Energy consumption data intelligent calling method and equipment

Abstract

The invention provides an energy consumption data intelligent calling method and device, and the method comprises the following steps: S1, initializing a collection period parameter, and obtaining the collection tasks of all devices from a database; s2, initializing an acquisition data link according to the acquisition task; s3, analyzing the received data file, and updating the data file to the collection data chain according to the data content; and S4, regularly counting the data of the collection data chain to generate a call item, updating the call item to a call data chain, further generating a call message, and sending the call message to the front collection equipment for data call collection. According to the invention, the problems that the urban rail transit network level energy management system is lack of data due to unstable communication, equipment failure and the like and cannot carry out data compensation and calling in time are solved, 24-hour intelligent compensation and calling of energy consumption data all day long are realized, the data integrity of the system is guaranteed, and the working efficiency of operators is improved.

Description

Technical Field

[0001] This invention belongs to the field of data acquisition and intelligent replenishment technology of urban rail transit energy management system, and in particular relates to an intelligent replenishment method and device for energy consumption data. Background Technology

[0002] With the intelligent development of urban rail transit systems, energy management systems, as the core support for operation and maintenance, need to collect and analyze various key information such as energy consumption data, power quality data, and environmental data in real time to support the formulation of energy conservation and emission reduction plans and the optimization of operational decisions. However, due to the complexity of the rail transit network environment (such as multi-device collaboration and long-distance transmission) and the large number of data collection devices, the data collection process often faces the following challenges: Existing data acquisition methods in rail transit energy management systems are mainly divided into two categories: proactive uploading and proactive acquisition. Proactive uploading relies on equipment actively transmitting data to the master station and uses breakpoint resume technology to compensate for data loss caused by transmission interruptions. Proactive acquisition, on the other hand, requires the master station to actively initiate data requests to retrieve data that was not successfully acquired. For proactive acquisition scenarios, efficiently identifying and retrieving missing data (hereinafter referred to as "deficient data items") is a key technology for ensuring the integrity of system data.

[0003] Currently, the data replenishment technology in the industry mainly suffers from the following shortcomings: 1. The supplementary collection technology based on historical database comparison is inefficient: This technology compares the collected data items in the historical database with the preset collection tasks to filter out the defective data items that were not successfully collected. However, it relies on traversing and matching the entire historical data, which has high computational complexity. It is only suitable for systems with a small collection volume and is difficult to adapt to large-scale data collection scenarios at the urban rail transit network level. 2. Insufficient reliability of memory database statistical technology: This technology temporarily stores the collection tasks and data item information in a memory database to improve the identification speed of defective data items. However, memory databases are volatile due to power failure. In scenarios where rail transit systems experience frequent power outages for maintenance, memory database data is easily lost, leading to interruption of recall tasks or data omissions, and thus failing to meet high reliability requirements. 3. Inefficiency of single-time-point statistics and recall: Existing recall technologies mostly perform statistics and recall for missing data at a single time point for the same device, without considering scenarios where the same device has missing data at multiple consecutive time points. For continuously missing data items, the statistics and recall operations need to be repeated at each time point, resulting in low recall efficiency and making it difficult to achieve real-time intelligent recall 24 hours a day.

[0004] In summary, existing data recall technologies suffer from low efficiency, poor reliability, and insufficient ability to recall data in complex network environments, large-scale data acquisition, and power outage maintenance scenarios. There is an urgent need for a highly adaptable, efficient, and reliable intelligent recall solution. Summary of the Invention

[0005] In view of this, the present invention aims to provide an intelligent energy consumption data replenishment method and device to at least solve one of the problems in the background art.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows: An intelligent energy consumption data-based replenishment method includes the following steps: S1. Initialize the acquisition cycle parameters and retrieve the acquisition tasks for all devices from the database; S2. Initialize the data acquisition chain according to the acquisition task; S3. Parse the received data file and update the data acquisition chain according to the data content; S4. Periodically collect data from the data chain to generate supplementary recall items, update them to the supplementary recall data chain, further generate recall messages and send them to the front-end acquisition device for supplementary data collection; The data acquisition chain is maintained through a circular linked list structure. Each data box contains a timestamp, acquisition period, and acquisition table. The acquisition table records an identifier, task quality code, and acquisition quality code. The identifier consists of terminal ID, meter ID, and data item set base address. The supplementary acquisition data chain is a linked list composed of several supplementary acquisition items. Each supplementary acquisition item contains terminal ID, meter ID, data item ID, start time, acquisition period, and number of data items.

