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

Abstract

The invention relates to the technical field of energy scheduling, and discloses an electric energy transaction data matching method and device, equipment and a storage medium. The method comprises the following steps: performing data matching operation on electricity selling intention data and electricity purchasing intention data of a local node; when the proportion of the electricity selling intention data and / or the electricity purchasing intention data which are / is not successfully matched for the first time in the local node exceeds a preset threshold proportion, performing data matching operation on the electricity selling intention data and the electricity purchasing intention data which are not successfully matched for the first time in the local node and a neighbor node of the local node; and carrying out data matching operation on the electricity selling intention data and / or the electricity purchasing intention data which are not successfully matched again in the local node and the electricity selling intention data and / or the electricity purchasing intention data in the target node. According to the embodiment of the invention, the time cost and the communication consumption cost in the electric energy transaction process can be reduced.

Description

Technical Field

[0001] The present application relates to the field of energy scheduling technology, and in particular to a method, device, equipment and storage medium for matching electric energy transaction data. Background Art

[0002] With the introduction of the concept of 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 is delivered. Consumers are responsible for the electricity bill, including any energy lost along the power lines. However, current methods for matching electricity trading data suffer from technical issues such as high latency, insufficient privacy protection, and rigid pricing. Providing a low-cost, low-latency localized electricity trading mechanism has become a critical technical challenge that needs to be addressed. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment and storage medium for matching electric energy transaction data, which can reduce the time cost and communication consumption cost in the process of electric energy transaction.

[0005] The present invention provides a method for matching electric energy transaction data, including: Performing a data matching operation on the electricity selling intention data and electricity purchasing intention data of the local node to determine the electricity selling intention data and / or electricity purchasing intention data in the local node that were not successfully matched initially; the electricity selling intention data is data published by the user to the edge gateway containing information about electricity that can be sold, and the electricity purchasing intention data is data published by the user to the edge gateway containing information about electricity that needs to be purchased; When the proportion of electricity selling intention data and / or electricity purchasing intention data that failed to be matched successfully for the first time in the local node exceeds a preset threshold ratio, a data matching operation is performed on the electricity selling intention data and electricity purchasing intention data that failed to be matched successfully for the first time in both the local node and the neighboring nodes of the local node, and the electricity selling intention data and / or electricity purchasing intention data that failed to be matched successfully again in the local node are determined; A data matching operation is performed on the electricity selling intention data and / or electricity purchasing intention data in the local node that has not been successfully matched again and the electricity selling intention data and / or electricity purchasing 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.

[0006] In some embodiments, before performing the data matching operation on the electricity selling intention data and the electricity purchasing intention data of the local node, the method further includes: Obtain electricity selling intention data and / or electricity purchasing intention data uploaded by user nodes; Performing hash conversion processing on the electricity selling intention data and / or the electricity purchasing intention data to generate a hash representation of the electricity selling intention data and / or a hash representation of the electricity purchasing intention data; The hash representation is subjected to noise processing based on the Laplace mechanism and corresponding time information and user information are added to obtain the electricity selling intention data and / or electricity purchasing intention data of the local node.

[0007] In some embodiments, matching the electricity selling intention data with the electricity purchasing intention data includes: When transaction price range information of the electricity selling intention data and the electricity purchasing intention data overlap, matching the electricity selling intention data with the electricity purchasing intention data according to time series information of the electricity purchasing intention data, and generating temporary transaction contract data; The transaction price information corresponding to the temporary transaction contract data is determined based on the power generation information and power storage information in the power selling intention data and the power demand information in the power purchasing intention data.

[0008] In some embodiments, the calculation formula for the transaction price information corresponding to the temporary transaction contract data is: , in, is the transaction price information at time t, is the power demand information at time t, is the power generation information at time t, is the basic price information of the power grid at time t, is the time period information, is the power storage information at time t, 、 and are respectively learnable coefficients, which are obtained by minimizing target deviation information, where the target deviation information is obtained by fitting the deviation information between the power demand information and the generated power information and the deviation information between the transaction price information and the predicted price information.

[0009] In some embodiments, the method of selecting the target node includes: Selecting, from each non-neighboring node of the local node, a candidate node whose network delay performance, node load performance, and data matching performance all meet the preset performance conditions; The priority weight information of the candidate nodes is determined according to the network delay performance, the node load performance and the data matching performance to select the target node.

[0010] In some embodiments, the electric energy transaction data matching method further includes: The electricity selling intention data and / or electricity purchasing intention data that have not been successfully matched three times in the local node are recorded, and the recorded electricity selling intention data and / or electricity purchasing intention data are preferentially performed when a data matching operation is performed on the electricity selling intention data and electricity purchasing intention data of the local node next time.

[0011] In some embodiments, the electric energy transaction data matching method further includes: After successfully matching the electricity selling intention data and the electricity purchasing intention data, sending transaction notification data to the user; the transaction notification data includes transaction power information, transaction price information, transaction time information and node signature information; 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 node.

[0012] The present application also provides an electric energy transaction data matching device, comprising: The first module is configured to perform a data matching operation on the electricity selling intention data and the electricity purchasing intention data of the local node, and determine the electricity selling intention data and / or electricity purchasing intention data in the local node that were not successfully matched initially; the electricity selling intention data is data published by the user to the edge gateway containing information about available electricity to be sold, and the electricity purchasing intention data is data published by the user to the edge gateway containing information about required electricity to be purchased; The second module is configured to, when the proportion of electricity selling intention data and / or electricity purchasing intention data that failed to be successfully matched in the local node for the first time exceeds a preset threshold ratio, perform a data matching operation on the electricity selling intention data and electricity purchasing intention data that failed to be successfully matched in the local node and the neighboring nodes of the local node, and determine the electricity selling intention data and / or electricity purchasing intention data that failed to be successfully matched again in the local node; The third module is used to perform data matching operations on the electricity selling intention data and / or electricity purchasing intention data in the local node that have not been successfully matched again and the electricity selling intention data and / or electricity purchasing 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.

