CN117395279A - Building intelligent energy management system based on Internet of things - Google Patents
Building intelligent energy management system based on Internet of things Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a building intelligent energy management system based on the Internet of things, which supports running block chains by a plurality of service nodes, and generates and protects unique IDs, public keys and private keys of users and devices. The equipment ID is encrypted through the public key of the affiliated user and is associated with the user ID, so that the safety of information and the privacy of the user are ensured. In addition, the system can also decrypt all device IDs associated with the user ID using the user's private key, providing a electricity record query service. The power utilization strategy model is built based on a neural network, and abnormal power utilization behaviors are detected through learning and understanding of power utilization modes. The implementation and updating of the model are carried out through a block chain consensus mechanism, so that the fairness and consistency of the system are ensured. The system comprises the step of feeding back the abnormal electricity consumption record to the user, so that the privacy and information safety of the user are ensured. The management system also comprises a step of counting the electricity utilization efficiency of all the devices in the building and reflecting the overall electricity utilization condition of the building.
Description
Technical Field
The invention relates to the technical field of digital processing. In particular to a building intelligent energy management system based on the Internet of things.
Background
With the increasing consumption of energy in the whole society, energy efficiency and management have become an important issue in the current generation. Particularly in building management systems, optimization of energy efficiency and monitoring of electricity consumption behavior become key factors for improving energy utilization rate, reducing energy cost and protecting environment. However, most building energy management systems currently on the market do not effectively address these issues, often lacking protection of user privacy, as well as accurate monitoring and anomaly detection of electricity usage.
Conventional building energy management systems typically rely on simple metering devices and independent management software, which results in fragmentation of data and inconsistent information. Furthermore, these systems often do not provide an effective mechanism to protect the privacy and data security of the user, who may be accessible to unauthorized third parties. Moreover, due to the lack of an effective data analysis and prediction model, these systems also cannot accurately monitor and predict electricity usage behavior, and cannot timely discover and handle abnormal electricity usage conditions.
In order to solve these problems, a new technical solution is needed, which can integrate various advanced technologies including the internet of things technology, the blockchain technology, the encryption technology and the neural network technology, and provides a comprehensive solution for building energy management. The scheme needs to be capable of protecting privacy and data safety of users, providing accurate and real-time electricity consumption monitoring and prediction, and effectively detecting and processing abnormal electricity consumption conditions, so that energy efficiency and management effect of the building are improved.
According to the disclosed technical scheme, the technical scheme with the publication number of JP2013057245A provides an energy-saving building scheme, which adopts a plurality of ventilation input and output ports with special designs and an optimized air duct, so that part of energy sources are saved in the process of ventilating, raising and lowering the temperature of an indoor air conditioner or a heating system; a technical solution disclosed in publication No. WO2018004017A1 proposes a building automatic control system capable of determining an operation mode for performing energy management of a building based on a time period divided into a plurality of time periods, based on a time period to which a current time belongs, automatically controlling an operation of an air conditioner corresponding to each case, and saving energy used in the air conditioner as much as possible; the technical scheme with the bulletin number of CN111799822B provides an energy coordination control method for a comprehensive energy system based on virtual energy storage, aims at minimizing building operation cost, and builds an intelligent building-level optimization operation model, so that the overall electricity utilization efficiency of a building is treated and optimized to a higher level.
The technical schemes all provide a plurality of technical means for improving the energy utilization rate in the modern building, but the privacy problem caused by the collection of a large amount of data of the electric equipment and the electricity consumption condition of the user in the process is further optimized by providing more technical schemes.
The foregoing discussion of the background art is intended to facilitate an understanding of the present invention only. This discussion is not an admission or admission that any of the material referred to was common general knowledge.
Disclosure of Invention
The invention aims to provide a building intelligent energy management system based on the Internet of things, which supports running blockchains by a plurality of service nodes, and generates and protects unique IDs of users and devices, public keys and private keys. The equipment ID is encrypted through the public key of the affiliated user and is associated with the user ID, so that the safety of information and the privacy of the user are ensured. In addition, the system can also decrypt all device IDs associated with the user ID using the user's private key, providing a electricity record query service. The power utilization strategy model is built based on a neural network, and abnormal power utilization behaviors are detected through learning and understanding of power utilization modes. The implementation and updating of the model are carried out through a block chain consensus mechanism, so that the fairness and consistency of the system are ensured. The system comprises the step of feeding back the abnormal electricity consumption record to the user, so that the privacy and information safety of the user are ensured. The management system also comprises a step of counting the electricity utilization efficiency of all the devices in the building and reflecting the overall electricity utilization condition of the building.
