DE202019000168U1 - Device for a system for the regulation of processes as a function of an intensity, preferably an interaction intensity or time intensity - Google Patents

Abstract

Apparatus for a system for controlling operations as a function of an intensity, preferably an interaction intensity or time intensity, and a respective computer-implemented method directed thereon, comprise at least the steps
- Input internally (16) in the module Business Requirements (3)
- Input external (17) in the module external influences (4)
- Entry in the user activities module (12)
- Entry in the Corporate actions module (13)
- Parameterization and definition of preferred numerical values in the input modules (3,4,12,13)
- Adjustment of values from the User Activities module (12) and the Corporate actions module (13) in the Status module (14)
- Transfer of the values from the module Status (14) to the module intensity driver (11.1, 11.2)
- Passing the values from module company requirements (3), the module external influences (4) to the intensity tensor (6,7) and the weighting function (8)
- Interaction of the module group database (1), conditions (2), models (5), intensity tensor (6, 7), intensity drivers (11.1, 11.2) as a control loop
Output of values of the intensity tensor (6, 7) via the module intensity differentiator (9.1, 9.2) to the module user interface (10)
Output of values of the intensity driver (11.1, 11.2) to the user interface module (10)

Description

The invention relates to a device for a system for regulating sequences as a function of an intensity, preferably an interaction intensity or time intensity, according to the preamble of the independent claims. The system itself combines in the device for the system the system-relevant links, such as data flow and communication, e.g. via hardware, which in turn has its own device characteristics. The description therefore also explains functions of the device by means of procedural, task-related information. It is clear that technical features such as control loop, Kl, cloud or internet can be claimed both as a device and as a method.

The system provides control, preferably iterative control, with detected models, tensors, weights, drivers and differentiators.

With the system, processes between entities, organizations, companies and / or institutions can be completely technically modeled and used for decision-making and decision optimization without intervention by the human mind.

The following terms are defined in the sense of the invention as follows:

The intensity is a numerically parameterized interval or switch values, e.g. I 1 to 10 I, (1 or 0).

Interaction intensity recording methods are currently colloquially equated with human action / reaction procedures. These are technically parameterized with the inventive system and stored in a database.

Time-consuming procedures are currently equated colloquially with time management. These are technically parameterized with the inventive system and stored in a database.

Control, regulation and subsequent control is by the Standard DIN 19226 (until 2002) and currently through the Standard DIN IEC 60050-351 Are defined. This concerns the structure of a control loop analogously. Control variables in the control loop are, for example, for an intensity of use by customers, instances or companies Measurements on the basis of clicks, actions, funds and the like. For each new customer interaction or after a predetermined period. The system, preferably supported by an AI, evaluates this behavior on the basis of the tensor data and automatically develops an action that, for example, increases customer loyalty. In another example, the system preferably provides support to the customer through an AI decision and acts actively for the customer in the interest of the customer.

A function is a mapping between sets of values in the mathematical sense where eg qualitative, quantitative solutions are determined, such as limit overruns / underruns, directions of a trend, trend evaluation (t), eg as F (x), F (x, y, z), F (x, ..., t).
A model defines the origin and conditions for the scaling (eg linear or non-linear behavior) of the value axes in the representing system, eg a Cartesian linear system for energy rating parameters of energy suppliers / energy customers. The system can represent static, business management, mathematical, technical and / or technical scaling for AI.

A tensor is a function that maps a defined number of vectors to one value. Wherein the tensor describes a function space of interdependent and independent functions, e.g. F (target (p (t)) and this parameterized, for example F (cost (currency, commodity values, ... energy costs, t)) with t = time.

The weighting is information with orientation for vectorization, e.g., for an interval of I 1 to 10 I, I 10 to 1 I, or I-10 to 10 I.

The weights are factors that normalize the functions of the tensor in relation to each other, make a baseline reference, or calibrate, e.g. positive or negative or in terms of parameterized terms such as quality degradation / quality increase over time.

