DE102015002367A1 - Secure data transfer and scaling, cloud over-load protection and cloud computing - Google Patents

Abstract

The invention relates to a method, apparatus for load defense, control, simulation, secure transmission of data, energy, for self-sufficient system, network, telecommunications, power supply, peer-to-peer, Internet of Things, Web 4.0, Industry 4.0, cyber Physical Device, High Performance Computing, Cloud Computing, Mobility z. As vehicle, swimming equipment, aircraft, drone that the data to be transmitted, energy in the start server and / or start network generates at least one virtual machine (VM) and / or virtual network (VN), under a set of static, dynamic parameters, probability , Ranking, transaction that z. B. after the execution of the VM and / or VN stopped, controlled, canceled or forwarded until the data, energy is received by the target system, after the execution of the local action, the VM and / or VN then delete and / or not processed further, in particular independently deleted. The control is done by means of swarm intelligence, for self-sufficient system, bionically integrated distribution function, mediator function, simulation (SI), z. For example, for overload protection in communication, a black out of the communication and energy supply.

Description

The invention relates to a method and apparatus for the transmission of data, in particular an allocation of resources to software applications, a group in a system, in particular in a computer cloud, in particular provider cloud, private cloud, public cloud, data centers, in particular on social media platforms; in particular virtual, cloud-based, physical and hybrid environments, in particular in the field of application and / or convergence of telecommunications, information, mobility and energy (TIME). In particular, in the communication, data transmission, telecommunications, control system, z. For self-sufficient system, self-sufficient networks. In particular, the mobility z. B. vehicles, and / or at least one computer-implemented invention, in particular software, in particular self-driving vehicles and / or networked vehicles, land vehicles, swimming equipment, aircraft, in particular aircraft, - spacecraft, spacecraft, - satellite, - earth satellites, - artificial satellites, - Spacecraft, in particular at a distance to Earth of greater than 160 kilometers altitude, less than 500 kilometers altitude, less than 800 kilometer altitude, in particular greater than 500 kilometers altitude. Under a distance to the earth is also understood a mean orbital height, in particular of greater than 350 km, especially the earth orbits, - drone, especially at a distance to the earth of less than 50 kilometers in height, especially greater than 50 kilometers in height, - projectile, in particular weapons technology , in particular passive and / or active controlled projectile, - projectile. In particular, in the power supply z. As gas, water, electricity, in particular by means of at least one energy supply network, especially in the peer-to-peer, especially in the Internet of things, especially in Web 4.0, especially Industry 4.0, in particular embedded device, eg. As microcomputer with sensors, actuators, a communication interface, a web server, especially cyber-physical device (CPS), high performance computing (HPC), the cloud, cloud computing, in particular using at least one system, in particular computer and / or at least one Network, in particular at least one computer network and / or at least one supply network for energy, in particular for electricity and / or at least one programmable device, in particular at least one implemented program, in particular computer program. The internet of things is understood to mean, in particular, systems and / or devices which are connected by at least one system and / or at least one network, in particular communicating with one another, such as smart products, in particular measuring systems, such as termostats, window handles, lamps, in particular devices for the household, in particular kitchen such as pot, pan, kettle, Römertopf, steamer, roasting pan, pressure cooker, Einkochtopf, Kokkotte, asparagus pot, simmer pot, kettle, Wook, can, espresso maker, coffee pot, teapot, thermos, containers, bowls, punch, food container , Fermenting basket, gastronorm container, mixing bowl, Sekko, baking tin, baking tin, ice cube mold, box mold, muffin baking molds, cup cake mold, pizza topping, quiche mold, springform dish, baking dish, dishes, cups, mugs, glasses, mug for hot drinks, cup with lid, mug for cold drinks, coffee service, tea service, mocha service, espresso service, plates, saucer, p dinner plate, soup plate, salad plate, dessert plate, bowl, plate, egg cup, cereal bowl, butter dish, honey pot, jam pot, pepper shaker, salt shaker, dinner plate, deep soup plates, salad plate, Dressert table, cutlery, hand tools, spoon, fork, knife, meat hammer, barbecue tongs, Knives, Japanese knives, oysters knives, cutting tools Vegetable peeler, potato peeler, asparagus peeler, carrot peeler, peeler, scissors, whipstick, ax, cleaver, dough roller, spatula, broom, broom, scrubber, cake lifter, in particular household appliances, such as equipment for laundry care, such as Washing machine, tumble dryer, ironing board, iron, in particular kitchen utensils, such as cooking and baking appliances, such as stove, oven, microwave oven, mini oven, pressure cooker, cooking machine, dishwasher, refrigerator, freezer, freezer, fridge-freezer, small domestic appliances, white goods, like hand mixer, coffee machine, K washing machine, blender, juicer, toaster, kettle, can opener, kitchen scales, cutlery, kitchen textiles, room air conditioner, fan, fan heater, humidifier, air conditioning, cleaning equipment, vacuum cleaner, wet-dry vacuum, lighting equipment, floor lamp, desk lamp, lamp systems, personal care, hair dryer, Curling iron, heat generators heating pad, radiant heater, sunbed, do-it-yourself machine, sewing machine, cordless drill, measuring instrument, scales, personal scales, digital clinical thermometer, skewers, staples, mills and graters, potato grater, nutmeg grater, parmesan grater, truffle grater, ginger grater, pounding and mincing, mortising machine, Measuring devices, oven thermometer, roasting thermometer, cooker, kitchen scale, measuring cup, alarm clock, kitchen timer, egg timer, wine thermometer, coffee, mixing device, for mixing or mincing food, blenders with a rotating knife, hand blender, mixer, dough hook and whisk, hand blender, Stirrer (dough mixer, bar tool for making cocktails, refrigerators, dishwashing machine, small electric kitchen appliances, fruit juice centrifuge, juicer, freezer bag closer, hand blender, blender, wand, beamer, blender, quick grinder, kitchen textiles, Geschirrtucvh, apron, potholder, oven mitt, scouring cloth, dishcloth, extractor hood , Coasters, Cleaning Appliances, Floor Cleaning Appliance, Vacuum Cleaner, Carpet Cleaner, Bathroom and Sanitary Cleaner, Universal Cleaning Equipment, Steam Cleaner, Pressure washer, cleaning bucket, hair dryer, mirror, shaving mirror, makeup mirror, tumbler, tumbler, clothes ironing machine, iron, ironing machine, steam iron station, folding board, ironcloth, clothespin, laundry basket, clothes line, clothes dryer, washing machine, clothes hanger, ladder, lock, lock, padlock, in particular Playground equipment, such as ball, doll, vehicle, models, game mobile, model railway, toy train, model airplane, toy car, car racing track, rowing racer, toy building, dollhouse, shop, doll stove, play equipment meeting the requirements of Toy Directive 2009/48 / EC comply with CE mark, in particular sports equipment, fitness equipment such as barbell, weight station, weight benches, ergometer, treadmill, stepper, cable traction system, cable trainer, chest trainer, shoulder trainer, arm trainer, abdominal trainer, back trainer, leg press, leg curler, cardio equipment, indoor cycle, rowing machine , Truck, multi-press rack, Medibow, Libra, bathroom scales, in particular functional clothing, such as shoes, sports shoes, jacket, shirt, pants, socks, in particular devices for residential use, in particular for the scope of VDE 0700 Part 1 2003-07 DIN EN 60335-1 , in particular residential object, such as sleeping pads, mattress pad, in particular that the sleep phases, temperatures, moisture recording heats in particular only at a load greater than 1 kg, especially in different temperature zones and areas, especially combined and / or networked with other smart products, smart home Products, Smarthpone. For example, that a kitchen appliance, in particular an embedded system, with at least one communication system, eg. B. smartphone networked and so technical parameters, eg. As temperature, humidity, locality monitors, displays and suggests, especially for further technical functionality, in particular to at least one communication system, eg. B. Smart product, with at least one visual and / or acoustic output, z. How to stir, when to turn the meat, possible ingredients, ingredients for cooling. For example, the at least one bottle, especially a bottle for personal care, z. B. Schampoflasche, the remaining amount of existing content, the locality, etc. in particular displays a smartphone and / or generates a purchase list, in particular taking into account and analysis of consumption and their scope in the use, in particular to automated ordering and / or payment.

Estimates assume 82.5 billion devices (GE) (2016) that can be connected to the Internet. In such a system, at least one group of software applications, data and / or resources are made available via a network, in particular an IT network, in particular via the Internet and / or an intranet. The individual software applications can be assigned system resources (SR), that is to say, for example, that various individual resources of the system are part of a totality of the individual resources available to the system, and for the ensuing description and claims this entirety of all the system available individual resources is also referred to as system capacity.

Under system resource (SR) and / or resources includes in particular also, the technical control mechanisms of a system and / or network and may be, for example, CPU (Central Processing Unit), in particular CPU times, RAM (Random Access Memory), in particular temporally associated memory , HDD (Hard Disk Drive), network bandwidth, in particular temporally allocated bandwidth, network bandwidth, data storage, server, personal computer, smartphone, control unit, virtual machines and / or Vrtuelle Netze (VN) and / or switching on and / or off networks and / or groups of networks, in particular radio, RFID, Bluetooth, WLAN, GSM, LTE, UMTS, radio networks, broadcast networks, television networks, satellite networks and / or other hardware and / or software and / or data, in particular data and / or metadata, in particular of sensors and / or devices (GE) and / or at least one computer-implemented invention, for. B. search engines and / or data storage and / or servers and / or virtual machines and / or virtual destop (VD) and / or virtual memory (VS) and / or other hardware and / or software and / or networks.

A group of software applications and / or data and / or systems and / or networks may for example be associated with one and / or more users and / or at least one other system and / or at least one other network, in particular certain one and / or more Categories of software and / or hardware, and / or has one and / or more specific properties for use and / or application of one or more software applications.

In the US 6650731 B1 from 23.09.1999 (Trinkel ...) is the overload defense of a switching network, in particular the intelligent network determined by means of a simulator depending on the load of one or more modules, bepielhaft depending on the queue length and / or user-definable parameters or value, bepielhaft the load limits. Defined limits have also been defined by means of a "fuzzy" decision, for example by means of fuzzy logic, whereby, by way of example, the call has a certain probability.

In the US 8345708 B2 from 26.06.2003 (Trinkel ...) a virtual machine, here a special software solution is used, which are usable also on other Provider servers, described here for the switching technology, which with insufficient switching capacity of the netzeigenen switching centers, a software on at least one another server of another selectable telecommunications network at least temporarily transmit and / or on this at least temporarily activated, in particular to increase the switching capacity.

In the DE 10 2006 039 664 A1 from 24.08.2006 (Daußmann ...), the predetermined network identifier is subjected to a probability test, whereby, depending on the determined probability, the predetermined first network identifier is confirmed or at least a second alternative network identifier is proposed or dialed directly.

In the US 000008750518 B2 (Daußmann, ...) ( US020090097658A1 from 10.10.2007) and DE 10 2007 048 518 A1 (Trinkel ...) is an example of a data track, in particular per time, per connection, from an Internet link z. For example, www.xy.com assigns a probability. Where the probability of having different characteristics, for example, the probability of potential harm to third parties, the probability of seeking help until the appearance of a content hot spot.

In the EP 000002378422 B1 from 14.04.2010 (Trinkel, Daußmann) a system, a virtual machine is described, which at least temporarily uses a hardware of a current transport server.

In the DE 10 2011 111 131 A1 from 20.08.2011 and DE 10 2012 011 831 A1 from 14.06.2012 (Trinkel ...) is subjected in a particular embodiment, the result of an intersection of weighting, classification, trigger, ranking and probability, for further actions, process steps, process flow, for influencing a device.

In the DE 10 2013 004 795 A1 from 20.03.2013 (Trinkel, ...) describes a random generator for the cloud, especially in cloud computing, here by means of software at least one hot spot is generated and used as a thermal noise generator via a sensor, in particular diode and / or diode function , for generating random numbers that in communication technology, especially for the security of data and information is becoming increasingly important and z. B. is understood as a trigger that the triggering event a pulse z. B. trigger pulse or a switching operation z. B. switching edge generates an event and has at least one triggering event and consists for example of at least one module and / or a circuit and / or a quadrupole and has at least one quadrupole is used for at least one trigger, characterized in that z. B. for data track the quadrupole parameters, value and are included.

Next, the prior art in the textbook of Tilli Thomas, entitled "Fuzzy Logic", (1991), Franzis-Verlag (ISBN 3-7723-4322-8) or with the title "Automation with Fuzzy Hardware", (1992), ISBN 3-7723-4412-7 , described in this context and the fuzzy-based control again in the DE 10 2012 016 967 A1 (2012) DE 10 2013 008 151 A1 (2013). Also in the article "Hakin9 Extra" 4/2012 - "Security in the Cloud" is titled "Security with Cloud Computing", by Sabine Wieland describes the impact of cloud computing on the security of IT and the variety of actions, aspects, attacks, and security impact around the cloud. Likewise in the lecture of the BITKOM, on the CeBIT Hannover, from 8. March 2013, 10:40 o'clock on the topic "security with Cloud Computing", and on the "ecd conference", with the topic "scaling logic for Cloud Computing" the speaker Sabine Wieland, see source inquiry of the presentation documents, with the presentation documents "scaling logic for cloud computing" under ecd-conference.de under http://ecd-conference.de/wp-content/blogs.dir/46/files/2011/03/130514_Wieland_Hochschule_fuer_Telekommunikation_Leipzig.pdf and http://www.cloudzone-karlsruhe.de/messe-karlsruhe-cloudzone/2013/de/mess_ka/die_kongresse/eurocloud_deutschland_conference_/ecdc_agenda/ecdc_agenda_1.jsp and in the lecture and publications in 2012 also the "Voice + ip", in Frankfurt / Main, from 31.10.2012 topic "Performance requirements by VoIP and cloud computing on IP-based networks" under http://de.slideshare.net/Connected_FFM/tag2-fpc1420-sabine-wieland and in the lecture of Siegmund and Wieland under http://www1.hft-leipzig.de/wieland/publikationen2012.html with the day before file http://www.ecd-conference.de/wp-content/blogs.dir/30/files/2011/03/130514_Wieland_Hochschule_fuer_Telekommunikation_Leipzig.pdf. last call 01.10.2014 12:00, among other things a method for scaling logic was described, which should compensate for load fluctuations in the cloud and is characterized in that it is a learning device, the algorithm takes into account statistical data, current and statistical load parameters, load values and the priority and security of all cloud-enabled applications in time to start a user surge to be awakened additional virtual machines or turn off again after the decrease of the load, in particular for possible reference to the VM, see also http://ecd-conference.de/vom lecture on 14.05.2013 Title "Partnering for the Cloud" with the report of the EuroCloud Germany Conference of the conference author Peter Koller from 21.05.2013 see http://www.ecd-conference.de/2013/05/21/partnering-for-the-cloud.html last call 01.10.2014 12:00 and further publications as of 03.11.2010 on the Science Days on - Virtualization in Information and Communication Technologies, the University of Telecommunications Leipzig (HfTL) with successful migrations on virtualized infrastructures, especially security aspects of virtualization, virtualization of security components and virtual security appliances, secure cloud computing and security in the cloud theme.

Disadvantage of the known device is that as a precaution, an oversizing of the network and / or device is made and / or created as a precaution back-up path and / or kept and / or precaution a traffic separation between data and language are made, and so valuable Resources are kept unused. The security of the devices, in particular the virtual machine, in particular of the virtual network, and / or the data, in particular in the case of unrecognized harmful data, is not guaranteed. that the number of random numbers depends on the number of applications, which is overprovisioned in case of a high number of applications in the cloud random numbers. That in the case of a content hotspot and / or connect rush, there is no or insufficient overload. The increasing complexity of the systems and the increasing dynamics of the processes lead to an increasing overload of systems. That the decentralized systems still need too much time, runtime of the data and consume additional energy, especially in the case of a required return channel for central confirmation.

The object of the invention is to provide a solution that enables a secure device and method, in particular for saving resources, in particular for data security, especially in overloading the device, in particular for optimizing the energy consumption, in particular the total energy consumption, especially in an automatic, adaptive, autonomous Allow resource allocation to one or more software applications based on current resource requirements even with changing target functions.

A preferred object of the invention is to allocate available resources of a system in the context of a system capacity available for a group of software applications, in order to make the most of these available resources and, secondly, the available resources of the system to the software applications in particular to allocate advantageous, in particular to allow secure network stability and / or data transmission.

The object is achieved with the device according to claim 1. Advantageous embodiments of the device are subject of the dependent claims or can be found in the following description and the embodiment, each of the listed embodiment, as well as any other, other described embodiment and form is arbitrarily combinable and not exhaustive.

In summary, the object is achieved by comprising for each user and / or for at least one transaction and / or at least one data record and / or at least one record part in that at least one virtual machine (VM) and / or at least one virtual network (VN ) and / or generates at least one neural network (NN) and / or at least one virtual channel (VK) and / or at least one virtual memory (VSp) and / or at least one virtual sensor (VSe) - starts and / or shuts down and / or is processed, in particular for the storage and / or processing and / or transport of data and / or applications and / or transactions. Insbesonderung by means of a controller comprises at least one soft computing and / or computer-implemented invention, in particular at least comprises a neural network (NN).

Soft computing also means that the set of rules, which includes, by way of example, as follows, in particular at least one fuzzy logic, computing with words, multicriteria analytics z. B. AHP, ANP, neural network (NN) z. Eg connectionism, genetic algorithms (evolutionary), symbol-based learning, probabilistic reasoning, such as confidence factor algebra, Dempster-Shafer evidence theory, abductive logics for non-monotone closure, stochastic approaches, eg. B. Bayesian closing, Bayesian Belief networks.

In this case, at least one VM and / or VN and / or NN, in a particular application also includes at least one virtual desktop (VD), in particular with the advantage that devices, in particular further Devices, in particular the employee can be more easily integrated into the system and / or network and so the data security, in particular against change and / or decryption, in particular privacy and / or technical performance z. B. in data throughput, increase CPU utilization, in particular by the fact that includes at least one virtualization-sensitive processing, insbesodnere is used as a security solution, in particular by means of at least one VM and / or VN and / or NN, in particular with at least one fuzzy-based control and / or neural network (NN) -based scheme.

• • ermitteln, mindestens ein Parameter, insbesondere mittels Messen mindestens einer Ressource, insbesondere mindestens einer zum Zeitpunkt t aktuellen Ressourcen-Nutzung, durch mindestens eine Software-Anwendungen der Gruppe, insbesondere jede Software-Anwendungen der Gruppe.

STEP 2 umfasst:

• • ermitteln mindestens ein momentaner Auslastungswert der Ressourcen, insbesondere zu einem Zeitpunkt t, insbesondere zum Zeitpunkt aus STEP 1, insbesondere mittels mindestens einer Parallelverarbeitung; für mindestens eine Software-Anwendung der Gruppe, insbesondere bei jeder Software-Anwendung der Gruppe, in Abhängigkeit von den ermittelten Ressourcen-Nutzung, insbesondere aus dem STEP 1, insbesondere mittels mindestens einer Parallelverarbeitung; in Bezug auf mindestens eine Ressource und/oder mindestens einem Ranking und/oder mindestens einer Wahrscheinlichkeit und/oder mindestens einer Kommunikationsadresse und/oder mindestens einer Gerätekennung; insbesondere mittels mindestens eines Simulation-System (SI).

STEP 3 umfasst:

• • Auswählen mindestens einer Softwareanwendung mit einem, insbesondere mit mindestens einem höchsten Ressourcenauslastungswert und/oder momentanen Ressourcenauslastungswert und/oder mindestens einem Ranking, insbesonderen zu mindestens einer Softwareanwendung; und/oder mindestens einer Wahrscheinlichkeit, insbesonderen zu mindestens einer Softwareanwendung; und/oder mindestens einer Kommunikationsadresse, insbesonderen zu mindestens einer Softwareanwendung; und/oder mindestens einer Gerätekennung, insbesonderen zu mindestens einer Softwareanwendung, insbesondere mittels mindestens einer Parallelverarbeitung;

STEP 4 umfasst:

• • Ermitteln und/oder Steuerung, zumindest für mindestens eine und/oder ausgewählte Software-Anwendung mindestens eines Skalierungswertes und/oder mindestens einer Gewichtung und/oder mindestens einer Ranking und/oder mindestens einer Wahrscheinlichkeit in Abhängigkeit von deren momentanen Ressourcenauslastungswert, von mindestens einem Relevanzwert und/oder von mindestens einem Kritikalitätswert mindestens einer Software-Anwendung, insbesondere der ausgewählten Software-Anwendungen und/oder einer Systemkapazität für die Gruppe, insbesondere mindestens einem Ranking, insbesonderen zu mindestens einer Gruppe; und/oder mindestens einer Wahrscheinlichkeit, insbesonderen zu mindestens einer Gruppe; und/oder mindestens einer Kommunikationsadresse, insbesonderen zu mindestens einer Gruppe; und/oder mindestens einer Gerätekennung, insbesonderen zu mindestens einer Gruppe; mit mindestens einem Kriterium, dass die Nutzung mindestens einer Ressourcen, insbesonderen zu mindestens einer Gruppe; durch mindestens einer Software-Anwendungen der Gruppe, insbesonderen aller Software-Anwendungen mindestens einer Gruppe; mindestens einen vorbestimmten Schwellwert und/oder mindestens einen vorbestimmten Ranking und/oder mindestens einen vorbestimmten Wahrscheinlichkeit nicht überschreitet, insbesondere mittels mindestens eines Simulation-System (SI), insbesondere in einer besonderen Anwendung mittels mindestens einer Parallelverarbeitung.

The object is further achieved and encompassed by means of a method and / or system for fuzzy-based and / or neural network (NN) -based control of an allocation of resources of at least one system, in particular a network, to one and / or more software Applications that make up a group include the following steps:

STEP 1 includes

• Determine at least one parameter, in particular by measuring at least one resource, in particular at least one resource usage current at the time t, by at least one software application of the group, in particular each software application of the group.

STEP 2 includes:

• Determine at least one momentary utilization value of the resources, in particular at a time t, in particular at the time from STEP 1, in particular by means of at least one parallel processing; for at least one software application of the group, in particular in each software application of the group, depending on the determined resource usage, in particular from the STEP 1, in particular by means of at least one parallel processing; with respect to at least one resource and / or at least one ranking and / or at least one probability and / or at least one communication address and / or at least one device identifier; in particular by means of at least one simulation system (SI).

STEP 3 includes:

• Selecting at least one software application having one, in particular at least one highest resource utilization value and / or current resource utilization value and / or at least one ranking, in particular to at least one software application; and / or at least one probability, in particular to at least one software application; and / or at least one communication address, in particular to at least one software application; and / or at least one device identifier, in particular for at least one software application, in particular by means of at least one parallel processing;

STEP 4 includes:

• Determining and / or controlling at least one and / or selected software application of at least one scaling value and / or at least one weighting and / or at least one ranking and / or at least one probability as a function of their current resource utilization value of at least one relevance value and / or at least one criticality value of at least one software application, in particular the selected software applications and / or a system capacity for the group, in particular at least one ranking, in particular to at least one group; and / or at least one probability, in particular to at least one group; and / or at least one communication address, in particular to at least one group; and / or at least one device identifier, in particular to at least one group; having at least one criterion that the use of at least one resource, in particular to at least one group; by at least one of the software applications of the group, in particular all software applications of at least one group; does not exceed at least one predetermined threshold value and / or at least one predetermined ranking and / or at least one predetermined probability, in particular by means of at least one simulation system (SI), in particular in a particular application by means of at least one parallel processing.

In a special application, the steps STEP 1 to STEP 4 can be combined in different combinations and / or only partially (eg STEP 1-2) and / or in sub-steps (eg STEP 2 and STEP 4) and / or multiple (n times STEP 3) and / or several times individual STEPs (n times STEP 1 and m times STEP 3) and / or triggered in parallel and is characterized in that in particular via at least one neural network (NN) Basic Controllers these steps are joined together and / or controlled. In a further particular application, the steps STEP 1 to STEP 4 need not be executed at each step and / or not executed completely, especially since the neural network (NN) - If the controller is also advantageous with partial information and / or conclusions about what has already been learned, the behavior of the system is particularly robust. In another particular application, especially at the first start (eg, without the STEP 1-3 passes, but the STEP 4 direct jump), at least one event from at least one neural network (NN) controller may be used.

In this case, in a particular application, at least one determination of at least one parameter and / or at least one controller and / or at least one simulation, in particular at least one simulation system (SI), comprises at least one VM and / or at least one VN, in particular includes at least one fuzzy-based control and / or neural network (NN) -based control.

In summary, with the advantageous, technical effect that the data on the start server to distribute, in particular split and each generate its own virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) and / or processed be and / or stored and be joined to at least one server, especially at the end server.

Advantageous that a controlled load distribution takes place, in particular by means of swarm behavior, in particular by means of automated aggregation of at least one data and / or at least one data record and / or at least one ranking and / or at least one probability and / or at least one identifier and / or at least one communication address and / or at least one device identifier and / or at least one VM and / or at least one VN, in particular by means of neural networks (NN), in particular resulting uniform load distribution of the device and / or system and / or network, in particular the load of the server, in particular the hardware, in particular the load of the network, in particular the load of the electrical power supply and / or at the same time when connected to load, in particular for generating and / or processing additionally random numbers to the load ratio, in particular by means of at least one VM and / or min at least one VN and / or at least one neural network (NN) is increased.

Advantageously, that an increase in the throughput speed of the data and / or control data and / or at least one identifier and / or communication data and / or communication, in particular by means of the separation of the data and / or data record and / or control data and / or content data in at least one, in particular jeweis its own virtual machine (VM) and / or virtual network (VN) is carried out, in particular by means of a learned neural networks (NN). Advantageously, the reliability and operational reliability against overload protection, manipulation of the data and / or data record and / or control data and / or content data by third parties, in particular by means of at least one VM and / or at least one VN and / or at least one neural network ( NN) is increased. Advantageous that a uniform utilization of the device, in particular servers, in particular the energy consumption and / or network, in particular of the telecommunications network is present, in particular by means of at least one VM and / or at least one VN and / or at least one neural network (NN) is increased. Advantageously, that the increase of the throughput speed of the data and / or data record and / or control data and / or content data and / or communication takes place, in particular in the real-time capability and / or in particular an improvement of the performance of the servers and / or networks by means of compensating load Energy, communication behavior, in particular comprises by means of at least one VM and / or at least one VN and / or at least one neural network (NN) - is increased. Advantageously, the reliability and reliability of the device, in particular server and / or network - is increased, in particular against manipulation of third parties, in particular by means of at least one VM and / or at least one VN and / or at least one neural network (NN) - is increased , Advantageous that the total energy consumption is lower compared to a separate device for a device as a stand-alone random number generator, in particular comprises at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention. is increased. Advantageously, it is thus possible to save and / or temporarily increase memory resources, memory space and memory segments, in particular by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented one Invention, in particular software, in particular active software - is increased. Advantageously, in a particular application that there is an adaptation and improvement of the required random numbers per number of applications, in particular by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software - in particular depending on the applications - is increased. Advantageously, an improvement in the response time of the data and / or data record and / or control data and / or content data and / or data packets, in particular the duration of the data packets, in the case of load by applications, takes place and comprises that the required Virtual machine VM and / or networks VN have already been started in advance, in particular by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software - In particular that the software at least one other parameter, in particular ranking, probability, communication address and / or at least one identifier, device identifier considered and / or processed and so before the other start of the application at least one VM and / or at least one VN starts and so the response time improved.

Advantageously, in particular in the event of a particular change, the device, in particular servers and / or networks, has a balanced distribution and / or networking of the technical execution structure, in particular that at least one virtual machine (VM) and / or virtual network (VN) is created for each transaction switched on and / or can be switched off, depending on the load of all considered networks, device architectures, thereby takes place and includes that the required virtual machine VM and / or networks VN already simulated in advance and / or started and / or turned off, in particular by means comprises at least a VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software.

Advantageously, in particular comprises within a black box structure, in particular a neural network (NN), and comprises at least one hardware, in particular at least one server, in particular with switching function and / or switch function, in particular for lines, memory, controller and software and / or at least one network.

Advantageous, that an increase of the Verarbetiungsgeschwindigkeit and / or reliability of the system takes place. Advantageous that an improvement of the architecture, in particular the technical architecture to model the possibility, in particular for the collection and storage of large data, especially metadata, big data.

Advantageous that an improvement of the systems, in particular subsystems in a technically manageable amount and thus allows to reduce the complexity of data collection, in particular to cope with their analysis, in particular the Big Datan, in particular by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software. It is advantageous that an improvement in data security takes place, in particular as more and more data sources are created, in particular for identification of authorship, responsibility, persons in charge, in particular for the generated data and / or metadata, by means of an identifier, in particular by means of at least one identifier of the devices (GE) and / or at least one communication address, in particular of the systems, in particular by means of at least one identifier, in particular takes place and comprises that the required virtual machine VM and / or networks VN have already been started in advance, in particular by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software.

It is advantageous that an improvement in data security, in particular since there is a real-time overview of the data, in particular where the data and / or how the data are stored and how, in particular usage and / or when and / or how often the data are used, in which case this temporary overview is safe, in particular by means of the distribution of the data on different VM and / or VN, in particular by and includes, by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least a computer-implemented invention, in particular software, in particular active software.

Advantageously, for improving the data security, in particular the simple, rapid solution of the data, in particular by deleting the VM and / or VN, in particular with at least one trigger and / or at least one parameter and thus advantageously the data, in particular an "expiration date", In particular, this takes place and includes that by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software - in particular with reference to the identifier.

According to the invention, this object is achieved in that, in a method of the aforementioned generic type, the data to be transmitted in the start server and / or start network generates at least one virtual machine (VM) and / or at least one virtual network (VN) Attention to the probability and / or ranking and / or at least one identifier of the data and / or transaction to be transmitted and after execution the virtual machine (VM) and / or virtual network (VN) is stopped, aborted, deleted or forwarded until that the virtual machine (VM) and / or virtual networks (VN) is received by the destination server and / or end server and / or after the execution of the action, the virtual machines (VM) and / or virtual networks (VN) subsequently is deleted, in particular self-extinguished.

In particular, at least one virtual machine (VM) and / or at least one virtual network (VN) comprises at least one fuzzy-based controller and / or neural network (NN) -based controller and / or at least one computer-implemented invention, in particular software, in particular active Software.

The object is further achieved by the fact that in a method of the aforementioned generic type that the data to be transmitted and / or at least one transaction and / or and / or at least one transaction and / or at least one program and / or at least one trigger, in the start server and / or start network, in particular taking into account the probability and / or ranking and / or at least one identifier, the data to be transmitted and / or transaction and / or application, at least one virtual machine (VM) and / or at least one virtual network (VN) are generated and / or integrated - in particular data and / or at least one transaction and / or at least one program and / or at least one trigger, at least one virtual machine (VM) and / or virtual network (VN) educate themselves and the Virtual Machine (VM) and / or Virtual Network (VN), in particular together with the data integrated therein and / or at least one transaction and / or m at least one program and / or trigger via at least one server and / or network, in particular the transport server is transported to the destination server, wherein during transport in at least one, preferably in each of the server and / or network, in particular transport server and / or transport network, the virtual machine (VM) and / or virtual network (VN) at least temporarily by means of at least one hardware and / or at least one software and / or at least one network, in particular line network of the current server and / or network, in particular transport server and / or transport network, in particular by means of a simulation system (SI), a processing and / or control is performed and / or executed and / or stored and / or processed and / or deleted, and / or after the execution of the virtual machine (VM) and / or virtual network (VN) stopped and / or canceled and / or resent and / or deleted until m At least one virtual machine (VM) and / or virtual network (VN) is received by the destination server and / or after the execution of the action, the virtual machine (VM) and / or virtual network (VN) deletes itself, in particular self-extinguishing in particular Consideration of the probability and / or ranking of the data to be transmitted and / or transaction and / or application.

With the advantage of increasing the speed and / or reliability, since there are enough virtual machine (VM) and / or virtual networks (VN), in particular in relation to the energy utilization, in particular in relation to the constant energy utilization, the security of the transaction, in particular Data security, in particular according to the principle of segmentation, in particular for industrial networks, in particular for control and regulation networks, in particular in the, automation, in particular by means of at least one simulation system (SI), in particular takes place and comprises that by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software.

The object is further achieved by a device in which the start server is set up to integrate the data to be transmitted into a virtual machine (VM) and / or a virtual network (VN) and / or through the data itself a virtual machine (VM ) and / or form a virtual network (VN), in particular for this purpose first to generate the virtual machine (VM) and / or virtual network (VN) at a time of origin and the virtual machine (VM) and / or virtual network (VN), in particular together with the data integrated therein for transport via the server and / or network, in particular transport server and / or transport network to the destination server and / or destination network, at least one of the servers and / or network, in particular transport server and / or network is set up, preferably all servers and / or networks, in particular transport server and / or transport network are set up during the transport, the Vi Execute virtual machine (VM) and / or virtual network (VN) at least temporarily by means of the hardware and / or software and / or networks of the current server and / or network, in particular transport server and / or transport network and after the execution, stop and / or abort and / or continue to send and / or delete and / or discontinue processing and / or to the next server and / or network, in particular transport server and / or transport network or to receive the virtual machine (VM) and / or virtual network (VN) by the destination server and / or target network, in particular taking into account the probability and / or ranking and / or Identification and / or device identifier and / or communication address of the data to be transmitted and / or transaction and / or application, in particular takes place and includes that by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software.

The essential core idea of the invention described here is that at least one trigger and / or the data to be transmitted at least one virtual machine (VM) and / or at least one virtual network (VN) is generated and / or simulated, in particular comprises by means of a simulation -System (SI) and the transmitted data and / or transaction and / or application no longer directly, whether encrypted and / or unencrypted, between the individual servers involved, in particular the telecommunications network to send, but the data to be transmitted and / or Transaction and / or application in an at least one virtual machine (VM) and / or at least one virtual network (VN) trained environment, in particular as a virtual machine (VM) and / or virtual network (VN) designed implementation device, in particular physical device and / or network, in particular, as at least one data container and / or or realized and / or integrated as at least one virtual machine (VM) and / or at least one virtual network (VN).

This has the advantage that unauthorized third parties can not attach directly to the data to be transmitted, application malware and / or access to the data to be transmitted because the data to be transmitted are not immediately visible to the outside and / or the defined Do not leave locality because they are as described above as a virtual machine (VM) and / or virtual network (VN) and integrated into at least one trained environment and thus during the transmission path essentially only the virtual machine (VM) and / or virtual Network (VN) is visible to the outside.

With the further advantage of increasing security, in particular data security according to the principle of segmentation, in particular for industrial networks, in particular for control and regulation networks, in particular comprises by means of a simulation system (SI) and / or controlling simulation system (SI) , in particular in automation, in particular according to the principle that in particular the network in compartments, in particular comprising at least one VM and / or VN is and / or subdivided and so accessible only from outside, only a few access points and so advantageous loss events are limited to certain areas and more individual compartments, in particular comprising at least one VM and / or VN is and / or interleaved to lower the probability of penetration and so the way to the innermost compartment, in particular comprises at least one VM and / or VN is and / or complicates. In particular, according to the cell-zone-site principle, here at least one interleaving is generated on three levels and the smallest unit the cell is the core and protects criter device z. B. only one machine, and in the zone other not so critical device are summarized and in the site level complete locations, localities are summarized, so that a site consists of at least one zone and in particular the inventive solution and / or routers ensure access Sites and at least one other inventive solution and / or router are in the zone for protection. Thus, the access via networks, in particular the inventive solution and / or VPN tunnel (Virtual Private Network), in particular takes place and comprises that by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software - takes place.

The inventive solution comprises at least one server and / or router and / or at least one computer-implemented invention, in particular software, in particular virtual machine (VM) and / or virtual network (VN), - which enables the possibility of an examination of the data packets, in particular by means at least one simulation system (SI), in particular by means of Deep Packet Inspection (DPI), in particular according to fieldbus types, in particular for Ethernet / IP Modbus TCP, in particular with Write commands block and / or pass and / or the purpose of the packets, in particular data and / or transaction and / or application checks.

With the further advantage of increasing security, in particular data security, in particular by means of at least one simulation system and / or method (SI) and / or according to the process principle of segmentation, in particular for industrial networks, in particular for control and regulation networks, in particular the automation, in particular according to the principle that takes place in particular on the type of content that includes, in particular at least one traffic separation, in particular a separation of the data packets, in particular at least one data packet, in particular at least one real-time data and at least one voice data and / or at least one general separation of data, in particular real-time data, in particular peer-to-peer; and / or language.

With the further advantage of increasing security, in particular data security according to the process principle of segmentation, in particular for industrial networks, in particular for control and regulation networks, in particular by means of at least one simulation system (SI), in particular in automation, in particular according to the process principle in that, in particular, according to the type of prioritization takes place and comprises, at least one strict priority queuing, in particular for facilitating the preferred transport of real-time data, and comprises at least one probability and / or at least one ranking and / or at least one identifier.

In a further particular embodiment, at least one automatic cleanup, in particular memory reset to the ground state, in particular memory cleanup, in particular for secure deletion and / or no further processing of data and / or programs, in particular for at least one virtual machine (VM) and / or at least a virtual network (VN) and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software; and includes the release and / or reuse of already allocated and / or no longer required areas, in particular memory areas (blocks / objects), in particular for at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one Neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software; computer memory is in particular by means of at least one computer-implemented invention, in particular software, characterized in that it comprises at least one list and / or matrix of at least one pointer and / or at least one identifier and / or at least one VM and / or VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software - and this is deleted and / or overwritten with zeros and / or with at least one random number, in particular coincidental with the state 1 or state 0 and / or is overwritten by means of at least one random state, in particular of an analogous type, wherein at least one random number and / or random state is effected by means of at least one noise generator, in particular by and comprises that by means of at least one VM and / or at least one VN and / or at least one neural network (N N) and / or at least one computer-implemented invention, in particular software, in particular active software - and thereby produces at least one thermal effect, in particular a hot spot and comprises by means of at least one sensor and / or the physically non-clonable functions (PUF, PUFs) described thermal effect and / or thermal action is based, in particular converted into at least one noise voltage, in particular for generating the random.

In another particular application and apparatus and / or method for generating a secure key, in particular by means of noise, also referred to herein as a noise generator, and uses the physically non-clonable functions (PUF, PUFs) described, for example in the EP 2191410 B1 , but also from the company Microsemi http://www.microsemi.com/ and is a hardware-based primitives. The inventive method and / or system used for hardware-based primitives, which is characterized in that in a further particular application and apparatus and / or method for generating a secure key, in particular by means of noise, in particular at least one noise generator, in particular takes place and comprises that for the hardware-based primitives at least one invention computer-implemented invention, in particular by means of software and comprises at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular software, in particular active software - in particular comprises at least one VM and / or at least one VN and / or neural network (NN) and / or neural network (NN) -based controller and thereby at least one thermal effect is generated, in particular at least a hot spot and in particular special comprises by means of at least one sensor and / or at least one neural network (NN) and / or at least one neural network (NN) -based controller and the thermal effect, in particular in at least one voltage, in particular noise voltage is converted to generate the random. The particular advantage is that the active software passes through certain areas of the hardware and thus generates certain areas more or less due to the movement of the electrons, temperature differences in the semiconductor body and / or printed conductors, in particular up to a hot spot. The active software here can be at least one VM and / or at least one VN and / or at least one neural network (NN).

Under a computer, in particular comprises at least one CPU, at least one memory, in particular RAM and / or ROM and / or SRAM; comprises in particular at least one control system comprising at least one computer-implemented invention, in particular comprising at least one VM and / or at least one VN and / or neural network (NN) and / or at least one neural network (NN) -based controller and / or at least one fuzzy -based controller and / or at least one simulation system.

In a further particular application and apparatus and / or method of the noise generator, in particular takes place and comprises that by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular Software, in particular active software - comprises at least one VM and / or at least one VN and / or neural network (NN) -based controller and thereby at least one bit-Zufallsmuster is generated and comprises by means of at least one unit, in particular memory z. B. SRAM and / or sensor and / or neural network (NN) -based controller and / or at least one activation code, in particular dual states; and generates at least one unit, in particular logic unit, in particular with at least one neural network (NN) -based controller and / or fuzzy-based controller random numbers, in particular a key is generated.

In a further particular application and apparatus and / or method of the noise generator, in particular takes place and comprises that by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular Software, in particular active software - comprises at least one VM and / or at least one VN and / or neural network (NN) -based controller and thereby at least randomly generates bit patterns, in particular memory z. B. SRAM and comprises by means of at least one module, in particular sensor and / or neural network (NN) -based controller and / or at least one activation code, in particular dual states; and at least one unit, in particular logic unit, in particular with at least one neural network (NN) -based controller and / or fuzzy-based controller random numbers, in particular a key generated.

In a further particular application and apparatus and / or method of the noise generator, in particular takes place and comprises that by means of at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one computer-implemented invention, in particular Software, in particular active software - comprises at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one neural network (NN) -based controller and thereby at least one random pattern, in particular random states, is generated , in particular in at least one memory and comprises by means of at least one module, in particular sensor and / or neural network (NN) -based controller and / or at least one activation code, in particular dual states; and comprises at least one unit, in particular a logic unit, in particular having at least one neural network (NN) -based controller and / or fuzzy-based controller random numbers, in particular for generating at least one key.

A memory advantageously comprises, in particular at least one memory PUF, in particular the SRAM PUFs, the z. B. in smard chips and / or in FPGAs can be used.

