Electric energy transaction data matching method, device and equipment and storage medium

Abstract

The application relates to the technical field of energy scheduling, and discloses an electric energy transaction data matching method, device, equipment and storage medium. The method comprises the following steps: performing data matching operation on the power selling intention data and the power buying intention data of a local node; when the proportion of the power selling intention data and / or the power buying intention data that fails to be successfully matched for the first time in the local node exceeds a preset threshold, performing data matching operation on the power selling intention data and the power buying intention data that fails to be successfully matched for the first time in both the local node and neighbor nodes of the local node; and performing data matching operation on the power selling intention data and / or the power buying intention data that fails to be successfully matched for the second time in the local node and the power selling intention data and / or the power buying intention data in a target node. The application can reduce the time cost and communication consumption cost in the electric energy transaction process.

Description

Technical Field

[0001] This application relates to the field of energy dispatching technology, and in particular to a method, apparatus, equipment and storage medium for matching electricity trading data. Background Technology

[0002] With the introduction of the concept of the energy internet, consumers in the power grid have gradually become prosumers.

[0003] In related technologies, electricity purchased by consumers from power plants must be transferred through a third party before it reaches them, and consumers bear the electricity costs, including the energy lost on the power lines they consume. However, current electricity trading data matching methods suffer from technical problems such as high latency, insufficient privacy protection, and rigid pricing. How to provide a low-cost, low-latency localized electricity trading mechanism has become a critical technical problem that urgently needs to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, device, and storage medium for matching electricity trading data, which can reduce the time and communication costs in the electricity trading process.

[0005] This application provides a method for matching electricity trading data, including:

[0006] A data matching operation is performed on the electricity sales intention data and electricity purchase intention data of the local node to identify the electricity sales intention data and / or electricity purchase intention data that failed to match initially in the local node; the electricity sales intention data is the data containing information about available electricity that the user publishes to the edge gateway, and the electricity purchase intention data is the data containing information about needing to purchase electricity that the user publishes to the edge gateway.

[0007] When the proportion of unsuccessful initial matching of electricity sales intention data and / or electricity purchase intention data in the local node exceeds a preset threshold, a data matching operation is performed on the unsuccessful initial matching of electricity sales intention data and electricity purchase intention data in both the local node and its neighboring nodes to determine the unsuccessful matching of electricity sales intention data and / or electricity purchase intention data in the local node.

[0008] A data matching operation is performed on the electricity sales intention data and / or electricity purchase intention data that failed to match again in the local node and the electricity sales intention data and / or electricity purchase intention data in the target node; the target node is a non-neighbor node of the local node and its network latency performance, node load performance and data matching performance all meet the preset performance conditions; the local node, the neighbor node and the non-neighbor node are all edge gateways.

[0009] In some embodiments, prior to the data matching operation on the electricity sales intention data and electricity purchase intention data for the local node, the method further includes:

[0010] Obtain electricity sales intention data and / or electricity purchase intention data uploaded by user nodes;

[0011] The electricity sales intention data and / or the electricity purchase intention data are subjected to hash transformation processing to generate a hash representation of the electricity sales intention data and / or a hash representation of the electricity purchase intention data;

[0012] The hash representation is noise-added using the Laplace mechanism, and corresponding time and user information are added to obtain the electricity sales intention data and / or electricity purchase intention data of the local node.

[0013] In some embodiments, matching the electricity sales intention data and the electricity purchase intention data includes:

[0014] When the transaction price range information of the electricity sales intention data and the electricity purchase intention data overlaps, the electricity sales intention data and the electricity purchase intention data are matched according to the time sequence information of the electricity purchase intention data, and temporary transaction contract data is generated.

[0015] The transaction price information corresponding to the temporary transaction contract data is determined based on the power generation and energy storage information in the electricity sales intention data and the electricity demand information in the electricity purchase intention data.

[0016] In some embodiments, the formula for calculating the transaction price information corresponding to the temporary transaction contract data is as follows:

[0017] ,

[0018] in, For the transaction price information at time t, For the electricity demand information at time t, For the power generation information at time t, This provides basic electricity grid price information at time t. This is time period information. For energy storage information at time t, , and These are the learnable coefficients, obtained by minimizing the target deviation information, which is fitted by the deviation information between the electricity demand information and the power generation information, and the deviation information between the transaction price information and the predicted price information.

[0019] In some embodiments, the method for selecting the target node includes:

[0020] Candidate nodes are selected from the non-neighbor nodes of the local node whose network latency performance, node load performance, and data matching performance all meet the preset performance conditions.

[0021] The priority weight information of the candidate nodes is determined based on the network latency performance, the node load performance, and the data matching performance, so as to select the target node.

[0022] In some embodiments, the electricity trading data matching method further includes:

[0023] Record the electricity sales intention data and / or electricity purchase intention data that failed to match three times in the local node. When performing data matching operation on the electricity sales intention data and electricity purchase intention data of the local node next time, prioritize the recorded electricity sales intention data and / or electricity purchase intention data for data matching operation.

[0024] In some embodiments, the electricity trading data matching method further includes:

[0025] After successfully matching the electricity sales intention data and the electricity purchase intention data, a transaction notification data is sent to the user; the transaction notification data includes transaction electricity information, transaction price information, transaction time information, and node signature information;

[0026] In response to the transaction confirmation data returned by the user, the transaction notification data, the user's identity data, and the authentication data are encrypted and stored in the memory of the local section.

[0027] This application embodiment also provides an electricity trading data matching device, including:

[0028] The first module is used to perform data matching operations on the electricity sales intention data and electricity purchase intention data of local nodes, and to determine the electricity sales intention data and / or electricity purchase intention data that failed to match successfully in the first match in the local nodes; the electricity sales intention data is data containing information on available electricity that users publish to the edge gateway, and the electricity purchase intention data is data containing information on electricity that users publish to the edge gateway.

[0029] The second module is used to perform a data matching operation on the unsuccessful electricity sales intention data and / or electricity purchase intention data in the local node and its neighboring nodes when the proportion of unsuccessful electricity sales intention data and / or electricity purchase intention data in the local node exceeds a preset threshold proportion, and to determine the unsuccessful electricity sales intention data and / or electricity purchase intention data in the local node that are matched again.

