Electric power terminal set copy data exception analysis method and intelligent equipment

Abstract

This application relates to the field of power system data analysis and fault diagnosis technology, specifically providing a method and intelligent device for anomaly analysis of power terminal meter reading data, aiming to solve the technical problem of low efficiency in anomaly analysis of power terminal meter reading data. The method includes: responding to a user query request by acquiring power terminal meter reading data, which includes meter reading data, meter file data, and configuration data; the user query request includes a target file address, a target configuration identifier, and a target time range; matching the corresponding target table serial number from the meter reading data based on the target file address and mapping relationship, and matching the corresponding target configuration information from the configuration data based on the target configuration identifier; extracting a target data subset from the meter reading data according to the target time range, target table serial number, and target configuration information; performing anomaly analysis on the target data subset according to the target configuration information and preset power operation parameter rules, and outputting the analysis results.

Description

Technical Field

[0001] This application relates to the field of power system data analysis and fault diagnosis technology, specifically providing a method and intelligent device for anomaly analysis of power terminal data collection. Background Technology

[0002] In power systems, power terminals (remote meter reading devices) are responsible for automatically reading and storing data from a large number of electricity meters, forming a massive meter reading database. However, during data collection, transmission, and storage, problems such as missing data and abnormal data values ​​often occur. When it is necessary to locate data anomalies in a specific meter within a specific time period, current technologies typically require testers to manually perform complex queries, filtering, comparisons, and analyses from the terminal database. This process is not only inefficient but also highly dependent on the operator's experience. Especially when it is necessary to correlate multi-source information such as table archive data, the complexity and time consumption of manual investigation increase significantly.

[0003] Therefore, a new method for anomaly analysis of power terminal data collection is needed in this field to solve the above problems. Summary of the Invention

[0004] This application aims to solve the aforementioned technical problem, namely, to address the low efficiency of abnormal data analysis in existing power terminal data collection technologies.

[0005] In a first aspect, this application provides a method for anomaly analysis of power terminal meter reading data. The method includes: in response to receiving a user query request, acquiring power terminal meter reading data, wherein the power terminal meter reading data includes meter reading data, meter file data, and configuration data; the meter reading data includes a meter serial number and the corresponding data content and configuration identifier; the meter file data includes a mapping relationship between the meter serial number and the file address; the configuration data includes a configuration identifier and the configuration information corresponding to the configuration identifier; the user query request includes a target file address, a target configuration identifier, and a target time range; matching a corresponding target meter serial number from the meter reading data based on the target file address and the mapping relationship; matching corresponding target configuration information from the configuration data based on the target configuration identifier; extracting a target data subset from the meter reading data according to the target time range, the target meter serial number, and the target configuration information; performing anomaly analysis on the target data subset according to the target configuration information and preset power operation parameter rules, and outputting the analysis results.

[0006] In one technical solution of the above-mentioned method for anomaly analysis of power terminal data collection, the configuration information includes a set of meter identifiers corresponding to the configuration identifier. Before extracting the target data subset from the meter reading data according to the target time range, the target meter number, and the target configuration information, the method further includes: in response to the fact that the set of meter identifiers corresponding to the target configuration information does not contain the target file address, outputting a configuration anomaly prompt information.

[0007] In one technical solution of the above-mentioned method for anomaly analysis of power terminal data collection, the configuration information also includes execution frequency and acquisition plan. The data content includes power operation parameters and acquisition storage timestamp. The step of extracting a target data subset from the meter reading data based on the target time range, the target table number, and the target configuration information includes: using the execution frequency and acquisition plan corresponding to the target time range, the target table number, and the target configuration information as joint filtering conditions, and extracting power operation parameters and acquisition storage timestamps that meet the joint filtering conditions from the meter reading data to construct the target data subset.

[0008] In one technical solution of the above-mentioned method for anomaly analysis of power terminal data collection, the method further includes: parsing and reorganizing the target data subset to generate a structured data table, wherein the structured data table includes a target file address column, a power operation parameter column, and a data collection and storage time stamp column, which are used to record the target file address value, the power operation parameter value, and the data collection and storage time stamp value, respectively.

