Transaction process approval method for power transaction system

Abstract

The invention discloses a transaction process approval method for a power transaction system, and relates to the technical field of power transaction management, and the method comprises the steps: extracting a historical transaction record and contract execution deviation information of a transaction node from a business database based on a block chain digital identity label of the transaction node, and forming a transaction node approval file; forming a process element set according to the content of the transaction process to be submitted, associating the process element set with the transaction node approval file according to a feature mapping rule, and outputting a process association feature set; constructing a fuzzy evaluation matrix based on the process association feature set, and calculating a process approval coefficient through a fuzzy synthesis rule; and according to a comparison result of the process approval coefficient and a preset approval threshold value, determining an approval level identifier and a matched approval path, and generating and outputting a final approval result set. According to the invention, while the approval accuracy is improved, the manual recheck burden is reduced, and the auditing performance of the approval decision and the system operation efficiency are enhanced.

Description

Technical Field

[0001] This invention relates to the field of power trading management technology, and in particular to a method for approving the transaction process of a power trading system. Background Technology

[0002] With the continuous deepening of the electricity market trading mechanism, the electricity trading system is gradually evolving from a centralized control model based primarily on planned indicators to a market-driven bidding model with multiple participants and price signals. During this process, the complexity of the trading process and the diversity of participating entities have significantly increased, prompting the continuous development of technologies for transaction review, credit evaluation, and process risk identification. Currently, mainstream review mechanisms largely rely on the node credit files, contract execution records, and price compliance rules of the trading center, using a combination of manual review and rule engines to achieve basic transaction declaration review. Some systems have introduced machine learning evaluation models, but these are typically based on single-dimensional behavioral characteristics or static scoring systems, making it difficult to perform multi-dimensional collaborative modeling of dynamic fluctuations in node behavior, contract performance deviations, and the reasonableness of bids. Simultaneously, with the expansion of data scale, the review process, heavily reliant on manual intervention, is gradually revealing problems such as inefficiency, strong subjective dependence, and difficulty in forming auditable and traceable decision-making paths.

[0003] However, existing technologies still have significant shortcomings in the areas of trusted management of transaction node identities, multi-dimensional feature correlation analysis of process scenarios, and approval judgment algorithms. First, existing transaction node identity systems rely on traditional account systems or centralized CAs, lacking a blockchain-based, immutable, and lifecycle-verifiable digital identity mechanism, making it difficult to establish a trusted binding relationship between node credit information and the review process. Second, existing transaction process analysis generally uses single-indicator or segmented logical processing, failing to structurally correlate historical node behavior data with current process elements, resulting in approval models that cannot effectively characterize key dimensions such as transaction stability, performance reliability, and price reasonableness. Third, existing risk control models are mostly simple rule overlays, lacking the flexible judgment capability to incorporate fuzzy logic, making it difficult to handle the numerous interval-type and uncertain behavioral characteristics present in transaction scenarios, and unable to provide stable and interpretable approval coefficients. Fourth, the decision-making mechanism for approval paths is generally fixed, lacking intelligent path scheduling functions based on dynamic grading of approval coefficients and the ability to trigger expert review within boundary intervals, leading to high-risk processes not being identified in a timely manner, while low-risk processes may be over-reviewed, affecting overall transaction efficiency. Summary of the Invention

[0004] In view of the problems existing in the approval method for the transaction process of a power trading system, this invention is proposed. Therefore, the problem to be solved by this invention is how to provide a method for approving the transaction process of a power trading system.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for approving the transaction process of a power trading system, which includes: extracting historical transaction records and contract execution deviation information of the transaction nodes from the business database based on the blockchain digital identity identifier of the transaction nodes to form an approval file for the transaction nodes; Based on the content of the proposed transaction process, collect the transaction varieties, declared capacity, declared price and historical execution differences to form a set of process elements. Then, according to the feature mapping rules, associate the set of process elements with the transaction node approval files and output the process association feature set. A fuzzy evaluation matrix is ​​constructed based on the process association feature set, and the process approval coefficient is calculated through fuzzy synthesis rules. Based on the comparison results between the process approval coefficient and the preset approval threshold, the approval level identifier and the matching approval path are determined, and the final approval result set is generated and output.