[0013] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned electricity transaction data matching method when executing the computer program.

[0014] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned electricity transaction data matching method is implemented.

[0015] The beneficial effects of the present application are as follows: by constructing a three-level matching mechanism at the edge gateway layer, firstly, a data matching operation is performed on the electricity selling intention data and the electricity purchasing intention data of the local node, and then a data matching operation is performed on the electricity selling intention data and the electricity purchasing intention data of the local node and the neighboring nodes of the local node that have not been successfully matched for the first time, and finally a data matching operation is performed on the electricity selling intention data and / or electricity purchasing intention data that have not been successfully matched again in the local node and the electricity selling intention data and / or electricity purchasing intention data in the target node. As a result, most of the data matching operations are limited to the local node or the neighboring nodes of the local node, and remote matching is only performed on a small amount of stubbornly unmatched intention data. This hierarchical processing mode reduces the amount of cross-regional data transmission, and at the same time, through a preset performance condition mechanism based on performance indicator screening, it ensures that remote matching is only performed between nodes with sufficient resource guarantees, avoids resource waste caused by invalid communication, and can reduce the time cost and communication consumption cost in the electricity trading process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is an application environment diagram of the electric energy transaction data matching method provided in an embodiment of the present application.

[0017] Figure 2 This is a flow chart of the electric energy transaction data matching method provided in an embodiment of the present application.

[0018] Figure 3 It is a structural diagram of the electric energy transaction data matching device provided in an embodiment of the present application.

[0019] Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0021] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps illustrated may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. Terms such as "first" and "second" in the specification, claims, and drawings are used to distinguish similar items and are not intended to describe a specific sequence or precedence.

[0022] 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 pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0023] The information, data, and signals involved in the embodiments of this application are all authorized by the relevant objects or fully authorized by all parties, and the collection, use, and processing of relevant data comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0024] Figure 1 This is an application environment diagram of the power transaction data matching method provided by the embodiment of the present application. Figure 1 This electric energy transaction data matching method is applied to an electric energy transaction data matching system. The electric energy transaction data matching system includes a user terminal 110 and an edge gateway 120. The user terminal 110 and the 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, a tablet computer, a laptop computer, etc. Multiple edge gateways 120 are connected via a network to form a distributed edge gateway network. The user terminal 110 is configured to send electricity selling intention data and / or electricity purchasing intention data to the edge gateway 120. The edge gateway 120 is used to perform data matching operations on the electricity selling intention data and electricity purchasing intention data of the local node, determine the electricity selling intention data and / or electricity purchasing intention data in the local node that failed to be matched successfully for the first time, and when the proportion of electricity selling intention data and / or electricity purchasing intention data in the local node that failed to be matched successfully for the first time exceeds a preset threshold ratio, perform data matching operations on the electricity selling intention data and electricity purchasing intention data in both the local node and the neighboring nodes of the local node that failed to be matched successfully for the first time, determine the electricity selling intention data and / or electricity purchasing intention data in the local node that failed to be matched successfully again, and perform data matching operations on the electricity selling intention data and / or electricity purchasing intention data in the local node that failed to be matched successfully again and the electricity selling intention data and / or electricity purchasing intention data in the target node. Among them, the electricity selling intention data is the data published by the user to the edge gateway containing information about the electricity that can be sold, and the electricity purchasing intention data is the data published by the user to the edge gateway containing information about the electricity that needs to be purchased. 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. The local node, neighbor node and non-neighbor node are all edge gateways 120.

[0025] Figure 2 This is a flow chart of the method for matching electric energy transaction data provided by the embodiment of the present application. Figure 2 In some embodiments, the method includes but is not limited to steps S201 to S203.

[0026] In this embodiment, the execution subject of the electric energy transaction data matching method is the local node.

[0027] Step S201 : performing a data matching operation on the electricity selling intention data and the electricity purchasing intention data of the local node, and determining the electricity selling intention data and / or electricity purchasing intention data in the local node that were not successfully matched initially.

[0028] Electricity selling intention data is data published by users to the edge gateway containing information about available electricity, while electricity purchasing intention data is data published by users to the edge gateway containing information about electricity to be purchased. Electricity selling intention data is data published by users with the intention to sell electricity to the edge gateway via electricity terminals, containing information about available electricity. This information may include the amount of electricity available, the expected price range, and the time period during which the electricity is available. Electricity purchasing intention data is data published by users with the intention to purchase electricity to the edge gateway via electricity terminals, containing information about electricity to be purchased. This information may include the amount of electricity required, the expected price range, and the time period during which the electricity is required.

[0029] The local node first performs a data matching operation on the local node's electricity sales intention data and electricity purchase intention data. The data matching operation is performed on pairs of electricity sales intention data and electricity purchase intention data that meet the data matching conditions. The successfully matched electricity sales intention data and electricity purchase intention data are then used 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 unmatched electricity sales intention data and / or electricity purchase intention data in the local node are the electricity sales intention data and / or electricity purchase intention data in the local node that were not successfully matched initially.

[0030] Step S202: When the proportion of electricity selling intention data and / or electricity purchasing intention data that failed to be matched successfully for the first time in the local node exceeds a preset threshold ratio, a data matching operation is performed on the electricity selling intention data and electricity purchasing intention data that failed to be matched successfully for the first time in both the local node and the neighboring nodes of the local node, and the electricity selling intention data and / or electricity purchasing intention data that failed to be matched successfully again in the local node are determined.

[0031] After determining the electricity selling intention data and / or electricity purchasing intention data that failed to be successfully matched initially in the local node, if there is a supply and demand imbalance in the local node, that is, the proportion of electricity selling intention data and / or electricity purchasing intention data that failed to be successfully matched initially in the local node exceeds a preset threshold ratio, the local node communicates with its neighboring nodes to perform a data matching operation on the electricity selling intention data and electricity purchasing intention data that failed to be successfully matched initially in both the local node and the local node's neighboring nodes, and performs data matching on each of the electricity selling intention data and electricity purchasing intention data that meet the conditions required for the data matching operation, so as to form a preliminary transaction contract using the successfully matched electricity selling intention data and electricity purchasing intention data. After traversing all electricity selling intention data and all electricity purchasing intention data in both the local node and the local node's neighboring nodes, the electricity selling intention data and / or electricity purchasing intention data that remain in the local node that have not been successfully matched are the electricity selling intention data and / or electricity purchasing intention data in the local node that failed to be successfully matched again.