The underlying principle is that once an SRAM block is powered, the memory cells and / or cells are initialized in any order and the bits take the value 1 or 0. Which value each bit prefers, is already predetermined by the production, in particular those of the IC, the computer, the CPU, RAM, ROM, EPROM, memory controller, but also those of the system of VM and / or VN and / or neural network (NN) and / or the neural network (NN) -based controller and / or fuzzy-based controller. By way of example, SRAM bits consist of two equal cross-coupled inverters. Since no two inverters are completely the same, the startup behavior of each bit and hence its preference is different. This random status of the bits in an SRAM block is measured. Should the inverter be exceptionally well balanced, thermal noise, in particular caused and in particular comprises by means of at least one computer-implemented invention, in particular of software, in particular by the construction and / or starting and / or deleting and / or processing at least one VM and / or at least a VN and / or neural network (NN) and / or at least one neural network (NN) -based controller and / or fuzzy-based controller; so that the predetermined preference can be overcome and the bit sometimes take the opposite status to the normal status when turned on. In most bits, the static preference is strong enough to overcome any dynamic noise. Temperature and / or lifetime and / or transactions and / or number of transactions and / or software load, especially the CPU, RAM, ROM and other environmental factors, may also cause interference. You can be met with error correction technology in the PUF design. When the PUFs are first used, a snapshot of the startup values of the bits is taken. An error correction code, the activation code, also helper is calculated so that at each subsequent Anschlaltzyklus, in particular by the construction and / or starting and / or deleting and / or processing at least one VM and / or at least one VN and / or At least one neural network (NN) -based controller and / or fuzzy-based controller - the noisy bits can be corrected. So it will be the same every time you turn it on Reconstructed key again. The SRAM PUFs have a further advantage, if the power supply is switched off, the states (secret) are lost, in particular by at least one virtual memory and / or at least one VM and / or at least one VN and / or at least one neural network ( NN) and / or at least one neural network (NN) -based controller and / or fuzzy-based controller takes place.

There are no methods that can predict the startup status of the SRAM bits when the power is off. In addition, the secret PUF key can not be reconstructed if the activation code is set to zero. If an SRAM is used to generate a single high-quality 256-bit cryptogram key, it uses about 1 to 2 Kbytes of SRAM. Ideally, each bit equally favors a 1 or a 0, a 50:50 probability, and this preference is independent of all other bits. In this case, every bit has a 100% entropy. Due to small slopes and correlations, the memory bits in the real world have an entropy of 95%. Dynamic rip and environmental effects can generate bit-flips compared to the original written memory image. In ideal situations, eg. B. at constant temperature and / or in the static state of VM and / or VN - is the number of interfering bits in a range of about 3 and 5 percent. Calculated over all conditions, including time and temperature, this value increases to about 12-15%, still within the range of the Error Correction Code, 25% or more. The SRAM PUF is a very reliable technique for reconstructing keys under all conditions and throughout the life cycle, with an error rate of e.g. Z. 1 per 1 billion. This rare mistake can be detected with a high probability and then attempted by another attempt to get the correct key.

The further advantage is that the private key for the identification of a device is generated by means of physically nonclonable functions (PUF, PUFs). The method and system are based on a physical characteristic that arises during the manufacture of the device and the inventive computer-implemented invention, in particular software, in particular active software - in particular at least one VM and / or VN and / or NN, - thanks to smaller technically uncontrollable, random manufacturing variants is unique to each building block, but in total provides a static, reproducible, recurring feature. Although these variations can not be predetermined or controlled, as long as they can be measured with a sufficiently small noise and are stable enough, PUFs can be used, with sufficient high noise due to the high quality thresholds required. Thus, for each individual system, in particular at least each module, - generate its own private key. Where the summary of many building blocks to a module, at least one private key to the module and / or several modules to a system, generates at least one private key to the system. PUFs are the fingerprint of the chip and can be used and understood according to the invention as identifier, in particular device identifier and / or system identifier. They are not clonable, because no two building blocks can be made with the identical properties, in particular by the beneficial use of the software is another random effect added. Hard to extract, because manufacturing variations often show up at the atomic level. PUF are thus almost forgery-proof and can on label, RFID, in semiconductors on storage elements z. As ROM, RAM, PROM, USB memory card, memory controller, memory elements of the CPU, but also be used on virtual memory. Also used on delay circuits, resistors, electrical circuitry, and / or other physical factors based devices and / or methods, and particularly advantageous for M2M applications, Industry 4.0, Internet of Things.

One of a special device and / or method for providing key, in particular this includes a physically non-clonable functions (PUF, PUFs) and is characterized in that it comprises at least one non-volatile memory, in particular which has at least one memory cell; and / or at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one neural network (NN) -based controller and / or fuzzy-based controller; and in particular by the construction and / or starting and / or deleting and / or processing and / or storing, - at least one VM and / or at least one VN and / or at least one neural network (NN) and / or at least one neural network (NN) -based controller and / or fuzzy-based controller generates thermal noise for at least one memory cell.

In a further particular application and / or method and / or system, at least one semiconductor ( 51 ), in particular a semiconductor chip ( 51 ), in particular at least one CPU and / or control unit and / or SRAM with at least one antenna, in particular a capacitive antenna ( 52 ) and / or magnetic antenna ( 53 ) and / or LOOP antenna and / or frame antenna, the at least the semiconductor ( 51 ), in particular a chip body and / or covers (see 5A ) and not shown, by means of computer-implemented invention, in particular software, in particular active software - be electromagnetic waves and / or magnetic and / or electric fields, in particular dynamic fields are recorded by means of at least one antenna and in at least one module ( 54 ) rectified and used in particular for triggering the memory and / or at least one memory cell, in particular at least one SRAM. The module ( 54 ) comprises at least one rectification, in particular diode, - and / or an inductance (L) and / or capacitance (C) can be effected for example by means of at least one diode, in particular on and / or in the chip body ( 51 ). In a further particular application and / or method and / or system, by means of an analysis of the electromagnetic wave and / or electric fields and / or magnetic fields and / or electrical voltage and / or current behavior, in particular over time and / or by means of minindestes a computer-implemented invention, in particular software, in particular active software - in particular by means of the active software - on the behavior of the software and / or other software running on the chip are concluded. The antenna consists in particular of an electrical conductor, in particular copper, silver, gold, lead, aluminum with the particular advantage that the heat generated during operation of the chip is additionally dissipated in an inventive manner (US Pat. 55 ), and / or as mechanical protection ( 56 ) Usage finds. With the advantage that the antenna is shielded from electromagnetic damage and / or shielded from electrical damage and / or shielded from electric fields and / or shielded from magnetic fields and at the same time be used as a trigger, in particular for PUF systems, to protect against mechanical damage Generation of coincidences, in particular keys, in the operation of at least one computer-implemented invention, in particular software. In a particular application, the antenna may be formed as a coil, in particular gößer the semiconductor and / or chip and / or module.

Under sensor is also understood, at least one Cyber-Physical Systems, Internet of Things, especially in the application domain Industry 4.0. Wherein the sensor, in particular by means of a simulator system (Si), in particular at least one VM and / or VN can be advantageously realized. Furthermore, in a particular application, the sensor may include at least one computer system, in particular at least one CPU and / or SRAM.

In a further particular embodiment, at least one allocated area, in particular a memory block, takes place, in particular for at least one virtual machine (VM) and / or at least one virtual network (VN), to which an inner pointer points, and comprises at least one dynamic tree structure which is searched with the aid of the inner pointer and determines at least one allocated block, in particular memory block, in particular at least one virtual machine (VM) and / or at least one virtual network (VN), in particular in the case of the garbage collection - mark-sweep algorithm Area, in particular memory, in particular for at least one virtual machine (VM) and / or at least one virtual network (VN) periodically scanned for objects comprising of running programs, at least one computer-implemented invention, in particular Softwareaus; can still be achieved. This usually puts all roots on a stack and then processes the topmost reference until the stack is empty. During processing, the system first checks whether the referenced object has already been selected. If this is not the case, then its "Mark Bit" is set, in particular a bit in the object header, and all references contained in this object are placed on the stack. If all accessible objects have been marked, the rest of the memory can be assumed to be free and subsequently overwritten and includes, in particular random states and / or random numbers, in particular at least one thermal noise generator, in particular at least one computer-implemented invention, in particular software for generating at least a thermal hot spot. In the sweep phase, the heap is traversed linearly and unmarked area, in particular memory areas, in particular for at least one virtual machine (VM) and / or at least one virtual network (VN) one and / or more lists and / or matrix, in particular free headers added and / or overwrites and includes, in particular, random numbers and / or or random states, in particular at least one thermal noise generator, in particular at least one computer-implemented invention, in particular software for generating at least one thermal hot spot, in particular for generating random numbers. The following allocations are then served from these lists and / or matrix. During the linear search by the heap all markers are also reset so that they are available and / or included in the next garbage collection, in particular at least one random number and / or random states, in particular at least one thermal noise generator, in particular at least one computer-implemented invention, in particular Software, in particular for generating at least one thermal hot spot, in particular for generating random numbers. In order to prevent fragmentation of the area, in particular memory, in particular for at least one virtual machine (VM) and / or at least one virtual network (VN), this method often ends with the area, in particular memory, in particular for at least one virtual machine ( VM) and / or at least one virtual network (VN) is rearranged such that the free area, in particular memory, in particular for at least one virtual machine (VM) and / or at least one virtual network (VN) is a single block, comprises at least a random number and / or random states, in particular at least one thermal noise generator, in particular at least one computer-implemented invention, in particular software, in particular for generating at least one thermal hot spot, in particular for generating random numbers. While garbage collection is performed using this method, the device is in an unstable state and "dead" objects are removed and / or includes at least one random number and / or at least one random state, in particular at least one thermal noise generator, in particular at least one computer-implemented invention, in particular Software, in particular for generating at least one thermal hot spot, in particular for generating random numbers, and / or recognized "living" objects and then renewed the references to objects.

In a further particular application, it can advantageously be concluded from the energy consumption and / or possible energy consumption that the intensity of the thermal power, in particular noise power, in particular for the generation of noise and / or coincidences and / or random numbers, be closed and comprises at least one sensor, in particular a detection device for detecting the energy consumption, in particular for detecting the energy consumption, at least one virtual machine (VM) and / or at least one virtual network (VN), in particular at least one CPU and / or memory, in particular SRAM, in particular at least one server, in particular at least one router, in particular at least one Vermilltungsstelle; and comprises at least one control logic, in particular for comparison and / or read-out and / or processing and / or at least one assignment of the thermal energy to the noise power, in particular to the number of possible random numbers, in particular the probability required coincidences, in particular random numbers, from at least a database DB, in particular at least one list and / or matrix, in particular from at least one SRAM and / or at least one virtual machine (VM) and / or at least one virtual network (VN).

Computer-implemented invention, comprising:

• • mindestens eine Software und/oder Transaktion unf/oder Applikation, insbesondere mindestens eines LOOP, insbesondere zur konstanten Belastung der CPU und/oder zur gezielten Erzeugung von Zufallszahlen umfasst, ein Vorrichtung, insbesondere eine lernend Vorrichtung, insbesondere mindestens ein Neuronales Netz (NN) und umfasst, insbesondere mindestens einen Algorithmus, insbesondere das Statistikdaten, insbesondere Kommunikationsdaten, insbesondere Leitungsparameter, Leitungswerte berücksichtigt und/oder aktuelle Lastparameter, und/oder statistische Lastparameter, und/oder die Priorität, insbesondere der Daten und/oder Transaktion und/oder Applikation; und/oder der Sicherheit, insbesondere der Daten und/oder Transaktion und/oder Applikation; und/oder Ranking, insbesondere der Daten und/oder Transaktion und/oder Applikation; und/oder Wahrscheinlichkeit, insbesondere der Daten und/oder Transaktion und/oder Applikation; dadurch gekennzeichnet, dass diese berücksichtig wird.

STEP 2: umfasst:

• • mindestens eine Applikation, insbesondere der Daten und/oder Transaktion; und/oder das erkennen und/oder erffassen der Parameter, mindestens einer weiteren aktiven Applikation, insbesondere weitere noch mögliche, erreichbare, aktive Applikationen, insbesondere Daten und/oder Transaktion; und/oder auf mindestens einem Server, insbesondere auf mindestens einem Router, insbesondere bei mindestens einem Cloud Provider, insbesondere aller aktiven Anwendungen

STEP 3: umfasst:

• • Das Verfahren dass dadurch gekennzeichnet ist, dass beim Änderung, insbesondere Ansteigen und/oder Abstieg; insbesondere möglcher Last-Änderung und/oder Energie-Änderung und/oder Änderung der Anzahl der VM und/oder VN, insbesondere mindestens ein wahrscheinlicher Last-Änderung, insbesondere für den Zeitraum t,
• • insbesondere das Kommunikationsaufkommen und/oder Datenaufkommen und/oder Transaktionsaufkommen und/oder Bandbreitenaufkommen, insbesondere des Energieverbrauches; dazu führt und

STEP 4 umfasst:

• • dass zugeschaltet und/oder abgeschaltet und/oder gestartet und/oder gelöscht wird, mindestens eine zusätzliche Virtuelle Maschinen VM und/oder mindestens ein Virtuelles Netze (VN) und/oder mindestesn ein Neuronales Netz (NN) und/oder mindestens eine zusätzliche Applikation, insbesondere Daten und/oder Transaktion;
• • Solange STEP 4 erfolgt bis mindestens ein ermittelten Parameter, insbesondere der zu erwachtenden Last erfüllt ist, insbesondere nach dem Rückgang der Last, dadurch gekennzeichnet ist, dass diese wieder abgeschaltet und/oder zurückgenommen und/oder gelöscht werden kann.

In another particular application, load fluctuations, especially in the cloud, are compensated for and included by going through the steps:

STEP 1 includes:

• At least one software and / or transaction unf / or application, in particular at least one LOOP, in particular for constant loading of the CPU and / or for specific generation of random numbers, a device, in particular a learning device, in particular at least one neural network (NN) and comprises, in particular at least one algorithm, in particular the statistical data, in particular communication data, in particular line parameters, line values taken into account and / or current load parameters, and / or statistical load parameters, and / or the priority, in particular the data and / or transaction and / or application; and / or security, in particular the data and / or transaction and / or application; and / or ranking, in particular the data and / or transaction and / or application; and / or probability, in particular the data and / or transaction and / or application; characterized in that it is taken into account.

STEP 2: includes:

• • at least one application, in particular the data and / or transaction; and / or recognizing and / or reading the parameters, at least one further active application, in particular further, still possible, achievable, active applications, in particular data and / or transaction; and / or on at least one server, in particular on at least one router, in particular in the case of at least one cloud provider, in particular of all active applications

STEP 3: includes:

• The method characterized in that in the case of change, in particular ascent and / or descent; in particular possible load change and / or energy change and / or change of the number of VM and / or VN, in particular at least one probable load change, in particular for the period t,
• • in particular the volume of communications and / or data and / or transaction volume and / or bandwidth, in particular energy consumption; leads to and

STEP 4 includes:

• That is switched on and / or turned off and / or started and / or deleted, at least one additional virtual machines VM and / or at least one virtual networks (VN) and / or at least one neural network (NN) and / or at least one additional application , in particular data and / or transaction;
• • As long as STEP 4 takes place until at least one determined parameter, in particular the load to be awakened is fulfilled, in particular after the load has dropped, characterized in that it can be switched off again and / or withdrawn and / or deleted.

In this case, the steps STEP 1 to 4 can be skipped serially and / or in parallel and / or also individual steps, in particular by combining the steps via at least one neural network (NN).

In a further application, at least one neural network (NN) and / or at least one sensor and / or at least one SI and / or at least one neural network (NN) -based controller and / or at least one fuzzy-based controller and / or at least a random generator is characterized in that it is controlled and / or processed and / or stored via at least one VN and / or at least one VN.

The advantage of the special application is that the performance in relation to the applications, applications is given, here by way of example in more applications, more machines are needed to allow a constant throughput - here z. B. Duchsatz of data packets, voice data paktet and at the same time additional more thermal power generated by software, to generate the more required random numbers, as more applications, especially on a server, more security and more random numbers required; and talked about.

In a particular application, in particular for the inventive scaling logic for cloud computing, an automated scaling logic is achieved by comprising static and / or dynamic values, in particular measurement data, in particular within a time interval, in particular less than 0.1 to 1 second, less than 10 seconds , in particular at least one further parameter, such as energy, current, voltage fluctuations, bandwidth, data throughput rate, security requirements, probability, priority of the applications, ranking, identifier z. B. Real-time applications, in particular of selected systems, especially cloud computing environments are used and so to a technical balance, in particular between the infrastructure, in particular between IT infrastructure and existing workload in the cloud computing environment and / or energy supply system and / or energy supply- Network arises, in particular this is done via at least one neural network (NN) in particular neural network (NN) -based controller.

This can lead to another special application for calculating the energy consumption, in particular in power distribution units (PDU), in particular individual virtual machines (VM) and / or VN in a network, in particular private cloud, and comprises by means of the basis of load fluctuations the applications, in particular by means of at least one value, in particular measured value and / or data set, in particular by means of at least one performance monitoring tool provided measured value, in particular program and / or program part, in particular CPU usage and / or input / output behavior and / or Memory utilization and / or bandwidth, - for at least one virtual machine (VM) and / or VN, so that a relative load is determined technically, in particular on the total energy consumption, in particular on the basis of measured values and / or static data and / or dynamic Data and / or static measured data and / or dynamic measured data should be determined by the energy consumption at least one virtual machine (VM) and / or VN. Thus, the exemplary performance requirements of VoIP and / or cloud computing to networks, especially IP-based networks, described by at least one method of scaling logic that balances load fluctuations and is characterized in that is at least one learning device and / or processing process in particular by means of neural networks (NN), the system and / or method taking into account static data, in particular measurement data and / or dynamic data, in particular measurement data and / or statistical data, in particular current and / or statistical load parameters and / or the priority and / or Security and / or likelihood, in particular of all applications active in the cloud provider, within a time, in particular less than 1 microsecond. In particular, an awakening user surge to create and / or start additional virtual machines (VM) and / or VN and / or shut down and / or clear and / or discontinue processing after the load has dropped. Furthermore, this can advantageously comprise and / or be controlled via at least one neural network (NN), in particular via at least one VM and / or at least one VN.

In a particular application for increasing security and / or bandwidth, at least one further VM and / or at least one further VN for transacton can be generated by means of and and, in particular with agents, in particular software-based systems. This has the advantage that this increases the overall reliability and / or bandwidth, in particular of the systems and / or networks, since several systems and / or networks run parallel, in particular simultaneously and / or in a chain and are combined at the end of the chain, in particular by means a logic circuit, in particular an OR circuit.

By way of example, a transaction (eg in 1 ) in the start node ( 1 ) started. Then creates several more nodes (VM and / or VN), here three more nodes ( 3.1 , middle, right VM, VN). Wherein a node (center) (here VM and / VN) disappears after the generation, in particular is deleted. The knot ( 3.1 ) generates four more nodes (here VM and / or VN) ... etc. In sum reach the main node ( 2 ) eight nodes (here VM and / or VN) even after different times t, of the 12 possible ones. For example, the minimum total safety is 3-fold, (weakest node), even if in particular the partial safety, especially in system-critical parts, the interpretation z. 4-fold (strongest node).

In a particular application, the inventive solution may also be implemented and / or used as a system and / or method for storing, in particular at least one virtual memory, in particular comprising at least one VM and / or at least one VN.

This has the advantage that in this way the VM and / or VN can be used as memory, in particular for saving resources and / or increasing the security of the system contributes, in particular by the fact that a collection of nodes, in particular at least one chain (serial and / or parallel) of nodes (in this case VM and / or VN), in particular here by means of the principle of storage by a time shift deta t, which in a particular embodiment be formed up to the LOOP.

In the 1 If this is shown by way of example, at least one data content and / or at least one data record and / or at least one transaction are stored, here called data, data set, transaction. (not shown) and is in the start node ( 1 ) and creates several more nodes 3.1 (VM and / or VN), to the node ( 3.n ) here in particular a chain of ( 3.1 to 3.n ) and is output at the node ( 2 ), here the output of the data, record, transaction. For this, a time t is needed, which is the possible length of storage, so that is the time t (processing of the node mesh) = t (storage time). In this case, the nodes can be generated serially, as a chain and / or in parallel, in particular as a network, architecture, mesh.

In the case of a LOOP, this can be done in an inventive manner such that a node (for example, here 3.1 ) at least one further node (here, for example, 3.n ) and at least one of the underlying nodes (here, for example, 3.1 ) after creating the new node (here, for example, 3.n ) to be deleted.

This has the advantage that VM and / or VN can be used for storing and a predetermined time, in particular of data, records, transactions are stored and / or resources saved. This has the further advantage that a "forgetting", in particular a controlled and / or automatic "forgetting" of data, records, transaction is possible. After deleting and / or no further processing, in particular switching off the VM and / or VN, here after a time t, in particular the data, data records, transaction are deleted.

A further advantage is the increase of the security of the data, in particular of the data protection, in particular with the data of the industry 4.0, in particular if the data are embedded in VM and / or VN and leaving the hardware, in particular system-related device, and the VM and / or VN is deleted

In a particular application, the inventive solution may be as a system and / or method for storing and / or processing, and / or delete and / or no further processing, in particular at least one virtual memory and / or VM and / or VN and / or NN , - comprises by means of at least one mobile system, in particular at least one communication system (MK), in particular mobile communication system.

• • Daten in einen definierten Datensatz teilen, so dass der Datensatz eine Teilmenge der Datern ist.

STEP 2 umfasst:

• • Datensatz versenden, insbesondere an mindestens einen Server und/oder Kennung und/oder Kommunikationsadresse und/oder Gerätekennung und/oder Netzwerk.

STEP 3 umfasst:

• • Verarbeitung des Datensatzes, auf mindestens einem zweiten Server, insbesondere umfasst mittels VM und/oder VN und/oder fuzzy-basierten Regler und/oder Neuronale Netz (NN) basierte Regler und/oder Neuronale Netz (NN).
• • Die technische Verarbeiten kann dabei das Lesen und/oder Erfassen und/oder das Löschen und/oder nicht weiter Verarbeite und/oder das Ausgeben und/oder Anzeigen der Daten, insbesondere Metadaten, Datensätze - beinhalten.

The processing and / or system comprises:

STEP 1 includes:

• • Split data into a defined record so that the record is a subset of the data.

STEP 2 includes:

• Send data record, in particular to at least one server and / or identifier and / or communication address and / or device identifier and / or network.

STEP 3 includes:

• Processing of the data record on at least one second server, in particular comprising by means of VM and / or VN and / or fuzzy-based controller and / or neural network (NN) based controller and / or neural network (NN).
• • The technical processing may include reading and / or recording and / or deleting and / or discontinuing processing and / or outputting and / or displaying the data, in particular metadata, data records.

• • Ermittlung mindestens einer Kennung und/oder mindestens eines Kommunikationsnetzzugang vom Start-Server und/oder ersten Server, insbesondere zu mindestens einem zweiten Server und/oder Netzwerk, insbesondere umfasst mindestens eines Funknetzes und/oder WLAN.

STEP 0.1 umfasst:

• • Suchen mindestens einer Kennungen und/oder einer weiteren Kennung des Kommunikationsnetzzugang und/oder Netzwerk und/oder einer vorgegebene Kennung.

STEP 0.2 umfasst:

• • Speichern mindestens eine Kennung, mindestens eines Zweiten-Server und/oder Ziel-Server, insbesondere der Kommunikationsnetzzugang und/oder Netzwerk, in mindestens eine Liste, insbesondere beim Start-Server und/oder ersten Server und/oder weiteren Server.

STEP 0.3 umfasst:

• • Ermittlung der vorhandenen Ressourcen, insbesondere Speichergröße RAM und/oder CPU-Leistung und/oder Betriebssystem, insbesondere mindestens eines zweiten Server und/oder mindestesn eines zweien Netzwerk.

STEP 1 umfasst:

• • Daten in einen definierten Datensatz teilen, - insbesondere auf einen ersten Server, insbesondere abgestimmt auf die Ressource, insbesondere mindestens eines zweiten Server, so dass der Datensatz eine Teilmenge der Datern ist und/oder die Kennung des Start- und/oder Ziel-Server enthalten ist.

STEP 2 umfasst:

• • Datensatz versenden, vom mindestens dem Start-Server und/oder ersten Server und/oder mindestens des zweiten Server, insbesondere an mindestens einen weiteren bestimmten zweiten Server und/oder mindestens weiteren bestimmten zweiten Kennung, insbesondere Kommunikationsadresse und/oder mindestens weiteren bestimmten zweiten Gerätekennung und/oder bestimmen Netzwerk und/oder Kennung des Netzwerk.

STEP 3 umfasst:

• • Verarbeitung des Datensatzes auf mindestens einem zweiten Server und/oder weiteren bestimmten zweiten Server, insbesondere umfasst diese mittels mindestens einer VM und/oder mindestens eines VN und/oder mindestesn einer fuzzy-basierten und/oder mindestesn einer Neuronale Netz (NN) basierte Regler und/oder mindestens eines Neuronalen Netz(NN).

STEP 4.0 umfasst:

• • STOP insbesondere, wenn Ziel-Server erreicht und/oder Kennung des Ziel-Server ist.

STEP 4.1 umfasst:

• • STOP insbesondere, wenn alle Datensätze der Daten versendet sind.

STEP 4.2 umfasst:

• • STOP insbesondere, wenn u. s. w. ...

STEP 5 umfasst:

• • zweiter Server = erster Server AND GOTO STEP 0.

ENDEIn a particular application, in particular by means of agents, in particular an agent control application and / or a central application, the inventive solution as a system and / or method for storing and / or processing, in particular at least one virtual memory and / or VM and / or VN comprises, by means of at least one system, in particular a mobile system, in particular at least one communication system (MK), in particular a mobile communication system. The processing and / or system comprises:

STEP 0 includes:

• Determining at least one identifier and / or at least one communication network access from the start server and / or first server, in particular to at least one second server and / or network, in particular comprises at least one radio network and / or WLAN.

STEP 0.1 includes:

• • Search for at least one identifier and / or another identifier of the communication network access and / or network and / or a predetermined identifier.

STEP 0.2 includes:

• • Store at least one identifier, at least one second server and / or destination server, in particular the communication network access and / or network, in at least one list, in particular at the start server and / or first server and / or further server.

STEP 0.3 includes:

• Determining the available resources, in particular memory size RAM and / or CPU power and / or operating system, in particular at least one second server and / or at least a second network.

STEP 1 includes:

• • Share data in a defined record, - in particular on a first server, in particular matched to the resource, in particular at least one second server, so that the record is a subset of the data and / or the identifier of the start and / or destination server is included.

STEP 2 includes:

• Send record from the at least the start server and / or first server and / or at least the second server, in particular to at least one other specific second server and / or at least another specific second identifier, in particular communication address and / or at least another specific second device identifier and / or determine the network and / or identifier of the network.

STEP 3 includes:

• Processing the data record on at least one second server and / or another specific second server, in particular comprising at least one VM and / or at least one VN and / or at least one fuzzy-based and / or at least one neural network (NN) based controller and / or at least one neural network (NN).

STEP 4.0 includes:

• • STOP especially if the destination server is reached and / or the destination server is identified.

STEP 4.1 includes:

• • STOP especially if all data records of the data have been sent.

STEP 4.2 includes:

• • STOP especially if etc ...

STEP 5 includes:

• • second server = first server AND GOTO STEP 0.

THE END

This advantageously distributes the data in the network and ensures, in particular the possible multiple routing, a secure transmission of the data.

In a further particular embodiment, the data records and / or data packets can be so large that a loss of the data records is now generated via a special method in the destination server and thus from the received data records and the missing and destroyed data records the data again be prepared, in particular mittesl mindesgtesn a check bit and / or return address and / or trigger to the start server.

In a further particular application, advantageously, the virtual network (VN) and / or virtual machine (VM) and / or neural network (NN), in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controller comprises at least one VM and / or at least one VN - in particular as memory, in particular as virtual memory (VS) are used for secure storage and / or temporary storage and / or solution, in particular by means of Overwriting with self-generated random data due to the inventive solution, in particular temporary data and / or data tracks, in particular on the Transportitweg from the start server to the destination server.

In a further particular application, advantageously, the virtual network (VN) and / or virtual machine (VM) and / or neural network (NN), in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers comprises at least one VM and / or at least one VN - in particular used as a memory, in particular virtual memory (VS), and / or patterns and / or indices and / or identifiers and / or ranking and / or probability - in particular, on the transport route, especially if they have an associated pattern. In heteroassociative retrieval of the patterns, in particular by noise and / or missing bits, this can be advantageously used to generate and / or determine coincidences. In this case, the neural network (NN), in particular neural network (NN) controller in particular as memory, in particular as virtual memory (VS) comprises at least one and / or two-layer networks, in particular store patterns that will be recognized later. In particular, they comprise the networks serving as content-addressable memories which allow the loading of data, in particular from at least one pattern key, which in particular consists of attributes of the stored data. In particular by means of at least one network and / or network, in particular at least one Hopfield network, which comprises at least one single-layer recursive network with symmetrical weight matrices, in which in particular all diagonal elements are equal to zero. In particular, it is advantageous that the Hopfield network can be characterized in particular by means of an energy function and this function, in particular energy functions, is a description, in particular of magnetic materials, of more intrusive material properties, in particular the Lyapunov function, in particular for the characterization of the dynamics, in particular the stability of nonlinear networks guaranteed, in particular guaranteed at least one group of networks.

The network, in particular neural network (NN), in particular neural network (NN) controller can be performed as VM and / or VN.

The network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controller and / or neural network (NN) controller, comprises at least one VM and / or at least one VN - in particular comprises at least one data compression, in particular By generating and / or learning at least one image that at least one reduction of the input pattern, in particular data takes place, in particular with respect to the memory, in particular virtual memory (VS).

The network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controller and / or neural network (NN) controller, comprises at least one VM and / or at least one VN - in particular comprises at least one recursive working Network, in particular with feedback connections, in particular recursive neural network, in particular linked recursive network and / or controller, in particular recursive neural network controller, in particular fully connected recursive network controller. With the advantage that a dynamic behavior occurs and so can perform mappings that are functions of time and / or space and / or probability and / or ranking - and / or - converges to a particular state, in particular by means of at least one Hopfield network and / or at least one Boltzmann machine and / or at least one Boltzmann machine network.

In a particular application, the Boltzmann machine network may include, in particular, hidden units as well as input and output units, the hidden units acting, in particular, as stochastic feature detectors. In particular, the units are not fully linked and there is a weighting and / or rank and / or probability again. The Boltzmann engine network is particularly advantageous in computational performance, particularly as an auto-associative memory in the application to convert incomplete and / or disturbed input data and / or patterns to original, particularly the new status only depends on the immediately preceding status , in particular by means of at least one VM and / or at least one VN and / or at least one neuroinal network (NN) are mapped.

The process of a system not remaining stuck in the extreme value minima (simulated annealing), in particular if the network does not recognize it, comprises, in particular, describing the at least one Markov chain as a series of experiments, the result of each experiment corresponding to the status and / or the configuration of the system and / or network, in particular network corresponds, in particular by means of at least one VM and / or at least one VN and / or at least one Neuroinales network (NN) are mapped.

The term "pattern" also means data, in particular data in the coordinate system, in particular (x) - and / or (x, y) - and / or (x, y, z) - and / or (X, y, z, i). Coordinates, in particular in at least one list and / or matrix and / or n-dimensional matrix, and can be imaged, in particular by means of at least one VM and / or at least one VN and / or at least one neuroinal network (NN).

The fuzzy-based and / or neural network (NN) -based networks and / or controllers comprise at least one VM and / or at least one VN. In a particular application, this may constructively include, that is, automatically grow itself, especially because the learning algorithm generates incrementally and at least one hidden layer during the monitored and / or automated training process. In this case, the fuzzy-based and / or neural network (NN) -based networks and / or controllers are generated by inserting nodes and / or connections until the errors during training provide an acceptable result, in particular an error rate of less than 10%, reached and / or has at least one layer.

Fuzzy-based and / or neural network (NN) -based networks and / or regulators, comprising at least one VM and / or at least one VN - in particular at least one Reduced Coulomb Energy Network (RCE), in particular with the advantages that the networks grow only up to a certain, fixed size and that no local minima occur and a low computational effort is required and has a high storage efficiency.

Fuzzy-based - and / or neural network (NN) -based networks and / or regulators, comprising at least one VM and / or at least one VN - includes in particular at least one Cascade Correlation Network (CCN), in particular with the advantages that it does not It is necessary to know in advance the network architecture, as it automatically generates a small-size network that meets the error criteria and allows partial learning without a complete re-training.

Fuzzy-based - and / or neural network (NN) -based networks and / or regulators, comprising at least one VM and / or at least one VN - comprises in particular at least one Neocognitron network, in particular with the advantages that it is particularly automated learning and / or a temporary weighting and / or ranking and / or probability for the input connections to S-layers can be adapted and present in the other layer, in particular C-layer connections fixed weighting and / or ranking and / or probability.

The network, in particular neural network (NN), in particular neural network (NN) controller can be done as VM and / or VN. and in particular comprises a sensor data fusion, comprising data from at least one source, in particular combining different sources and / or determining and / or associating and / or correlating and / or extrapolating and / or estimating and / or combining data, in particular by means of a matrix, in particular fuzzy-based and / or neural network (NN) -based control - and to be able to make a statement about the identity and / or the temporal classification, in particular to determine more information compared to the individual data, in particular with the Application of security technology and / or control technology, in particular for driverless vehicles, in particular aircraft, swimming devices, in particular for assessing and / or extrapolating the situation and / or threat, in particular in the cloud, in particular in parts of the cloud, in particular in at least one group of the cloud ,

The fuzzy-based and / or neural network (NN) -based control in particular have the advantage in the application of artificial intelligence (AI), in particular in the knowledge representation, in particular with uncertainty, in particular represented by at least one identifier and / or a Ranking and / or probability and uncertainty, in particular represented by means of at least one ranking and / or probability deals, in particular the self-adaptation and / or generalization and / or robust parallel processing.

The fuzzy-based and / or neural network (NN) -based control in particular comprise at least one binary logic, in particular (0,1) and / or a trivalent logic, in particular (0,1, neutral) and / or (0,1 , Unknown) and / or (0,1, coincidence) and / or (0,1, do not know) and / or (0,1, ranking and / or probability and / or identifier) and / or bipolar (-1, 1) and / or n-valued logic, in particular (0,1, ranking, probability, identifier).

Fuzzy-based networks and / or neural network (NN) and / or fuzzy-based controllers and / or neural network (NN) -based controllers, in particular comprising at least one VM and / or VN, can be improved, in particular in the Systemperformace, in particular in the detection rate, by contrast to a single large network (1st part), in particular fuzzy-based network and / or neural network (NN) and / or fuzzy-based and / or neural network (NN) -based controller, an n-part network, in particular an n-part controller, in particular fuzzy-based network and / or neural network (NN), in particular at least one n-part fuzzy-based and / or neural network (NN) - based controller, is used with (n = 2, 3 ...). As a result, it could be achieved in an exemplary comparison that the system was networks <90% correct results for non-trained data compared to a 1-part network with <65% correct results for non-trained data.

The network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controller and / or neural network (NN) controller, comprises at least one VM and / or at least one VN - in particular comprises at least one VM and / or at least one VN, and especially for dynamically recursive networks, configuring them themselves to solve a particular technical condition in different ways, depending on the training scheme, in particular learning to behave like a finite automaton, with the ability to learn for the learned patterns in general, in particular by adding additional hidden layers and / or changing the connections of the layer, in particular the context layer, in particular hybrid networks comprising a mixture comprising sigmoidal and radial activation functions with partial feedback, in particular at least one signal delay at least one system loop and / or the system loop Interference and / or noise, in particular by means of computer-implemented invention, in particular Softwer can be added using the PUF effect to more realistic simulate the overall system, in particular the simulation (SI) and ss the system more stable to model and / or control.

In a further particular application can advantageously by means of the inventive solution, in particular processing and / or control, in particular by means of distribution networks, in particular taking into account swarm intelligence, in particular for mesh networks, in particular the load of communication networks and / or energy consumption and / or possible energy consumption and / or energy control is closed and / or controlled intervene, so the beneficial against a blackout, especially for smart energy; is contributed.

Swarm intelligence also includes and encompasses at least one processing step, the Distributed Artificial Intelligence (VKI), agents, multi-agent systems, in particular by means of networked processes, in particular software agent systems. In this case, the technical swarm intelligence can be linked, in particular by means of logic, cognitive logic. Logic circuits, in particular by means of AND, OR, NOT, - with social networks, in particular of homogeneous and / or inhomogeneous social networks, such. Eg wikis, blogs, apps, activity streams, collaboration software, crowdfunding, hands-on funds, social trading sites, document sharing, data trails, movement profile, information profile, news profile, big data, especially at least one data track, especially in the sense the documents US00000870518B2 . DE 10 2011 111 131 A1 . DE 10 2013 004 795 A1 . DE 10 2012 011 831 A1 . DE 10 2012 024 423 A1 ,

The network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN; In particular, this includes at least one swarm intelligence.

Here, swarm intelligence represents the advantage that it is technically effective, by the central strategy, in particular the central processing and / or the rules, the processing is decentralized and in particular autonomous, technically robust, modular, parallel and scalable, especially after predetermined central rules. In addition, the swarm intelligence represents the integration of complex information from several heterogeneous sources and opens up many possibilities for optimization, in particular in energy consumption, in the security and reliability of the systems, in the saving of resources, and in increasing the quality of life. Wherein the strategy of the Verarbetiungsprozesse and / or rules as follows and includes, at least one logic-based (predicate logic) and / or at least a strategy-based (state space search) and / or at least one rule-based (production systems) and / or at least one case-based (Case-based reasoning ) and / or at least one model-based (simulation) and / or at least one frame-based (OOP paradigm) and / or at least one hybrid-based.

In particular, the swarm intelligence, in particular the processing process, in particular by means of symmetrically distributed processes, in particular algorithms, of at least one VM and / or at least one VN, by means of local interactions, in particular of at least one VM and / or at least one VN. The interactions, for example, depend on typical, basic few skills, especially processing, saving, starting, stopping, sending. In particular, the Confidential Query and Manipulation Language (KQML) can also be used for the communication between the VM and / or VN. Thus, VM and / or VN can also replace decentralized P2P architectures and / or hardware-based systems, in particular networks.

This swarm intelligence can take place, for example, in that at least one VM and / or at least one VN and / or NN neighbor recognize and / or process and / or recognize the interaction, in particular based on probabilities, priorities, information, identifier, in particular on the data rate and / or data throughput and / or environment. Receive the VM and / or VN and / or NN in particular on the behavior, in particular load behavior of the system and / or network information from neighbors, especially in a defined environment of less than 1 meter, less than 10 meters, less than 100 meters, less than or equal to 1000 meters , greater than 1 km, greater than 10 km, in particular wireless environment, in particular WLAN, in particular WLAN coupling and / or in particular the presence of adjacent VM and / or VN and / or NN in the immediate vicinity reveals.

As advantageous processing processes and / or processing parameters are carried out for each VM and / or VN and / or NN that is characterized the at least one common information infrastructure is used and / or common policy, in particular processing steps and / or at least shared a common technical goal has achieved, in particular the system stability, esbesonde maximum system bandwidth and / or the individual goals of at least one VM and / or at least one VN by maintaining the system stability.

The processing, in particular the swarm intelligence, can also take place, for example, advantageously in that at least one virtual switching function takes place, in particular for technical services, in particular for at least one VM and / or at least one VN, in particular for data throughput, throughput rates, CPU times.

The virtual technical mediation can once again belong to a swarm. In this way, the distribution of resources can be easily adapted to new and / or different conditions, so that, for example, swarm intelligence supports blackout in smart energy. The swarm intelligence may in particular comprise at least one VN and / or at least one VN and / or at least one neural network (NN).

The inventive processing, in particular the swarm intelligence, can for example also be advantageous in that cloud resources, in particular computing power (compute), memory (memory), storage capacity (storage), broadband data throughput, general fluctuations, in particular load fluctuations in the cloud, convey and / or process and / or compensates, characterized in that it is a learning device, in particular by means of at least one neural network and / or VM and / or VN, wherein the processing, in particular the algorithm, in particular also measurement data dynamic and / or measurement data static and / or statistics, current load parameters and / or statistical load parameters, and the priority and / or probability and / or identifier and / or security, in particular depending on the content and / or at least one identifier and / or at least one telecommunications address, eg. Eg CLI; HLR, IP address, in particular of all applications active in the cloud provider, in order to start in time an awakening rush of users additional virtual machines or to switch them off again after the load has dropped.

Thus, for example, simulations show that the network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN - in particular robust in the overall system against interference from inside and outside. Furthermore, this method, further technical advantages, here in the localization, brings performance advantages in particular with respect to the method of a queue, in particular in an environment, a defined digital network and / or system and / or network system, in particular a defined "internal mark" and / or " National Cloud ".

The simulation system (SI) comprises, in particular, earmarked for solving the technical problem, in this case for optimal use of resources, in particular for overload protection of systems and / or networks, in particular by means of VM and / or VN, in particular for secure transmission of data and / or Programs in systems and / or networks, in particular by means of VM and / or VN.

The simulation system (SI) comprises the technical function of processing at least one technical input, in particular data and / or programs, in particular by means of at least one neural network (NN) and / or at least one VM and / or at least one VN in particular for possible technical effects and / or the technical response to the input data, to process their potential technical implications, to select a possible technical solution and to control output by means of technical signals to systems and / or networks, in particular VM and / or VN , In In a particular embodiment, the simulation system (SI) can virtually refuse to give an output to the input.