[0006] As a preferred embodiment of the power trading system transaction process approval method of the present invention, the step of forming the transaction node approval file includes: Using blockchain digital identity identifiers as indexes, all historical transaction records corresponding to transaction nodes are retrieved and extracted from the business database, and a transaction time series is constructed in chronological order. The transaction time series is standardized by mapping each transaction to a unified set of data items, forming a structured set of entries; Based on the structured set of entries, key statistical indicators of transaction nodes are obtained, including cumulative transaction volume, number of transactions, number of historical defaults, average settlement delay, and contract execution deviation. The structured set of entries, key statistical indicators, and contract execution deviations are filled in according to a unified data format to form the transaction node approval file.

[0007] As a preferred embodiment of the power trading system transaction process approval method of the present invention, the output process associated feature set includes: Analyze historical transaction statistics and contract execution deviations from transaction node approval files; Based on the feature mapping rules, the numerical items reflecting the current transaction behavior in the process element set are mapped to the corresponding historical statistical indicators in the transaction node approval file to generate associated indicators; The associated indicators are merged with the original fields in the process element set according to the mapping order to form a process associated feature set.

[0008] As a preferred embodiment of the power trading system transaction process approval method described in this invention, the construction of the fuzzy evaluation matrix includes: The original indicator sequence constituting transaction stability, performance reliability and pricing rationality is extracted from the process-related feature set, and each original indicator is normalized to the interval. The normalized indicators are then fuzzified using a Gaussian membership function, which is expressed as follows: ; in, This represents the Gaussian membership value. For normalized index values, Center of Gaussian function Standard deviation; A membership vector is generated for each evaluation dimension, and the membership vectors of each dimension are arranged to form a three-dimensional fuzzy evaluation matrix.

[0009] As a preferred embodiment of the power trading system transaction process approval method of the present invention, the step of calculating the process approval coefficient through fuzzy synthesis rules includes: Normalize the membership vector for each evaluation dimension to obtain the probability distribution matrix, and calculate the information entropy of the dimension, expressed as: ; in, Indicates the first Information entropy of evaluation dimensions Represents the fuzzy level number, Indicates the first Evaluation Dimension 1 Normalized membership values ​​for fuzzy levels; The weight vector is calculated based on information entropy and is expressed as follows: ; in, For the first Weights of evaluation dimensions; The comprehensive membership degree is calculated using a fuzzy synthesis rule, which employs a weighted minimum-maximum combination: the comprehensive membership degree vector is obtained by weighting the three-dimensional fuzzy evaluation matrix by rows, as shown below: ; in, To synthesize the membership vector at the th Components at the fuzzy level, The fuzzy evaluation matrix is ​​the first... Line number Column elements; Calculate the initial value of the approval coefficient and the fuzzy entropy of the comprehensive membership vector. Adjust the initial value of the approval coefficient based on the fuzzy entropy of the comprehensive membership vector to generate the process approval coefficient, expressed as: ; in, This is the process approval coefficient. This is the initial value for the approval coefficient. To synthesize the fuzzy entropy of the membership vector, For uncertainty threshold, This is the entropy adjustment coefficient. This is a predetermined conservative coefficient.

[0010] In a preferred embodiment of the power trading system transaction process approval method described in this invention, the initial value of the approval coefficient is calculated using the following formula: ; in, This is the initial value for the approval coefficient. For the first The scalar representative value corresponding to the fuzzy level; The formula for calculating the fuzzy entropy of the comprehensive membership vector is as follows: ; in, This is the entropy value of the comprehensive membership vector.

[0011] As a preferred embodiment of the power trading system transaction process approval method of the present invention, the step of determining the approval level identifier and the matching approval path includes: if the process approval coefficient is greater than a first preset approval threshold, then the approval level identifier is set to level one, and a regular approval sub-path that is automatically approved is matched; if the process approval coefficient is less than a second preset approval threshold, then the approval level identifier is set to level two, and a regular approval sub-path that is automatically rejected is matched; if the process approval coefficient is greater than the second preset approval threshold and less than the first preset approval threshold, then the approval level identifier is set to level three, and a specialist review path is matched; wherein, the first predetermined approval threshold is greater than the second predetermined approval threshold.

[0012] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a power trading system transaction process approval method.

[0013] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements the steps of a power trading system transaction process approval method.