The invention adopts the following technical scheme: the building intelligent energy management system based on the Internet of things comprises a server and a plurality of service nodes in communication connection with the server; each of the service nodes is configured to interface with one or more users and to perform the following management operations:
carrying out electricity utilization access registration on electric equipment of a user; the access registration at least comprises recording rated working parameters of the electric equipment;
storing the working time of the electric equipment and the electricity utilization characteristics in the working process as electricity utilization records;
providing electricity utilization record inquiring service of the electric equipment for users;
encrypting the electricity records of the plurality of service nodes and storing the electricity records in a distributed storage system of a management system;
wherein, a block chain is operated and maintained by a server and a plurality of service nodes as nodes in the management system; the service node periodically writes the electricity utilization access registration and the electricity utilization record into the latest block of the block chain, and one service node packs block data and then accesses the end of the block chain;
each service node evaluates each of the electricity records in the block according to the electricity policy model, sets the record with unreasonable electricity characteristics as an in-doubt item, broadcasts the in-doubt item to other service nodes in the management system, and simultaneously requests a plurality of service nodes to authenticate the in-doubt item; after the service nodes exceeding the verification quantity authenticate that the suspicious item is an unreasonable item, storing the unreasonable item in a blockchain and sending a user of electric equipment to which the unreasonable item belongs to, and reminding the user of paying attention to the electricity utilization problem;
preferably, the management system generates a unique user ID, a public key and a private key corresponding to the user ID for the user under a consensus mechanism of a plurality of nodes of the blockchain, and generates a device ID for each electric device; the management system encrypts the equipment ID through a public key provided by a user, and then binds the encrypted equipment ID with the user ID;
preferably, when the user wants to query the electricity records of one or more electric equipment to which the user belongs, the specified equipment IDs are required to be provided to the management system, or all the equipment IDs associated with the user IDs are decrypted by the management system by using the private key of the user, and the electricity records of the equipment are returned;
preferably, the electricity utilization strategy model comprises a neural network-based establishment for auditing electricity utilization records;
preferably, a server and a service node in the management system perform consensus confirmation on a blockchain based on a consensus mechanism so as to establish and update the electricity utilization strategy model;
preferably, the management system includes calculating an overall electricity efficiency index E of a building, and the calculating method includes:
e i ＝f(r i ,m i )；
wherein e i The power utilization efficiency index f () is a function for calculating the actual power utilization condition r i And a theoretical electricity utilization model m which is derived from the electricity utilization strategy model and is set based on the equipment type of the ith electric equipment i Is the degree of coincidence of (2); omega i Calculating a weight value for the ith electric equipment;
and ω is i And function f () is determined by the management system under a consensus mechanism of a plurality of nodes of the blockchain;
preferably, the management system includes encrypting the unreasonable item by using a public key of the user of the electric equipment to which the unreasonable item belongs, and feeding back the unreasonable item to the user by a service node to which the user belongs.
The beneficial effects obtained by the invention are as follows:
1. the management system ensures the privacy security of the user data by carrying out public key encryption on the unique IDs of the user and the equipment and decrypting by using the private key; the application of the blockchain technology further improves the security and the non-falsifiability of the data and protects the sensitive information of the user from unauthorized access and modification;
2. the management system can learn and understand the electricity consumption mode by utilizing the neural network model, monitor the electricity consumption behavior in real time and timely detect and process abnormal electricity consumption conditions; in addition, the calculation of the overall electricity utilization efficiency provides a quantized index, and can be used for evaluating and optimizing the energy utilization efficiency of the building;
3. all system operations of the management system, including implementation and updating of the electricity utilization strategy model, are performed through a block chain consensus mechanism; this ensures fairness and consistency of the system, allowing all users to use the system in a fair and transparent environment;
4. the management system adopts modularized design for each software and hardware part, thereby being convenient for upgrading or replacing related software and hardware environments in the future and reducing the use cost.