The simulation system (SI) comprises as input, in particular technical signals, in particular data and / or data packets and / or data track and / or programs and / or rules, in particular ranking and / or weighting and / or probability and / or identifier and / or or further technical features, in particular communication address and / or device identifier. The output comprises a signaling, in particular for controlling a system and / or network, in particular a group of systems and / or networks, which is dependent and comprises, in particular, data and / or data packets and / or data track and / or programs of the ranking and / or or weighting and / or probability and other technical features, in particular communication address and / or device identifier. The functionality of the simulation includes, in particular via rules and / or fuzzy-based control and / or neural network (NN) -based Regelunf, in particular these include the technical maximum and / or minimum parameters and / or structures of the systems, in particular VM and / or the network, in particular the UN and / or NN.

With the technical advantage of the simulation system (SI) that not only a Boolean result takes place but at least one fuzzy-based result, in particular output, in particular weighted result with regard to the overall system and / or overall network. The advantage that otherwise rigid rule system and is capable of learning, in particular by means of at least one neural network (NN). The simulation system (SI) comprises at least one controller and / or at least one processing and / or at least one controlled simulative processing - and thus several modules, in a module, in particular there by means of at least one neural network (NN), at least one more Module is learned and / or can still be learned at any time.

In a further particular embodiment, at least one simulation system (SI), in particular according to input data, performs a simulation and is characterized and comprises that the simulation system (SI) itself acts as at least one real load and / or as at least a functionality required for controlling a system and / or network and / or group and / or technical control mechanisms comprises, in particular in the load behavior, bandwidth behavior when generating, processing, delete and / or not further processing of VM and / or VN; and / or any underlying system and / or network and / or group specified. their functionality and / or technical control mechanisms includes, in particular in the load behavior, bandwidth behavior. In this case, the system and / or network provided for the control system is not real, technically controlled by the simulation system (SI), but advantageously the total system and / or overall network is advantageously controlled. Thus, in particular black-out, the avalanche take place in the distant and branched system network, especially in decentralized systems and / or networks, here by means of the inventive solution, be blocked by means of a simulation and real mapping to the outside and / or inside world and thus advantageously retain the overall stability of the system and / or network, by means of real mapping of the decentralized functionalities and / or resources and thus produce a stable network, in particular overall network. Real mapping also includes at least one distributed systems and / or networks, which is switched off by the overall system and / or network and has no effect on the overall system and / or network, but actually exist for the overall system and / or network.

In a further particular embodiment, at least one simulation system (SI), in particular according to input data, performs a simulation and in particular comprises at least one fuzzy and / or neural network (NN) control system for controlling the allocation of resources of at least one system and / or at least one network,

In a further particular embodiment, the inventive solution comprises at least one simulation system (SI), at least one device (GE), in particular at least one intelligent counter, in particular smart meter, smart grid, intelligent meters for gas, water and heat consumption ,

The network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN - in particular comprises at least one simulation system (SI) and / or at least one device (DS). Here according to the European Directive on Energy Efficiency and Energy Services (EDL 2006/32 / EC) Article 13 , in particular "Billing on the basis of actual consumption is carried out so frequently that customers are able to control their own energy consumption", and according to the German § 21c EnWG the installation of smart meters "measuring devices [...], which reflect the actual power consumption and the actual usage time for the respective connection user "becomes technically advantageous here expanded, in particular by at least the probability and / or ranking and / or identifier and / or simulation and / or by means of VM and / or by means of VN and / or fuzzy-based and / or neural network (NN) -based controller.

Under the processing, in particular comprises at least one simulation, is further understood, at least one forecast and / or verification and / or identification and / or trend analysis and / or solutions of system problems, in particular for monitoring the system and / or their operating data, in particular A network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN.

In a further particular embodiment, in combination of local and / or private cloud, in particular cloud server architecture and robust field agents, the evaluation can enable the operating data and / or the forecast, in particular of plant failures, and comprises at least one simulation system (SI) at least one method, in particular for forecasting and / or trend analysis and / or solutions of system problems, in particular for monitoring the system and / or operating data, in particular by means of Proficey software recorded and / or management of the plant monitoring server, in particular long-term operating data on performance level and trigger points, in particular alarms and in particular handles the user access management. On the basis of the data, in particular processing, in particular by means of at least one VM and / or VN, in particular software, generates a trend analysis for a single machine and / or a group of machines, in particular machinery. In a further step, the users receive, in particular via mobile apps and / or at least one web browser, in particular task-specific alarm messages and / or trend data and / or controlled, based on a, in particular software-based and / or hardware-based system simulation and / or system model. This provides the advantage of real-time operational intelligence, data, in particular raw data, resulting in fast, reliable, and economical transfers and / or conversions to increase operational efficiency, productivity, and technical reliability, particularly through probability and reliability / or ranking and / or identifier, in particular by means of at least one of the individual risk index an advanced analysis and / or simulation and / or forecast done. In a further particular application, at least one field agent can in particular carry at least one data, in particular system data from control devices, in particular fuzzy-based and / or neural network (NN) -based controllers, in particular devices (GE). These data are stored and / or temporarily buffered, in particular for security reasons, in particular against power failure, in particular stored in the device (GE), in particular by means of at least one VM and / or VN and / or encrypted and / or to the servers, in particular the plant monitoring server, in particular start server and / or destination server by means of at least one VM and / or VN, transmitted and / or verified. In particular, at least one network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN.

In a further particular embodiment, in particular to the prior art of DE 00 0010 261 682 B4 includes at least one system, in particular simulation system (SI) and / or device (DS) - at least one receiver module and / or transmitting module, in particular for transmitting the signals and / or electrical energy. In particular, the signals for control and / or data. The receiver module comprises at least one antenna, in particular for receiving electromagnetic waves and / or electric fields and / or magnetic fields; and at least one transducer for converting and / or generating signals from the antenna. The transmission module comprises at least one antenna, in particular for transmission and at least one transducer, for converting and / or generating transmission energy.

In particular, a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controller and / or neural network (NN) controller, comprises at least one VM and / or at least one VN.

In this case, the module, in particular the receiver and / or transmitting module, comprises at least one further module for generating energy, in particular electrical energy and / or energy conversions, here called energy module, comprising at least one receiving module, in particular with at least one Antenna and a module, in particular Gleicherichter module, in particular for demodulation.

In a further embodiment, the energy module may contain a further processing, in particular for smoothing, in particular by means of at least one capacitance, in particular parallel to the output, in particular by means of at least one capacitor - and / or - by means of at least one inductor, in particular in series with the output, in particular mittesl at least one coil - and / or by means of at least one resonant circuits (L, C).

In a particular embodiment, at least one comprises an antenna, in particular an electrically conductive wire, in particular connected to a diode to ground, and provide electrical energy, in particular by means of electromagnetic waves and / or electric waves and / or electric fields and / or magnetic waves and / or magnetic fields - and / or - by means of a resonant circuit (LC), in particular at least one resonant circuit (LC), in particular a resonant antenna.

Antenna is also understood and includes, at least one electrically conductive wire and / or a dipole and / or a Yagi Richtantene and / or mangetische antenna, in particular loop antenna and / or at least one long wire <1 meter, in particular> 10 meters and / or capacitive antenna, in particular shortened symmetric antenna, in particular EH antenna, magnetic loop, isotron antenna. In this case, in a particular application, the EH antennas comprise two cylinders which have an inductance (in Henry) of less than 3 mH and a capacitance of less than 30 pF, in particular greater than 5 pF, and advantageously have a stronger electric field component, E component in the Short range and are advantageous Rotary omnidirectional with vertical polarization, E and H field is in phase, with the advantage of a highly capacitive antenna with strong spatial shortening, the low amount of noise, background noise, no difference between sending and receiving have. very space-saving antenna with good reception characteristics especially for the interior.

The transmission of electrical energy comprises in particular electromagnetic waves (E cross H) and / or transverse wave, in particular for use in a vacuum and / or electric waves and / or electric fields (E field) and / or magnetic waves and / or magnetic fields (H-field).

Electromagnetic waves includes in particular radio waves, microwave, heat radiation, light, X-ray, gamma radiation, in particular a frequency less than 3 GHz, in particular a frequency less than 30 GHz, in particular a frequency less than 300 GHz, in particular a frequency greater than 3 kHz and less than 30 GHz.

In a further particular advantageous embodiment, the transmission comprises both the electrical energy, in particular for the supply of the device (DS) and / or the simultaneous transmission of the signals, in particular at least one control signal and / or data signals, in particular for a data track.

With the advantageous embodiment for generating the supply voltage by means of energy harvesting, in particular the use of the antenna voltage caused by the transmitter of any kind comprises electromagnetic waves, WLAN, mobile radio, radio transmitter, mobile phones, in particular of the device (GE), in particular by means of a network, in particular Fuzzy-based and / or neural networks (NNs), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprise at least one VM and / or at least one VN.

• • mindestens ein Signal im ersten Schritt die Information des Signals ausgelesen wird und im zweiten Schritt das elektrische Signale zur Energieversorgung verwendet wird.
• • Im Fall A umfasst die beispielhafte technische Lösung, insbesondere für mindestens ein AM moduliertes Signal, mittels eines elektrischen Bauteil, insbesondere Gleichrichter und/oder Diode, die mittels einer Detektorschaltung verdrahtet ist.

Fall B umfasst:

• • mindestens ein Signal im ersten Schritt das elektrische Signale zur Energieversorgung verwendet wird und im zweiten Schritt die Information des Signals ausgelesen, insbesondere generiet wird.
• • Im Fall B umfasst die beispielhafte technische Lösung, insbesondere für mindestens ein AM moduliertes Signal, das mittels mindestens ein Modul mit der Funktion der Schritte:

STEP 1 umfasst:

• • Auslesen der Hüllkurve, insbesondere der darin enthalten Information zur weiteren Verarbeitung.

STEP 2 umfasst:

• • Verwendung der „Fläche” der Hüllkurve, insbesondere für die elektrische Energieversorgung.

STEP 3 umfasst: Gehe nach STEP 1

STOP.This is advantageously done by simultaneously containing the energy, in particular electrical energy, in the signal and comprises a signal, in particular in the

Case A includes:

• • At least one signal in the first step, the information of the signal is read and in the second step, the electrical signal is used for power supply.
• In case A, the exemplary technical solution, in particular for at least one AM modulated signal, comprises by means of an electrical component, in particular rectifier and / or diode, which is wired by means of a detector circuit.

Case B includes:

• • At least one signal in the first step, the electrical signals is used for power supply and read in the second step, the information of the signal, in particular generiet.
• In case B, the exemplary technical solution, in particular for at least one AM modulated signal, comprises by means of at least one module having the function of the steps:

STEP 1 includes:

• • Reading the envelope, in particular the information contained therein for further processing.

STEP 2 includes:

• • Using the "area" of the envelope, especially for the electrical energy supply.

STEP 3 includes: Go to STEP 1

STOP.

In a particular embodiment, in particular solved and comprises, at least one system, in particular communication system (MK) and / or control system, in particular fuzzy-based and / or neural network (NN) -based controller that an electrical and / or magnetic alternating signal convert into at least one signal, in particular electrical signal converts, with an electrical and / or magnetic receiving unit and / or a converter, in particular electrical converter and the receiving unit is part of the electrical system and / or converter, in particular comprises at least one rectification, in particular comprises at least one rectification, in particular by means of at least one diode and at least one coil (L) and / or resonant circuit (L, C), in particular voice coil (L), - and - is arranged so that a receiving unit, in particular at least one antenna, in particular the coil (L) and / or resonant circuit (L, C), in particular the voice coil (L), - the Emp is catching unit for the energy, in particular of the electrical and / or magnetic alternating signal, by means of the in series and / or parallel circuit to the coil (L) connected rectification, in particular diode and / or rectifier, the energy, in particular the electrical and / or magnetic alternating signal in a modulated by the signal information, in particular pulsating DC voltage, - and - the DC voltage corresponding to the signal information is converted by the converter, in particular into electrical waves. With the advantage that the device (DS) works without its own power supply by it draws its energy from the received alternating signal, in particular electrical and / or magnetic alternating signal, in particular control signal, data signal.

In a further particular application, the rectification can take place by means of at least one diode and / or a rectifier function and / or full-wave rectifier, in particular by means of at least one transistor and / or transistor function. The interconnection can also be done by means of at least a bridge circuit and / or full-wave rectification in push-pull circuit.

The function is as follows, as an input signal comes a variety of waves, in particular the electric and / or magnetic waves in the vicinity of the antenna. Electrons in the antenna, in particular coil, are vibrated so that alternating currents, in particular weak alternating currents, flow. These alternating currents are in particular a mixture of currents of different frequency and amplitude. If this total current is demodulated, there is a mixture of all the transmission energy of all the electric and / or magnetic transmitters in the environment. This total current can be used advantageously for the energy supply of the system and / or for monitoring surrounding systems in a certain radius. A coil, in particular a resonant circuit makes it possible to filter out a desired frequency, in particular of the signals, in particular of the control signals and / or data trace signals. A single resonant circuit works but still imperfect. In particular, the diode eliminates by rectification (demodulation) the symmetry of the coil, in particular voice coil. The demodulation is achieved, for example, by cutting off either the positive and / or the negative half wave of the received AC voltage at the oscillating circuit, this is done by means of the rectifier component, with rectification functionality, for example, crystal detector, diode, transistor, peak voltage rectification, allowing only one half-wave and making the AC voltage a pulsating DC voltage whose envelope in the desired low frequency. In practice, for example, so peak voltage of at least 200 mV and less than 600 mV takes place. The envelope of the pulsating DC voltage is the transmitted technical signals, in particular information, in the case of amplitude modulation (AM). The high-frequency components still contained can be used in particular for the energy supply of the system and can be transformed in particular by means of cascade connection to at least a predetermined value and / or the pulsating output voltage can by means of other electronic measures, such as smoothing, in particular by means of at least one capacitor C and / or at least one parallel connected to the load capacitor C of sufficient capacity (in farads) and / or by at least one inductance L (in Henry) takes place in series with the consumer. Wherein the condenser C is particularly advantageous from the cascade circuit.

The cascade circuit comprises the series connection or concatenation of several modules and / or assemblies, wherein the connection between the individual modules is directed unilaterally and / or a feedback possible by means of the return connection to the beginning of the chain. It can advantageously the Modules and / or assembly, in particular the rectification, in particular mindestersn a diode advantageously be used simultaneously for demodulation and / or as a cascade connection.

The cascade circuit has the functionality that has a higher effect, especially voltage; is achieved, as with only a single module, in particular by interconnecting a plurality of voltage doubler to reach a multiple of the electrical voltage, in particular to achieve a predetermined and / or specific value, in particular the required power voltage. Another advantage of the cascade circuit is that despite increasing the effect, in particular the voltage, the signal is usually without change, thus advantageously can be done first cascade and processing and then the signals are processed. The cascade circuit comprises in particular a voltage multiplier, which is achieved by means of at least one capacitor and potential-controlled switch, in particular rectifier, in particular diode - a voltage multiplication and generates from a supplied AC voltage a magnitude higher DC voltage than can be achieved with only one rectifier. In rectifiers in a cascaded design advantageously distributes the high output voltage to a plurality of diodes, whereby they must have a lower blocking voltage than a rectifier for a directly high-transformed AC voltage and it can be tapped advantageous all partial voltages. In particular, cascade circuits may be interconnected as, Balanced Duplication with the Delon or Greinacher Circuit, and Unbalanced Duplication with the Villard Circuit

In a particular application, the voltage can be increased and / or doubled by means of a switching regulator, in particular at least one fuzzy-based and / or neural network (NN) -based regulator, in particular boost regulator at least one inductance and at least one regulation of the pulse width modulation. Signal (PWM) and so advantageous to produce a decentralized, simulated load, in particular a SI, which is present in the network and / or central but real, in particular by means of inductive load, in particular of at least one motor, electromagnet and / or additional inductances.

In a further particular embodiment, at least one overload and vice versa, in particular defined limit by means of at least one sensor and / or automatesierte decision, in particular includes at least one "blurred" decision, for example by means of fuzzy logic, probability, ranking, neural networks; and / or uncontrolled state of the processing of the communication data and / or at least the power supply, in particular by means of an energy provider (EVU), in particular for a possible local black out z. B. loaded locality, in particular less than 50 km, less than 100 km, less than 300 km; and / or global black out in particular less than 500 km, 1000 km, greater 700 km, in particular due to a probability greater than 50 percent, in particular determined 80 percent and includes at least one sensor and / or at least one mathematical models, in particular at least one function and / or Algorithm and / or at least one trigger point and comprises at least one communication device, in particular at least one virtual machine (VM) and / or at least one virtual network (VN), and / or at least one power supply, in particular within the communication device, in particular at least one supply network ( RU), in particular in the local area of the communication device z. As the telecommunications provider, in particular the local supply network z. As the power generator (RU), characterized and includes at least one shutdown and / or stop and / or delete and / or not further processing and vice versa, at least one computer processing, in particular at least one virtual machine (VM) and / or at least one virtual Network (VN) and / or at least one real network (RN) and / or at least one real machine, in particular server, exchange, machine, missile, in particular within the communication time (KT) and / or within the communication device (KS) and in particular that the data, in particular telecommunications-based data are stored, in particular temporarily, in particular to the resumption and vice versa, after the elimination of overload and vice versa the power supply, in particular due to a probability of possible overloading and vice versa, of less than 50 percent, in particular less than 20 percent determined and included at least one sensor and / or mathematical models, in particular functions and / or algorithms, in particular within the communication device.

The determination of an overloading and / or underloading of the device and / or communication device can be carried out in particular by means of at least one simmulation program. The burden of the device must not be limited only to the telecommunication and / or energy supply, as an example also to the water supply and / or transport path control and / or data supply, in particular the data track; and can also be included as an example in the Simmulation and / or Simmulationsprogramm and control, in particular the mediation.

• • Aktivierung und/oder Detektion einer Überlastung der Vorrichtung, insbesondere des Stromnetzes und/oder Telekommunikationsnetz und/oder Transportnetz und/oder Contentnetz und/oder Datennetz dadurch gekennzeichnet und umfasst, dass mindestens ein Erhöhen, insbesondere Starten von mindestens einer zweiten VM und/oder mindestens eines VN;

STEP 2 umfasst:

• • und/oder Verringerung der Zustände und umfasst, Beenden und/oder Löschen und/oder nicht weiter Verarbeiten und/oder Anhalten, insbesondere mindestens einer VM und/oder VN.

In a further particular application for the control, in particular in the locality, in particular in the system loaded locality and the specific time t, in particular the electrical energy intake, at least one device and / or at least one network, in particular tele-communication device and / or telecommunications network depending on conditions in at least one network, in particular power grid and / or transport control device and / or network, in particular content network and / or social media device z. For example, the content traffic, in particular the data track; and / or its network, which is responsible for the supply and / or control of the telecommunication device and / or telecommunication network in the locality, in particular affected locality and / or time, wherein the network, in particular power grid and / or communication network and / or transport control network and / or content network z. B. advent of many friends z. Eg the death of an important content user, here with many fellows, followers; a central monitoring unit, in particular, this comprises at least one sensor and / or processing process, which monitors the operating state of at least part of the components in the device and / or network, in particular power grid and in particular the telecommunications network has a telecommunications control unit, at least part of the telecommunications network components in Telecommunications network controls, the power grid monitoring unit and / or the transport monitoring unit and / or the content monitoring unit coupled to the telecommunications control unit and comprises at least one device and / or network and / or interface is connected to digital information, in particular at least signals, at least data, at least one data packet and / or data track and includes in particular the steps:

STEP 1 includes:

• Activation and / or detection of an overload of the device, in particular of the power grid and / or telecommunication network and / or transport network and / or content network and / or data network characterized and includes that at least one increase, in particular start of at least one second VM and / or at least one VN;

STEP 2 includes:

• • and / or reducing the states and includes, stopping and / or deleting and / or discontinuing processing and / or stopping, in particular at least one VM and / or VN.

Device is also understood, in particular the power supply and / or content, in particular in a locality, data track, in particular affected locality; and / or time and / or time range, by exchanging signals, in particular trigger signals, in particular information between the power grid monitoring unit and / or the telecommunication control unit and / or transport control unit and / or content control unit.

Increase and / or decrease the power consumption and / or communication performance and / or content of the telecommunication network in the locality, especially affected locality and / or time and / or time domain, by configuring the telecommunication network components in the locality, especially affected locality and / or time and / or or time range by the power grid monitoring unit and / or telecommunications monitoring unit and / or transport monitoring unit for the period of higher and / or low power supply and / or higher and / or low communication volumes, in particular also dummy communication volumes; and / or higher and / or lower content; and / or lower transport volumes; and / or - detection of an overload of the power grid; and / or communication network and / or content network and / or transport network; due to a low and / or high power supply; and / or due to insufficient and / or high communication volumes; and / or characterized by a low and / or high content, and / or by a low and / or high transport performance characterized and includes at least one by exchange of information and / or signals and / or content, in particular messages between at least one Power network monitoring unit and / or at least one telecommunications control unit and / or at least one transport control units and / or at least one content control units, lowering and / or increasing the power consumption and / or communication and / or content and / or transport volume of the telecommunications network in the affected locality, by configuring the Komponeten , in particular the telecommunication network and / or transport network and / or content network in the affected locality by the power grid monitoring unit and / or unit of communication monitoring and / or transport monitoring unit and / or content monitoring unit for the time and / or the period of low and / or high power supply and / or communication and / or transport and / or content.

• • Ankopplung und/oder Abkoppeln der Komponente, insbesondere der Telekommunikations; und/oder Transport und/oder Content, insbesondere Datenspur; vom Stromnetz und/oder Vermittlungsnetz, insbesondere Virtuelle Netz (VN) und/oder Virtuelle Maschine (VM);
• • und Betrieb und/oder Zuschaltung von mindestens einer weiteren Energieversorung, insbesondere Batterie, Akkumulatoren, unterbrechungsfreie Stromversorgung;
• • Verändern der Verschaltung des Netzes, insbesondere der Topologie des Vermittlungsnetzes, insbesondere umfasst mindestens eines Virtuellen Maschine (VM) und/oder mindestens eines Virtuellen Netze (VN), insbesondere Routings von Signale, insbesondere Kommunikationsdaten, insbesondere Kommunikationskennung z. B. CLI, HLR, IP-Adresse, Router-Adresse; Gerätekennung, insbesondere Kommunikationsinformationen;

STEP 2 umfasst:

• • Und/oder so beschaltet wird, insbesondere das System und/oder Netz, insbesondere um die betroffenen Lokalität und/oder System- die Last; um die belastete Lokalität herumzuleiten

STEP 3 umfasst:

• • Und/oder zusätzlich durchzuleiten, insbesondere zur Erhöhung der Last;

STEP 4 unfasst:

• • Und/oder umfasst Zuschalten und/oder Abschalten von redundanten Komponenten, insbesondere mindestens einer Virtuelle Maschine (VM) und/oder mindestens eines Virtuellen Netzes (VN), insbesondere der Telekommunikationsnetzwerkkomponenten, insbesondere in dem belasteten Lokalität und umfasst,
• • Erhöhung und/oder Reduzierung, insbesondere der Übertragungsgeschwindigkeiten und/oder Content und/oder Kommunikationsaufkommen und/oder mindestens einer Virtuellen Maschine (VM) und/oder mindestens eines Virtuellen Netzes (VN), in den Telekommunikationsnetzwerkkomponenten in der betroffenen Lokalität.

In a further particular embodiment, the method according to the preceding claim, characterized in that at least one increase and / or at least one reduction of the power consumption and / or communication volume and / or transport volume, in particular of the telecommunications network components, can be achieved by at least a configuration and / or method and / or device comprises:

STEP 1 includes:

• Coupling and / or decoupling of the component, in particular the telecommunications; and / or transport and / or content, in particular data track; from the power grid and / or switching network, in particular virtual network (VN) and / or virtual machine (VM);
• • and operation and / or connection of at least one other energy supply, in particular battery, accumulators, uninterruptible power supply;
• • Changing the interconnection of the network, in particular the topology of the switching network, in particular comprises at least one virtual machine (VM) and / or at least one virtual networks (VN), in particular routing of signals, in particular communication data, in particular communication identifier z. CLI, HLR, IP address, router address; Device identifier, in particular communication information;

STEP 2 includes:

• • And / or so connected, in particular the system and / or network, in particular the affected locality and / or system load; to pass around the polluted locality

STEP 3 includes:

• • and / or additionally pass, in particular to increase the load;

STEP 4 includes:

• • and / or includes connection and / or disconnection of redundant components, in particular at least one virtual machine (VM) and / or at least one virtual network (VN), in particular the telecommunication network components, in particular in the loaded locality and comprises,
• Increasing and / or reducing, in particular the transmission speeds and / or content and / or communication volume and / or at least one virtual machine (VM) and / or at least one virtual network (VN), in the telecommunications network components in the affected locality.

Under Transportsteuevorrichtung and network is also understood and includes that device and network for road traffic control, water supply, gas supply, flight control, navigation route control, but also control device and networks for services such. B. social media in particular Facebook, Twitter, WhatsApp, in particular services that generate content, in particular within a short time, for some specific device and / or persons and / or for a circle of communication addresses and / or device identifiers, in particular with short contents, especially news.

In a particular embodiment, in at least one underload and / or overload, in particular defined limit, in particular by means of at least one sensor and / or automated decision, in particular "fuzzy" decision, for example by means of fuzzy logic and / or ranking and / or probability; and / or at least one communication data processing and / or at least one energy supply (RU), in particular in the case of a possible local area, in particular affected area, in particular less than 50 km, less than 100 km, less than 300 km; and / or global area, in particular less than 500 km, 1000 km, greater 700 km; in particular due to a probability and / or ranking greater than 50 percent, in particular determined 80 percent and includes at least one sensor and / or at least one mathematical models, in particular at least one function and / or algorithm and / or at least one trigger point and comprises at least one communication device and / or at least one electrical energy supply, in particular within the communication device, in particular at least one electric power supply network (EVU), in particular in the local periphery of the communication device z. As the telecommunications provider, in particular the local supply network z. As the power generator (RU), characterized and includes that at least one start (S) and / or at least one connection and / or at least a temporal extension of the processing takes place, in particular comprises a programmed LOOP and / or at least one queue and comprises at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one real network (RN) and / or at least one real machine, in particular servers, Switching center, in particular within the communication time (KT) and / or within the communication device (KS) and includes, in particular that the telecommunication-based data, in particular dummy data, test data are stored, especially temporarily, in particular until the resumption after the elimination of the underutilization of the power supply , in particular Based on a probability and / or ranking of a possible underload of less than 50 percent, in particular less than 20 percent determined and includes at least one sensor and / or mathematical models, in particular functions and / or algorithms, in particular within the communication device. Thus, it can be advantageous to be switched on as an example machine, such as night storage heaters if too much electrical energy in the network is available, but the telecommunications and the device technology and networks can now be taken as a buyer of too much electrical energy produced by energy consumers such as the virtual machines and / or networks that are generated and / or closed, terminated, and enable faster / slower communication transmission. In particular for overload protection and in crises, it is advantageous to have a slower communication transmission.

The advantage is that different networks can join together and thus together a skipping of the damage, malfunction, can be simmuliert to other networks and / or prevented. So z. B. in case of crisis, a controlled shutdown and / or resumption of the device and / or networks are made or prevented by the controlled and targeted technical intervention. Here, the different types of networks are advantageously protected, as they are not only dependent on the power supply, but also on the transport routes and content. All networks interlock and so can a sudden high drop and / or volume of content, a so-called content hot spot z. B. provide the information where, in particular the locality, the identifier, in which system; Help is needed, as in the case of flooding, in the event of a crisis to a Unterlasst and / or overloading, especially the local switching network lead, either by the damage itself, z. As the flooding, the shutdown and / or connection of the electrical energy supply and / or the increased content, in particular Datgenspur. A broad-based local content hotspot also takes place, for example, on Twitter, electronic voting, vote in particular dramatic incidents, which leads to increased content and short communication time and to a particular local congestion, data traffic, in the system itself and their communication channels , in particular networks and / or communication and energy demand that is to be prevented and / or obtained, in particular for the information of the population and / or internal security.

The generation (E) comprises in particular the automated program provision and / or program creation and / or data, in particular data track. Starting (S) comprises, in particular, the execution of at least one program and / or program part and / or data. The processing (V) is carried out by means of at least data, in particular data track and / or control signals and / or programs and / or program parts, in particular malicious programs.

The communication layer (KE) comprises in particular the layer 0 with the level of the data, signals, content, in particular for information, images. The layer 1 for identifying the customer includes the device z. By means of CLI, HLR, IP address, router address; Device identifier, customer z. B. by means of customer number PAN, PIN; the location z. B. by HLR, GPS data. The layer 2 the switching device and / or application, program z. B. by means of switching device, router, computer, personal computer. Layer 3 of the identification of the networks and the layer 4 of the transmission networks z. B. by means of WLAN, LAN, LTE, GSM, UMTS. With the advantage of increasing the bandwidth, security and parallelization of communication processing, in particular so will prevent at least one denial of service attacks, here that the networks can lame by congestion, in particular by means of cyber-physical device (CPS) includes in a intelligent environment, in particular with at least one virtual machine (VM) and / or at least one virtual network (VN), are embedded and can communicate with at least one second and / or further cyber-physical device (CPS) in their vicinity via the Internet Protocol (IP) and thus autonomously organize the processes, in particular production, by means of semantic technologies, characterized by the Cyber-Physical Device (CPS) and at least one second Cyber-Physical Device (CPS), in particular machines understand what the objects in their environment For example, which services have at least one second cyber-physical predi In particular, the production is linked to the ERP devices of the companies which control the business processes.

In a further particular application, for at least one server, in particular routers, by means of at least one network IP stack and at least one server, in particular web server, comprises - at least one virtual machine (VM) and / or at least one virtual network (VN); and includes for the data, in particular to be delivered data of the applications the shortest possible bridge from Device memory is taken to the network card, by means of at least one framework, in particular netmap framework, a neural network (NN); map the buffer of the network card in the user space and put the bufferless processes and the approach, applications and network stack in the same address space.

In a further particular application, that static and / or dynamic content can be additionally accelerated and includes that the data, in particular at least one data record, in particular data track; to finished packages, in particular to at least one package, pre-segmented and / or kept ready and includes at least one VM and / or at least one VN. With the advantage that demands on TCP resources decrease and the accesses become faster.

In a particular embodiment, for at least one customer and / or at least one application and / or system and / or system group and / or at least one transaction and / or trigger, in particular overload Unterlastrigger the electrical energy supply and / or at least one program start and / or at least one communication level (KE), characterized and comprises at least one virtual machine (VM) and / or at least one virtual network (VN) and / at least one real network (RN); - At least generated (E) and / or started (S) and / or processed (V) is carried out and / or within the communication time (KT) and / or within the communication device (KS), in particular within the active communication device, in particular within the actively from the communication device used communication device and / or by means of at least one trigger.

In a particular embodiment, the solution for identification and / or verification and / or classification, in particular of at least one system and / or at least one network and / or at least one VM and / or at least one VN and / or at least one neural network (NN ), and includes at least one assignment of the locality, in particular the spatial distance x, here on the x-axis - and their frequency of communication f (x), here on the y-axis, in particular their negative correlation, the Thomas J Allen (1977) proved, especially after the function of the Zipfschen law. This assignment is called probability function of distance to communication (WEK). So there is a different function curve for the behavior: communication to the distance at E-Mail, in particular IP address, identification, in particular the communication identification and personal meetings. This functional curve is coupled with technical means, in particular technical parameters, and used in an inventive manner, in particular for identification and / or verification and / or classification. Here, the Lokalitatsentfernung, here the distance, in particular between at least one physical server and / or network done on which at least one VN and / or at least one VN is present.

Where distance is also understood, the spatial distance in meters, the line length of the connections of the networks in meters, but also the field strength and / or signal strength of the signal, in particular radio signal from one system to another and / or the local energy consumption from one system to another ,

Here, the communication frequency here, in particular the create at least one VM and / or VN and / or - the communication between at least one VM and / or the communication between at least one VN and / or the communication between at least one VM and / or VN. In this case, the probability function of the distance communication (WEK) can be used in a particular application and comprises at least one ranking and / or probability and / or identifier. With the advantage that this function gives a specific, quasi-static function curve and can thus be advantageously used for identification, verification, classification, ranking, probability, identification. An intrusion of an unauthorized third party and / or the intrusion of an unauthorized system into the system and / or network has changed the existing functional structure of the probability function of distance-communication (WEK), in particular, changes from less than 1 percent, 1 to less than 3 percent, in particular less than 5 percent, and in particular then at least one trigger point takes place that an unauthorized intervention in the system and / or network, in particular in the cloud, - carried out and other measures, in particular system-specific done, in particular delete the affected VM and / or VN. In this case, the assignment function can be equal to the position, in particular the distance (x) to the function frequency of communication f (x), in particular in a certain assignment function range.

• – insbesondere f(x) := (a/x) + b,
• – insbesondere f(x) := (a*x) + b,
• – insbesondere f(x) := a*(log zur Basis ½ (x)) + b mit dem Faktor a und einer n natürlichen Zahlen (...–1, 0, +1...) und einem Summanden b der natürlichen Zahlen (...–1, 0, +1...).
• – Dabei wird mit der Funktion f(x) eine spezifische Form der Korrelation der Kommunikation zur Lokalität, insbesondere der Lokalitätsentfernung, - wiedergeben.

The mapping function includes

• In particular f (x): = (a / x) + b,
• In particular f (x): = (a * x) + b,
• In particular f (x): = a * (log to the base ½ (x)) + b with the factor a and one n natural numbers (...- 1, 0, +1 ...) and one summand b of the natural numbers (...- 1, 0, +1 ...).
• - With the function f (x) a specific form of the correlation of the communication to the locality, in particular the locality distance, - is reproduced.

Another essential advantage of a virtual machine (VM) and / or virtual network (VN) is that such a virtual machine (VM) and / or virtual network (VN) on each hardware, in particular each computer on which the VM, VN is executed, such as the aforementioned servers, is restarted and / or the execution of the virtual machine (VM) and / or virtual network (VN) is continued from a defined state in which a previous server, so start server, Transport server or destination server that has stopped Virtual Machine (VM) and / or Virtual Network (VN). Likewise, any server deployed during the transport path may either pause before resending the Virtual Machine (VM) and / or Virtual Network (VN) for a next server to continue or abort from the suspend point, thus a next Server then restarts the Virtual Machine (VM) and / or Virtual Network (VN).

This results in further special advantages for the method according to the invention and for secure data transmission. Thus, by breaking off a virtual machine (VM) and / or virtual network (VN) after execution by at least one server, in particular transport server and before forwarding to the next server, in particular transport server or the destination server can be ensured that due the subsequently occurring restart at the next server, in particular transport server or destination server, if necessary, interim manipulations are automatically destroyed, as by restarting each subsequent server, the virtual machine (VM) and / or virtual network (VN) each of a defined ground state and or locality is restarted from, in particular, that was defined before being sent by the start server. Thus, by the respective restart at each of the servers during the transport path, but at least one and in particular for example at the destination server, the interim manipulations are lost, so that the data is received in an original state, which defined by the start server at the beginning of the shipping activity Basic state of the virtual machine (VM) and / or virtual network (VN) corresponds.

The stopping of a virtual machine (VM) and / or virtual network (VN) after execution by a transport server before forwarding to a subsequent transport server or to the destination server has the further advantage that desired defined activities and / or locality of the virtual machine ( VM) and / or virtual network (VN) can be carried out continuously throughout the transport path and such activities can serve to ensure the integrity of the data during transport.

The described advantages of both stopping and aborting a virtual machine (VM) and / or virtual network (VN) can be combined as desired. For example, it may be provided that not every server, in particular transport server, terminates a virtual machine (VM) and / or virtual network (VN), so that it has to be restarted by the next server, in particular transport server, but only a few servers , in particular transport server during the transport route, for example, the last server, in particular transport server before the destination server performs. Other servers that do not terminate the virtual machine (VM) and / or virtual network (VN), in particular transport servers, can stop them, for example, only the virtual machine (VM) and / or virtual network (VN) for a subsequent transport server or destination server to stop the virtual Execute machine from the stopped time or the state defined by the stop.

In an advantageous development, it can be provided that the virtual machine (VM) and / or virtual network (VN) already checks the integrity and / or locality violation of the data transmitted with it on a server, in particular transport server, during its execution time. This can take place, for example, by virtue of the fact that a virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN) continuously generates a checksum of the data to be transmitted and checks it with a stored value or else in that at least a virus scanner comprises infestation of the transmitted data by defective software, such as viruses or Trojans, being checked and / or by monitoring the energy consumption, in particular the next server, of the current server in retrospect, in particular per data record and / or transaction, in particular by means of at least one neural network (NN). If such an infestation is detected, it can be directly determined by such an executed test program, in particular the infestation by the malware in the Communication chain are eliminated early, so that this already gives the advantage of having to check the data to be transmitted not only on the destination server and thus leads to energy savings and / or optimal use of existing bandwidth, as lying in the Kommumungskketter server and networks are not unnecessary Process data, especially in the destination server already protected against the introduction of malware.

In another possible embodiment, which can also be combined with the one described above, provision can be made for start data to be integrated into the virtual machine (VM) and / or virtual network (VN) as data to be transmitted with the start server Virtual machine (VM) and / or virtual network (VN) are converted in a conversion process in target data, in particular on each server, in particular the transport server, in particular anyone who executes the virtual machine (VM) and / or virtual network (VN), during the execution time of the virtual machine (VM) and / or virtual network (VN), in particular by means of at least one neural network (NN); the conversion process is carried out in part, in particular in such a way that the conversion process does not take place until reception or after reception of the virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); completed at the destination server.

This embodiment of the invention further security and security of the data to be transmitted and use of the bandwidth of the network and hardware is achieved in that it is not the data to be transmitted is only those data that should arrive at the destination server, but to those Data from which during the transport path through the execution of the virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); by at least some of the servers, in particular transport server, those data which are to be received by the destination server are first generated.

In a particular embodiment, data on server, in particular server, in particular cloud server, in particular communication device, in particular mobile devices, in particular comprises at least one virtual machine (VM) and / or at least one virtual network (VN) and / or by means of at least one neural network (NN); in particular the copying of the shared data to a server, holds, in particular in a database, in particular in at least one virtual machine (VM) and / or at least one virtual network (VN); and / or when deleting and / or not further processing the used virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN) is deleted and / or overwritten.

In a particular advantageous application, the device includes at least one cloud raid (CR) method by connecting multiple physical disks to a logical drive, in particular enabling redundant array of independent disks (RAID) technology, higher data transfer rates, and lower failure rates so inventively solved that the data is not completely entrusted to a single cloud device and / or cloud network, in particular a provider and / or user device, but divided and / or encrypted and / or distributed to different storage services and / or different communication channels, in particular evenly and comprises at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); and at least data and / or at least one trigger and the data, in particular at least one data record is split and encrypted into at least one data record part, in particular block and for each block and / or each data record and / or at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); generated and / or started and distributed to different, in particular independent device, in particular service providers. Advantageously, no device has complete ownership of all data fragments. This approach advantageously makes external data storage more reliable, reduces the risk of getting dependent on a particular service provider, and reduces the potential for data misuse and data center operational security.

Wherein the distribution and / or splitting and / or encryption and / or allocation comprises at least one random number and / or at least one random state, wherein at least one random number and / or random state by means of at least one random number generator, in particular noise generator, in particular by means of thermal action of the invention Processing, in particular software processing, see also ( DE 10 2013 004 795 A1 ), in particular in the generation and / or processing of at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); takes place, in particular at least one content and / or data and / or data set and / or transaction and / or virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN).

A virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); is triggered and / or started and includes at least one trigger and / or signal and / or application. In this case, the trigger, in particular at least one transaction and / or at least one data record and / or data track and / or at least one APP and / or at least one identifier and / or at least a new customer identifier.

Cloud, cloud computing describes, among other things, the approach and / or abstracted IT infrastructures, for example, the device technology, device platforms, mobility platforms z. B. for platforms for networking mobility, in particular for networking of motor vehicle traffic, the protocol interfaces, computing capacity, data storage, network capacities, but also the software that should be made available together dynamically adapted to the needs, here via a network using central (Cloud Computing) and / or distributed control (Grid Computing), in particular by means of at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN). Cloud, cloud computing includes, for example, at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN).

The term "data" also includes, among other things, and includes, in particular and includes, data that is fragmented by metadata and / or data traces and / or data acids with metadata, in particular and / or data scattered across different apps, platforms, integrators, stores and technologies Data and / or data track, especially in records, and thus the data fragmentation in particular at different locations, at different providers, and / or different times and / or different locations different systems and / or different networks collected, recorded, stored, processed and output , Data and / or data track are in particular electrical signals and / or signal sequences, in particular comprising different intensity and different time and / or different locations and / or different systems and / or different networks. Furthermore, the term "data and / or data track" also includes programs, in particular program parts, in particular applications, in particular APP. Data and / or data lane also mean network and / or system and / or personal data and / or data lane, in particular also preventively with a lifetime, in particular with regard to the right to be forgotten, and / or prohibition for processing within certain locality boundaries, in particular national borders and / or networks, in particular network in and / or intersection with other networks, in particular special country borders and / or systems, in particular with location in special country borders to provide.

In a particular application, in particular if data subjects and / or systems are entitled to their systemic and / or personal data being deleted and not further processed, in particular if the purposes for which the data was collected have become obsolete, this may by at least one parameter.