[0014] The beneficial effects of this invention are as follows: This invention structurally associates real-time reporting elements with historical archives through feature mapping, thereby improving the targeting of abnormal behavior identification; it generates process approval coefficients based on fuzzy evaluation and information entropy weight synthesis of comprehensive membership degree; it realizes hierarchical path scheduling according to predetermined thresholds and can trigger specialist review, thereby improving the approval accuracy, reducing the burden of manual review, and enhancing the auditability of approval decisions and system operating efficiency. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a transaction process approval method for an electricity trading system. Detailed Implementation

[0017] To make the above-mentioned objects, features, and advantages of the present invention more readily understood, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figure 1 This is the first embodiment of the present invention, which provides a method for approving the transaction process of a power trading system, including: S1: Based on the blockchain digital identity of transaction nodes, historical transaction records and contract execution deviation information of transaction nodes are extracted from the business database to form transaction node approval files; S2: Collect the trading varieties, application capacity, application price and historical execution differences based on the content of the proposed transaction process, form a set of process elements, and associate the set of process elements with the transaction node approval file according to the feature mapping rules, and output the process association feature set; S3: Construct a fuzzy evaluation matrix based on the process association feature set, and calculate the process approval coefficient through fuzzy synthesis rules; S4: Based on the comparison results between the process approval coefficient and the preset approval threshold, determine the approval level identifier and the matching approval path, and generate and output the final approval result set.

[0021] Specifically, after receiving the blockchain digital identity token submitted by the receiving node, the system queries the registration status of the digital identity token in the blockchain registry and verifies the identity status. After the verification is successful, the system uses the digital identity token as an index to retrieve all corresponding historical transaction record entries in the business database, and extracts the original transaction events in chronological order to construct a transaction time series.

[0022] The extracted transaction time series is standardized by mapping the core fields of each transaction (transaction number, transaction time, transaction type, declared capacity, committed supply / demand, settlement amount, actual supply / fulfillment, settlement completion time, anomaly identifier, etc.) to a unified set of data items, forming a set of structured entries according to a preset data format.

[0023] Based on the structured set of entries, key statistical indicators of nodes are obtained, including cumulative transaction volume, number of transactions, number of historical defaults, average settlement delay, and maximum single transaction deviation.

[0024] For example, for each contract performance record of a node, the performance deviation is calculated for each contract, and the overall contract performance deviation of the node is calculated, expressed as: ; in, For the overall contract execution deviation at the node, Indicates the number of contracts. Indicates the first The actual amount of performance of this contract. Indicates the first The amount of performance promised in the contract.

[0025] The latest transaction summary, key statistical indicators, and overall contract execution deviation of the node in the structured entry set are filled in item by item according to the field order of the unified data format to form the transaction node approval file.

[0026] After receiving the proposed transaction process content, the system extracts the transaction type, declared capacity, declared price, and execution difference data of the corresponding entity in previous transactions from the submitted information according to the predefined process element structure, and assembles them into a process element set in a fixed field order.

[0027] Using the transaction node approval files as input, the system analyzes the historical transaction statistics and contract execution deviations, and constructs a feature comparison table based on the feature correspondence.

[0028] Based on the feature mapping rules, the numerical items reflecting the current transaction behavior in the process element set are mapped to the corresponding historical statistical indicators in the transaction node approval file to generate associated indicators.

[0029] The associated indicators are summarized in a structured form after feature mapping and merged with the original fields in the process element set in the mapping order to form a process associated feature set.

[0030] After receiving the process-related feature set, the original indicator sequence constituting transaction stability, performance reliability and pricing rationality is extracted from it in a fixed order, and each indicator is normalized to the interval. The features in the process association feature set were extracted and quantified into three categories of evaluation indicators according to dimensions. The transaction stability consists of the recent declaration volatility, transaction deviation variance and price jump frequency of the transaction node; the performance reliability consists of the frequency of contract default, deviation assessment deduction rate and bilateral cooperation stability; and the price reasonableness consists of the proportion of the current declaration price deviating from the average electricity price in the range, the historical price deviation and counterparty matching degree. The normalized index set is fuzzified using a family of membership functions, with the membership function expressed as a parameterized Gaussian function: ; in, This represents the Gaussian membership value. For normalized index values, Center of Gaussian function The standard deviation is denoted as .

[0031] For each evaluation dimension, a membership vector is generated according to a predetermined set of levels. After obtaining the membership vectors of each dimension, a three-dimensional fuzzy evaluation matrix is ​​constructed in dimensional order. A weight determination method based on information entropy is introduced. The membership vector of each evaluation dimension is normalized to obtain a probability distribution matrix. Then, the information entropy of the dimension is calculated, expressed as: ; in, Indicates the first Information entropy of evaluation dimensions This represents the number of fuzzy levels (3 in this method). Indicates the first Evaluation Dimension 1 Normalized membership values ​​for fuzzy levels.