[0007] Furthermore, the initialization acquisition cycle parameters in step S1 include: Obtain the parameters for each collection period from the configuration file. The collection period parameters include the number of supplementary collection periods, the number of delayed supplementary collection periods, the number of valid periods, and the supplementary collection polling strategy.

[0008] Furthermore, the initialization of the acquisition data chain in step S2 includes: Construct a circular linked list consisting of several data bins. The timestamp of the first data bin is the current time rounded down by the collection period, and the timestamps of subsequent data bins are decremented by one collection period. The collection period of each data bin is consistent with the collection period of its respective collection task. Initialize the collection table and generate an identifier based on the collection task. Initialize the task quality code as the bitwise AND result of the bitwise OR of the data item ID set and the bitwise inverse of the data item set base address. Initialize the collection quality code to 0. Set the head and tail pointers of the circular linked list.

[0009] Furthermore, the parsing of the received data file in step S3 includes: Receives SQLite format data files, categorizes them by collection period, and stores them in the corresponding directories; parses the data types, timestamps, and collection periods in the files to match the corresponding collection data chains; reads data items line by line, extracts terminal IDs, meter IDs, and data quality codes, generates identifiers, and updates them to the collection table of the corresponding data bin; deletes the data file after parsing is complete.

[0010] Furthermore, step S4 includes: The defect detection task is initiated according to the preset supplementary recall polling strategy; starting from the tail of the data collection chain, several data boxes are continuously checked, and the result of the bitwise inversion of the collection quality code is calculated and the result of the bitwise AND operation with the task quality code is calculated. If the result is greater than 0, it is determined that there is a defective data item; the identifier of the defective data item is parsed, the terminal ID, meter ID, and data item set base address are extracted, and the positions and number of 1s in the above bitwise AND operation result are counted to generate a supplementary recall item; if there is no identical supplementary recall item in the supplementary recall data chain, it is created; if there is, it is merged or the start time and number of data are updated; the head and tail pointers of the collection data chain are updated, and the collection quality code and timestamp of the head data box are reset; Among them, data items that were not successfully collected are defined as defective data items.

[0011] Furthermore, this solution discloses an intelligent energy consumption data-based replenishment device, comprising: The system comprises an initialization module, a task maintenance module, a file management module, a data parsing module, a defect detection module, and a supplementary data collection module. The initialization module acquires data acquisition cycle parameters. The task maintenance module initializes and maintains the data acquisition chain based on task templates and acquisition tasks. The file management module receives and stores data files and loads historical data files. The data parsing module parses data files and updates the acquisition quality codes in the corresponding data bins. The defect detection module periodically checks the data acquisition chain, generates supplementary items, and updates the supplementary data collection chain. The supplementary data collection module categorizes supplementary items and sends them to the front-end acquisition device for supplementary data collection.

[0012] Furthermore, the initialization module is used to: obtain parameters for each collection cycle from the configuration file, including the number of supplementary recall cycles, the number of delayed supplementary recall cycles, the number of valid cycles, and the supplementary recall polling strategy; The task maintenance module is used to: construct a circular linked list consisting of several data bins when initializing the data acquisition chain; initialize the timestamp, acquisition period, and acquisition table; periodically load task templates and data acquisition task data; generate or update the data acquisition chain by combining task templates and data acquisition tasks; update the data acquisition chain; delete invalid acquisition items or add new acquisition items. The file management module is used to: receive SQLite format data files sent by the front-end acquisition device, and store them in the corresponding directories according to the acquisition cycle; load historical data at startup, store it as files and classify it in the corresponding directories; and support the reception and storage of real-time data files and supplementary data files. The data parsing module is used to: poll a specified path to search for data files, parse the file name to obtain the data type, collection period, and timestamp; match the data collection chain corresponding to the collection period, and find the data box corresponding to the timestamp; read data items line by line, extract the terminal ID, meter ID, and data quality code, generate an identifier and update it to the collection table of the data box; and delete the file after parsing is complete. The defect detection module is used to: start a timer according to the supplementary recall polling strategy, and continuously check several data boxes starting from the tail of the data collection chain; calculate the result of bitwise AND operation between the bitwise inverted result of the acquisition quality code and the task quality code, and if the result is greater than 0, parse the identifier to obtain the terminal ID, meter ID, and data item set base address; count the positions and number of 1s in the above bitwise AND operation result, and generate or update the supplementary recall items in the supplementary recall data chain; update the head and tail pointers of the acquisition data chain, and reset the acquisition quality code and timestamp of the head data box; The supplementary recall data module is used to: receive supplementary recall messages from the defect investigation module, classify supplementary recall items in the supplementary recall data chain according to terminal ID, and send supplementary recall items of the same terminal to the front-end acquisition device for data supplementation.

