Blockchain electric meter control method and system based on edge computing

Abstract

The present invention discloses a blockchain meter control method and system based on edge computing, which relates to the field of smart grid technology. The method includes: interacting with the target power grid to obtain multiple smart meters, extracting their intrinsic characteristics and historical data groups to generate a block sub-chain and marking the patrol meters, activating the meters based on the block sub-chain to collect electric energy data (including usage, transaction and source data), using the meters as edge computing units to process data locally to determine multi-target blockchain data, parsing and synchronizing the data to the block sub-chain, and performing random mutual trust verification. This solves the technical problems of existing smart meters that cannot ensure the credibility, privacy protection and traceability of energy data, rely on cloud processing, resulting in large data transmission delays, slow responses, heavy cloud loads, and lack of local processing capabilities, and achieves the technical effect of improving the management level and utilization efficiency of electric energy.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a blockchain electricity meter control method and system based on edge computing. Background Art

[0002] In the context of power energy management, with the complex development of power systems and increasing user demands for energy service quality, technical issues in smart meter applications have become particularly prominent, and the contradictions between technical optimization needs have become more pronounced. Building a more comprehensive power energy management system has become crucial for promoting the development of the power industry. Traditional smart meter technology is often relatively rudimentary and limited, only enabling basic remote meter reading functions. It lacks in-depth processing and analysis capabilities for energy data, and cannot ensure data credibility, privacy protection, and traceability. It relies heavily on the cloud for data processing, resulting in large data transmission delays, slow responses, and excessive cloud load. Furthermore, the ability to interact with surrounding devices is insufficient, resulting in irrational resource allocation, making some key functions impossible to implement. The development of power energy management solutions is relatively simple and fixed, and cannot effectively meet the ever-changing actual needs of the power industry.

[0003] At present, relevant technologies have technical problems such as smart meters cannot ensure the credibility, privacy protection and traceability of energy data, and rely on cloud processing, resulting in large data transmission delays, slow responses, heavy cloud load, and lack of local processing capabilities. Summary of the Invention

[0004] This application provides a blockchain meter control method and system based on edge computing, which interacts with the target power grid to obtain multiple smart meters, extracts their intrinsic characteristics and historical data groups to generate a block sub-chain and marks the patrol meters, activates the meters based on the block sub-chain to collect power energy data (including usage, transaction and source data), uses the meters as edge computing units to process data locally to determine multi-target blockchain data, parses and synchronizes the data to the block sub-chain, and performs random mutual trust verification. By constructing an integrated power energy management system with high data security, efficient local processing, good device interaction and intelligent sustainability, the application achieves the technical effect of improving the management level and utilization efficiency of power energy.

[0005] This application provides a blockchain electricity meter control method based on edge computing, including:

[0006] Interact with the target power grid to obtain multiple smart meters; extract the intrinsic operating characteristics and historical operating data of the multiple smart meters, group them, and generate multiple block sub-chains, wherein the multiple block sub-chains correspond to multiple sub-chain patrol smart meters; based on the multiple block sub-chains, activate multiple smart meters to collect power energy data, wherein the power energy data includes power usage data, power transaction data and power source data; use the multiple smart meters as multiple edge computing units to process the power energy data locally, and determine multi-target blockchain data based on the local processing results; parse the multi-target blockchain data, synchronize the multi-target blockchain data to the multiple block sub-chains, and perform random mutual trust verification.

[0007] This application also provides a blockchain electricity meter control system based on edge computing, including:

[0008] A smart meter acquisition module, which is used to interact with the target power grid and acquire multiple smart meters; a grouping module, which is used to extract the intrinsic operating characteristics and historical operating data of multiple smart meters, perform grouping, and generate multiple block sub-chains, wherein the multiple block sub-chains correspond to multiple sub-chain patrol smart meters; an electric power energy data collection module, which is used to activate multiple smart meters to collect electric power energy data based on multiple block sub-chains, wherein the electric power energy data includes electric power usage data, electric power transaction data and electric power source data; an on-site processing module, which is used to use multiple smart meters as multiple edge computing units to perform on-site processing on the electric power energy data, and determine multi-target blockchain data based on the on-site processing results; a mutual trust verification module, which is used to parse the multi-target blockchain data, synchronize the multi-target blockchain data to multiple block sub-chains, and perform random mutual trust verification.

[0009] The edge computing-based blockchain meter control method and system proposed in this application first interacts with the target power grid to obtain multiple smart meters, extracts their intrinsic characteristics and historical data groups to generate a block sub-chain and marks the patrol meters, activates the meters based on the block sub-chain to collect power energy data (including usage, transaction and source data), uses the meters as edge computing units to process data locally to determine multi-target blockchain data, parses and synchronizes the data to the block sub-chain, and performs random mutual trust verification. By constructing an integrated power energy management system with high data security, efficient local processing, good device interaction and intelligent sustainability, the technical effect of improving the management level and utilization efficiency of power energy is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A flowchart of a blockchain electricity meter control method based on edge computing provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the structure of a blockchain electricity meter control system based on edge computing provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: smart meter acquisition module 10 , grouping module 20 , electric power data collection module 30 , local processing module 40 , mutual trust verification module 50 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The embodiment of the present application provides a blockchain electricity meter control method based on edge computing, such as Figure 1 As shown, the method includes:

[0018] Step S100: Interact with the target power grid to obtain multiple smart meters. Specifically, interacting with the target power grid and obtaining multiple smart meters involves establishing a connection with a specific target power grid and obtaining relevant information about the multiple smart meters. The target power grid is selected, the communication equipment and software required for interaction are prepared, and interaction parameters such as the communication protocol and data format are set. Interaction with the target power grid is performed to obtain preliminary information about the smart meters in the target power grid, including the number and basic identification of the smart meters. The interaction continues to obtain detailed information about the smart meters, such as their model, accuracy, and installation location. For example, if this is an electric energy management system, it may first send a request packet in a specific format to the target power grid, requesting information about smart meters in a specific area or type. After receiving this packet, the target power grid selects eligible smart meters from its database or real-time monitoring system based on the requirements and sends their detailed information back to the electric energy management system.

[0019] Step S200 extracts the intrinsic operating characteristics and historical operating data of multiple smart meters, performs grouping, and generates multiple blockchain subchains, wherein each of the multiple blockchain subchains is labeled with multiple subchain patrol smart meters. Specifically, extracting the intrinsic operating characteristics and historical operating data of multiple smart meters, performing grouping, and generating multiple blockchain subchains, wherein each of the multiple blockchain subchains is labeled with multiple subchain patrol smart meters, refers to obtaining the intrinsic operating characteristics and historical operating data of multiple smart meters, performing grouping operations to create multiple blockchain subchains, and then labeling each of these blockchain subchains to identify the multiple subchain patrol smart meters. Multiple smart meters to be processed are selected, data extraction and analysis tools are prepared, and grouping parameters, such as meter computing power thresholds and operating load rate ranges, are set. Data extraction and grouping operations are performed to obtain preliminary grouping results, which reflect the classification of the meters based on the set parameters. The grouping is then refined to generate multiple final blockchain subchains, each representing a collection of meters in different groups. At the same time, smart meters suitable for patrol are identified in each blockchain subchain. For example, if there are 200 smart meters, grouping parameters are first set, such as grouping meters with similar computing power and operating load rates. Data from these meters is then extracted and analyzed, and if 50 of them meet the grouping criteria, a blockchain subchain is generated. Ten of these 50 smart meters are selected as the subchain patrol smart meters. The remaining meters are grouped, blockchain subchains are generated, and the smart meters marked for patrol are identified in the same manner.

[0020] In one possible implementation, the intrinsic operating characteristics and historical operating data of multiple smart meters are extracted and grouped to generate multiple block subchains, wherein the multiple block subchains correspond to multiple subchain patrol smart meters. Step S200 further includes step S210, where the computing power of the multiple smart meters is evaluated based on the intrinsic operating characteristics to obtain a computing power coefficient set. Specifically, the intrinsic operating characteristics of each smart meter are studied, including characteristics such as hardware configuration, processing core performance, and memory capacity. By analyzing these characteristics, coefficients reflecting the computing power level of each smart meter are calculated for each smart meter. The coefficients are combined to form a computing power coefficient set. For example, if a smart meter has a powerful processing core and a large memory, its computing power coefficient may be higher.

[0021] Step S220 parses the historical operating data, statistically analyzes the operating load rates and computing loads of multiple smart meters, and outputs the data as a typical load dataset. Specifically, the historical operating data of the smart meters is parsed to extract information related to the operating load rate (e.g., the meter's workload during different time periods) and computing load (e.g., the amount of data processed, the number of computing tasks performed, etc.). This information is then processed and summarized using statistical analysis methods, such as average, maximum, and minimum value calculations. The results are ultimately output as a typical load dataset. For example, statistics show that a particular meter had an average load rate of 60% over the past month, with the highest computing load occurring in the evening hours each day.

[0022] In step S230, based on the topological relationship spectrum of the multiple smart meters, pairing is performed to generate multiple direct connection relationship groups. The connection delays of the multiple direct connection relationship groups are evaluated based on the historical operating data to obtain a delay information set. Specifically, based on the topological relationship spectrum between the multiple smart meters, the meters are paired two by two to form multiple direct connection relationship groups, representing possible direct connection methods between the meters. The direct connection relationship groups are evaluated with the help of historical operating data, focusing on the data transmission delay between them. Through measurement and calculation, the delay information of each direct connection relationship group is obtained and summarized into a delay information set. For example, through testing, it was found that the connection delay between meter A and meter B is generally lower in the morning and higher in the afternoon.