The driver is a type of data interpreter / translator that takes external status data as a parameter, compares such status data with tensor results, corrects weights in the tensor if necessary, and outputs parameters to actuators, e.g. for a network.

The differentiator provides defined, filtered parameter values for defined interfaces in accordance with the respective interface or bus standards. The filter is defined in each case depending on the interface.

When using an iterative control loop, the defined tensor is supported by using methods of so-called artificial intelligence (AI). Whereby the used AI works as a knowledge-based system, so-called expert system, and questions translated into technical parameters to provide a user with answers based on formalized expertise and logical conclusions, which in turn are provided to the system as technical parameters.
The search and removal of inaccuracies in the technical systems is supported and preferably based on the Kl, which can act as a switch, corrected. On the basis of this iterative process, it is thus also possible to use the available data to determine and carry out a control, whereby the closest known known data record is used for a model, tensor, weighting, driver and differentiator. The controller benefits from the increasingly extensive database through the iterative control loop.

State of the art

From the prior art is the DE 10 2011 051 671 A1 5, which iteratively models and validates an optimization system using an iterative expert engine which determines investment data as a numerical solver on the basis of input data.

From the state of the art are further individual methods such as feedback systems or statistical evaluations eg about raised interest behavior, such as by the DE 10163002A1 shown, known. Such interaction control systems of companies, which are mostly processes controlled on the basis of information by the human mind, respond to shortcomings or deficiencies by using any measure of a series of further developed measures, which determine which interactions should be used at a given time in response to a particular type of disturbance or outcome. In some cases, actions are selected, canceled, or modified based on a preset priority that can be changed.

In some cases, algorithms have been used to determine the content or order in which the interaction actions are to be applied.

In some known systems, grossly simplified interaction systems are used to manage the use of cross-linking effects and thus cost savings through computerized methods. The evaluation of such systems is based on the technically generated information, which, however, is usually assessed and decided by the human mind. Before the introduction of smartphones and apps, networked digital interaction was a very small part of the tertiary sector, such as services, and parts of the secondary sector, such as industrial products. Most such solutions have not changed to this day and are rather non-technical. In addition, interaction intensity is not captured by digital interaction in most cases, and essentially relates to providing information.
Known interaction intensity management systems in the form of interaction intensity deficit systems are generally limited to the use of the content and nature of an interaction measure and do not decide when which measure will be used by whom. Such systems are currently limited to interaction intensity information that is obtained only within a company and does not reflect the actual customer behavior.
In general, the behavior of a customer is rarely discussed, but rather the behavior of the market, ie how the individual customer reacts and can not be reached but how the intensity of interaction of a customer can be increased again. The restoration or increase of the interaction intensity of a single customer is not planned by the information and not executed.

The restoration of interaction intensity to a target audience or market is then conventionally performed manually by human acquisition / market. Manually restoring the interaction intensity of a target group is generally expensive.

Although interaction intensity detection methods are currently known, these are individual and very limited systems that are not configured to operate e.g. by the system itself, finding the best way to increase the intensity of interaction that could be achieved by detailed analysis of instance behavior and instance response in a given configuration or situation, because here human experience functions as an external decision maker of the human mind.

Thus, the methods of interaction intensity management in use today do not achieve the advantages (such as savings, range, effect, control) that one could achieve in the particular situation, using various different patterns of interaction, e.g. Instances and / or companies may require the use of interaction measures.

The currently known such entrepreneurial interaction intensity control systems are thus very limited in scope, simply constructed and not configurable, for example, the optimization of a relationship, eg customer to Companies, with a view to ensuring all interactions of the past. This is essentially due to the fact that the detailed, time-consuming analysis of the costs and benefits of each individual measure is currently predominantly non-technical. As a result, complex relationships are not captured because the view of the totality of all measures is very complex and can not be grasped by the human mind. On this basis, at most a temporally delayed and partial evaluation of the measures that are available through information available through the human mind, which makes it impossible on the other hand, to react quickly to the current trend or unexpected events with an optimized action plan. The systems in use today therefore do not achieve the value added, eg in customer loyalty and the turnover that could be achieved in situations that can be achieved using the complete customer history for the company and the information eg from the whole industry.