[0013] Furthermore, this solution discloses an electronic device, including a processor and a memory communicatively connected to the processor and used to store executable instructions of the processor, wherein the processor is used to execute an intelligent energy consumption data replenishment method.

[0014] Furthermore, this solution discloses a server, including at least one processor and a memory communicatively connected to the processor. The memory stores instructions executable by the at least one processor, which are executed by the processor to cause the at least one processor to perform an intelligent energy consumption data replenishment method.

[0015] Furthermore, this solution discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements an intelligent energy consumption data replenishment method.

[0016] Compared with existing technologies, the intelligent energy consumption data replenishment method and device described in this invention have the following advantages: (1) The intelligent energy consumption data replenishment method and equipment described in this invention solves the problem of data loss caused by unstable communication and equipment failure in urban rail transit network-level energy management systems, which cannot replenish data in a timely manner. It realizes intelligent replenishment of energy consumption data 24 hours a day, ensuring the data integrity of the system and improving the work efficiency of operators. (2) The intelligent energy consumption data replenishment method and device described in this invention, by constructing a circular linked list structure for the collection data chain (each data box contains a timestamp, collection period and collection table), combined with preset multi-dimensional collection period parameters (minute, hour, day, month, meter reading day), effective period (number of effective periods ≥ number of collection periods) and replenishment polling strategy (interval / timed), can accurately identify missing data items at multiple consecutive time points of the same device (instead of traditional single time point statistics); (3) The intelligent energy consumption data replenishment method and device described in this invention adopts a non-volatile configuration parameter storage and task maintenance mechanism (the initialization module obtains the collection cycle parameters from the configuration file, and the task maintenance module loads the task template from the historical database and updates the collection data chain), avoiding the problem of power loss in traditional memory databases; at the same time, the data parsing module updates the collection quality code in the data box in real time (by bit or update), and the defect investigation module continuously investigates the data box from the head of the queue based on the timestamp, ensuring that the replenishment task can be quickly restored from the historical data chain after the system is powered off for maintenance. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of an intelligent energy consumption data replenishment method according to an embodiment of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0019] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] This invention discloses an intelligent energy consumption data replenishment method and device, relating to the field of urban rail transit energy. The energy consumption data replenishment method includes: Step S1, initializing the acquisition cycle parameters and retrieving acquisition tasks for all devices from the database; Step S2, initializing the acquisition data chain according to the acquisition tasks; Step S3, parsing the received data file and updating the acquisition data chain according to the data content; Step S4, periodically statistically analyzing the data in the acquisition data chain to generate replenishment items, updating them in the replenishment data chain, further generating a recall message and sending it to the front-end acquisition device for data replenishment. This invention solves the problem of data loss and inability to promptly replenish data in urban rail transit network-level energy management systems due to unstable communication, equipment failure, etc., achieving 24 / 7 intelligent replenishment of energy consumption data, ensuring system data integrity and improving the work efficiency of operators.

[0023] It mainly includes the following modules: 1) Initialization module: used to obtain each collection cycle parameter from the configuration file, including the number of supplementary recruitment cycles, the number of delayed supplementary recruitment cycles, the number of effective cycles, and the supplementary recruitment polling strategy; 2) Task Maintenance Module: Initializes the data acquisition chain, constructs a circular linked list consisting of several data bins, initializes timestamps, acquisition cycles, and acquisition tables; periodically loads task templates and acquisition tasks, combines task templates and acquisition tasks to generate or update the data acquisition chain; updates the data acquisition chain, deletes invalid acquisition items, or adds new acquisition items; 3) File Management Module: Receives SQLite format data files forwarded by the front-end acquisition device, categorizes and stores them in the corresponding directories according to the acquisition cycle; loads historical data upon startup, stores it as files and categorizes them in the corresponding directories; supports the reception and storage of real-time data files and supplementary data files; 4) Data parsing module: Polls the specified path to search for data files, parses the file name to obtain data type, collection period, and timestamp; matches the collected data chain of the corresponding collection period, and finds the data box of the corresponding timestamp; reads data items line by line, extracts terminal ID, meter ID, and data quality code, generates identifiers and updates them to the collection table of the data box; deletes the file after parsing is complete. 5) Defect detection module: Starts a timer according to the supplementary recall polling strategy, and continuously checks several data boxes starting from the tail of the data acquisition chain; calculates the result of bitwise inversion of the acquisition quality code and bitwise AND operation with the task quality code. If the result is greater than 0, it parses the identifier to obtain the terminal ID, meter ID, and data item set base address; counts the positions and number of 1s in the above bitwise AND operation result, and generates or updates the supplementary recall items in the supplementary recall data chain; updates the head and tail pointers of the acquisition data chain, and resets the acquisition quality code and timestamp of the head data box. 6) Supplementary Recall Data Module: Receives supplementary recall messages from the defect investigation module, classifies supplementary recall items in the supplementary recall data chain according to terminal ID, and sends supplementary recall items of the same terminal to the front-end acquisition device for supplementary data collection.