[0023] Step S240, performing adaptive grouping according to preset grouping constraints, combining the computing power coefficient set, the typical load data set and the delay information set, wherein the grouping constraints include meter quantity constraints, load constraints and delay extreme value constraints. Specifically, grouping is performed based on pre-set grouping constraints. The grouping constraints include meter quantity constraints (specifying the maximum number of meters included in each group to ensure that the transmission efficiency and number of connections during blockchain upload synchronization meet the requirements and avoid congestion), load constraints (limiting the maximum data throughput that each group can carry to prevent excessive data concentration for easy management), and delay extreme value constraints (setting the maximum allowable delay between the two smart meters with the longest logical path in the block sub-chain). Taking into account the previously obtained computing power coefficient set, typical load data set, and delay information set, multiple smart meters are adaptively grouped through complex algorithms and optimization strategies to generate multiple block sub-chains. The smart meters in the block sub-chain have good connectivity, and their computing performance can meet the requirements of the computing task volume. For example, when grouping, meters with strong computing power, low load, and small connection delay will be prioritized and grouped into the same group to meet the grouping constraints and optimize the overall performance.

[0024] In one possible implementation, the intrinsic operating characteristics and historical operating data of multiple smart meters are extracted and grouped to generate multiple block subchains, wherein the multiple block subchains correspond to multiple subchain patrol smart meters. Step S200 further includes step S250, which generates a sample evaluation dataset based on the edge computing requirements of the target power grid and constructs a patrol evaluation operator. Specifically, the edge computing requirements of the target power grid are analyzed, including strict requirements on data processing latency, data transmission security guarantees (such as encryption methods and transmission protocols), and local cache backup strategies. Based on the specific requirements, a batch of meter samples for evaluation are generated, or a portion of existing meters are selected as samples. Professional technicians or expert systems conduct evaluation experiments on the samples according to the computing capacity regulations and evaluation methods of the target power grid. The data obtained from the experiments are used to construct a sample evaluation dataset, and based on this, a patrol evaluation operator is constructed for subsequent patrol evaluation. For example, if the target power grid has extremely high latency requirements, when generating the sample evaluation dataset, a higher priority will be placed on selecting meter samples with fast processing speeds.

[0025] In step S260, multiple smart meters in the first blockchain subchain are traversed, and patrol evaluation is performed based on the patrol evaluation operator to obtain a patrol index set. Specifically, each smart meter in the first blockchain subchain is inspected and evaluated one by one. Using the previously constructed patrol evaluation operator, each meter's ability to meet the target grid edge computing requirements is quantitatively evaluated. The evaluation results are recorded in the form of a patrol index. The patrol indices of all meters are aggregated to form a patrol index set. For example, for a specific smart meter, a corresponding patrol index is assigned based on its performance in terms of latency, security, etc.

[0026] In step S270, multiple smart meters are serialized based on the patrol index set, and the top N smart meters are extracted to generate a candidate meter sequence. Specifically, based on the obtained patrol index set, multiple smart meters in the first block subchain are sorted by patrol index to achieve serialization. The top N smart meters with the highest patrol index are selected from the sorted sequence and extracted to form a candidate meter sequence, indicating that these meters perform well in meeting the target grid edge computing requirements.

[0027] Step S280: Activate the random number generator to randomly select from the candidate meter sequence to determine the sub-chain patrol smart meter. Specifically, to increase randomness and fairness, activate the random number generator and randomly select from the candidate meter sequence based on the result of the random number generator to ultimately determine the smart meter for the sub-chain patrol. This avoids selection bias caused by fixed rules and ensures the fairness and rationality of the selection. For example, if the number generated by the random number generator corresponds to a certain position in the candidate meter sequence, the meter at that position is determined to be the sub-chain patrol smart meter.

[0028] Step S300 activates multiple smart meters based on the multiple blockchain subchains to collect power data, where the power data includes power usage data, power transaction data, and power source data. Specifically, activating multiple smart meters based on the multiple blockchain subchains to collect power data, where the power data includes power usage data, power transaction data, and power source data, refers to enabling data collection for multiple smart meters based on the relevant information of the multiple blockchain subchains that have been established. The blockchain subchain to be activated is selected, the necessary activation instructions and communication conditions are prepared, and activation parameters such as the frequency and start and end times of data collection are set. The activation operation is executed to obtain power usage data. Power usage data represents power consumption by different power-consuming devices over different time periods and is characterized by detailed information about power usage behavior. The activation operation is continued to obtain power transaction data. Power transaction data represents various information related to the power purchase and sale process, such as the time, power consumption, price, and participants of the transaction, and is characterized by reflecting the market flow of power. Activate again to obtain electricity source data. This data indicates how electricity is generated, such as thermal, hydropower, or solar power. Its characteristic is that it reveals the source of electricity. For example, within a large power supply area, there are multiple different blockchain subchains, each corresponding to different user groups or regions. After the system activates the smart meters in these blockchain subchains according to the set rules, the meters will collect daily electricity usage data of residents, including the power consumption of different appliances. They will also record electricity transaction data between businesses and power suppliers, such as the specific time and amount of transactions. They will also obtain regional electricity source data to clarify whether electricity comes from local power plants or external transmission networks.

