Full-life-cycle archive management method and system suitable for electric power spot market

Abstract

The invention discloses a full-life-cycle archive management method and system suitable for an electric power spot market, and the method comprises the steps: obtaining market daily clearing data, carrying out the data solidification processing, and synchronizing the data to all transaction systems of the electric power spot market, and obtaining the current-day transaction data of each transaction system, the method comprises the following steps: performing data screening and warehousing quality inspection on transaction data of the day through a predefined data rule, performing warehousing archiving on the transaction data of the day passing the quality inspection to obtain transaction archive data of the day, and performing data consistency comparison on the transaction archive data and a local archive database; and sequentially storing the transaction archive data with inconsistent comparison results, analyzing the data development trend difference of the local archive database before and after the storage of the transaction archive data, and carrying out alarm mechanism matching on abnormal archive data according to the data development trend difference to obtain full-cycle archive management data. The method has the effect of improving the data management efficiency of the electric power spot market.

Description

Technical Field

[0001] This invention relates to the technical field of power record management, and in particular to a method and system for full life-cycle record management adapted to the power spot market. Background Technology

[0002] Currently, with the full implementation of the electricity spot market, the number of market participants has increased dramatically, and the trading frequency has changed from long-term annual and monthly transactions to daily or even shorter cycles. The shortened trading frequency and the surge in trading data have placed higher demands on power companies in managing the trading records of each market participant in the face of these changes in electricity spot market trading data.

[0003] The existing methods for managing transaction archive data in the electricity spot market typically involve each system managing its own generated transaction archive data. However, each electricity spot transaction may generate transaction data across multiple systems. Due to inconsistent data formats between systems, data silos are created between the power company's trading and management systems. Furthermore, the traditional archive data storage adopts a "one-time registration, static update" model, which cannot adapt to the dynamically changing market participant information in the spot market environment. The aforementioned technologies also suffer from the drawbacks of complex and varied sources of transaction data in electricity spot transactions, inconsistent data quality, and erroneous or inaccurate data, which directly affect the accuracy of electricity spot clearing and settlement. Summary of the Invention

[0004] In response to the problems of complex and varied sources of transaction data and inconsistent data quality in existing electricity spot trading technologies, where erroneous or inaccurate data directly affects the accuracy of electricity spot clearing and settlement, this invention provides a method and system for full lifecycle file management adapted to the electricity spot market. This method and system can adapt to the dynamic changes in the electricity spot market, and manage the file data of electricity market participants in a unified, dynamic, high-quality, and full lifecycle manner, effectively improving the data management efficiency of the electricity spot market and supporting its efficient and stable operation.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution: A method for managing the entire lifecycle of data in the electricity spot market, the method comprising: Acquire daily market clearing data, perform data solidification processing, and synchronize the daily clearing solidified data to all trading systems in the electricity spot market to obtain the daily trading data for each trading system. The daily transaction data is filtered and inspected for quality before being stored by predefined data rules. The daily transaction data that passes the quality inspection is then stored and archived to obtain the daily transaction archive data. Acquire transaction file data from all trading systems and compare the data consistency with the local file database. Transaction file data with inconsistent comparison results are stored sequentially. The differences in data development trends of the local archive database before and after the transaction archive data is stored are analyzed. Based on the differences in data development trends, an alarm mechanism is used to match abnormal archive data to obtain full-cycle archive management data.

[0006] In a preferred embodiment, this application can be further configured as follows: the acquisition of daily market clearing data is processed for data solidification, and the solidified daily clearing data is synchronized to all trading systems in the electricity spot market to obtain the daily trading data of each trading system, specifically including: Obtain the unique identifier of each registered market entity, conduct data verification on the registration data of the registered market entities, and perform access permission matching processing on the registered market entities that pass the verification; Based on the access permission matching result, the real-time daily clearing data of the registered market entities is obtained, and the real-time daily clearing data is processed for data quality verification according to the preset data quality verification rules to obtain the data verification result; If the data verification result is passed, the real-time daily clearing data is solidified, and the master data table of the registered market entity is updated according to the power trading time to obtain the daily clearing sub-file data; The daily clearing and filing data is synchronized to all trading systems in the electricity spot market, and the filing data in each trading system is updated according to the unique identifier to obtain the daily trading data of the registered market participants.