[0032] Step S203 : performing a data matching operation on the electricity selling intention data and / or electricity purchasing intention data in the local node that has not been successfully matched again and the electricity selling intention data and / or electricity purchasing intention data in the target node.

[0033] The target node must be a non-neighboring node of the local node and meet pre-set performance requirements for network latency, node load, and data matching. Network latency refers to the round-trip time between nodes and can be measured using the PING command to ensure real-time cross-node matching. Node load refers to the current computing resource utilization and 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 and can be calculated from statistical database records to screen high-efficiency nodes.

[0034] After determining that the electricity sales intention data and / or electricity purchase intention data in the local node have not been successfully matched again, the local node selects a corresponding target node from its multiple non-neighboring nodes based on preset performance conditions. The local node communicates with the target node to perform a data matching operation on the electricity sales intention data and / or electricity purchase intention data in the local node that have not been successfully matched again and the electricity sales intention data and / or electricity purchase intention data in the target node. The electricity sales intention data and electricity purchase intention data that meet the required conditions for the data matching operation are matched pairwise, and a preliminary transaction contract is formed using the successfully matched electricity sales intention data and electricity purchase intention data. Thus, by constructing a three-level matching mechanism, most data matching operations are restricted to the local node or its neighboring nodes, and remote matching is only performed on a small amount of stubbornly unmatched intention data. This hierarchical processing mode reduces the amount of cross-regional data transmission. At the same time, through a preset performance condition mechanism based on performance indicator screening, it ensures that remote matching is only performed between nodes with sufficient resource guarantees, avoiding resource waste caused by ineffective communication.

[0035] Local nodes, neighbor nodes, and non-neighbor nodes are all edge gateways. A local node refers to an edge gateway deployed within the local area. It can be implemented as an embedded system with data caching and computing capabilities, processing electricity sales and purchase data from users within the local area. A neighbor node refers to an edge gateway deployed in an adjacent area with a direct communication connection to the local node. These nodes can be automatically identified using a network topology discovery protocol to build a low-latency local matching network. A non-neighbor node refers to an edge gateway deployed outside the adjacent area and without a direct communication connection to the local node. These nodes can be automatically identified using a network topology discovery protocol based on a number of neighbor nodes to build a low-latency local matching network. Edge gateway deployment is accomplished using the "Community Deployment Kit." First, the edge gateway automatically performs a hardware pre-check to ensure the following conditions are met: CPU ≥ 2 cores, memory ≥ 4GB, storage ≥ 32GB, ARMv8 / x86\_64 architecture compatibility, dual network port redundancy, and IPv6 protocol. If the pre-check fails, an adaptation report is generated and sent to the operations center. Secondly, the edge gateway is flashed with a pre-installed Ubuntu + Docker image, which enables AppArmor by default, closes non-essential ports, and issues a unique device identity certificate signed by the local community PKI to prevent firmware tampering. The pre-installed Ubuntu + Docker image is essentially a "one-click launch" system disk that already packages the operating system kernel, security modules, and container runtime environment. The image includes all the core service containers required by the platform, including intent publishing, a matching engine, dynamic pricing, encrypted storage, a security agent, and operations monitoring. It also includes pre-installed Docker Compose or Kubernetes startup scripts, as well as initial configuration for networking, storage, certificates, and TPM2.0 drivers. This means that after flashing this image to the edge gateway, the device automatically pulls and starts all service components in the correct order upon reboot, eliminating the need for manual step-by-step installation or configuration. This enables fast, standardized, and secure one-click deployment. Subsequently, upon initial 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 mandatory rotation for two-factor authentication is triggered every seven days or upon detecting an unusual login. Finally, user terminals access the gateway via Ethernet or Wi-Fi 6 in a star topology, ensuring latency ≤ 10ms and bandwidth ≥ 1Gbps. IPv6 is used to isolate the public internet and form an independent subnet. When a user terminal first comes online, it applies for an X.509 certificate containing a unique ID and public key from the community PKI and completes two-way authentication in a TLS 1.3 handshake. Failure to authenticate triggers a disconnection alarm. Inter-zone communication between edge gateways is achieved through IKEv2-negotiated IPsec tunnels and SM4 encryption.

[0036] In some embodiments, before performing data matching operations on the electricity selling intention data and electricity purchasing intention data of the local node, it also includes: obtaining the electricity selling intention data and / or electricity purchasing intention data uploaded by the user node; performing hash conversion processing on the electricity selling intention data and / or electricity purchasing intention data to generate a hash representation of the electricity selling intention data and / or a hash representation of the electricity purchasing intention data; performing noise processing on the hash representation based on the Laplace mechanism and adding corresponding time information and user information to obtain the electricity selling intention data and / or electricity purchasing intention data of the local node.

[0037] Hashing involves converting raw data into a fixed-length hash value using a one-way hash function, typically using the SHA-256 algorithm. This hashing eliminates user identity associations from the raw data, preventing reverse engineering of transaction details. Laplace noise addition involves adding random noise that conforms to a Laplace distribution to the hash value. The noise intensity can be controlled by setting a privacy budget parameter, thereby achieving differential privacy 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, for use in subsequent matching operations to verify data timeliness. User information refers to user identifiers that have undergone desensitization, such as by using anonymized encoding to generate a unique sequence, allowing data source tracing without revealing the true identity.