[0029] In step S400, the power energy data is processed locally using the multiple smart meters as edge computing units, and multi-target blockchain data is determined based on the local processing results. Specifically, using the multiple smart meters as edge computing units to process the power energy data locally and determining multi-target blockchain data based on the local processing results refers to treating the multiple smart meters as multiple independent edge computing units. The smart meters to process the power energy data are selected, the necessary software and hardware are prepared, and relevant processing parameters, such as data screening rules and analysis algorithms, are set. Local processing operations are performed to obtain preliminary processing results, including periodic statistics of power usage data and compliance assessments of power transaction data. Based on these local processing results, data integration and optimization are continued to determine multi-target blockchain data. The multi-target blockchain data contains refined and classified power energy-related information, such as the electricity usage patterns of key users and the characteristics of high-frequency power transactions. For example, in the power system of an industrial park, multiple smart meters serve as edge computing units to process their collected power energy data. One meter detects a surge in electricity consumption at a factory during a specific time period and uses this information as key data. Another meter performs a preliminary screening of multiple energy transactions, flagging any that may be anomalous. Ultimately, these key and anomalous data from different meters are integrated to form multi-target blockchain data for further analysis and storage.

[0030] In one possible implementation, multiple smart meters are used as multiple edge computing units to process the electric energy data locally, and based on the local processing results, multi-target blockchain data is determined. Step S400 further includes step S410, which obtains the electric energy data in real time and performs local preprocessing. Specifically, by connecting to the smart meter, electric energy data is obtained in real time. The data source contains noise, missing values, and data in different formats. Then, preprocessing operations are performed locally to clean the data, remove invalid and erroneous data, normalize the data, unify data of different units and ranges into a standard scale, compress the data, and reduce the amount of data to improve the efficiency of subsequent processing and save storage space. For example, some abnormal high or low value data are eliminated, the electricity consumption data under different voltage levels are uniformly converted into standard units, and a large amount of similar data is compressed and stored.

[0031] In step S420, based on the preprocessing results, multiple edge computing units perform real-time data processing and analysis, and based on the processing and analysis results, extract a storage data set and an exchange data set, wherein the exchange data set includes transaction exchange data and collaborative control exchange data. Specifically, based on the preprocessed data results, multiple edge computing units begin to work together to perform real-time data processing and analysis. The processing and analysis include identifying power usage patterns, predicting power trading trends, and evaluating the operating status of the power system. Based on the processing and analysis results, a storage data set and an exchange data set are further extracted. The storage data set is used to store important historical data and statistical information for a long time, while the exchange data set is used for information exchange and collaboration between different systems or devices. The transaction exchange data in the exchange data set covers detailed information on power trading, such as transaction amount, power consumption, and transaction time. The collaborative control exchange data contains instructions and parameters for coordinating the operation of various parts of the power system. For example, if the edge computing unit discovers that power consumption in a certain area has continued to rise recently, it extracts the relevant data into a storage data set for subsequent analysis, and simultaneously extracts collaborative control exchange data indicating that the area may need to increase power supply.

[0032] Step S430: Output the stored dataset and the exchanged dataset as multi-target blockchain data. Specifically, the extracted stored dataset and exchanged dataset are integrated and packaged, and then output as multi-target blockchain data. This data is then prepared for uploading to the blockchain network to achieve distributed storage, sharing, and immutable records. For example, key information in the stored dataset and exchanged dataset is encrypted and signed to form data blocks that conform to the blockchain format. This data is then output to the blockchain for verification and storage by other nodes.

[0033] In one possible implementation, based on the preprocessing results, real-time data processing and analysis are performed by multiple edge computing units, and based on the processing and analysis results, a storage data set and an exchange data set are extracted, wherein the exchange data set includes transaction exchange data and collaborative control exchange data. Step S420 further includes step S421, extracting the power usage data, calculating the power usage, power usage duration, and device information of multiple smart meters, and outputting the data as the storage data set. Specifically, the power usage data is accurately extracted from the acquired data. For multiple smart meters, their power usage, i.e., the total amount of power consumed, is calculated; the power usage duration, such as the specific power usage time period, is determined; and device information is identified to determine which devices are consuming power. The calculation and extraction results are organized and output as a storage data set for subsequent long-term storage and in-depth analysis. For example, for a smart meter, it is calculated that its power usage in a day is 100 degrees, the usage duration is 10 hours, and the main devices used are air conditioners and refrigerators.

[0034] Step S422 extracts the electric energy transaction data, performs natural language recognition, extracts the electric energy transaction condition set, and converts the electric energy transaction condition set based on a preset uniform coding format to generate the transaction exchange data. Specifically, the electric energy transaction data is extracted from the overall data, and natural language recognition technology is used to parse the text descriptions and terms in the transaction data to extract the key electric energy transaction condition set, such as the transaction price, transaction volume, transaction time, etc. The extracted transaction condition set is converted according to a pre-set unified coding format to generate transaction exchange data that can be used for exchange and transmission. For example, a condition such as "50 kWh of electricity at a price of 0.5 yuan per kWh at 2:00 p.m." is identified from a transaction description and converted into a specific coding format.