[0007] In a preferred embodiment, this application can be further configured as follows: if the data verification result is passed, the real-time daily clearing data is solidified, and the master data table of the registered market entity is updated according to the electricity trading time. The master data table update process in the daily clearing sub-file data specifically includes: According to the preset data storage rules, the data hash value of the real-time daily clearing data is calculated to obtain the incremental data hash value of the registered market entity; The incremental data hash value is compared with the last data hash value of the main data table, and the main data table is determined to be stored repeatedly based on the comparison result. If not, the solidified real-time daily clearing data is updated at the end of the master data table according to the power trading time, and the master data table is updated.

[0008] In a preferred embodiment, this application can be further configured as follows: updating the master data table by updating the solidified real-time daily clearing data to the end position according to the power trading time, and updating the master data table, further includes: The real-time daily clearing data is compared item by item with the last data in the master data table, and the power trading trend of the registered market participants is predicted based on the comparison results.

[0009] In a preferred embodiment, this application can be further configured as follows: The step of obtaining real-time daily clearing data of registered market entities based on the access permission matching result, and performing data quality verification processing on the real-time daily clearing data through preset data quality verification rules to obtain the data verification result, further includes: If the data verification result is unsuccessful, then all abnormal data in the real-time daily clearing data that do not conform to the data quality inspection rules are obtained; The abnormal data is associated with the unique identifier and packaged into an abnormal work order. The abnormal work order is then sent to the electricity spot market management terminal through a preset message queue.

[0010] In a preferred embodiment, this application can be further configured as follows: The step of synchronizing the daily clearing and filing data to all trading systems in the electricity spot market, and updating the filing data in each trading system according to a unique identifier to obtain the daily trading data of the registered market participant, specifically includes: Obtain the business data requirements of all trading systems in the electricity spot market, analyze the common characteristics of the data of all trading systems based on the business data requirements, and obtain the common characteristics of system data in the electricity spot market; Data training is performed on the common characteristics of the system data and the corresponding power trading business to construct a unified data model that covers the business requirements of all trading systems; The daily clearing and filing data is input into the unified data model for common data feature analysis. Based on the analysis results, user profiles are created for registered market entities to obtain the entity profile data for the current power trading business.

[0011] In a preferred embodiment, this application can be further configured such that the method further includes: Obtain each data processing step and corresponding data status of the daily market data stored in the local archive database, and package the data processing steps and data status into data blocks; The block hash value of each data block is calculated based on the data processing time. The packaged data blocks are then stored in a chain according to the block hash value to obtain the full lifecycle archive blockchain data.

[0012] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions: A full lifecycle record management system adapted to the electricity spot market, the system being applied to the aforementioned full lifecycle record management method adapted to the electricity spot market, the system comprising: The unified archive data module is used to acquire daily market clearing data, perform data solidification processing, and synchronize the daily clearing solidified data to all trading systems in the electricity spot market to obtain the daily trading data of each trading system. The data quality verification engine module is used to filter the transaction data of the day and perform entry quality verification through predefined data rules. The transaction data of the day that passes the quality verification is then entered into the database and archived to obtain the transaction archive data of the day. The document data processing module is used to acquire transaction document data from all transaction systems and compare the data consistency with the local document database. Transaction document data with inconsistent comparison results are stored sequentially. The work order management module is used to analyze the differences in data development trends in the local archive database before and after the transaction archive data is stored, and to match abnormal archive data with an alarm mechanism based on the differences in data development trends, so as to obtain full-cycle archive management data.

[0013] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described method for managing the full lifecycle records adapted to the electricity spot market.