Where, in a particular application, "when the purposes for which the data was collected have become superfluous" include, in particular, when the data, at least one data set has collected, measured, and has measured the particular system, in particular the system generating the data / or network that generates, collects, measures, forwards the data and / or data track and / or leaves the system and / or network.

In particular, at least one ranking and / or probability and / or identifier include, certain locality boundaries and / or network boundaries, in particular country borders and / or networks, in particular network in and / or intersection with specific other networks and / or country borders and / or Systems, in particular with location in special country borders, leave in particular this done by means of an identifier, in particular a device identifier and / or communication identifier, in particular country code.

Where, according to the European Commission: Proposal for a General Data Protection Regulation to inform Article 17 (2) "that its data subject requires them to erase all cross-references to such personal data or to make copies or replicas of such data" may appear in a particular embodiment, the person responsible and / or the responsible system, this comprises, by means of at least one virtual machine (VM) and / or VN and / or at least one neural network (NN); insbeonderre with the at least one address and / or at least one identifier of the data and / or data track, in particular if this is done automatically, that to delete data and / or data track, at least one Virtual machine (VM) and / or VN and / or at least one neural network (NN); generates, creates and / or this sends to at least a destination address of the data, in particular the record.

• – Datensatz := Summe (mindestens einer Kennung und/oder mindestens ein Teil (der Daten, insbesondere des Inhaltes, Nutzdaten, Content)) und/oder Datensatz := Summe (mindestens ein Teil (der Daten, insbesondere des Inhaltes, Nutzdaten, Content)) und/oder mindestens einer Kennung) und/oder Datensatz := Summe (mindestens ein Teil (der Daten, insbesondere des Inhaltes, Nutzdaten, Content)) und/oder mindestens einer Kennung (Kommunikationsadresse und/oder Gerätekennung und/oder Ranking und/oder Wahrscheinlichkeit) ist.
• – Mit Content := Summe der Teile (der Daten des Inhaltes, Nutzdaten, Content) ist.

For example, the data and / or data track can be constructed and includes as follows: Data: = sum (of at least one record)
With

• - Dataset: = sum (at least one identifier and / or at least one part (of the data, in particular of the content, user data, content)) and / or data set: = sum (at least a part of the data, in particular of the content, user data, content )) and / or at least one identifier) and / or data set: = sum (at least a part of the data, in particular of the content, user data, content) and / or at least one identifier (communication address and / or device identifier and / or ranking and / or probability).
• - With Content: = sum of the parts (of the data of the content, user data, content) is.

• – n = n + 1
• – Datensatz (n) := ist Teil von Daten
• – IF Summe Datensatz (1...n) = Daten THEN GOTO B

GOTO A

B LOOP M = 1 to n

• – Umfasst: TODO mindestens jeder Datensatz (M), mindestens eine GOSUB VM (n, M) und/oder GOSUB VN (n, M), erzeugt und/oder verarbeitet und/oder versendet und/oder löscht.

Next m

STOPIn particular with the method A comprises:

Process (DS) for n = 0 holds:

A LOOP (includes)

• - n = n + 1
• - Record (s): = is part of data
• - IF sum record (1 ... n) = data THEN GOTO B

GOTO A

B LOOP M = 1 to n

• - Includes: TODO at least each record (M), at least one GOSUB VM (n, M) and / or GOSUB VN (n, M), generates and / or processes and / or sends and / or deletes.

Next m

STOP

• – n = n + 1
• – Datensatz (n) := ist Teil von Daten
• – TODO mindestens jeder Datensatz (n), mindestens eine GOSUB VM (n) und/oder GOSUB VN (n), erzeugen und/oder verarbeiten und/oder versenden und/oder löschen.
• – IF Summe Datensatz (1...n) = Daten THEN GOTO STOP
• – GOTO A

STOPIn particular in another method B comprises:

Process (DS) for n = 0 holds:

A LOOP (includes.)

• - n = n + 1
• - Record (s): = is part of data
• Generate and / or process and / or send and / or delete at least one data set (s), at least one GOSUB VM (n) and / or GOSUB VN (n).
• - IF sum data record (1 ... n) = data THEN GOTO STOP
• - GOTO A

STOP

• – n = n + 1
• – Datensatz (n) := ist Teil von Daten
• – TODO mindestens jeder Datensatz (n), mindestens eine GOSUB VM (n) und/oder GOSUB VN (n), erzeugen und/oder verarbeiten und/oder versenden und/oder löschen.
• – IF Summe Datensatz (1...n) = Daten THEN GOTO STOP
• – GOTO A

STOPIn particular, in the method C comprises:

Process (DS) for n = 0 holds:

A LOOP (includes)

• - n = n + 1
• - Record (s): = is part of data
• Generate and / or process and / or send and / or delete at least one data set (s), at least one GOSUB VM (n) and / or GOSUB VN (n).
• - IF sum data record (1 ... n) = data THEN GOTO STOP
• - GOTO A

STOP

• – Datensatz (n) := ist Teil von REST
• – REST(n) := (REST - Datensatz (n))
• – TODO mindestens jeder Datensatz (n), mindestens eine VM (n) und/oder VN (n), erzeugen und/oder verarbeiten und/oder versenden und/oder löschen.
• – TODO REST (n) := mindestens eine VM(REST) und/oder VN(REST)
• – Endebedingung: z. B. IF Summe Datensatz (1...n) = REST THEN GOTO STOP
• – GOTO A

STOP In particular in another method D comprises:

Process (DS) applies:

A DEATH

...

Record (s): = is part of data

REST (n): = (data record (s))

TODO at least each record (s), at least one GOSUB VM (s) and / or GOSUB VN (n), generate and / or process and / or send and / or delete.

TODO REST (n): = at least one VM (REST) and / or VN (REST)

STOP

...

z. B. for VM (REST) applies

A LOOP (includes)

• - Record (s): = is part of REST
• - REST (n): = (REST record (s))
• Generate and / or process and / or send and / or delete at least one data record (s), at least one VM (s) and / or VN (s).
• - TODO REST (n): = at least one VM (REST) and / or VN (REST)
• - end condition: z. B. IF Sum Record (1 ... n) = REST THEN GOTO STOP
• - GOTO A

STOP

• • NN und/oder VM und/oder VN(n) := (Kennung, Datensatz (1...m)...) hier insbesondere mit einem Datensatz - das nur eine VM und/oder VN erzeugt wird.

und/oder

• • NN und/oder VM und/oder VN(n + 1) := (Kennung, insbesondere Kennung (VM(n + 1)), Datensatz (1 + 1...m)) Auslösung der VM und/oder VN ist der Datensatz (1 + 1...m))
• • NN und/oder VM und/oder VN(n + 2) := (Kennung, insbesondere Kennung (VM(n + 2)), Datensatz (1 + 2...m)) Auslösung der VM und/oder VN ist der Datensatz (1 + w...m))

General, in particular for the virtual machine (VM) and / or VN and / or NN, can be described:

• • NN and / or VM and / or VN (n): = (identifier, data record (1 ... m) ...) here, in particular with a data record - that only one VM and / or VN is generated.

and or

• • NN and / or VM and / or VN (n + 1): = (identifier, in particular identifier (VM (n + 1)), data set (1 + 1 ... m)) triggering of the VM and / or VN the data record (1 + 1 ... m))
• • NN and / or VM and / or VN (n + 2): = (identifier, in particular identifier (VM (n + 2)), data record (1 + 2 ... m)) triggering of the VM and / or VN the record (1 + w ... m))

• • NN und/oder VM und/oder VN(n + 1) := (Kennung, insbesondere Kennung (VM(n + 1)), Rest-Datensatz (1 + 1...m)) Auslösung der VM und/oder VN ist der Rest-Datensatz (1 + 1...m))
• • NN und/oder VM und/oder VN(n + 2) := (Kennung, insbesondere Kennung (VM(n + 2)), Datensatz (1 + 2...m)) Auslösung der VM und/oder VN ist der Datensatz (1 + w...m))

mit dem technischen Effekt, das die Datensätze immer kleiner werden, je mehr Schritte durchlaufen werden, bzw. VM und/oder VN angestoßen werden. Dies hat den Vorteil, das technisch nahe Systeme und/oder Netze ein gewisser maßen ein geringeres Gefahrenpotential darstellt, als eine sehr viele Schritte entferntes System. Somit werden mit jedem Schritt, die Datensätze immer kleiner und z. B. im 2 oder 3. Schritt nur noch kleine Datensatzschnipsel enthalten. Insbesondere „ist Teil von Daten” nicht ein konstanter Teil darstellen kann, sondern im
Schritt 1: „ein Teil” := 2 ist, im
Schritt 2: „ein Teil” := 4 ist und im
Schritt 3: „ein Teil” := REST ist von Schritt 1 und Schritt 2.General, in particular for the NN and / or virtual machine (VM) and VN, can be described:

• • NN and / or VM and / or VN (n + 1): = (identifier, in particular identifier (VM (n + 1)), remainder data set (1 + 1 ... m)) triggering of the VM and / or VN is the remainder record (1 + 1 ... m))
• • NN and / or VM and / or VN (n + 2): = (identifier, in particular identifier (VM (n + 2)), data record (1 + 2 ... m)) triggering of the VM and / or VN the record (1 + w ... m))

with the technical effect that the data sets become smaller and smaller as more steps are taken or VM and / or VN are triggered. This has the advantage that technically close systems and / or networks to a certain extent a lower risk potential represents, as a very many steps away system. Thus, with each step, the records are getting smaller and z. B. in 2 or 3 step only contain small data record clippings. In particular, "is part of data" can not represent a constant part, but in the
Step 1: "one part": = 2, in
Step 2: "one part": = 4 and im
Step 3: "one part": = REST is from step 1 and step 2.

Thus, here z. B. maximum 3 steps and three levels of VM and / or VN.

• • NN und/oder VM und/oder VN(n + 2) := (Kennung, Summe Kennung (VM(n + 1), VM(n + 2)), Datensatz (1 + 2...m) erzeugt

wobei, insbesondere dann die

• • Summe der KennungNN, (VM(n + 1), VM(n + 2)) := ein Kennzeichen der Datenspur über die Stufen VM und/oder VN des Datensatzes (Datensatz (1 + 2...m)) ist.

General, in particular for NN and / or the virtual machine (VM) and / or VN, can be described:
In particular, with a string of an identifier:

• • NN and / or VM and / or VN (n + 2): = (identifier, sum identifier (VM (n + 1), VM (n + 2)), data set (1 + 2 ... m) generated

being, especially then the

• • Sum of the identifier NN, (VM (n + 1), VM (n + 2)): = is a characteristic of the data track over the levels VM and / or VN of the data set (data record (1 + 2 ... m)).

General, in particular for NN and / or the virtual machine (VM) and / or VN, can be described. In particular, with a trace of records - the z. B. only one NN and / or VM and / or VN is generated and in the rest of the record thereby further VM and / or VN are generated.

For example, a virtual network can be realized by means of at least one VMware server. VMware Server is a virtualization solution that supports common operating devices, host and guest, and offers three types of virtual networking for a guest device: bridged networking, network address translation (NAT), and host-only networking. The network connections for the virtual machines (VM) are realized by VMware Server via ten virtual networks (VMnet0 to VMnet9). Each of these networks forms a virtual switch. The host and host network adapters connect to this switch. In addition, a DHCP server, a NAT router and a DNS proxy can be realized. An AMD PCNet adapter is emulated for the guest device.

The inventive apparatus and method comprises at least one virtual machine (/ VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); and is embedded in at least one server, in particular start server, in particular router, in particular network.

The inventive apparatus and method comprises at least one virtual machine NM) and / or at least one virtual network (VN) and / or at least one neural network (NN); and is embedded in at least one server, in particular start server, in particular router, in particular network and can additionally by means of a thermal hot spot from the software of the virtual machine (VM) and / or virtual networks (VN) coincidences, especially for the application and / or virtual machine (VM) and / or virtual networks (VN) and / or at least one neural network (NN); to generate.

The abovementioned and claimed components to be used according to the invention described in the exemplary embodiments are not subject to special exceptions in their size, shape, design, material selection and technical conceptions, so that the selection criteria known in the field of application can be used without restriction.

In a further special embodiment and by extension of the prior art, according to the invention, the data and / or data track to be received is converted from the data and / or data track to be transmitted, so that correspondingly there is no possibility of the data and / or data track to be received of the transport route and / or leave the defined locality. Even if a tapping of the data and / or data track by unauthorized third parties should take place during the transport, then it is such a tap to data and / or data track with which the unauthorized third party nothing and thus can not cause harm, as it the data and / or data track is simply not the one that the destination server should receive. For this reason, it is particularly advantageous if the processing of the virtual machine (VM) and / or virtual network (VM) and / or at least one neural network (NN); is controlled during the transport path such that from the data to be transmitted or the start data to be received data and / or data track or the target data are converted so that they are generated only by a final conversion step in the destination server and / or the generation the target data is at least completed only in such a server, in particular transport server, which is arranged in the transmission chain of the individual different servers near the destination server, in particular directly in the transmission chain is positioned in front of this. In one embodiment, it may also be provided that with at least one Virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); or data and / or data track sent as virtual machine (VM) and / or virtual network (VN) each time the virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN) is executed; to convert and / or at least one virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); to generate, so that at any time of transport, the actual data to be transmitted in the virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); or as a virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); and then using the destination server to reconvert the received data that was converted from the original during transport to reconstruct the original data. In a particular embodiment, at least one virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); local, local be limited, such that no virtual machine (VM) and / or virtual network (VN) outside a defined location, especially network, especially transport network, especially within at least one defined cloud, especially personalized cloud, especially national cloud, in particular for intra-European data traffic or also "Schengen network" takes place, mittes at least one defined virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); and / or data track and / or data description, in particular data record description and / or defined lists, in particular content list and / or communication addresses, in particular CLI, HLR, IP address, router address, WLAN identifier, device identifier.

A content, in particular a content list, comprises, for example, data and / or data track, a list and / or matrix and / or n-dimensional martix with data and / or data track, in particular text, images, sound, messages, such that this describes a location, by way of example, such that in particular z. B. German text, poems, newspaper articles; Pictures z. B. National monuments, eg. B. Cologne Cathedral; Sound z. B. Kölscher dialect - to draw the conclusion that this is the locality Dautschland, here the Rhineland, especially since a content list can be generated independently, includes mittells social networks, logbook contributions, local newspaper articles - in particular complying with the locality, In particular, the link is made by means of at least one neural network (NN).

For this purpose, the target server and / or destination network can have information about the reconversion or back transformation to be carried out, in particular by means of at least one simulation system (SI). z. B. by separate reception of this information from the start server and / or start network. Even if a tap of the data and / or data track would take place during transport and even if an unauthorized third party would have the information about the reversion to be performed, a conversion would not be possible, since the conversion according to the invention only at the last transport server and / or transport network or at the destination server and / or target network is completed and thus only the reconversion at the destination server and / or target network restores the original data and / or data track. In another possible embodiment, which can likewise be combined as desired with each of the aforementioned embodiments, it can also be provided that, independently of the transmission of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) ; is transmitted with the data and / or data track integrated therein to the destination server and / or destination network of time information either about the expected total execution time and / or the total transmission time and / or both times and / or energy consumption during the transmission of the virtual Machine (VM) and / or Virtual Network (VN) and / or Neural Network (NN); and then the destination server and / or destination network the total execution time and / or total transmission time and / or power consumption of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); after receiving them compares with the received time information. For example, it may be provided that a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); or software running thereon is set up such that on each of the servers, and / or networks used during the transaction, in particular the transport, in particular the transport server, the virtual machine (VM) and / or virtual network (VN) and / or neural Network (NN); is executed for a certain predefined period and / or locality and / or energy demand, so that the multiple execution of the virtual machine (VM) and / or virtual networks (VN) and / or neural network (NN); results in a total execution time and / or total energy consumption over the entire transport path, in addition to a transmission time required for the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); to send between the individual servers and / or network via the telecommunications network. Here it may be provided that the respective execution times and / or total energy consumption of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) by the start server or by a user programming this start server; is specified on each or certain transport servers and / or transport network and that the pure transmission time and / or energy consumption and / or energy requirements, in particular total energy consumption, for example, immediately before sending the virtual machine (VM) and / or virtual network (VN) and / or neural Network (NN); with the data and / or data track integrated therein. Thus, the determination of the pure transmission time and / or energy consumption and / or energy demand, between the individual servers from the start server to the destination server in an exemplary application by using a so-called ping command to be checked, which between start server and / or start network and destination server and / or target network, the pure transport time and / or energy consumption and / or energy demand, determined by data packets and / or data track. This pure transport time and / or energy consumption and / or energy requirement, can immediately before sending the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); determined, so that hereby it is determined that at the target server and / or target network a total time and / or energy consumption and / or energy needs must have elapsed, the sum of the required transport time and / or energy consumption and / or energy demand, plus the sum of the individual Execution times and / or energy consumption and / or energy requirements, on each server and / or network, in particular transport server and / or transport network corresponds. This time information can be parallel to the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); or exchanged in this between start server and / or start network and destination server and / or target network, so that the destination server and / or target network after receiving this time information and / or energy consumption and / or energy needs, in the position, this obtained time information and / or energy consumption and / or energy consumption, to be compared with the total time and / or energy consumption and / or energy demand that has passed since the reception of the time information and / or energy consumption and / or energy demand, by the start server, which preferably the time information and / or Energy consumption and / or energy requirements, and the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); sent simultaneously or temporally correlated. Likewise, it may be provided in one embodiment that a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); during transport via the individual transport server and / or transort network only stopped and continued respectively on the subsequent server and / or network and that the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); an internal clock and / or measuring device for energy consumption and / or measuring device for energy demand, continuously updates, thus their overall execution time and / or total energy consumption and / or total energy requirements, logged and that this total execution time and / or total energy consumption and / or total energy demand, after receiving the Virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); is compared by the target server and / or target network with that when sending the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); total execution time and / or total energy consumption and / or total energy demand determined by the start server and / or start network. Here too, therefore, information can be obtained from the comparison of the two data, start data and result data, in particular the times, whether in the meantime a manipulation on the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) ; has taken place, as these usually lead to a changed, in particular a prolonged total execution time and / or total energy consumption and / or total energy needs. The review of only the total execution time and / or total energy consumption and / or total energy needs, this has the advantage that any uncertainties in the temporal transmission are irrelevant, so it is therefore uncritical if a transfer between two servers and / or networks during the transport path, in particular So between the multiple transport servers and / or transport network needed more time than originally assumed. These mentioned times and / or energy consumption and / or energy requirement, with reference to the aforementioned embodiment, can also be used to serve as a parameter or also value of a back transformation function or back transformation. In the method according to the invention, it can also be provided, in particular in order to eliminate temporal uncertainties as described above, that the transport path that lies between the start server and / or start network and destination server and / or destination network, and thus represented by the individual transport server and / or transport network is, not arbitrary, but is prescribed, by way of example by means of local, national cloud. For example, when transmitting the data within the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); be provided that together with the virtual machine (VM) and / or virtual network (VN) a server list and / or network list is sent indicating on which specific transport and / or transport server this virtual machine (VM) and / or virtual Network (VN) and / or neural network (NN); is to be sent via the telecommunications network to the destination server and / or destination network. The individual transport server and / or transport network to be used during this transport, z. B. By their respective device identifiers, CLI's, IP address, router address; Device identifier, communication address are represented. In this case, in addition to the definition of the transport path, in such a piece of information attached to the shipment it can also be determined which of the individual transport servers the virtual machine (VM) and / or virtual network (VN) and / or network (NN); execute and stop or cancel. A server list and / or network list of the transport server and / or transport network to be used for the transport may e.g. B. as a file and / or list and / or executable program code in the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); be integrated or even a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); or before the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); be shipped, so that such a list already exists in the transport servers and / or transport network, if they are a virtual machine (VM) and / or virtual network (VN) and / or network (NN); receive. In this case, a server list z. B. only national or local transport server listed, so that the transport route can be limited nationally. It can also be provided that the transformation or conversion to be carried out is not within the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); is stored, but the individual transport server, in particular by prior information, are informed, which transformation or conversion they have to perform each. In a server list can thus be included not only which transport server in the transport chain follows what previous, but also what kind of transformation each transport server has to perform. According to the previous embodiments, the data can be stored in at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); integrated, the data but can itself at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); form. Furthermore, at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); also a static image, in particular a "noisy image" and / or a video sequence, in particular "noisy bold sequence", which comprise the data and / or data track or which are the data itself. It can thus also be provided with reference to the aforementioned conversions that as part of these conversions an image or a film is changed during transport. The "noisy picture" and / or "noisy picture sequence" and / or "noisy content" can additionally be used as a key and / or for encryption by the data, in particular the generated virtual machine (VM) and / or generated virtual network (VN) and / or neural network (NN), in particular data and / or data track, in particular noise data, are superposed in a processing process, in particular in the content, in particular in an additive and / or multiplizierent method and / or integrated in a content , in particular by means of at least one simulation system (SI).

In a particular application, data and / or at least one data track can be used as noise data to generate the random, in particular this comprises at least one neural network (NN);

The Virtual Machine (VM) and / or Virtual Network (VN) and / or Neural Network (NN); can continue z. B. be pronounced as at least one Turing machine and / or at least one basement machine and so advantageously improve the encryption and / or concealment and enlarge. A virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); of the type according to the invention can be a stand-alone machine (VM, VN) and / or hardware and / or software and / or at least one neural network (NN); in particular without being influenced by the outside and / or one which by means of at least one parameter, value of outside z. At interfaces of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); can be controlled or influenced. It may be provided that a possible signaling of an influence / manipulation of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); is performed, for. B. if via such an interface to the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); is accessed. In this case, a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); in a particular embodiment completely sealed off from the outside world and / or not be influenced by external processes, eg. B. in that a stop and / or possible interference from outside z. B. by further data or the attempt of a data tap, the virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); internally one, in particular later verifiable signaling z. B. sets by at least one trigger and / or trigger point and / or destroyed immediately, and / or that the program of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); deletes and / or overwrites and / or is described with random data and / or data track and / or marked as deleted and / or disabled or manipulated. In this case, in a particular embodiment, the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); be destroyed without tax information to the sender. Only by exceeding at least one parameter, value, in particular the energy consumption, possible Energieverbracuh, in particular a time and / or time limit in the communication chain, in particular in the server chain, in particular at the end of the communication chain, - from the start server to the destination server via the server, in particular transport server at least one control information is given to the next server and / or the next server in the communication chain, in particular to the destination server. In a further embodiment, the data and / or data track may include a code and / or an executable code, e.g. B. an interpreted code, a program and / or time-dependent data and / or content, in particular time-dependent content. The data can be a transformation in a closed frame z. B. the virtual machine (VM) and / or virtual network (VN) and / or virtual database and / or virtual memory and / or at least one neural network (NN); Experienced. The data and / or data track z. B. present in a bit stream, can be transformed z. B. in at least one schema of the form and / or function s1, s2, s3, ... sn, here for example x, y-form (for an image) and / or in x, y, z-form (here for a video), ie a 2 or 3 or more dimensional transformation can be applied to the data. In practice, this may mean that the data and / or data track, in particular the data, at least a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); form a changing picture or a walking movie. To transform the data and / or data track can be provided as a parameter, value in particular at least one communication parameter, such. CLI, HLR, IP address, router address; Identifier, device identifier, communication start, communication time, standard time z. B. GMT and / or at least one classification and / or at least one ranking and / or at least one probability z. The probability of communication, the importance of the information, the importance of the information content, the likelihood of a communication crash, the likelihood of energy demand, the likelihood of energy consumption; and / or as a parameter, value the energy requirement and / or energy consumption, in particular per server, per transaction.

Probability is also understood as the probability of production, use, contacting and / or an identifier, at least one technical interface and / or at least one device, in particular to at least one database and / or at least one neural network (NN); in particular to at least one program, in particular traffic management program, in particular to a service creation device (SCE), in particular in the intelligent network (IN). Furthermore, the likelihood that the communication and / or communication data record and / or start of the communication is preferred with the size of the probability, in particular of the size less than or equal to one z. B. 1 volt, 1 bit with state 1 and greater than zero z. B. 0 volts, 1 bit with state 0, in particular set equal to a flag corresponds to the one and no flag set the zero.

In a further particular application, the time is used as a limit parameter, limit value for the virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); and / or at least one file and / or data and / or data track used. In this case, it can be provided by way of example that the data is stored as a content, for. Example, an image, x, y-shape, which varies with time, or for example as a film and / or mathematical function F function values are calculated, the function F z. B. externally modifiable parameters, value contains. So it is z. For example, it is possible to recognize, on the basis of the final image, the final film or the end-function values, where and when an influence has taken place. From a trigger point z. B. from a certain point of change of the archetype, Urfilm or primal function, the virtual machine can be made to initiate further actions, to a deletion and / or not further processing of the data and / or data track and / or the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) ;. For example, it may be assumed that there is a data point A to be transmitted. As virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); or in a virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); the data point A of a transformation, z. B. with the mathematical function GF (eg, straight-line equation) and the function (A times the time t plus a B); executed, wherein the variable B is a counter of accesses, z. B. Interface accesses to the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); from outside. If no attack on the data of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) occurred, at least one graph and / or algorithm comes as data and / or data trace at the end of the transmission chain and / or or equation, in particular with the variable time t and the point result GF of t an, in particular with the data point A equal to GF divided by the time t and / or energy consumption and / or energy requirement. In the case of an attack, access to the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); at the interface S at the time ts, the graph has a jump at the time ts, in particular since the variable B has been counted up due to the access. Is one or too many requests, eg. As jump points exist, for example, the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); delete itself and / or discontinue processing and / or start further actions and / or transaction eg. B. Request for repeated transmission of the data. In a particular application, a virtual movie can be calculated as a result, which has a different result depending on the influence, z. B. different time lengths, times, color dots, pixel deletions.

In a further particular application and / or method and / or system, in particular at least one simulation system (SI) for the transmission of data from a start server ( 1 ) and / or destination network ( 1 ) to a destination server ( 2 ) and / or destination network ( 2 ) via several servers and / or network, in particular transport server ( 3.1 , ..., 3.N ) and / or transort network ( 3.1 , ..., 3.N ) of a telecommunication network and / or telecommunication server, wherein, the data to be transmitted in the start server ( 1 ) and / or startup network ( 1 ) are integrated and / or transmitted and / or stored in a virtual machine (VM) and / or virtual network (VN) and the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); together with the data and / or data track integrated therein via the server and / or destination network, in particular transport server ( 3.1 , ..., 3.N ) and / or transport network to the destination server ( 2 ) and / or destination network ( 2 ), wherein during the transport in at least one server and / or network, in particular transport server ( 3.1 , ..., 3.N ) and / or transport network, the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); at least at times by means of the hardware of the current server and / or network, in particular transport server and / or transort network ( 3.1 , ..., 3.N ) and stopped or aborted after execution and retransmitted until the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); from the destination server ( 2 ) and / or destination network / 2) is received and where as the data to be transmitted with the start server ( 1 ) and / or destination network ( 1 ) Start data (S) in the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); integrated by the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); be converted in a conversion process into target data (Z), whereby on each of the servers, in particular transport server ( 3.1 , ..., 3.N ) executing the virtual machine (VM) and / or virtual network (VN) during the execution time of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); the conversion process is carried out in part, characterized in that at least one server and / or at least one network, in particular each further server and / or network, in particular transport server and / or transport network is informed about the conversion to be carried out in parallel with the dispatch and / or storage and / or or processing before sending the virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); by means of a list and / or matrix and / or n-dimenional matrix, in which for each server and / or network at least one type of parameter, value is defined, in particular the conversion, and / or address, in particular communication address and / or probability and / or ranking and / or identifier and / or the expected total execution time and / or total transmission time and / or total energy consumption, in particular possible and / or probable total energy consumption and / or energy consumption, in particular the next to be awakened energy consumption of the other first server and / or network and / or other servers and / or network; and / or - in the case of data in particular with at least one data record and / or at least one application and / or at least one customer identifier and / or at least one communication identifier, the apparatus comprises at least one virtual machine (VM) and / or at least one virtual network (VN ) and / or at least one neural network (NN); and started and / or stopped and / or at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN) is stored and / or deleted; In particular, by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN and / or at least one Neural network (NN);

In a further particular application and / or method and / or system, in particular at least one simulation system (SI), for the transmission of data from a start server ( 1 ) and / or startup network ( 1 ) to a destination server ( 2 ) and / or destination network ( 2 ) via several servers and / or network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) of a telecommunications network and / or telecommunication device, in particular of the Internet, characterized in that the data to be transmitted and / or data track in the start server ( 1 ) and / or startup network ( 1 ) in a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); integrated or the data is a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); train themselves and the virtual machine (VM) and / or virtual network (VN), in particular together with the data integrated therein via the server and / or network, in particular transport server and / or destination network ( 3.1 , ..., 3.N ) to the destination server ( 2 ) and / or destination network ( 2 ), wherein during transport in at least one, preferably in each of the transport server and / or transport network ( 3.1 , ..., 3.N ) the virtual machine (VM) and / or virtual network (VN) at least temporarily by means of the hardware of the current server and / or network, in particular transport server and / or destination network ( 3.1 , ..., 3.N ) and stopped or aborted after execution and retransmitted until the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); from the destination server ( 2 ) and / or destination network ( 2 ) and / or - in the case of data, in particular with at least one data record and / or at least one application and / or at least one customer identifier and / or at least one communication identifier, the apparatus comprises at least one virtual machine (VM) and / or at least one virtual Network (VN) and / or at least one neural network (NN); and started and / or stopped and / or stored and / or deleted is at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); , in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controller and / or neural network (NN) controller, comprises at least one VM and / or at least one VN and / or at least one neural network (NN) ,

In a further particular application and / or method and / or system, in particular at least one simulation system (SI), for the transmission of data and / or data track from a start server ( 1 ) and / or startup network ( 1 ) to a destination server ( 2 ) and / or destination network ( 2 ) via several servers and / or destination network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) of a telecommunications network and / or telecommunication device, in particular of the Internet, characterized in that the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); already during their execution time on a server and / or network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) checks the data transmitted with it for integrity, in particular by means of one in the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); executed checking program, in particular virus scanner and / or - the data and / or data track in particular at least one record and / or at least one application and / or at least one customer identifier and / or at least one communication identifier, the device comprises at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); and started and / or stopped and / or stored and / or deleted is at least one virtual machine (VM) and / or at least one virtual network (VN), in particular by means of a network, in particular fuzzy-based and / or neural network ( NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN and / or at least one neural network (NN).

In a further particular application and / or method and / or system, in particular at least one simulation system (SI), for the transmission of data and / or data track from a start server ( 1 ) and / or launch network to a destination server ( 2 ) and / or destination network ( 2 ) via several servers and / or network, in particular transport server and / or destination network ( 3.1 , ..., 3.N ) of a telecommunication network and / or telecommunication device, in particular of the Internet, characterized in that as data to be transmitted and / or data track with the start server and / or start network ( 1 ) Start data (S) in the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); integrated and / or the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); formed by the Virtual Machine (VM) and / or Virtual Network (VN) and / or Neural Network (NN); be converted in a conversion process in target data (Z), wherein on each of the server and / or network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) during the execution time of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); the conversion process is carried out in part, in particular in such a way that the conversion process does not take place until reception and / or after reception of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); at the destination server and / or destination network ( 2 ) is / is and / or characterized in that the conversion to be performed as at least one program in the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); integrated and / or stored, in particular as an address, in particular as a link, in particular comprises at least one neural network (NN); and / or characterized in that - at least one server and / or network, in particular each server and / or network, in particular transport server and / or transport network is informed about the conversion to be carried out, in particular in parallel to the dispatch and / or before the dispatch and / or or after sending the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); in particular by means of a list and / or matrix and / or n-dimensional matrix, in particular at least one link list, link matrix, in which the type of transformation is defined for each server and / or network and / or that in the case of data Particularly with at least one data record and / or at least one application and / or at least one customer identifier and / or at least one identifier and / or communication identifier, the apparatus comprises at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); and started and / or stopped and / or stored and / or deleted is at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); In particular, by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN and / or at least one Neural network (NN);

In a further particular application and / or method and / or system, in particular a simulation system (SI), for the transmission of data and / or data track from a start server and / or Startup network ( 1 ) to a destination server and / or destination network ( 2 ) via at least one server and / or destination network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) of a telecommunication network and / or telecommunication device, in particular of the Internet, characterized in that, independently for the transmission of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); to the destination server and / or destination network ( 2 ) Time information about during the transmission of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); expected total execution time and / or total transmission time and / or total energy consumption, in particular possible and / or probable total energy consumption is transmitted and the target server and / or target network, the total execution time and / or total transmission time and / or energy consumption, especially in the next server and / or network and / or Total energy consumption, in particular possible and / or probable total energy consumption of at least one virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); after receipt of which it compares with the information obtained, in particular time information and / or uses the time information for the reconstruction and / or transformation of the data and / or total energy consumption, in particular possible and / or probable total energy consumption as a parameter; - and / or - in the case of data, in particular in the case of at least one data record and / or at least one application and / or at least one customer identifier and / or at least one communication identifier, the apparatus comprises at least one virtual machine (VM) and / or at least one virtual network ( VN) and / or neural network (NN); and started and / or stopped and / or stored and / or deleted is at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); In particular by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN and / or at least a neural network (NN).

In a further particular application and / or method and / or system, in particular at least one simulation system (SI). for transmitting data and / or data track from a start server and / or start network ( 1 ) to a destination server and / or destination network ( 2 ) via several transport servers and / or transport network ( 3.1 , ..., 3.N ) of a telecommunications network and / or telecommunication device, in particular of the Internet, characterized in that at least one server and / or network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) from one of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); attached and / or parallel or previously sent server list and / or network list information about which server and / or network, in particular probable server and / or network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); be forwarded, in particular wherein the server and / or network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) in the server list and / or network list by their parameters, value, in particular identifier and / or communication address and / or energy consumption, in particular at the next server and / or network and / or total energy consumption, in particular possible and / or probable total energy consumption and / or information, in particular time information and / or the time information for at least one data, in particular represents data record; and / or - in the case of data and / or data track, in particular in the case of at least one data record and / or at least one application and / or at least one customer identifier and / or at least one communication identifier, the device comprises at least one virtual machine (VM) and / or at least one Virtual network (VN) and / or at least one neural network (NN); and started and / or stopped and / or stored and / or deleted is at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); In particular, by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN and / or at least one Neural network (NN).

In a further particular application and / or method and / or system, in particular at least one simulation system (SI), for the transmission of data and / or data track comprising a start server and / or start network ( 1 ) and a destination server and / or destination network ( 2 ) between which the data and / or data track is via several transport servers and / or transport network ( 3.1 , ..., 3.N ) of a telecommunications network and / or telecommunication device, in particular of the Internet, can be transmitted, characterized in that the start server and / or destination network ( 1 ) is set up, the data to be transmitted in a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); to integrate and / or by the data itself a virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); form, in particular before the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); to generate at the time of origin and the virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN); in particular together with the data and / or data track integrated therein for transport via the transport server and / or transport network ( 3.1 , ..., 3.N ) to the destination server and / or destination network ( 2 ), at least one of the servers and / or network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ), preferably all are set up, during the transport, the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); at least at times by means of the hardware and / or software of the current server and / or network, in particular transport server and / or transport network ( 3.1 , ..., 3.N ) and to stop and / or cancel and / or resend after execution until the reception of the Virtual Machine (VM) and / or Virtual Network (VN) and / or Neural Network (NN); at the destination server and / or destination network ( 2 ) - and / or - the data and / or data track, in particular at least one data record and / or at least one application and / or at least one customer identifier and / or at least one communication identifier, the device comprises at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); and started and / or stopped and / or stored and / or deleted is at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN); In particular by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN and / or at least a neural network (NN).

The description and characteristics of the cloud, in particular the national cloud, in particular the personalized cloud can be described by means of at least one algorithm and / or method, in particular as at least one machine, in particular as a Turing machine and / or cellar machine and / or mathematical equation, in particular as body, ring, surface and other structures as and / or at least as Markov chain, whereby the Markov chain is defined over that by knowledge of a limited history just as good prognoses over the future development are possible as with knowledge of the entire history of the process. The Markov chain of first order states that the future of the device depends only on the present, the current state and not on the past. The mathematical formulation in the case of a finite state quantity requires the discrete distribution as well as the conditional probability, while in the case of time the concepts of filtration and conditional expectation are needed. The goal in the application, in particular the Markov chain, in particular in the queue, the Markov chain is suitable for specifying the probability of the occurrence of future events, especially in networks, especially in packet-oriented networks, especially in hybrid networks with a mixed application, in particular Data and voice, especially of Voice over IP (VOIP) and classical IP data communication, especially as the Internet shows the well-known Erlang approach and its formulation an inadequate image of the technical behavior. With the advantage of the description by means of a machine, in particular Markov model, that the model can be used as a controllable parameter, value and / or trigger, in particular for determining the probability and / or ranking and / or identifier.

Thus, by means of the Markov chain, instead of the classical calculation using streamlines of individual points, point distributions are calculated here. The flow map is stored in a so-called transition matrix, in particular by means of at least one time-discrete Markov chains. The transition matrix is then further used in the inventive apparatus and / or method.

In particular for cloud optimization, in particular for hot spot optimization, at least one controller (CD), at least one dynamic computer (DC), comprises at least one virtual machine (VM) and / or virtual network (VM) and / or neural network (NN); , at least one profiler (PR) and at least one native machine code (NMC). Where the dynamic compiler (DC) is a combination of the advantageous properties of the JIT compiler and interpreter, where the bytecode is always first interpreted in the JVM, if the compiled code has an error after optimization, this can be reinterpreted. The profiler (PR), while running a function, collects tracking information about the performance, especially runtime, and then selects a method after a first heuristic for compilation and the compiled methods are stored in the cache of the native machine code (NMC) and the call is checked, whether the native version of the method is in the cache, otherwise it is interpreted. Wherein the controller (CO) comprises at least one intelligent data hub, in particular data carousel such, in particular comprising a queue ring comprising comprising at least one parameter and / or at least one trigger and / or at least one ranking and / or at least one probability, in particular communication addresses held and connects in particular the various components and thus the device involved in particular connect directly online, in particular for communication. The Java HotSpot Client VM and Java HotSpot Server VM use different compilers, however an interface to the same virtual machine (VM) and / or virtual network (VN) and / or neural network (NN); and thus have the same GC routine, interpreter, thread sync.

By means of at least one data track and / or data hub, in particular data carousel, in particular pronounced as a black box and comprises at least one rule (RULE), at least one policy, at least one layer. The rule (RULE) comprises at least one trigger and one of the subsequent action, in particular processing, in particular the method comprises:

In particular, the method includes, here with at least one chain:

With at least one policy, includes at least one rule and d. f. At least one layer with at least one method at least comprises virtual patching and d. f. at least one compliance template and d. f. at least one customer-defined.

In particular, the method includes at least one multidimensional communication path:

In a particular embodiment, the mutual simultaneous influencing operated virtual machines (VM) and / or VN and / or neural network (NN); be prevented and / or eliminated, by means of at least one assignment of at least one VM and / or VN and / or NN to at least one probability and / or ranking and / or identifier and / or parameters and / or fuzzy-based controller and / or neural network (NN) -based controller, in particular to at least one hypervisor, in particular by means comprises a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controller and / or neural network (NN) controller, comprises at least a VM and / or at least one VN and / or at least one neural network (NN).

The term "parameter" includes and understands the term value, in particular the time, a unique, unique parameter at a time, in the inventive method and also in combination with other technical features, in particular of the content. Since a particular local action, content, personalized content, data track, can not be done at the same time, the time is especially an important parameter for the content and / or record and / or ranking and / or probability. In this case, a time stamp can be used and / or a time stamp that synchronizes the possible different time device, so that a consecutive number, time number (NR) is assigned. Thus, by way of example, for each data track and / or communication address, a separate communication time number (NR) of time (t) can take place, with t> 0 for the running variable n greater than zero then NR (tn + 1) <NR (tn + 2). In a particular embodiment, the time is determinable due to secondary device such. B. of navigation satellites, MobilfunkVorrichtungn. In this case, according to the invention, the function time can be taken by the satellite device and / or that of a mobile radio device. Furthermore, a comparison can be made by way of example, based on the communication time number. In this case, the external device specifies the time t1 and another device the time t2 where t1> t2. The communication time number (NR) of time (t) is inventive, with t1> t2> 0 for the running variable n greater than zero, NR (tn + 2)> NR (tn + 1). Here, a preferential time can be taken. NR (t) => t1> t2 or NR (t) = <t2 <t1. In a particular embodiment, time differences can be determined and taken into account and a corrective communication time difference can be inserted as a parameter of the track. Likewise, the communication time number can be used as a time stamp, here in the context of the other data records, data tracks and / or in the entire database of the inventive device. It is also possible that the communication time number and / or communication time difference can be used as an additional sub-parameter at the time of the invention. By way of example, here can be a timestamp of the form date time (27.10.2007 08:53:20 key äd2aögkh3öädosg) or date time communication number (27.10.2007 08:53:20 1234567). Further combinatorial possibilities are possible.