[0035] Step S423: Perform a correlation analysis based on the power usage data and the power source data, and based on the correlation analysis results, associate and combine the power usage data with the power source data to output the coordinated control exchange data. Specifically, a correlation analysis is performed on the power usage data and the power source data to evaluate the relationship and dependency between the two. For example, it is determined whether the peak power usage period matches the supply of different power sources. Based on the results of the correlation analysis, closely related data are combined. Coordinated control is performed on the meters corresponding to the highly correlated data. For example, if two smart meters have high power consumption during off-peak periods, they can be set to prioritize renewable energy. If multiple smart meters have the same power source, load balancing control can be performed to prevent overload. The associated and combined data is output as coordinated control exchange data for optimizing and efficiently operating the power system. For example, if analysis reveals that some smart meters are always in a high power consumption state at the same time during a specific time period and have the same power source, their power consumption data is combined to generate coordinated control exchange data for load balancing control.

[0036] Step S500: parse the multi-target blockchain data, synchronize the multi-target blockchain data to multiple block sub-chains, and perform random mutual trust verification. Specifically, parsing the multi-target blockchain data, synchronizing the multi-target blockchain data to multiple blockchain subchains, and performing random mutual trust verification involves analyzing and interpreting the acquired multi-target blockchain data, selecting the data range to be processed, preparing the tools and algorithms required for parsing, setting relevant parsing parameters such as data format and key information extraction rules, executing the parsing operation, extracting key content from the data, such as detailed power energy data and key elements of transaction records, synchronizing the parsed multi-target blockchain data to multiple blockchain subchains, selecting the blockchain subchains to be synchronized, preparing the data transmission channels and conditions, setting the synchronization method and frequency, and executing the synchronization operation to ensure that each blockchain subchain has timely access to the latest and complete data. Performing random mutual trust verification involves selecting the blockchain subchain range to be verified, preparing the verification rules and standards, setting the random sampling method and sample size, and executing the verification operation to check the data's integrity, accuracy, and compliance with blockchain specifications and consensus mechanisms. For example, assuming the multi-target blockchain data covers electricity usage and transaction details for multiple smart meters during a specific time period, during the parsing phase, key information such as the specific electricity usage and transaction amount in each record is identified. This data is then synchronized to different blockchain subchains, ensuring that each subchain has the same copy of the data. During random mutual trust verification, several smart meter records may be randomly selected from multiple subchains to verify whether their electricity usage and transaction amounts are consistent with the original data and whether they comply with the blockchain's encryption and verification rules.

[0037] In one possible implementation, the multi-target blockchain data is parsed and synchronized to multiple blockchain subchains, and random mutual trust verification is performed. Step S500 further includes step S510, activating the subchain patrol smart meter and synchronizing the stored data set to multiple blockchain subchains for decentralized storage. Specifically, smart meters marked as patrolling are activated. The subchain patrol smart meters are assigned specific tasks and permissions and synchronize the stored data set to multiple blockchain subchains. The synchronization process ensures that each blockchain subchain has access to the same stored data, achieving widespread data distribution and decentralized storage. In this process, data is not dependent on a single central node, but rather is jointly stored and maintained by multiple blockchain subchains, improving data security and reliability. For example, when the subchain patrol smart meters are activated, they begin coordinating data synchronization, simultaneously sending important stored data sets, such as long-term electricity usage statistics, to multiple blockchain subchains, ensuring that the data is backed up on different nodes.

[0038] In step S520, the exchange data set is parsed according to the sub-chain patrol smart meter, and data exchange instructions are generated. The data exchange instructions are then executed based on the transaction smart contract deployed by the target power grid. Specifically, the exchange data set is parsed by the sub-chain patrol smart meter, and through in-depth analysis and understanding of the data, key information and requirements are extracted to generate corresponding data exchange instructions. The instructions are intended to achieve data exchange and sharing between different block sub-chains or with external systems. Based on the transaction smart contract pre-deployed by the target power grid, these data exchange instructions are executed strictly in accordance with the rules and conditions in the contract to ensure the legality, accuracy and security of the data exchange. For example, the sub-chain patrol smart meter parses that a block sub-chain needs to provide electricity transaction data for a specific time period to another sub-chain, generates corresponding exchange instructions, and ensures that the data is exchanged accurately under the specified conditions based on the transaction smart contract.

[0039] Step S530: Based on the shared ledgers of the multiple block subchains, a plurality of groups of the block subchains are randomly selected for data verification, wherein the data verification includes consensus verification and hash verification. Specifically, the shared ledgers shared by the multiple block subchains are used to determine the multiple groups of block subchains to be verified by random selection. The purpose of data verification is to ensure the integrity, consistency and accuracy of the data. Consensus verification is used to check whether the understanding and recording of the data by each block subchain are consistent; hash verification determines whether the data has been tampered with by comparing the hash value of the data. For example, several groups are randomly selected from the numerous block subchains, and the data stored in them are subjected to consensus verification to confirm whether everyone has the same opinion on a certain power transaction record. By calculating and comparing the hash value of the data, it is ensured that the data has not been illegally modified during transmission and storage.