[0032] The weight vector is calculated based on information entropy and is expressed as follows: ; in, For the first Weights of evaluation dimensions; An improved fuzzy synthesis rule is used to calculate the comprehensive membership degree. The synthesis rule adopts a weighted minimum-maximum combination: the comprehensive membership degree vector is obtained by weighting the three-dimensional fuzzy evaluation matrix by row, as follows: ; in, To synthesize the membership vector at the th Components at the fuzzy level, The fuzzy evaluation matrix is ​​the first... Line number Column elements.

[0033] The initial value for calculating the approval coefficient is expressed as: ; in, This is the initial value for the approval coefficient. For the first Scalar representative values ​​corresponding to fuzzy levels (arranged in ascending order); To further improve the ability to distinguish between transactions with fuzzy boundaries, the fuzzy entropy index is calculated to reflect the uncertainty of the membership degree distribution, and is expressed as: ; in, The fuzzy entropy of the comprehensive membership vector is used; the initial value of the obtained approval coefficient is adjusted based on the fuzzy entropy of the comprehensive membership vector to generate the process approval coefficient, expressed as: ; in, This is the process approval coefficient. For uncertainty threshold, This is the entropy adjustment coefficient. This is a predetermined conservative coefficient.

[0034] The process approval coefficient is compared sequentially with two predetermined approval thresholds (derived from statistical analysis of historical approval decision data). The approval level identifier is determined based on the comparison results, and the approval path is determined based on the approval level identifier. If the process approval coefficient is greater than the first predetermined approval threshold, the approval level identifier is set to level one, and the automatic passage sub-path in the regular approval path is matched. After the approval record is written, it is immediately issued and a notification is sent to all relevant entities. If the process approval coefficient is less than the second predetermined approval threshold, the approval level identifier will be set to level two, matching the automatic rejection sub-path in the regular approval path. After the approval record is written, a rejection notice will be issued to the submitter. If the process approval coefficient is greater than the second predetermined approval threshold but less than the first predetermined approval threshold, the approval level identifier will be set to level three, the specialist review path will be matched, and after the specialist votes and generates the review conclusion, the review conclusion will be issued to the relevant subject; wherein, the first predetermined approval threshold is greater than the second predetermined approval threshold. The standard approval path (automatic approval / automatic rejection) includes the following sequential processing nodes: path confirmation, approval record assembly, approval result signing, approval result archiving and result notification.

[0035] The specialist review path includes the following sequential processing nodes: review form generation, review material packaging, specialist allocation and voting, review conclusion synthesis, review conclusion signing, review result archiving and result notification. When assigned to a specialist review path, three available specialists are selected from the specialist pool as a review group according to a first-come, first-served rule; specialist voting adopts a binary voting method (for / against), and the review conclusion is determined by the majority principle.

[0036] This embodiment also provides a computer device applicable to a power trading system transaction process approval method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement all or part of the steps of the method described in the above embodiments of the present invention.

[0037] This embodiment also provides a storage medium storing a computer program thereon. When the computer program is executed by a processor, it performs the method in any optional implementation of the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0038] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0039] In summary, this invention improves the targeting of abnormal behavior identification by structurally associating real-time reporting elements with historical archives through feature mapping; it generates process approval coefficients based on fuzzy evaluation and information entropy weight synthesis of comprehensive membership degree; it realizes hierarchical path scheduling according to predetermined thresholds and can trigger specialist review, thereby improving the approval accuracy, reducing the burden of manual review, and enhancing the auditability of approval decisions and system operating efficiency.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for approving the transaction process in a power trading system, characterized in that: include, Based on the blockchain digital identity of transaction nodes, historical transaction records and contract execution deviation information of transaction nodes are extracted from the business database to form transaction node approval files; Based on the content of the proposed transaction process, collect the transaction varieties, declared capacity, declared price and historical execution differences to form a set of process elements. Then, according to the feature mapping rules, associate the set of process elements with the transaction node approval files and output the process association feature set. A fuzzy evaluation matrix is ​​constructed based on the process association feature set, and the process approval coefficient is calculated through fuzzy synthesis rules. Based on the comparison results between the process approval coefficient and the preset approval threshold, the approval level identifier and the matching approval path are determined, and the final approval result set is generated and output.