Drawings
The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate corresponding parts throughout the different views.
1-building; 10-a server; 12-a service node; 14-network; 100-user; 500-a computer system; 502-bus; 504-a processor; 506-main memory; 508-read-only memory; 510-a storage device; 512-display; 514-input means; 516-cursor control device; 518-a network device;
FIG. 1 is a schematic diagram of a management system according to the present invention;
FIG. 2 is a schematic diagram of a connection between a management system and a powered device according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating steps of the management system for establishing an electricity policy model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a deep neural network of an electricity consumption strategy model according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a computer system for the server and the service node according to an embodiment of the present invention.
Detailed Description
In order to make the technical scheme and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the following examples thereof; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. Other systems, methods, and/or features of the present embodiments will be or become apparent to one with skill in the art upon examination of the following detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description. Included within the scope of the invention and protected by the accompanying claims. Additional features of the disclosed embodiments are described in, and will be apparent from, the following detailed description.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if any, the terms "upper," "lower," "left," "right," and the like indicate an orientation or a positional relationship based on the orientation or the positional relationship shown in the drawings, this is for convenience of description and simplification of the description, and does not indicate or imply that the apparatus or component to be referred to must have a specific orientation. The terms describing the positional relationship in the drawings are merely for illustrative purposes and are not to be construed as limiting the present patent, and specific meanings of the terms are understood by those of ordinary skill in the art according to specific circumstances.
Embodiment one: an intelligent building energy management system based on the Internet of things is exemplarily described, and comprises a server and a plurality of service nodes in communication connection with the server; each of the service nodes is configured to interface with one or more users and to perform the following management operations:
carrying out electricity utilization access registration on electric equipment of a user; the access registration at least comprises recording rated working parameters of the electric equipment;
storing the working time of the electric equipment and the electricity utilization characteristics in the working process as electricity utilization records;
providing electricity utilization record inquiring service of the electric equipment for users;
encrypting the electricity records of the plurality of service nodes and storing the electricity records in a distributed storage system of a management system;
wherein, a block chain is operated and maintained by a server and a plurality of service nodes as nodes in the management system; the service node periodically writes the electricity utilization access registration and the electricity utilization record into the latest block of the block chain, and one service node packs block data and then accesses the end of the block chain;
each service node evaluates each of the electricity records in the block according to the electricity policy model, sets the record with unreasonable electricity characteristics as an in-doubt item, broadcasts the in-doubt item to other service nodes in the management system, and simultaneously requests a plurality of service nodes to authenticate the in-doubt item; after the service nodes exceeding the verification quantity authenticate that the suspicious item is an unreasonable item, storing the unreasonable item in a blockchain and sending a user of electric equipment to which the unreasonable item belongs to, and reminding the user of paying attention to the electricity utilization problem;
preferably, the management system generates a unique user ID, a public key and a private key corresponding to the user ID for the user under a consensus mechanism of a plurality of nodes of the blockchain, and generates a device ID for each electric device; the management system encrypts the equipment ID through a public key provided by a user, and then binds the encrypted equipment ID with the user ID;
preferably, when the user wants to query the electricity records of one or more electric equipment to which the user belongs, the specified equipment IDs are required to be provided to the management system, or all the equipment IDs associated with the user IDs are decrypted by the management system by using the private key of the user, and the electricity records of the equipment are returned;
preferably, the electricity utilization strategy model comprises a neural network-based establishment for auditing electricity utilization records;
preferably, a server and a service node in the management system perform consensus confirmation on a blockchain based on a consensus mechanism so as to establish and update the electricity utilization strategy model;
preferably, the management system includes calculating an overall electricity efficiency index E of a building, and the calculating method includes:
e i ＝f(r i ,m i )；
wherein e i The power utilization efficiency index f () is a function for calculating the actual power utilization condition r i And a theoretical electricity utilization model m which is derived from the electricity utilization strategy model and is set based on the equipment type of the ith electric equipment i Is the degree of coincidence of (2); omega i Calculating a weight value for the ith electric equipment;
and ω is i And function f () is determined by the management system under a consensus mechanism of a plurality of nodes of the blockchain;
preferably, the management system includes encrypting the unreasonable item by using a public key of a user of the electric equipment to which the unreasonable item belongs, and feeding back the unreasonable item to the user by a service node to which the user belongs;
