Concentrated bidding result data analysis method and device, equipment and storage medium

Abstract

The invention discloses an analysis method and device for centralized bidding result data, equipment and a storage medium. The method comprises the following steps: acquiring a centralized bidding result data set of an electricity market; segmenting the centralized bidding result data set into a plurality of data subsets, and constructing a binary tree to be analyzed by using data elements in the data subsets; the to-be-analyzed binary tree is analyzed according to a preset YAML rule file through a binary tree YAML analyzer, so that a first data analysis result is obtained, the binary tree YAML analyzer comprises a plurality of YAML sub-analyzers, and the YAML sub-analyzers are of a binary tree structure. According to the technical scheme, the YAML rule file of the pre-configured binary tree structure is used for analyzing the multiple data subsets of concentrated bidding of the electricity market, the analysis speed and efficiency of the electricity transaction data are optimized, the electricity transaction data with the incidence relation are accurately interpreted, and the analysis efficiency of the electricity transaction data is improved. The problems of low power data analysis efficiency and poor accuracy are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power trading, and in particular to a method, device, equipment and storage medium for analyzing centralized bidding result data. Background Art

[0002] In the electricity market, the monthly centralized bidding is a key pricing and trading mechanism, which involves the processing of a large amount of complex data. At present, the electricity market trading system generally adopts a static parsing strategy, that is, using pre-set parsing rules to process the result data of the centralized bidding.

[0003] Although static analysis strategies have met the needs of data processing to a certain extent, with the development of the power market and the frequent adjustment of rules, traditional static analysis strategies have problems such as low efficiency and high error rate. Specific manifestations include: in the data analysis process, the dynamic nature of the data is not fully considered. When the amount of data surges, the processing speed and resource utilization are limited, which is easy to cause misinterpretation, affecting the accuracy and fairness of transactions. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for analyzing centralized bidding result data to solve the problems of low efficiency and poor accuracy in power data analysis.

[0005] In a first aspect, the present invention provides a method for analyzing centralized bidding result data, comprising:

[0006] Obtain the centralized bidding results dataset of the electricity market;

[0007] Dividing the centralized bidding result data set into multiple data subsets, and constructing a binary tree to be parsed using the data elements in the data subsets;

[0008] The binary tree to be parsed is parsed using a binary tree YAML parser according to a preset YAML rule file to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure.

[0009] In a second aspect, the present invention provides a centralized bidding result data parsing device, comprising:

[0010] A data acquisition module is used to obtain a centralized bidding result data set in the power market;

[0011] A data segmentation module, used to segment the centralized bidding result data set into multiple data subsets, and construct a binary tree to be parsed using the data elements in the data subsets;

[0012] The data parsing module is used to parse the binary tree to be parsed according to a preset YAML rule file using a binary tree YAML parser to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure.

[0013] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0014] at least one processor;

[0015] and a memory communicatively coupled to the at least one processor;

[0016] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the centralized bidding result data parsing method of the first aspect mentioned above.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, which are used to implement the centralized bidding result data parsing method of the first aspect when executed by a processor.

[0018] The parsing solution for centralized bidding result data provided by the present invention obtains a centralized bidding result data set of the power market, divides the centralized bidding result data set into multiple data subsets, and uses the data elements in the data subsets to construct a binary tree to be parsed, and uses a binary tree YAML parser to parse the binary tree to be parsed according to a preset YAML rule file to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure. By adopting the above technical solution, multiple data subsets of centralized bidding in the power market are parsed using a pre-configured binary tree structured YAML rule file, which can not only quickly parse large amounts of data and optimize the parsing speed and efficiency of power trading data, but also accurately interpret the power trading data with associated relationships, solving the problems of low efficiency and poor accuracy in power data parsing.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flowchart of a method for analyzing centralized bidding result data provided according to the first embodiment of the present invention;

[0022] Figure 2 is a flow chart of a method for analyzing centralized bidding result data provided according to Embodiment 2 of the present invention;

[0023] Figure 3 2 is a schematic diagram of the structure of a centralized bidding result data analysis device provided according to Embodiment 3 of the present invention;

[0024] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In the description of the present invention, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are an "or" relationship. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 A flowchart of a method for parsing centralized bidding result data is provided for the first embodiment of the present invention. This embodiment is applicable to the case of parsing centralized bidding result data. The method can be executed by a parsing device for centralized bidding result data. The parsing device for centralized bidding result data can be implemented in the form of hardware and / or software. The parsing device for centralized bidding result data can be configured in an electronic device. The electronic device can be composed of two or more physical entities or one physical entity.