2. The power trading system transaction process approval method as described in claim 1, characterized in that: The approval file for the formation of the transaction node includes: Using blockchain digital identity identifiers as indexes, all historical transaction records corresponding to transaction nodes are retrieved and extracted from the business database, and a transaction time series is constructed in chronological order. The transaction time series is standardized by mapping each transaction to a unified set of data items, forming a structured set of entries; Based on the structured set of entries, key statistical indicators of transaction nodes are obtained, including cumulative transaction volume, number of transactions, number of historical defaults, average settlement delay, and contract execution deviation. The structured set of entries, key statistical indicators, and contract execution deviations are filled in according to a unified data format to form the transaction node approval file.

3. The power trading system transaction process approval method as described in claim 1, characterized in that: The output process associated feature set includes: Analyze historical transaction statistics and contract execution deviations from transaction node approval files; Based on the feature mapping rules, the numerical items reflecting the current transaction behavior in the process element set are mapped to the corresponding historical statistical indicators in the transaction node approval file to generate associated indicators; The associated indicators are merged with the original fields in the process element set according to the mapping order to form a process associated feature set.

4. The power trading system transaction process approval method as described in claim 1, characterized in that: The construction of the fuzzy evaluation matrix includes: The original indicator sequence constituting transaction stability, performance reliability and pricing rationality is extracted from the process-related feature set, and each original indicator is normalized to the interval. The normalized indicators are then fuzzified using a Gaussian membership function, which is expressed as follows: ； in, This represents the Gaussian membership value. For normalized index values, Center of Gaussian function Standard deviation; A membership vector is generated for each evaluation dimension, and the membership vectors of each dimension are arranged to form a three-dimensional fuzzy evaluation matrix.

5. The power trading system transaction process approval method as described in claim 1, characterized in that: The process approval coefficient calculated using fuzzy synthesis rules includes: Normalize the membership vector for each evaluation dimension to obtain the probability distribution matrix, and calculate the information entropy of the dimension, expressed as: ； in, Indicates the first Information entropy of evaluation dimensions Represents the fuzzy level number, Indicates the first Evaluation Dimension 1 Normalized membership values ​​for fuzzy levels; The weight vector is calculated based on information entropy and is expressed as follows: ； in, For the first Weights of evaluation dimensions; The comprehensive membership degree is calculated using a fuzzy synthesis rule, which employs a weighted minimum-maximum combination: the comprehensive membership degree vector is obtained by weighting the three-dimensional fuzzy evaluation matrix by rows, as shown below: ； in, To synthesize the membership vector at the th Components at the fuzzy level, The fuzzy evaluation matrix is ​​the first... Line number Column elements; Calculate the initial value of the approval coefficient and the fuzzy entropy of the comprehensive membership vector. Adjust the initial value of the approval coefficient based on the fuzzy entropy of the comprehensive membership vector to generate the process approval coefficient, expressed as: ； in, This is the process approval coefficient. This is the initial value for the approval coefficient. To synthesize the fuzzy entropy of the membership vector, For uncertainty threshold, This is the entropy adjustment coefficient. This is a predetermined conservative coefficient.

6. The power trading system transaction process approval method as described in claim 5, characterized in that: The formula for calculating the initial value of the approval coefficient is as follows: ； in, This is the initial value for the approval coefficient. For the first The scalar representative value corresponding to the fuzzy level; The formula for calculating the fuzzy entropy of the comprehensive membership vector is as follows: ； in, This is the entropy value of the comprehensive membership vector.

7. The power trading system transaction process approval method as described in claim 1, characterized in that: The determination of the approval level identifier and the matching approval path includes: If the process approval coefficient is greater than the first preset approval threshold, the approval level identifier will be set to level one, and the regular approval sub-path that is automatically approved will be matched. If the process approval coefficient is less than the second preset approval threshold, the approval level identifier will be set to level two, and the regular approval sub-path that is automatically rejected will be matched. If the process approval coefficient is greater than the second preset approval threshold but less than the first preset approval threshold, the approval level will be set to level three and a specialist review path will be matched. Among them, the first predetermined approval threshold is greater than the second predetermined approval threshold.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the power trading system transaction process approval method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the power trading system transaction process approval method according to any one of claims 1 to 7.

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