[0038] Before matching the electricity sales and purchase intention data of a local node, the electricity sales and / or purchase intention data uploaded by the user through the user terminal is first converted into a hash value. For example, fields such as the electricity price range and power demand are hashed to generate an irreversible string. Subsequently, Laplace noise is added to the hash value, for example, by adding a zero-mean random perturbation to each bit of the hash value, making it impossible to associate and identify multiple uploads by the same user. The noisy hash value is then bound to the data generation time and an anonymous user identifier, for example, by appending a timestamp and user code as metadata to the end of the hash value, forming a standard data format stored by the local node. Consequently, in subsequent matching operations, the node matches transactions based solely on the desensitized hash value, without accessing the original sensitive information. This dual process of hashing and differential privacy noise removal prevents attackers from retrieving the original content from the stored data. The addition of noise also destroys the statistical correlation between the data. Furthermore, the embedded time information prevents outdated data from being included in the matching process. For example, electricity purchase requests that have expired will automatically become invalid, thereby improving transaction efficiency.

[0039] In a specific embodiment, the hash conversion processing of the electricity selling intention data and / or the electricity purchasing intention data can be implemented using the SHA-256 hash function, and the electricity selling intention data and / or the electricity purchasing intention data uploaded by the user are input into the SHA-256 hash function to generate a hash value of a fixed length.

[0040] In some embodiments, a matching operation is performed on the electricity selling intention data and the electricity purchasing intention data, including: when there is overlap in the transaction price range information of the electricity selling intention data and the electricity purchasing intention data, matching the electricity selling intention data and the electricity purchasing intention data based on the timing information of the electricity purchasing 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 power information and power storage information in the electricity selling intention data and the power demand information in the electricity purchasing intention data.

[0041] Transaction price range information refers to the price fluctuation range proposed by the electricity seller and the electricity buyer. Specifically, it can be stored as a pair of interval values. For example, the electricity seller sets a lower price limit of 0.5 yuan / kWh and an upper price limit of 0.8 yuan / kWh, while the electricity buyer sets a lower price limit of 0.6 yuan / kWh and an upper price limit of 1.0 yuan / kWh. Transaction price range information is used to screen potential suppliers and buyers to avoid invalid matches. The timing information of electricity purchase intention data refers to the timestamp when the electricity purchase intention data is submitted to the edge gateway. Specifically, it can be recorded in a time series database. For example, electricity purchase intention data that is submitted earlier than other electricity purchase intention data can be marked as high priority. The timing information of electricity purchase intention data is used to ensure fairness in first-come, first-served transactions and optimize the matching order. Power generation information refers to the current output power value of the electricity seller. Power storage information refers to the remaining capacity of the electricity seller's energy storage equipment. Power demand information refers to the total amount of electricity required by the electricity buyer. Specifically, it can be collected by real-time 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.

[0042] When matching electricity sales intention data with electricity purchase intention data, if the system detects an intersection between the seller's price range and the buyer's price range, the system will add both parties with a basis for price negotiation to the candidate matching pool. The electricity purchase intention data are then sorted by submission time, with sales resources allocated preferentially to those submitted earlier. After a successful match, temporary transaction contract data is generated, including elements such as transaction volume and time window. The transaction price is calculated based on the power generation and storage information in the power sales intention data and the power demand information in the power purchase intention data. The power generation information is used to assess the real-time power supply capacity of the power seller, the storage information is used to determine whether the price needs to be adjusted to compensate for storage losses, and the power demand information is used to measure the urgency of the power buyer's demand. For example, during peak hours, 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.

[0043] The calculation process for transaction price information corresponding to temporary trading contract data is constructed as a "learnable linear function," enabling transaction price information to simultaneously reflect electricity supply and demand factors, trading time period factors, time period factors, and energy storage factors, and to adjust coefficients online to adapt to system changes. In developing this learnable linear function, it is assumed that transaction price information can be linearly approximated for electricity supply and demand factors, trading time period factors, time period factors, and energy storage factors (if nonlinear, this can be expanded to basis functions or neural networks). The electricity supply and demand factors, trading time period factors, time period factors, and energy storage factors are scaled (or dimensioned within the model). The learning objective is to minimize the weighted quadratic loss of the sum of supply and demand deviations and price forecast deviations.

[0044] In some embodiments, the calculation formula for the transaction price information corresponding to the temporary transaction contract data is: , in, is the transaction price information at time t, is the power demand information at time t, is the power generation information at time t, is the basic price information of the power grid at time t, is the time period information, is the power storage information at time t, 、 and are learnable coefficients, obtained by minimizing target deviation information, which is obtained by fitting the deviation information between power demand information and generated power information, and the deviation information between transaction price information and predicted price information. Based on this, the formula for calculating the transaction price information corresponding to temporary transaction contract data is based on the electricity supply and demand factors at the transaction time, the transaction time factor, the time period factor, and the power storage factor. This calculation ensures that the calculated transaction price information adapts to the real-time supply and demand relationship.

[0045] When generating temporary transaction contract data, the current power demand information, power generation information, and power storage information are first obtained, and the transaction price information is calculated in real time in combination with the basic price information of the power grid. Time period information is used to distinguish electricity pricing strategies for different time periods, such as adopting a higher base price coefficient during peak power consumption periods. Power storage information is used to adjust the supply and demand balance. When the energy storage equipment has sufficient remaining power, the transaction price can be appropriately lowered to promote power consumption. The learnable coefficient enables the transaction price to dynamically adapt to market changes by continuously optimizing the weighted objective function of the supply and demand deviation and the price forecast deviation. For example, when there are short-term fluctuations in power generation and power demand, the system automatically adjusts the coefficient weight to balance the supply and demand relationship.

[0046] In some embodiments, the learning process of the learnable coefficients can be expressed as: , , , , in, is the target deviation information, is the weight coefficient, is the total duration of electricity consumption, is the predicted price information at time t, is obtained from the kth learning , is obtained by the k+1th learning , is obtained from the kth learning , is obtained by the k+1th learning , is obtained from the kth learning , is obtained by the k+1th learning , is the learning rate. Based on this, the target deviation between the transaction price information and the predicted price information is calculated using the expression of the learning process of the learnable coefficient. The learnable coefficient in the calculation formula for the transaction price information corresponding to the temporary transaction contract data is iteratively updated based on the target deviation information until the target deviation information meets the preset deviation condition. This ensures that the transaction price information calculated using the calculation formula for the transaction price information corresponding to the temporary transaction contract data is closer to the predicted price information.