[0030] The third module is used to perform data matching operations on the unsuccessfully matched electricity sales intention data and / or electricity purchase intention data in the local node and the electricity sales intention data and / or electricity purchase intention data in the target node; the target node is a non-neighbor node of the local node and its network latency performance, node load performance and data matching performance all meet the preset performance conditions, and the local node, the neighbor node and the non-neighbor node are all edge gateways.

[0031] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described electricity trading data matching method.

[0032] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described electricity trading data matching method.

[0033] The beneficial effects of this application are as follows: By constructing a three-level matching mechanism at the edge gateway layer, the first step is to perform data matching operations on the electricity sales intention data and electricity purchase intention data of the local node. Then, data matching operations are performed on the electricity sales intention data and electricity purchase intention data of the local node and its neighboring nodes that failed to match initially. Finally, data matching operations are performed on the electricity sales intention data and / or electricity purchase intention data that failed to match again in the local node and the electricity sales intention data and / or electricity purchase intention data in the target node. This ensures that most data matching operations are limited to the local node or its neighboring nodes, with remote matching only performed on a small number of persistently unmatched intention data. This layered processing mode reduces the amount of cross-regional data transmission. Simultaneously, through a preset performance condition mechanism based on performance indicators, it ensures that remote matching only occurs between nodes with sufficient resource guarantees, avoiding resource waste caused by invalid communication, and reducing time and communication costs in the electricity trading process. Attached Figure Description

[0034] Figure 1 This is an application environment diagram of the electricity trading data matching method provided in the embodiments of this application.

[0035] Figure 2 This is a flowchart of the electricity trading data matching method provided in the embodiments of this application.

[0036] Figure 3 This is a schematic diagram of the structure of the power trading data matching device provided in the embodiments of this application.

[0037] Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and drawings are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0041] The information, data, and signals involved in the embodiments of this application are all authorized by the relevant parties or fully authorized by all parties, and the collection, use, and processing of the relevant data comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0042] Figure 1 This is an application environment diagram of the electricity trading data matching method provided in the embodiments of this application. (See also...) Figure 1This electricity trading data matching method is applied to an electricity trading data matching system. The system includes a user terminal 110 and an edge gateway 120. The user terminal 110 and edge gateway 120 are connected via a local area network. The user terminal 110 can be a desktop terminal or a mobile terminal; the mobile terminal can be at least one of a mobile phone, tablet, or laptop. Multiple edge gateways 120 are connected via a network to form a distributed edge gateway network. The user terminal 110 is used to send electricity sales intention data and / or electricity purchase intention data to the edge gateway 120. Edge gateway 120 is used to perform data matching operations on the electricity sales intention data and electricity purchase intention data of local nodes. It identifies the electricity sales intention data and / or electricity purchase intention data that failed to match initially in the local nodes. When the proportion of the electricity sales intention data and / or electricity purchase intention data that failed to match initially in the local nodes exceeds a preset threshold, it performs data matching operations on the electricity sales intention data and electricity purchase intention data that failed to match initially in both the local nodes and the neighboring nodes of the local nodes. It identifies the electricity sales intention data and / or electricity purchase intention data that failed to match again in the local nodes. It performs data matching operations on the electricity sales intention data and / or electricity purchase intention data that failed to match again in the local nodes and the electricity sales intention data and / or electricity purchase intention data in the target nodes. Among them, the electricity sales intention data is the data containing information about sellable electricity published by users to the edge gateway, and the electricity purchase intention data is the data containing information about electricity to be purchased published by users to the edge gateway. The target node is a non-neighbor node of the local node and the network latency performance, node load performance and data matching performance all meet the preset performance conditions. The local node, neighbor node and non-neighbor node are all edge gateways 120.

[0043] Figure 2 This is a flowchart of the electricity trading data matching method provided in an embodiment of this application. (See attached document.) Figure 2 In some embodiments, the method includes, but is not limited to, steps S201 to S203.

[0044] In this embodiment, the entity executing the electricity trading data matching method is the local node.

[0045] Step S201: Perform a data matching operation on the electricity sales intention data and electricity purchase intention data of the local node to identify the electricity sales intention data and / or electricity purchase intention data that failed to match successfully in the first attempt in the local node.

[0046] Electricity sales intention data refers to data published by users to the edge gateway containing information about available electricity for sale, while electricity purchase intention data refers to data published by users to the edge gateway containing information about the electricity they wish to purchase. Electricity sales intention data is data published by users with electricity sales intentions to the edge gateway through their electricity consumption terminals, containing information about available electricity, such as the amount of electricity available, the desired price range, and the effective time period for the electricity. Electricity purchase intention data is data published by users with electricity purchase intentions to the edge gateway through their electricity consumption terminals, containing information about the electricity they wish to purchase, such as the amount of electricity needed, the desired price range, and the time period for the electricity demand.

[0047] The local node first performs a data matching operation on its own electricity sales intention data and electricity purchase intention data. It matches pairs of electricity sales intention data and electricity purchase intention data that meet the requirements of the matching operation, using the successfully matched data to form a preliminary transaction contract. After traversing all electricity sales intention data and all electricity purchase intention data in the local node, the remaining electricity sales intention data and / or electricity purchase intention data that have not been matched are the electricity sales intention data and / or electricity purchase intention data that failed to match initially in the local node.

[0048] Step S202: When the proportion of unsuccessful electricity sales intention data and / or unsuccessful electricity purchase intention data in the local node exceeds a preset threshold, a data matching operation is performed on the unsuccessful electricity sales intention data and unsuccessful electricity purchase intention data in both the local node and its neighboring nodes to determine the unsuccessful electricity sales intention data and / or unsuccessful electricity purchase intention data in the local node.