Direct digitizable reactions e.g. of the customer and the company, which are convertible into technical editable parameters allow automation, i. an action sequence not influenced by the human mind, the same. This type of automation is represented by a closed-loop control circuit that operates according to the destinations, e.g. the customer and the company take appropriate measures to improve the relationship.

At the latest with the development of Evolutionary Algorithms (EA) within the framework of AI research, management is no longer limited to non-technical action. This affects u.a. the intensity management, time management and management generally claimed with this invention.

In the present invention, the central content of the management is represented by a technical method, which technically models complex data based on information, functionally prepared and provided and that runs on a machine. This makes the management function transferable to a control loop for a machine.

In previous systems, this was usually solved incompletely by human interpretation, experience, analysis and reaction as an approximation to reality only with material and time-consuming effort for individual cases.

For example, the direct impact of a marketing measure is only measurable by the customer in individual cases and then not across the entire breadth of customer interaction. In traditional customer retention, market analysis, and marketing strategy planning, the delays, e.g. to collect the essential data of the market, to an ineffective way of working. There is no permanent, direct contact with customers, who continuously draw conclusions and improvements, e.g. through corrective intervention by humans. Previous forms of the survey provide only a momentary result: the customer can actually behave differently than he indicates. The direct direct connection does not exist. A self-regulating real-time interaction intensity is therefore not possible with the previous solutions. This becomes possible only with the invention.

The present invention thus provides by the technical form of an iterative control with models, tensors weightings, drivers and differentiators a kind of optimization system, which is for example by an interaction intensity management (control) or time management (control) capable of the interaction intensity management or the time management eg between instances or customer and company to optimize, such as the effectiveness and efficiency of marketing campaigns, in which also technical devices are integrated.

For example, an interaction intensity control system uses an intensity driver and an intensity tensor to determine an interaction intensity control between an entity or a legal person, hereafter referred to as a "customer", and a offering entity or entity, hereafter "entity" for example, to increase customer loyalty among the parties involved through action, and is particularly applicable to defined customer relationships that require different types of defined action by the offering party.
Instances can also be defined as networks, KIs and other systems.
The interaction intensity control system thereby defines the device's (customer loyalty) regulatory unit offering units to increase interaction intensity over time or at various different times while still meeting certain technical requirements, conditions or requirements in the device. This may include input, such as the company's commitment to specifically promote the sale of a certain product line, to increase social media awareness, to sensitize customers to a particular brand, to comply with legal requirements, to ensure optimal utilization of systems guarantee efficiency, increase costs Also, the interaction intensity control system takes into account the interactions that have taken place between all customers and companies, as well as their omission. Furthermore, for example, the scalability of the technical devices and methods for a product is recorded and taken into account.

Interaction intensity-gathering methods are currently colloquially equated with human action / response behavior as a method. These are technically parameterized with the inventive system and stored in a database in terms of valuation.

The term interaction intensity defined above in the sense of the invention includes, for example, parameterizable terms such as: (customer) commitment, purchase frequency, purchase volume, communication frequency, product commitment, intentional action, reliability, penetration and the like.

Measures for obtaining parameters or stimulating parameters can be, for example, surveys, gratuities, bonus programs, stimuli (visual, auditory), improvement of the customer's quality of life, advertising, participation, videos, coins, process optimizations, comparisons, analyzes, adjustments (of conditions, prices, dates, production quantities, stocks) and the like.

The weighting is calculated on the basis of automatically recorded customer behavior and automated market analyzes. Customer is defined as any entity and legal entity.

Interaction intensity control systems are generally used to track, evaluate and manage actions by the company in an effort to ensure that appropriate measures are properly operated to meet customer and corporate requirements - such as the cost / benefit balance of the marketing measure - and also to be able to ensure this in case of unforeseen or unexpected events.