[0040] In one possible implementation, the multi-target blockchain data is parsed, and the multi-target blockchain data is synchronized to multiple block sub-chains, and random mutual trust verification is performed. Step S500 further includes step S540, which defines the rotation update constraints based on the security requirements of the target power grid, wherein the rotation update constraints include adaptive topology constraints, patrol cycle constraints, and patrol cooling constraints. Specifically, the security requirements of the target power grid are evaluated and analyzed. The security requirements involve aspects such as confidentiality, integrity, availability of data, and stability and reliability of the system. The constraints for rotation update are defined. The adaptive topology constraint means that when a new smart meter is detected to be connected to the target power grid, the system needs to update the rotation configuration accordingly to ensure that the newly connected meter can be reasonably included in the rotation system to ensure comprehensive data collection and processing. The patrol cycle constraint stipulates the replacement cycle of the sub-chain patrol smart meter. For example, a new patrol meter needs to be reselected at certain intervals to avoid fatigue or failure caused by a certain meter taking on patrol tasks for a long time. The patrol cooling constraint stipulates that after a certain number of patrol cycles, a sub-chain patrol smart meter cannot be re-elected for the next several cycles. This allows a certain "cooling-off" period to balance the usage frequency and load of each meter. For example, if the target grid's security policy requires that newly connected meters be quickly monitored, the adaptive topology constraint will be triggered, and the system will automatically adjust the patrol settings. Also, assuming the patrol cycle is set to one week, the patrol smart meter will be replaced every week. If the patrol cooling constraint is set to three cycles, then after completing one patrol cycle, a meter must wait at least three weeks before being re-elected as a patrol meter.

[0041] Step S550, based on the rotation update constraint, updates the multiple sub-chain patrol smart meters corresponding to the multiple block sub-chains. Specifically, according to the previously defined rotation update constraint conditions, the sub-chain patrol smart meters marked in the multiple block sub-chains are updated, and the operation status of the power grid and the satisfaction of various constraint conditions are continuously monitored. When the conditions for updating are met, such as the end of the patrol cycle or the detection of a new meter connection, a new sub-chain patrol smart meter will be reselected and marked according to the constraint rules. Through the update mechanism, it is ensured that the selection of patrol meters always meets the security requirements and operation status of the power grid, thereby improving the stability and reliability of the entire system. For example, when the patrol cycle of one week ends, the system will re-select new sub-chain patrol smart meters from the smart meters in each block sub-chain according to the current power grid topology, the operation status of the meter, and the patrol cooling constraint conditions, so as to ensure the continuous effectiveness and optimization of the patrol work.

[0042] The embodiment of the present application uses interaction with the target power grid to obtain multiple smart meters, extracts their intrinsic characteristics and historical data groups to generate a block sub-chain and marks the patrol meters, activates the meters based on the block sub-chain to collect power energy data (including usage, transaction and source data), uses the meters as edge computing units to process data locally to determine multi-target blockchain data, parses and synchronizes the data to the block sub-chain, and performs random mutual trust verification, thereby achieving the technical effect of improving the management level and utilization efficiency of power energy by building an integrated power energy management system with high data security, efficient local processing, good device interaction and intelligent sustainability.

[0043] In the above, refer to Figure 1 The blockchain electric meter control method based on edge computing according to an embodiment of the present invention is described in detail. Figure 2 The present invention describes a blockchain electricity meter control system based on edge computing according to an embodiment of the present invention.

[0044] The edge computing-based blockchain meter control system according to an embodiment of the present invention is designed to address the technical issues of existing smart meters, such as their inability to ensure energy data credibility, privacy protection, and traceability, their reliance on cloud processing, which results in significant data transmission delays, slow responses, heavy cloud load, and a lack of local processing capabilities. By building an intelligent and sustainable integrated power energy management system with high data security, efficient local processing, good device interaction, and intelligent sustainability, the system achieves the technical effect of improving the management level and utilization efficiency of power energy. The edge computing-based blockchain meter control system includes: a smart meter acquisition module 10, a grouping module 20, a power energy data collection module 30, a local processing module 40, and a mutual trust verification module 50.

[0045] The smart meter acquisition module 10 is used to interact with the target power grid and acquire multiple smart meters;

[0046] The grouping module 20 is used to extract the intrinsic operating characteristics and historical operating data of multiple smart meters, perform grouping, and generate multiple block subchains, wherein the multiple block subchains are correspondingly marked with multiple subchain patrol smart meters;

[0047] The electric energy data collection module 30 is used to activate multiple smart meters to collect electric energy data based on multiple block sub-chains, wherein the electric energy data includes electric energy usage data, electric energy transaction data and electric energy source data;

[0048] The local processing module 40 is used to process the electric energy data locally using the multiple smart meters as multiple edge computing units, and determine multi-target blockchain data based on the local processing results;

[0049] The mutual trust verification module 50 is used to parse the multi-target blockchain data, synchronize the multi-target blockchain data to multiple block sub-chains, and perform random mutual trust verification.

