An abnormal electricity consumption identification method and system based on a blockchain oracle

Abstract

This invention discloses a method and system for identifying abnormal electricity consumption based on a blockchain oracle. The method involves collecting electricity consumption information and categorizing it according to meter number and time. The categorized electricity consumption information is then stored on the blockchain. An abnormal electricity consumption identification command is initiated by packaging the electricity consumption information stored on the blockchain within the current period. The electricity consumption information is verified and parsed to obtain the associated transformer substation and topology information, and off-chain calculations are performed to identify abnormal electricity consumption behavior. The meter number, abnormal data identifier, and identification result are packaged and signed. After signing, the identification is confirmed based on the identification result. If successful, the relevant information is stored on the blockchain; otherwise, the identification process continues until the next period's data is stored on the blockchain. Advantages: This method improves the efficiency and accuracy of abnormal electricity consumption behavior analysis and identification while minimizing on-chain computing resources and balancing data security and computational efficiency.

Description

Technical Field

[0001] This invention relates to a method and system for identifying abnormal electricity consumption based on a blockchain oracle, belonging to the field of blockchain application technology. Background Technology

[0002] Power losses in the power grid are generally divided into two categories: technical losses and non-technical losses. Technical losses mainly consist of normal losses in the power system's production and transmission processes, such as line transmission losses. Non-technical losses include losses caused by various situations, such as abnormal metering equipment and abnormal electricity consumption behavior by users. As the main factor of non-technical losses, abnormal electricity consumption behavior by users causes power companies to suffer large economic losses and also poses a hidden danger to the stability of power grid operation.

[0003] As the application of smart meters and electricity information collection systems continues to improve, the characteristics and means of abnormal electricity consumption behavior by users are also changing accordingly. Traditional methods of electricity theft, such as invasive damage to metering equipment, are no longer feasible. Tampering with electricity meter data through programming and network attacks has become a new means of electricity theft. Therefore, the importance of the security and reliability of source metering data is becoming increasingly prominent.

[0004] Blockchain technology has demonstrated tremendous application value in multiple industries by leveraging its advantages such as decentralized consensus and distributed storage. In the field of electricity metering, the application of blockchain technology will enable tamper-proof and traceable source data, effectively improving the security and reliability of electricity information collection.

[0005] Oracles, as an important technological direction of blockchain, can effectively break down the data barriers between on-chain and off-chain, expand the application scenarios of smart contracts, and enable oracles with data analysis capabilities to effectively improve the processing capacity of complex business and massive data of smart contracts, making it possible to realize data-driven businesses such as abnormal electricity consumption identification on the blockchain. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method and system for identifying abnormal electricity consumption based on blockchain oracles. Under the premise of ensuring the security and reliability of the source data of metering data, the method and system use oracles to achieve the identification of abnormal electricity consumption behavior through the collaboration of on-chain data and off-chain computing, so as to improve the security of electricity consumption information collection and the accuracy of abnormal behavior identification.

[0007] To address the aforementioned technical problems, this invention provides a method for identifying abnormal electricity consumption based on a blockchain oracle, comprising:

[0008] Electricity consumption information is collected and categorized according to the electricity meter number and time. The categorized electricity consumption information is then stored on the blockchain. The electricity consumption information that has been stored on the blockchain is identified by its hash value.

[0009] According to the predetermined cycle, the electricity consumption information uploaded to the blockchain within the current cycle is packaged and an abnormal electricity consumption identification command is initiated.

[0010] According to the abnormal power consumption identification command, the power consumption information is verified and parsed to obtain the data-related transformer and topology information. Based on the built-in abnormal power consumption data analysis module, the off-chain calculation for abnormal power consumption behavior identification is performed. If an abnormality is identified, the identification result is determined and the abnormal data identifier is determined.