The term "parameter" includes and describes the locality, the parameter locality and / or position and / or environment and / or local environment (LU), the parameter which can be determined and in particular by means of the Global Positioning Device GPS and / or comprises at least one communication device. In a particular embodiment, the position can be determined via the existing communication device. In this case, the communication address in the communication device is used to look up where the device is located. In a fixed communication device (fixed network device) is exemplified by the CLI, (Calling Line Identification), at the phone number where the communication device is located. Since the phone number consists of a fixed scheme, the local area code and individual unique number. Thus, by way of example, it can be recognized from the local area code 0202 that it is the place Wuppertal. Thus, the data can be read from the communication device and transmitted to the track to the identifier belonging to the communication address. This can also be done by means of a list and / or matrix and / or n-dimensional matrix, which is stored in a centralized and / or decentralized device and z. B. the area code, call number with geodata link. By way of example, the telephone number 0202 is linked to the position Wuppertal, the telephone number 0202-6986269 with position Wuppertal Lönsstrasse 59. At the same time with the phone number z. B. 0202-6986269 with position Wuppertal, Lönsstrasse, number 59, z. As the CLI, HLR, device identifier Axel NN of mobile communication device, etc. are linked as a fix-source company patent attorney and other metadata, that is, that here is the fictitious location, not at the time of the real - as the mobile device to move can. The particular advantage here is that different data is already contained in the data record and thus faster access to the data, saving of the computing time can be done. In a mobile device, the position of the communication device can be determined via the radio cell or by a combination of several radio cells. Here, the HLR (Home Location Register) is tracked which is a unique communication address and the radio cells in which the HLR arrives. In a particular embodiment, the position parameter can be determined, for example, by means of an optical image, image sequence, video, photo transmitted via the communication device. The communication device may trigger this image automated or triggered by the customer, e.g. B. an image of a particular feature of an object, a cathedral, a train station, the starry sky, the position of the sun. In a further embodiment, barcodes can also be detected by machines, telephone booths, cash machines, emergency call columns. Triggering can be deliberately triggered by the customer by "shooting" a photo. In a temporary triggering, the communication device z. B. at intervals, in jerky movements trigger a picture or a sequence of images. At the same time z. B. RFID, WLAN, LTE, UMTS, GMS, near field reception, - an image and / or image sequence are triggered An evaluation device z. B. centrally, decentrally determined on the basis of the transmitted photo, voice the possible position of the communication device and sends this back to the device for assembling the data track and / or the communication device. It also monitors the evaluation device whether it is a real picture, sound recording or a deception pretending to be only in this place. In a further particular embodiment, it is possible to deduce the position from the content, transaction, content, quantity. Here, for example, a picture and / or alone only the sound - for location. If z. For example, if a photograph of the "Meadows in Munich" is made and the sound is not "Bavarian" in the majority, then the device may close that the location is outside of Munich and a local, national Meadow Festival (eg Wuppertal, Beijing) due to regional language colorings.

• • I(N)_Spur = Id(1/(–p·Content)) zur Zeit = t und Wahrscheinlichkeit und/oder Ranking und/oder Kennung
• • I(N)_Spur = –Id(p·Content) zur Zeit = t d. f. H_Spur(t) = Summe (I(N)_Spur(t)) bzw.
• • I(N)_Spur = Summe(Id(1/(–p·Content)) zur Zeit = t = 0)
• • I(N)_Spur = Summe (abs(–Id(p·Content) zur Zeit = t d. f. H_Spur(t) = Summe(I(N)_Spur(t))) und/oder ...
• • hieraus erfolgt mindestens eine Virtuelle Maschine (VM) = f((I(N)_Spur)i) und/oder
• • mindestens eine Virtuelles Netz (VN) = f((I(N)_Spur)) und/oder mindestens ein Neuronales Netz (NN) = f((I(N)_Spur)).

Among other things, content means all parameters and / or information and / or data and / or data track and / or metadata, in particular of systems, networks; and / or programs and / or messages, in particular of search engines, which in particular comprises detected by means of a technical device and / or processed and / or stored and / or transported, as well as by means of a z. B. mathematical transformation data, information that can be subsequently converted into technical parameters, reshaped. For example, as content here is understood among other things, the communication identifiers, voice data, image data, state data, including the information content, the probability of parts of the content. Communication identifier (eg 0202-69862, C communication identifier, C = ({C / c ε identifier, probability, ranking, HLR, IP address, router address, device identifier, CLI, ....} communication identifier), At the same time, the Connect can also contain destinations, addresses (eg who and / or where has been sent by this device - knows this Also, the content is a subset of transactions (T) (eg, content / quantity) .There may be different transactions in forms of, for example, text, images, sound (eg, with A-type of transaction A = {A / A <message, picture, text, language, control_signal>} where message = 0000 0000 0000 card_number, content, destination with the M-content set, e.g. M = in Bit = {0, 1}, M = in Bitsec, M = in Pieksel, Color Level and / or S-Information Set S = k In P, P - the prob einlichkeit. The parameter Content comprises at least one record and / or at least one list and / or at least a matrix and / or at least one n-dimensional matrix and in a particular application, at least one identifier and / or communication address and / or at least one device identifier, in particular a communication device, in particular server, at least one identifier and / or communication address and / or at least one Device identifier, in particular a communication device, in particular network, in particular network router. By way of example, a Calling Line Identification (CLI), Home Location Register (HLR), IP Address, Router Address; Device identification. By way of example, this can be a destination CLI / HLR / IP address, router address; Device identifier and / or his own address. From this, it is possible to identify and process a destination / user profile from the sum of the addresses - it is also clear who knows whom. In a particular embodiment, the data track can be represented by means of at least one technical parameter and / or comprises at least one trigger point and / or at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one trigger point set , in particular in that it is determined from the data track, in particular the information content. Here by way of example includes:

• • I (N) _track = Id (1 / (- p · Content)) at time = t and probability and / or ranking and / or identifier
• • I (N) _track = -Id (p · Content) at time = t df H _track (t) = sum (I (N) _{track (t)} ) or
• • I (N) _trace = sum (Id (1 / (- p · Content)) at time = t = 0)
• • I (N) _track = sum (abs (-Id (p · content) at time = t df H _track (t) = sum (I (N) _{track (t)} )) and / or ...
• • This results in at least one virtual machine (VM) = f ((I (N) _track ) i) and / or
• At least one virtual network (VN) = f ((I (N) _lane )) and / or at least one neural network (NN) = f ((l (N) _lane )).

By weighting and / or ranking and / or probability is also understood and encompassed, at least one neural network (NN) and / or the processing hereby takes place and / or by means of Fussy logic and / or at least one ranking and / or by means of at least one data set , in particular from the social networks z. B. Facebook, fuck, schufa list.

• • Ranking := ist eine Funktion vom (Content und/oder Datenspur und/oder Informationsgehalt und/oder Ranking-Algorithmus, insbesondere PageRank)

Ranking can also be understood and comprises at least one parameter which is composed in particular:

• • Ranking: = is a function of (content and / or data track and / or information content and / or ranking algorithm, especially PageRank)

The PageRank algorithm is a method, a set of linked content, especially documents, each element is assigned a weight due to the structure. In particular, the PageRank, assigned due to its linking structure. special method to determine the link popularity of a page or a document, with the basic principle; The more links to a page, the higher the weight of this page. The higher the weight of the referring pages, the greater the effect. The goal of the method is to sort the links according to weight so as to produce a result order in a search query, i. H. Links to more important pages further up the list of results.

The PageRank algorithm improves, especially in the content, especially in texts, images, moving images, film, sound, - especially in compromising texts, images, moving images, film, sound, - especially in legally prohibited texts, images, moving images, film, sound , at least one ranking, in particular ranking parameters, is characterized and comprises the basic principle that at least one parameter - which characterizes the allocation and / or structure of points, in particular of pixels and / or image color to each other, in particular the summary, especially as a sum, multiplication - and so at least one parameter forms. This can advantageously be done filtering of content based on a ranking, which is done in particular by the z. B. no VN and / or VM is started, in particular by means of fuzzy-based controller and / or neural network (NN) -based controller, in particular by means of a fast learning neural network (NN) -based controller adjustments of the filter can be performed quickly and promptly become.

The allocation and / or structure of points comprises in a particular embodiment in particular at least one image processing program, image viewer and analysis program.

• • Der Content, insbesondere das Bild, - liegt in digitaler Form vor, insbesondere binäre Daten z. B. x, y, Farbpunkt.

STEP 1 umfasst:

• • Ermittelt der Zuordnung und/oder Struktur von Punkten und/oder Bildfarbe, insbesondere mittels umfasst mindestens eines fuzzy-basiert- und/oder Neurnale Netz(NN)-basierten Reger.

STEP 2 umfasst:

• • Verarbeitung und/oder verknüpfung der Zuordnungen, insbesondere der ermittelten Zuordnungen, insbesondere mit Analyse Programme, z. B. Big Date Analyse Programme für Strukturen, Zuordnungen der Strukturen zu mindestsns einem Parameter, hier Rankingparameter, inbesondere mittels umfasst mindestens eines fuzzy-basierten-Regler und/oder Neurnale Netz(NN)-basierten Reger.

The method comprises at least the program steps:

STEP 0 includes:

• • The content, in particular the image, - is in digital form, in particular binary data z. B. x, y, color point.

STEP 1 includes:

• • Determines the allocation and / or structure of points and / or image color, in particular by means of at least one fuzzy-based and / or Neurnal Network (NN) -based Reger.

STEP 2 includes:

• • Processing and / or linking of the assignments, in particular of the determined assignments, in particular with analysis programs, eg. For example, Big Date Analysis programs for structures, assignments of structures to at least one parameter, here ranking parameters, in particular by means of at least one fuzzy-based controller and / or Neurnale Netz (NN) -based Reger.

In another particular embodiment, the weighting and / or ranking and / or probability and / or trigger and / or trigger point, at least one n-pole, in particular by means of line parameters at least one n-pole, in particular at least one quadrupole (VP) and / or their interconnection of at least one further n-pole, in particular quadrupole (VP). In this case, an n-pole is understood as meaning a network which has n input and output terminals each and in which the currents in the input and output terminals are equal in magnitude within a respective pair of terminals. The n-pole as well as further arrangements of active and passive elements, in particular for at least one virtual network (VN) and / or at least one virtual machine (VM) and / or at least one neural network (NN), comprises the line parameters, in particular at least one line equation of a homogeneous linear two-wire line, in particular the quantities of the length dx multiplied line coverings, the inductance coating L ', the capacitance C', the Widerstandsbealg R 'and the derivative coating G', in particular includes the determination and / or determination z. The resistance parameters, conductance parameters, chain parameters, hybrid parameters, inverse hybrid parameters, characteristic impedance, Z-parameters, Y-parameters, H-parameters, C-parameters, A-parameters, their conversion of quadrupole parameters, their series connection, parallel connection, series-parallel Circuit (hybrid), derailleur, parallel series (inverse hybrid), equivalent resistances of a connected VP, equivalent source voltage of an active two-terminal connected VP; for at least one virtual network (VN) and / or at least one virtual machine (VM) and / or at least one neural network (NN), in particular by means of a network, in particular fuzzy-based and / or neural network (NN), in particular Fuzzy-based controller and / or neural network (NN) controller, comprises at least one VM and / or at least one VN and / or at least one neural network (NN).

The method for quadrupole analysis, in particular with high bandwidth, here for realizing the measurement of the measured variables takes place and comprises such that with a test signal and / or data and / or at least one data set and / or at least one Datenpaktet, in particular at least one data track, in particular pulse-shaped is controlled, characterized in that from the output signal of the measurement object comprises, a bandwidth-reduced signal and / or at least data and / or at least one data set and / or at least one Datenpaktet, in particular at least one data track is formed, - the bandwidth reduction by superposition with a Pulse signal and / or at least one data and / or at least one data record and / or at least one Datenpaktet, in particular at least one data track, takes place - and used for superposition pulse signal and / or at least one data and / or at least one record and / or at least oneDatenpaktet, in particular at least one data track, a relatively small, constant frequency difference to the test signal and / or at least one data and / or at least one data record and / or at least one Datenpaktet, in particular at least one data track has, and in front of or behind the DUT a passport, In particular, a low pass and / or high pass and / or bandpass is installed and the output, in particular mixer output a passport, in particular a low pass and / or high pass and / or a bandpass is installed and / or comprises by means of at least one virtual network (VN) and / or at least one virtual machine (VM) and / or at least one neural network (NN) is simulated and / or generated, in particular by means of at least one program, in particular software program, in particular at least one virtual network (VN) and / or at least one virtual Machine (VM) and / or at least one neural network (NN), in particular by means of a network, in In particular, fuzzy-based and / or neural networks (NNs), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprise at least one VM and / or at least one VN and / or at least one neural network (NN).

With the advantage of easy transformability, in particular modeling, in particular for energy-efficient calculation, in particular current limitation and simplification of calculations, complex n-poles, gates can be summarized using simplified parameters simplified circuits and exemplary computational and thus temporal advantage in the processing process, in particular In the case of cloud computing, in particular when taking into account the past time, in particular by means of the memristor, in particular the memristance function, in this case via the rate of change of the flow with the charge, in particular when used in the field of neural networks, the control engineering, in particular for Simulation of synapses and so the size of the circuit and thus reduce the cost, in particular advantageous because it can accept binary values and any intermediate values.

The n-pole, in particular a quadrupole (VP) and the equivalent circuit is a network and exists in passive elements, in particular quadripolar z. B. IC filter or attenuator and / or a combination of passive basic bipolar R, L, C and at least one memristor. In active quadripoles such. B. Transistors contains the equivalent circuit and / or active basic bipoles, as well as ideally controlled current source and / or the ideal controlled voltage source are possible.

In a particular embodiment, in particular at least one simulation system (SI), device and method for transmitting data and / or data track from a start server ( 1 ) and / or destination network ( 1 ) to a destination server ( 2 ) and / or destination network ( 2 ) via several servers and / or network, in particular transport server ( 3.1 , ..., 3.N ) and / or transport network ( 3.1 , ..., 3.N ) of a telecommunication network and / or telecommunication server, characterized in that the data to be transmitted and / or data track in the start server ( 1 ) and / or startup network ( 1 ) program and / or computer program implemented on the basis of a parameter and / or trigger and / or at least one computer program; at least one virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) is generated and / or integrated and / or transmitted and / or transported and / or transmitted and / or stored and / or deleted, in particular, in a particular embodiment, the deletion of the VM and / or VN takes place immediately after the initiation of a new virtual machine (VM) and / or virtual network (VN) or n-steps, with a deletion the particular advantage that the virtual machine (VM) and / or virtual network (VN) is only a small time and so the risk of attack against harmful Softwafre is reduced.

In a further special application, the virtual machine (VM) and / or virtual network (VN) together with the data and / or data track integrated therein via the server and / or network, up to the destination server ( 2 ) and / or destination network ( 2 ), and during transport in at least one server and / or network at least at times by means of the hardware and / or software and / or network, in particular taking into account the probability and / or ranking and / or identifier and / or parameters of the current server and / or network is executed and / or stopped after execution and / or aborted and / or forwarded and / or stored and / or deleted and / or at the latest until that the virtual machine (VM) and / or virtual network (VN) from the destination server ( 2 ) and / or destination network ( 2 ), in particular by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN and / or at least one neural network (NN).

In a further special application, the data (S) are integrated into the virtual machine (VM) and / or virtual network (VN), which are processed by the virtual machine (VM) and / or virtual network (VN) in a conversion process in target data ( Z), wherein on each of the server and / or network, in particular transport server and / or Transportnegtzwerk ( 3.1 , ..., 3.N ) executing the virtual machine (VM) and / or virtual network (VN), during the execution time of the virtual machine (VM) and / or virtual network (VN), the conversion process is partially performed, and that at least one server and / or at least one network, in particular each further server and / or network, in particular transport server and / or transport network, is informed about the conversion to be carried out in parallel with the sending and / or storage and / or processing before the sending of the virtual machine (VM) and / or virtual network (VN) by means of mindesgtesn a record and / or at least one database entry and / or at least one list in which at least one address is stored for each server and / or network, - in particular by means comprises a network, in particular fuzzy-based and / or neural Network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least a UN.

In a further embodiment, at least one parameter and / or at least one triggering trigger takes place and comprises; at least one address, in particular communication address, network address, Device identifier, customer address, customer identifier; and / or at least one probability, in particular the probability of a communication, communication volume, but also the probability of a failure; and / or at least one ranking, in particular taking into account technical features such. B. frequency of communication based on the communication protocols of the frequency of communication addresses in one location, within one time slot, to another communication address; and / or at least one feature of at least one n-port, in particular Merkamle z. Z, Y, line characteristics, simulated performance parameters; and / or at least the expected total execution time, in particular the communication time, the network setup time, the time to set up a VM and / or VN and / or NN; and / or total transmission time; and / or total energy consumption, in particular the exchange, of the VM and / or VN and / or NN; and / or possible and / or probable energy consumption; and / or the next energy consumption to be aroused; and / or the next energy consumption to be awakened to the next server; and / or network and / or other servers and / or further network; and / or at least one algorithm, in particular those associated with technical parameters, measured values, signals, triggers; and / or by means of at least one measurement, in particular the simmulation of the line parameters of the virtual machine (VM) and / or virtual network (VN) and / or the communication channel.

In a further particular embodiment, at least one trigger and / or at least one parameter and / or data and / or at least one data record and / or at least one application and / or at least one customer identifier and / or at least one communication identifier and / or at least one application, at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN) are started and / or stopped and / or stored and / or deleted and / or forwarded, In particular by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN and / or at least a neural network (NN).

Ranking and / or probability can also be used and / or used in a particular application for identifying data and / or data track, in particular the importance of the data and / or data track, in particular data for identifying the required transmission speed and / or transmission bandwidth and / or transmission time, in particular medical data, in particular in an emergency and / or ambulance and / or videos on Youtube, in particular for the identification and / or evaluation of large amounts of data, in particular for the identification of adaptive data, in particular data of production, in particular data for neural networks (NN) and / or artificial intelligence data, in particular by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN.

In a further particular embodiment, in particular the technical and / or legal assignment of the data becomes more and more difficult, in particular due to the multiplicity of data sources, in particular for the legal assignment of authorship, responsibility, responsible persons, in particular for the generated data and / or metadata, this is done by means of at least one identifier and / or ranking, in particular by means of the identifier of the devices (GE) and / or communication address.

In a further particular embodiment, in particular advantageous for improving data security and / or authorship and / or identification, that at least the data and / or at least one data record and / or at least one transaction receives at least one identifier and / or ranking, in particular one Communication identifier and / or device identifier and / or network identifier and / or version identifier and / or time identifier and / or Lokilisationskennung, in particular for authorship and / or identification and / or verification of the data. Thus, the origin of the data generation can advantageously be determined and then assigned to a system and / or network and / or an identifier, in particular a customer identifier and / or a natural person.

In a special application, the identifier comprises at least one identifier and comprises, in particular at least one communication identifier of the systems along the data track of the data and / or the networks along the data track of the data. And / or at least one identifier comprises, in particular at least one device identifier, in particular of the systems along the data track of the data and / or the networks along the data track of the data. And / or at least one identifier comprises, in particular at least one network identifier, in particular the systems along the data track of the data and / or the networks along the data track of the data. And / or includes at least one identifier, in particular at least one software version identifier, in particular the systems along the data track of the data and / or the networks along the data track of the data. And / or at least one identifier comprises, in particular at least one version identifier of the systems along the data track of the data and / or the networks along the data track of the data. And / or comprises at least one identifier, in particular at least one operating system identifier, in particular along the data track of the data. And / or comprises at least one identifier, in particular at least one time and / or time identifier and / or date and / or runtime, in particular along the data track of the data. And / or comprises at least one identifier, in particular at least one localization and / or a localization identifier and / or latitude-longitude, in particular comprises by means of the radio networks (eg GSM, LTE, UMTS, radio) and / or communication addresses (eg CLI, HLR) and / or IP address, in particular along the data track of the data. And / or at least one identifier comprises, in particular at least one customer address of the systems and / or networks and / or software, - in particular along the data track of the data. And / or comprises at least one identifier, in particular at least one customer identifier, in particular of the systems and / or networks and / or software, in particular along the data track of the data. And / or at least one identifier comprises, in particular at least one probability, in particular the probability of communication and / or communication and / or cancellation and / or failure and / or overload and / or ranking and / or at least one technical feature and / or frequency of Communication based on the communication protocols and / or frequency of the communication addresses in a locality and / or within a time window and / or to another communication address, in particular along the data track of the data.

In a further particular embodiment, at least one identifier can be used, in particular as a trigger, in particular as a criterion for deleting and / or not further processing and / or administering and / or outputting the data. Particularly advantageous for "geeseen he data or as a" digital eraser ", in particular comprises that the data and / or at least one record, in particular independently by means of at least one software and / or agents and / or digital data container comprises mindestents with a date and / or expiration date and / or expiration date, and after expiration of this date and / or reach the date and / or date difference, the data and / or data track and / or information by at least one system and / or comprises a program, in particular agent and / or the operating system of the computer and / or by the data set itself, in particular by means of a VM and / or VN and / or NN, - automatically deleted.

The deletion and / or no further processing, in particular of data, also means that the systems, in particular computers and / or memory, where the data was located, are overwritten at least once by means of data and / or by VM and / or VN and / or NN; in particular by means of random data and / or random VN and / or random VN and / or random NN, where the coincidence takes place here from a list and / or by means of at least one software and / or by means of at least one noise generator and / or at least one VM and / or VN and / or NN itself is generated.

In a further particular embodiment, by means of at least one trigger and / or at least one parameter and / or at least one application and / or at least one sensor and / or at least one computer program, a specific number of random numbers is generated and / or load-dependent, in particular at an electrical load, in particular an onslaught of communication volumes and / or by means of the VM and / or VN and / or NN by means of thermal noise, in particular by means of computer programs and / or software and / or Betgriebssoftware and / or Schnittstellensoftwae - thermal hot spot are generated and by means of a Sensonr a thermal noise is generated and then by means of a device generates random numbers for processing, in particular for the VN and / or VM and / or NN, - in particular by means comprises a network, in particular fuzzy-based and / or Neural network (NN), especially fuzzy-ba Controlled controller and / or neural network (NN) controller includes at least one VM and / or at least one VN.

In a particular application, the inventive solution can be used in particular for aircraft, in particular missiles and / or flying machine, which in particular form at least one network and / or are networked and / or form a cloud and / or generate at least one VM and / or VN.

In a particular application, in particular for aircraft, in particular missile and / or flying machine and / or their systems, in particular for VM and / or VN, in particular at a height above the earth between 200 and 3000 km (LEO) and / or less than 500 km and / or less than 400 km and / or less than 150 km. Height is also understood, in particular a height in an average circular and / or elliptical orbit of the earth body.

In a further particular application, in particular for aircraft, in particular missile and / or flying machine and / or their systems, in particular for VM and / or VN and / or NN, in particular for generating electrical energy, comprises electric and / or magnetic fields and in particular comprises at least one transducer and / or at least one sensor and / or at least one actuator, in particular in electrically charged air layers of the ionosphere, in particular the ionospheric layer F2 at a height of 250 to 400 km, with the advantage that here is the highest and strongest ionized layer , with an exemplary maximum ionization of less than 1 million free electrons per cm ³

The energy supply, in particular of the systems, in particular for aircraft, in particular missiles and / or flying machines takes place and comprises, at least one electrical energy system, in particular by means of energy harvesting, in particular for at least one system, in particular at a certain average altitude, the surrounding electrical and / or magnetic fields, in particular the electrical energy generated by the movement of the missile and / or flying machine in the ionosphere, by means of electric and / or magnetic fields and in particular comprises at least one transducer and / or at least one sensor and / or at least one actuator.

figure description

Further details, features and advantages of the subject matter of the invention will become apparent from the subclaims and from the following description of the accompanying drawings, in which - by way of example - a preferred embodiment of the invention is shown. The figures shows:

1 1 shows a node-network model of the solution according to the invention, which comprises the transmission of data, in particular of at least one simulation system (SI), from a start server ( 1 ) and / or destination network ( 1 ) to a destination server ( 2 ) and / or destination network ( 2 ) via several servers and / or network, in particular transport server ( 3.1 , ..., 3.N ) and / or transort network ( 3.1 , ..., 3.N ) of a telecommunication network and / or telecommunication server is used, whereby the data to be transmitted in the start server ( 1 ) and / or startup network ( 1 ) are integrated and / or transmitted and / or stored in at least one virtual machine (VM) and / or at least one virtual network (VN) and the virtual machine (VM) and / or virtual network (VN) together with the data integrated therein via the server and / or destination network, in particular transport server ( 3.1 , ..., 3.N ) and / or transport network to the destination server ( 2 ) and / or destination network ( 2 ), wherein during the transport in at least one server and / or network, in particular transport server ( 3.1 , ..., 3.N ) and / or transport network, the virtual machine (VM) and / or virtual network (VN) at least temporarily by means of the hardware and / or software of the current server and / or network, in particular transport server and / or transort network ( 3.1 , ..., 3.N ) and stopped or aborted after execution and retransmitted until the virtual machine (VM) and / or virtual network (VN) from the destination server ( 2 ) and / or destination network / 2) is received and where as the data to be transmitted with the start server ( 1 ) and / or destination network ( 1 ) Start data (S) are integrated into the virtual machine (VM) and / or virtual network (VN), which are converted by the virtual machine (VM) and / or virtual network (VN) in a conversion process into target data (Z), being on each of the servers, in particular transport server ( 3.1 , ..., 3.N ) executing the virtual machine (VM) and / or virtual network (VN), during the execution time of the virtual machine (VM) and / or virtual network (VN), the conversion process is partially carried out and characterized in that at least comprises at least a server and / or at least network, in particular each further server and / or network, in particular transport server and / or transport network, is informed about the conversion to be carried out in parallel with the sending and / or storage and / or processing prior to the sending of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) by means of a list and / or matrix and / or n-dimensional matrix in which at least one type of parameter is defined for each server and / or network, in particular the conversion , and / or identifier and / or address, in particular communication address and / or probability and / or ranking and / or the expected total Ausff ührungszeit and / or total transmission time and / or total energy consumption, in particular possible and / or probable total energy consumption and / or energy consumption, in particular the next to be awakened energy consumption of the other first server and / or network and / or at least one other servers and / or at least one other network ;
and / or - in the case of data in particular with at least one data record and / or at least one application and / or at least one customer identifier and / or at least one communication identifier, the apparatus comprises at least one virtual machine (VM) and / or at least one virtual network (VN ) and / or at least one neural network (NN); and started and / or stopped and / or stored and / or deleted is at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN), in particular by means of a network, in particular fuzzy based and / or neural network (NN), in particular fuzzy-based controller and / or neural network (NN) controller, comprises at least one VM and / or at least one VN.

The technical solution of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) comprises, as a technical architecture, an inventive structure in layers, in particular the base layer, layer 1: the virtual machine (VM) and / or Virtual Network (VN) and / or Neural Network (NN); in particular as a Java Virtual Machine (JVM) and / or Java Virtual Network (JVN) and in particular one or parts of computer languages, computer programs in particular C, C ++, Java be written platform, in particular to execute Java code. This increases Advantageously, in particular the standardization reduces complexity and customer-specific adjustments.

The fuzzy and / or neural network (NN) can in particular be executed from at least one virtual machine (VM) and / or at least one virtual network (VN) and / or virtual channel (VK) and / or at least one neural network (NN) be, in particular on at least one computer and / or computer system and / or distributed computer system.

The simulation system (SI) can take place in particular by means of at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN), in particular by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN.

To determine at least one parameter. In particular for ranking and / or probability, and advantageously for controlling the system and / or network, a simulation can advantageously be carried out, in particular at least one simulation system (SI), in particular an intelligent communication network (IN) and / or intelligent energy network (IE). , in particular at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN) and / or the at least one user system and / or at least one application network, in particular by means of at least one hardware and / or or at least one software and / or computer-implemented system. This is done by means of a formula, in particular of the form ERLANG = ERLANG PLUS = ERLANG +: = A + M * (mathematical function) * X, in particular ERLANG is a function of the line parameters of the quadrupole. Thus, advantageously, the computer-implemented simulating device (SI) and / or simulation method (SV) can take place in its entirety an improved control of the overall inventive system, in particular by means of a network, in particular fuzzy-based and / or neural network (NN ), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN.

By means of a method and / or apparatus for simulating the performance, also in relation to DE 00 0019 811 097 A1 and the patent US000006650731 B1 in particular with the components, at least one module for simulating any typical event sequences, in particular the load and / or traffic simulator and / or data track, in particular according to the traffic theory; especially for VM and VN; according to the rules, in particular according to the rule ERLANG = ERLANG + = ERLANG PLUS = M * ErI + A, in particular taking into account the priority of the VoIP packets, in particular taking into account the separate logical channels of VoIP and data packets, especially taking into account the measures on a hardware basis, in particular the MPLS and / or VLAN with hardware-based QoS measures as the transport network, with the advantage that the virtual channels (VK) support a large number of VoIP channels in a given system, in particular with the advantage of the low delay time in package transport; and at least one module for event-oriented simulation of the system, in particular the VM and / or VN using process models, in particular at least one module for controlling and / or distributing and / or stocking and / or mediating the resources of the systems and / or networks and / or or VM and / or VN and / or VK and / or NN takes place, in particular by means of a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controller and / or neural network (NN) - Controller comprising at least one VM and / or at least one VN.

The technical solution, in particular at least one simulation system (SI), the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) can also be described as at least one virtual channel (VK). virtual circuit) and / or virtual connection (VC) executed and / or be technically resolved. Particularly advantageous can be solved as virtual network (VN) from at least one virtual channel (VK) technically. A virtual channel (VK) may also be a virtual connection that is dynamically assimilated and degraded (English: switched virtual circuit, SVC) or permanently established (English: permanent virtual circuit, PVC). physically by lines or logically by own virtual channels such as MPLS, ATM, VLAN with QoS measures, DiffServ and Overprovisioning done.

A virtual connection (VC) represents at least one connection, in particular (end-to-end) connection, of at least one system, in particular at least one terminal device (EE), in particular data terminal device (DEE) via a network to be used. Here, a route belonging to a connection can be selected for each data packet, and for this purpose the data packets receive at least one identification for the associated virtual connection and / or at least one destination address. For example, two virtual channels, in particular VLAN, MPLS, ATM or VLAN can be set up with QoS measures for one transmission link. This can also separate a separation of data in data and real-time communication. And now advantageously more VoIP channels can be transmitted with lower delay times. and in the resolution of speed deviations, in particular in the repetition of transmissions in case of transmission errors and in the conversion of content running over other network connections.

Identifier receives, in particular an identifier of the system, system environment, communication identifier and / or device identifier and / or network identifier and / or version identifier and / or timestamp

In a particular application example, in particular for specifying the inventive solution to the prior art, here the textbook by the author Thomas Till entitled "Fuzzy Logic" (from 1991) and "Automation with Fuzzy Logic" (from 1992) comprises a method and system by means of a fuzzy-based control, in particular controller; inventive and includes at least one neural network (NN) and / or at least one neural network (NN) -based control, in particular controller; comprises by means of at least one VM and / or VN, in particular for regulation, in particular for consumption, and / or total energy consumption and / or transaction volume and / or transaction speed of several systems, in particular electrical appliances (GE) and / or sensors and / or VM and / or or VN.

The sensor, in particular GE; comprises in a particular application at least one actuator and / or at least one sensor and / or at least one virtual sensor, in particular a group of sensors, in particular those generated and / or represented by means of VM and / or VN and / or NN, in particular the one mapping and / or processing and / or processing and / or storing and / or controlling the physical effect in computer-technical form, in particular by means of at least one simulation system (SI), in particular by means of at least one fuzzy-based control system and / or network; and / or neural network (NN) -based control system and / or network, in particular by means of at least one VM and / or VN.

A sensor, in particular as an actuator, comprising in a particular application, in particular the measurement control and regulation technology, at least one actuator, in particular in at least one control loop, in particular set at least one signal, in particular a control z. B. in mechanical work and / or thermal work and / or electrical work.

At least one sensor, in a particular application, includes at least one GE and / or simulation system (SI) and / or at least one simulation system (SI) and / or vice versa. A simulation system (SI) is at least one sensor, in particular at least one virtual sensor, in particular by means of at least one fuzzy-based control system and / or network; and / or at least one neural network (NN) and / or neural network (NN) -based control system and / or network, in particular by means of at least one VM and / or VN.

VM and / or VN are also understood to mean software, in particular computer-implemented programs, in particular software agents, in particular distributed intelligence, in particular centralized and / or decentralized expert systems, in particular by means of cyber-physical systems (CPS) and their software, in particular software agents and / or intelligent software systems, with the possible Verarbetiungsschritte, in particular tasks and / or goals and / or algorithm with a strategy function and / or are then assigned systems, in particular product and / or machine and / or components and / or sensor and / or actuator and / or control and / or fuzzy and / or neural network (NN) controllers and / or at least one neural network (NN).

Data is also understood to mean physical states, in particular data, data sets, in particular big data; in particular for use in INDUSTRIE 4.0, detected by way of example with a sensor, in particular virtual sensor (VSE), z. With a flow rate sensor of 0.18 terabytes / a of data, e.g. B. a vibration analysis sensor with 1.3 terabytes / a data and z. B. an optical sensor with 3.9 terabytes / a data.

With a method and system for neural network (NN), in particular fuzzy-based control and / or neural network (NN) -based control of the total energy consumption and / or transaction volume and / or transaction speed of multiple electrical devices, and / or sensors where the current energy consumption and / or transaction volume and / or transaction speed of each electrical device and / or sensor and / or VM and / or VN and / or NN of the group is measured for each electrical device and / or sensor and / or VM and / or VN and / or or NN is determined as a function of its current energy consumption and / or transaction volume and / or transaction speed a priority value and / or probability, at least the system, in particular electrical device with the highest priority value and / or probability is selected, a control value for the selected system, in particular electrical device depending on the dazug the priority value and / or probability of which current energy consumption and / or transaction volume and / or transaction speed and current total energy consumption and / or total transaction volume and / or total transaction speed of the group is determined such that the total energy consumption of the group does not exceed an adjustable threshold exceeds.

Probability is also understood to mean the probability value, in particular at least one probability value, in particular a technical value, in particular at least one analog and / or digital value. As a digital value, in particular a state on / off; (0/1); a bit and / or bit sequence. As analog value, in particular at least one a technical signal, in particular voltage and / or current and / or electric field and / or magnetic field and / or electro-magnetic spectrum, in particular light defined.

The probability and / or ranking can be influenced and / or taken into account in a particular embodiment and / or the market value of the network, in particular VN and / or group, in particular for at least one VN, here in the measured variable in Erlang and / or Erlang- B and / or Erlang-C. Erlang is the dimensionless auxiliary unit used in traffic theory for the traffic value in the communication network (Erl, er or 1 Erl = 36 CCS in the USA) and the inventive consideration of additional technical facts, in particular with at least one compensation curve, in particular the form Erl: = Erl + A, A-fixed parameter and / or the form Erl: = M * (mathematical function) * X + A; with M slope, X = Erl - the variables and the mathematical function, such as log, In, Hyp, e and combination like (e ** x) and in particular by means of at least one simulation, in particular computer-implemented device and / or system. Probability is further understood, especially taking into account the analog and / or probability parameters for A - limited patience of the caller - the caller does not wait indefinitely - in the queue. B - Limited Waiting Room - The waiting room is limited by the number of lines available, in particular B. limited in the call center. C-decreasing marginal utility - above a certain level of service - eg. B. threshold value (80-90%) brings the use of additional staff only marginal improvements in accessibility. D - Unproductivity - Break times of employees. E - Dynamic arrival rates - Often, the assumption of arrivals does not apply. z. B. After a hotspot (commercials) a massive accumulation of calls occurs. F - hetrogenic agents - As a rule, the agents are not all in a state of knowledge, but have certain "specialties". G - Heterogeneous callers - Often the callers do not form a homogeneous group, especially VN, but several heterogeneous groups.

The simulation, also called a simulator (SI), can be carried out in particular by means of at least one system and / or system architecture and / or software.

In this case, at least one decentralized simulation (DS) and / or central simulation (ZS) and / or a combination of both types can be carried out for simulation, in this case combination simulation (KS), in particular by means of VM and / or VN and / or NN, in particular a network, in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controllers, comprises at least one VM and / or at least one VN.

• • Die dezentrale Simulation-System (DS) simuliert, insbesondede kontinuierlich und/oder diskret, insbesonder VM und/oder VN und/oder NN, insbesonder in seinem definierten und/oder temporäre definierten Umfeld (z. B. in Meter) und/oder Zeit (z. B. in Sekunde) und/oder Energie (z. B. in Watt) und/oder Datenbandbreite (z. B. in bit pro Sekunde) und/oder Bewertung und/oder Rankung und/oder Wahrscheinlichkeit.
• • Das definierte Umfeld (DU) kann insbesondere auch verstanden werden, eine Entfernung, insbesondere zu einem System und/oder Sytemgruppe und/oder mindestens ein Systeme, insbesondere im Umfeld mindestens eines System, insbesondere im definierten Umfeld (DU), z. B. < 1 Meter, - Home-Bereich, z. B. 2–5 Meter, - Nahbereich < 100 Meter, - Nahbereich < 1.000 Meter.
• • Unter temporäre definierten Umfeld (tDU) kann insbesondere auch verstanden werden, eine für eine bestimmte Zeit (t) und/oder mindestens eines bestimmten Zustand, insbesondere Triggerpunkt mindestens eine Zuordnung zum definierten Umfeld (DU) erfolgt, insbesondere von Netze und/oder Systeme, insbesondere VM und/oder VN.
• • Das dezentrale Simulation-System (DS) simuliert und/oder steuert, mittels mindestens einer Steuerung (ST) als Vorrichtung, insbeondere mittels mindestens eines Smart Metering, insbesondere in seinem definierten und/oder temporär definierten Umfeld.
• • Unter Steuerung (ST) wird insbesondere auch verstanden das An- und/oder Abschalten und/oder Verringerung von mindestens einem System und/oder mindestens einer Transaktionen und/oder mindestens einer VM und/oder mindestens einer VN. Wobei die Verringerung, insbesondere auch verstanden wird von der Verringerung der Anzahl von Transaktionen und/oder VM und/oder VN. Weiter kann auch hierunter verstaden werden ein kontinuierlicher Wert und/oder Parameter zwischen Null und Eins.

STEP 2 umfasst:

• • Die dezentrale Simulation-System (DS) steuert, insbesondere mittels einer Steuerung (ST), insbesondere nach mindestens einer simulation, mindestens ein System und/oder Netz, insbesondere Lokale System, insbesondere im definierten Umfeld. Nach der Steuerung erfolgt mindestens ein Protokoll, insbesondere über mindestens einen Protokollrückkanal (PRK) an die Zentrale Simulation insbesondere kann das Protokoll über den Protokollrückkanal (PRK) zeitversetzt erfolgen, insbesondere über den normalen Kommunikationskanal, insbesondere über den Protokollkanal, insbesondere über Container und/oder VM und/oder VN und/oder NN.

STEP 3 umfasst:

• • Die zentrale Simulation (ZS) verarbeitet das Protokoll, insbesondere mit internen Regeln und/oder Simulation und/oder Gewichtung und/oder Ranking und gibt mindestens ein Protokoll (ZP), insbesondere zentrale Protokoll (ZP), insbesondere mindestens ein Datenprotokoll, an die dezentrale Simulation (DS) zurück, insbesondere ein OK oder NOOK und/oder fuzzy-basierte bewertet ist, insbeondere fuzzy-basierte bewerte größer Null.

STEP 4 umfasst:

• • Die dezentrale Simulation-System (DS) nimmt das Protokoll auf, insbesondere zur kenntnis, insbesondere auf zum lernen des Regelwerkes der Simulation, insbesondere zum Lernalgorithmus.

A particularly advantageous embodiment comprises at least one neural network (NN) and / or at least one VM and / or at least one VN, in particular the swarm behavior and / or swarm intelligence, in particular the simulation, in particular for autonomous systems, by means of at least one protocol back channel (PRK). , in particular such as method comprises:

STEP 1 includes:

• The decentralized simulation system (DS) simulates, in particular continuously and / or discretely, in particular VM and / or VN and / or NN, in particular in its defined and / or temporary defined environment (eg in meters) and / or Time (eg in seconds) and / or energy (eg in watts) and / or data bandwidth (eg in bits per second) and / or rating and / or ranking and / or probability.
• The defined environment (DU) can in particular also be understood to mean a distance, in particular to a system and / or group of systems and / or at least one system, in particular in the environment of at least one system, in particular in the defined environment (DU), e.g. B. <1 meter, - Home area, z. 2-5 meters, - short range <100 meters, - short range <1,000 meters.
• • Temporary defined environment (tDU) may in particular also be understood as meaning that at least one assignment to the defined environment (DU) takes place for a specific time (t) and / or at least one specific state, in particular trigger point, in particular of networks and / or systems , in particular VM and / or VN.
• The distributed simulation system (DS) simulates and / or controls by means of at least one control (ST) as a device, in particular by means of at least one smart metering, in particular in its defined and / or temporarily defined environment.
• • Control (ST) is in particular also understood as the activation and / or deactivation and / or reduction of at least one system and / or at least one transaction and / or at least one VM and / or at least one UN. The reduction, in particular also understood by the reduction in the number of transactions and / or VM and / or VN. Furthermore, a continuous value and / or parameter between zero and one can also be used.

STEP 2 includes:

• The decentralized simulation system (DS) controls, in particular by means of a controller (ST), in particular after at least one simulation, at least one system and / or network, in particular local system, in particular in the defined environment. After the control, at least one protocol, in particular via at least one protocol return channel (PRK) to the control center simulation in particular, the protocol via the protocol return channel (PRK) is carried out with a time delay, in particular via the normal communication channel, in particular via the protocol channel, in particular via containers and / or VM and / or VN and / or NN.