[0050] The specific configuration of the grouping module 20 will be described in detail below. As described above, the intrinsic operating characteristics and historical operating data of multiple smart meters are extracted, grouped and divided, and multiple block sub-chains are generated, wherein the multiple block sub-chains correspond to multiple sub-chain patrol smart meters. The grouping module 20 further includes: a computing power coefficient set acquisition unit, which is used to evaluate the computing power of multiple smart meters based on the intrinsic operating characteristics and obtain a computing power coefficient set; a statistical analysis unit, which is used to parse the historical operating data, statistically analyze the operating load rate and operating calculation amount of multiple smart meters, and output A typical load data set; a relationship group generation unit, the relationship group generation unit is used to perform pairing based on the topological relationship spectrum of multiple smart meters to generate multiple direct connection relationship groups, and based on the historical operation data, evaluate the connection delay of the multiple direct connection relationship groups to obtain a delay information set; an adaptive group division unit, the adaptive group division unit is used to perform adaptive group division according to preset grouping constraints, combined with the computing power coefficient set, the typical load data set and the delay information set, wherein the grouping constraints include meter quantity constraints, load constraints, and delay extreme value constraints.

[0051] Among them, the grouping module 20 can further include: an operator construction unit, which is used to generate a sample evaluation data set based on the edge computing requirements of the target power grid and construct a patrol evaluation operator; a patrol index set acquisition unit, which is used to traverse multiple smart meters in the first block sub-chain, perform patrol evaluation based on the patrol evaluation operator, and obtain a patrol index set; a meter sequence generation unit, which is used to serialize multiple smart meters according to the patrol index set, and extract the first N smart meters to generate an alternative meter sequence; a random selection unit, which is used to activate a random number generator, perform random selection in the alternative meter sequence, and determine the sub-chain patrol smart meter.

[0052] The specific configuration of the local processing module 40 will be described in detail below. As described above, the multiple smart meters are used as multiple edge computing units to perform local processing on the electric energy data, and based on the local processing results, the multi-target blockchain data is determined. The local processing module 40 further includes: a preprocessing unit, which is used to obtain the electric energy data in real time and perform local preprocessing; a data set extraction unit, which is used to perform real-time data processing and analysis through multiple edge computing units based on the preprocessing results, and extract the storage data set and the exchange data set based on the processing and analysis results, wherein the exchange data set includes transaction exchange data and collaborative control exchange data; a blockchain data output unit, which is used to output the storage data set and the exchange data set as multi-target blockchain data.

[0053] Wherein, based on the preprocessing results, real-time data processing and analysis are performed through multiple edge computing units, and based on the processing and analysis results, storage data sets and exchange data sets are extracted, wherein the exchange data sets include transaction exchange data and collaborative control exchange data, and the data set extraction unit further includes: a storage data set output subunit, the storage data set output subunit is used to extract the electric energy usage data, calculate and obtain the power usage, power usage time and usage device information of multiple smart meters, and output it as the storage data set; an exchange data generation subunit, the exchange data generation subunit is used to extract the electric energy transaction data, perform natural language recognition, extract the electric energy transaction condition set, and convert the electric energy transaction condition set based on the preset same encoding format to generate the transaction exchange data; a collaborative control exchange data output subunit, the collaborative control exchange data output subunit is used to perform correlation analysis based on the electric energy usage data and the electric energy source data, and based on the correlation analysis result, associate and combine the electric energy usage data and the electric energy source data to output the collaborative control exchange data.

[0054] The specific configuration of the mutual trust verification module 50 will be described in detail below. As described above, the multi-target blockchain data is parsed, and the multi-target blockchain data is synchronized to multiple block sub-chains, and random mutual trust verification is performed. The mutual trust verification module 50 further includes: a centralized storage unit, the centralized storage unit is used to activate the sub-chain patrol smart meter, synchronize the stored data set to multiple block sub-chains, and perform decentralized storage; a data exchange instruction generation unit, the data exchange instruction generation unit is used to parse the exchange data set according to the sub-chain patrol smart meter, generate data exchange instructions, and execute the data exchange instructions based on the transaction smart contract deployed by the target power grid; a data verification unit, the data verification unit is used to randomly select multiple groups of the block sub-chains for data verification based on the shared ledgers of the multiple block sub-chains, wherein the data verification includes consensus verification and hash verification.

[0055] Among them, the mutual trust verification module 50 further includes: an update constraint unit, which is used to define a patrol update constraint based on the security requirements of the target power grid, wherein the patrol update constraint includes an adaptive topology constraint, a patrol cycle constraint and a patrol cooling constraint; a patrol smart meter unit, which is used to update multiple sub-chain patrol smart meters corresponding to the marked blocks based on the patrol update constraint.