[0011] The electricity meter number, abnormal data identifier, and identification results are packaged and signed. After signing, the identification results are used to confirm whether the identification is complete. If so, the signed data is uploaded to the blockchain for evidence storage. Otherwise, the identification is continued after the data of the next cycle is uploaded to the blockchain until the identification is complete.

[0012] Furthermore, the collection of electricity consumption information and the categorization of this information according to meter number and time include:

[0013] The electricity consumption information actively transmitted by the electricity meter is merged and classified according to the electricity meter number and time on a periodic basis. The electricity consumption information includes active energy, reactive energy, voltage, current, active power, reactive power, and power factor.

[0014] Furthermore, it also includes:

[0015] Based on the different characteristics of electricity consumption data after merging and classification, an on-chain data table is constructed. The meter number is used as the primary key of the data table to store metering data in separate tables. Data upload and query interfaces are provided for each type of data.

[0016] Furthermore, the step of packaging the electricity consumption information uploaded to the blockchain within the current period and initiating an abnormal electricity consumption identification command according to a predetermined period includes:

[0017] The oracle call timing strategy is determined based on the number of metering points and the data collection frequency of the electricity meter. According to the oracle call timing strategy, the electricity consumption information data within the current period is obtained at set intervals. The electricity consumption information data is packaged and signed based on the electricity meter number before calling the oracle. After receiving the information, the oracle returns a reception response and a task ID, and the reception response and task ID are stored on the blockchain.

[0018] Furthermore, the step of verifying and parsing the electricity consumption information according to the abnormal electricity consumption identification instruction, obtaining the data-related transformer and topology information, and performing off-chain calculations for abnormal electricity consumption behavior identification based on the built-in abnormal electricity consumption data analysis module to obtain the identification results, including:

[0019] Upon receiving the abnormal electricity consumption identification command, the electricity consumption information is verified by the signature verification module. After the signature is verified, the data is parsed to obtain the electricity consumption information to be analyzed. Combined with the historical electricity consumption information of the same electricity meter number that already exists in the database, it is determined whether the conditions for abnormal electricity consumption identification and analysis are met.

[0020] If available, the data analysis module is invoked to analyze and obtain the data-related transformer and topology information, including: transformer name, transformer model, transformer capacity, low-voltage line length, power supply radius, and conductor type. Then, based on the built-in abnormal power consumption data analysis module, the off-chain calculation for abnormal power consumption behavior identification is performed to obtain the identification results.

[0021] If not available, the response result will be returned directly, and the existing data will be stored in the database for subsequent analysis.

[0022] Furthermore, the off-chain calculation for identifying abnormal electricity consumption behavior based on the built-in abnormal electricity consumption data analysis module yields the identification results, including:

[0023] Cluster analysis combined with linear regression is used for identification. Based on the influencing factors of abnormal electricity consumption, relevant characteristic parameters are determined, and then intermediate factors for abnormal electricity consumption analysis are formed as input vectors for the cluster model. The electricity consumption data is clustered, and supervised training is carried out using typical abnormal electricity consumption data as training samples to obtain a well-trained cluster model.

[0024] The trained model is used to identify abnormal electricity consumption behavior from the parsed electricity consumption information, and the identification results are obtained.

[0025] An abnormal electricity consumption identification system based on a blockchain oracle includes: a blockchain module and an oracle module;

[0026] The blockchain module includes a blockchain client, an electricity consumption information smart contract module, and an oracle interaction smart contract module.

[0027] The oracle is used to collect electricity consumption information and classify the electricity consumption information according to the electricity meter number and time. The classified electricity consumption information is stored on the blockchain and the hash value of the electricity consumption information that has been stored on the blockchain is used as the data identifier.

[0028] The oracle interaction smart contract module is used to package the electricity consumption information uploaded to the chain within the current period and initiate abnormal electricity consumption identification instructions according to a predetermined period.