STEP 3 includes:

• The central simulation (ZS) processes the protocol, in particular with internal rules and / or simulation and / or weighting and / or ranking and gives at least one protocol (ZP), in particular central protocol (ZP), in particular at least one data protocol to which decentralized simulation (DS) back, in particular an OK or NOOK and / or fuzzy-based is evaluated, in particular fuzzy-based ratings greater than zero.

STEP 4 includes:

• • The decentralized simulation system (DS) records the protocol, in particular to the knowledge, in particular to learn the rules of the simulation, in particular the learning algorithm.

The particular advantage of this technical procedure and / or system and / or network is that the time-fast control, in particular the reaction time, especially after a simulation is no loss of time for waiting for the decision from the central system. It is a kind of autonomous decision, which is evaluated retrospectively, approved and / or negated and / or fuzzy-based and thus incorporated into the rules of the decentralized decision of the simulation (DS), in particular re-evaluated and / or weighted and / or or fuzzy-based decentralized simulation (DS).

At the same time, the system and / or network as a whole becomes more stable, especially since the central rules are adhered to everywhere, in particular with a difference in the rating and / or fuzzy-based and / or time-delayed (delta t), in particular in a rating of Centrals receive a greater than zero weighting advantageous.

For example, an intelligent consumption display and / or control for the shower can be made. The device (DS) is connected as an example between shower head and hose. With each shower, it measures the water and heat energy consumption and simulates the further course z. B. temporally and / or all lying in a given environment showers z. B. in the Hallebad and / or acts controlling, z. B. in case of unforeseen actions, in particular additional consumption z. B. of water, for. B. due to pipe burst, energy. The consumption data and / or the result of the control and / or the simulation are z. B. transmitted via Bluetooth 4.0 to a mobile devices and the wireless network and / or cloud to the central System control (ZS) in particular the power generator and / or water generator sent. Subsequently, the central system control (ZS) sends the device (DS) a confirmation, in particular a weighted confirmation back. In this way, users advantageously receive information about the usage behavior, in particular about their individual energy and / or water consumption, in particular information about the energy efficiency class, the water temperature, the water consumption since the start of the showering process, the energy consumption in watt-hours, the amount of water consumed in liters. The device (DS) is almost adapted to the central system (ZS) and can thus be carried out decentralized central requirements advantageous.

The invention relates to a system for controlling, in particular comprising at least one neural network (NN), in particular fuzzy-based control and / or neural network (NN) -based control, the total energy consumption and / or transaction volume and / or transaction speed of at least one system, in particular a plurality of electrical devices and / or sensors, in particular a control system, in particular fuzzy control system, in particular at least a virtual control system by means least a fuzzy control system and / or by means of at least one neural network (NN), in particular by means of at least one VM and / or VN , As well as a method for controlling, in particular fuzzy-based control and / or neural networks (NN) -based control of the total consumption, in particular total energy consumption of several electrical appliances, these electrical appliances form a group, in particular a virtual group, in particular by means of VM and / or VN.

Fuzzy-based controls, neural networks (NN), self-organizing controllers (SOC) are known as state of the art. Self-organizing controllers (SOC) have the disadvantages that in a rule modification, the entire rule base is changed. If the modification is performed repeatedly, the rule base may return to an earlier state. The controller oscillates around an ideal value. This is very often a modification of the controller with low optimization potential. Another disadvantage of the fuzzy-based control in particular is the lack of learning ability and adaptability, in particular to autonomous systems.

Further, in the particular embodiment, the object can be achieved that resources are saved, in particular by means of a learned automatic, adaptive control of consumption, in particular in the overload defense, in particular in the energy consumption of several electrical appliances on the basis of instantaneous consumption values and / or statistical values, In particular, algorithms, in particular the energy consumption values with changing target functions, are enabled and / or safe to transmit the data, in particular data and / or data track and / or communication data and / or electrical energy.

To solve the problem, in particular the particular embodiment, an inventive system and / or method for controlling by means of fuzzy-based control and / or at least one neural network (NN), in particular neural networks (NN) -based control is characterized in that Load defense, in particular the overload protection, in particular the total consumption, in particular the total energy consumption, in particular the total transaction volume and / or total transaction speed of multiple systems, in particular electrical devices, a group, in particular virtual group, in particular VM and / or VN and / or NN form, in which the current consumption, in particular energy consumption and / or transaction volume and / or transaction speed of each system, in particular electrical device of the group is measured for each system, in particular electrical device depending on its current consumption, in particular energy consumption and / or transaction volume and / or transaction speed, a priority value is determined, in particular characterized in that at least one system, in particular electrical device having the highest priority value and / or probability and / or identifier is selected and for this purpose at least one control value for the selected system, in particular electrical device as a function of at least one of the associated priority value and / or probability value of the current consumption particular energy consumption and the current total consumption, especially total energy consumption of the group is determined such that the total energy consumption of the group does not exceed an adjustable threshold, in particular by means at least one optimized distribution of the networking, in particular by means of at least one VM and / or VN and / or NN, in particular by distribution of the load, by means of at least one VM and / or VN.

Transaction is also understood to mean a sequence of operations and / or program steps and / or digital data and / or data track and / or technical signals which are only completely considered as a logical unit and / or not performed at all and after a faultless and complete execution in a consistent state. Erroneous transactions must be aborted and the previous changes in the database must be reversed so that they do not have any Affect the state of the database. Transaction volume is also understood to mean the volume, in particular in the case of communication, in particular data volume and / or storage, in particular data volume and / or processing volume, in particular throughput volume, in the unit of measure byte, in particular Gbyte. At the transaction speed, the speed from the beginning to the end of the transaction and / or amount of processing and / or processing speed, in particular in bits / sec, is also understood in particular

The inventive, particular embodiment, at least one control and / or at least one neural network (NN), in particular fuzzy-based control is used and / or at least one neural network (NN) -based control, which so input and output values, in particular with Linguistic, in particular fuzzy sets and / or by means of at least one neural networks (NN) sets containing variables, allows a combination of variables and / or device-specific identifiers and / or communication identifier such that thereby an automatic optimization of consumption, in particular their Distribution, in particular of the energy consumption and / or transaction volume and / or transaction speed of a group-forming systems, in particular VM and / or VN, in particular electrical devices even with different competing parameters, changing target functions and taking into account different communication protocols and / or he system variables and / or transaction volume and / or transaction speed, for communication with the systems, in particular electrical equipment can be made.

An advantage is that the determination of a control value for controlling the consumption and / or networking and / or energy consumption of a selected system, in particular electrical appliance, which for example defines a time of switching on or the clocking of the selected system, in particular electrical device, not only of current parameters of the selected electrical device and a determined based on this selected, belonging to this selected electrical device target function, but additionally at least one target function of the total consumption, in particular total energy consumption and / or total transaction volume and / or total transaction speed of the group of electrical equipment in the form an adaptable threshold value is taken into account and the learning ability and the adaptability is present here, in particular in he new learning ability, that all already learned patterns plus the new patterns are learned z For better technical use of the systems, especially in relation to product liability, since no knowledge is lost.

Consequently, by appropriate control of a selected system, in particular electrical device, the activity of this selected electrical device within a current total energy consumption profile temporally shifted to a phase of lesser load, especially within the group, especially in the network, in particular by means of VM and / or VN and / or NN and so the total consumption, in particular reduced total energy consumption and avoided load peaks and / or moved in the network, without affecting the functionality of the selected system, in particular electrical device for control over a user.

A further advantages is that a control takes place over several levels, in particular the communication level and / or control level and / or regulation level and / or electrical energy level, characterized in that different, at least one VM and / or VN and / or VK and / or NN is used, in particular by means of a network and / or at least one neural network (NN), in particular fuzzy-based and / or neural network (NN), in particular fuzzy-based controllers and / or neural network (NN) controller, comprising at least one VM and / or at least one VN.

Another advantage is that the technical resource utilization, the physical systems through the virtualization, in particular the VM and / or VN and / or NN is improved, in particular within the communication, in particular by means of duplicate check, in particular before starting, at least one VM and / or VN and / or NN, in particular before the possible generation of the VM and / or VN and / or NN, in particular on multiple data and thus leads to an improvement of about 50% and / or in particular by means of the neural network (NN) a data reduction can be done simultaneously and serially. A further advantage is that the bandwidth is bundled, in particular improved, by means of the virtualization systems and / or the neural network (NN).

A further advantage is that the regulation, in particular by means of neural networks (NN) and / or at least one neural network (NN) takes place, and so advantageously a learning system is present and parallel processing enabled and in particular control intervene and / or record new measured values and / or process and / or learn. With the further advantage, in particular by means of neural networks (NN) and / or at least one neural network (NN), that this also conventional controller can replace and / or the system and / or network to specify at least one target value, the neural network (NN) and / or at least one neural network (NN) from one of self-developed forecast, in particular statistics about the course of the process can be determined. Controllers, in particular fuzzy systems and / or neural networks (NN) and / or at least one neural network (NN), can also be advantageous because of the bidirectional course, in particular also across the different levels, in particular of the electrical energy consumption and / or communication network and / or control network - a conversion of the scheme, in particular by means of at least one fuzzy systems and / or means neural networks (NN) systems and / or at least one neural network (NN) are designed. The advantage of the control, in particular fuzzy-based control and / or neural network (NN) -based control and / or at least one neural network (NN), is that the learning behavior changes as the activity of the neurons or the learning rate of the network changes / or the infinite state spaces can be approximated by means of neural networks (NN) the utility function, in particular for the Markov decision process.

With the further advantage of overload protection and / or regulation and / or encryption of the data by means of the control, in particular fuzzy-based control and / or neural networks (NN) based control and / or at least one neural network (NN).

Accordingly, the particular embodiment also proposes a control system and / or at least one neural network (NN), in particular a fuzzy control system and / or at least one neural network (NN) control system, for controlling the total energy consumption of a plurality of electrical devices which form a group, in front. In particular, the fuzzy control system and / or neural network (NN) control system, a first device, in particular fuzzy-based device, and / or neural network (NN) -based device, which is adapted to each electrical device within the group, in particular VM and / or VN and / or NN depending on its current energy consumption to determine a priority value and / or probability and at least to select the electrical device with the highest priority value, and wherein the control system, in particular fuzzy control system, and Neural network (NN) control system, a second device, in particular fuzzy-based device and / or neural network (NN) -based device and / or at least one neural network (NN), which is designed, at least for each selected electrical device depending on its associated priority value and its current energy consumption as well as the current total tenergieverbrauch the group of electrical equipment to determine a control value, such that the total energy consumption of the group, in particular VM and / or VN and / or NN does not exceed an adjustable threshold.

Basically, the controller, in particular the fuzzy-based devices and / or neural networks (NN) -based device and / or at least one neural network (NN) of the inventive system for performing the method according to the invention part of a single controller, in particular fuzzy controller and / or neural network (NN) controllers and / or at least one neural network (NN). Since this then but depending on the included electrical equipment within the group, in particular VM and / or VN and / or NN; is complex and complex constructed of electrical equipment and optionally application specific also slower than separate, but interconnected controller, in particular fuzzy controller and / or neural network (NN) controller and / or at least one neural network (NN) is preferably provided the first controller, in particular fuzzy-based device and / or neural network (NN) -based device and / or at least one neural network (NN), as a first separate controller, in particular fuzzy controller and / or neural network (NN) - Controller, and the second controller, in particular fuzzy-based device and / or neural network (NN) -based device and / or at least one neural network (NN), as a second separate controller, in particular fuzzy controller and / or neural network ( NN) controller and / or at least one neural network (NN) to form, wherein for linking the two controllers, in particular fuzzy controller and / or neural network (NN) controller and / or at least one Ne uronic network (NN), the second controller, in particular fuzzy controller and / or neural network (NN) controller and / or at least one neural network (NN), with the output of the first controller, in particular fuzzy controller and / or neural Network (NN) controller is connected.

It is thus expedient for each priority value and / or probability value determined as a target function to be taken into consideration to be supplied as a separate manipulated variable to the subsequent determination of the manipulated variable for the selected electrical device within the method according to the invention.

In an expedient refinement, at least one further, in particular a third device, in particular at least one neural network (NN), in particular the fuzzy-based device and / or neural network (NN) -based device, is also provided and configured in dependence on at least one predetermined desired value and / or probability to determine a target control value of the current time and the current total energy consumption, in which case the second device, in particular fuzzy-based device and / or neural network (NN) -based device, or the second controller, in particular at least one neural network (NN), in particular fuzzy controller and / or neural network (NN) controller is adapted for the selected electrical device in addition to the dependence on the associated priority value and / or probability of the current Energy consumption and the current total energy consumption of the group, in particular VM and / or VN and / or NN, of electrical devices also depending on the setpoint control value to determine a control value, so that the total energy consumption does not exceed the adjustable threshold.

In this way, the control behavior with respect to the entire group, in particular VM and / or VN and / or NN, of electrical devices can be assigned a target specification directly, since a control value determined as a result influences the dynamics of the control as a whole.

Also preferred is the third device, in particular at least one neural network (NN), in particular fuzzy-based device and / or neural network (NN) -based device, as a separate third controller, in particular at least one neural network (NN), in particular fuzzy Regulator, and / or neural network (NN) controller, formed, which then for linking at least to the second controller, in particular at least one neural network (NN), in particular fuzzy controller and / or neural network (NN) controller, also On the output side with this second controller, in particular at least one neural network (NN), in particular fuzzy controller and / or neural network (NN) controller is connected.

Suitably, in turn, each as a further objective function to be taken into account certain desired control value within the particular embodiment of the invention method as at least one separate control variable of the subsequent determination of the control value for the selected system, in particular electrical device supplied.

In a further development of the particular embodiment, it is further provided that the determination of the priority value and / or probability and / or identifier and / or ranking for each system, in particular electrical device not only depending on its current energy consumption and / or transaction volume and / or transaction speed but in addition depending on a to each system, in particular electrical device individually associated Kritikalitätsmaßes perform. In this case, according to the invention, the criticality measure can in particular also be linked to at least one identifier and / or time and / or time duration and / or device identifier and / or communication identifier and / or machine identifier.

Under a communication address, communication identifier is understood here among other things, and includes at least one identifier and vice versa. Communication identifier consisting of at least one and / or several classes. The individual classes are technical features are listed here by way of example and not exhaustive. The class Customer ID- for identification of the customer, owner, owner of the device, eg. Eg customer number, PAN (personal address number), telephone number, customer number, billing account number, identity card number, TIN (tax identification number), tax number 1-digit. The class communication identifier, e.g. Calling Line Identification (CLI), Visitor Location Register (VLR), Home Location Register (HLR), Internet Protocol (IP) address, telephone number, telephone number, telecard number, radio cell, radio range. Under a communication address, communication identifier and / or with the class device identifier for identification of the individual device, by way of example with the device identification number, device serial number z. IMEI (International Mobile Equipment Identity), definition IMEI (International Mobile Equipment Identity), for example in the GSM system, the number uniquely identifying a terminal which is always checked during authentication, with the IMEI on different lists, White List, Gray List, Black List is listed in the Equipment Identification Register (EIR). The IMSI (International Mobile Subsciber Identity) is the term for a number of up to 15 digits that uniquely identifies a subscriber to a mobile service (mobile subscriber) and is composed of Mobile Country Code (MCC) and Mobile Network Code (MNC), Mobile Subscriber Identity Number. (MS-IN) Fixed per communication user and oriented to the ITU Recommendation E.212, is stored in the SIM (Subscriber Identity Module) and communicated with each login and is used to identify the records in the HLR (Home Location Register), AUC , VLR (Visotor Location Register). An electronic product code EPC, or localization number EAN number for the unique identification of RFID detection location (s), radio cell identification, WAP address (WAP push-link) sent to the telephone by SMS, an electronic product code (EPC) for be unique identification of products through individual allocation of serial numbers and also the number of the shipping unit (NVE) also the European Article number (EAN), transaction data in the communication z. B. start time of the communication, time end of the communication, date data volume, transaction data for the money traffic. Under a communication address, communication identifier and / or class of the device identifier and / or the class transaction identifier - for the transaction during the payment process, payment transaction, date, time, tax number, goods, position. Under a communication address, communication identifier and / or class of the device identifier and / or the class transaction identifier and / or class biometric identifier, in particular for securing the SI and / or VM and / or VN, in particular at least one technical parameter, in particular for the result, at least one technical parameter, records that are based on a possible algorithm z. From fingerprint, voiceprint, iris. The aim of the biometric feature, biometric parameters is the biometric recognition z. B. with a finger scanner system, it is the identity of a person to identify (identification) or a claimed identity to confirm or disprove (verification). In an identification, a biometric feature is compared with all the reference features stored in the system (1: n comparison). The legitimate user must be recognized and distinguished. The unlawful user must be prevented from using it. During the verification, the user gives the system his identity, identification identifier in advance (eg via a customer number) and the system only has to compare the biometric characteristic with the characteristic stored in the identity (1: 1 comparison). In authentication, the verification of an identity or authenticity (authenticity) is given by the disclosure of a knowledge (eg passwords), use of a possession (eg card, certificates), presentation of biometric data (eg. Fingerprint, voice, iris) between man and machine, machine and man and machine to machine. It must be proven that an asserted identity of an object or subject agrees with the measured characteristic properties. The user must be sure that they are working with the right system. The system must be sure that the user is who he claims to be. Authentication is a prerequisite for access control, access control, and liabilities. Biometric features, biometric parameters is e.g. B. Physical Human Metrics - are physical parameters such. Resistance, capacitance, inductance, permeability constants, mechanical frictional resistance, roughness, weight, fluid evaporation, etc. due to human areas, such as arm, head, right hand to left hand, right hand to ground (feet, ground), between mouth and ear z. B. which can be measured by a bone sound parameter. Physical metric = physical human metric = a function of a defined range z. Hand, head, mouth-head and a physically measurable parameter in the range z. As length, resistance, use of different biometric functions, eg. Eg voicID and / or FingerPrint.

Under device identifier is also understood and includes the machine identifier and is in particular the identification of at least one system and / or components and / or products, in particular by means of barcode, DMC, RFID, Internet address, software, in particular software agents. In particular, by means of a machine product memory, especially in embedded products, in particular by means of RFID and / or virtually the product associated data, databases, cloud z. Eg "virtual RFID tag". In particular, so that the product knows how it wants to be built, in particular the product tells the machines how it wants to be processed, process processing. Next is also understood by device identification machine identification code (MIC), in particular a color printer labeling, z. For example, yellow dots or secret dots, - a digital watermark, and the code is an identification of the devices that created a particular document, referring to the manufacturer of the document.

A communication system is understood here inter alia and includes at least one communication system z. B. at least one radio system, insbesodnere base station of a provider, the exchange of a fixed network operator. Likewise, this also means a communication system of a customer. In this case, the communication system, for example a mobile, can be a fixed communication system, to a landline connection, to an Internet connection. The communication system supports the communication z. From customer to customer, man to machine, machine to machine or machine to man.

In a particular embodiment, the parameter position can be determined, for example, by means of an optical image, image sequence, video, photo transmitted via the communication system. The communication system can trigger this image automatically or triggered by the customer, for. B. an image of a particular feature of an object, a cathedral, a train station, the starry sky, the position of the sun. In a further embodiment, barcodes can also be detected by machines, telephone booths, cash machines, emergency call columns. Triggering can be deliberately triggered by the customer by "shooting" a photo. For a temporary trigger, the communication system z. B. at intervals, in jerky movements trigger a picture or a sequence of images. At the same time by other trigger system z. B. RFID, WLAN, LTE, GSM, near field reception, a picture and / or image sequence are triggered Evaluation system z. B. centrally, decentrally determined on the basis of the transmitted photo, voice the possible position of the communication system and sends this back to the system for assembling the data track and / or the communication system. It also monitors the evaluation system whether it is a real picture, sound recording or a deception pretending to be only in this place. In a further particular embodiment, it is possible to deduce the position from the content, transaction, content, quantity. Here, for example, a picture and / or alone only the sound - for location. If z. For example, if a photograph of the "Meadows in Munich" is made and the sound is not "Bavarian" in the majority, then the system may close that the location is outside of Munich and a local, national meadow festival (eg in Wuppertal, Beijing) due to regional language colorings.

The parameter Content is u. a. according to the invention from a set of parameters and / or data. By way of example, as parameters, for. B. the content, the communication identifier (eg 0202-69862, C-communication identifier, C = {C / c ε HLR, IP, CLI} identifier and / or communication identifier), a communication systems (eg device identifier, mobile , Landline systems, K-communication system, K = {K / K ε mobile, screen, landline} and / links (eg IP addresses, www.dot.com, link L = {L / I ε www.dot At the same time the Connect can also contain destinations, addresses (eg who and / or where has been sent by this system - knows this communication system, this communication address, this link) Subset of transactions (T) (eg content / quantity) .There may be different transactions in forms of, for example, text, images, sound (eg with A type of transaction A = { A / a <message, image, text, language, control signal>} where message = 0000 0000 0000 card number, content, destination with the M content set eg M = in bits = {0, 1}, M = in bitsec, M = in Piksel, color level and / or S - information set S = k In P, P - the probability.

The parameter Content also includes at least one communication address. For example, a Calling Line Identification (CLI), Home Location Register (HLR), IP address This can be, for example, a destination CLI / HLR / IP address or your own address. From this, it is possible to identify and process a destination / user profile from the sum of the addresses - it is also possible to identify who knows whom. In a particular embodiment, the data track can be represented by a technical parameter and set further trigger points, thereby determining the information content from the data track.

• • I(N)_Spur = Id(1/(–p·Content)) = –Id(p·Content) und insbesondere erweitert die H_Spur = Summe (p_ilog_2pi·_Content.)
• • mit einer Wahrscheinlichkeit unf/oder Ranking insbesondere gespeichert in mindestens einer Liste und/oder Matrix und/oder n-dimensionalen Matrix, insbesondere in mindestens ein Neuronales Netz (NN).

Shown here includes:

• • I (N) _track = id (1 / (-p * content)) = -Id (p * content) and in particular extends the H _track = sum (p _i log _2pi * _content. ) _.
• • with a probability unf / or ranking in particular stored in at least one list and / or matrix and / or n-dimensional matrix, in particular in at least one neural network (NN).

The functional value and / or functional deviation comprises in particular at least one technical trigger point. In this case, the data track can be extended by this trigger point and / or be a trigger point. This has the advantage, for example, that a crypto key can be developed that changes over time.

A transactional system includes any system that is particularly involved in telecommunications, power, big-date - to perform a process, particularly financial transaction, from start to end of a process and is adapted to identify the user of the transactional system and another intended action to enable control, triggering, in particular to be able to make a transactions, financial transaction related to a possible money account of the user. In this case, a transaction system, for example, an access security system z. B. to a stadium, to secure access at major events, business premises or a time account system z. B. to capture the mobile work or use-dependent content usage or be a product z. In a large shopping center, for example, by marking and / or securing products with product code and the customer wants to purchase the product.

A correspondingly designed first device and method, in particular at least one neural network (NN), in particular fuzzy-based device, and / or neural network (NN) device, is expediently configured such that the criticality measure associated with an electrical device is dependent on a device-specific activity value and / or ranking and / or probability, a device-specific contact value and / or a general device-specific security relevance value and / or probability, in particular device-specific probability and / or communication Probability, especially in the machine control, in particular the machine to machine communication, determine.

Additionally or alternatively, the second device, in particular fuzzy-based device and / or neural network (NN) -based device, may be designed appropriately, the control value for each selected electrical device not only depending on its current energy consumption, the current total energy consumption of the group , in particular VM and / or VN and / or NN, and the priority value and / or probability value to be determined additionally in dependence of a to each selected electrical device individually associated relevance measure, in particular in relation to communication behavior and / or transaction volume and / or transaction speed - For this purpose, the second device, in particular fuzzy-based device and / or neural network (NN) -based device, is preferably designed to match the relevance measure associated with a selected electrical device as a function of the current time, in particular Ze and / or time span, a device-specific activity value and / or a device-specific service life and / or transaction volume and / or transaction speed.

In a further preferred embodiment it is provided that the second and / or third controller, in particular fuzzy-based device and / or at least one neural network, each at least one memory, in particular at least one virtual memory and / or at least one VM and / or at least one VN and / or at least one neural network (NN) and / or cloud, in which predetermined temporal usage profiles, in particular at least one learned usage profile are stored, and each adapted to, in response to the determined at predetermined and / or predeterminable times total energy consumption to recognize a predetermined usage profile, in particular at least one learned usage profile. On the basis of a recognized and / or learned usage profile, potential future load peaks of the total energy consumption can be included as an expectation into the determination of the manipulated variable, in particular for a selected electrical device, so that the regulation proposed according to the invention can be learned early for different, recurring total energy consumption profiles and / or is adaptable or can be learned and / or adapted.

Further advantages and features of the invention will become apparent from the following description of preferred embodiments with reference to the accompanying drawings.

1A shows a simplified representation of a preferred embodiment of a first neural network (NN), in particular as a fuzzy controller and / or neural network (NN) controller, trained, fuzzy-based device and / or neural network (NN) -based device, a fuzzy control system and / or neural network (NN) control system according to the invention, to illustrate various embodiments,

2A 1 shows a simplified representation of a preferred exemplary embodiment of a second fuzzy-based device and / or neural network (NN) -based device designed as a controller, in particular fuzzy controller and / or neural network (NN) controller, within a control system according to the invention, in particular fuzzy control system and / or neural network (NN) control system for illustrating various embodiments,

3A shows a simplified sketch of a preferred embodiment of a third, as a controller, in particular fuzzy controller and / or neural network (NN) controller trained, fuzzy-based device and / or neural network (NN) based device within a control system according to the invention, in particular fuzzy control system and / or neural network (NN) control system, to illustrate various embodiments,

4A a schematic diagram of a preferred embodiment of a method according to the invention for controlling the total energy consumption of several electrical devices which form a group, in particular VM and / or VN, to illustrate various embodiments.

On 1A and 2A A first device, in particular fuzzy-based device and / or neural network (NN) -based device, 100 or a second fuzzy-based device and / or neural network (NN) -based device, 200 a fuzzy control system according to the invention and / or neural network (NN) control system, for controlling the total energy consumption of several electrical devices 10-1 to 10-N which form a group, in particular VM and / or VN, show. The first device, in particular fuzzy-based device and / or neural network (NN) -based Device is expediently designed as a separate fuzzy controller and / or neural network (NN) controller, and also the second fuzzy-based device and / or neural network (NN) -based device according to 2 is expediently designed as a separate second fuzzy controller and / or neural network (NN) controller wherein for linking the two controllers, in particular fuzzy controller, and / or neural network (NN) controller, the second controller, in particular fuzzy controller and / or neural network (NN) controller is connected to the output of the first controller, in particular fuzzy controller and / or neural network (NN) controller.

Fuzzy controllers belong to the class of characteristic controller, which correspond to the theory of fuzzy logic, see also prior art. Usually, in each control step, three sub-steps are carried out, that is, the step of fuzzyfication, an inference, and finally the step of defuzzification, the individual inputs and outputs being described as linguistic variables, each of which includes fuzzy sets.

Under neural network (NN) controllers, inter alia, also to the class of artificial neural networks understood and referred to the topology of the structure of the network, which states how many artificial neurons (CN) are on how many layers (Hidden Lagers) and how these are interconnected. Artificial neurons can be connected in any way to an artificial neural network. Preferably, neurons (NE) are arranged in successive layers. Using graphs, the neurons can be represented as nodes and their connections as edges. The inputs are treated like nodes. The last layer of the network, whose neuron output is usually the only one visible outside the network, is called the output layer. Prior layers are accordingly called a hidden layer. The structure of the network is directly related to the learning method used. By way of example, only a single-layer network can be trained with the delta rule. At the same time, nets do not necessarily have to be homogeneous and can also be made of combinations of different models in order to combine the different advantages. Further, there are examples of pure feedforward networks in which a layer is always connected only to the next higher layer. In addition, there are networks in which connections are allowed in both directions. However, the finding of the optimal, especially suitable network structure can be adjusted automatically with the aid of evolutionary algorithms and / or backpropagation.

• • Entwicklung neuer Verbindungen, Löschen bestehender Verbindungen.

Lerntyp2 umfasst:

• • Ändern der Gewichtung, Ranking, Wahrscheinlichkeit, insbesondere der Gewichte von einem Neuron i zu anderen Neuron j).

Lerntyp3 umfasst:

• • Anpassen der Schwellwerte der Neuronen.

Lerntyp4 umfasst:

• • Hinzufügen oder Löschen von Neuronen.

Lerntyp5 umfasst:

• • Kombination der Lerntypen, insbesondere Lenrtyp2 UND Lerntyp3 ODER Lerntyp4.

Typical structures are exemplary, single-layer feedforward networks, with the feedforward property being the simplest structures of artificial neural networks (NN). They only have one output layer. The feedforward property states that neuron outputs are directed only in the processing direction and can not be returned by a recurrent edge (acyclic, directed gaph). Typical structures are exemplary, multi-layer feedforward network - and in addition to the output layer also have hidden layers whose output as described, are not visible outside the network. Hidden layers enhance the abstraction of such networks. Typical structures are exemplary, Recurrent network - in contrast, also have reverse (recurrent) edges (English feedback loops) and thus contain a feedback. Such edges are then often provided with a time delay (delta t), so that, in a stepwise processing, the neuron outputs of the past unit can be reapplied as inputs. The feedbacks allow a network to have a dynamic behavior and thus equip it with a memory - which can be used here in an advantageous manner. A neural network (NN) can be exemplified by the following. Learning types take place:

Learning Type1 includes:

• • Development of new connections, deletion of existing connections.

Learning type 2 includes:

• • Changing the weighting, ranking, probability, especially the weights of one neuron i to another neuron j).

Learning type 3 includes:

• • Adjusting the thresholds of the neurons.

Learning type 4 includes:

• • Add or delete neurons.

Learning type 5 includes:

• • combination of learning types, in particular Lenrtyp2 AND Lerntyp3 OR Lerntyp4.

The network learns, for example, by modifying the weights and / or ranking and / or probability of the neurons, adjusting the threshold by an ON neuron, etc. Thus, the neural network is capable of complex non-linear functions, mathematical functions via a "learning" Algorithm, which attempts to determine the parameters of the function, in particular by iterative and / or recursive procedure from existing input and desired output processing values. Neural networks (NNs) consist of many simple similar functional parts, but in sum, only the behavior of the system becomes complex, not its results.

A linguistic input variable may be, for example, the temperature, the electrical state, the power consumption of an electrical device and a linguistic output variable, for example, a valve position for a cooling air supply and / or the control of a machine and / or object, in particular in the Internet of things be ZB in turn, the fuzzy sets are formed from a set of membership functions, also called fuzzy sets. These membership functions or fuzzy sets are usually preconfigured and may take the form of a triangle, trapezium or curve, for example. The fuzzy sets classify the linguistic input variables into "fuzzy" classes and corroborate the input linguistic variables by measuring or otherwise receiving "sharp" input values having a class membership value between 0% and 100%. Accordingly, the "crisp" output values to be outputted corresponding to a linguistic output variable may be based on membership functions and occupy a membership value with respect to the fuzzy class division of 0% to 100%.

The fuzzy controller and / or neural network (NN) controller determines a control value to be output from the control difference, that is to say from the difference between a setpoint value and / or integral area under the curve and a measured actual value and / or area of an IST Value times, in particular a time difference and / or time duration by processing the steps, in particular the fuzzyfication, inference and defuzzification.

In fuzzyfication, fuzzy sets assigned to the input values described by means of linguistic variables are evaluated by way of example and values for class membership are determined, in particular by means of neural networks, in particular by means of VM and / or VN and / or NN.

In the step of inference, the rules for the calculation of the output are predefined on the basis of the values for class membership and thereby also a weighting and / or probability and / or key of the variables flowing into the calculation are determined. In particular, a weighting by means of the communication behavior and / or transaction volume and / or transaction speed and / or communication identifier and / or device identifier

In the case of defuzzification, the output values described by means of linguistic variables are assigned to membership functions based on the rules for the calculation and weighting and / or probability, for example, and determined according to a membership value, in particular the key.

A fuzzy controller and / or neural network (NN) controller is z. As a non-linear map and / or state controller and has in principle no internal dynamics and may in particular contain by means of at least one neural networks, in particular at least one VM and / or VN. Further, the regulator is an advantageous system for making at least one key to the lock) and decrypting.

1A and 2A comprising the first fuzzy-based device and / or neural network (NN) -based device according to 1A expediently as a separate fuzzy controller, and / or neural network (NN) controller, 100 and also the second fuzzy-based device, and / or neural network (NN) -based device, according to 2A useful as a separate second fuzzy controller and / or neural network (NN) controller, 200 It should be noted that the first fuzzy-based device and / or neural network (NN) -based device, in modification to 1A and the second fuzzy-based device and / or neural network (NN) -based device, as a modification to 2A may be both parts of a single fuzzy controller, and / or neural network (NN) controller together, but the structure then becomes more complex and expensive due to the internal cross-feedbacks. For the sake of simplicity, the following description therefore refers to the preferred embodiment that the first fuzzy-based device, and / or neural network (NN) -based device, according to 1A and the second fuzzy-based device and / or neural network (NN) -based device according to 2A two separate fuzzy controllers and / or neural network (NN) controllers, 100 respectively. 200 are that are so interconnected that the second fuzzy controller and / or neural network (NN) controller, 200 two according to 2A with the output "A100" of the first fuzzy controller and / or neural network (NN) controller 100 according to 1A connected is.

Exemplary for several electrical devices, which form a group, in particular VM and / or VN and / or NN, whose total energy consumption is controllable according to the invention, is a first electrical device 10-1 For example, a refrigerator, a machine, a bottle (on the Internet of Things) at 1A to the fuzzy controller and / or neural network (NN) controller, 100 turned on and at least a second electrical device 10-N , For example, a washing machine, in particular at least one machine, at least one in particular to the Internet of Things.

In principle, a number of N electrical devices, which form a common group, in particular VM and / or VN and / or NN, can be connected successively and thus one after the other on the same processing channel, so that in the simplest version and in a modified form for fuzzy-based Device and / or neural network (NN) based device, 100 according to 1A also a fuzzy-based device and / or neural network (NN) -based device can be used with only a least one processing channel within the scope of the invention, in particular for the formation of the key, in particular by means of at least one neural network (NN).

Conveniently, the as fuzzy controller and / or neural network (NN) controller, according to 1A trained fuzzy-based device and / or neural network (NN) -based device, 100 However, for each powered electrical device at least one processing channel available, so that for an N-channel fuzzy controller and / or neural network (NN) controller, 100 a number of N electrical devices, which form a common group, in particular VM and / or VN, to the fuzzy controller and / or neural network (NN) controller, 100 together, that can be connected in parallel.

The first fuzzy controllers and / or neural network (NN) controllers, 100 constructed first fuzzy-based device and / or neural network (NN) -based device is formed according to the invention, for each electrical device, ie according to 1A for the devices 10-1 to 10-N depending on its respective current energy consumption to determine a priority value and / or probability and at least the electrical device 10-1 , ..., 10-N with the highest priority value and / or probability value.

For this purpose, each electrical device is also appropriate 10-1 to 10-N via a connection device 1-1 to 1-N to one channel each of the first fuzzy controller and / or neural network (NN) controllers, 100 turned on.

Such a connection device 1-1 thus comprises at least one sensor, in particular consumption meter 14-1 , the consumption, in particular of energy of the connected electrical device 10-1 detected as an input value for channel 1. Are in the above-indicated, modified embodiment, the number of N electrical devices that form a common group, in particular VM and / or VN, and / or NN, successively and thus in turn to connect to a same processing channel, all electrical devices can, for. B. to be connected to the same Anschalteinrichtung, which then expediently includes a corresponding selection device, with which the electrical devices can in turn be connected successively to the same processing channel.

The according to the embodiment according to 2A further Anschalteinrichtungen include according to the Anschalteinrichtung 1-1 equally at least one sensor, in particular consumption meter, which detect the consumption, in particular the energy consumption of the electrical devices respectively connected via the connection devices, as an input value for the further channels.

Because thus both the Anschalteinrichtungen 1-1 to 1-N as well as the channels 1 to N of the first fuzzy controller and / or neural network (NN) controller, 100 may correspond to one another in terms of their construction, reference will now be made only to a preferred structure of the channel 1 of the first fuzzy controller and / or neural network (NN) controller, 100 received.

The initial value of the measurement, especially the consumption meter 14-1 which indicates the current energy consumption of the electrical device 10-1 measures, thus corresponds to a current consumption value and is the input value of a first adapted to the fuzzification device 113-1 channel 1 of the first fuzzy controller and / or neural network (NN) controller, 100 fed. The channel 1 points the device 113-1 followed by a logically adapted to perform an inference means 115-1 and this downstream of a correspondingly adapted to perform a Defuzzyfizierung device 116-1 on.

For fuzzy-based processing and / or neural network (NN) -based processing, the output value of the consumption meter 14-1 are in the fuzzy-based device, and / or neural network (NN) based device, 100 for describing a linguistic variable "consumption" for example the electrical device 10-1 associated fuzzy sets and / or neural network (NN) sets, deposited, which the linguistic input variable "consumption" in "fuzzy" classes z. From "very little" to "very much" and to which the fuzzy controller and / or neural network (NN) controller performing fuzzy-based processing and / or neural network (NN) -based processing . 100 according to the internal controller interconnection. Based on the to the electrical device 10-1 corresponding output value of the consumption meter 14-1 evaluates z. B. the Fuzzyfizierungseinrichtung and, 113-1 These fuzzy sets determine the values for class membership and / or ranking and / or probability for the measured energy consumption. Energy consumption is thus described by means of linguistic variables. The values of the class membership can usually be between 0% and 100%. One of the consumption meter 14-1 measured energy consumption of the electrical device 10-1 and accordingly spent consumption value could be ranked 80% as normal and 60% as little based, for example.

Such fuzzy sets and / or neural net (NN) sets are preferably preconfigured and may for example take the form of a triangle or a trapezium but also a curve piece, in this regard and to document the expertise of the skilled person to be considered here in full in the textbook from Tilli Thomas, fuzzy logic, Franzis from 1991 u. 1992 ISBN 3-7723-4322-8 or from Tilli Thomas, automation with fuzzy hardware, Franzis from 1992 and 1993, ISBN 3-7723-4412-7 and the later publication of 1995 of the DE 195 02 230 A1 and specified here in an inventive manner. The z. From the fuzzifier device, 113-1 as fuzzy sets, designated sets, ie the sets that have a membership greater than 0% to the measured input, and thus according to the example above, the fuzzy set "normal" and the fuzzy set "little", then the inferencing device 115-1 which prepares these fuzzy sets designated as active by predetermined rules for calculating an output. In the inferencing facility 115-1 Thus, rules for the individual fuzzy sets are deposited, which are selected by this and set accordingly for the subsequent calculation of an output, if a respective fuzzy set as active by the Fuzzyfizierungseinrichtung 113-1 the inferencing facility 115-1 has been communicated. The inferencing facility 115-1 By doing so, it also defines a weighting of variables used in the calculation. The result of the selection and determination becomes the defuzzification means 116-1 passed, which then determines a sharp output value. In this case, as is known to the person skilled in the art, various methods can be used, wherein, for example, in the case of triangular or trapezoidal fuzzy sets, a centroid of the fuzzy sets designated as active is calculated. The default is the defuzzification device 116-1 then a priority value 101-1 for the electrical device 10-1 ready on a scale of 0 to 1 or 0% to 100% a priority of this device 10-1 with regard to its current energy supply, and thus the urgent need to intervene (100%) or, for example, because of the current energy consumption, there is no such urgent need, so that, for example, a priority value of 75% exists. In the case of a refrigerator as an electrical device 10-1 would the need to intervene regulate, for. As a reduction in energy consumption, for example, by 25%, thus lead to an increase in temperature in the refrigerator, with a reduction in energy consumption by 25%, for example, would be accompanied by a rise in temperature by 5 °, which was assumed acceptable in the present example ,

The channels 1 to N of the first controller, in particular fuzzy controller and / or neural network (NN) controller 100 are appropriately constructed according to each other, so that according to 1A z. B. via the channel N, a priority value 101-N and / or ranking and / or probability and / or key for that to this channel N via the Anschalteinrichtung 1-N powered electrical device 10-N based on the current energy consumption measured for this device.

All priority values and / or ranking and / or probability and / or key and / or identifier 101-1 to 101-N will point to a selector 117-1 the first fuzzy controller and / or neural network (NN) controller, 100 which at least selects the electrical appliance with the highest associated priority value. The voltage applied to the output A100 output value is then z. B is a priority value output by the selector 101 , about which the at least one corresponding selected electrical device is clearly identifiable. The one of the selection device 117-1 output priority value 101 thus corresponds to at least one of the priority values 101-1 to 101-N ,

Is in modification to the appropriate training according to 1A As indicated above, a number of N electrical devices, which form a common group, in particular VM and / or VN and / or NN, are connected successively and thus one after the other to a same processing channel, are the respectively determined priority values 101-1 to 101-N consequently for selecting the electrical appliance with the highest associated priority value and / or ranking and / or probability and / or key previously in a memory provided for this purpose, in particular at least one virtual memory and / or VM and / or VN and / or NN and / or VK and / or to store at least one cloud, in particular to store in at least one virtual memory (VS) and / or at least one NN, in particular to store between. Caching can be done before handover to the selector 117-1 done or the selection device 117-1 itself may for this purpose include a corresponding memory, in particular at least one virtual memory and / or VM and / or VN and / or NN and / or cloud.