[0009] In one technical solution of the above-mentioned method for anomaly analysis of power terminal data collection, the method further includes: generating a visualization chart based on the power operation parameter values ​​and the collection and storage time stamp values ​​in the structured data table, wherein the visualization chart includes a line chart with the collection and storage time stamp values ​​on the horizontal axis and the power operation parameter values ​​on the vertical axis.

[0010] In one technical solution of the above-mentioned method for anomaly analysis of power terminal data collection, the preset power operation parameter rules include the changing trends and parameter ranges corresponding to the power operation parameters. The step of performing anomaly analysis on the target data subset based on the target configuration information and the preset power operation parameter rules, and outputting the analysis results, includes: determining whether the power operation parameters conform to the changing trends in the time series; if not, determining that there is a trend anomaly; determining whether the power operation parameters exceed the parameter range; if so, determining that there is an over-limit anomaly; determining the expected number of data records based on the execution frequency; comparing the actual number of records in the target data subset with the expected number of data records; if the actual number of records is less than the expected number of data records, determining that there is a missing record anomaly.

[0011] In one technical solution of the above-mentioned method for anomaly analysis of power terminal data collection, the method further includes: marking outlier values ​​on the structured data table and / or the visualization chart for target data subsets that are determined to have trend anomalies, limit-crossing anomalies, and missing value anomalies.

[0012] In one technical solution of the above-mentioned method for anomaly analysis of power terminal data collection, the configuration data includes task configuration data and scheme configuration data; the task configuration data includes a task identifier as the configuration identifier, the execution frequency corresponding to the task identifier, and a scheme identifier; the scheme configuration data includes a scheme identifier and a set of meter identifiers and a collection plan corresponding to the scheme identifier, wherein the task configuration data and the scheme configuration data form a many-to-many combination relationship through the scheme identifier.

[0013] In a second aspect, a smart device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the method described in any of the above-described technical solutions for the anomaly analysis method of power terminal data collection.

[0014] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the method described in any of the technical solutions of the above-described method for anomaly analysis of power terminal data collection.

[0015] This application integrates meter reading data, meter archive data, and configuration data, and automatically matches them using user query requests. This allows for efficient and accurate filtering of target data subsets from complex and redundant meter reading data. Then, it performs anomaly analysis on the extracted target data subset using preset power operation parameter rules, thereby achieving automatic analysis and location of abnormal data. This method transforms the original analysis process, which relied on manual experience and multi-step cross-comparison, into an automated and intelligent analysis method, significantly improving the efficiency and accuracy of anomaly location in power terminal meter reading data. Furthermore, by generating structured data tables and visual charts and anomaly annotation, the analysis results are readily apparent, greatly reducing the professional threshold and operational difficulty for maintenance personnel. Attached Figure Description

[0016] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. In the drawings: Figure 1This is a flowchart illustrating the main steps of an anomaly analysis method for power terminal data collection according to an embodiment of this application; Figure 2 This is a schematic diagram of the main structure of a smart device according to an embodiment of this application.

[0017] Reference numerals: 11: memory; 12: processor. Detailed Implementation

[0018] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0019] In the description of this application, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection via an intermediate medium; a connection within two elements; a wireless connection or a wired connection.

[0020] Furthermore, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, and memory, and may also include software components such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor can be implemented in software, in hardware, or a combination of both. Computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc.

[0021] Furthermore, if the term "and / or" appears in this application, it includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that simultaneously satisfies A and B. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B," and can include only A, only B, or A and B. The singular forms of the terms "a" and "this" can also include plural forms.

[0022] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of an anomaly analysis method for power terminal data collection according to an embodiment of this application. Figure 1 As shown, the method in this application embodiment mainly includes the following steps S2 to S8.

[0023] Step S2: In response to receiving a user query request, obtain the power terminal data collection data.