[0029] After receiving the abnormal electricity consumption identification command from the oracle interaction smart contract module, the oracle verifies and parses the electricity consumption information to obtain the associated transformer substation and topology information. Based on the built-in abnormal electricity consumption data analysis module, it performs off-chain calculations to identify abnormal electricity consumption behavior. If an anomaly is identified, the identification result is confirmed and the abnormal data identifier is determined. The electricity meter number, the abnormal data identifier, and the identification result are packaged and signed. After signing, the identification result is used to confirm whether the identification is complete. If so, the signed data is uploaded to the blockchain for notarization. Otherwise, the identification process continues until the data in the next cycle is uploaded to the blockchain.

[0030] Furthermore, the oracle includes:

[0031] The data interface module provides a network interface for blockchain nodes and smart contracts.

[0032] The task scheduling module is used to verify and parse the data received by the oracle, and to assign computing tasks to the data analysis blocks according to the parsing results.

[0033] The data analysis module is used to decompose electricity consumption information data layer by layer and use big data methods and artificial intelligence algorithms to identify abnormal electricity consumption behavior.

[0034] The task response module is used to construct and sign a task response result data packet based on the analysis results of the data analysis module, and then call the data interface module to initiate a task response.

[0035] The data storage module is used to cache or persist electricity consumption information data.

[0036] A computer-readable storage medium for storing one or more programs, characterized in that the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described.

[0037] A computing device, comprising,

[0038] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described.

[0039] The beneficial effects achieved by this invention are as follows:

[0040] This invention leverages the advantages of blockchain technology, such as decentralization, tamper-proofing, and traceability, to enable electricity consumption information to be recorded on the blockchain at the source end of the metering device. This effectively solves the problem of difficulties in circumventing emerging electricity theft methods such as network attacks and data hijacking, ensuring the secure and reliable collection of electricity consumption information. Simultaneously, based on the trusted data on the blockchain, it conducts analysis and identification of abnormal electricity consumption behavior through efficient collaboration between smart contracts and oracles with off-chain computing capabilities. This improves the efficiency and accuracy of abnormal electricity consumption identification while minimizing on-chain computing resources and balancing data security and computational efficiency. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the abnormal electricity consumption identification method based on a blockchain oracle proposed in the embodiments of this specification;

[0042] Figure 2 This is a system architecture diagram of a blockchain oracle used for identifying abnormal electricity consumption, as described in the embodiments of this specification. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0044] The basic idea of ​​this invention is to first ensure the tamper-proof and traceability of electricity metering data through blockchain technology, and then combine the off-chain computing capabilities of oracles to achieve efficient on-chain and off-chain collaboration in the analysis and identification of abnormal electricity consumption behavior. On the one hand, by putting the data on the chain, the risk of source data being tampered with or inaccurate in traditional centralized analysis and calculation is avoided. On the other hand, to address the problems of insufficient on-chain computing capabilities, low computing efficiency, and incomplete data of smart contracts, oracles with big data and artificial intelligence computing capabilities are introduced to realize the abnormal electricity consumption behavior analysis function that requires high computing power.

[0045] To address the above technical objectives, this invention provides a method for identifying abnormal electricity consumption based on a blockchain oracle. (See [link to relevant documentation]). Figure 1 As shown, it includes the following steps

[0046] Step S1: The electricity information collection concentrator periodically merges and classifies the metering data actively transmitted by the electricity meters according to the electricity meter number and time. The metering data includes: active energy, reactive energy, voltage, current, active power, reactive power, power factor and other metering information. Then, the electricity information smart contract is called through the blockchain client to initiate a request for electricity information to be stored on the blockchain.

[0047] Electricity information smart contracts are designed to store electricity information based on different electricity data characteristics and construct on-chain data tables. The electricity meter number is used as the primary key of the data table to store metering data in separate tables. The contracts provide on-chain data and query interfaces for different types of data to meet the needs of analysis, calculation and data traceability.