[0047] In some embodiments, a data matching operation is performed on the electricity selling intention data and the electricity purchasing intention data in the local node, including: inputting the electricity selling intention data and the electricity purchasing intention data in the local node into a preset matching priority scoring model to calculate the priority scores corresponding to the electricity selling intention data and the electricity purchasing intention data in the local node; determining the priorities corresponding to the electricity selling intention data and the electricity purchasing intention data in the local node based on the priority scores, and performing a data matching operation on several electricity selling intention data and several electricity purchasing intention data with the highest current priorities. In this way, the efficiency and success rate of data matching operations in the local node can be improved. The expression of the matching priority scoring model is: , , in, 、 and are all learnable coefficients, Score the priority, It is a transaction price range tightness indicator, which represents the degree of closeness between the expected transaction price set by the user and the median or boundary value of the current transaction price. It 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 intention data or electricity purchase intention data in the current period. It is the user's historical credit indicator and the user's positive contribution to the electricity transaction data matching system. 、 and It can be set by expert experience setting method, or by data-driven learning method, by conducting supervised learning modeling on historical intention data, and using linear regression or neural network methods to train a weight group that can minimize matching deviation, or by using reinforcement learning adjustment method, with matching success rate or response speed as reward feedback, and dynamically updated through Q-learning policy optimization model or policy gradient algorithm.

[0048] In some embodiments, the electric energy transaction data matching further includes: caching a hot data set; constructing a hot data index structure for the hot data set; when performing a data matching operation on the electricity selling intention data and the electricity purchasing intention data in the local node, using a Bloom filter to query the hot data index structure; when the query result indicates that the hot data index structure has electricity selling intention data and / or electricity purchasing intention data that may be suitable for the data matching operation, obtaining the corresponding electricity selling intention data and / or electricity purchasing 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 a target data template. The target intention data is the electricity selling intention data and / or electricity purchasing intention data that have not been successfully matched for a preset number threshold within the validity period. The target user intention data is the electricity selling intention data and / or electricity purchasing intention data uploaded by a user whose activity reaches a preset activity threshold. The target data template is an intention data template whose matching success rate reaches a preset matching success rate threshold. In this way, the amount of data traversed in the data matching operation can be reduced, node resources can be saved, and the efficiency of the data matching operation in the local node can be improved.

[0049] In some embodiments, before performing a data matching operation on the electricity selling intention data and electricity purchasing intention data that were not successfully matched initially between the local node and the neighboring nodes of the local node, it also includes: performing a node performance evaluation on each neighboring node of the local node, and determining the neighboring node for performing data matching operations with the local node based on the node performance score.

[0050] The node performance of a neighboring node can be evaluated based on its average communication delay, current node load, historical match success count, and current intended data volume. The deviations between the average communication delay, current node load, historical match success count, and current intended data volume of the neighboring node and their respective maximum values ​​are normalized. A greater deviation from the maximum value indicates better node performance, while a smaller deviation indicates worse node performance. The node performance evaluation score for the neighboring node is determined by taking a weighted sum of the normalized scores.

[0051] In some embodiments, the expression for computing the node performance evaluation score is: , , in, The node performance evaluation score of the i-th neighbor node, is the average communication delay of the i-th neighbor node, is the maximum communication delay of the i-th neighbor node, is the current node load of the i-th neighbor node, is the maximum node load of the i-th neighbor node, is the number of successful historical matches of the i-th neighbor node, is the maximum number of successful matches of the i-th neighbor node, is the current intention data volume of the i-th neighbor node, is the maximum amount of intended data of the i-th neighbor node, 、 、 and are all learnable coefficients.

[0052] By evaluating the node performance of each neighboring node of the local node, it is possible to automatically initiate cooperative matching to the neighboring node with the best local performance, so as to further shorten the response time from matching failure to the next round of attempts. 、 、 and It can be set through static rules, that is, to configure a fixed ratio coefficient according to the needs of the expected transaction scenario. For example, in an environment with high response speed requirements, it can be set = 0.4, = 0.2, = 0.2, = 0.2, or data-driven optimization, by dynamically adjusting the historical matching logs through regression analysis or minimum deviation fitting. 、 、 and Minimize the error between the comprehensive score and the actual matching success probability, or use the reinforcement learning adaptive method to model the neighbor node selection behavior as a strategy learning process, use the transaction success rate or response time as reward feedback, and automatically optimize 、 、 and By combining the values ​​of , we can achieve adaptive adjustment for different operating environments and load conditions. This allows for differentiated settings based on neighbor nodes' regional characteristics, time period changes, or node roles, thereby improving the accuracy of neighbor node selection and matching efficiency.

[0053] In some embodiments, a method for selecting a target node includes: selecting a candidate node from each non-neighbor node of a local node whose network delay performance, node load performance, and data matching performance all meet preset performance conditions; determining priority weight information of the candidate node based on the network delay performance, node load performance, and data matching performance to select the target node.

[0054] Priority weight information refers to an evaluation value formed by comprehensively analyzing network delay performance, node load performance, and data matching performance. Specifically, it can be achieved by converting each performance indicator into a unified scoring standard using a weighted summation algorithm, which is used to quantitatively compare the comprehensive capabilities of candidate nodes.

[0055] When selecting a target node, the local node's non-neighbor node set is screened for candidate nodes that simultaneously meet the network delay threshold, load pressure threshold, and matching success rate threshold. For example, the network delay threshold can be set to 200 milliseconds, and the load pressure threshold can be set to a CPU utilization rate not exceeding 70%. For each candidate node, its network delay performance score, node load performance score, and data matching performance score are calculated separately. These scores are then weighted using a preset weight coefficient to generate a priority weight value. Ultimately, the candidate node with the highest priority weight value is selected as the target node for cross-node data matching operations.