[0024] In this embodiment, the power terminal data collection data is a raw data set exported from the data collection equipment (terminal equipment) deployed in the power system, including meter reading data, meter file data, and configuration data. Specifically, the meter reading data includes, but is not limited to: the meter serial number and the corresponding data content, the collection and storage timestamp, and the configuration identifier. The meter serial number refers to the logical number assigned to each meter for quick indexing within the data collection equipment, which is unique within the equipment, such as 1, 2, 3, etc. The data content is the specific data collected, such as total forward active energy, total reverse active energy, voltage, current, active power, and other power operation parameters. The collection and storage timestamp is a time identifier that accurately records the specific time of data collection and storage. The configuration identifier is an ID used for quick indexing of configuration information.

[0025] Meter archive data includes, but is not limited to: the mapping relationship between meter serial number and archive address, protocol type, meter reading port information, number of rates, and user type. The archive address refers to the meter's own physical communication address, typically 6 bytes long and unique. Configuration data includes a configuration identifier and its corresponding configuration information, which specifies when and which data from the meter will be read by the centralized meter reading device.

[0026] A user query request is an analysis command entered by the user through the interactive interface. It mainly includes the target file address, the target configuration identifier, and the target time range. The target file address is the file address of the meter to be analyzed, the target configuration identifier is used to locate specific configuration information, and the target time range is used to determine the time interval to be analyzed.

[0027] In one implementation, the method can be executed on a host computer (such as a central server or host computer). The acquired meter reading data and meter archive data can be stored in the meter reading database and meter archive database on the host computer, respectively. For example, the meter reading database stores the raw data records collected by the centralized meter reading equipment. Each record includes the meter serial number, configuration identifier, collection and storage timestamp, and specific power operation parameters (such as voltage, current, active power, active energy, etc.). The meter archive database stores the basic archive information of the electricity meter, including the meter serial number and its corresponding archive address, which is the key to linking the meter reading data with the specific physical device (electricity meter).

[0028] In one implementation, the configuration data defines the rules for data collection, including task configuration data and scheme configuration data. The task configuration data includes a task identifier (serving as a configuration identifier), the execution frequency corresponding to the task identifier (e.g., meter reading every 15 minutes / day), and the scheme identifier bound to the task identifier. The scheme configuration data includes a scheme identifier, a set of corresponding meter identifiers (which meters the scheme will read), and a collection plan (which data to collect). The task configuration data and scheme configuration data form a many-to-many combination through the scheme identifier, jointly defining when, at what frequency, and from which meters what type of data to collect. This embodiment, by splitting the configuration data into task configuration data and scheme configuration data, achieves the goal of decoupling time scheduling from the collection content, improving the flexibility of the configuration data in use and modification.

[0029] In one embodiment, the task configuration data may further include task start time, end time, execution delay time, task execution type, runtime segment, etc.; the scheme configuration data may further include data type to be copied (such as frozen data, real-time data), storage timestamp, etc. Those skilled in the art can select according to actual needs, and this application does not impose specific limitations in this regard.

[0030] Step S4: Match the corresponding target table number from the meter reading data based on the target file address and mapping relationship, and match the corresponding target configuration information from the configuration data based on the target configuration identifier.

[0031] In this embodiment, if a user query request is received, the table archive data is first queried based on the target archive address to obtain the target table sequence number corresponding to the target archive address. Then, the configuration data is matched based on the target configuration identifier to find the corresponding target configuration information. As a specific example, the target archive address "000000000001" is matched with the corresponding target table sequence number "1" in the table archive data. Next, the scheme identifier S01 is used to match the corresponding set of meter identifiers (e.g., a set of archive numbers or table sequence numbers) and the collection plan (e.g., collection type and collection content) in the scheme configuration data. Finally, the corresponding execution frequency is matched in the task configuration data based on the scheme identifier S01.

[0032] In one optional implementation, the anomaly analysis method for power terminal data collection further includes the following step S5.

