Electric power settlement method and system based on self-arrangement settlement algorithm rule

Abstract

The invention relates to an electric power settlement method and system based on a self-arrangement settlement algorithm rule. The method comprises the following steps: determining a corresponding service data source according to an electric power settlement service type; and based on a self-defined capturing rule, capturing the service data in the determined service data source through the multi-time-point window. Processing the captured business data and storing the processed business data in a database; abstracting a data table in the database as a data source node as a starting node of a settlement algorithm rule self-arrangement process; according to the business data of the starting node, constructing a data processing node to perform custom settlement rule configuration; constructing a computing node to carry out user-defined calling combination on a pre-configured algorithm formula, and generating a self-arrangement settlement algorithm rule in combination with a user-defined settlement rule; and based on the self-arrangement settlement algorithm rule, processing the business data in parallel through a plurality of nodes in the distributed memory computing architecture, and outputting a power settlement result. The self-arrangement settlement algorithm rule can adapt to changeable business environments.

Description

Technical Field

[0001] The present application relates to the technical field of power transaction settlement, and in particular to a power settlement method and system based on self-compiled settlement algorithm rules. Background Art

[0002] The gradual entry of general industrial and commercial market players into the market, the introduction of differentiated retail packages, and the promotion of the electricity spot market have led to a shift from the original monthly peak and valley electricity price mechanism to hourly and 15-minute time-sharing transactions. As a result, the complexity of electricity settlement, the number of settlement entities, and the amount of settlement data have all experienced explosive growth.

[0003] At present, the settlement of power transactions is characterized by large settlement data and frequent policy changes. At the same time, the settlement items of power transactions are numerous, the calculation process is complex, and there are interrelated relationships in the calculation, which increases the time consumption of data calculation. Under different policy environments, the development of settlement algorithm programs and inefficient program operation have brought high time costs to settlement, and the policy adaptation agility is insufficient. Summary of the invention

[0004] In order to at least to some extent overcome the problems of high time cost and insufficient adaptability in electricity trading settlement in related technologies, the present application provides an electricity settlement method and system based on self-organized settlement algorithm rules.

[0005] The scheme of this application is as follows: According to a first aspect of an embodiment of the present application, there is provided a method for electricity settlement based on a self-organized settlement algorithm rule, comprising: Determine the corresponding business data source according to the type of power settlement business; Capture business data from a specific business data source through multiple time point windows based on custom capture rules; Process the captured business data and store it in the database; Abstract the data table in the database as a data source node, which serves as the starting node of the self-organization process of the settlement algorithm rules; According to the business data of the starting node, a data processing node is constructed to configure a custom settlement rule; Build computing nodes to perform custom call combinations on pre-configured algorithm formulas, and generate self-compiled settlement algorithm rules in combination with custom settlement rules; Based on the self-organized settlement algorithm rules, business data is processed in parallel by multiple nodes in the distributed memory computing architecture to output power settlement results.

[0006] Preferably, in the settlement algorithm rule self-organization process, the method further includes: Process the previous node data according to the user-defined SQL statement.

[0007] Preferably, the constraints for configuring custom settlement rules include: Market types and settlement elements; Among them, market types include: medium- and long-term market transactions, day-ahead spot market transactions, and real-time market transactions; Settlement elements include: clearing method, unbalanced funds, deviation assessment and settlement allocation.

[0008] Preferably, the method further comprises: Through virtualization technology, physical resources, CPU, memory and disk resources are abstracted into a state that can be divided and recombined, simulating an independent operating system environment; Execute business data storage, self-organization of settlement algorithm rules and business data processing in an independent operating system environment.

[0009] Preferably, the electricity settlement business types include: Basic support services, core application services, decision-making support services and customer billing services; The basic support services include: business parameter configuration center, settlement algorithm configuration center, data flow monitoring center and verification rule configuration center; the basic support services are used to provide support for system functional technology; The core application business includes: daily settlement, monthly settlement and demand-side response; the core application business is used to collect data and settle accounts in combination with market rules; The auxiliary decision-making business includes: real-time measurement, simulation, data mining, data auditing, customer service and intelligent analysis; the auxiliary decision-making business is used to provide support for diversified customer service; The customer billing service includes: electricity quantity confirmation and review, settlement result confirmation and review; the customer billing service is used to strengthen the business interaction between the settlement system and market entities.