In any case, however, is also useful in the selection device 117-1 If necessary, two or more electrical devices with the highest priority values and / or ranking and / or probability values can be selected. As a result, the priority value and / or ranking and / or probability value output by the selector can be 101 also several of the priority values and / or ranking and / or probability values 101-1 to 101-N include.

The fuzzy control system and / or neural network (NN) control system according to the invention has a second fuzzy-based device and / or neural network (NN) -based device, 200 , which in a preferred embodiment according to 2A is constructed as a second fuzzy controller and / or neural network (NN) controller, and to which of the priority value and / or probability and / or key output by the selection device at the output A100 101 is handed over. Again, this second fuzzy-based device and / or neural network (NN) device suitably points to each electrical device 10-1 to 10-N from the group, in particular VM and / or VN of electrical appliances whose total energy consumption is to be regulated according to the invention, a processing channel.

This second fuzzy-based device and / or neural network (NN) -based device, 200 is now configured for the selected electrical device, ie the device which has been selected by the first fuzzy-based device and / or neural network (NN) -based device, as a function of which, in the given priority value and / or or probability and / or key 101 associated associated priority value and / or probability and / or key, e.g. B. 101-1 , as well as from the current energy consumption of the selected electrical device and the current total energy consumption of the group, in particular VM and / or VN, of electrical devices to determine a control value and / or probability and / or key such that the total energy consumption of the group, in particular VM and / or VN does not exceed an adjustable threshold and / or probability.

For the sake of simplicity, this in turn is each of the electrical appliances 10-1 to 10-N via a connection device 2-1 to 2-N each with a channel of the second fuzzy-based device and / or neural network (NN) -based device, 200 connected, which comprise at least in each case a consumption meter for determining the current energy consumption, z. B. the connection device 2-1 a consumption meter 23-1 for determining the consumption of the electrical device 10-1 respectively. 23-n , The consumption meters of the connection devices 2-1 to 2-N this can be the consumption meter of the Anschalteinrichtungen 1-1 to 1-N correspond or be separate consumption meter. Further, the first fuzzy-based device and / or neural network (NN) -based device 100 the output priority value 101 to the second fuzzy-based device and / or neural network (NN) -based device, 200 to hand over.

The second fuzzy-based device and / or neural network (NN) device, 200 based thereon identifies the device based on the first fuzzy-based device and / or neural network (NN) -based device 100 selected electrical device and the associated, in the given priority value and / or probability and / or key 101 contained priority value, which is not shown for reasons of clarity in the figures.

According to example 2A in which for each of the electrical appliances 10-1 to 10-N By means of the respectively assigned consumption meter the current energy consumption is measured, the Identification of the first fuzzy-based device and / or neural network (NN) -based device, 100 the second fuzzy-based device and / or neural network (NN) based device, 200 notified selected electrical device to a later processing step, for. B. only when performing an inference.

However, the identification can also be modified from 2A to an earlier processing step, so that, for example, only the channels of the second fuzzy-based device and / or neural network (NN) -based device 200 are actively operated, which are connected to the at least one selected electrical device. For example, the first fuzzy-based device and / or neural network (NN) -based device has priority value and / or probability and / or key 101 pass a value containing only the priority value and / or probability and / or key value 101-1 includes and therefore the electrical device 10-1 identified as a selected electrical device, it may for example be sufficient, only the consumption meter 23-1 and the associated channel 1 according to 2A active, but not the other consumption meters and / or channels.

Furthermore, according to 2A a consumption meter 24 provided, the current total energy consumption of the group, in particular VM and / or VN and / or network of electrical equipment 10-1 to 10-N detected. This can be connected directly to all electrical devices or be connected to the, the individual electrical appliances each associated consumption meters or at a central location, such as a main supply line for the group, in particular VM and / or VN of electrical equipment.

For fuzzy-based processing and / or neural network (NN) -based processing in the fuzzy-based device and / or neural network (NN) -based device 200 are for description of a linguistic variable "consumption" according to the description too 1A in turn for the electrical equipment associated fuzzy sets and / or neural network (NN) sets, as well as to describe a linguistic variable "total consumption" according to the description 1A in turn, for the electrical appliances associated fuzzy sets and / or neural network (NN) sets, and deposited on which the fuzzy controller and the fuzzy-based processing, and / or neural network (NN) -based processing performing facilities of the fuzzy controller / or neural network (NN) controllers 200 according to the internal controller interconnection.

Similar to the first fuzzy-based device and / or neural network (NN) -based processing 100 is z. B. for that of the consumption meter 23-1 for the electrical device 10-1 consumption determined a consumption value z. B. to at least one Fuzzyfizierungseinrichtung 212-1 which then pass a fuzzyed output variable and / or probability and / or key to an inferencing device 316-1 passes. Also, the total energy consumption, ie a total consumption value of the consumption meter 24 determined total consumption is z. B. at least one Fuzzyfizierungseinrichtung 213-1 passed and then from this a fuzzyfiziert output and / or probability and / or key also to the Inferenteinrichtung 216-1 to hand over.

Further, according to the embodiment according to 2A also the priority value and / or ranking and / or probability and / or key 101 from the first fuzzy-based device and / or neural network (NN) -based device 100 z. B. the Fuzzyfizierungseinrichtung 218-1 passed as input value, which their output size and / or probability and / or key also the Inferenteinrichtung 216-1 as another input value passes. Depending on the internal specific configuration of the second fuzzy-based device and / or neural network (NN) -based device 200 may be that of the first fuzzy-based device and / or neural network (NN) -based device 100 certain priority value 101 possibly also directly the Inferenteinrichtung 216-1 be supplied.

For fuzzy-based processing and / or neural network (NN) -based device of activity value are in the fuzzy-based device and / or neural network (NN) -based device 100 thus useful also to describe a linguistic variable "priority" z. B. Fuzzy sets deposited on which the fuzzy-based processing and / or neural network (NN) -based processing performing facilities of the fuzzy controller and / or neural network (NN) controller can fall back according to the internal controller interconnection.

According to sketch after 2A the priority value and / or ranking and / or probability and / or key 101 as well as by the total consumption meter 24 In addition, total energy consumption determined in an identical manner to all other channels of the second fuzzy-based device and / or neural network (NN) -based device 200 as previously mentioned, the identification of the device by the first fuzzy-based device and / or neural network (NN) -based device 100 selected electrical device by the second fuzzy-based device and / or neural network (NN) -based device 200 by way of example only in a subsequent step, in particular within the inferencing facilities 216-1 for channel 1 and in a corresponding manner in the Inferenzeinrichtungen the other channels for the other electrical equipment.

Would exemplify the unique assignable priority value and / or probability and / or key 101-1 from the first fuzzy-based device and / or neural network (NN) -based device 100 to the second fuzzy-based device and / or neural network (NN) -based device 200 passed, takes place in the Inferenzeinrichtung 216-1 a high weighting according to the rules established there for the calculation of the to the electrical device 10-1 associated issue and / or probability and / or key. Is in the given priority value and / or probability and / or key 101 the priority value and / or probability and / or key 101-1 In particular, a weighting of 0 is thus not included.

The output size of the inferencing device 216-1 is then z. B. a Defuzzyfizierungseinrichtung 217-1 pass, which has a setpoint and / or probability and / or key 201-1 for the electrical device 10-1 determined, if this is a selected device, such that the total energy consumption of the group, in particular VM and / or VN of electrical equipment does not exceed an adjustable threshold.

Such an adjustable threshold value, for example, which shows an exemplary total energy consumption of a plurality of electrical devices which form a group, in particular VM and / or VN, over time and in relation to a maximum, predetermined or predefinable energy consumption, such a maximum consumption value over the Time.

Such a maximum consumption value over time is thus adjustable or stored in the fuzzy-based device, and / or neural network (NN) -based device such that the inferencing devices are already set up for this in order to determine the calculations and / or z. B. the Defuzzyfizierungseinrichtungen can use to determine the control values for the at least one selected electrical device.

Such a control value and / or ranking and / or probability and / or key 201-1 for the electrical device 10-1 and correspondingly defined for the other channels, for example, the time of switching on or off, the clocking, ie the duration of a shift of a switch-on of the electrical device, so that the power consumption of the selected electrical device controllable and as a result of the total consumption of the group, in particular VM and / or VN, the electrical equipment is controlled by energy. So for example is the electrical device 10-1 a refrigerator, so for example, the manipulated variable 201-1 the activity of the electrical device and / or VM and / or VN expedient move until a time phase with low total energy consumption, within which the threshold is then not exceeded.

The manipulated variable and / or ranking and / or probability and / or key 201-1 is this, in particular the electrical device and / or VM and / or VN 10-1 supplied to the appropriate controller.

From the description above 2A It can be seen that also with regard to the second fuzzy-based device and / or neural network (NN) -based device, in a modification, a single processing channel may be sufficient, to which then the selected electrical device will be turned on after selection or several selected ones electrical devices and / or VM and / or VN after appropriate selection successively and are turned on in each case one after the other.

As 1A it can be seen, is the first fuzzy-based device and / or neural network (NN) -based device 100 expediently further designed to determine the associated priority value and / or ranking and / or warning for each electrical device and / or VM and / or VN as a function of its current energy consumption and an associated criterion of criticality.

The criterion of criticality indicates a measure reflecting how critical the operation of the electrical equipment is and thus it is useful in weighting the calculation of a priority value. A high degree of criticality thus expediently reduces the priority value.

The measure of the criticality is furthermore preferably determined as a function of a device-specific activity value, a device-specific contact value and / or a device-specific security relevance value.

The activity here too is a device-specific, linguistic variable which describes the mode of operation of the device. A value for the activity can thus be transmitted by the latter itself, depending on the electrical device and / or VM and / or VN, or can be transmitted by a corresponding activity sensor 11-1 within the connection group 1-1 be determined. For a washing machine this would be, for example, main wash, gentle cycle, spin, etc., in the case of a refrigerator as an electrical device, this would be, for example, a high-cooling phase or a temperature-maintenance phase. For a VM and / or VN this would be, for example, the number of started transactions, the processing time, the number of further started VMs and / or VNs.

For fuzzy-based processing and / or neural network (NN) -based processing of a value for the activity are consequently in the fuzzy-based device and / or neural network (NN) -based device 100 for describing the linguistic variables "activity" in turn the fuzzy sets assigned to electrical devices and / or VM and / or VN, in particular neural network and / or neural network (NN) sets, to which the fuzzy-based processing and / or Neural network (NN) based processing facilities of the fuzzy controller and / or neural network (NN) controllers 100 according to the internal controller interconnection.

Likewise, the contact value is described according to the invention as a communication identifier (eg HLR, CLI, IP address) and / or device-specific (eg IMA) and / or linguistic variable indicating how a user and / or user VM and / or user VN operates and / or controls the device and / or VM and / or VN, in the case of a refrigerator as an electrical device, for example, how often the refrigerator door is opened or is open. Again, this can be a corresponding contact sensor 12-1 in the connection device 1-1 provided for fuzzy-based processing and / or neural network (NN) -based processing of at least one value for the activity are consequently in the fuzzy-based device and / or neural network (NN) -based device, 100 for the description of the linguistic variables "contact" z. B. also the electrical equipment associated fuzzy sets deposited on which the fuzzy-based processing and / or neural network (NN) -based Verarbetung performing facilities of the fuzzy controller and / or neural network (NN) controller 100 according to the internal controller interconnection.

In the case of a VM and / or VN as an electrical device, for example, how often the transaction is initiated t is completed. Again, this can be a corresponding sensor 12-1 (eg HotSpot sensor, diode) in the connection device 1-1 provided for fuzzy-based processing and / or neural network (NN) -based processing of at least one value for the activity are consequently in the fuzzy-based device and / or neural network (NN) -based device, 100 for the description of the linguistic variables "contact" z. As well as the electrical equipment associated fuzzy setsz. B. according to 5 to which the fuzzy controller and / or neural network (NN) controllers performing fuzzy-based processing and / or neural network (NN) based processing are placed 100 according to the internal controller interconnection.

Likewise, a device-specific security relevance and / or probability and / or key is described according to the invention as at least one telecommunication identifier and / or device-specific (eg device identifier) and / or linguistic variable, and can be detected, for example, in the case of a motion detector a refrigerator can be detected as an electrical device, for example, with respect to a temperature monitoring. In other words, the security relevance and / or probability and / or key represents a variable that determines the relevance of an activity of an electrical device and / or VM and / or VN with respect to a certain security and / or probability and / or key (eg B. in terms of health, safety and life of the user, the overall system, the infrastructure, the network). So z. As security systems, such as a security camera and / or hot spot sensor, usually a high security relevance.

For fuzzy-based processing and / or neural network (NN) -based processing of a value for security relevance and / or probability and / or keys are consequently in the fuzzy-based device and / or neural network (NN) -based device 100 for description z. B. the linguistic variables "safety relevance" also z. B. the electrical equipment associated fuzzy sets deposited on which the fuzzy-based processing and / or neural network (NN) -based processing performing facilities of the fuzzy controller and / or neural network (NN) controller 100 according to the internal controller interconnection.

Thus, the criterion of criticality of a respective electrical device and / or VM and / or VN is expedient in parallel to the z. B. fuzzyfizierten consumption value of this respective electrical device of the Inferenzeinrichtung 115-1 z. B. the first fuzzy-based device 100 so that the rules for the calculation of the output are determined based on both quantities and finally the defuzzified priority value 101-1 from the defuzzifier device 116-1 is determined and issued. If the criterion of criticality, as described above, is determined based on an activity value, a contact value and / or security relevance value, it is expedient in each case to fuzzify the corresponding values in the fuzzy means 110-1 . 111-1 and or 112-1 , which then the fuzzyfizierten values of an Inferenteinrichtung 114-1 pass the measure of criticality to the inferencing device as the output variable 115-1 passes.

Further, like the 2A to refer to the second fuzzy-based device and / or neural network (NN) -based device 200 expediently also designed for each selected electrical device and / or VM and / or VN depending on a relevance measure and / or probability and / or key the current energy consumption, the current total energy consumption and the priority value and / or probability and / or key Determine control value.

The relevance measure and / or probability and / or key are z. B. at least one measure that reflects how relevant the operation and / or processing or the influence of the electrical device and / or VM and / or VN on a specific objective function and thus flows into the calculation of the control value weighting appropriate. This objective function can be compliance with the threshold, for example.

The measure of the relevance is preferably determined as a function of the current time and / or time difference and / or telecommunications address and / or device identifier and / or the device-specific activity value and / or a device-specific service life and / or user behavior.

Again, this is again exemplary of the electrical device and / or VM and / or VN 10-1 at least one sensor, in particular activity sensor 21-1 provided to the in 1A described sensor, in particular activity sensor 11-1 may be identical or may correspond to this, so that in this respect to the comments on 1A is referenced. Furthermore, z. B. by means of a measuring device 22-1 the service life of the electrical appliance 10-1 measured and further is a timer to all channels 25 turned on.

Since according to the invention z. If, for example, the activity, the service life and the time are also described here as linguistic variables, then for fuzzy-based processing and / or neural network (NN) -based processing respectively for the activity, the service life and the time of the values obtained Fuzzy-based device and / or neural network (NN) -based device 200 for description z. B. corresponding to these linguistic variables associated fuzzy sets z. In relation to the activity and / or with respect to time and / or in relation to the ranking and / or with respect to the probability to which the fuzzy-based processing and / or neural network (NN) -based processing performing facilities of the fuzzy controller, and / or neural network (NN) controllers 200 according to the internal controller interconnection.

Appropriately, z. B. the relevance measure of a respective electrical device and / or VM and / or VN in parallel with the fuzzyfizierten consumption value of this respective electrical device, the fuzzyfizierten total consumption value and the fuzzyfizierten priority value of the Inferenteinrichtung 216-1 the second fuzzy-based device and / or neural network (NN) -based device 200 supplied, so that based on these sizes, the rules for the calculation of the output are determined and ultimately z. B. the defuzzyfizierte control value 201-1 from the defuzzifier device 217-1 is determined and issued.

If z. If, for example, the relevance measure, as described above, is determined based on a current time value, a device-specific activity value and / or a device-specific useful life value, it is expedient in each case to fuzzify the corresponding values in the fuzzyfication devices 214-1 . 210-1 and or 211-1 , which then the fuzzyfizierten values of an Inferenteinrichtung 215-1 passed, which is the measure of relevance to the Inferenteinrichtung 216-1 passes.

2A but also from the 3A It can be seen, has a particularly preferred embodiment of a fuzzy control system according to the invention and / or neural network (NN) control system, here a third fuzzy-based device and / or neural network (NN) -based device 300 on, which is a setpoint control value 316 determined, depending on a particular setpoint, z. From the current time and the current total energy consumption and / or probability and / or key. This target control value 316 becomes the second fuzzy-based device and / or neural network (NN) -based device 200 to hand over.

The second fuzzy-based device and / or neural network (NN) -based device 200 is in this case designed for the selected electrical device and / or VM and / or VN not only as a function of the associated priority value, of its current energy consumption, the current total energy consumption of the group, in particular VM and / or VN, but also as a function of Setpoint control value to determine the control value, which ensures that the total energy consumption does not exceed the adjustable threshold and / or probability and / or key.

In turn, the third fuzzy-based device and / or neural network (NN) -based device is a separate fuzzy controller and / or neural network (NN) controller 300 trained, provides the target control value 316 the output of the third fuzzy-based device and / or neural network (NN) -based device present at the output A300, so that consequently designed as a separate fuzzy controller and / or neural network (NN) controller third fuzzy-based device and / or neural network (NN) based device 300 on the output side with the second fuzzy-based device and / or neural network (NN) -based device 200 connected is. The third fuzzy-based device and / or neural network (NN) -based device 300 certain reference control value 316 becomes expedient according to 2 initially z. B. a Fuzzyfizierungseinrichtung, 220-1 and Z. B. then also the Inferenteinrichtung 216-1 fed. Depending on the internal specific configuration of the second fuzzy-based device and / or neural network (NN) -based device 200 may be that of the third fuzzy-based device and / or neural network (NN) -based device 300 certain reference control value 316 possibly also directly the Inferenteinrichtung 216-1 be supplied.

To capture z. B. the current time and the current total energy consumption of the group, in particular VM and / or VN, is the third fuzzy-based device and / or neural network (NN) -based device 300 again z. B. a timer device 32 and a total consumption meter 33 upstream, the timer 25 and the total consumption meter 24 according to 2A may also be separate, but accordingly working facilities.

The desired value can influence the internal dynamics of the fuzzy control system according to the invention and / or the neural network (NN) control system. For example, the setpoint may be a value between 1 and 10, and represents e.g. B. also a linguistic variable whose fuzzy sets, then in classes "very early" to "very late", for example, are divided. The setpoint is via the setpoint generator 31 , the current time via the timer 32 and the measured total energy consumption of the group, in particular VM and / or VN, over the total consumption meter 33 the third fuzzy-based device and / or neural network (NN) -based device, 300 and Z. B. in this one Fuzzyfizierungs device 311 . 312 respectively. 313 supplied and their fuzzyfickte outputs of an Inferenteinrichtung 314 , whose determined output is then a Defuzzyfizierungseinrichtung 315 is supplied to calculate a defuzzyfizierten setpoint control value.

The fuzzy-based device and / or neural network (NN) -based device 300 can therefore be constructed at least one channel, since the calculated setpoint control value 360 refers to the dynamics of the regulation with respect to the entire group, in particular VM and / or VN, of electrical devices, in particular VM and / or VN and thus according to 2A Each channel of the second fuzzy-based device and / or neural network (NN) -based device 200 is handed over. If, for example, a setpoint of 10 is specified, which determines a very late dynamic intervention of the control system, the calculated setpoint control value, for example, can be used 316 "8" amount, so that in addition depending on the reference control value 360 determined control value 201-1 to 201-N In each case ensures a timely and safe control of the selected device and, for example, via the manipulated variable 201-1 the selected device 10-1 can be moved in time into a time phase with low total energy consumption.

In a further particularly expedient embodiment, the second and / or the third fuzzy-based device and / or neural network (NN) -based device 200 respectively. 300 at least one memory 219 . 319 , in particular at least one virtual memory and / or VM and / or VN and / or cloud, in which predetermined z. B. temporal usage profiles are stored, in which case the second or third fuzzy-based device and / or neural network (NN) -based device is suitably designed to respond in response to the at predetermined times z. B. determined total energy consumption to recognize a predetermined use profile.

at 2A is an example of such memory 219 in particular at least one virtual memory and / or VM and / or VN and / or cloud in different areas possible to the Inferenzeinrichtungen 215-1 and 216 switched on in particular at least one virtual memory and / or VM and / or VN and / or cloud by way of example to the inferencing device 314 , The usage profiles stored in the memories, in particular at least one virtual memory and / or VM and / or VN and / or cloud, can represent different scenarios or different time sequences for the total energy consumption, and z. B. reflect a total energy consumption over the weekend, over a holiday period, or a certain period of time, at a party or during normal everyday life. These values can be based on measurements or trend analyzes and reflect, for example in the case of a holiday scenario, a lower total energy consumption, because usually the ice compartment is defrosted or in the case of a party that an increased total energy consumption of the refrigerator due to constantly cooling beer is needed. Such usage profiles may represent exemplary energy consumption profiles over time based on exemplary electrical devices, and / or which may include an exemplary total energy consumption of multiple electrical devices and / or overall ranking of electrical devices and / or overall probability of electrical seeds representing a group, in particular VM and / or VN form over the time and / or use represent, be stored.

The second fuzzy-based device, especially neural network, 200 or the third fuzzy-based device, especially neural network, 300 determined during continuous periods and thus regularly, ie at predetermined times the total energy consumption and balances this with the memory in particular at least one virtual memory and / or VM and / or VN and / or cloud 219 respectively. 319 stored usage profiles, so that upon detection of a predetermined, stored usage profile this then with respect to the rules of Inferenteinrichtung 215-1 . 216-1 and or 314 Application finds.

With the above-described fuzzy control system and / or neural network (NN) control system is thus an inventive method for controlling z. B. the total energy consumption and / or overload of a plurality of electrical devices, which form a group, in particular VM and / or VN, feasible, of which 4A in a schematic diagram of a preferred embodiment for illustrating various embodiments, in particular with an exemplary possible direct coupling of the time 32 . 25 in particular for synchronization and / or with a relative reference point of time in the coupling of 22-2 and 23-2 and or 22-2 and 23-2 ,

Accordingly, the current power consumption 14-1 Each electrical device of the group, in particular VM and / or VN, measured for each electrical device depending on its current energy consumption, a priority value 101 determined, at least the electrical device with the highest priority value selected and a control value 201 at least for the one selected electrical device for a time shift (eg switch-on time, timing, transaction start) of the operation of the at least one selected electrical device as a function of the associated priority value 101 , from its current energy consumption 23-2 and the current total energy consumption 24 the group, in particular VM and / or VN, determined such that z. B. the total energy consumption of the group, insbesoridere VM and / or VN, does not exceed an adjustable threshold.

Furthermore, preference is given in dependence on a predetermined desired value 31 , from the current time 32 and Z. B. the current total energy consumption 33 a setpoint control value 316 determined, such that for the at least one selected electrical device in dependence on the associated priority value 101 , from its current energy consumption 23-2 , the current total energy consumption 24 the group, in particular VM and / or VN, and the setpoint control value 316 the control value 201 is determined.

Alternatively, but in addition, in addition, for each electrical device and / or VM and / or VN depending on z. B. from its current energy consumption and / or prioritization of the telecommunications address and / or device identifier 14-1 and an associated Critikalitätsmaß the priority value determined, wherein z. B. preferably for each electrical device a criticality measure in response to a device-specific and / or communication address and / or probability of activity value 11-1 one device-specific contact value 12-1 and / or a device-specific security relevance value and / or probability value and / or key value and / or communication address 13-1 is determined.

Alternatively, but in particular supplementary is further for each selected electrical device and / or VM and / or VN depending on a relevance measure, for. B. the current energy consumption 23-2 , the current total energy consumption 24 and the priority value 101 the control value determined, wherein preferably the relevance measure as a function of the current time 25 , a device-specific activity value 21-2 and / or a device-specific period of use 22-2 and / or probability and / or key and / or communication address determined.

Particularly preferred, though in 4A not shown, are stored in at least one memory, in particular at least one virtual memory and / or VM and / or VN and / or cloud predetermined temporal usage profiles, in particular chronologically stored, in particular stored synchronously, so that in response to the determined at predetermined times total energy consumption at least one predetermined usage profile can be recognized and in a particular embodiment, not shown here, comprises at least one simulation device (SE) for the future, in particular> 1 second,> 0.1 minutes,> 10 minutes,> 100 minutes. simulated using the existing historical data and the current time <1 second, <0.1 minutes, less than 10 minutes, less than 100 minutes.

In summary, the invention thus makes it possible to optimize the energy consumption and / or overload protection and / or increase the reliability of a plurality of electrical devices and / or VM and / or VN, in particular in a household, according to different, competing parameters and can as a result of the description of substantially all input values in kind z. B. of linguistic variables also different communication protocols and / or at least one communication address and / or at least one device identifier of the respective electrical devices and / or VM and / or VN considered. The knowledge z. As the various load parameters, the criticality of each device, etc. allows quasi a dynamic orchestration, so a flexible combining and adjusting several electrical devices in terms of z. B. on their energy consumption and in dependence of the requirements and may change according to the respective usage situation or a recognized usage profile. This allows z. B. in an intelligent house and / or network z. B. reduce energy consumption and avoid peak loads and / or allow shutdowns particular specific equipment and / or VM and / or VN.

The flexible combining and adaptation takes place in various stages and is expediently repeated cyclically and / or if necessary and / or provided for learning, in particular by neural networks (NN). Thus, an optimum can be achieved at any time even under changed environmental conditions.

An essential prerequisite for this is a fuzzy-based control and / or neural network (NN) -based control z. B. with linguistic variables and / or communication address and / or device identifier and / or probability and / or fuzzy sets that are expedient preconfigured. These fuzzy sets, thus can in particular take the form of a triangle, trapezium or a curved piece cf. also textbook of Tilli Thomas, fuzzy logic, Franzis 1991 u. 1992 ISBN 3-7723-4322-8 or from Tilli Thomas, automation with fuzzy hardware, Franzis 1992 and 1993, ISBN 3-7723-4412-7 , In a particular embodiment by means of elephant curves, in particular for the encryption of the control systems used.

These linguistic variables are linked together in a plurality of fuzzy devices and / or neural network (NN) devices and it is expedient to intervene in the system only when necessary in order to prevent the exceeding of a selected threshold value. For this purpose, z. B. the energy consumption of at least the electrical device and / or VM and / or VN with the highest priority and / or probability and / or key and / or communication address and / or device identifier in a phase with low load shifted and / or turned off, so that z. B. the basic functionality and / or Mindestestfunktionaltität and / or not use and / or at least one VM and / or VN - is maintained for the user. For this purpose, user profiles are expediently also created and stored, in particular at least one virtual memory and / or VM and / or VN and / or cloud and / or neural network (NN) which z. B. can log the energy consumption per time and thus typical peak load and / or overload in the power supply and / or overload in processing in particular the VM and / or VN and / or overload in the communication channel - can predict.

In particular, the device with the specific identifier, in particular communication address and / or device identifier and / or highest priority and / or probability z. B. according to especially However, the oven can be used for the regulation of consumption, since it does not heat continuously and. The preferred embodiment with the highest current energy consumption but selected with the least criticality, examples include: refrigerator, heating, washing machine, air conditioning, freezer, dishwasher Consequently, there is no continuous power consumption and a temperature drop of 5 ° C does not spoil the cake or the roast. Ideal for the regulation of consumption are devices with high periodic energy consumption such as freezer, refrigerator, heating etc.

Then the duration of the shift, or the timing of the selected device and / or VM and / or VN or the selected devices and / or VM and / or VN in phases z. B. with the low energy consumption and / or with at least one communication address and / or at least one device identifier determined to avoid exceeding the threshold. The energy consumption z. B. the individual devices is described with linguistic variables, the values can be based on measurements and trend analysis, in particular results of at least one neural network (NN). The total energy consumption is z. B. also described in a linguistic variable.

The invention thus enables an automatic, adaptive and adaptive control z. As the energy consumption based on learned and the current consumption values, in particular within a smart home control and / or central control and / or decentralized control, in which the scheme for different recurrent scenarios and / or learned scenarios and / or spontaneous scenarios can be optimized and insbesonder only intervene regulatively, if the objective function, ie z. B. not to exceed the threshold, can not be met.

Because z. B. can be assumed that the real system usually fulfills the objective function without control, is preferably intervened only in cases of expected overload (exceeding the desired maximum energy consumption regulatory in the system and / or if trigger points are met, in particular of communication addresses, Device identifications Probability, key, in particular for load defense and targeted decentralized control, The real system is constantly observed and learned silently.The measured values are evaluated statistically and / or dynamically and particularly preferably assigned to different scenarios.The users and / or energy operators and / or Communication operators further support the system through other targets.

As soon as deviations of the current measured values from the values and / or ranges of values and / or triggers suitably stored in the fuzzy control system and / or neural network (NN) control system are detected, at least one controller and / or the specific controller is adapted accordingly. For this purpose, different scenarios (usage profiles) are provided and / or learned and / or triggered in order to be able to carry out the adaptation quickly.

• • Messen mindestens eines aktuellen Verbrauches, insbesondere Energieverbrauchs (32-1) und/oder Transaktionsvolumen und/oder Transaktionsgeschwindigkeit jedes elektrischen Geräts (10-1, ... 10-N) und/oder Sensoren und/oder VM und/oder VN und/oder NN der Gruppe, insbesondere VN,;

Schritt A2 umfasst:

• • Ermitteln für jedes elektrische Gerät (10-1, ... 10-N) und/oder Sensoren und/oder VM und/oder VN und/oder NN in Abhängigkeit von dessen aktuellem Energieverbrauch Transaktionsvolumen und/oder Transaktionsgeschwindigkeit einen Prioritätswert (101-1, ... 101-N); und/oder Wahrscheinlichkeit

Schritt A3 umfasst:

• • Auswählen wenigstens mindestens eines des elektrischen Geräts und/oder Sensoren und/oder VM und/oder VN mit dem höchsten Prioritätswert und/oder Wahrscheinlichkeit;

Schritt A4 umfasst:

• • Ermitteln mindestens einem Stellwerts (201-1, ... 201-N) und/oder Schlüssel zumindest für das eine ausgewählte elektrische Gerät und/oder Sensoren und/oder VM und/oder VN und/oder NN in Abhängigkeit von dem dazugehörenden Prioritätswert und/oder Wahrscheinlichkeit, von dessen aktuellem Energieverbrauch und/oder Transaktionsvolumen und/oder Transaktionsgeschwindigkeit und dem aktuellen Gesamtenergieverbrauch und/oder Gesamt-Transaktionsvolumen und/oder Gesamt-Transaktionsgeschwindigkeit der Gruppe, insbesondere VN und/oder VM und/oder NN, derart, dass der Gesamtenergieverbrauch und/oder Gesamt-Transaktionsvolumen und/oder Gesamt-Transaktionsgeschwindigkeit der Gruppe, insbesondere VN und/oder VM und/oder NN, mindestens einen einstellbaren Schwellwert und/oder Virtuellen Stellwert und/oder Schlüssel nicht überschreitet.

In a further particular application and / or method and / or system for fuzzy and / or neural network (NN) based regulation, in particular neural network, the total energy consumption and / or transaction volume and / or transaction speed of several electrical devices and / or sensors and / or or VM and / or VN and / or NN, which form a group, in particular VN, - here called Description A -, comprising the following steps:

Step A1 includes:

• • Measuring at least one current consumption, especially energy consumption ( 32-1 ) and / or transaction volume and / or transaction speed of each electrical device ( 10-1 , ... 10-N ) and / or sensors and / or VM and / or VN and / or NN of the group, in particular VN ,;

Step A2 includes:

• • Determine for each electrical device ( 10-1 , ... 10-N ) and / or sensors and / or VM and / or VN and / or NN depending on its current energy consumption transaction volume and / or transaction speed has a priority value ( 101-1 , ... 101-N ); and / or probability

Step A3 includes:

• Selecting at least one of the electrical device and / or sensors and / or VM and / or VN having the highest priority value and / or probability;

Step A4 includes:

• • determining at least one control value ( 201-1 , ... 201-N ) and / or key at least for the one selected electrical device and / or sensors and / or VM and / or VN and / or NN depending on the associated priority value and / or probability, its current energy consumption and / or transaction volume and / or Transaction speed and the current total energy consumption and / or total transaction volume and / or total transaction speed of the group, in particular VN and / or VM and / or NN such that the total energy consumption and / or total transaction volume and / or total transaction speed of the group , in particular VN and / or VM and / or NN, at least one adjustable threshold and / or virtual control value and / or key does not exceed.

In another particular application and / or method and / or system fuzzy and / or neural network (NN) control system, here called B, for controlling the total energy consumption and / or total transaction volume and / or overall transaction speed of multiple electrical appliances and / or sensors which form a group, in particular VN, with a first fuzzy and / or neural network (NN) based device ( 100 ), which is designed for each electrical device ( 10-1 , ... 10-N ) and / or at least one sensor and / or at least one VM and / or VN depending on its current consumption, in particular energy consumption and / or transaction volume and / or transaction speed to determine at least one priority value and / or probability and / or key and at least that electrical device and / or sensor with the highest priority value, a second fuzzy and / or neural network (NN) based device ( 200 ), which is designed, at least for the one selected electrical device and / or at least one sensor and / or at least VN and / or VM and / or NN, depending on the associated priority value and / or probability and / or telecommunications identifier and / or Device identifier and / or key of its current consumption, in particular energy consumption and / or transaction volume and / or transaction speed and the current total consumption, in particular total energy consumption and / or total transaction volume and / or total transaction speed of the group, in particular and / or VM and / or NN, a control value ( 201-1 , ... 201-N ), such that the total consumption, in particular total energy consumption and / or total transaction volume and / or total transaction speed of the group, in particular VN and / or VM and / or NN, does not exceed an adjustable threshold value and / or key.

In a further particular application and / or method and / or system fuzzy and / or neural network (NN) control system, here called description C and / or description B, characterized in that the second fuzzy and / or neural network ( NN) based device ( 200 ) a memory ( 219 ), in particular at least one virtual memory and / or at least one VM and / or VN and / or NN and / or cloud, in which stored at least a predetermined temporal usage profiles and / or keys, in particular energy consumption and / or transaction volume and / or transaction speed in particular by means of at least one virtual memory and / or VM and / or VN and / or NN and / or cloud, wherein the second fuzzy and / or neural network (NN) based device ( 200 ) is adapted to recognize at least one predetermined usage profile and / or key, in particular at least one, in response to at least one total consumption determined and / or learned at predetermined times, in particular total energy consumption and / or total transaction volume and / or total transaction speed Communication behavior and / or content behavior, in particular at least at least one predetermined period of time.

In a further particular application and / or method and / or system fuzzy and / or neural network (NN) control system and here called description D, with description B and / or description C, characterized by a third and / or at least further fuzzy and / or neural network (NN) based device ( 300 ), which is designed to determine a desired control value as a function of at least one predetermined desired value and / or of a current time span and / or the current time and the current total consumption, in particular total energy consumption and / or total transaction volume and / or total transaction speed wherein the second and / or at least further fuzzy and / or neural network (NN) based device is adapted for at least one of the selected system, in particular at least one electrical device and / or sensor in dependence on at least one of the associated priority value and / or probability of its current consumption, in particular energy consumption and / or transaction volume and / or transaction speed, the current total energy consumption and / or total transaction volume and / or total transaction speed of the group, in particular VM and / or VN and / or NN and at least one em the setpoint control value to determine at least one of the control value and / or key and / or trigger.

In another particular application and / or method and / or system fuzzy and / or neural network (NN) control system described here description E, with description D and / or description C, characterized in that the third fuzzy and / or neural network (NN) based device ( 300 ) at least one memory ( 219 ) has in particular at least one virtual memory and / or VM and / or VN and / or cloud in which predetermined temporal usage profiles are stored, wherein the third fuzzy and / or neural network (NN) based device ( 300 ) is adapted to recognize at least one predetermined usage profile and / or key in response to the total consumption determined at a predetermined time and / or time period, in particular total energy consumption and / or total transaction volume and / or total transaction speed.

In another particular application and / or method and / or system fuzzy and / or neural network (NN) control system here called Description F, with description B and / or And Description C and / or description D and / or description E wherein the first fuzzy and / or neural network (NN) based device ( 100 ) as a first fuzzy and / or neural network (NN) controller ( 100 ), and / or the second fuzzy and / or neural network (NN) based device ( 200 ) as the second and / or the nth fuzzy and / or neural network (NN) based device as nth, with the output of the first fuzzy and / or neural network (NN) controller ( 100 ) connected fuzzy and / or neural network (NN) controller is formed.

In another particular application and / or method and / or system fuzzy and / or neural network (NN) control system here called description G, with description D and / or description E wherein the first fuzzy and / or neural network (NN) based device ( 100 ) as a first fuzzy and / or neural network (NN) controller ( 100 ), the second fuzzy and / or neural network (NN) based device ( 200 ) as a second fuzzy and / or neural network (NN) controller ( 200 ) and the third fuzzy and / or neural network (NN) based device ( 300 ) as a third fuzzy and / or neural network (NN) controller ( 300 ) and / or the n-th fuzzy and / or neural network (NN) based device as n-th, wherein the second fuzzy and / or neural network (NN) controller ( 200 ) with the output of the first fuzzy and / or neural network (NN) controller ( 100 ) and with the output of the third fuzzy and / or neural network (NN) controller ( 300 ) and / or the nth fuzzy and / or neural network (NN) based device is connected as nth.

In a further particular application and / or method and / or system fuzzy and / or neural network (NN) control system - here called H-description - with description B and / or description C and / or description D and / or description E and / or description F and / or description F, characterized in that the first fuzzy and / or neural network (NN) based device ( 100 ) is designed for each system, in particular electrical device and / or sensor and / or VM and / or VN depending on its current consumption, in particular energy consumption and / or transaction volume and / or transaction speed, in particular in a period of time and / or time and at least one associated measured value, in particular Kritikalitätsmaß the priority value and / or probability and / or key to determine.

In a further particular application and / or method and / or system fuzzy and / or neural network (NN) control system - here called K description - with description B and / or description C and / or description D and / or description E and / or description F and / or description F and / or description H, characterized in that the first fuzzy-based device ( 100 ) is designed for each system, in particular electrical device and / or sensor mindesgtesn a measured value, in particular Kritikalitätsmaß depending on at least one system-specific, especially device-specific activity value and / or characteristic and / or device identifier and / or communication identifier, at least one device-specific contact value and / or at least one system-specific, in particular device-specific and / or characteristic value and / or device identifier and / or communication identifier, at least one safety relevance value and / or probability and / or key to be determined.

In another particular application and / or method and / or system fuzzy and / or neural network (NN) control system here called description L, with description B and / or And description C and / or description D and / or description E and / or description F and / or description F and / or description H and / or description K, characterized in that the second fuzzy and / or neural network (NN) based device ( 200 ) is designed for each selected system, in particular electrical device and / or sensor depending on a relevance measure and / or probability, the current consumption, in particular energy consumption and / or transaction volume and / or transaction speed, the current, in particular in a period of time and / or time, at least one Total consumption, in particular total energy consumption and / or transaction volume and / or transaction speed and the priority value and / or probability to determine at least one control value and / or key.

In a further particular application and / or method and / or system fuzzy and / or neural network (NN) control system - here called M description - with description B and / or And description C and / or description D and / or description E and / or description F and / or description F and / or description H and / or description K and / or description L, characterized in that the second fuzzy and / or neural network (NN) based device ( 200 ) is adapted to at least one relevance measure as a function of the current time, in particular in a time period and / or time, a system-specific, in particular at least one device-specific activity value, in particular from the characteristic value and / or the device identifier and / or the communication identifier and / or a device-specific service life, in particular from the characteristic value and / or device identifier and / or communication identifier and / or keys to determine.

In a further particular embodiment, the set of rules, in particular at least one fuzzy and / or neural network (NN) control system, in particular at least one neural network - for encryption and / or coding and / or decoding - of data, in particular the control data and / or Energy and / or outgoing data and / or energy, in particular the communication data and / or machine data, characterized in that the control system, in particular the fuzzy and / or neural network (NN) performs the processing, insbesonde using at least one communication address and / or at least one device identifier and / or at least one random number, in particular by means of at least one hotspot on the processing hardware and / or the hardware of at least one VM and / or VN and / or NN.

Advantageous, in particular by means of the behavior of the control system, in particular at least one neural network (NN), for the formation of at least one key 100 . 100-1 , Here as Münchhausen effect, with at least one record, in particular arising from the big data, especially in a particular communication time section.

This advantageously improves the security of the communication and / or of the systems and / or subsystem and / or network, in particular of the machine, in particular in applications in the field of industry 4.0 and / or the Internet of Things. In particular, since the at least one key is a replica of the consumption and, in particular, for comprehending the dish for third parties, all data (big data) must be accessed and / or stored by all systems and / or networks and / or consumed by third parties. In this case, in addition to the behavior of the control system, in particular at least one neural network (NN), in particular by means of a history file, in particular learned data patterns and / or energy patterns, in particular of data, in particular at least one communication data record and / or at least one device identifier and / or communication identifier, within a certain period of time and / or time difference, in particular between communication start and current communication time can be used.

In particular, the processing and / or the formation of the key and / or encryption and / or decryption can take place by means of at least one VM and / or VN. Furthermore, at least one key can be determined by means of at least one parameter of the fuzzy and / or neural network (NN) control system, in particular of the neural network and / or with at least one threshold value of the system, in particular with at least one fuzzy and / or neural Network (NN) controller, in particular neural network, in particular at least one on-neuron and / or at least one non-linear function, in particular via at least one "learning" algorithm and / or at least one parameter of the function in the neural network (NN).