[0048] Step S2, as follows Figure 2 As shown, an oracle call timing strategy is formulated based on the number of metering points and the data collection frequency of the electricity meter. The oracle interaction smart contract obtains the electricity consumption information data in the current period through the data query function of the electricity consumption information storage smart contract according to the set period. After the data is packaged and signed based on the electricity meter number, the oracle is called. After receiving the information, the oracle returns the receiving response and task ID. The smart contract stores the above information on the blockchain.

[0049] Step S3: After receiving the task from the smart contract, the abnormal electricity consumption identification oracle completes data verification through the signature verification module. After successful verification, it parses the data to obtain the electricity consumption information to be analyzed. Combined with the historical electricity consumption information of the same electricity meter number already existing in the database, it determines whether the conditions for abnormal electricity consumption identification and analysis are met. If they are met, it calls the data analysis module to analyze and obtain the data-related transformer and topology information, including: area name, transformer model, transformer capacity, low-voltage line length, power supply radius, and conductor type. Then, it performs off-chain calculations for abnormal electricity consumption behavior identification based on the built-in abnormal electricity consumption data analysis module. If the conditions are not met, it directly returns the response result to the smart contract and stores the existing data in the database for subsequent analysis.

[0050] Optionally, the oracle's abnormal electricity consumption analysis module can use cluster analysis combined with linear regression for identification. Based on the influencing factors of abnormal electricity consumption, relevant feature parameters are determined, and then intermediate factors for abnormal electricity consumption analysis are formed as input vectors for the clustering model. The electricity consumption data is clustered, and supervised training is carried out using typical abnormal electricity consumption data as training samples. The trained model can then be used to effectively identify abnormal electricity consumption behavior.

[0051] Step S4: Based on the identification results of the abnormal electricity consumption analysis module, the oracle module packages and signs the electricity meter number, abnormal data identifier and identification results, and then wakes up the oracle interaction smart contract. The contract will record the relevant information on the blockchain if the identification is completed; otherwise, it will wait for the data in the next cycle to be recorded on the blockchain and continue to initiate the oracle call until the identification is completed.

[0052] Accordingly, the present invention also provides an abnormal power consumption identification system based on a blockchain oracle, comprising: a blockchain module and an oracle module;

[0053] The blockchain module includes a blockchain client, an electricity consumption information smart contract module, and an oracle interaction smart contract module.

[0054] The oracle is used to collect electricity consumption information and classify the electricity consumption information according to the electricity meter number and time. The classified electricity consumption information is stored on the blockchain and the hash value of the electricity consumption information that has been stored on the blockchain is used as the data identifier.

[0055] The oracle interaction smart contract module is used to package the electricity consumption information uploaded to the chain within the current period and initiate abnormal electricity consumption identification instructions according to a predetermined period.

[0056] After receiving the abnormal electricity consumption identification command from the oracle interaction smart contract module, the oracle verifies and parses the electricity consumption information to obtain the associated transformer substation and topology information. Based on the built-in abnormal electricity consumption data analysis module, it performs off-chain calculations to identify abnormal electricity consumption behavior. If an anomaly is identified, the identification result is confirmed and the abnormal data identifier is determined. The electricity meter number, the abnormal data identifier, and the identification result are packaged and signed. After signing, the identification result is used to confirm whether the identification is complete. If so, the signed data is uploaded to the blockchain for notarization. Otherwise, the identification process continues until the data in the next cycle is uploaded to the blockchain.

[0057] The oracle includes:

[0058] The data interface module provides a network interface for blockchain nodes and smart contracts.

[0059] The task scheduling module is used to verify and parse the data received by the oracle, and to assign computing tasks to the data analysis blocks according to the parsing results.

[0060] The data analysis module is used to decompose electricity consumption information data layer by layer and use big data methods and artificial intelligence algorithms to identify abnormal electricity consumption behavior.

[0061] The task response module is used to construct and sign a task response result data packet based on the analysis results of the data analysis module, and then call the data interface module to initiate a task response.