[0014] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for managing full lifecycle records adapted to the electricity spot market.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This application transforms the decentralized and static file management of various systems into a centralized, dynamic, and full-process management covering all aspects of market entities from registration to delisting by solidifying and synchronously pushing daily market data. This solves the problem of information silos caused by different data formats between different systems. The quality of file data is strictly controlled through quality inspection before storage, and the data consistency comparison analysis is used to analyze whether the same data has been stored in the local file database, reducing the probability of duplicate data storage. In addition, combined with the difference in data development trend before and after incremental file data storage in the local file database, abnormal file data is promptly alerted. This realizes unified, dynamic, and full life cycle automated management of file data in the electricity spot market, improving the data management efficiency of the electricity spot market. 2. This application uses daily clearing and file processing to determine whether incremental daily clearing data is stored repeatedly in the master data table by comparing hash values. This ensures that the file data used for spot trading and settlement is always updated daily, which can accurately support the decision-making needs of the electricity spot market and fundamentally reduce the probability of settlement disputes and market risks caused by data inconsistency. 3. This application transforms the data in the electricity spot market from passive cleaning to proactive prevention through a configurable data quality verification engine. By strictly controlling the quality of incoming data and promptly generating work orders for abnormal data to allow management intervention, it improves the data governance capabilities of the electricity spot market and provides a reliable data foundation for market decision-making. Through the full-cycle traceability and storage of data processing steps and data status in the archives, the application uses blockchain to reduce the possibility of data tampering and improve data reliability. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart illustrating the implementation of the full lifecycle record management method adapted to the electricity spot market in this embodiment.

[0018] Figure 2 This is the overall data framework diagram of the full lifecycle file management method in this embodiment.

[0019] Figure 3 This is a technical architecture diagram of the full lifecycle file management method in this embodiment.

[0020] Figure 4 This is a flowchart illustrating the implementation of step S10 of the full lifecycle file management method in this embodiment.

[0021] Figure 5 This is a flowchart illustrating the implementation of step S103 of the full lifecycle file management method in this embodiment.

[0022] Figure 6 This is a flowchart illustrating the implementation of the exception handling process in the full lifecycle file management method of this embodiment.

[0023] Figure 7 This is a flowchart illustrating the implementation of step S104 of the full lifecycle file management method in this embodiment.

[0024] Figure 8 This is a flowchart illustrating the implementation of data block storage in the full lifecycle archive management method of this embodiment.

[0025] Figure 9 This is a structural block diagram of the full lifecycle record management system adapted to the electricity spot market in this embodiment.

[0026] Figure 10 This is a schematic diagram of the internal structure of a computer device used to implement a full lifecycle record management method. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0029] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0031] In one embodiment, this application discloses a method for managing the entire lifecycle of records adapted to the electricity spot market. The overall data framework diagram of the method for managing the entire lifecycle of records in this application is as follows: Figure 2 As shown in the diagram, the technical architecture is as follows: Figure 3 As shown, specifically, such as Figure 1 As shown, the full lifecycle file management method adapted to the electricity spot market in this embodiment specifically includes the following steps: S10: Obtain daily market clearing data, perform data solidification processing, and synchronize the daily clearing solidified data to all trading systems in the electricity spot market to obtain the daily trading data of each trading system.

[0032] Specifically, such as Figure 4 As shown, step S10 includes: S101: Obtain the unique identifier of each registered market entity, conduct data verification on the registration data of registered market entities, and perform access permission matching processing on registered market entities that pass the verification.

[0033] Specifically, based on the user access request of the registered market entity, the unique identification code of the registered market entity is obtained. The unique identification code is either the enterprise ID or the unified social credit code of the enterprise. The registration data of the registered market entity is reviewed for data qualification, including basic enterprise information, business information, credit information, etc. For the registered market entities that pass the review, the transaction center conducts internal review and relevant system data review. When the data meets the preset review requirements, access permissions are matched.