[0033] Step S5: In response to the fact that the set of meter identifiers corresponding to the target configuration information does not contain the target file address, output a configuration error message.

[0034] In this embodiment, step S5 is used to verify the correctness of the user's query logic. For example, a user wants to query whether there are any anomalies in the data of the electricity meter with the file address "000000000001" under the scheme identifier "S01". However, through matching, it is found that the set of electricity meter identifiers corresponding to the scheme identifier "S01" does not contain the file address "000000000001". This indicates that there is an error in the association between the electricity meter and the configuration data. At this time, the system can directly output a configuration anomaly prompt message, quickly terminate the invalid query, avoid subsequent meaningless data matching and analysis, and improve user experience and query efficiency.

[0035] Step S6: Extract a subset of target data from the meter reading data based on the target time range, target table number, and target configuration information.

[0036] In this embodiment, the meter reading data is filtered using a target time range (such as query start time, query time interval, query end time, etc.), target table sequence number, and target configuration information to obtain a target data subset. As an example, assuming the target configuration information includes a target scheme identifier, the target time range, target table sequence number, and target scheme identifier can be used as joint filtering conditions to extract power operation parameters that meet the joint filtering conditions and the corresponding acquisition and storage timestamps from the meter reading data, thereby constructing the target data subset.

[0037] In one implementation, the target subset of data to be constructed can be a data table containing multiple records, as shown in Table 1.

[0038] Table 1. Target Data Subset

[0039] Table number scheme identification Collection and storage time stamp Total positive active power Phase A voltage …… 1 S01 2025-01-0100:00:00 1000.5 220.3 1 S01 2025-01-0200:00:00 1010.2 221.1 1 S01 2025-01-0300:00:00 (null) (null) ……

[0040] In an optional implementation, the anomaly analysis method for power terminal data collection further includes step S7, specifically steps S71 and S72.

[0041] Step S71: Parse and reorganize the target data subset to generate a structured data table. The structured data table includes a target file address column, a power operation parameter column, and a data acquisition and storage time stamp column, which are used to record the target file address value, the power operation parameter value, and the data acquisition and storage time stamp value, respectively.

[0042] In this embodiment, after extracting the target data subset, it is integrated to obtain a structured data table for easier analysis and display. Specifically, each data record is first parsed, and the file address (converted from the table sequence number), the acquisition and storage time stamp, and the values ​​of various power operation parameters are reorganized to generate a structured data table. For example, each row of the table represents a acquisition time, and the columns include a file address column, an acquisition and storage time stamp column, and a power operation parameter column. This structured data table is the basis for batch data comparison, statistics, and further calculations.

[0043] In one implementation, Table 2 shows a specific structured data table.

[0044] Table 2. Structured Data Table

[0045] Archive address Power operating parameters 2025-01-0100:00:00 2025-01-0200:00:00 2025-01-0300:00:00 …… 000000000001 Total positive active power 1000.5 1010.2 (null) 000000000001 Phase A voltage 220.3 221.1 (null) ……

[0046] Step S72: Generate a visualization chart based on the power operation parameter values ​​and the acquisition and storage time scale values ​​in the structured data table. The visualization chart includes a line chart with the acquisition and storage time scale values ​​on the horizontal axis and the power operation parameter values ​​on the vertical axis.

[0047] Step S8: Based on the target configuration information and the preset power operation parameter rules, perform anomaly analysis on the target data subset and output the analysis results.

[0048] In this embodiment, the power operation parameter rules are anomaly detection rules constructed based on general rules and best practices of power business. They are used to perform automated anomaly analysis on the extracted target data subset in combination with target configuration information and output the analysis results.

[0049] In one embodiment, the preset power operation parameter rules include the changing trend and parameter range of the power operation parameters, and the above step S8 mainly includes the following steps S81 to S84.

[0050] Step S81: Determine the expected number of data records based on the execution frequency. Compare the actual number of records in the target data subset with the expected number of data records. If the actual number of records is less than the expected number of data records, it is determined that there is a missing record anomaly.