[0049] After identifying the unsuccessful initial matching of electricity sales intentions and / or purchase intentions in the local node, if there is a supply-demand imbalance at the local node (i.e., the proportion of unsuccessful initial matching of electricity sales intentions and / or purchase intentions in the local node exceeds a preset threshold), the local node communicates with its neighboring nodes to perform data matching operations on the unsuccessful initial matching of electricity sales intentions and purchase intentions in both the local node and its neighboring nodes. The electricity sales intentions and purchase intentions that meet the conditions for data matching are matched pairwise to form preliminary transaction contracts. After traversing all electricity sales intentions and all electricity purchase intentions in both the local node and its neighboring nodes, the remaining unmatched electricity sales intentions and / or purchase intentions in the local node are the unsuccessful matching of electricity sales intentions and / or purchase intentions in the local node.

[0050] Step S203: Perform a data matching operation on the unsuccessful electricity sales intention data and / or electricity purchase intention data in the local node and the electricity sales intention data and / or electricity purchase intention data in the target node.

[0051] The target node is a non-neighbor node of the local node, and its network latency, node load, and data matching performance all meet preset performance conditions. Network latency refers to the round-trip time between nodes, which can be measured using the PING command to ensure real-time cross-node matching. Node load performance refers to the current utilization of computing resources, which can be obtained through the system monitoring interface to prevent high-load nodes from becoming performance bottlenecks. Data matching performance refers to the historical transaction matching success rate, which can be calculated from statistical database records to select high-efficiency nodes.

[0052] After identifying unsuccessful electricity sales intentions and / or purchase intentions in the local node, the local node selects a target node from its multiple non-neighboring nodes based on preset performance conditions. The local node communicates with the target node to perform data matching operations on the unsuccessful electricity sales intentions and / or purchase intentions in the local node and the target node. Electricity sales intentions and purchase intentions that meet the requirements of the matching operation are matched pairwise to form a preliminary transaction contract. Thus, by constructing a three-level matching mechanism, most data matching operations are limited to the local node or its neighboring nodes, with remote matching only performed on a small number of persistently unmatched intentions. This hierarchical processing mode reduces cross-regional data transmission volume, and the preset performance condition mechanism based on performance indicators ensures that remote matching only occurs between nodes with sufficient resources, avoiding resource waste caused by ineffective communication.

[0053] Local nodes, neighbor nodes, and non-neighbor nodes are all edge gateways. Local nodes are edge gateways deployed within the local area, typically implemented using embedded systems with data caching and computing capabilities, used to process user electricity sales and purchase intention data within the local area. Neighbor nodes are edge gateways deployed in adjacent areas and have direct communication connections with local nodes, automatically identified through network topology discovery protocols, used to build low-latency local matching networks. Non-neighbor nodes are edge gateways deployed outside adjacent areas and do not have direct communication connections with local nodes, automatically identified based on several neighbor nodes through network topology discovery protocols, used to build low-latency local matching networks. Edge gateway deployment is achieved through the "Community Deployment Suite." First, the edge gateway automatically performs hardware pre-checks, meeting the following conditions: CPU ≥ 2 cores, memory ≥ 4GB, storage ≥ 32GB, compatible with ARMv8 / x86 / 64 architecture, dual network port redundancy, and IPv6 protocol. If the check fails, an adaptation report is generated and pushed to the operations and maintenance center. Secondly, the edge gateway is flashed with a pre-installed Ubuntu+Docker image using a one-click process. AppArmor is enabled by default, unnecessary ports are closed, and a unique device identity certificate is issued by the local community PKI to prevent firmware tampering. The pre-installed Ubuntu+Docker image is essentially a "one-click deployment" system disk that already packages the operating system kernel, security modules, and container runtime environment. The image contains all the core service containers required by the platform: intended release, matching engine, dynamic pricing, encrypted storage, security proxy, and operation and maintenance monitoring. It also includes pre-installed DockerCompose or Kubernetes startup scripts, as well as initialization configurations for network, storage, certificates, and TPM2.0 drivers. This means that after flashing this image onto the edge gateway, the device can automatically pull and start all business components in the correct order upon reboot, without requiring manual installation or configuration, thus achieving fast, standardized, and secure one-click deployment. Subsequently, upon first startup, the edge gateway automatically requests an SM4 symmetric key and IPsec certificate from the community PKI. All keys and certificates are encrypted and stored by TPM2.0, and a mandatory two-factor authentication rotation is triggered every seven days or when an abnormal login is detected. Finally, user terminals connect to the gateway via Ethernet or Wi-Fi 6 in a star topology, ensuring latency ≤10ms and bandwidth ≥1Gbps, and forming an independent subnet isolated from the public internet via IPv6. Upon initial connection, user terminals apply for an X.509 certificate containing a unique ID and public key from the community PKI, and complete mutual authentication during the TLS 1.3 handshake. If authentication fails, an alarm is triggered and the connection is closed. Cross-area communication between edge gateways is achieved through IKEv2 negotiation of an IPsec tunnel and SM4 encryption.

[0054] In some embodiments, before performing data matching operations on the electricity sales intention data and electricity purchase intention data of the local node, the method further includes: obtaining the electricity sales intention data and / or electricity purchase intention data uploaded by the user node; performing hash transformation processing on the electricity sales intention data and / or electricity purchase intention data to generate hash representations of the electricity sales intention data and / or electricity purchase intention data; adding noise to the hash representations based on the Laplace mechanism and adding corresponding time information and user information to obtain the electricity sales intention data and / or electricity purchase intention data of the local node.

[0055] Hash transformation refers to converting the original data into a fixed-length hash value using a one-way hash function, such as the SHA-256 algorithm. This transformation eliminates user identity associations in the original data, preventing reverse engineering of transaction content. Laplace noise enhancement adds random noise conforming to a Laplace distribution to the hash value. The noise intensity can be controlled by setting a privacy budget parameter, thus achieving differential privacy protection and preventing correlation analysis of transaction data. Time information refers to the timestamp of data generation or upload, typically recorded in Coordinated Universal Time (UTC) format, used to verify data timeliness in subsequent matching operations. User information refers to de-identified user identifiers, such as using anonymized encoding to generate unique sequences, used to trace data origins without revealing the real identity.