[0010] Preferably, the captured business data is processed, including: Establish a data pool to clean, convert, verify and govern the captured business data; Data pools are used to share data between different business entities, as well as between business data and the outside world.

[0011] Preferably, the method further comprises: Output the processed business data to the specified data table or database; After outputting the electricity settlement results, the calculation process and electricity settlement results are verified by combining the model self-check and theorem prover.

[0012] Preferably, when processing business data in parallel through multiple nodes in a distributed memory computing architecture, the method further includes: Determine the algorithm data size based on data volume fluctuations and changes in computing requirements; Select computing task types of different sizes based on the amount of algorithm data.

[0013] Preferably, the method further comprises: Record the self-compiled settlement algorithm rules generated historically based on the timeline.

[0014] According to a second aspect of an embodiment of the present application, there is provided an electricity settlement system based on a self-organized settlement algorithm rule, comprising: Processor and memory; The processor and the memory are connected via a communication bus: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store a program, and the program is at least used to execute an electricity settlement method based on self-organized settlement algorithm rules as described in any one of the above items.

[0015] The technical solution provided by this application may have the following beneficial effects: The electric power settlement method based on self-organized settlement algorithm rules in the present application includes: determining the corresponding business data source according to the type of electric power settlement business; capturing business data in the determined business data source through multiple time point windows based on custom capture rules; storing the captured business data in a database after processing; abstracting the data table in the database as a data source node as the starting node of the self-organized process of the settlement algorithm rules; constructing a data processing node to configure a custom settlement rule according to the business data of the starting node; constructing a computing node to perform a custom call combination on the pre-configured algorithm formula, and generating a self-organized settlement algorithm rule in combination with the custom settlement rule; based on the self-organized settlement algorithm rule, processing business data in parallel through multiple nodes in the distributed memory computing architecture, and outputting the electric power settlement result.

[0016] In this technical solution, business data is flexibly captured based on multiple time point windows and custom capture rules to provide support for subsequent functions. Data processing nodes support user-defined settlement rule configuration, allowing users to dynamically adjust calculation logic, and construct calculation nodes to support users to customize the call combination of pre-configured algorithm formulas, so that the algorithm can adapt to the changing business environment to meet the needs and data characteristics of different users, with higher accuracy and flexibility. Its flexible configuration capabilities can realize the customization of differentiated settlement services, breaking the barriers between users and system managers in function customization, while improving settlement efficiency, paying more attention to the intuitive display of results, improving user experience, and enhancing the adaptability of rules.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] Figure 1 It is a flowchart of a method for electricity settlement based on self-organized settlement algorithm rules provided by an embodiment of the present application; Figure 2 It is a structural diagram of an electricity settlement system based on self-organized settlement algorithm rules provided by an embodiment of the present application.

[0020] Reference numerals: processor-21; memory-22. DETAILED DESCRIPTION

[0021] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0022] Figure 1 This is a flow chart of a method for electricity settlement based on self-organized settlement algorithm rules provided by an embodiment of the present application, with reference to Figure 1 , a method for electricity settlement based on self-organized settlement algorithm rules, comprising: S11: Determine the corresponding business data source according to the power settlement business type; It should be noted that the types of electricity settlement services in this technical solution include: Basic support services, core application services, decision-making support services and customer billing services; Basic support services include: business parameter configuration center, settlement algorithm configuration center, data flow monitoring center and verification rule configuration center; basic support services are used to provide support for system functional technologies; Core application businesses include: daily clearing, monthly settlement and demand-side response; core application businesses are used to collect data and settle accounts in accordance with market rules; Decision-making support services include: real-time measurement, simulation, data mining, data auditing, customer service and intelligent analysis; decision-making support services are used to provide support for diversified customer services; Customer billing services include: electricity quantity confirmation and review, settlement result confirmation and review; customer billing services are used to strengthen the business interaction between the settlement system and market entities.

[0023] S12: Capture business data from a determined business data source through multiple time point windows based on the custom capture rules; It should be noted that the captured business data is processed, including: Establish a data pool to clean, convert, verify and govern the captured business data; Data pools are used to share data between different business entities, as well as between business data and the outside world.

[0024] A settlement data pool is established within the settlement business for sharing between business data and with external data. Preferably, the data interaction and synchronization tool in this embodiment can realize data interaction and integration, and supports two-way push in timing, active triggering, fixed frequency and other modes.

[0025] In this embodiment, business data can be flexibly captured based on the customized capture rules.