[0062] The data storage module is used to cache or persist electricity consumption information data.

[0063] A computer-readable storage medium storing one or more programs, said one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods described.

[0064] A computing device, comprising,

[0065] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described.

[0066] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0068] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0070] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying abnormal electricity consumption based on a blockchain oracle, characterized in that, The method comprises the following steps: Collecting power consumption information and classifying the power consumption information according to the electric energy meter number and time, and storing the classified power consumption information on the blockchain, wherein the power consumption information stored on the blockchain is used as the data identifier; Packing the power consumption information uploaded in the current period according to the predetermined setting period to initiate an abnormal power consumption identification instruction; According to the abnormal power consumption identification instruction, the power consumption information is verified and parsed, the data associated transformer distribution and topology information is obtained, and the off-chain calculation of abnormal power consumption behavior identification is carried out based on the built-in abnormal power consumption data analysis module. If the identification is abnormal, the identification result is determined and the abnormal data identifier is determined; The electric energy meter number, abnormal data identifier and identification result are packaged and signed. After signing, according to the identification result, it is determined whether the identification is completed. If yes, the signed data is uploaded and stored on the chain; if not, the next period data is uploaded and stored on the chain, and the identification is continued until the identification is completed. According to the abnormal power consumption identification instruction, the power consumption information is verified and parsed, the data associated transformer distribution and topology information is obtained, and the off-chain calculation of abnormal power consumption behavior identification is carried out based on the built-in abnormal power consumption data analysis module. If the identification is abnormal, the identification result is determined and the abnormal data identifier is determined; Receive the abnormal power consumption identification instruction, verify the data of the power consumption information through the verification module, and parse the data to obtain the power consumption information to be analyzed after the verification is passed. Combine the historical power consumption information of the same electric energy meter number in the database to determine whether the abnormal power consumption identification analysis condition is met; If yes, the data analysis module is called for analysis, the data associated transformer distribution and topology information is obtained, including: transformer name, transformer type, transformer capacity, low-voltage line length, power supply radius, conductor type, and then the off-chain calculation of abnormal power consumption behavior identification is carried out based on the built-in abnormal power consumption data analysis module to obtain the identification result; If not, directly return the response result, and store the existing data in the database for subsequent analysis and use; The off-chain calculation of abnormal power consumption behavior identification based on the built-in abnormal power consumption data analysis module to obtain the identification result comprises: Adopting the method of clustering analysis combined with linear regression to identify, determining the relevant characteristic parameters based on the abnormal power consumption influencing factors, and then forming the abnormal power consumption analysis intermediate factor as the input vector of the clustering model to cluster the power consumption data, and performing supervised training by taking the typical abnormal power consumption data as the training sample to train the trained clustering model; Using the trained model to identify the abnormal power consumption behavior of the parsed power consumption information to obtain the identification result. 2.The blockchain-based oracle abnormal electricity usage identification method of claim 1, wherein, The method of collecting power consumption information and classifying the power consumption information according to the electric energy meter number and time comprises: According to the period, the power consumption information actively uploaded by the electric energy meter is merged and classified according to the electric energy meter number and time, and the power consumption information contains active electric energy, reactive electric energy, voltage, current, active power, reactive power and power factor. 3.The blockchain oracle-based abnormal electricity usage identification method of claim 2, wherein, Further comprising: According to the different power consumption data characteristics after merging and classifying, a chain data table is constructed, the electric energy meter number is used as the data table primary key to store the metering data, and data uploading and querying interfaces are provided for each type of data. 4.The blockchain-based oracle abnormal electricity usage identification method of claim 1, wherein, The step of packaging the electricity consumption information uploaded to the blockchain within the current period according to a predetermined cycle and initiating an abnormal electricity consumption identification command includes: The oracle call timing strategy is determined based on the number of metering points and the data collection frequency of the electricity meter. According to the oracle call timing strategy, the electricity consumption information data within the current period is obtained at set intervals. The electricity consumption information data is packaged and signed based on the electricity meter number before calling the oracle. After receiving the information, the oracle returns a reception response and a task ID, and the reception response and task ID are stored on the blockchain. 