Direct digitizable reactions of the customer and the company, which can be converted into technical editable parameters, allow automation of the same. This automation is represented by a loop that takes appropriate action to improve the relationship, in accordance with both the customer's and the company's objectives.

With the technically parameterized terms of interaction intensity, not only can the impact of marketing actions be measured and evaluated immediately, but also the intent behind each interaction can be directly related to decisions to initiate control.

For example, the tensor (intensity or time) can functionally evaluate each value-related and / or statistical anomaly of each interaction and weight it according to its own functional algorithms. By using AI, a kind of self-optimization of the control loop takes place, from which a control via the dynamic database and the adapted models profits.

Interaction intensity methods, for example, determine the defined interaction measure based on analyzes that already refer to market behavior in the past at the time of their completion, or determination of content of a measure in the present, but without being able to change the measure itself or a weighting and thus be able to control or regulate the measures in a targeted manner or to be able to regulate them.

Interaction intensity determination measures are used, for example, to keep businesses efficient and operational, as without a sufficient assessment it can quickly lead to strategic wrong decisions, which can lead to inefficiency.

The interaction intensity determination is carried out in response to a disturbance or change in a market or customer behavioral history and the subsequent eventual additional disturbances or changes resulting from a primary disturbance or change, resulting in a deficient state in the affected areas, eg information retrieval, Production, storage, predictability.
Common system failures that can cause an interaction intensity deficit include: expertise shortcomings such as detection flaws, transparency, access to data, ability to evaluate; System deficits, such as structural deficiencies due to lack of procedures and procedures and / or large company deficits, loss of a target group or partner company and / or customers that may cause an interaction intensity deficit, errors in previous procedures, changes in the situation that result that previous methods become unusable, errors in planning, errors in implementation, such as wrong target group, wrong medium, errors in the evaluation, etc ..

As digital interaction becomes an ever greater part of corporate communications and strategic business planning and enterprise organization, it is necessary to have an interaction based on a defined quality modeled, for example, by parameters such as cost reduction, range, intensity enhancing effect determine their scope and when a company's interaction measures are deployed or exchanged.

The inventive system makes it possible, for example, to digitally map findings such as experiences, human impulses and complex interacting relations, and thus, for example, to automatically manage the interaction intensity.
Such systems make it possible, for example, for companies or entities to adapt individually by means of interaction measures, for example to a customer, and vice versa.

Here, the measurement of the respective interaction intensity is technically continuous and can be regulated by, for example, a weighting of all measures for a customer takes place and / or the behavior of the customer is taken into account in the market. At the same time, it is possible to immediately analyze and evaluate any intervention measure that is necessary and appropriately defined, and then automatically start targeted, appropriate interaction measures. For example, you do not have to wait until a measure has gone through its complete lifecycle and evaluation in order to have enough new, qualified information to be able to carry out further measures to optimize the interaction intensity.
A value determination determines, for example, a loss or a weakening of the interaction intensity and can control the sequence and nature of further measures, preferably using Kl, in order, for example, to induce an increase in interaction intensity.

Object of the invention

It is an object of the invention to eliminate the deficiency of the prior art and to allow the technical merging of such systems for a device to provide and validate a technical parameter input with a model-based analysis, weighting and provision of technical interface interface technical data which are suitable for transfer to systems for entrepreneurial or social issues.

It is a further object of the invention to merge and provide a technical system via a device of interacting modules in a defined technical control loop, which can be used as a functional technical interface between different instances and / or organizational structures. It goes without saying that parts of the claimed control circuit have elements of a control and can also be operated as a follow-up control.

Thus, the invention goes far beyond storing existing data according to an organizational structure, calculating relations between the data and monitoring the validity of the data (17W (pat) 6/14). Rather, by the invention hitherto only non-technically processable tasks technically solvable or supported in their solution, improved and expanded.