FIG. 1 is a schematic diagram of an embodiment of a management system embodying the present invention; wherein a plurality of independent or associated users 100 are included in the building 1; a plurality of service nodes 12 are deployed in a plurality of locations of the building 1; in some exemplary embodiments, the service nodes 12 may be distributed in units of floors; while in other embodiments one or more service nodes 12 may be distributed in a company, home, or other manner to serve different users 100;
a plurality of service nodes 12 are connected to the server 10 via a network 14; the server 10 and each service node 12 may be a computer device with computing capabilities; which is configured with a processor and a memory; the memory stores codes, programs, data and the like related to the management system; when the processor executes the code, program stored in the memory, the management as described above may be implemented;
further, as shown in fig. 2, each user 100 has a plurality of electric devices 200; illustratively, powered device 200 may take a variety of forms, including, but not limited to, the following:
low power consumption device: such devices are typically relatively low in power consumption, including lighting fixtures, computer devices, printers, televisions, charging devices, and the like; the energy consumption of these devices is relatively low, but due to the large number, the overall impact on the electrical energy consumption is still considerable;
high power consumption device: the equipment has high energy consumption in use, such as air conditioners, electric heaters, washing machines, dryers, electromagnetic ovens, water heaters and the like; the power consumption of these devices determines to some extent the overall power efficiency of the building;
long-term power-on device: such devices include refrigerators, air purifiers, safety monitoring devices, etc., which operate for a long time, and thus the demand for electric power is also continuous;
the electric equipment can be accessed into the Internet of things through a communication module configured by the electric equipment, and data interaction is carried out between the electric equipment and the management system; the communication module may include a Wi-Fi module, a bluetooth module, a ZigBee module, etc., and even include a more advanced 5G communication module; through the communication modules, the electric equipment can send data such as state information, electricity consumption and the like to the management system;
in addition, if the electric equipment does not have a self-communication module or can not be directly connected to the Internet of things, the electric equipment can communicate with the Internet of things through communication intermediary equipment; the communication intermediary equipment comprises a router, a switch, a gateway and other equipment, and the equipment can forward the information of the electric equipment to the Internet of things, so that the data interaction with the management system is realized;
illustratively, the consumer registers with the management system for power access, including nominal operating parameters submitted to the management system, including, illustratively: brand, type, model, origin, rated power, maximum power, rated operating voltage, maximum instantaneous current, operating frequency; further may include energy efficiency ratio, noise level, operating temperature, vibration level, etc.;
for example, the power usage characteristic data continuously submitted by the powered device to the management system may include one or more of:
power consumption, real-time current, real-time voltage, real-time power, frequency, and power factor;
illustratively, the electric equipment can also submit information of the working state of the equipment, such as a switch state, a working mode, a fault state and the like, to the management system;
the above electricity utilization characteristic data and rated working parameters are only exemplified, and the management system can collect more characteristic data about electric equipment through the service node and sort the characteristic data into electricity utilization records;
further, the process of multiple service nodes recording electricity usage records on a blockchain may generally include the following steps:
and (3) data collection: firstly, the service node needs to collect the above mentioned working data from the electric equipment; this may be achieved by the device's own communication module, or by some form of intermediary device (e.g., router, switch, etc.);
and (3) data processing: the collected data may require some processing, such as formatting, encoding, or encryption, for the purpose of ensuring the readability and security of the data on the blockchain;
creating a record on a chain: the service node packages the processed data into a record item; the content of this entry is the device work data to be recorded onto the blockchain; the record may contain some other information, such as the originator of the record (i.e., the service node), the recipient of the record (possibly one management node or another service node), etc.;
broadcasting record items: broadcasting the created record item to a blockchain network by the service node; other nodes in the network (possibly including other service nodes, management nodes, authentication nodes, etc.) receive this entry;
and (3) record item verification: once the record is broadcast into the network, the service nodes on the full blockchain are required to verify; the purpose of the verification is to ensure the validity and validity of the record item; verification may include checking the format of the record, checking the digital signature of the record, checking the content of the record, etc.;
packaging the record items: once the entries generated at the time of generation of a block are verified as valid, the entries may be packed into a new block; packaging is typically done by a designated node or delegated node in the network; the selection of the service node for executing the packaging can be generated after voting is carried out by all nodes in the blockchain, or the server designates a certain service node according to a certain rule and then carries out the publicity to the whole blockchain;
block chain updating: the new block is added to the end of the blockchain, which is updated; the new block contains the record item of the equipment working data, so that the working data of the equipment is recorded on the block chain;
through the above process, the service node can store the electricity usage record onto the blockchain; and the working data of the electric equipment can be reliably, safely and nondefectively stored, and can be easily shared and inquired across nodes.