[0056] Before performing data matching operations on electricity sales intention data and electricity purchase intention data at local nodes, the electricity sales intention data and / or electricity purchase intention data uploaded by users through their terminals are first converted into hash values. For example, fields such as electricity price range and electricity demand are generated into irreversible strings using a hash function. Subsequently, Laplace noise is superimposed on the hash values. For example, a random perturbation following a zero-mean distribution is added to each bit of the hash value, making it impossible to correlate and identify data uploaded multiple times by the same user. The noisy hash value is bound to the data generation time and anonymous user identifier. For example, the timestamp and user code are appended as metadata to the end of the hash value, forming a standard data format stored at local nodes. Thus, in subsequent matching operations, nodes only perform transaction matching based on the anonymized hash values, without accessing the original sensitive information. Therefore, through the dual processing of hash conversion and differential privacy noise addition, attackers cannot recover the original content from the stored data. At the same time, the addition of noise disrupts the statistical correlation between data. Furthermore, the embedding of time information prevents expired data from participating in matching; for example, electricity purchase demands that have exceeded their validity period will automatically expire, thereby improving transaction efficiency.

[0057] In one specific embodiment, the hash conversion processing of electricity sales intention data and / or electricity purchase intention data can be implemented using the SHA-256 hash function. The electricity sales intention data and / or electricity purchase intention data uploaded by the user are input into the SHA-256 hash function to generate a hash value of fixed length.

[0058] In some embodiments, the matching operation of electricity sales intention data and electricity purchase intention data includes: when the transaction price range information of the electricity sales intention data and the electricity purchase intention data overlaps, matching the electricity sales intention data and the electricity purchase intention data according to the time series information of the electricity purchase intention data, and generating temporary transaction contract data; determining the transaction price information corresponding to the temporary transaction contract data based on the power generation and energy storage information in the electricity sales intention data and the electricity demand information in the electricity purchase intention data.

[0059] The transaction price range information refers to the price fluctuation range proposed by both the electricity seller and the electricity buyer. This can be stored as a range of numerical pairs. For example, the electricity seller sets a lower limit of 0.5 yuan / kWh and an upper limit of 0.8 yuan / kWh, while the electricity buyer sets a lower limit of 0.6 yuan / kWh and an upper limit of 1.0 yuan / kWh. This price range information is used to filter supply and demand parties with a possibility of transaction, avoiding invalid matching. The time-series information of the electricity purchase intention data refers to the timestamp of the electricity purchase intention data submitted to the edge gateway. This can be recorded using a time-series database. For example, electricity purchase intention data earlier than other data can be marked as high priority. This time-series information ensures the fairness of first-come, first-served transactions and optimizes the matching order. Generation power information refers to the current output power value of the electricity seller. Energy storage information refers to the remaining capacity of the electricity seller's energy storage devices. Electricity demand information refers to the total amount of electricity demanded by the electricity buyer. This can be achieved by real-time data collection from sensors. For example, the instantaneous power output of the photovoltaic power generation system is 5kW, and the remaining capacity of the energy storage battery is 20kWh. These features are used to dynamically calculate transaction prices, reflecting real-time supply and demand relationships.

[0060] When matching electricity sales intention data and electricity purchase intention data, if an overlap is detected between the price ranges of the electricity seller and the price range of the electricity buyer, the system will include both parties with a basis for price negotiation in the candidate matching pool. Subsequently, the electricity purchase intention data is sorted according to the order of submission time, prioritizing the allocation of electricity sales resources to earlier submitted data. After a successful match, temporary transaction contract data containing elements such as the transaction volume and time window is generated. During the calculation of transaction price information, the transaction price is determined based on the power generation and energy storage information in the electricity sales intention data and the electricity demand information in the electricity purchase intention data. Power generation information is used to assess the real-time power supply capacity of the electricity seller; energy storage information is used to determine whether a price adjustment is needed to compensate for energy storage losses; and electricity demand information is used to measure the urgency of the electricity buyer's demand. For example, during peak electricity consumption periods, when power generation is lower than the average load, the transaction price can be dynamically increased based on the degree of supply and demand tension.

[0061] The calculation process for transaction price information corresponding to temporary transaction contract data is constructed into a "learnable linear function," enabling the transaction price information to simultaneously reflect electricity supply and demand factors, transaction time factors, time period factors, and energy storage factors, and to adjust coefficients online to adapt to system changes. In obtaining the learnable linear function, it is assumed that the transaction price information can be linearly approximated by electricity supply and demand factors, transaction time factors, time period factors, and energy storage factors (if nonlinear, this can be extended to basis functions or neural networks). The electricity supply and demand factors, transaction time factors, time period factors, and energy storage factors have been scaled (or their dimensions have been handled in the model). The learning objective is to minimize the weighted quadratic loss of the sum of supply and demand deviations and price prediction deviations.

[0062] In some embodiments, the formula for calculating the transaction price information corresponding to temporary transaction contract data is as follows:

[0063] ,

[0064] in, For the transaction price information at time t, For the electricity demand information at time t, For the power generation information at time t, This provides basic electricity grid price information at time t. This is time period information. For energy storage information at time t, , and These are the learnable coefficients, obtained by minimizing the target deviation information. The target deviation information is fitted by the deviation information between electricity demand information and power generation information, and the deviation information between transaction price information and predicted price information. Based on this, the calculation formula for the transaction price information corresponding to the temporary transaction contract data is based on the electricity supply and demand factors at the transaction time, the transaction period factors, the time period factors, and the energy storage factors to calculate the transaction price information corresponding to the temporary transaction contract data, so that the calculated transaction price information is adapted to the real-time supply and demand relationship.

[0065] When generating temporary trading contract data, the system first acquires current electricity demand, power generation, and energy storage information, and then calculates the trading price in real time by combining this with basic grid price information. Time period information is used to differentiate electricity pricing strategies for different time periods; for example, a higher benchmark price coefficient is used during peak electricity consumption periods. Energy storage information is used to regulate supply and demand balance; when energy storage devices have sufficient remaining capacity, the trading price can be appropriately reduced to promote energy consumption. Learnable coefficients continuously optimize the weighted objective function of supply-demand deviations and price prediction deviations, enabling the trading price to dynamically adapt to market changes. For example, when power generation and electricity demand experience short-term fluctuations, the system automatically adjusts the coefficient weights to balance the supply and demand relationship.