[0029] like Figure 1 As shown, the method for parsing centralized bidding result data provided by the first embodiment of the present invention specifically includes the following steps:

[0030] S101. Obtain a centralized bidding result data set in the electricity market.

[0031] S102: Divide the centralized bidding result data set into multiple data subsets, and construct a binary tree to be parsed using the data elements in the data subsets.

[0032] In this embodiment, the centralized bidding result data set can be pre-divided into multiple data subsets. Then, the data elements in each data subset are used to construct the binary tree to be parsed. Each node in the binary tree to be parsed represents at least one data element or data structure, and the connection relationship between the nodes represents the hierarchical relationship of the data in the data subset, such as the hierarchical relationship of grandfather, father and grandson. Among them, centralized bidding for electricity refers to the electricity trading model in the electricity market, which mainly adopts centralized bidding to conduct electricity trading. Its core is to gather the electricity purchasing needs of market participants, determine the electricity trading price through bidding, so as to achieve the effective operation and stable development of the electricity market.

[0033] S103. Parse the binary tree to be parsed according to a preset YAML rule file using a binary tree YAML parser to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure.

[0034] In this embodiment, a YAML rule file can be defined in advance using a rule definition language (YAML) to obtain a preset YAML rule file. Since there is usually an association between the centralized bidding result data and the rules in the corresponding YAML rules, the YAML rule file can be further parsed using a binary tree YAML parser. The preset YAML rule file is converted into an executable parsing logic using a pre-configured binary tree YAML parser, and the parsing logic is used to traverse and parse the binary tree to be parsed to obtain and extract the required data and information, that is, to obtain the first data parsing result. When abnormal data is detected, the binary tree YAML parser can automatically select the most suitable parsing path, reduce the error rate, and improve the robustness of data processing.

[0035] Optionally, the development process of a YAML rule file, a YAML editor, and a YAML parser includes:

[0036] YAML rule files can be generated by pre-defining the basic YAML syntax structure, including keywords, operators, and data types. Then, a rule editor is developed by creating a graphical interface system or a text editor, which supports the writing, editing, and testing of YAML rules. A parser that can read YAML rule files is then developed, which can convert rule files into executable parsing logic. This parser is the binary tree YAML parser.

[0037] The method for parsing centralized bidding result data provided by the embodiment of the present invention obtains a centralized bidding result data set of the power market, divides the centralized bidding result data set into multiple data subsets, and uses the data elements in the data subsets to construct a binary tree to be parsed, and uses a binary tree YAML parser to parse the binary tree to be parsed according to a preset YAML rule file to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure. The technical solution of the embodiment of the present invention uses a pre-configured binary tree structure YAML rule file to parse multiple data subsets of centralized bidding in the power market, which not only quickly parses large amounts of data and optimizes the parsing speed and efficiency of power trading data, but also accurately interprets power trading data with correlation relationships, solving the problems of low efficiency and poor accuracy in power data parsing.

[0038] Optionally, before the binary tree to be parsed is parsed using a binary tree YAML parser according to a preset YAML rule file to obtain a first data parsing result, it also includes: using a machine learning algorithm to identify the multiple data subsets in parallel to obtain a recognition result, wherein the recognition result includes a normal data subset and an abnormal data subset; wherein the use of data elements in the data subset to construct the binary tree to be parsed includes: using data elements in the normal data subset to construct the binary tree to be parsed. The advantage of this setting is that multiple data subsets are identified in parallel using a machine learning algorithm, which reduces false positives and false negatives and ensures the cleanliness and accuracy of the data.