[0024] The above modules are implemented as follows: 1. Initialization module ① Obtain parameter information for each acquisition cycle from the configuration file.

[0025] ② Obtain the acquisition period parameters , The value range is [1, 2, 3, 4, 5], representing minute, hour, day, month, and meter reading day respectively (the content of this patent). The values ​​are the same, so I won't repeat them.

[0026] ③ Obtain the number of recall cycles This indicates the number of data boxes checked in a single recall. ④ Obtain the number of delayed recall cycles This indicates pushing forward from the front of the line. The deadline for the replenishment of one data box is set as the deadline.

[0027] ⑤ Obtain the number of valid periods ,and , indicating the number of data boxes in the data acquisition chain.

[0028] ⑥ Obtain the polling strategy for each collection cycle, which is divided into two strategies: interval and timed. The interval strategy further obtains the interval time in seconds; the timed strategy further obtains the timed type, including hour, day and month.

[0029] 2. Task Maintenance Module ① Create multiple data acquisition chains based on the acquisition cycle parameters. Each data acquisition chain creates... Each data box is initialized. Each data box consists of a timestamp, a collection period, and a collection table. The collection table consists of multiple rows of collection items; each collection item consists of an identifier, a task quality code, and a collection quality code. a. Initialize timestamps: The timestamp of the head data box is the current time rounded down by the acquisition period. The timestamps of subsequent data boxes are equal to the timestamps of each acquisition period. Therefore, the timestamp of the tail data box differs from that of the head data box. One collection cycle.

[0030] b. Initialize the acquisition cycle: The data box acquisition cycle is equal to the corresponding acquisition cycle of the acquisition data link, in seconds; c. Initialize the data acquisition table: The data acquisition table is empty during initialization; ② Periodically retrieve the task template table and collection task table from the database and update them to the collection data chain, steps a to d.

[0031] a) Obtain the task template table and the data collection task table. If the data is updated, proceed to step b; otherwise, terminate the update process. The data in the task template table includes: template ID, data set base address, data item ID set, collection period, collection type, time interval, and template status. The data in the data collection task table includes: task ID, terminal ID, meter ID, template ID, set time, and task status. b. Based on the template ID, process the task template data and the collection task data together to obtain the collection task. The collection task includes the terminal ID, meter ID, data item set base address, data item ID set, collection period, set time, task status, and template status.

[0032] c. Filter out invalid data collection tasks based on task status, template status, terminal ID, and meter ID.

[0033] d. Transform each collection task into a collection item in sequence, find the collection data chain according to the collection cycle, and update the collection table of all data boxes with timestamps greater than the set time, that is, delete invalid collection items and add new collection items.

[0034] The transformation principle of the identifier is as follows: a unique string is composed of the terminal ID, meter ID, and data item set base address, i.e., identifier = terminal ID | (meter ID >> 8) | (data item set base address >> 16); the transformation principle of the task quality code is as follows: the result of the bitwise OR of the data item ID set and the bitwise NOT of the data item set base address, i.e., task quality code = (data item ID_1 | data item ID_2 | ... | data item ID_n) & (~ data item set base address); the acquisition quality code is set to 0.

[0035] 3. File processing module ① When this module starts, it retrieves data for a certain period from the database and stores it in a file based on the initialization module's data acquisition period parameters. The end time is the current time rounded down by the acquisition period, and the start time is calculated backwards from the end time. One collection cycle.

[0036] ② This module is used to receive data files sent from the front-end acquisition device. It places the data files into the corresponding file directory based on the acquisition period parameter in the file name. The data file format is SQLite, and the file name format is: DR_data type_acquisition period_timestamp.sqlite.

[0037] ③ The data files include real-time data files and supplementary data files.