A further object of the invention is to provide, by the control in the device, a simple regulatory procedure which enables the real-time mapping and evaluation of the instance behavior and regulates the interaction and binding intensity.

Another object is to provide a simple and secure handling of all the variables needed for the control loop, simple tensor-based adaptation of the strategies and, if necessary, to provide automatic detection of an action in a device that is being implemented by a machine. The design of the regulator does not require any additional operations, such as manually executed measurement methods and analyzes. For the installation of the regulator only hardware is required, preferably a USB stick with computer-implemented method, The connection to the system via the Internet, preferably via a cloud connection.

It is another object of the invention to provide a corresponding device by which the regulation of interaction intensity, measures and weighting work easier, safer and more reliable, low-maintenance and reduced with less individual, independent parts in the effort.

This object is achieved by the device according to the main claim 1 and the embodiments according to the dependent claims.

• - Eingabe intern (16) im Modul Unternehmensanforderungen (3)
• - Eingabe extern (17) im Modul äußere Einflüsse (4)
• - Eingabe im Modul Benutzeraktivitäten (12)
• - Eingabe im Modul Unternehmensmaßnahmen (13)
• - Parametrisierung und Definition der bevorzugt numerischen Werte in den Eingabemodulen (3,4,12,13)
• - Abgleich der Werte aus dem Modul Benutzeraktivitäten (12) und dem Modul Unternehmensmaßnahmen (13) im Modul Status (14)
• - Weitergabe der Werte aus dem Modul Status (14) an das Modul Intensitätstreiber (11.1, 11.2)
• - Weitergabe der Werte aus Modul Unternehmensanforderungen (3), dem Modul äußere Einflüsse (4) an den Intensitätstensor (6,7) und die Gewichtungsfunktion (8)
• - Interaktion der Modulgruppe Datenbank (1), Auflagen (2), Modelle (5), Intensitätstensor (6, 7), Intensitätstreiber (11.1, 11.2) als Regelkreis
• - Ausgabe von Werten des Intensitätstensors (6, 7) über das Modul Intensitätsdifferentiator (9.1, 9.2) an das Modul Benutzerschnittstelle (10)
• - Ausgabe von Werten des Intensitätstreibers (11.1, 11.2) an das Modul Benutzerschnittstelle (10)

The method includes a system for controlling operations as a function of an intensity, preferably an intensity of interaction or time intensity, and a respective one directed thereto Computer-implemented method, include at least the steps

• - Input internally (16) in the module Business Requirements ( 3 )
• - Input external (17) in the module external influences ( 4 )
• - Entry in the user activities module ( 12 )
• - Entry in the module Corporate actions ( 13 )
• - Parameterization and definition of preferred numerical values in the input modules ( 3 . 4 . 12 . 13 )
• - Matching the values from the User Activities module ( 12 ) and the module Corporate actions ( 13 ) in the module Status ( 14 )
• - Passing the values from the module Status ( 14 ) to the module intensity driver ( 11.1 . 11.2 )
• - Passing the values from module company requirements ( 3 ), the module external influences ( 4 ) to the intensity tensor (6,7) and the weighting function ( 8th )
• - Interaction of the module group database ( 1 ), Pads ( 2 ), Models ( 5 ), Intensity tensor ( 6 . 7 ), Intensity drivers ( 11.1 . 11.2 ) as a control loop
• - Output of values of the intensity tensor ( 6 . 7 ) via the module Intensitätsdifferentiator ( 9.1 . 9.2 ) to the module user interface ( 10 )
• - Output of values of the intensity driver ( 11.1 . 11.2 ) to the module user interface ( 10 )