Embodiment two: this embodiment should be understood to include at least all of the features of any one of the preceding embodiments, and be further modified based thereon;
in further embodiments, the management system may also collect relevant environmental parameters, such as temperature, humidity, illuminance, air purity, etc., through sensors disposed in the user's use environment for subsequent generation and continuous stacking of the power usage policy model;
further, as shown in fig. 3, a method for performing rationality analysis on the electricity records by a service node based on an electricity policy model of a deep neural network is shown, which comprises the following steps:
s101: acquiring electricity utilization record data of a plurality of electric equipment from the block chain;
s102: processing the electricity recording data by using a neural network; a pretrained power utilization strategy model based on a deep neural network is adopted to output a label with reasonable power utilization or unreasonable power utilization;
s103: the management system transmits the electricity records with reasonable electricity consumption or unreasonable electricity consumption to the service node connected with the appointed user;
in one or more exemplary embodiments, the types of deep neural networks employed may include Recurrent Neural Networks (RNNs), long-short-term memory networks (LSTM), gated loop units (GRUs), or other types of neural networks;
in step S102, the pre-trained electricity policy model based on the deep neural network may be pre-trained by the server, and then distributed to a plurality of service nodes for use; the training steps comprise:
(1) Data preprocessing: preprocessing the original electricity utilization record so as to facilitate training of the neural network model; the pretreatment step typically includes:
data cleaning: deleting or filling the missing values, processing abnormal values, etc.;
feature selection: selecting characteristics which have an influence on the problem, such as electricity utilization time, power consumption and the like;
feature standardization: scaling the features to the same range, e.g., 0-1 or a standard normal distribution, to avoid the impact of dimensional differences between features on model training;
serializing: converting the continuous electrical records into a time series format; for example, regarding daily electricity records as a sequence, the characteristic value of each time point is the electricity time, power consumption, etc. of the time point;
(2) Building a training set: the training set is the power consumption data characteristics of different electric equipment which are marked by the reasonable power consumption or the unreasonable power consumption of the manual standard; and further, based on the electricity rating parameter and the electricity consumption characteristic of one electric equipment, a theoretical electricity consumption model m can be established by a relevant technician i For indicating the electricity-using deviceA reserve theory-based power usage feature;
(3) And (3) constructing a model: the deep neural network based electricity usage policy model preferably includes an input layer comprising n neurons representing inputs of n feature dimensions set by the relevant technician in the electricity usage record;
and, the power usage policy model preferably includes 1 to 3 hidden layers, each including m hidden neurons; the number of layers of the hidden layer is determined based on the total number of electric equipment and the number of feature dimensions of the set electric features; preferably, the number of hidden layers may be 2;
also, the number of hidden layer neurons is typically dependent on the dimensions of the input data and the number of output classes; in general, if the number of hidden layer neurons is too small, all features of the data may not be captured; if the number of neurons is too large, overfitting and longer training times may result; preferably, a balance can be found between the number of hidden layer neurons and the training time/overfit; a smaller value can be set at the beginning of model training, and then the number of neurons is gradually increased until the performance of the model is no longer significantly improved;
further, the power consumption policy model preferably is an output layer of a sigmoid activation function;
(4) Model training: selecting an appropriate loss function and optimizer, and then training the model using the training set; the loss function defines the difference between the model predicted and actual values, and the optimizer determines how to update the parameters of the model to minimize the loss function; for the binary classification problem, for example, in this embodiment, it is reasonable or unreasonable to determine an electricity record, and a common loss function is binary cross entropy loss, and a common optimizer includes SGD, adam, and the like;
(5) Model evaluation: after training the model, it is necessary to evaluate the performance of the model on a validation set or test set; common evaluation indexes comprise accuracy, precision, recall rate, F1 score and the like; the related technicians can adjust the model structure or parameters according to the indexes so as to improve the performance of the model;
further, the management system comprises a power utilization strategy model, a calculation function f () for calculating the actual power utilization condition r i And a theoretical electricity utilization model m which is derived from the electricity utilization strategy model and is set based on the equipment type of the ith electric equipment i Is the degree of coincidence of (2); wherein the difference between the two may be calculated based on a combination of one or more of the following mathematical tools: euclidean distance, dynamic time regularity, pearson correlation coefficient, cosine similarity, and further learning two model sequences r by long-term and short-term memory neural network i And m is equal to i And calculates the similarity based on these representations.