[0034] S102: Based on the access permission matching result, obtain the real-time daily clearing data of the registered market entities, and perform data quality verification processing on the real-time daily clearing data through preset data quality verification rules to obtain the data verification result.

[0035] Specifically, based on the access permission matching results, real-time daily clearing data of registered market entities after access is obtained. In this application, the latest daily clearing data is automatically retrieved from external systems such as the marketing system daily through a preset automatic retrieval mechanism. Preset data quality verification rules are used to perform quality checks on key data quality items such as data format, data logic, and data missing information. For data format quality checks, such as the measurement point coding of real-time daily clearing data not conforming to the coding rules in the preset data quality verification rules (including incorrect digits, illegal characters, etc.), for data logic quality checks, such as inconsistencies between the account number and user relationship and the information reserved during registration, and for data missing information, such as the missing measurement value in the current daily clearing data compared to historical daily clearing data in the marketing system, the real-time daily clearing data is checked item by item against the preset data quality verification rules to obtain the data verification results.

[0036] In this embodiment, the data quality verification rules are stored in the database. When data quality verification is triggered, the rule set is loaded through KieS session to verify the real-time daily clearing data and collect the verification results.

[0037] In this embodiment, the daily clearing data retrieval time point is set to trigger the daily clearing task on time every day. The deployed interface server retrieves all the archive data that has changed the previous day from the marketing system's API interface in an incremental manner, including user additions, metering point locations and relationship changes, etc. The data is transmitted in JSON format using the HTTPS protocol.

[0038] S103: If the data verification result is passed, the real-time daily clearing data will be solidified, and the master data table of the registered market entities will be updated according to the power trading time to obtain the daily clearing sub-file data.

[0039] Specifically, if the data verification result is passed, that is, the real-time daily clearing data meets the preset data quality verification rules, then the real-time daily clearing data is solidified according to the preset data solidification method, and the real-time daily clearing data is stored in the master data table according to the power trading time. The master data table of the registered market entity is updated to obtain the daily clearing sub-file data.

[0040] Specifically, such as Figure 5 As shown, step S103 specifically includes: S1031: Calculate the data hash value of the real-time daily clearing data according to the preset data storage rules, and obtain the incremental data hash value of the registered market entities.

[0041] Specifically, according to the preset data storage rules, this embodiment uses the MD5 algorithm to calculate the data hash value of the real-time daily clearing data. The newly retrieved real-time daily clearing data is used as the incremental data of the registered market entities, and the incremental data hash value of the registered market entities is calculated by the MD5 algorithm.

[0042] S1032: Compare the hash value of the incremental data with the hash value of the last data in the main data table, and determine whether the main data table should be stored repeatedly based on the comparison result.

[0043] Specifically, the hash value of the incremental data is compared with the hash value of the last data in the master data table. Based on the comparison result, it is determined whether the master data table has stored duplicate data. If the two hash values ​​are the same, it means that there is daily clearing data with the same hash value in the master data table. If the two hash values ​​are different, it means that there is no daily clearing data with the same hash value in the master data table.

[0044] S1033: If not, then update the solidified real-time daily clearing data at the end of the master data table according to the power trading time, and update the master data table.

[0045] Specifically, if the master data table does not store daily clearing data with the same hash value, the solidified real-time daily clearing data will be updated to the end of the master data table according to the power trading time, and the master data table will be updated.

[0046] Step S1033 in this embodiment further includes: S1034: Compare the real-time daily clearing data with the last data in the master data table item by item, and predict the power trading trend of registered market participants based on the comparison results.

[0047] Specifically, the real-time daily clearing data is stored in a temporary table and compared with the end data of the master data table in an independent process. By comparing each item and each field, the data differences between the real-time daily clearing data and the end data of the master data table are found. These differences include fields such as metering point ID, electricity user number, and electricity address, as well as the values ​​before and after the field changes. Based on the data difference results obtained from the comparison, the electricity trading trend of registered market entities is predicted, which helps to promptly detect abnormal situations of registered market entities.