[0056] The priority of a candidate node can be evaluated based on its network delay, load pressure, and data matching performance evaluation. The deviation between the candidate node's network delay and load pressure values ​​and the corresponding maximum parameters is normalized. A greater deviation from the maximum parameters indicates a higher priority, and vice versa. The priority weight of the candidate node is determined by taking a weighted sum of the normalized evaluation information and the data matching performance evaluation value.

[0057] In some embodiments, the calculation formula for the priority weight information of the candidate node is: , in, is the priority weight information of the candidate node, is the network delay value, is the network delay threshold, is the load pressure value, is the load pressure threshold, is the data matching performance evaluation value, 、 and are weight coefficients. Based on this, the priority weight information of the candidate node is determined based on the deviation between the network delay value and load pressure value of the candidate node and the corresponding maximum value parameters. The greater the deviation from the maximum value parameter, the higher the node priority and the greater the priority weight of the candidate node. Conversely, the lower the node priority, the smaller the priority weight of the candidate node.

[0058] In some embodiments, the priority weight information of the candidate nodes is determined based on the network delay performance, node load performance and data matching performance, including: constructing an intention graph containing each edge gateway, fitting the network delay performance, node load performance and data matching performance of the graph nodes into the edge weight credibility values ​​of the graph nodes; selecting the graph nodes whose edge weight credibility values ​​exceed the preset minimum edge weight credibility value and are neighbor nodes of non-local nodes from the intention graph as target nodes.

[0059] In some embodiments, a data matching operation is performed on the electricity selling intention data and / or electricity purchasing intention data in the local node that has not been successfully matched again and the electricity selling intention data and / or electricity purchasing intention data in the target node, including: sending the electricity selling intention data and / or electricity purchasing intention data in the local node that has not been successfully matched again to multiple target nodes in parallel, and for each electricity selling intention data and / or electricity purchasing intention data that has not been successfully matched again, responding to the data matching success information fed back by the first target node and terminating the data matching operation with other target nodes.

[0060] Specifically, the local node may select multiple target nodes from multiple non-neighboring nodes based on network delay performance, node load performance, and data matching performance, and then simultaneously transmit the power selling intention data and / or power purchasing intention data that were not successfully matched again in the local node to each target node, wait for the data matching success information of each target node, wait for the data matching success information fed back by the target node, and for each power selling intention data and / or power purchasing intention data that were not successfully matched again, upon first receiving the data matching success information fed back by the target node and responding to the data matching success information and generating temporary transaction contract data, terminate the data matching operation with other target nodes. For example, the local node simultaneously sends three groups of power selling intention data that were not successfully matched again in the local node to three target nodes. The first group of power selling intention data is first successfully matched in the first target node and fed back to the local node. The local node then generates the corresponding temporary transaction contract data and terminates the data matching success information fed back by the second and third target nodes. The second group of power selling intention data is first successfully matched in the second target node and fed back to the local node. The local node then generates the corresponding temporary transaction contract data and terminates the data matching success information fed back 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 electricity selling intention data and / or electricity purchasing intention data that have not been successfully matched in the local node will be sent to multiple target nodes in parallel in the next matching cycle.

[0061] In some embodiments, after successfully matching the electricity selling intention data and the electricity purchasing intention data, the process also includes: generating a number of 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 smallest comprehensive path cost as the target transmission path from the power supply node to the power consumption node. Specifically, a weighted sum of multiple objectives (power transmission loss, average node response time, and estimated time for power to reach the user) is used to convert the comprehensive path cost into a single objective, which facilitates minimization and node selection. The calculation formula for the comprehensive path cost is: , , in, is the kth candidate transmission path, for The comprehensive path cost, for The power transmission loss, for The average response time of nodes, for The estimated time it takes for the electricity to reach the user, 、 、 and are weight coefficients, is the unit energy loss between the i-th hop and the i+1-th hop on the candidate transmission path, The target amount of electricity requested by the user in the transaction. Based on this, a weighted calculation is performed on the power transmission loss, average node response time, and estimated time for the power to reach the user along the candidate transmission path to obtain the comprehensive path cost of the candidate transmission path. The greater the power transmission loss, average node response time, and estimated time for the power to reach the user, the greater the comprehensive path cost; conversely, the smaller the comprehensive path cost.

[0062] After calculating the comprehensive path cost of each candidate transmission path, an energy supply instruction is issued to the power supply node on the target transmission path to ensure that the power is delivered to the power consumption node along the target transmission path. ( is a preset cost threshold), it can be dispatched in parallel according to the concurrent path sending strategy to achieve redundancy and multi-path load balancing. To reduce the remote power supply response delay, a virtual power supply proxy node set is used. This virtual power supply proxy node set is composed of some power supply nodes pre-registering their available power generation capacity snapshots at the local node. The available power generation capacity snapshot includes the node number of the power supply node, the available power supply energy, the quotation range, and the power supply available time period. The local node can display the supply and demand hotspot areas based on the predictive heat map model and prioritize the activation of potential power supply nodes, allowing them to preheat, pre-communicate, and pre-configure, thereby completing energy supply preparations before the match is triggered, shortening the response cycle.

[0063] In some embodiments, the data matching operation for the electricity selling intention data and / or electricity purchasing intention data in the local node that has not been successfully matched again and the electricity selling intention data and / or electricity purchasing intention data in the target node is implemented using a reinforcement learning algorithm. Specifically, a state space describing the current environment state is constructed. , describing the action space of the matching order strategy and a reward function based on factors such as matching success rate, response delay, and bandwidth consumption , the matching order strategy is , is all permutations of the candidate set. The optimal strategy is obtained through the reinforcement learning algorithm, which maximizes the expected long-term reward and enables dynamic adaptation of the optimal matching path under different conditions. The expression for obtaining the optimal strategy is: , in, is the optimal strategy, is the discount factor.

[0064] In some embodiments, the electric energy transaction data matching method further includes: recording the electricity selling intention data and / or electricity purchasing intention data that have not been successfully matched three times in the local node, and giving priority to performing data matching operations on the recorded electricity selling intention data and / or electricity purchasing intention data when performing data matching operations on the electricity selling intention data and electricity purchasing intention data of the local node next time.