Embodiment III: the present embodiment should be understood to include at least all of the features of any one of the foregoing embodiments and be further modified based thereon
FIG. 5 depicts a schematic diagram of a computer system 500 in which the management system described herein may be implemented, wherein the computer system 500 may be employed as a service node or client in embodiments;
wherein computer system 500 includes a bus 502 or other communication mechanism for communicating information, and one or more processors 504 coupled with bus 502 for processing information; processor 504 may be, for example, one or more general-purpose microprocessors;
computer system 500 further includes a main memory 506, such as a Random Access Memory (RAM), cache memory, and/or other dynamic storage device, coupled to bus 502 for storing information and instructions to be executed by processor 504; main memory 506 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 504; these instructions, when stored in a storage medium accessible to processor 504, present computer system 500 as a special purpose machine that is customized to perform the operations specified in the instructions;
computer system 500 may also include a Read Only Memory (ROM) 508 or other static storage device coupled to bus 502 for storing static information and instructions for processor 504; a storage device 510, such as a magnetic disk, optical disk, or USB drive (flash drive), among others, is coupled to bus 502 for storing information and instructions;
and further, coupled to bus 502 may also include a display 512 for displaying various information, data, media, etc., an input device 514 for allowing a user of computer system 500 to control, manipulate computer system 500, and/or interact with computer system 500;
and further, computer system 500 may also include a network device 518 coupled to bus 502 to enable computer system 500 to have data transfer over a network;
a preferred way of interacting with the management system may be through a cursor control device 516, such as a computer mouse or similar control/navigation mechanism;
in general, as used herein, the words "engine," "component," "system," "database," and the like may refer to logic embodied in hardware or firmware, or to a set of software instructions, possibly with entries and exit points, written in a programming language such as Java, C, or C++; the software components may be compiled and linked into an executable program, installed in a dynamic linked library, or may be written in an interpreted programming language (e.g., BASIC, perl, or Python); it should be appreciated that software components may be invoked from other components or from themselves, and/or may be invoked in response to a detected event or interrupt;
software components configured to execute on a computing device may be provided on a computer readable medium, such as an optical disk, digital video disk, flash drive, magnetic disk, or any other tangible medium, or as a digital download (and may be initially stored) in a compressed or installable format, requiring installation, decompression, or decryption prior to execution; such software code may be stored in part or in whole on a memory device executing the computing device for execution by the computing device; the software instructions may be embedded in firmware, such as EPROM. It should also be appreciated that the hardware components may be comprised of connected logic units (e.g., gates and flip-flops) and/or may be comprised of programmable units (e.g., programmable gate arrays or processors);
computer system 500 includes computing devices that can implement the techniques described herein using custom hardwired logic, one or more ASICs or FPGAs, firmware, and/or program logic, which in combination with a computer system, makes computer system 500 a special purpose computing device;
in accordance with one or more embodiments, the techniques herein are performed by computer system 500 in response to processor 504 executing one or more sequences of one or more instructions contained in main memory 506; such instructions may be read into main memory 506 from another storage medium, such as storage device 510; execution of the sequences of instructions contained in main memory 506 causes processor 504 to perform the process steps described herein; in alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions;
the term "non-transitory medium" and similar terms as used herein refer to any medium that stores data and/or instructions that cause a machine to operate in a specific manner; such non-transitory media may include non-volatile media and/or volatile media; nonvolatile media includes, for example, optical or magnetic disks, such as storage device 510; volatile media includes dynamic memory, such as main memory 506;
common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge, and network versions thereof;
non-transitory media are different from, but may be used in conjunction with, transmission media; the transmission medium participates in information transmission between the non-transient mediums; for example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 502; transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
While the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the invention. That is, the methods, systems and devices discussed above are examples. Various configurations may omit, replace, or add various procedures or components as appropriate. For example, in alternative configurations, the methods may be performed in a different order than described, and/or various components may be added, omitted, and/or combined. Moreover, features described with respect to certain configurations may be combined in various other configurations, such as different aspects and elements of the configurations may be combined in a similar manner. Furthermore, as the technology evolves, elements therein may be updated, i.e., many of the elements are examples, and do not limit the scope of the disclosure or the claims.