[0048] In this embodiment, as Figure 6 As shown, after step S102, the following is also included: S1021: If the data verification result is unsuccessful, then retrieve all abnormal data in the real-time daily clearing data that does not conform to the data quality inspection rules.

[0049] Specifically, if the data verification structure fails, all abnormal data in the real-time daily clearing data that does not conform to the data quality inspection rules will be retrieved, including abnormal metering point IDs, abnormal electricity user numbers, abnormal electricity addresses, etc.

[0050] S1022: Associate the abnormal data with the unique identifier and package it into an abnormal work order. Send the abnormal work order to the electricity spot market management terminal through a preset message queue.

[0051] Specifically, abnormal data is associated with a unique identifier and packaged into an abnormal work order. This work order is then sent to the electricity spot market management terminal via a preset message queue. In this embodiment, the message queue is Kafka. The abnormal data is then visualized using the work order visualization rules of the management terminal.

[0052] S104: Synchronize the daily clearing and filing data to all trading systems in the electricity spot market, update the filing data in each trading system according to the unique identifier, and obtain the daily trading data of registered market participants.

[0053] Specifically, such as Figure 7 As shown, step S104 includes: S1041: Obtain the business data requirements of all trading systems in the electricity spot market, analyze the common characteristics of the data of all trading systems based on the business data requirements, and obtain the common characteristics of the system data in the electricity spot market.

[0054] Specifically, it involves acquiring the business data requirements of all trading systems in the electricity spot market, analyzing the common characteristics of data related to the current electricity trading business in all trading systems based on the current data requirements of the current electricity trading business, including the entity name and entity code in the basic identity characteristics, the trading qualifications and credit information in the business characteristics, the generating units and metering points in the asset characteristics, and the organizational structure relationships in the relationship characteristics.

[0055] S1042: Conduct data training on the common characteristics of system data and corresponding power trading business to build a unified data model that covers the business requirements of all trading systems.

[0056] Specifically, using the common characteristics of system data for each power trading business as training samples, data training is performed on the relevant data of all power trading businesses. Based on the training results, the common attributes of business requirements of different power trading businesses in all trading systems are analyzed, and then a unified data model is constructed.

[0057] S1043: Input the daily clearing and filing data into the unified data model for common data feature analysis, and create user profiles for registered market entities based on the analysis results to obtain the entity profile data for the current power trading business.

[0058] Specifically, the daily clearing and filing data is input into the same data model and compared with the common features of the data in the model in terms of numerical and logical consistency. Based on the comparison results, user profiles are created for registered market entities to obtain the entity profile data of the current power trading business.

[0059] S20: Filter the daily transaction data using predefined data rules and perform an entry quality inspection. Archive the daily transaction data that passes the quality inspection to obtain the daily transaction archive data.

[0060] Specifically, data filtering and entry rules are dynamically configured and enabled by administrators through a built-in configurable rule engine. These rules mainly include format verification such as ID card number and social credit code, enumeration value verification such as voltage level enumeration, logical verification, and cross-entity association verification. The daily transaction data is filtered and the entry quality is checked according to predefined data rules. For example, if relevant data in the daily transaction data is filtered according to the entry quality check items, and all relevant data meet the preset entry quality check requirements, it is considered to have passed the quality check. The daily transaction data that has passed the quality check is collected into the local archive database for archiving, resulting in the daily transaction archive data.

[0061] S30: Obtain transaction file data from all transaction systems and compare the data consistency with the local file database. Transaction file data with inconsistent comparison results are stored sequentially.

[0062] Specifically, the transaction archive data of all transactions is obtained, and each transaction archive data is compared with the local archive database for data consistency. The transaction archive data is compared item by item and field by field. Transaction archive data with inconsistent comparison results are stored in order of transaction time.

[0063] S40: Analyze the differences in data development trends in the local archive database before and after the transaction archive data is stored, and match the alarm mechanism for abnormal archive data based on the differences in data development trends to obtain full-cycle archive management data.