[0065] 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 the electricity sales intention data and / or electricity purchase intention data in the local node that have failed to match three times. 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 them and stores them in a specific database or cache area. At the beginning of the next matching cycle, the matching engine prioritizes scanning the database for historical unmatched data and cross-checking them with newly added intention data in the current cycle. For example, recorded intention data can be placed at the front of the matching queue, or a hash index can be used to quickly locate associated transaction objects, thereby reducing the computational overhead caused by repeated traversals. This significantly improves the processing efficiency of long-term unmatched intention data, avoids the waste of system resources caused by repeated traversals, and reduces the retention time of data with high failure rates through the prioritized matching mechanism, thereby optimizing the overall transaction throughput and response speed of the local node.

[0066] In some embodiments, the electric energy transaction data matching method further includes: after successfully matching the electricity sales intention data and the 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 the authentication data in the local node's memory. The transaction notification data includes transaction quantity information, transaction price information, transaction time information, and node signature information.

[0067] Transaction notification data refers to notification information containing key transaction parameters. It can be implemented in a structured data packet format, such as encapsulating the transaction power, price, time, and node signature in JSON or XML format to clarify transaction terms and ensure the integrity of data transmission. Node signature information refers to the digital signature of the edge gateway on the transaction notification data. It can be implemented using an asymmetric encryption algorithm, such as using RSA or ECDSA to generate a signature to verify the authenticity of the data source and prevent tampering. Encrypted storage refers to the cryptographic protection of sensitive data. It can be implemented using a symmetric encryption algorithm, such as using AES-256 to encrypt identity data and transaction confirmation data, combined with a memory storage mechanism to reduce disk access latency and prevent unauthorized access. Authentication data refers to the user's credentials for confirming a transaction. It can be implemented using a one-time token or biometric hash value, such as a text message verification code or fingerprint hash, to ensure the legitimacy of the transaction confirmation operation.

[0068] Once the electricity sales intention data matches the electricity purchase intention data, 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 pre-set 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. Upon receiving the confirmation data, the local node encrypts the transaction notification data, user identity information, and verification data, and stores them directly in the local node's memory. In this process, in-memory storage avoids the I / O bottlenecks 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 millisecond-accurate timestamp, and node signatures can be generated based on the hash value of the transaction data, thereby ensuring data integrity while reducing storage overhead. This instant encryption of transaction data during generation reduces the risk of privacy leaks caused by exposed storage media. The in-memory storage mechanism reduces latency in data persistence operations, improving system responsiveness in high-concurrency scenarios. The combination of node signatures and verification data ensures the immutability of transaction records, providing a trusted data foundation for subsequent dispute resolution.

[0069] See also Figure 3 The present application also provides an electric energy transaction data matching device, which can implement the above-mentioned electric energy transaction data matching method, and the device includes: The first module 301 is configured to perform a data matching operation on the electricity selling intention data and electricity purchasing intention data of the local node, and determine the electricity selling intention data and / or electricity purchasing intention data of the local node that were not successfully matched initially. The electricity selling intention data is data published by the user to the edge gateway containing information about available electricity to be sold, and the electricity purchasing intention data is data published by the user to the edge gateway containing information about required electricity to be purchased. The second module 302 is configured to, when the proportion of electricity selling intention data and / or electricity purchasing intention data that failed to be successfully matched initially in the local node exceeds a preset threshold ratio, perform a data matching operation on the electricity selling intention data and electricity purchasing intention data that failed to be successfully matched initially in both the local node and its neighboring nodes, and determine the electricity selling intention data and / or electricity purchasing intention data that failed to be successfully matched again in the local node; The third module 303 is used to perform data matching operations on the electricity selling intention data and / or electricity purchasing intention data in the local node that have not been successfully matched again and the electricity selling intention data and / or electricity purchasing 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, neighbor node and non-neighbor node are all edge gateways.

[0070] The specific implementation of the power transaction data matching device is basically the same as the specific embodiment of the above-mentioned power transaction data matching method, and will not be repeated here.

[0071] Figure 4 It is a block diagram of an electronic device according to an exemplary embodiment.

[0072] Refer to the following Figure 4 4 to describe the electronic device 400 according to this embodiment of the present disclosure. Figure 4 The electronic device 400 shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0073] like Figure 4 As shown, electronic device 400 is implemented as a general-purpose computing device. Components of 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 various system components (including storage unit 420 and processing unit 410), a display unit 440, and the like.

[0074] The storage unit stores program code, which can be executed by the processing unit 410, so that the processing unit 410 executes the steps described in the above-mentioned electric energy transaction data matching method part of this specification according to various exemplary embodiments of the present disclosure.

[0075] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 4201 and / or a cache memory unit 4202 , and may further include a read-only memory unit (ROM) 4203 .

[0076] The storage unit 420 may also include a program / utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0077] Bus 430 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0078] The electronic device 400 may also communicate with one or more external devices 400′ (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 400, and / or any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may occur via an input / output (I / O) interface 450. Furthermore, the electronic device 400 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 460. The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the 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.

[0079] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned electricity transaction data matching method is implemented.

[0080] The electric energy transaction data matching method, apparatus, device and storage medium provided in the embodiments of the present application, by constructing a three-level matching mechanism at the edge gateway layer, first performs a data matching operation on the electricity selling intention data and the electricity purchasing intention data of the local node, then performs a data matching operation on the electricity selling intention data and the electricity purchasing intention data of the local node and the neighboring nodes of the local node that failed to match successfully for the first time, and finally performs a data matching operation on the electricity selling intention data and / or electricity purchasing intention data in the local node that failed to match successfully again and the electricity selling intention data and / or electricity purchasing intention data in the target node. As a result, most of the data matching operations are limited to the local node or the neighboring nodes of the local node, and remote matching is only performed on a small amount of stubbornly unmatched intention data. This hierarchical processing mode reduces the amount of cross-regional data transmission. At the same time, through a preset performance condition mechanism based on performance indicator screening, it ensures that remote matching is only performed between nodes with sufficient resource guarantees, avoiding resource waste caused by invalid communication, and can reduce the time cost and communication consumption cost in the electric energy transaction process.