[0066] In some embodiments, the learning process of the learnable coefficients is expressed as follows:

[0067] ,

[0068] ,

[0069] ,

[0070] ,

[0071] in, For target deviation information, These are the weighting coefficients. Total electricity usage time For the predicted price information at time t, For the kth learning iteration , The result of the (k+1)th learning iteration , For the kth learning iteration , The result of the (k+1)th learning iteration , For the kth learning iteration , The result of the (k+1)th learning iteration , The learning rate is used. Based on this, the target deviation information between the transaction price information and the predicted price information is calculated through the expression of the learning process of the learnable coefficients. The learnable coefficients in the calculation formula of the transaction price information corresponding to the above temporary transaction contract data are iteratively updated based on the target deviation information until the target deviation information meets the preset deviation conditions. This makes the transaction price information calculated using the calculation formula of the transaction price information corresponding to the above temporary transaction contract data closer to the predicted price information.

[0072] In some embodiments, a data matching operation is performed on the electricity sales intention data and electricity purchase intention data in a local node, including: inputting the electricity sales intention data and electricity purchase intention data in the local node into a preset matching priority scoring model to calculate the priority score corresponding to the electricity sales intention data and electricity purchase intention data in the local node; determining the priority corresponding to the electricity sales intention data and electricity purchase intention data in the local node based on the priority score; and performing a data matching operation on a number of electricity sales intention data and a number of electricity purchase intention data with the highest current priority. This can improve the efficiency and success rate of data matching operations within the local node. The expression for the matching priority scoring model is:

[0073] ,

[0074] ,

[0075] in, , and All are learnable coefficients. Rate the priority. This is a price range tension indicator, representing how close a user-defined expected trading price is to the median or boundary value of the current trading price. This is an indicator of the urgency of electricity supply and demand, representing the degree of imbalance in the supply and demand relationship of electricity sales intentions or purchase intentions within the current time period. This is a historical credit metric for users, representing their positive contribution to the electricity trading data matching system. , and The settings can be configured using expert experience, or by using data-driven learning, which involves supervising the modeling of historical intention data and training a weighted reorganization that minimizes matching bias using methods such as linear regression or neural networks. Alternatively, a reinforcement learning adjustment method can be used, with matching success rate or response speed as reward feedback, and dynamically updated through a Q-learning policy optimization model or policy gradient algorithm.

[0076] In some embodiments, electricity trading data matching further includes: caching a hot data set; constructing a hot data index structure for the hot data set; when performing data matching operations on electricity sales intention data and electricity purchase intention data in the local node, using a Bloom filter to query the hot data index structure; if the query results indicate that the hot data index structure contains electricity sales intention data and / or electricity purchase intention data that may be suitable for data matching operations, retrieving the corresponding electricity sales intention data and / or electricity purchase intention data from the hot data index structure; otherwise, skipping the hot data index structure. The hot data set includes target intention data, target user intention data, and target data templates. Target intention data refers to electricity sales intention data and / or electricity purchase intention data whose number of unsuccessful matches within the validity period reaches a preset threshold. Target user intention data refers to electricity sales intention data and / or electricity purchase intention data uploaded by users whose activity level reaches a preset activity level threshold. Target data templates are intention data templates whose matching success rate reaches a preset matching success rate threshold. This reduces the amount of data traversed for data matching operations, saves node resources, and improves the efficiency of data matching operations within local nodes.

[0077] In some embodiments, before performing a data matching operation on the unsuccessful initial matching of electricity sales intention data and electricity purchase intention data between the local node and its neighboring nodes, the method further includes: performing node performance evaluation on each neighboring node of the local node, and determining the neighboring node to perform the data matching operation with the local node based on the node performance score.

[0078] The performance of a neighboring node can be evaluated based on its average communication latency, current load, historical successful matches, and current intention data volume. By standardizing the deviations of these parameters from their corresponding extreme values, the greater the deviation, the better the node's performance; conversely, the smaller the deviation, the worse the performance. The node performance score is determined by weighted summation of the standardized scores.

[0079] In some embodiments, the expression for the performance evaluation score of a computing node is:

[0080] ,

[0081] ,

[0082] in, The performance score of the i-th neighbor node is given. Let be the average communication delay of the i-th neighbor node. Let be the maximum communication delay of the i-th neighbor node. Let be the current node load of the i-th neighbor node. Let be the maximum node load of the i-th neighbor node. Let i be the number of successful matches in the history of the i-th neighbor node. Let be the maximum number of successful matches for the i-th neighbor node. Let be the current intention data volume of the i-th neighbor node. Let be the maximum amount of intended data for the i-th neighbor node. , , and All are learnable coefficients.

[0083] By evaluating the performance of each neighboring node of the local node, it can automatically initiate cooperation matching with the neighboring node with the best local performance, thereby further reducing the response time from the failure of matching to the next round of attempts. , , and Static rules can be set, meaning a fixed ratio coefficient can be configured based on the needs of the expected transaction scenario. For example, it can be set in environments with high response speed requirements. = 0.4, = 0.2, = 0.2, = 0.2, or a data-driven optimization approach, which dynamically adjusts the value by performing regression analysis or minimum deviation fitting on historical matching logs. , , and To minimize the error between the overall score and the actual probability of successful matching, or through an adaptive reinforcement learning approach, the neighbor node selection behavior is modeled as a policy learning process, using transaction success rate or response time as reward feedback to automatically optimize. , , and The combination of values ​​allows for adaptive adjustment to different operating environments and load states. Therefore, differentiated settings can be made based on the regional characteristics, time period changes, or node roles of neighboring nodes, thereby improving the accuracy and matching efficiency of neighboring node selection.

[0084] In some embodiments, the method for selecting a target node includes: selecting candidate nodes from each of the non-neighboring nodes of the local node whose network latency performance, node load performance, and data matching performance all meet preset performance conditions; and determining the priority weight information of the candidate nodes based on the network latency performance, node load performance, and data matching performance to select the target node.