[0038] 4. Data parsing module ① Loop through the specified path to search for data files, and parse out the data type, collection period, and timestamp based on the file name; ② Locate the corresponding data collection chain based on the collection cycle, then locate the data box based on the timestamp. If the data box cannot be found, delete the file. ③ Read the data items in the data file line by line, and execute steps ④ to ⑤ until the file is completely read; ④ Obtain the terminal ID, meter ID, and data quality code from the data items. It generates an identifier based on the terminal ID, meter ID, and data item set base address. Each data type maps to a base address of a data itemset. ⑤According to Search for the collection item in the collection table; if found, update the collection quality code for that item. The formula is: ; = 6 \* GB3 ⑥ Delete the data file after parsing is complete.

[0039] 5. Defect detection module ① The recall data chain consists of several recall items, and the format of a recall item is as follows: [Terminal ID][Meter ID][Data Item ID][Start Time][Collection Period][Number of Data]; ② Start the timer according to the supplementary recall polling strategy. After the timer expires, start from the tail pointer of the data collection link and continuously check. One data box; ③ The steps for checking the data box are as follows: a. First, obtain the timestamp of the data box. and collection cycle ; b. Retrieve the identifier of the collection table row by row. Task quality code and collection quality code ; c. Calculate the task quality code and the acquisition quality code. ,like If the value is 0, then there are no defective data items; return to step b to retrieve the next row of data. If the value is greater than 0, then there is a defective data item, and step d is executed; d. Resolve identifiers Obtain the terminal ID, meter ID, and data item set base address; e. Calculation Number of bits that are 1 and its location Follow steps f to h. Second-rate; f. Based on the terminal ID, meter ID, and data item ID, search for the replacement item in the replacement data chain, where data item ID = data item set base address + ; g. If not found, generate a new supplementary entry [Terminal ID][Meter ID][Data Item ID][] ][ [1], and added to the recall data chain; h. If found, there may be multiple entries; in this case, obtain the start time of each supplementary entry sequentially. Collection cycle Number of data items ,judge and Check if they are equal; if they are equal, update the supplementary call item. for Return to step e; if none are equal, create a new replacement item and add it to the replacement data chain; ④ Update the data acquisition chain. Move the head pointer and tail pointer of the data acquisition chain one node to the right; then set the timestamp of the data box pointed to by the head pointer to the current time rounded down by the acquisition period, and set all acquisition quality codes in the acquisition table to 0.

[0040] 6. Recall Data Module The system receives the supplementary recall message from the defect investigation module, classifies the supplementary recall items in the supplementary recall data chain according to the terminal ID, and then sequentially sends the supplementary recall items of the same terminal to the front-end acquisition device for data supplementation.

[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent replenishment of energy consumption data, characterized in that, Includes the following steps: S1. Initialize the acquisition cycle parameters and retrieve the acquisition tasks for all devices from the database; S2. Initialize the data acquisition chain according to the acquisition task; S3. Parse the received data file and update the data acquisition chain according to the data content; S4. Periodically collect data from the data chain to generate supplementary recall items, update them to the supplementary recall data chain, further generate recall messages and send them to the front-end acquisition device for supplementary data collection; The data acquisition chain is maintained through a circular linked list structure. Each data box contains a timestamp, acquisition period, and acquisition table. The acquisition table records an identifier, task quality code, and acquisition quality code. The identifier consists of terminal ID, meter ID, and data item set base address. The supplementary acquisition data chain is a linked list composed of several supplementary acquisition items. Each supplementary acquisition item contains terminal ID, meter ID, data item ID, start time, acquisition period, and number of data items.

2. The intelligent energy consumption data replenishment method according to claim 1, characterized in that, The initial acquisition cycle parameters in step S1 include: Obtain the parameters for each collection period from the configuration file. The parameters for each collection period include the number of supplementary collection periods, the number of delayed supplementary collection periods, the number of valid periods, and the supplementary collection polling strategy.

3. The intelligent energy consumption data replenishment method according to claim 1, characterized in that, The initialization of the acquisition data chain in step S2 includes: Construct a circular linked list consisting of several data bins. The timestamp of the first data bin is the current time rounded down by the collection period, and the timestamps of subsequent data bins are decremented by one collection period. The collection period of each data bin is consistent with the collection period of its respective collection task. Initialize the collection table and generate an identifier based on the collection task. Initialize the task quality code as the bitwise AND result of the bitwise OR of the data item ID set and the bitwise inverse of the data item set base address. Initialize the collection quality code to 0. Set the head and tail pointers of the circular linked list.