[0081] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above-mentioned method according to the embodiments of the present disclosure.

[0082] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0083] Computer-readable storage media may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0084] Those skilled in the art will appreciate that the modules described above can be distributed in the device according to the description of the embodiment, or can be modified accordingly to be used in one or more devices that are different from the embodiment. The modules of the above embodiment can be combined into one module or further divided into multiple submodules.

[0085] While the exemplary embodiments of the present disclosure have been specifically illustrated and described above, it should be understood that the present disclosure is not limited to the detailed structures, configurations, or implementations described herein; rather, the present disclosure is intended to encompass various modifications and equivalent configurations within the spirit and scope of the appended claims.

Claims

1. A method for matching electric energy transaction data, characterized in that: include: Performing a data matching operation on the electricity selling intention data and electricity purchasing intention data of the local node to determine the electricity selling intention data and / or electricity purchasing intention data in the local node that were not successfully matched initially; the electricity selling intention data is data published by the user to the edge gateway containing information about electricity that can be sold, and the electricity purchasing intention data is data published by the user to the edge gateway containing information about electricity that needs to be purchased; When the proportion of electricity selling intention data and / or electricity purchasing intention data that failed to be matched successfully for the first time in the local node exceeds a preset threshold ratio, a data matching operation is performed on the electricity selling intention data and electricity purchasing intention data that failed to be matched successfully for the first time in both the local node and the neighboring nodes of the local node, and the electricity selling intention data and / or electricity purchasing intention data that failed to be matched successfully again in the local node are determined; Performing a data matching operation on the electricity selling intention data and / or electricity purchasing intention data in the local node that has not been successfully matched again and the electricity selling intention data and / or electricity purchasing intention data in the target node; The target node is a non-neighbor node of the local node and its network delay performance, node load performance and data matching performance all meet preset performance conditions. The local node, the neighbor node and the non-neighbor node are all edge gateways.

2. The electric energy transaction data matching method according to claim 1, characterized in that: Before performing the data matching operation on the electricity selling intention data and the electricity purchasing intention data of the local node, the method further includes: Obtain electricity selling intention data and / or electricity purchasing intention data uploaded by user nodes; Performing hash conversion processing on the electricity selling intention data and / or the electricity purchasing intention data to generate a hash representation of the electricity selling intention data and / or a hash representation of the electricity purchasing intention data; The hash representation is subjected to noise processing based on the Laplace mechanism and corresponding time information and user information are added to obtain the electricity selling intention data and / or electricity purchasing intention data of the local node.

3. The electric energy transaction data matching method according to claim 1, characterized in that: The electricity selling intention data and the electricity purchasing intention data are matched, including: When transaction price range information of the electricity selling intention data and the electricity purchasing intention data overlap, matching the electricity selling intention data with the electricity purchasing intention data according to time series information of the electricity purchasing intention data, and generating temporary transaction contract data; The transaction price information corresponding to the temporary transaction contract data is determined based on the power generation information and power storage information in the power selling intention data and the power demand information in the power purchasing intention data.

4. The electric energy transaction data matching method according to claim 3, characterized in that: The calculation formula for the transaction price information corresponding to the temporary transaction contract data is: ， in, is the transaction price information at time t, is the power demand information at time t, is the power generation information at time t, is the basic price information of the power grid at time t, is the time period information, is the power storage information at time t, 、 and are respectively learnable coefficients, which are obtained by minimizing target deviation information, where the target deviation information is obtained by fitting the deviation information between the power demand information and the generated power information and the deviation information between the transaction price information and the predicted price information.

5. The electric energy transaction data matching method according to claim 1, characterized in that: The method for selecting the target node includes: Selecting, from each non-neighboring node of the local node, a candidate node whose network delay performance, node load performance, and data matching performance all meet the preset performance conditions; The priority weight information of the candidate nodes is determined according to the network delay performance, the node load performance and the data matching performance to select the target node.

6. The electric energy transaction data matching method according to claim 1, characterized in that: Also includes: The electricity selling intention data and / or electricity purchasing intention data that have not been successfully matched three times in the local node are recorded, and the recorded electricity selling intention data and / or electricity purchasing intention data are preferentially performed when a data matching operation is performed on the electricity selling intention data and electricity purchasing intention data of the local node next time.

7. The electric energy transaction data matching method according to claim 1, characterized in that: Also includes: After successfully matching the electricity selling intention data and the electricity purchasing intention data, sending transaction notification data to the user; The transaction notification data includes transaction power information, transaction price information, transaction time information and node signature information; 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 node.

8. An electric energy transaction data matching device, characterized in that: include: The first module is configured to perform a data matching operation on the electricity selling intention data and the electricity purchasing intention data of the local node, and determine the electricity selling intention data and / or electricity purchasing intention data in the local node that were not successfully matched initially; the electricity selling intention data is data published by the user to the edge gateway containing information about available electricity to be sold, and the electricity purchasing intention data is data published by the user to the edge gateway containing information about required electricity to be purchased; The second module is configured to, when the proportion of electricity selling intention data and / or electricity purchasing intention data that failed to be successfully matched in the local node for the first time exceeds a preset threshold ratio, perform a data matching operation on the electricity selling intention data and electricity purchasing intention data that failed to be successfully matched in the local node and the neighboring nodes of the local node, and determine the electricity selling intention data and / or electricity purchasing intention data that failed to be successfully matched again in the local node; The third module is used to perform a data matching operation on the electricity selling intention data and / or electricity purchasing intention data in the local node that has not been successfully matched again and the electricity selling intention data and / or electricity purchasing intention data in the target node; The target node is a non-neighbor node of the local node and its network delay performance, node load performance and data matching performance all meet preset performance conditions. The local node, the neighbor node and the non-neighbor node are all edge gateways.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the electric energy transaction data matching method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the electric energy transaction data matching method according to any one of claims 1 to 7 is implemented.

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