[0085] Priority weight information refers to the evaluation value formed by combining network latency performance, node load performance and data matching performance. Specifically, a weighted summation algorithm can be used to convert each performance indicator into a unified scoring standard to quantify and compare the comprehensive capabilities of candidate nodes.

[0086] When selecting a target node, candidate nodes that simultaneously meet the network latency threshold, load pressure threshold, and matching success rate threshold are selected from the set of non-neighbor nodes of the local node. For example, the network latency threshold can be set to 200 milliseconds, and the load pressure threshold can be set to CPU utilization not exceeding 70%. For each candidate node, its network latency performance score, node load performance score, and data matching performance score are calculated separately, and then weighted using preset weight coefficients to generate a priority weight value. Finally, the candidate node with the highest priority weight value is selected as the target node for cross-node data matching operations.

[0087] The priority of candidate nodes can be evaluated based on their network latency, load stress, and data matching performance evaluation values. By standardizing the deviations of the candidate node's network latency and load stress values ​​from their corresponding extreme parameters, a larger deviation indicates a higher priority, and vice versa. The priority weights of the candidate nodes are then determined by weighted summation of the standardized evaluation information and the data matching performance evaluation values.

[0088] In some embodiments, the formula for calculating the priority weight information of candidate nodes is as follows:

[0089] ,

[0090] in, This refers to the priority and weight information of candidate nodes. This represents the network latency value. This is the network latency threshold. This is the load pressure value. The load pressure threshold, To match performance evaluation values ​​to the data, , and All of these are weighting coefficients. Based on this, the priority weight information of a candidate node is determined by the deviation between its network latency value and load pressure value and the corresponding extreme value parameter. The greater the deviation from the extreme value parameter, the higher the node priority and the greater the priority weight of the candidate node; conversely, the smaller the deviation, the lower the node priority and the smaller the priority weight of the candidate node.

[0091] In some embodiments, determining the priority weight information of candidate nodes based on network latency performance, node load performance, and data matching performance includes: constructing an intention graph containing each edge gateway; fitting the network latency performance, node load performance, and data matching performance of the graph nodes to the edge weight confidence value of the graph nodes; and selecting graph nodes from the intention graph whose edge weight confidence value exceeds a preset minimum edge weight confidence value and which are not local nodes as target nodes.

[0092] In some embodiments, a data matching operation is performed on the unsuccessfully matched electricity sales intention data and / or electricity purchase intention data in the local node and the electricity sales intention data and / or electricity purchase intention data in the target node, including: sending the unsuccessfully matched electricity sales intention data and / or electricity purchase intention data in the local node to multiple target nodes in parallel; and for each unsuccessfully matched electricity sales intention data and / or electricity purchase intention data, terminating the data matching operation with other target nodes in response to the data matching success information fed back by the first target node.

[0093] Specifically, a local node can select multiple target nodes from among multiple non-neighboring nodes based on network latency performance, node load performance, and data matching performance. It then simultaneously transmits unsuccessfully matched electricity sales intention data and / or electricity purchase intention data from the local node to each target node, awaiting successful data matching information from each target node. For each unsuccessfully matched electricity sales intention data and / or electricity purchase intention data, upon first receiving successful data matching information from a target node, the local node responds to this information, generates temporary transaction contract data, and terminates the data matching operation with other target nodes. For example, if a local node simultaneously sends three sets of unsuccessfully matched electricity sales intention data to three target nodes, and the first set of electricity sales intention data is successfully matched by the first target node and returned to the local node, the local node generates the corresponding temporary transaction contract data and terminates the successful data matching information returned by the second and third target nodes. If the second set of electricity sales intention data is successfully matched by the second target node and returned to the local node, the local node generates the corresponding temporary transaction contract data and terminates the successful data matching information returned by the first and third target nodes. If no data matching success information is received from the target node within the preset waiting time, the unmatched electricity sales intention data and / or electricity purchase intention data in the local node will be sent to multiple target nodes in parallel during the next matching cycle.

[0094] In some embodiments, after successfully matching electricity sales intention data and electricity purchase intention data, the method further includes: generating several candidate transmission paths from the power supply node to the power consumption node; calculating the path cost of each candidate transmission path to obtain the comprehensive path cost of the candidate transmission path; and selecting the candidate transmission path with the minimum comprehensive path cost as the target transmission path from the power supply node to the power consumption node. Specifically, a comprehensive path cost is obtained by weighting and summing multiple objectives (power transmission loss, average node response time, and estimated time for power to reach the user) to a single objective, which facilitates minimization and node selection. The formula for calculating the comprehensive path cost is:

[0095] ,

[0096] ,

[0097] in, For the k-th candidate transmission path, for The overall path cost for Power transmission loss, for The average response time of the nodes, for The estimated time it takes for electricity to reach the user. , , and All are weighting coefficients. Let be the unit energy loss between the i-th hop and the (i+1)-th hop on the candidate transmission path. The target electrical energy requested by the user in the transaction is calculated. Based on this, the power transmission loss, average node response time, and estimated time for power to reach the user of the candidate transmission path are weighted and calculated to obtain the comprehensive path cost of the candidate transmission path. The higher the power transmission loss, average node response time, and estimated time for power to reach the user, the higher the comprehensive path cost, and vice versa.

[0098] After calculating the comprehensive path cost of each candidate transmission path, a power supply command is issued to the power supply nodes on the target transmission path to ensure that electricity is delivered to the power consumption nodes along the target transmission path. If multiple candidate transmission paths satisfy... ( If a preset cost threshold is set, parallel scheduling can be implemented according to the concurrent path sending strategy to achieve redundancy protection and multi-path load balancing. To reduce remote power supply response latency, a virtual power supply proxy node set is used. This set consists of snapshots of available power generation capacity pre-registered by some power supply nodes on the local node. These snapshots include the node number, available power supply energy, price range, and available power supply time period of the power supply node. The local node can display supply and demand hotspots based on a predictive heat map model and prioritize the activation of potential power supply nodes, enabling them to preheat, pre-communicate, and pre-configure, thereby completing power supply preparation before matching is triggered and compressing the response cycle.