Specific details are given in the description to provide a thorough understanding of exemplary configurations involving implementations. However, configurations may be practiced without these specific details, e.g., well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring configurations. This description provides only an example configuration and does not limit the scope, applicability, or configuration of the claims. Rather, the foregoing description of the configuration will provide those skilled in the art with an enabling description for implementing the described techniques. Various changes may be made in the function and arrangement of elements without departing from the spirit or scope of the disclosure.
It is intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is intended that it be regarded as illustrative rather than limiting. Various changes and modifications to the present invention may be made by one skilled in the art after reading the teachings herein, and such equivalent changes and modifications are intended to fall within the scope of the invention as defined in the appended claims.
Claims (7)
1. The building intelligent energy management system based on the Internet of things is characterized by comprising a server and a plurality of service nodes in communication connection with the server; each of the service nodes is configured to interface with one or more users and to perform the following management operations:
carrying out electricity utilization access registration on electric equipment of a user; the access registration at least comprises recording rated working parameters of the electric equipment;
storing the working time of the electric equipment and the electricity utilization characteristics in the working process as electricity utilization records;
providing electricity utilization record inquiring service of the electric equipment for users;
encrypting the electricity records of the plurality of service nodes and storing the electricity records in a distributed storage system of a management system;
wherein, a block chain is operated and maintained by a server and a plurality of service nodes as nodes in the management system; the service node periodically writes the electricity utilization access registration and the electricity utilization record into the latest block of the block chain, and one service node packs block data and then accesses the end of the block chain;
each service node evaluates each of the electricity records in the block according to the electricity policy model, sets the record with unreasonable electricity characteristics as an in-doubt item, broadcasts the in-doubt item to other service nodes in the management system, and simultaneously requests a plurality of service nodes to authenticate the in-doubt item; and after the service nodes exceeding the approved number authenticate the suspicious item as an unreasonable item, storing the unreasonable item in a blockchain and sending the user of the electric equipment to which the unreasonable item belongs to remind the user of paying attention to the electricity utilization problem.
2. The management system of claim 1, wherein a unique user ID, a public key and a private key corresponding to the user ID are generated for a user by the management system under a consensus mechanism of a plurality of nodes of the blockchain, and a device ID is generated for each powered device; the management system encrypts the device ID through a public key provided by the user, and then binds the encrypted device ID with the user ID.
3. A management system according to claim 2, characterized in that when a user wants to query the electricity usage record of the consumer or consumers to which they belong, the management system is provided with the specified device IDs, or all device IDs associated with the user IDs are decrypted by the management system using the user's private key and the electricity usage record of these devices is returned.
4. A management system according to claim 3, wherein the electricity usage policy model includes neural network based creation for auditing electricity usage records.
5. The management system of claim 4 wherein a consensus acknowledgement is made on a blockchain by a server and a service node in the management system based on a consensus mechanism to implement the building and updating of the power usage policy model.
6. The management system of claim 5, wherein the management system includes a calculation of a building overall electricity efficiency index E by:
e i ＝f(r i ,m i )；
wherein e i The power utilization efficiency index f () is a function for calculating the actual power utilization condition r i And a theoretical electricity utilization model m which is derived from the electricity utilization strategy model and is set based on the equipment type of the ith electric equipment i Is the degree of coincidence of (2); omega i Calculating a weight value for the ith electric equipment;
and ω is i And function f () is determined by the management system under a consensus mechanism of a plurality of nodes of the blockchain.
7. The management system of claim 6, wherein the management system includes using a public key of a user of the powered device to which the unreasonable item belongs to encrypt the unreasonable item, and feeding back the unreasonable item to the user by a service node to which the user belongs.
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