[0039] Specifically, the following processing may be performed on multiple data subsets in advance:

[0040] 1) Data cleaning: such as using a binary tree YAML parser to remove empty values, correct format errors, fill in missing values, and match numeric key pairs (such as matching buy and sell when the value is 0 for buy and 1 for sell).

[0041] 2) Format conversion: For example, the binary tree YAML parser is used to convert the date, electricity, transaction price, and enumeration type fields (such as buying and selling direction) into a unified format. For example, the time data parsed by the binary tree YAML parser is "2024-01-01 or January 1, 2024". The binary tree YAML parser can make conditional judgments, specifically determine the format of the time data, and convert it to obtain the target time format.

[0042] 3) Standardization: Scale and normalize the data to ensure the consistency of the values.

[0043] Finally, using machine learning algorithms, such as the isolation forest algorithm, each data subset can be identified in parallel to obtain an abnormal result.

[0044] For example, if the power market rule in the preset YAML rule file is: "The power value of the power station is not negative", when the binary tree YAML parser parses the transaction volume of a certain transaction as negative, it means that the data may be entered incorrectly. This transaction can be marked for abnormal processing and a prompt message can be issued to remind the trader to intervene and review manually.

[0045] Optionally, the use of a binary tree YAML parser to parse the binary tree to be parsed according to a preset YAML rule file to obtain a first data parsing result includes: determining a first correspondence between nodes in the binary tree to be parsed and a plurality of the preset YAML rule files; determining a second correspondence between nodes in the binary tree to be parsed and YAML sub-parsers in the binary tree YAML parser; based on the first correspondence and the second correspondence, in combination with multi-site technology or multi-process technology, parsing the multiple data subsets in parallel to obtain the first data parsing result. The advantage of such a setting is that the speed and efficiency of data parsing can be further optimized.

[0046] Specifically, the correspondence between the preset YAML rule file and the YAML sub-parser can be determined based on the correspondence between the nodes in the binary tree to be parsed and the preset YAML rule file, and the correspondence between the nodes in the binary tree to be parsed and the YAML sub-parser. Then, using multi-site or multi-process technology, multiple YAML sub-parsers are used to parse the preset YAML rule file at the same time under a high-performance parallel computing architecture to process multiple different data subsets. In addition, intelligent caching strategies can be used to store parsed data fragments and intermediate results, as well as to store various YAML parsers that have been developed and written, to reduce repeated calculations and data extraction. Dynamic resource scheduling can also be used to dynamically adjust the priority of parsing tasks and allocate computing resources based on real-time resource monitoring to ensure that in a multi-node environment, parsing tasks can be reasonably allocated to avoid single-point overload and improve stability.

[0047] Embodiment 2

[0048] Figure 2 A flowchart of a method for parsing centralized bidding result data provided in the second embodiment of the present invention. The technical solution of the embodiment of the present invention is further optimized on the basis of the above-mentioned optional technical solutions, and a specific method for parsing centralized bidding result data is given.

[0049] Optionally, the above method further includes: when it is determined by using a file system listener that there is an updated preset YAML rule file in the preset YAML rule file, determining the target data subset corresponding to the updated preset YAML rule file in the binary tree to be parsed; determining the target YAML sub-parser corresponding to the updated preset YAML rule file in the binary tree YAML parser; and parsing the target data subset using the target YAML sub-parser to obtain a second data parsing result. The advantage of such a setting is that by using the file system listener and the binary tree YAML parser to dynamically read and execute the parsing logic, it can quickly adapt to changes in market rules, ensure immediate response to changes in market rules, and reduce the time and cost of parsing system maintenance.

[0050] Optionally, before obtaining the centralized bidding result data set of the power market, it also includes: using a pre-configured graphical interface system and / or text editor to write, edit and test YAML rules according to the dynamic information of the power market rules to generate a preset YAML rule file, wherein the graphical interface system and the text editor support real-time syntax prompts and real-time error prompts for YAML. The advantage of this setting is that market managers can flexibly use the parsing rule language to dynamically write and adjust parsing rules according to the latest market rules without changing the core code of the system.