[0099] In some embodiments, the data matching operation for unsuccessfully matched electricity sales intention data and / or electricity purchase intention data in the local node and the electricity sales intention data and / or electricity purchase intention data in the target node is implemented using a reinforcement learning algorithm. Specifically, a state space describing the current environmental state is constructed. Describe the action space of the matching order strategy. And a reward function based on factors such as matching success rate, response latency, and bandwidth consumption. The matching order strategy is , Let be all permutations of the candidate set. An optimal policy is obtained through a reinforcement learning algorithm, maximizing the expected long-term reward and dynamically adapting to the optimal matching path under different conditions. The expression for obtaining the optimal policy is:

[0100] ,

[0101] in, The optimal strategy is... This is the discount factor.

[0102] In some embodiments, the electricity trading data matching method further includes: recording the electricity sales intention data and / or electricity purchase intention data that failed to match three times in the local node, and prioritizing the recording electricity sales intention data and / or electricity purchase intention data when performing the next data matching operation on the electricity sales intention data and electricity purchase intention data of the local node.

[0103] After traversing all electricity sales intention data and all electricity purchase intention data in both the local node and the target node, the remaining unmatched electricity sales intention data and / or electricity purchase intention data in the local node are those that failed to match three times consecutively. When electricity sales intention data and / or electricity purchase intention data fail to match three times consecutively in the local node, its neighboring nodes, and the target node, the local node marks it and stores it in a specific database or cache area. At the start of the next matching cycle, the matching engine prioritizes scanning the historical unmatched data in this database and cross-compares it with the newly added intention data in the current cycle. For example, the recorded intention data can be placed at the front of the matching queue, or the associated transaction object can be quickly located through a hash index, thereby reducing the computational overhead caused by repeated traversal. This can significantly improve the processing efficiency of long-term unmatched intention data, avoid the waste of system resources caused by repeated traversal, and shorten the retention time of high-failure data through the priority matching mechanism, thereby optimizing the overall transaction throughput and response speed of the local node.

[0104] In some embodiments, the electricity trading data matching method further includes: after successfully matching electricity sales intention data and electricity purchase intention data, sending transaction notification data to the user; and in response to the transaction confirmation data returned by the user, encrypting and storing the transaction notification data, the user's identity data, and authentication data in the memory of the local node. The transaction notification data includes transaction electricity information, transaction price information, transaction time information, and node signature information.

[0105] Transaction notification data refers to notification information containing key transaction parameters. This can be implemented using a structured data packet format, such as encapsulating transaction volume, price, time, and node signature into JSON or XML format. This clarifies transaction terms and ensures the integrity of data transmission. Node signature information refers to the edge gateway's digital signature on the transaction notification data. This can be implemented using asymmetric encryption algorithms, such as RSA or ECDSA, to generate the signature and verify the authenticity of the data source and prevent tampering. Encrypted storage refers to cryptographic protection of sensitive data. This can be implemented using symmetric encryption algorithms, such as AES-256, to encrypt identity data and transaction confirmation data. Combined with in-memory storage mechanisms, this reduces disk access latency and prevents unauthorized access. Authentication data refers to the user's credential for confirming the transaction. This can be implemented using one-time tokens or biometric hash values, such as SMS verification codes or fingerprint hashes, to ensure the legitimacy of the transaction confirmation operation.

[0106] Once the electricity sales intention data and electricity purchase intention data are matched, the local node automatically generates transaction notification data containing the transaction amount, price, time, and node signature, and sends it to the user terminal via a preset communication protocol. Upon receiving the notification, the user terminal generates transaction confirmation data by inputting pre-set verification information and returns it to the local node. After receiving the confirmation data, the local node encrypts the transaction notification data, user identity information, and verification data, and stores them directly in its memory area. In this process, memory storage avoids the I / O bottleneck of traditional disk storage, while encryption ensures the security of sensitive information during storage and transmission. For example, transaction time information can be recorded as a timestamp accurate to milliseconds, and the node signature can be generated based on the hash value of the transaction data, thereby reducing storage overhead while ensuring data integrity. Thus, real-time encryption protection of transaction data during the generation stage reduces the risk of privacy leaks due to exposed storage media; the memory storage mechanism reduces the latency of data persistence operations, improves system response efficiency in high-concurrency scenarios, and the combination of node signature and verification data ensures the immutability of transaction records, providing a trusted data foundation for subsequent dispute resolution.

[0107] Please see Figure 3 This application also provides an electricity trading data matching device, which can implement the above-mentioned electricity trading data matching method. The device includes:

[0108] The first module 301 is used to perform data matching operations on the electricity sales intention data and electricity purchase intention data of the local node, and to identify the electricity sales intention data and / or electricity purchase intention data that failed to match successfully in the first match in the local node; the electricity sales intention data is the data containing information on sellable electricity published by the user to the edge gateway, and the electricity purchase intention data is the data containing information on electricity to be purchased published by the user to the edge gateway.

[0109] The second module 302 is used to perform a data matching operation on the unsuccessful electricity sales intention data and / or electricity purchase intention data in the local node and its neighboring nodes when the proportion of unsuccessful electricity sales intention data and / or electricity purchase intention data matched again in the local node exceeds a preset threshold ratio.

[0110] The third module 303 is used to perform data matching operations on the unsuccessful electricity sales intention data and / or electricity purchase intention data in the local node and the electricity sales intention data and / or electricity purchase intention data in the target node; the target node is a non-neighbor node of the local node and the network latency performance, node load performance and data matching performance all meet the preset performance conditions, and the local node, neighbor node and non-neighbor node are all edge gateways.

[0111] The specific implementation of the electricity trading data matching device is basically the same as the specific implementation of the electricity trading data matching method described above, and will not be repeated here.

[0112] Figure 4 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0113] The following reference Figure 4 To describe an electronic device 400 according to such an embodiment of the present disclosure. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0114] like Figure 4 As shown, the electronic device 400 is presented in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, a bus 430 connecting different system components (including storage unit 420 and processing unit 410), a display unit 440, etc.