[0064] Specifically, the differences in data development trends in the local archive database before and after the transaction archive data is stored are compared, including differences in data values ​​and differences in data development direction. Based on the differences in data development trends, corresponding alarm mechanisms are matched for abnormal archive data. For example, corresponding approval roles are set at different process nodes. When the data development trends are consistent, the process is automated. When the data development trends are inconsistent, the data at the corresponding process node is manually reviewed according to the preset approval roles, thereby obtaining full-cycle archive management data for the electricity spot market.

[0065] like Figure 8 As shown, the full lifecycle file management method adapted to the electricity spot market in this embodiment also includes: S50: Obtain each data processing step and corresponding data status of the daily market data stored in the local archive database, and package the data processing steps and data status into data blocks.

[0066] Specifically, the system retrieves data from each data processing step and its corresponding data status, storing daily market data in a local archive database. Then, through a pre-defined smart contract, it invokes a blockchain storage algorithm to package each data operation step and its corresponding data status into data blocks. The data processing steps in this embodiment include multiple steps such as entity registration, information modification, asset transfer, and delisting application.

[0067] S60: Calculate the block hash value of each data block according to the data processing time, and store the packaged data blocks in a chain according to the block hash value to obtain the full life cycle archive blockchain data.

[0068] Specifically, the block hash value of each data block is calculated according to the data processing time. In this embodiment, the MD5 algorithm is used to calculate the hash value. The packaged data blocks are stored in a chain according to the data processing time based on the block hash value to obtain the full life cycle archive blockchain data.

[0069] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0070] In one embodiment, a full lifecycle record management system adapted to the electricity spot market is provided, which corresponds one-to-one with the full lifecycle record management method adapted to the electricity spot market described in the above embodiments. For example... Figure 9 As shown, this full lifecycle record management system adapted to the electricity spot market includes a unified record data module, a data quality verification engine module, a record data processing module, and a work order management module. Detailed descriptions of each functional module are as follows: The unified archive data module is used to acquire daily market data, perform data solidification processing, and synchronize the solidified daily data to all trading systems in the electricity spot market to obtain the daily trading data for each trading system.

[0071] The data quality verification engine module is used to filter and inspect the transaction data of the day according to predefined data rules, and archive the transaction data of the day that passes the quality inspection to obtain the transaction archive data of the day.

[0072] The document data processing module is used to acquire transaction document data from all transaction systems and compare it with the local document database. Transaction document data with inconsistent comparison results are stored sequentially.

[0073] The work order management module is used to analyze the differences in data development trends in the local archive database before and after the transaction archive data is stored. Based on the differences in data development trends, an alarm mechanism is used to match abnormal archive data to obtain full-cycle archive management data.

[0074] Preferably, the unified archive data module specifically includes: The access control submodule is used to obtain the unique identifier of each registered market entity, review the registration data of registered market entities, and perform access control matching for registered market entities that pass the review.

[0075] The quality verification submodule is used to obtain the real-time daily clearing data of registered market entities based on the access permission matching results, and to perform data quality verification processing on the real-time daily clearing data according to the preset data quality verification rules to obtain the data verification results.

[0076] The data archiving submodule is used to solidify the real-time daily clearing data if the data verification result is passed, and to update the master data table of registered market entities according to the power trading time to obtain the daily clearing sub-archive data.

[0077] The data update submodule is used to synchronize the daily clearing and filing data to all trading systems in the electricity spot market. It updates the filing data in each trading system according to the unique identifier to obtain the daily trading data of registered market participants.

[0078] Preferably, the master data table update process in the data archiving submodule specifically includes: The hash value calculation unit is used to calculate the data hash value of real-time daily clearing data according to preset data storage rules, so as to obtain the incremental data hash value of registered market entities.

[0079] The duplicate detection unit is used to compare the hash value of the incremental data with the hash value of the last data in the main data table, and determine whether the main data table should store duplicate data based on the comparison result.