[0026] Through multi-time point window triggering, business data can be fully captured in terms of time and space scales.

[0027] Preferably, this embodiment also supports customization of data cleaning, data conversion, data verification and data governance.

[0028] In specific practice, custom data governance fully combines SQL queries, ETL tools, API calls, file transfers, data flow technologies, etc., and introduces custom functions in defining data ranges, functional nodes, and process selection. Among them, SQL statements are used to query and extract data from relational databases, and combined with API calls, the custom range function design of the data source is completed; combined with tools such as Informatica and Talend, the system supports custom crawling functions in nested processes; combined with tools such as Apache Kafka and Amazon Kinesis, the extraction and processing of real-time data streams is realized.

[0029] S13: Processing the captured business data and storing it in a database; It should be noted that in this technical solution, virtualization technology is used to abstract physical resources, save CPU, memory and disk resources into a state that can be divided and recombined, simulating an independent operating system environment; Execute business data storage, self-organization of settlement algorithm rules and business data processing in an independent operating system environment.

[0030] That is, in this embodiment, the data storage adopts sandbox technology, which uses virtualization technology to abstract physical resources, such as CPU, memory, disk resources, etc., into a state that can be divided and recombined, simulating an independent operating system environment so that applications or processes can run in this environment. The running program can only access the resources loaded in the independent environment and will not affect external applications, systems or platforms.

[0031] S14: abstract the data table in the database into a data source node, which serves as the starting node of the self-organization process of the settlement algorithm rules; Business data includes market transaction data, user online electricity consumption (power sellers), user electricity consumption (power buyers), etc. The data table in the database is abstracted as a data source node, which serves as the starting node for the self-organization process of the settlement algorithm rules. The setting of the data source node is the basis of the algorithm process. It contains all the necessary raw data and provides input for subsequent data processing and calculation.

[0032] S15: Based on the business data of the starting node, a data processing node is constructed to configure a custom settlement rule; Data processing nodes can be configured based on the business data of the previous node, which means that users can customize the business data according to their business needs. After configuration, you can run to view real-time data information and processing process log information to ensure the correctness of the algorithm flow configuration.

[0033] The specific constraints for configuring custom settlement rules include: ① Market type: including medium- and long-term market transactions, day-ahead spot market transactions, real-time market transactions, etc.; ② Settlement elements: clearing method, unbalanced funds, deviation assessment, settlement allocation, etc.

[0034] S16: construct a computing node to perform a custom call combination on the pre-configured algorithm formula, and generate a self-compiled settlement algorithm rule in combination with the custom settlement rule; When configuring the core calculation formula for settlement in the computing node, users can intuitively view the configured formula information, for example: electricity fee = electricity * electricity price * coefficient. This intuitive formula display method allows users to easily understand and verify the calculation logic of the algorithm and easily make custom call combinations.

[0035] It should be noted that in the self-compilation process of the settlement algorithm rules, the method also includes: Process the previous node data according to the user-defined SQL statement.

[0036] In order to further improve the flexibility of data processing, custom SQL nodes are provided. Users can customize SQL statements to process the previous node data, which greatly improves the flexibility at the data processing level and allows users to perform more detailed operations on the data according to specific business needs.

[0037] S17: Based on the self-organized settlement algorithm rules, business data is processed in parallel through multiple nodes in the distributed memory computing architecture to output the electricity settlement results.

[0038] In this embodiment, a distributed memory computing architecture is built to enhance computing power. The distributed memory computing architecture automatically distributes huge amounts of data on the memory of multiple nodes in the cluster, and completes short-term computing tasks of massive data through simultaneous processing of multiple nodes.

[0039] Specifically, each node only stores part of the data in its memory, which can improve the efficiency of data processing by the node. When executing computing tasks, since the data has been stored in a decentralized manner, multiple nodes in the cluster can independently process their own data fragments at the same time, thus achieving parallel computing.

[0040] At the same time, the distributed memory computing architecture reduces the delay caused by data transmission in the network while allowing data to be processed locally, thereby improving the efficiency of data processing.

[0041] When processing business data in parallel through multiple nodes in a distributed memory computing architecture, the method further includes: Determine the algorithm data size based on data volume fluctuations and changes in computing requirements; Select computing task types of different sizes based on the amount of algorithm data.