Furthermore, a computer-implemented method for controlling processes as a function of an intensity on a device is possible, comprising at least the steps
Input intern (16) in the module Business Requirements ( 3 )
Input external (17) in the module external influences ( 4 )
Entry in the user activities module ( 12 )
Entry in the module Corporate actions ( 13 )
Parameterization and definition of preferred numerical values in the input modules ( 3 . 4 . 12 . 13 )
Matching the values from the user activities module ( 12 ) and the module Corporate actions ( 13 ) in the module Status ( 14 )
Passing the values from the module Status ( 14 ) to the module intensity driver ( 11.1 . 11.2 )
Passing the values from module company requirements ( 3 ), the module external influences ( 4 ) to the intensity tensor ( 6 . 7 ) and the weighting function ( 8th )
Interaction of the module group database ( 1 ), Pads ( 2 ), Models ( 5 ), Intensity tensor ( 6 . 7 ), Intensity drivers ( 11.1 . 11.2 ) when
loop
Output of values of the intensity tensor ( 6 . 7 ) via the module Intensitätsdifferentiator ( 9.1 . 9.2 ) to the module user interface ( 10 )
Output of values of the intensity driver ( 11.1 . 11.2 ) to the module user interface ( 10 )

Further steps for the computer-implemented method of controlling processes as a function of intensity by the apparatus include the step of outputting values of the intensity driver ( 11.1 . 11.2 ) to the module enterprise controller ( 15 ).

Further steps for the computer-implemented method of controlling processes as a function of intensity through the device include the step
Control of actuators, bus systems, communication devices and / or networks with the outputs of values from the user interface module ( 10 ).

Further steps for the computer-implemented method of controlling processes as a function of intensity through the device include the step of controlling actuators, bus systems, communication devices and / or networks with the outputs of values from the user interface module ( 10 ) and from the module enterprise controller ( 15 ).

The invention further includes a dependent apparatus for a system for controlling processes as a function of intensity, for processing data comprising means for carrying out the method.

As an example of the use of an inventive system for controlling operations as a function of an intensity of intensity or time intensity and a respective computer-implemented process addressed thereto, local governments, utilities, mail order companies, internet-based companies, insurance companies, transport companies, trading companies and the like are addressed by the Automation of as many processes and their optimization in the customer context eg Focus on customer acquisition and customer loyalty. The invention is based on the approach that a direct technical digital detection of customer behavior, e.g. through online, mobile, etc., combined with access to all of the company's services and products, enable more complex, accurate and accurate inferences and adjustments. Overall, the invention reduces expenses in the analysis and reaction to customer interaction and their intensification in a simple and reliable manner.

• 1 zeigt ein beispielhaftes Blockdiagramm für ein System zur Regelung von Abläufen als Funktion einer Intensität hier einer Interaktionsintensität und ein jeweiliges darauf gerichtetes computerimplementiertes Verfahren
• 2 zeigt ein beispielhaftes Blockdiagramm für ein System zur Regelung von Abläufen als Funktion einer Intensität hier einer Zeitintensität und ein jeweiliges darauf gerichtetes computerimplementiertes Verfahren
• 3 zeigt ein beispielhaftes vereinfachtes Blockdiagramm für ein System zur Regelung von Abläufen als Funktion einer Intensität

Further advantages, features and applications of the present invention will become apparent from the following description taken in conjunction with the figures. Show it:

• 1 FIG. 12 shows an exemplary block diagram for a system for controlling operations as a function of an intensity here of an interaction intensity and a respective computer-implemented method directed thereto
• 2 Figure 4 shows an exemplary block diagram for a system for controlling operations as a function of intensity here of time intensity and a respective computer-implemented method directed thereto
• 3 FIG. 10 shows an exemplary simplified block diagram for a system for controlling operations as a function of intensity

1 shows a block diagram with directional connections (arrows), which show the technical data flow as a flowchart.

The input parameters internally ( 16 ), such as appointment calendars, targets, user input and the like are displayed in the module Enterprise Requirements ( 3 ) parameterized and defined.

The input parameter external ( 17 ), such as costs, prices, deadlines and the like are in the module External influences ( 4 ) parameterized and defined.

It is to be understood that the input parameters are kept dynamic, and the data is subject to temporally different input requests interrogatable by a machine, e.g. CMS, EAP systems.