[0051] Optionally, before dividing the centralized bidding result data set into a plurality of data subsets, the method further includes: parsing the centralized bidding result data set using a pattern recognition algorithm and metadata parsing technology to obtain a parsing result set, wherein the parsing result set includes the file format, metadata, and file structure of the data files in the centralized bidding result data set; wherein, dividing the centralized bidding result data set into a plurality of data subsets includes: dividing the parsing result set into a plurality of data subsets. The advantage of such a setting is that the recognition and preview of the data format are realized by using the pattern recognition algorithm and metadata parsing technology.

[0052] like Figure 2 As shown, a method for parsing centralized bidding result data provided by Embodiment 2 of the present invention specifically includes the following steps:

[0053] S201. Write, edit and test YAML rules according to the dynamic information of power market rules by using a pre-configured graphical interface system and / or text editor to generate a preset YAML rule file, wherein the graphical interface system and the text editor support real-time syntax prompts and error prompts of YAML.

[0054] S202. Obtain a centralized bidding result data set of the electricity market.

[0055] S203. Analyze the centralized bidding result data set using a pattern recognition algorithm and metadata analysis technology to obtain a parsing result set, wherein the parsing result set includes the file format, metadata, and file structure of the data files in the centralized bidding result data set.

[0056] Specifically, pattern recognition algorithms and metadata parsing technology can be used to automatically detect the file format of uploaded data (i.e., centralized bidding result data set), such as automatically determining the file type based on the file signature and structural features, including but not limited to xlsx, CSV, and JSON formats. Then extract the metadata, specifically by reading the file header information and parsing out metadata such as column names and data types. And perform structural analysis to analyze the internal structure of the file, such as whether there are nested objects or arrays, and their hierarchical relationships. Among them, the identification and preview of the file format are the key to the parsing process, which will affect the efficiency and accuracy of the parsing. Identifying the file format in advance can quickly discover potential problems and prevent parsing errors.

[0057] Exemplarily, pattern recognition algorithms and metadata parsing techniques may be used to automatically identify the transaction ID, transaction date, transaction type, and buying and selling direction contained in the CSV file, and determine that the "transaction date" is a date type.

[0058] S204: Divide the analysis result set into multiple data subsets.

[0059] S205. Use a machine learning algorithm to identify the multiple data subsets in parallel to obtain identification results, and use the data elements in the normal data subsets to construct a binary tree to be parsed.

[0060] S206, determining a first correspondence between the nodes in the binary tree to be parsed and the plurality of preset YAML rule files; determining a second correspondence between the nodes in the binary tree to be parsed and the YAML sub-parsers in the binary tree YAML parser.

[0061] For example, if the rule in the preset YAML rule file includes: "If the buying and selling direction is buying, the quantity is negative and the calculated price should be negative", when this rule is read by the binary tree YAML parser, the corresponding check can be automatically performed during the data parsing process of the normal data subset.

[0062] S207: Based on the first corresponding relationship and the second corresponding relationship, the multiple data subsets are analyzed in parallel in combination with multi-site technology or multi-process technology to obtain the first data analysis result.

[0063] Optionally, the connection relationship between binary tree nodes in the binary tree YAML parser is determined according to the association relationship between multiple preset YAML rule files, the binary tree node in the binary tree YAML parser is the YAML sub-parser, and the multiple YAML sub-parsers include multiple types.

[0064] Specifically, the connection paths between binary tree nodes in the binary tree YAML parser may be connected according to the association relationship between preset YAML rule files.

[0065] S208. When it is determined by using the file system listener that there is an updated preset YAML rule file in the preset YAML rule file, determine the target data subset corresponding to the updated preset YAML rule file in the binary tree to be parsed; determine the target YAML sub-parser corresponding to the updated preset YAML rule file in the binary tree YAML parser; and use the target YAML sub-parser to parse the target data subset to obtain a second data parsing result.