[0042] Specifically, two task types, Low Resource and Hight Resource, are used. The computing task type is selected according to the amount of algorithm data to cope with fluctuations in data volume and changes in computing requirements.

[0043] •High Resource Tasks: Suitable for computing tasks with large data volumes. For this type of task, you can customize more server resources to ensure computing efficiency and performance. You can customize server resources, including memory, number of cores, number of partitions, etc. When the amount of data increases, add nodes or resources to expand processing capabilities to adapt to the growth of data volume. This elastic expansion capability ensures that high-performance computing capabilities can be maintained even when massive data is calculated or the amount of data suddenly increases.

[0044] •Low Resource Tasks: Suitable for computing tasks with a small amount of data. This type of task does not require too many resources, can reduce resource usage, improve resource utilization, and maximize the use of limited resources to run more computing tasks.

[0045] It should be noted that when the number of submitted computing tasks exceeds a certain number (the number is user-configurable), if resources are limited, the system can execute an algorithm queuing strategy and only run a specified number of tasks. Other tasks are queued in the order of submission time.

[0046] It should be noted that the settlement plan runs as a computing task, and the computing tasks run in the independent memory space of the system. Even if the same program runs at the same time, it can be executed in parallel at the resource level without interfering with each other. The temporary results of the computing tasks are also saved in their own independent data tables to ensure that the intermediate data of different tasks will not affect each other, and will not affect the original data of the calculation.

[0047] It should be noted that in the distributed memory computing architecture, data is stored in memory instead of on disk, which has a significant impact on improving data processing speed. Traditional data processing relies on disk I / O operations, and due to the limited read and write speed of the disk, the computing process takes a long time. The memory computing method can process data directly in memory, which not only reduces disk I / O operations, but also improves the speed and response time of data processing.

[0048] In addition, since the virtualization technology is used in this embodiment to abstract physical resources, save CPU, memory and disk resources into a state that can be divided and recombined, an independent operating system environment is simulated. For the same algorithm, it can be carried out simultaneously without affecting each other. For example, for the same set of monthly settlement plan algorithms, monthly settlement calculations for multiple months can be performed at the same time according to the input month parameters.

[0049] It should be noted that the method also includes: Output the processed business data to the specified data table or database; After the business data is processed, the data output node is used as the end point of the algorithm flow. At the data output node, the processed business data can be flexibly output to the specified database and specified table. This step ensures the availability and traceability of the data processing results, which facilitates subsequent data analysis and auditing. According to business needs, multiple algorithms are configured as settlement plans. The algorithms in the settlement plan can be configured as front-to-back dependencies. The big data computing engine automatically builds a hierarchical order based on the relationship between the algorithms. At the same time, the automatic execution time and frequency of the plan can be configured.

[0050] In this technical solution, after outputting the power settlement results, the calculation process and results are verified by combining the model self-check and theorem prover.

[0051] The system has a built-in model self-checking function, which can self-check the calculation results by simulating various situations that may occur during the settlement process. It can identify and correct potential errors, such as data input errors, calculation logic errors, or algorithm implementation errors. The model self-checking function verifies the rationality of the final output by comparing it with the known correct results or by checking the intermediate steps in the calculation process.

[0052] The system also integrates a theorem prover, a formal verification tool used to prove the logical correctness of the settlement algorithm. The theorem prover verifies whether the algorithm follows the correct mathematical principles and logical structure through a series of mathematical rules and logical reasoning. It ensures that the algorithm is theoretically correct and can produce correct outputs for all possible inputs.

[0053] In addition, the theorem prover can also check the boundary conditions and exception handling mechanisms of the algorithm to ensure that the algorithm can still run stably and output correct results under extreme conditions. This double verification mechanism - model self-checking and theorem proving - is also one of the advantages of the flexible settlement system. They not only improve the accuracy of settlement, but also enhance users' trust in the system results.

[0054] It should be noted that the method also includes: Record the self-compiled settlement algorithm rules generated historically based on the timeline.

[0055] The self-organized settlement algorithm rules generated in this technical solution also have a version control function, which can track the change history of the self-organized settlement algorithm rules and roll back to any officially released version. After the self-organized settlement algorithm rules are configured, they can be released as an official version. If the self-organized settlement algorithm rules are modified, they can be released as another version after modification. When the self-organized settlement algorithm rules are running, any effective version can be selected to enable them.