The inputs are sent directly to the Intensity Tensor module, here as the interaction tensor ( 6 ) and there functionally incorporated. At the same time, the tensor with a defined weighting function ( 8th ) coupled. The weighting function is preferably recursively coupled to the statistical functions of the tensor. The decision for a coupling is preferably obtained by a switch of the AI.

The module intensity tensor, here as interaction tensor ( 6 ) interacts via the module Models ( 5 ) with the modules database ( 1 ) and conditions ( 2 ) and on the other hand with the module intensity driver, here as an interaction driver ( 11.1 ). This module group ( 1 . 2 . 5 . 6 . 8th . 11.1 ) preferably uses a group of evolutionary algorithms (EA), which also use iterative processes, eg for evaluation within the framework of the weighting function, and thus use their own defined AI.

The intensity tensor, here as the interaction tensor ( 6 ) has a first edition on an intensity differentiator, here as an interaction differentiator ( 9.1 ), the technical parameter checks, for example, against regional standards, legal and economic demarcations, so that, for example, no results that are not admissible under administrative law are transmitted to the user interface as digitally defined data ( 10 ).

The intensity tensor, here as the interaction tensor ( 6 ) has a second edition about a module intensity driver, here as an interaction driver ( 11.1 ), the digitally defined data to the user interface ( 10 ) and also digitally defined data to a definable optional module, here eg enterprise controller ( 15 ) can pass on.

The Intensity Driver module, here as an interaction driver ( 11.1 ) in addition to the connection in the control loop to the intensity tensor, here as the interaction tensor ( 6 ) the input modules user activities ( 12 ) and corporate actions ( 13 ) via the parameterized defined data, which has a module status ( 14 ) to the intensity driver, here as an interaction driver ( 11.1 ).

The technical control unit module enterprise controller ( 15 ) refers explicitly to internal company parameters and is an optional issue.

2 shows a block diagram with directional connections (arrows), which show the technical data flow as a flowchart.

The input parameters internal (16), such as appointment calendars, targets, user input and the like are displayed in the module Enterprise Requirements ( 3 ) parameterized defined.

The input parameter external ( 17 ), such as costs, prices, deadlines and the like are in the module External influences ( 4 ) parameterized defined.

It is to be understood that the input parameters are kept dynamic, and the data is subject to temporally different input requests interrogatable by a machine, e.g. CMS, EAP systems.

The inputs are sent directly to the Intensity Tensor module, here as a time tensor ( 7 ) and there functionally incorporated. At the same time, the tensor with a defined weighting function ( 8th ) coupled. The weighting function is preferably recursively coupled to the statistical functions of the tensor. The decision for a coupling is preferably obtained by a switch of the AI.

The module intensity tensor, here as module time tensor ( 7 ) interacts via the module Models ( 5 ) with the modules database ( 1 ) and conditions ( 2 ) and on the other hand with the module intensity driver, here as module time driver ( 11.2 ). This module group ( 1 . 2 . 5 . 7 . 8th . 11.2 ) preferably uses a group of evolutionary algorithms (EA), which also use iterative processes, eg for the evaluation within the framework of the weighting function, and thus use their own defined AI.

The module intensity tensor, here as module time tensor ( 7 ) has a first issue on a module intensity differentiator, here as module time differentiator ( 9.2 ), the technical parameter checks, for example, against regional standards and economic demarcations, so that, for example, no unauthorized administrative results as digitally defined data to the user interface ( 10 ).

The module intensity tensor, here as module time tensor ( 7 ) has a second edition about a module intensity driver, here as a module time driver ( 11.2 ), the digital defined data to the user interface ( 10 ) and also digitally defined data to a definable optional module, here eg enterprise controller ( 15 ) can pass on.

The module intensity driver, here as module time driver ( 11.2 ) has next to the connection in the control loop to the module intensity tensor, here time tensor ( 7 ) the input modules user activities ( 12 ) and corporate actions ( 13 ) via the parameterized defined data, which has a module status ( 14 ) to the module time driver ( 11.2 ).