[0115] The storage unit stores program code, which can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the above-described section of the electricity trading data matching method according to various exemplary embodiments of this disclosure.

[0116] Storage unit 420 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 4201 and / or cache memory 4202, and may further include a read-only memory (ROM) 4203.

[0117] Storage unit 420 may also include a program / utility 4204 having a set (at least one) program module 4205, such program module 4205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0118] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0119] Electronic device 400 can also communicate with one or more external devices 400' (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. Network adapter 460 can communicate with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0120] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described electricity trading data matching method.

[0121] The electricity trading data matching method, apparatus, device, and storage medium provided in this application construct a three-level matching mechanism at the edge gateway layer. First, data matching is performed on the electricity sales intention data and electricity purchase intention data of the local node. Then, data matching is performed on the electricity sales intention data and electricity purchase intention data of the local node and its neighboring nodes that failed to match initially. Finally, data matching is performed on the electricity sales intention data and / or electricity purchase intention data that failed to match again in the local node and the electricity sales intention data and / or electricity purchase intention data in the target node. This restricts most data matching operations to the local node or its neighboring nodes, with remote matching only performed on a small number of persistently unmatched intention data. This layered processing mode reduces the amount of cross-regional data transmission. Simultaneously, through a preset performance condition mechanism based on performance indicators, it ensures that remote matching only occurs between nodes with sufficient resource guarantees, avoiding resource waste caused by invalid communication and reducing time and communication costs in the electricity trading process.

[0122] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this disclosure.

[0123] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0124] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0125] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0126] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. An electric energy transaction data matching method, characterized by, The method comprises the following steps: Data matching operation is performed on the power selling intention data and the power buying intention data of the local node to determine the power selling intention data and / or the power buying intention data that fails to be matched for the first time in the local node; the power selling intention data is the data containing the information of the sellable power energy published by the user to the edge gateway, and the power buying intention data is the data containing the information of the power energy to be purchased published by the user to the edge gateway; When the proportion of the power selling intention data and / or the power buying intention data that fails to be matched for the first time in the local node exceeds a preset threshold, data matching operation is performed on the power selling intention data and the power buying intention data that fails to be matched for the first time in the local node and the neighbor node of the local node to determine the power selling intention data and / or the power buying intention data that fails to be matched for the second time in the local node; Data matching operation is performed on the power selling intention data and / or the power buying intention data that fails to be matched for the second time in the local node and the power selling intention data and / or the power buying intention data in the target node; The target node is a non-neighbor node of the local node, and the network delay performance, the node load performance and the data matching performance all meet the preset performance conditions; the local node, the neighbor node and the non-neighbor node are all edge gateways.

2. The method of claim 1, wherein, Before the data matching operation is performed on the power selling intention data and the power buying intention data of the local node, the method further comprises the following steps: The power selling intention data and / or the power buying intention data uploaded by the user node are acquired; Hash conversion processing is performed on the power selling intention data and / or the power buying intention data to generate the hash representation of the power selling intention data and / or the hash representation of the power buying intention data; Noise processing is performed on the hash representation based on the Laplace mechanism, and corresponding time information and user information are added to obtain the power selling intention data and / or the power buying intention data of the local node.

3. The method of claim 1, wherein, The matching operation on the power selling intention data and the power buying intention data comprises the following steps: When the transaction price interval information of the power selling intention data and the power buying intention data both exists overlap, the power selling intention data and the power buying intention data are matched according to the time sequence information of the power buying intention data, and temporary transaction contract data is generated; The transaction price information corresponding to the temporary transaction contract data is determined according to the power generation power information and the power storage information in the power selling intention data and the power consumption information in the power buying intention data.

4. The method of claim 3, wherein, The calculation formula of the transaction price information corresponding to the temporary transaction contract data is: ， wherein is the transaction price information at time t, is the electricity demand information at time t, is the power generation information at time t, is the grid base price information at time t, is the time period information, is the electricity storage information at time t, , and are learnable coefficients, obtained by minimizing target deviation information, which is fitted from deviation information between the electricity demand information and the power generation information and deviation information between the transaction price information and the predicted price information.

5. The method of claim 1, wherein, The method for selecting the target node comprises the following steps: A candidate node that meets the preset performance conditions in the network delay performance, the node load performance and the data matching performance is selected from each non-neighbor node of the local node; The priority weight information of the candidate node is determined according to the network delay performance, the node load performance and the data matching performance to select the target node.

6. The method of claim 1, wherein, The method further comprises the following steps: Record the unsucessful selling intention data and / or buying intention data in the local node, and prioritize the recorded selling intention data and / or buying intention data for data matching operation next time.

7. The method of claim 1, wherein, Also includes: After successfully matching the selling intention data and the buying intention data, send transaction notification data to the user; The transaction notification data contains transaction power information, transaction price information, transaction time information and node signature information; In response to the transaction confirmation data returned by the user, encrypt and save the transaction notification data, the identity data and the identity verification data of the user in the memory of the local node.

8. An electric energy transaction data matching apparatus characterized by comprising: Includes: A first module for performing data matching operation on the selling intention data and the buying intention data of the local node, and determining the unsucessful selling intention data and / or buying intention data in the local node for the first time; The selling intention data is the data containing the information of the sellable electric energy published by the user to the edge gateway, and the buying intention data is the data containing the information of the electric energy to be purchased published by the user to the edge gateway; A second module for performing data matching operation on the unsucessful selling intention data and buying intention data in the local node and the neighbor node of the local node for the first time when the proportion of the unsucessful selling intention data and buying intention data in the local node for the first time exceeds the preset threshold proportion, and determining the unsucessful selling intention data and / or buying intention data in the local node for the second time; A third module for performing data matching operation on the unsucessful selling intention data and / or buying intention data in the local node for the second time and the selling intention data and / or buying intention data in the target node; The target node is a non-neighbor node of the local node, and the network delay performance, node load performance and data matching performance all meet the preset performance conditions, and the local node, the neighbor node and the non-neighbor node are all edge gateways.

9. An electronic device, comprising: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the power transaction data matching method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the power transaction data matching method of any one of claims 1 to 7.

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