4. The intelligent energy consumption data replenishment method according to claim 1, characterized in that, The data file received in step S3 includes: Receives SQLite format data files, categorizes them by collection period, and stores them in the corresponding directories; parses the data type, timestamp, and collection period in the file name to match the corresponding collection data chain; reads data items row by row, extracts terminal ID, meter ID, and data quality code, generates identifiers, and updates the collection table of the corresponding data box; deletes the data file after parsing is complete.

5. The intelligent energy consumption data replenishment method according to claim 1, characterized in that, Step S4 includes: The defect detection task is initiated according to the preset supplementary recall polling strategy; starting from the tail of the data collection chain, several data boxes are continuously checked, and the result of the bitwise inversion of the collection quality code is calculated and the result of the bitwise AND operation with the task quality code is calculated. If the result is greater than 0, it is determined that there is a defective data item; the identifier of the defective data item is parsed, the terminal ID, meter ID, and data item set base address are extracted, and the positions and number of 1s in the above bitwise AND operation result are counted to generate a supplementary recall item; if there is no identical supplementary recall item in the supplementary recall data chain, it is created; if there is, it is merged or the start time and number of data are updated; the head pointer and tail pointer of the collection data chain are updated, and the collection quality code and timestamp of the head data box are reset; Among them, data items that were not successfully collected are defined as defective data items.

6. An intelligent energy consumption data-based replenishment device, characterized in that, include: Initialization module, task maintenance module, file management module, data parsing module, defect detection module, and data replenishment module; The initialization module is used to obtain the acquisition cycle parameters; the task maintenance module is used to initialize and maintain the acquisition data chain based on the task template and acquisition task; the file management module is used to receive and store data files and load historical data files; the data parsing module is used to parse data files and update the acquisition quality code in the corresponding data box; the defect investigation module is used to periodically investigate the acquisition data chain, generate supplementary items, and update them in the supplementary data chain; the supplementary data module is used to classify supplementary items and send them to the front-end acquisition device for data supplementation.

7. The intelligent energy consumption data replenishment device according to claim 6, characterized in that, The initialization module is used to: obtain parameters for each collection period from the configuration file, including the number of supplementary recall periods, the number of delayed supplementary recall periods, the number of valid periods, and the supplementary recall polling strategy; The task maintenance module is used to: initialize the data acquisition chain, construct a circular linked list consisting of several data boxes, and initialize the timestamp, acquisition period, and acquisition table; Scheduled loading of task templates and data collection tasks; combined task templates and data collection tasks to generate or update the data collection chain. Update the data collection chain, delete invalid collection items or add new collection items; The file management module is used to: receive SQLite format data files forwarded by the front-end acquisition device, classify and store them in the corresponding directories according to the acquisition cycle; and load historical data upon startup, store it as files and classify it in the corresponding directories. Supports the reception and storage of real-time data files and supplementary data files; The data parsing module is used to: poll a specified path to search for data files, parse the file name to obtain the data type, collection period, and timestamp; match the data collection chain of the corresponding collection period, and find the data bin of the corresponding timestamp; Read data items row by row, extract terminal ID, meter ID, and data quality code, generate identifiers, and update them in the data collection table of the data box; Delete the file after parsing is complete; The defect detection module is used to: start a timer according to the supplementary recall polling strategy, and continuously check several data boxes starting from the tail of the data collection chain; calculate the result of bitwise AND operation between the bitwise inverted result of the acquisition quality code and the task quality code, and if the result is greater than 0, parse the identifier to obtain the terminal ID, meter ID, and data item set base address; count the positions and number of 1s in the above bitwise AND operation result, and generate or update the supplementary recall items in the supplementary recall data chain; update the head and tail pointers of the acquisition data chain, and reset the acquisition quality code and timestamp of the head data box; The supplementary recall data module is used to: receive supplementary recall messages from the defect investigation module, classify supplementary recall items in the supplementary recall data chain according to terminal ID, and send supplementary recall items of the same terminal to the front-end acquisition device for data supplementation.

8. An electronic device comprising a processor and a memory communicatively connected to the processor and used for storing processor-executable instructions, characterized in that: The processor is used to execute the intelligent energy consumption data replenishment method according to any one of claims 1-5.

9. A server, characterized in that: It includes at least one processor and a memory communicatively connected to the processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the processor to cause the at least one processor to perform an intelligent energy consumption data replenishment method as described in any one of claims 1-5.

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 intelligent energy consumption data replenishment method according to any one of claims 1-5.

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