[0056] The blockchain electricity meter control system based on edge computing provided by an embodiment of the present invention can execute the blockchain electricity meter control method based on edge computing provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0057] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0058] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. The blockchain electric meter control method based on edge computing is characterized by: The method comprises: Interact with the target grid and acquire multiple smart meters; Extracting intrinsic operating characteristics and historical operating data of multiple smart meters, grouping and dividing them, and generating multiple block subchains, wherein the multiple block subchains are correspondingly marked with multiple subchain patrol smart meters; Based on the plurality of block subchains, a plurality of smart meters are activated to collect electric energy data, wherein the electric energy data includes electric energy usage data, electric energy transaction data, and electric energy source data; Using the plurality of smart meters as a plurality of edge computing units, the electric energy data is processed locally, and based on the local processing results, multi-target blockchain data is determined; Parsing the multi-target blockchain data, synchronizing the multi-target blockchain data to the multiple block subchains, and performing random mutual trust verification; Extract the intrinsic operating characteristics and historical operating data of multiple smart meters and group them, including: Performing computing power evaluation on multiple smart meters based on the intrinsic operating characteristics to obtain a computing power coefficient set; Parsing the historical operation data, statistically analyzing the operation load rate and operation calculation amount of multiple smart meters, and outputting the data as a typical load data set; Based on the topological relationship spectrum of the plurality of smart meters, pairing is performed to generate a plurality of direct connection relationship groups, and based on the historical operation data, the connection delays of the plurality of direct connection relationship groups are evaluated to obtain a delay information set; According to the preset grouping constraints, the computing power coefficient set, the typical load data set and the delay information set are combined to perform adaptive grouping. The grouping constraints include meter quantity constraints, load constraints and delay extreme value constraints. The meter quantity constraint is the number of meters included in each group. The load constraint is the maximum data throughput that each group can carry. The delay extreme value constraint is the maximum value allowed for the delay between the two smart meters with the longest logical path in the block subchain.

2. The blockchain electric meter control method based on edge computing according to claim 1 is characterized in that: Extract the intrinsic operating characteristics and historical operating data of multiple smart meters and group them. This also includes: Based on the edge computing requirements of the target power grid, a sample evaluation data set is generated and a patrol evaluation operator is constructed; Traversing multiple smart meters in the first block subchain, performing patrol evaluation based on the patrol evaluation operator, and obtaining a patrol index set; According to the patrol index set, multiple smart meters are serialized, and the top N smart meters are extracted to generate a candidate meter sequence; Activate the random number generator, randomly select from the candidate meter sequence, and determine the sub-chain patrol smart meter.

3. The blockchain electric meter control method based on edge computing according to claim 2 is characterized in that: The plurality of smart meters are used as a plurality of edge computing units to perform local processing on the electric energy data, including: Acquire the electric power energy data in real time and perform local pre-processing; Based on the preprocessing results, real-time data processing and analysis are performed through multiple edge computing units, and based on the processing and analysis results, a storage data set and an exchange data set are extracted, wherein the exchange data set includes transaction exchange data and collaborative control exchange data; The storage data set and the exchange data set are output as multi-target blockchain data.

4. The blockchain electric meter control method based on edge computing according to claim 3 is characterized in that: Based on the preprocessing results, real-time data processing and analysis are performed through multiple edge computing units, including: Extracting the electric energy usage data, calculating and obtaining the power usage, power usage duration, and usage device information of multiple smart meters, and outputting the data as the stored data set; Extracting the electric energy transaction data, performing natural language recognition, extracting the electric energy transaction condition set, and converting the electric energy transaction condition set based on a preset uniform coding format to generate the transaction exchange data; A correlation analysis is performed based on the power usage data and the power source data, and based on the correlation analysis result, the power usage data and the power source data are associated and combined to output the collaborative control exchange data.

5. The blockchain electric meter control method based on edge computing according to claim 4 is characterized in that: Parsing the multi-target blockchain data, synchronizing the multi-target blockchain data to multiple block subchains, and performing random mutual trust verification, including: Activate the sub-chain patrol smart meter and synchronize the stored data set to multiple block sub-chains for decentralized storage; According to the sub-chain patrol smart meter, the exchange data set is parsed, a data exchange instruction is generated, and the data exchange instruction is executed based on the transaction smart contract deployed by the target power grid; Based on the shared ledgers of the plurality of block subchains, a plurality of groups of the block subchains are randomly selected for data verification, wherein the data verification includes consensus verification and hash verification.

6. The blockchain electric meter control method based on edge computing according to claim 1 is characterized in that: The method further comprises: Based on the security requirements of the target power grid, the rotation update constraints are defined, wherein the rotation update constraints include adaptive topology constraints, patrol cycle constraints and patrol cooling constraints; Based on the rotation update constraint, multiple sub-chain patrol smart meters corresponding to the multiple block sub-chains are updated.

7. The blockchain electricity meter control system based on edge computing is characterized by: The system is used to implement the blockchain electricity meter control method based on edge computing according to any one of claims 1 to 6, and the system includes: A smart meter acquisition module, which is used to interact with the target power grid and acquire multiple smart meters; A grouping module, which is used to extract the intrinsic operating characteristics and historical operating data of multiple smart meters, perform grouping, and generate multiple block subchains, wherein the multiple block subchains are correspondingly marked with multiple subchain patrol smart meters; An electric energy data collection module, which is used to activate multiple smart meters to collect electric energy data based on multiple block subchains, wherein the electric energy data includes electric energy usage data, electric energy transaction data, and electric energy source data; An on-site processing module, configured to process the electric energy data on-site using the plurality of smart meters as a plurality of edge computing units, and determine multi-target blockchain data based on the on-site processing results; A mutual trust verification module is used to parse the multi-target blockchain data, synchronize the multi-target blockchain data to multiple block sub-chains, and perform random mutual trust verification.

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