[0080] The data update unit is used to update the master data table by updating the solidified real-time daily clearing data at the end of the master data table according to the power trading time if no.

[0081] Preferably, the data update unit further includes: The trend prediction unit is used to compare the real-time daily clearing data with the data at the end of the master data table item by item, and predict the power trading trend of registered market participants based on the comparison results.

[0082] Preferably, the quality verification submodule also includes: The abnormal data extraction unit is used to extract all abnormal data in the real-time daily clearing data that does not meet the data quality inspection rules if the data verification result is unsuccessful.

[0083] The abnormal work order processing unit is used to associate abnormal data with unique identifiers and package them into abnormal work orders, and send the abnormal work orders to the power spot market management terminal through a preset message queue.

[0084] Preferably, the data update submodule specifically includes: The feature analysis unit is used to obtain the business data requirements of all trading systems in the electricity spot market, analyze the common data characteristics of all trading systems based on the business data requirements, and obtain the common system data characteristics of the electricity spot market.

[0085] The model building unit is used to train data on the common characteristics of system data and corresponding power trading business, and to build a unified data model that covers the business requirements of all trading systems.

[0086] The user profiling unit is used to input daily clearing and filing data into a unified data model for common data feature analysis. Based on the analysis results, user profiles are created for registered market entities to obtain the main profile data of the current power trading business.

[0087] Preferably, the full lifecycle file management method adapted to the electricity spot market in this embodiment further includes: The data packaging module retrieves each data processing step and corresponding data status of the daily market data stored in the local archive database, and packages the data processing steps and data status into data blocks.

[0088] The block storage module is used to calculate the block hash value of each data block according to the data processing time, and to perform chain storage of the packaged data blocks according to the block hash value to obtain the full life cycle archive blockchain data.

[0089] Specific limitations regarding the full lifecycle record management system adapted to the electricity spot market can be found in the above-mentioned limitations on the full lifecycle record management method adapted to the electricity spot market, and will not be repeated here. Each module in the aforementioned full lifecycle record management system adapted to the electricity spot market can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0090] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores the entire management process data for the daily clearing records of the electricity spot market. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a full lifecycle record management method adapted to the electricity spot market.

[0091] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a full lifecycle record management method adapted to the electricity spot market.

[0092] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0093] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0094] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for managing the entire lifecycle of records adapted to the electricity spot market, characterized in that, The method includes: Acquire daily market clearing data, perform data solidification processing, and synchronize the daily clearing solidified data to all trading systems in the electricity spot market to obtain the daily trading data for each trading system. The daily transaction data is filtered and inspected for quality before being stored by predefined data rules. The daily transaction data that passes the quality inspection is then stored and archived to obtain the daily transaction archive data. Acquire transaction file data from all trading systems and compare the data consistency with the local file database. Transaction file data with inconsistent comparison results are stored sequentially. The differences in data development trends of the local archive database before and after the transaction archive data is stored are analyzed. Based on the differences in data development trends, an alarm mechanism is used to match abnormal archive data to obtain full-cycle archive management data.

2. The method for full lifecycle record management adapted to the electricity spot market according to claim 1, characterized in that, The process of acquiring daily cleared market data, performing data solidification processing, and synchronizing the solidified daily cleared data to all trading systems in the electricity spot market to obtain the daily trading data for each trading system specifically includes: Obtain the unique identifier of each registered market entity, conduct data verification on the registration data of the registered market entities, and perform access permission matching processing on the registered market entities that pass the verification; Based on the access permission matching result, the real-time daily clearing data of the registered market entities is obtained, and the real-time daily clearing data is processed for data quality verification according to the preset data quality verification rules to obtain the data verification result; If the data verification result is passed, the real-time daily clearing data is solidified, and the master data table of the registered market entity is updated according to the power trading time to obtain the daily clearing sub-file data; The daily clearing and filing data is synchronized to all trading systems in the electricity spot market, and the filing data in each trading system is updated according to the unique identifier to obtain the daily trading data of the registered market participants.