The technical control unit enterprise controller ( 15 ) refers explicitly to internal company parameters and is an optional issue.

3 shows a simplified block diagram with directional connections (arrows), which clearly show the technical data flow. Such representations show a subscriber A who, for example, with actuators, machines in his network A via a control circuit module with at least database ( 1 ) Module, models ( 5 ) Module and Intensity Tensor (6,7) Module and Weighting ( 8th ) Module interacts and communicates and how the structure for the technical flow of technical parameters between input loop output is constructed.

LIST OF REFERENCE NUMBERS

1

Database

2

pads

3

Business Needs

4

External influences

5

models

6

Intensity tensor, here as an interaction tensor

7

Intensity tensor, here as a time tensor

8th

weighting function

9.1

Intensity differentiator, here as interaction differentiator

9.2

Intensity differentiator, here as time differentiator

10

User interface

11.1

Intensity driver, here as an interaction driver

11.2

Intensity driver, here as a time driver

12

user activity

13

Corporate actions

14

status

15

Corporate Controller

16

Input parameters internally

17

Input parameters externally

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

• DE 102011051671 A1 [0016]
• DE 10163002 A1 [0017]

Cited non-patent literature

• Standard DIN 19226 [0008]
• Standard DIN IEC 60050-351 [0008]

Claims (6)

Apparatus for a system for controlling operations as a function of an intensity, preferably an interaction intensity or time intensity, and a respective computer-implemented method directed thereon, comprise at least the steps - Input internally (16) in the module Business Requirements (3) - Input external (17) in the module external influences (4) - Entry in the user activities module (12) - Entry in the Corporate actions module (13) - Parameterization and definition of preferred numerical values in the input modules (3,4,12,13) - Adjustment of values from the User Activities module (12) and the Corporate actions module (13) in the Status module (14) - Transfer of the values from the module Status (14) to the module intensity driver (11.1, 11.2) - Passing the values from module company requirements (3), the module external influences (4) to the intensity tensor (6,7) and the weighting function (8) - Interaction of the module group database (1), conditions (2), models (5), intensity tensor (6, 7), intensity drivers (11.1, 11.2) as a control loop Output of values of the intensity tensor (6, 7) via the module intensity differentiator (9.1, 9.2) to the module user interface (10) Output of values of the intensity driver (11.1, 11.2) to the user interface module (10) Device for a system for controlling processes as a function of an intensity preferably of an interaction intensity or time intensity and a respective computer-implemented method directed thereto Claim 1 comprising the step of - output of values of the intensity driver (11.1, 11.2) to the module enterprise controller (15). Device for a system for controlling processes as a function of an intensity preferably of an interaction intensity or time intensity and a respective computer-implemented method directed thereto Claim 1 comprising the step of controlling actuators, bus systems, communication devices and / or networks with the outputs of values from the user interface module (10). Device for a system for controlling processes as a function of an intensity preferably of an interaction intensity or time intensity and a respective computer-implemented method directed thereto Claim 2 comprising the step of controlling actuators, bus systems, communication devices and / or networks with the outputs of values from the user interface module (10) and from the enterprise controller module (15). An apparatus for a system for controlling operations as a function of intensity, for processing data according to any one of the preceding claims, comprising means for carrying out the steps, wherein at least the Interaction of the module group database (1), requirements (2), models (5), intensity tensor (6, 7), intensity drivers (11.1, 11.2) via the Internet, preferably a cloud service / operator. An apparatus for an intensity management system for processing data as claimed in any one of the preceding claims, comprising means for performing the steps, the modules being enterprise requirements (3), external influences (4), user activities (12), corporate actions (13), user interface (10) and enterprise controller (15) are embodied as electronic devices which provide auditory, visual and / or haptic inputs via the modules enterprise requirements (3), external influences (4), user activities (12), corporate measures (13 ) as well as auditory, visual and / or haptic outputs via the user interface module (10) and enterprise controller (15).

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