[0066] Specifically, a dynamic rule loading and execution framework can be developed, which can be used to load the rules in the preset YAML rule file in real time without restarting the entire parsing engine, achieving efficient execution of parsing tasks and having good expansion potential. The framework can be used to ensure real-time updates of rules through file system monitoring and event-driven mechanisms. Among them, file system monitoring specifically includes: using a file system listener to continuously monitor whether the preset YAML rule file has been updated. When there are multiple preset YAML rule files, an incremental update algorithm can be pre-designed, which can load and execute the updated preset YAML rule file separately, avoiding unnecessary waste of resources.

[0067] When the file system listener is used to determine that there is an updated preset YAML rule file in the preset YAML rule file, the incremental update algorithm can be used to update the changed preset YAML rule file and / or the target data subset, rather than reprocessing the entire data subset. This is especially important for large-scale data sets, which can significantly improve efficiency and reduce resource consumption. For example, when the electricity market rules change, the preset YAML rule file is also updated immediately, and the updated rules can be automatically loaded and applied in the next parsing task. The updated rules are parsed in parallel by using the binary tree YAML parser to parse the updated rules to parse the currently unparsed data subset.

[0068] The specific process of incremental update includes:

[0069] 1) Change detection: First, determine the updated preset YAML rule file and the target data subset corresponding to the rule file in the binary tree to be parsed. This can be achieved by comparing the new and old data versions, such as by checking the modification timestamp of the file or using hash values ​​to identify data blocks.

[0070] 2) Difference calculation: Once the changed preset YAML rule file and / or target data subset are determined, the next step is to calculate the impact of these changes on the data structure of the preset YAML rule file and / or target data subset to obtain the difference calculation result.

[0071] 3) Local update: Determine the target YAML subparser corresponding to the updated preset YAML rule file in the binary tree YAML parser. Based on the result of the difference calculation, use the target YAML subparser and the updated preset YAML rule file to update and parse only those parts affected by the change, that is, the target data subset. For the binary tree to be parsed, this means that only some nodes and their child nodes may need to be adjusted without rebuilding the entire tree.

[0072] 4) Consistency maintenance: After the update is completed, that is, after the second data analysis result is obtained, in order to ensure the consistency and integrity of the entire data structure, consistency maintenance can be performed, such as recalculating certain summary information and repairing pointers.

[0073] For example, suppose there is a binary tree of user information to be parsed, and the age of a user in the binary tree has changed. If the incremental update algorithm detects that the data under the path "user / 222 / age" of the preset YAML rule file has changed, it is determined by calculating the difference that only the value of the age field is different. Then, the target YAML subparser and the updated preset YAML rule file can be used for local update, and only the age attribute of the user is updated. Finally, consistency maintenance is performed. For example, if the average age of all users is also stored in the binary tree, the average age will be recalculated.

[0074] Optionally, you can also pre-set rule hot swap to implement the rule hot swap function, that is, dynamically update the preset YAML rule file without interrupting the current parsing task.

[0075] The method for parsing centralized bidding result data provided by the embodiment of the present invention enables market managers to flexibly use the parsing rule language to dynamically write and adjust parsing rules according to the latest market rules without changing the core code of the system. It uses pattern recognition algorithms and metadata parsing technology to realize the recognition and preview of data formats, and dynamically reads and executes parsing logic by using a file system listener and a binary tree YAML parser to quickly adapt to changes in market rules, ensure immediate response to changes in market rules, and reduce the time and cost of parsing system maintenance.

[0076] Embodiment 3

[0077] Figure 3 This is a schematic diagram of the structure of a centralized bidding result data analysis device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a data acquisition module 301, a data segmentation module 302 and a data analysis module 303, wherein:

[0078] A data acquisition module is used to obtain a centralized bidding result data set in the power market;

[0079] A data segmentation module, used to segment the centralized bidding result data set into multiple data subsets, and construct a binary tree to be parsed using the data elements in the data subsets;

[0080] The data parsing module is used to parse the binary tree to be parsed according to a preset YAML rule file using a binary tree YAML parser to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure.