[0056] In summary, in this technical solution, business data is flexibly captured based on multi-time point windows and custom capture rules to provide support for subsequent functions. Data processing nodes support user-defined settlement rule configuration, allowing users to dynamically adjust calculation logic, and construct calculation nodes to support users to customize the call combination of pre-configured algorithm formulas, so that the algorithm can adapt to the changing business environment to meet the needs and data characteristics of different users, with higher accuracy and flexibility. Its flexible configuration capability can realize the customization of differentiated settlement services, breaking the barriers between users and system managers in function customization, while improving settlement efficiency, paying more attention to the intuitive display of results, improving user experience, and enhancing the adaptability of rules.

[0057] Embodiment 2 An electricity settlement system based on self-organized settlement algorithm rules, referring to Figure 2 ,include: Processor 21 and memory 22; The processor 21 and the memory 22 are connected via a communication bus: The processor 21 is used to call and execute the program stored in the memory 22; The memory 22 is used to store a program, and the program is used to execute at least one of the electricity settlement methods based on the self-organized settlement algorithm rules in the above embodiments.

[0058] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0059] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0060] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0061] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0062] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0063] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0064] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0065] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0066] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for electricity settlement based on self-compiled settlement algorithm rules, characterized in that: include: Determine the corresponding business data source according to the type of power settlement business; Capture business data from a specific business data source through multiple time point windows based on custom capture rules; Process the captured business data and store it in the database; Abstract the data table in the database as a data source node, which serves as the starting node of the self-organization process of the settlement algorithm rules; According to the business data of the starting node, a data processing node is constructed to configure a custom settlement rule; Build computing nodes to perform custom call combinations on pre-configured algorithm formulas, and generate self-compiled settlement algorithm rules in combination with custom settlement rules; Based on the self-organized settlement algorithm rules, business data is processed in parallel by multiple nodes in the distributed memory computing architecture to output power settlement results.

2. The method according to claim 1, characterized in that In the settlement algorithm rule self-compilation process, the method further includes: Process the previous node data according to the user-defined SQL statement.

3. The method according to claim 1, characterized in that The constraints for configuring custom settlement rules include: Market types and settlement elements; Among them, market types include: medium- and long-term market transactions, day-ahead spot market transactions, and real-time market transactions; Settlement elements include: clearing method, unbalanced funds, deviation assessment and settlement allocation.

4. The method according to claim 1, characterized in that: The method further comprises: Through virtualization technology, physical resources, CPU, memory and disk resources are abstracted into a state that can be divided and recombined, simulating an independent operating system environment; Execute business data storage, self-organization of settlement algorithm rules and business data processing in an independent operating system environment.

5. The method according to claim 1, characterized in that The types of electricity settlement services include: Basic support services, core application services, decision-making support services and customer billing services; The basic support services include: business parameter configuration center, settlement algorithm configuration center, data flow monitoring center and verification rule configuration center; the basic support services are used to provide support for system functional technology; The core application business includes: daily settlement, monthly settlement and demand-side response; the core application business is used to collect data and settle accounts in combination with market rules; The auxiliary decision-making business includes: real-time measurement, simulation, data mining, data auditing, customer service and intelligent analysis; the auxiliary decision-making business is used to provide support for diversified customer service; The customer billing service includes: electricity quantity confirmation and review, settlement result confirmation and review; the customer billing service is used to strengthen the business interaction between the settlement system and market entities.

6. The method according to claim 1, characterized in that Process the captured business data, including: Establish a data pool to clean, convert, verify and govern the captured business data; Data pools are used to share data between different business entities, as well as between business data and the outside world.

7. The method according to claim 1, characterized in that The method further comprises: Output the processed business data to the specified data table or database; After outputting the electricity settlement results, the calculation process and electricity settlement results are verified by combining the model self-check and theorem prover.

8. The method according to claim 1, characterized in that When processing business data in parallel through multiple nodes in a distributed memory computing architecture, the method further includes: Determine the algorithm data size based on data volume fluctuations and changes in computing requirements; Select computing task types of different sizes based on the amount of algorithm data.

9. The method according to claim 1, characterized in that: The method further comprises: Record the self-compiled settlement algorithm rules generated historically based on the timeline.

10. An electric power settlement system based on self-compiled settlement algorithm rules, characterized in that: include: Processor and memory; The processor and the memory are connected via a communication bus: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store a program, and the program is used at least to execute an electricity settlement method based on self-organized settlement algorithm rules as described in any one of claims 1-9.

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