Under the possible application and Industry 4.0 is also understood, the computerization of manufacturing technology, especially the smart factory with the versatility and / or resource efficiency and / or integration of business model and value creation processes is characterized and in particular by means of cyber-physical systems and / or Internet of Things.

In particular, Industry 4.0 also identifies the field of industrial production and personalization and / or individualization, in particular up to lot size 1 of the products under the conditions of a highly flexibilized high-volume production. By means of automation technology and methods of self-optimization and / or self-configuration and / or self-diagnosis and / or cognition, in particular here behavioral control system implemented behavioral control system implemented information transformation - to be optimized, become more intelligent and better support people in their increasingly complex work. Wherein the information transformation in Netzverken and / or systems can take place, in particular by means of VM and / or VN. The self-configuration by a system adapts itself to a new, previously unknown, situation, in particular by means of neural network (NN). And if it is able to configure itself without external help. In particular, by loading the appropriate drivers for the found hardware and / or behavior and / or load behavior and configuring itself this way. In particular, using all traceability data and / or use of sensors and / or actuators and / or recording particular data (big data), instead of selected data, this can be done within a central data storage and / or even decentralized data storage and / or at least one dynamic data storage.

By neural network (NN) is also understood neural circuits, in particular artificial neural networks, (KNN), engl. artificial neural network (ANN) to expert systems, are advantageous in the possibility and method of learning the internal structure of the CNN, in particular by developing new compounds and / or deleting existing compounds and / or changing the weight and / or weights of neuron (a) to neuron (b) and / or adjusting the threshold values of the neurons and / or adding and / or deleting neurons The "learns" in particular by modification of the weights of the neurons takes place and / or an adjustment of the threshold value can be characterized by an on -Neuron be dealt with. Thus, they are able to perform complicated non-linear functions through an algorithm, in particular by iterative and / or recursive approach from existing input and desired output values to determine all parameters of the function trying to learn. Furthermore, they represent an equivalent model to the Turing machine in terms of predictability. The structures may in particular be at least one single-layer feedforward network and / or multilayer feedforward network and / or recurrent network. Basically, the classes of networks differ mainly by the different network topologies and types of connections, such as single-layer, multi-layer, feedforward or feedback networks - which have no immediate disadvantages. At the same time, the learning behavior of the neural networks can be advantageously used, in particular to generate the learning method for a given input pattern associated output patterns, by means of at least one supervised learning and / or encouraging learning and / or unsupervised learning and / or stochastic learning. In supervised learning, an input pattern is given to the neural network and the output that the neural network produces in its current state is compared to the value it is supposed to output. By comparing desired and actual output, it is possible to deduce the changes to be made to the network configuration, in particular by means of delta rule, perceptron learning rule, backpropagation, backpropagation with inertia term, SuperSAB, quickprop, resilient propagation. It is not always possible to have the appropriate output dataset for training for each input dataset. For example, in Industry 4.0 it can not always be said which action is best. But after a test run, which consists of several time steps, the system can be evaluated. Based on this assessment, a function can be learned through encouragement learning, as a Markov Decision Process, especially with the assumption that the Markov property is satisfied. The unsupervised learning takes place exclusively by entering the pattern to be learned. The neural network changes itself according to the input patterns, in particular by means of adaptive resonance theory, Hebbian learning rule, Kohonen networks. The neural network is advantageously oriented to the similarity of the input values and adapts the weights and / or likelihood accordingly. can be learned in particular through the automatic segmentation (clustering) and / or the compression of data for dimensionality reduction. In this case, the compression can be used particularly advantageously, in which case it is attempted to represent the input values in a more compact form, with little information being lost. Stochastic learning using neural networks is based on a random search with the ability to recognize laws. A learning algorithm attempts to make the most accurate prediction possible with at least one hypothesis, in particular by means of an image that assigns the expected output value to each input value. For this, the algorithm alters the free parameters of the chosen hypothesis class, in particular the set of all hypotheses that can be modeled by a particular artificial neural network. In this case, the freely selectable parameters are the weights of the neurons. The goal of stochastic learning is to use a random search to control the weights w of the mesh against a good minimum of a previously chosen error function E (w), with the advantage that it is applicable even if the weights are limited to discrete values or if the activation function is not differentiable. By means of iterative improvement and / or simulated annealing, this being particularly advantageous because the new weights also accept with a given probability p, in particular the inventive probability, if they have not achieved a reduction of the error compared to the preceding weights. Neural networks (NN) can be combined homogeneously as well as using fuzzy logic.

• • Umfasst: Messen für jede Ressource einer aktuellen Ressourcen-Nutzung (21-1, ... 28-N), insbesondere zu einem Zeitpunkt durch jede der Software-Anwendungen (10-1, ... 10-N) der Gruppe;

STEP 2 umfasst:

• • Ermitteln eines momentanen Ressourcenauslastungswertes VM (VM-1, ... VM-N) und/oder Virtuelle Maschine VM (VM-1, ... VM-N) und/oder Virtuelles Netz VN (VN-1, ... VN-N) und/oder NN (NN-1...NNN) für mindestenseine Software-Anwendung und/oder jede Software-Anwendung (10-1, ... 10-N) der Gruppe in Abhängigkeit von deren aktuellen Ressourcen-Nutzungen (21-1, ... 28-N) jeder Ressource; und/oder Simulations des System, mittel mindestens eines Simulations-System (SI),

STEP 3 umfasst:

• • Auswählen wenigstens einer Softwareanwendung mit mindestens einem momentanen Ressourcenauslastungswert und/oder mit mindestens einem vorgegeben Ressourcenauslastungswert und/oder mit mindestens einem höchsten momentanen Ressourcenauslastungswert (VM-1, ... VM-N) und/oder Virtuelle Maschine VM (VM-1, ... VM-N) und/oder Virtuelles Netz VN (VN-1, ... VN-N) und/oder NN (NN-1...NNN);

STEP 4 umfasst:

• • Ermitteln eines Skalierungswertes (SV1, ... SVN) zumindest für jede ausgewählte Software-Anwendung in Abhängigkeit von deren momentanen Ressourcenauslastungswert VM (VM-1, ... VM-N) und/oder Virtuelle Maschine VM (VM-1, ... VM-N) und/oder Virtuelles Netz VN (VN-1, ... VN-N) und/oder NN (NN- 1...NNN), von einem Relevanzwert (RV-1, ... RV-N) und von einem Kritikalitätswert (CV-1, ... CV-N) der ausgewählten Software-Anwendungen und/oder einer Systemkapazität für die Gruppe, mit dem Kriterium, dass die Nutzung aller Ressourcen durch alle Software-Anwendungen (10-1, ... 10-N) der Gruppe einen einstellbaren Schwellwert und/oder Wahrscheinlichkeit und/oder Ranking nicht überschreitet.

In a further particular application and / or method and / or system AA0 a method for fuzzy and / or neural network (NN) based control of the allocation of resources of a system, in particular at least one network and / or system, in particular computer system, in particular an IT Network, to at least one and / or a predetermined, in particular by means of probability and / or ranking and / or multiple software applications ( 10-1 , ... 10-N ), which form a group,
includes the following steps:

STEP 1 includes:

• • Includes: Measuring for each resource of a current resource usage ( 21-1 , ... 28-N ), in particular at one time by each of the software applications ( 10-1 , ... 10-N ) the group;

STEP 2 includes:

• Determining a current resource utilization value VM (VM-1,... VM-N) and / or virtual machine VM (VM-1,... VM-N) and / or virtual network VN (VN-1, ... VN-N) and / or NN (NN-1 ... NNN) for at least one software application and / or software application ( 10-1 , ... 10-N ) of the group depending on their current resource uses ( 21-1 , ... 28-N ) of each resource; and / or simulation of the system, by means of at least one simulation system (SI),

STEP 3 includes:

• Selecting at least one software application with at least one current resource utilization value and / or with at least one predetermined resource utilization value and / or with at least one highest current resource utilization value (VM-1,... VM-N) and / or virtual machine VM (VM-1, ... VM-N) and / or virtual network VN (VN-1, ... VN-N) and / or NN (NN-1 ... NNN);

STEP 4 includes:

• • Determining a scaling value (SV1, ... SVN) at least for each selected software application depending on its current resource utilization value VM (VM-1, ... VM-N) and / or virtual machine VM (VM-1,. .. VM-N) and / or virtual network VN (VN-1, ... VN-N) and / or NN (NN-1 ... NNN), of a relevance value (RV-1, ... RV -N) and a criticality value (CV-1, ... CV-N) of the selected software applications and / or a system capacity for the group, with the criterion that the use of all resources by all software applications ( 10-1 , ... 10-N ) of the group does not exceed an adjustable threshold and / or probability and / or ranking.

• • einer ersten Fuzzy- und/oder Neuronales Netz(NN)-basierten Vorrichtung (200), die dazu ausgebildet ist, für jede Software- und/oder Hardware-Anwendung (10-1, ... 10-N) in Abhängigkeit von einer Messung mindestens einem momentanen Ressourcenauslastungswert und/oder mit mindestens einem vorgegeben Ressourcenauslastungswert und/oder mit mindestens für jede Ressource einer aktuellen Ressourcen-Nutzung (21-1, ... 28-N) und/oder einer zu Bestimmten Ressourcen-Nutzung (21-1, ... 28-N), insbesondere mittels Wahrtscheinlichkeit und/oder Rankiung, - durch jede der Software- und/oder Hardware-Anwendungen (10-1, ... 10-N) der Gruppe, insbesondere in Abhängigkeit von einem CPU-Wert, insbesondere einem CPU Delta-Wert und/oder einem RAM-Wert, insbesondere einem RAM-Delta-Wert und/oder einem HDD-Wert, insbesondereeinem HDD-Delta-Wert und/oder einem Netzwerk-Wert insbesondere einem Netzwerk-Delta-Wert und/oder von mindestens eine Kommunikationsadresse und/oder einen Wert einer Kommunikationsadresse, insbesondere Wahrscheinlichkeit und/oder Rankling ztur Kommunikationsadresse, insbesondere mindestens einer CLI, HLR, IP-Adresse, WLAN-Adresse; und/oder von mindestens einer Gerätekennung, insbesondere mindestens einer IMA; und/oder eine momentane Ressourcenauslastungswert (VM-1, ... VM-N) und/oder Virtuelle Maschine VM (VM-1, ... VM-N) und/oder Virtuelles Netz VN (VN-1, ... VN-N) und/oder NN (NN-1...NNN); zu ermitteln, wobei das Fuzzy- und/oder Neuronales Netz(NN)-Regelsystem mindestens eine Auswahleinrichtung (291) umfasst, um wenigstens eine Software- und/oder Hardware-Anwendung mit einem höchsten und/oder vorbestimmten; momentanen Ressourcenauslastungswert (VM-1, ... VM-N) und/oder NN (NN-1...NNN) und/oder Wahrscheinlichkeit und/oder Ranking auszuwählen,

STEP 2 umfasst:

• • einer zweiten Fuzzy- und/oder Neuronales Netz(NN)-basierten Vorrichtung (300), die dazu ausgebildet ist, für mindestens eine, insbesondere jede Software- und/oder Hardware-Anwendung (10-1, ... 10-N) in Abhängigkeit von einem Nutzungsverhalten, insbesondere von der Uhrzeit, und/oder einem Tagesnutzungsprofil und/oder einem Wochennutzungsprofil und/oder Wahrscheinlichkeit und/oder Ranking mindestens einen Relevanzwert (RV-1, ... RV-N) zu ermitteln und/oder zu speichern und/oder verarbeiten und/oder senden,

STEP 3 umfasst:

• • einer dritten Fuzzy- und/oder Neuronales Netz(NN)-basierten Vorrichtung (400), die dazu ausgebildet ist, zumindest für die eine ausgewählte Software- und/oder Hardware-Anwendung in Abhängigkeit von einem dazugehörenden Prioritätswert und/oder Wahrscheinlichkeit und/oder Ranking und/oder von einem Sicherheitsrelevanzwert, insbesondere einen Kritikalitätswert (CV-1, ... CV-N) zu ermitteln und/oder zu speichern und/oder verarbeiten und/oder senden, und

STEP 4 umfasst:

• • einer vierten Fuzzy- und/oder Neuronales Netz(NN)-basierten Vorrichtung (500), die dazu ausgebildet ist, aus mindestene einen Ressourcenauslastungswerten (VM-1, ... VM-N), insbesondere den momentanen Ressourcenauslastungswerten (VM-1, ... VM-N), mindestens einer Software-Anwendungen (10-1, ... 10-N), insbesondere der ausgewählten Software- und/oder Hardware-Anwendungen (10-1, ... 10-N), dem Relevanzwert der ausgewählten
• • Software- und/oder Hardware-Anwendungen (10-1, ... 10-N), dem Kritikalitätswert (CV-1, ... CV-N) der Software- und/oder Hardware-Anwendungen (10-1, ... 10-N), insbesondere ausgewählten Software- und/oder Hardware-Anwendungen (10-1, ... 10-N), und/oder der Systemkapazität (51) für die Gruppe und/oder mindestens ein Teil der Gruppe, einen Skalierungswert (SV-1, ... SV-N) und/oder Wert eines Ranking und/oder Wert der Wahrscheinlichkeit auszuwählen, insbesondere für zumindest
• • ausgewählte Software- und/oder Hardware-Anwendungen, insbesondere der Gruppe zu ermitteln und/oder speichern und/oder verarbeiten und/oder senden, mit dem Kriterium, dass die Nutzung aller Ressourcen, insbesondere Virtuelle Maschine VM (VM-1, ... VM-N) und/oder Virtuelles Netz VN (VN-1, ... VN-N) und/oder durch alle Software- und/oder Hardware-Anwendungen (10-1, ... 10-N) und/oder NN (NN-1...NNN) der Gruppe einen bestimmten, insbesondere mittels mindestens eines Ranking und/oder einstellbaren Schwellwert nicht überschreitet und/oder nur mit einer spezifischen Differenz, insbesonde 5 Prozent, insbesondere 10 bis 20 Prozent, kleiner 10 Prozent, kleiner 40 Prozent überschreitet.

In a further particular application and / or method and / or system AA1 a fuzzy and / or neural network (NN) control system for controlling the allocation of resources of a system and / or at least one network, in particular an IT network, at least one and / or a predetermined, in particular by means of probability and / or ranking and / or multiple software and / or hardware applications ( 10-1 , ... 10-N ), which form a group with

STEP 1 includes:

• A first fuzzy and / or neural network (NN) based device ( 200 ), which is designed for each software and / or hardware application ( 10-1 , ... 10-N ) in response to a measurement of at least one current resource utilization value and / or at least one predetermined resource utilization value and / or at least for each resource of a current resource usage ( 21-1 , ... 28-N ) and / or a specific resource usage ( 21-1 , ... 28-N ), in particular by probability and / or rank, by each of the software and / or hardware applications ( 10-1 , ... 10-N ) of the group, in particular as a function of a CPU value, in particular a CPU delta value and / or a RAM value, in particular a RAM delta value and / or an HDD value, in particular an HDD delta value and / or a network value, in particular a network delta value and / or at least one communication address and / or a value of a communication address, in particular probability and / or ranking for the communication address, in particular at least one CLI, HLR, IP address, WLAN address ; and / or at least one device identifier, in particular at least one IMA; and / or a current resource utilization value (VM-1,... VM-N) and / or virtual machine VM (VM-1,... VM-N) and / or virtual network VN (VN-1, ... VN-N) and / or NN (NN-1 ... NNN); where the fuzzy and / or neural network (NN) control system has at least one selection device ( 291 ) to at least one software and / or hardware application having a highest and / or predetermined; select current resource utilization value (VM-1, ... VM-N) and / or NN (NN-1 ... NNN) and / or probability and / or ranking,

STEP 2 includes:

• A second fuzzy and / or neural network (NN) based device ( 300 ), which is designed for at least one, in particular any software and / or hardware application ( 10-1 , ... 10-N ) and / or determine at least one relevance value (RV-1, ... RV-N) as a function of a usage behavior, in particular of the time of day, and / or a daily use profile and / or a weekly usage profile and / or probability and / or ranking store and / or process and / or send,

STEP 3 includes:

• A third fuzzy and / or neural network (NN) based device ( 400 ), which is designed, at least for the one selected software and / or hardware application, depending on an associated priority value and / or probability and / or ranking and / or on a security relevance value, in particular a criticality value (CV-1,. .. CV-N) to determine and / or store and / or process and / or send, and

STEP 4 includes:

• A fourth fuzzy and / or neural network (NN) based device ( 500 ), which is formed from at least one resource utilization value (VM-1, ... VM-N), in particular the current resource utilization values (VM-1, ... VM-N), at least one software application ( 10-1 , ... 10-N ), in particular the selected software and / or hardware applications ( 10-1 , ... 10-N ), the relevance value of the selected
• • software and / or hardware applications ( 10-1 , ... 10-N ), the criticality value (CV-1, ... CV-N) of the software and / or hardware applications ( 10-1 , ... 10-N ), in particular selected software and / or hardware applications ( 10-1 , ... 10-N ), and / or system capacity ( 51 ) for the group and / or at least a part of the group, a scaling value (SV-1, ... SV-N) and / or value of a ranking and / or value of the probability to select, in particular for at least
• • identify and / or store and / or process and / or send selected software and / or hardware applications, in particular the group, with the criterion that the use of all resources, in particular virtual machine VM (VM-1, .. VM-N) and / or virtual network VN (VN-1, ... VN-N) and / or by all software and / or hardware applications ( 10-1 , ... 10-N ) and / or NN (NN-1 ... NNN) of the group does not exceed a specific, in particular by means of at least one ranking and / or adjustable threshold and / or only with a specific difference, in particular 5 percent, in particular 10 to 20 percent, less than 10 percent, less than 40 percent exceeds.

In a further particular application and / or method and / or system AA2 a fuzzy and / or neural network (NN) control system with the system AA1 and / or method AA0 wherein the second fuzzy and / or neural network (NN) - based device ( 300 ) has a memory, in particular at least one virtual memory (VSP) in which predetermined temporal usage profiles and / or probabilities and / or ranking results and / or usage profiles are stored, wherein the second fuzzy and / or neural network (NN) based device ( 300 ) is designed to recognize and / or store and / or process and / or send at least one predetermined usage profile in response to the resource usage and / or probabilities and / or ranking results determined at predetermined times.

In a further particular application and / or method and / or system AA3 a fuzzy and / or neural network (NN) control system with Sdem ystem AA2 and / or system AA1 and / or device AA0 wherein the first fuzzy and / or neural Network (NN) based device ( 200 ) as a first fuzzy and / or neural network (NN) controller ( 200 ), and the second fuzzy and / or neural network (NN) based device ( 300 ) as a second fuzzy and / or neural network (NN) controller ( 300 ), and the third fuzzy and / or neural network (NN) based device ( 400 ) as a third fuzzy and / or neural network (NN) controller ( 400 ), and the first fuzzy and / or neural network (NN) controller ( 200 ), the second fuzzy and / or neural network (NN) controller and the third fuzzy and / or neural network (NN) controller on the output side with the fourth fuzzy and / or neural network (NN) controller ( 500 ) and / or by means of at least one virtual machine VM (VM-1, ... VM-N) and / or at least one virtual network VN (VN-1, ... VN-N) and / or NN (NN -1 ... NNN) is formed.

In a further particular application and / or method and / or system AA4 a fuzzy and / or neural network (NN) control system with the system AA1 and / or the system AA2 and / or system AA3 and / or device AA0 being at least one simulation and / or at least one controller, at least one fuzzy and / or neural network (NN) -based device ( 200 ) and / or VM and / or VN and / or NN and / or VK.

According to the particular method of the invention, the current usage of each resource of the system is measured by each of the software and / or hardware applications of the group and an instantaneous resource utilization value for each software and / or hardware application of the group depending on their current resources. Use of each resource determined, in particular by means of at least one fuzzy controller and / or by means of a neural network (NN) controller. Subsequently, at least one software and / or hardware application, preferably and / or deferred, however, is selected a plurality of software and / or hardware applications, for which, in particular the highest, current resource utilization value has been determined. These one or more selected software and / or hardware applications may then be scaled by allocating resources based on a scaling value and / or ranking and / or probability and / or communication address and / or device identifier. For this purpose, a scaling value and / or ranking and / or probability and / or communication address and / or device identifier for the at least one or more selected software and / or hardware applications is determined according to the invention. Scaling is understood to mean the increase or decrease in the use of system resources of one or more software and / or hardware applications by allocating resources, with an allocation of resources to these software and / or hardware applications depending on the scaling value Either resources are added to these software and / or hardware applications or subtracted from these software and / or hardware applications. A software application is also understood to mean at least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one NN and / or at least one virtual memory (VS), in particular SRAM NN and / or virtual channel ( VK) and the control, in particular software-based control of systems, in particular of hardware. A software application is understood in particular to mean at least one computer-implemented invention. Under a hardware application is in particular also understood an energy consumer, in particular telecommunications device, controller, hair dryer, shaving. Under a software and / or hardware application is in particular also understood a quais passive hardware z. As a room or ventilation fan, an elevator motor, an electric motor, a refrigerator that is supplied as a feature with electrical energy and / or electromagnetic energy and / or magnetic energy and / or electrical energy and / or radio - and / or - then their software application, in particular central and / or decentralized is contained in at least one system, in particular in the power grid and / or in at least one transformer for electricity and / or in at least one intelligent transformer and / or in at least one controller, insbender at least one heater and / or radiator and / or door opener and / or payment machine and / or at least one exchange for telecommunications and / or at least one branching box for telephone z. B. on the street, in the house. A software and / or hardware application is also understood to mean, in particular, at least one module, consisting in particular of at least one receiver and / or transmitter, in particular memory and / or processing; in particular at least one passive RFID transponder, in particular according to ISO 14443 or ISO 15693 , especially ISO 18092 . 21481 ECMA 340 . 352 . 356 . 362 . ETSI TS 102 190 , In particular, active RFID transponders, in particular near field communication (NFC), in particular for the Internet of Things. As such, increasing a scaling value increases resources and reduces a scaling value reduces resources. It should be noted that a scaling value for the same software and / or hardware applications may be different and different software and / or hardware applications may be identical or different, in particular depending on a ranking and / or probability and / or communication address and / or device identifier and / or key. A scaling value for a software and / or hardware application is determined according to the invention at least for each and / or only selected software and / or hardware application depending on a determined instantaneous resource utilization value of each and / or only selected software and / or hardware. Application determined. Furthermore, the scaling value becomes dependent on a relevance value of the selected software and / or hardware applications, on a criticality value of these selected software and / or hardware applications, and on system capacity for the group of software and / or hardware Determined applications, in particular depending on a ranking and / or probability and / or communication address and / or device identifier and / or keys. The relevance value for a software and / or hardware application indicates a measure, in particular in ERLANG and / or ERLANG +, how relevant the operation or the influence, in particular currently or in the future, of a software and / or hardware application on one certain objective function and / or load function is and thus flows in a weighting manner into the determination of the scaling value. This objective function can, for. B. be the threshold. In particular, the relevance value is determined as a function of a device-specific and / or application-specific usage behavior of a software and / or hardware application, for example, an application-specific daily or weekly profile and / or the time of day. In particular, a device-specific usage behavior of a system, in particular hardware and / or software. A low relevance value thus expediently reduces the scaling value and a high relevance value thus expediently increases the scaling value. The criticality value gives a measure of how critical the operation and / or the control system and / or a software and / or hardware application is, and thus expediently flows into the determination of the scaling value, in particular as a function of a ranking and / or probability and / or communication address and / or device identifier and / or key. A high criticality value thus expediently increases the scaling value, and a low relevance value thus expediently reduces the scaling value. The measure of the criticality is furthermore preferably determined as a function of an application-specific priority value and / or an application-specific security relevance value and / or a ranking and / or probability and / or communication address and / or device identifier and / or key. The priority value indicates a relative relationship between the individual software and / or hardware applications by assigning priorities. The security relevance value indicates a measure of how critical a software and / or hardware is for the security of the system, in particular of the overall system, in particular of the considered overall system, in particular of the overall network system, in particular of the considered overall network system. For example, a web application, for example, a mid-monthly payroll program may have a higher priority than an application for a firewall, but which has a high security relevance and therefore a high security relevance value is assigned, or has a high security relevance, due to the communication address z. B. a security-relevant person, in particular the device identifier z. B. a certain special group of companies. In this respect, it should also be noted that the boundaries between the variables which enter into a priority value and a security relevance value, but in particular also into a relevance value and a criticality value, can be fluid, since these values can also influence one another. For example, a software application may be time-dependent more or less relevant, such as in a payroll application, and / or be critical, such as starting a firewall or virus scanner application. The inventively proposed fuzzy-based and / or neural network (NN) -based control, which describes input and output values with linguistic, each containing fuzzy sets and / or neural network (NN) criteria variables, allows linking the Variables such that an automatic optimization of the resource allocation of one or more software and / or hardware applications forming a group can also be undertaken with different, competing parameters and changing target functions, in particular in the case of neural network (NN) -based ones z. For example, if software and / or hardware applications have different priorities at different times. A significant further advantage is that the determination of a scaling value for controlling the resource allocation to one or more selected software and / or hardware applications, which, for example, an initialization of the scaling of the selected software and / or hardware application and / or Starting one or more virtual machines (VM) defines not only depends on current parameters of the selected software and / or hardware application and a based on this, based on this selected software and / or hardware application target function, but in addition at least an objective function with respect to a total resource usage of the group of software and / or hardware applications, in particular in the form of an adjustable threshold, is taken into account. Furthermore, by appropriate scaling of a selected software and / or hardware application, the activity of this selected software and / or hardware application within a current overall resource utilization profile may be temporally or spatially moved to a lower utilization area, and thus the overall resource usage lowered and peak loads are avoided without affecting the functionality of the selected software and / or hardware applications) for a user.

In a further particular application, at least one virtual memory (VR) can be made up of at least one neural network (NN), in particular it comprises at least one virtual machine (VM) and / or at least one virtual network (VN). With the advantage that the data and / or data track after deletion of at least one - virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) - the stored data and / or data track is deleted and so beneficial , a secure temporary memory exists, in particular for the transport path of the data and / or data track via the transport server. In a further particular application, at least one neural network (NN) can be made up of at least one virtual machine (VM) and / or at least one virtual network (VN) with the advantage that when deleting the virtual machine (VM) and / or virtual Network (VN) the neural network (NN) is irrevocably deleted.

Controlling an allocation of resources of a system, in particular of an IT network, to one or more software and / or hardware applications which form a group, comprising, with a first fuzzy-based device, which is designed for each software and / or hardware application as a function of a measurement for each resource of a current resource usage by each of the software and / or hardware applications of the group, in particular as a function of a CPU value, a CPU delta value, a RAM value, a RAM delta value, an HDD value, an HDD delta value, a network value, and / or a network delta value to determine a current resource utilization value , The fuzzy control system includes, for example, in the first fuzzy-based device, a selector to select at least one software and / or hardware application having a highest current resource utilization value. The control system, in particular NN, in particular fuzzy control system further comprises a second fuzzy-based device, which is adapted for each software and / or hardware application depending on a usage behavior, in particular depending on the time, a daily use profile and / or a weekly usage profile, to determine a relevance value, and a third fuzzy-based device, which is designed to determine a criticality value for at least one selected software and / or hardware application depending on an associated priority value and on a security relevance value , and a fourth fuzzy-based device adapted from the current resource utilization values of the selected software and / or hardware applications, the relevance value of the selected software and / or hardware applications, the criticality value of the selected software and / or or hardware applications, and determining the system capacity for the group to provide a scaling value for at least selected software and / or hardware applications of the group, with the criterion that use of all resources by all software and / or hardware applications of the group does not exceed an adjustable threshold , In principle, the fuzzy-based devices of the system according to the invention for carrying out the method according to the invention can be part of a single fuzzy controller. However, since this is complex and complex depending on the software and / or hardware applications involved within the group of software and / or hardware applications and may also be slower than separate, but interconnected fuzzy controllers, depending on the application, it is preferred provided, the first fuzzy-based device as a first separate fuzzy controller, the second fuzzy-based device as a second separate fuzzy controller, the third fuzzy-based device as a third separate fuzzy controller and the fourth fuzzy-based device form as a fourth separate fuzzy controller, wherein the input of the fourth fuzzy controller is connected to the outputs of the other three fuzzy controller subcontroller for linking these four fuzzy controller.

According to the method and system according to the invention, the ascertained relevance value, criticality value and an instantaneous resource utilization value are again supplied as target functions to be considered as separate manipulated variables for the subsequent determination of a scaling value for each selected software and / or hardware application. In a preferred embodiment of a fuzzy control system according to the invention, the second fuzzy-based device has a memory in which predetermined temporal usage profiles are stored, wherein the second fuzzy-based device is adapted to respond to the resource usage determined at predetermined times to recognize a predetermined usage profile.

The commercial use of the method, device is used for load defense, control, simulation, for secure transmission of data, energy, eg. For self-sufficient system, network, telecommunications, power supply, peer-to-peer, Internet of Things, Web 4.0, Industry 4.0, cyber-physical device, high-performance computing, cloud computing, - the mobility z. B. vehicle, swimming equipment, aircraft, drone, - that the data to be transmitted, energy in the start server and / or start network generates at least one virtual machine (VM) and / or virtual network (VN) and / or neural network (NN), under a set of static, dynamic parameters, probability, ranking, transaction that z. B. after the execution of the VM and / or VN stopped, controlled, canceled or forwarded until the data, energy is received by the target system, after the execution of the local action, the VM and / or VN then delete and / or not processed further, in particular independently deleted. The control is done by means of swarm intelligence, for self-sufficient system, z. B. bionically integrated stock exchange function, mediator function, simulation (SI), - in particular for overload protection in the communication against a black out of the communication and power supply, and to encrypt the communication. Furthermore, the industrial application is exemplary in parts or entirely in the field and / or system and / or method of telecommunications and / or mobility and / or power engineering and / or power distribution and / or energy networks and / or computer technology and / or network technology and / or the Internet, in particular in cloud computing, in particular in applications in networked life, in particular in industry 4.0, in particular in the Internet of Things. Next in the industrial technical area of simulation, computing capacity, data transmission, data storage, network capacity and software.

LIST OF REFERENCE NUMBERS

C '

capacitance

CLI

Calling Line Identification, z. B. identifier, communication identifier

DPI

Deep Packet Inspection, parameters

dates

z. B. include records, trigger points, parameters, binary states, in particular text, image, moving images, sound, identifier

DS

decentralized simulation z. System, rules, procedures, SI

YOU

defined environmental z. B. spatially, temporally, z. B. radio network

delta t

Delta time, time difference, z. In seconds

e

in particular, generates data, data sequences, data records, transactions, program parts, programs

G'

Discharge coating, z. B. first mathematical derivative, second derivative, nth derivative

GE

Devices, systems, sensor, computer, in particular also VM, VN, VSE

GF

mathematical function z. B. line equation, diode characteristic, transistor gain line

HLR

Home Location Register, z. B. identifier, communication identifier

KS

Combination simulation z. B. system, rules, procedures

L '

inductance

PRK

Protocol return channel z. B. as a record

PUF

Physical Unclonable function

PUFs

Physical Unclonable function

R '

resistance coating

S

start, in particular data, data sequences, program parts, programs

SI

Simulation system, z. B. decentral, central, agent controlled z. B. system, rules, procedures, z. B. Boolean logic, fuzzy logic, z. Fussy-based controls, neural network (NN) controllers

SR

system resources

ST

Control z. System, procedures, triggers

t

Time, z. In seconds, e.g. Processing time, storage time, delivery time

TDU

temporary defined environment z. B. spatially, temporally

V

Processing, eg. As data, data sequences, program parts, programs, SI

VD

Virtual Desktop, eg. B. VM, VN, VS, VSE, virtual memory

VS

Simulation method z. Eg by means of tables, rules, Boolean rules, fuzzy rules, neural networks (NN)

VSE

Virtual sensor, z. SI, GE

VM

Virtual machines, z. B. Virtual Environment, Virtual Server, VD, Virtual Storage

KT

Communication time z. B. from the time of the start of communication, data transport

KS

Communication device z. B. switching device, computer device, personal computer, telephone, mobile phone, smart phone, router, wireless router

RN

real network, z. Network, Network, Network Router, WLAN, LAN, Intelligent Network (IN), LTE Network, GSM Network, UMTS, Virtual Private Network VPN, Near Field NCF

VM

Virtual machine z. Personal computer, client, router, network router

VN

Virtual network, virtual network z. VLAN, VXLAN

VP

n-pole, especially quadrupole

hardware

z. As exchange, PC, RAM, ROM, RFID, systems of the network, systems of network connectivity

server

z. As exchange, PC, start server, computer, telecommunication system, communication system, includes z. As network, peer-to-peer, z. RAM, ROM, RFID network connection,

content

Image, image sequence, sound, sequence, signal, signal sequence, message, text

MK

Communication system, z. Eg landline telephone, mobile phone, networked devices, networked devices in the Internet of Things and smart home and smart industry, smartphones, tablets, computers, smartwatches, data glasses, smart glasses, data glasses attached to eyeglasses, data glasses module

network

z. As network, an attachment architecture of systems includes radio, wireline, optical, z. B. WLAN, LAN, GSM. LTE, UMTS

NN

Neural network, artificial neural network, neural network and fuzzy system, neuro-fuzzy logic

ZP

central protocol z. B. at least one protocol from the central office in at least one decentralized system

ZS

central simulation z. B. system, rules, procedures

1

Start server and / or startup network z. As server, PC, exchange, router, smart phone, LAN, WLAN, VPN, IN, LTE, UMTS, GSM

2

Destination server and / or destination network z. As server, PC, exchange, router, smart phone, LAN, WLAN, VPN, IN, LTE, UMTS, GSM

3.1, ..., 3.N

Transport server and / or destination network z. As server, PC, exchange, router, smart phone, LAN, WLAN, IN, LTE, UMTS, GSM

51

Semiconductors, e.g. B. chip, CPU, control unit, semiconductor device

52

Capacitive antenna, z. As surface antenna, capacitance, capacitor, plate capacitor

53

Magnetic antenna, z. B. loop antenna, loop antenna, induction, coil z. B. with a filling of density Halz, plastic, paint

54

Module z. B. Rectifier with and without capacitance (capacitor) and / or inductance (coil), in particular with grounding to ground, grounding

55

Functionality of thermal conductors with a density, eg. As copper, silver, gold, aluminum, lead, metal, plastic, ceramics, paint

56

Functionality mechanical protection with a density, eg. As copper, silver, gold, aluminum, lead, metal, plastic, ceramics, paint

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited patent literature

• US 6650731 B1 [0005]
• US 8345708 B2 [0006]
• DE 102006039664 A1 [0007]
• US 000008750518 B2 [0008]
• US 020090097658 A1 [0008]
• DE 102007048518 A1 [0008]
• EP 000002378422 B1 [0009]
• DE 102011111131 A1 [0010, 0096]
• DE 102012011831 A1 [0010, 0096]
• DE 102013004795 A1 [0011, 0096, 0160]
• DE 102012016967 A1 [0012]
• DE 102013008151 A1 [0012]
• EP 2191410 B1 [0044]
• US 00000870518 B2 [0096]
• DE 102012024423 A1 [0096]
• DE 000010261682 B4 [0116]
• DE 000019811097 A1 [0239]
• US 000006650731 B1 [0239]
• DE 19502230 A1 [0317]

Cited non-patent literature

• Toy Directive 2009/48 / EC [0001]
• VDE 0700 Part 1 2003-07 DIN EN 60335-1 [0001]
• Tilli Thomas, entitled "Fuzzy Logic", (1991), Franzis-Verlag (ISBN 3-7723-4322-8) [0012]
• "Automation with Fuzzy Hardware", (1992), ISBN 3-7723-4412-7 [0012]
• "Hakin9 Extra" 4/2012 - "Security in the Cloud" is titled "Security with Cloud Computing", by Sabine Wieland [0012]
• http://ecd-conference.de/wp-content/blogs.dir/46/files/2011/03/130514_Wieland_Hochschule_fuer_Telekommunikation_Leipzig.pdf [0012]
• http://www.cloudzone-karlsruhe.de/messe-karlsruhe-cloudzone/2013/en/mess_ka/die_kongresse/eurocloud_deutschland_conference_/ecdc_agenda/ecdc_agenda_1.jsp [0012]
• http://en.slideshare.net/Connected_FFM/tag2-fpc1420-sabine-wieland [0012]
• http://www1.hft-leipzig.de/wieland/publikationen2012.html [0012]
• http://www.ecd-conference.de/wp-content/blogs.dir/30/files/2011/03/130514_Wieland_Hochschule_fuer_Telekommunikation_Leipzig.pdf. [0012]
• http://ecd-conference.de/vom lecture on 14.05.2013 Title "Partnering for the Cloud" [0012]
• http://www.ecd-conference.de/2013/05/21/partnering-for-the-cloud.html [0012]
• (EDL 2006/32 / EC) Article 13 [0113]
• Tilli Thomas, fuzzy logic, Franzis from 1991 u. 1992 ISBN 3-7723-4322-8 [0317]
• Tilli Thomas, automation with fuzzy hardware, Franzis from 1992 and 1993, ISBN 3-7723-4412-7 [0317]
• Tilli Thomas, fuzzy logic, Franzis 1991 u. 1992 ISBN 3-7723-4322-8 [0375]
• Tilli Thomas, automation with fuzzy hardware, Franzis 1992 and 1993, ISBN 3-7723-4412-7 [0375]
• ISO 14443 [0405]
• ISO 15693 [0405]
• ISO 18092 [0405]
• 21481 [0405]
• ECMA 340 [0405]
• 352 [0405]
• 356 [0405]
• 362 [0405]
• ETSI TS 102 190 [0405]

Claims (4)

Method, in particular for the transmission of data and / or for generating a random, in particular at least one key - from at least one server ( 1 ), in particular start server ( 1 ) and / or destination network ( 1 ) to a destination server ( 2 ) and / or destination network ( 2 ) via at least one server and / or network, in particular transport server ( 3.1 , ..., 3.N ) and / or transport network ( 3.1 , ..., 3.N ), in particular - a telecommunications network and / or telecommunication server characterized in that - in the start server ( 1 ) and / or startup network ( 1 ) based on at least one parameter and / or at least trigger and / or at least one computer implemented program and / or at least one computer program; - At least one virtual machine (VM) and / or at least one virtual network (VN) and / or at least one neural network (NN) is and - generates and / or integrates and / or transmits and / or transports and / or and / or saved and / or deleted. A method according to claim 1, characterized in that • comprises at least one trigger, in particular at least one transaction; and / or • is controlled, in particular by means of soft computing, in particular fuzzy logic and / or neural networks (NN); at least one neural network (NN) and / or at least one thermal noise for at least one memory cell is generated and / or comprises at least one virtual machine (VM) and / or virtual network (VN) and / or at least one neural network (NN) is generated in the steps and / or programs, in particular data is processed and / or programs, in particular data is sent and / or virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) is deleted each further server and / or network, in particular transport server and / or transport network is informed about the conversion to be carried out in parallel with the sending and / or storage and / or processing prior to the sending of the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) by means of at least one data record and / or at least one database entry and / or at least one list and / or matrix and / or n-dimensional martix, in welc is stored for each server and / or network at least one address. System for transmitting data and / or energy characterized and comprising • at least one computer, in particular server, the start server ( 1 ) and a destination server ( 2 ) between which the data and / or energy is transmitted via several transport servers ( 3.1 , ..., 3.N ) of a telecommunication network, whereby the start server ( 1 ) is set up to integrate the data and / or energy to be transferred into at least one virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) and for this purpose first the virtual machine (VM) and / or Generate virtual network (VN) and / or neural network (NN) at a time of origin and the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) together with the data integrated therein for transport via the transport server ( 3.1 , ..., 3.N ) to the destination server ( 2 ) to send; At least one trigger, in particular a transaction, at least one virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) at least temporarily by means of the hardware of the current transport server ( 3.1 , ..., 3.N ) and to halt and / or terminate after execution and forward it until the virtual machine (VM) and / or virtual network (VN) and / or neural network (NN) is received at the destination server ( 2 ) and the start server ( 1 ) is set up as data to be transmitted and / or energy start data (S) in the virtual machine (VM) and / or virtual network (VN) to integrate and the virtual machine (VM) and / or virtual network (VN) is set up in a conversion process convert the start data (S) into target data (Z) and / or data and / or transport energy, each of the transport servers ( 3.1 , ..., 3.N ) executing the virtual machine (VM) and / or virtual network (VN) is arranged to perform the conversion process partially during the execution time of the virtual machine (VM) and / or virtual network (VN). System according to claim 3, characterized in that The system comprises at least one computer, in particular a control system and / or at least one simulation system (SI), in particular the system comprises at least one transmission module and / or reception module and / or at least one antenna and transmission module and / or a control system for switching and / or controlling data and / or energy - in particular at least one neural network (NN), in particular fuzzy control system and / or at least one neural network (NN) control system, for controlling systems, in particular the total energy consumption of several electrical equipment, which form a group, - in particular by means of at least one neural network (NN), in particular fuzzy control system and / or neural network (NN) control system includes and is formed for each electrical device within the group, in particular VM and / or VN and / or NN depending on its current energy consumption to determine a priority value and / or probability and whom at the very least, select and steer the electrical appliance with the highest priority value.

Images