[0081] The device for parsing centralized bidding result data provided in the embodiment of the present invention utilizes a pre-configured binary tree structured YAML rule file to parse multiple data subsets of centralized bidding in the power market. It not only rapidly parses large amounts of data and optimizes the parsing speed and efficiency of power trading data, but also accurately interprets power trading data with associated relationships, thereby solving the problems of low efficiency and poor accuracy in power data parsing.

[0082] Optionally, the device further comprises:

[0083] The identification module is used to use a machine learning algorithm to parallelly identify the multiple data subsets to obtain an identification result before using the binary tree YAML parser to parse the binary tree to be parsed according to a preset YAML rule file to obtain a first data parsing result, wherein the identification result includes a normal data subset and an abnormal data subset.

[0084] Optionally, the data segmentation module includes:

[0085] A binary tree construction unit is used to construct a binary tree to be parsed using the data elements in the normal data subset.

[0086] Optionally, the data parsing module includes:

[0087] A first correspondence determination unit, used to determine a first correspondence between the nodes in the binary tree to be parsed and the plurality of preset YAML rule files;

[0088] A second correspondence determination unit, configured to determine a second correspondence between the nodes in the binary tree to be parsed and the YAML sub-parsers in the binary tree YAML parser;

[0089] A data parsing unit is used to parse the multiple data subsets in parallel based on the first corresponding relationship and the second corresponding relationship in combination with multi-site technology or multi-process technology to obtain the first data parsing result.

[0090] Optionally, the connection relationship between binary tree nodes in the binary tree YAML parser is determined according to the association relationship between multiple preset YAML rule files, the binary tree node in the binary tree YAML parser is the YAML sub-parser, and the multiple YAML sub-parsers include multiple types.

[0091] Optionally, the device further comprises:

[0092] A target data subset determination module, configured to determine a target data subset corresponding to the updated preset YAML rule file in the binary tree to be parsed when determining that there is an updated preset YAML rule file in the preset YAML rule file by using a file system listener;

[0093] A target sub-parser determination module, used to determine the target YAML sub-parser corresponding to the updated preset YAML rule file in the binary tree YAML parser;

[0094] The data parsing result determining module is used to parse the target data subset using the target YAML sub-parser to obtain a second data parsing result.

[0095] Optionally, the device further comprises:

[0096] A rule file generation module is used to write, edit and test YAML rules according to the dynamic information of power market rules by utilizing a pre-configured graphical interface system and / or text editor before obtaining the centralized bidding result data set of the power market to generate a preset YAML rule file, wherein the graphical interface system and the text editor support real-time syntax prompts and error prompts of YAML.

[0097] Optionally, the device further comprises:

[0098] A pre-parsing module is used to parse the centralized bidding result data set using a pattern recognition algorithm and metadata parsing technology before dividing the centralized bidding result data set into multiple data subsets to obtain a parsing result set, wherein the parsing result set includes the file format, metadata and file structure of the data files in the centralized bidding result data set.

[0099] Optionally, the data segmentation module is specifically used to segment the analysis result set into multiple data subsets.

[0100] The centralized bidding result data analysis device provided in the embodiment of the present invention can execute the centralized bidding result data analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0101] Embodiment 4

[0102] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0103] like Figure 4As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0104] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0105] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a method for parsing centralized bidding result data.

[0106] In some embodiments, the method for parsing the centralized bidding result data may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method for parsing the centralized bidding result data described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the method for parsing the centralized bidding result data in any other appropriate manner (e.g., by means of firmware).

[0107] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0108] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0109] The computer device provided above can be used to execute the method for analyzing the centralized bidding result data provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0110] Embodiment 5

[0111] In the context of the present invention, the computer-readable storage medium may be a tangible medium, and the computer-executable instructions, when executed by a computer processor, are used to perform a method for parsing centralized bidding result data, the method comprising:

[0112] Obtain the centralized bidding results dataset of the electricity market;

[0113] Dividing the centralized bidding result data set into multiple data subsets, and constructing a binary tree to be parsed using the data elements in the data subsets;

[0114] The binary tree to be parsed is parsed using a binary tree YAML parser according to a preset YAML rule file to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure.