5.A blockchain oracle-based abnormal electricity usage identification system, characterized in that, include: Blockchain module and oracle module; The blockchain module includes a blockchain client, an electricity consumption information smart contract module, and an oracle interaction smart contract module. The oracle is used to collect electricity consumption information and classify the electricity consumption information according to the electricity meter number and time. The classified electricity consumption information is stored on the blockchain and the hash value of the electricity consumption information that has been stored on the blockchain is used as the data identifier. The oracle interaction smart contract module is used to package the electricity consumption information uploaded to the chain within the current period and initiate abnormal electricity consumption identification instructions according to a predetermined period. After receiving the abnormal electricity consumption identification command from the oracle interaction smart contract module, the oracle verifies and parses the electricity consumption information to obtain the associated transformer substation and topology information. Based on the built-in abnormal electricity consumption data analysis module, it performs off-chain calculations to identify abnormal electricity consumption behavior. If an anomaly is identified, the identification result is confirmed and the abnormal data identifier is determined. The electricity meter number, the abnormal data identifier, and the identification result are packaged and signed. After signing, the identification result is used to confirm whether the identification is complete. If so, the signed data is uploaded to the blockchain for notarization. Otherwise, the identification process continues after the next cycle of data is uploaded to the blockchain until the identification is complete. The process involves verifying and parsing the electricity consumption information according to the abnormal electricity consumption identification command, obtaining the associated transformer and topology information, and performing off-chain calculations for abnormal electricity consumption behavior identification based on the built-in abnormal electricity consumption data analysis module to obtain the identification results, including: Upon receiving the abnormal electricity consumption identification command, the electricity consumption information is verified by the signature verification module. After the signature is verified, the data is parsed to obtain the electricity consumption information to be analyzed. Combined with the historical electricity consumption information of the same electricity meter number that already exists in the database, it is determined whether the conditions for abnormal electricity consumption identification and analysis are met. If available, the data analysis module is invoked to analyze and obtain the data-related transformer and topology information, including: transformer name, transformer model, transformer capacity, low-voltage line length, power supply radius, and conductor type. Then, based on the built-in abnormal power consumption data analysis module, the off-chain calculation for abnormal power consumption behavior identification is performed to obtain the identification results. If not available, the response result will be returned directly, and the existing data will be stored in the database for subsequent analysis. The off-chain calculation based on the built-in abnormal power consumption data analysis module for identifying abnormal power consumption behavior yields the identification results, including: The method for identifying is performed by using cluster analysis combined with linear regression, relevant characteristic parameters are determined based on abnormal power consumption influencing factors, and then intermediate factors in abnormal power consumption analysis are formed as input vectors of a cluster model, power consumption data is clustered, and a trained cluster model is obtained through supervised training by using typical abnormal power consumption data as training samples; Abnormal power consumption behavior identification is performed on the analyzed power consumption information by using the trained model, and an identification result is obtained.

6. The blockchain oracle-based anomalous electricity usage identification system of claim 5, wherein, The oracle includes: A data interface module for providing a network interface for a blockchain node and a smart contract; A task scheduling module for verifying and analyzing data received by the oracle, and distributing computing tasks to a data analysis block according to an analysis result; A data analysis module for performing layer-by-layer decomposition on power consumption information data, and performing abnormal power consumption behavior identification by using a big data method and an artificial intelligence algorithm; A task response module for constructing a task response result data packet and signing the data packet based on an analysis result of the data analysis module, and initiating a task response by calling the data interface module; A data storage module for buffering or persistently storing power consumption information data.

7. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions that when executed by a computer cause the computer to perform a method of any of claims 1-6. The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-4.

8. A computing device, comprising: including, one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-4.

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