3. The method for full lifecycle record management adapted to the electricity spot market according to claim 2, characterized in that, If the data verification result is successful, the real-time daily clearing data is solidified, and the master data table of the registered market entity is updated according to the electricity trading time. The specific process of updating the master data table in the daily clearing sub-file data includes: According to the preset data storage rules, the data hash value of the real-time daily clearing data is calculated to obtain the incremental data hash value of the registered market entity; The incremental data hash value is compared with the last data hash value of the main data table, and the main data table is determined to be stored repeatedly based on the comparison result. If not, the solidified real-time daily clearing data is updated at the end of the master data table according to the power trading time, and the master data table is updated.

4. The method for full lifecycle record management adapted to the electricity spot market according to claim 3, characterized in that, The step of updating the master data table by updating the solidified real-time daily clearing data to the end of the master data table according to the power trading time further includes: The real-time daily clearing data is compared item by item with the last data in the master data table, and the power trading trend of the registered market participants is predicted based on the comparison results.

5. The method for full lifecycle record management adapted to the electricity spot market according to claim 2, characterized in that, The step of obtaining real-time daily clearing data of registered market entities based on the access permission matching result, and performing data quality verification processing on the real-time daily clearing data through preset data quality verification rules to obtain data verification results, further includes: If the data verification result is unsuccessful, then all abnormal data in the real-time daily clearing data that do not conform to the data quality inspection rules are obtained; The abnormal data is associated with the unique identifier and packaged into an abnormal work order. The abnormal work order is then sent to the electricity spot market management terminal through a preset message queue.

6. The method for full lifecycle record management adapted to the electricity spot market according to claim 2, characterized in that, The process of synchronizing the daily clearing and filing data to all trading systems in the electricity spot market, and updating the filing data in each trading system according to the unique identifier to obtain the daily trading data of the registered market participants, specifically includes: Obtain the business data requirements of all trading systems in the electricity spot market, analyze the common characteristics of the data of all trading systems based on the business data requirements, and obtain the common characteristics of system data in the electricity spot market; Data training is performed on the common characteristics of the system data and the corresponding power trading business to construct a unified data model that covers the business requirements of all trading systems; The daily clearing and filing data is input into the unified data model for common data feature analysis. Based on the analysis results, user profiles are created for registered market entities to obtain the entity profile data for the current power trading business.

7. The method for full lifecycle record management adapted to the electricity spot market according to claim 1, characterized in that, The method further includes: Obtain each data processing step and corresponding data status of the daily market data stored in the local archive database, and package the data processing steps and data status into data blocks; The block hash value of each data block is calculated based on the data processing time. The packaged data blocks are then stored in a chain according to the block hash value to obtain the full lifecycle archive blockchain data.

8. A full lifecycle record management system adapted to the electricity spot market, characterized in that, The system is applied to the full life-cycle file management method for the electricity spot market as described in any one of claims 1-7, and the system includes: The unified archive data module is used to acquire daily market clearing data, perform data solidification processing, and synchronize the daily clearing solidified data to all trading systems in the electricity spot market to obtain the daily trading data of each trading system. The data quality verification engine module is used to filter the transaction data of the day and perform entry quality verification through predefined data rules. The transaction data of the day that passes the quality verification is then entered into the database and archived to obtain the transaction archive data of the day. The document data processing module is used to acquire transaction document data from all transaction systems and compare the data consistency with the local document database. Transaction document data with inconsistent comparison results are stored sequentially. The work order management module is used to analyze the differences in data development trends of the local archive database before and after the transaction archive data is stored, and to match abnormal archive data with an alarm mechanism based on the differences in data development trends to obtain full-cycle archive management data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the full lifecycle file management method for the electricity spot market as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the full lifecycle file management method for the electricity spot market as described in any one of claims 1 to 7.

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