[0115] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by an instruction execution system, device or equipment or used with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] The computer device provided above can be used to execute the method for analyzing the centralized bidding result data provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0117] It is worth noting that in the embodiment of the above-mentioned centralized bidding result data analysis device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0118] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

[0119] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing centralized bidding result data, characterized in that: include: Obtain the centralized bidding results dataset of the electricity market; Dividing the centralized bidding result data set into multiple data subsets, and constructing a binary tree to be parsed using the data elements in the data subsets; The binary tree to be parsed is parsed using a binary tree YAML parser according to a preset YAML rule file to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure.

2. The method according to claim 1, characterized in that Before the binary tree to be parsed is parsed using the binary tree YAML parser according to the preset YAML rule file to obtain the first data parsing result, the method further includes: Using a machine learning algorithm to identify the multiple data subsets in parallel to obtain a recognition result, wherein the recognition result includes a normal data subset and an abnormal data subset; The step of constructing a binary tree to be parsed using the data elements in the data subset includes: The binary tree to be parsed is constructed using the data elements in the normal data subset.

3. The method according to claim 1, characterized in that The using a binary tree YAML parser to parse the binary tree to be parsed according to a preset YAML rule file to obtain a first data parsing result includes: Determine a first corresponding relationship between the nodes in the binary tree to be parsed and the plurality of preset YAML rule files; Determine a second correspondence between nodes in the binary tree to be parsed and YAML sub-parsers in the binary tree YAML parser; Based on the first corresponding relationship and the second corresponding relationship, in combination with multi-site technology or multi-process technology, the multiple data subsets are analyzed in parallel to obtain the first data analysis result.

4. The method according to claim 1 or 3, characterized in that: The connection relationship between binary tree nodes in the binary tree YAML parser is determined according to the association relationship between multiple preset YAML rule files. The binary tree nodes in the binary tree YAML parser are the YAML sub-parsers, and the types of the multiple YAML sub-parsers include multiple types.

5. The method according to claim 1, characterized in that Also includes: When it is determined by using the file system listener that there is an updated preset YAML rule file in the preset YAML rule file, determining a target data subset corresponding to the updated preset YAML rule file in the binary tree to be parsed; Determine a target YAML sub-parser corresponding to the updated preset YAML rule file in the binary tree YAML parser; The target data subset is parsed using the target YAML sub-parser to obtain a second data parsing result.

6. The method according to claim 1, characterized in that Before obtaining the centralized bidding result data set of the power market, the method further includes: By utilizing a pre-configured graphical interface system and / or text editor, YAML rules are written, edited and tested according to dynamic information of electricity market rules to generate a preset YAML rule file, wherein the graphical interface system and the text editor support real-time syntax prompts and real-time error prompts for YAML.

7. The method according to any one of claims 1, 2, 5 or 6, characterized in that: Before dividing the centralized bidding result data set into a plurality of data subsets, the method further includes: Parsing the centralized bidding result data set using a pattern recognition algorithm and metadata parsing technology to obtain a parsing result set, wherein the parsing result set includes a file format, metadata, and file structure of a data file in the centralized bidding result data set; The method of dividing the centralized bidding result data set into multiple data subsets includes: The parsing result set is divided into multiple data subsets.

8. A centralized bidding result data analysis device, characterized in that: include: A data acquisition module is used to obtain a centralized bidding result data set in the power market; A data segmentation module, used to segment the centralized bidding result data set into multiple data subsets, and construct a binary tree to be parsed using the data elements in the data subsets; The data parsing module is used to parse the binary tree to be parsed according to a preset YAML rule file using a binary tree YAML parser to obtain a first data parsing result, wherein the binary tree YAML parser includes multiple YAML sub-parsers, and the YAML sub-parsers are in a binary tree structure.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for parsing centralized bidding result data according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for parsing centralized bidding result data according to any one of claims 1 to 7 when executed.

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