[0051] In this embodiment, based on the execution frequency specified in the target configuration information (e.g., once a day) and the target time range input by the user, the theoretically expected number of data records within that time period is calculated. For example, assuming the target time range is from January 1, 2025 to January 3, 2025, once a day should yield 3 records. If the actual number of records in the acquired target data subset is less than 3, a missing record anomaly is determined. In this case, the missing time point or time period can be recorded, and an anomaly prompt message can be sent to the user.

[0052] Step S82: Determine whether the power operation parameters exceed the parameter range. If they do, it is determined that there is an over-limit anomaly.

[0053] In this embodiment, the power operation parameter rules include safe and reasonable parameter ranges for each power operation parameter (e.g., the normal voltage range is 198V-242V). The system iterates through the value of each power operation parameter in the target data subset, determining whether it exceeds the corresponding parameter range. If it exceeds the parameter range, an out-of-limit anomaly is identified. It should be noted that the parameter range can be configured according to the meter type, power grid standards, or user needs; this application does not impose specific restrictions on this.

[0054] Step S83: Determine whether the power operation parameters conform to the changing trend in the time series. If they do not conform, it is determined that there is an abnormal trend.

[0055] In this embodiment, the power operation parameter rules also define the expected trends of certain parameters over time. For example, for positive active energy, under normal circumstances, this value should monotonically and non-decreasing over time, meaning it can only remain constant or increase, and should not decrease. If the positive active energy value in the target data subset shows a situation where the reading at a later time is less than the reading at the previous time, then an abnormal trend is determined to exist.

[0056] Step S84: Mark outliers on structured data tables and / or visualization charts for the target data subsets that are determined to have trend anomalies, limit-crossing anomalies, and missing value anomalies.

[0057] In this embodiment, after completing the analysis in steps S81 to S83, the system generates an analysis result report containing all the above-mentioned anomaly types, thereby further improving the readability and operability of the results. Specifically, the target data subsets determined to have trend anomalies, limit-crossing anomalies, and missing data anomalies can be marked as outliers on the structured data table and / or corresponding visualization charts, and the analysis results containing the marked values ​​can be output. For example, the background of the abnormal data rows in the structured data table can be highlighted in red, and the abnormal data points in the visualization chart (such as a line chart) can be highlighted with a conspicuous symbol (such as a red "X"). This allows maintenance personnel to quickly locate the anomalies.

[0058] Using the method described in this application, users only need to input a query request to automatically complete the entire process from multi-source data association and precise filtering to multi-dimensional intelligent diagnosis, and finally to the structured and visualized presentation of results. This reduces the manual investigation work that originally took hours or even days to seconds, greatly improving the intelligence level of power data operation and maintenance and the efficiency of fault location. The method described in this application supports custom query items (such as specifying meters, time ranges, and configuration data), which can not only quickly and accurately locate abnormal meter reading data, but also automatically generate structured data tables and visual charts, presenting the analysis results intuitively, and significantly improving the efficiency and accuracy of data verification and anomaly location.

[0059] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore will also fall within the protection scope of this application.

[0060] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0061] Another aspect of this application provides a computer-readable storage medium.

[0062] In one embodiment of a computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program for performing anomaly analysis of power terminal data collection data in the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described anomaly analysis method for power terminal data collection data. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0063] Another aspect of this application provides a smart device.

[0064] In one embodiment of a smart device according to this application, the smart device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the method described in any of the above embodiments. The smart device described in this application may include driving equipment, smart vehicles, robots, and other devices. See appendix. Figure 2 , Figure 2 The image exemplarily illustrates a communication connection between memory 11 and processor 12 via a bus.

[0065] In some embodiments of this application, the smart device may further include at least one sensor for sensing information. The sensor is communicatively connected to any type of processor mentioned in this application. Optionally, the smart device described in this application may be, but is not limited to, a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, etc., and the embodiments of this application do not limit this.

[0066] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for anomaly analysis of power terminal data collection, characterized in that, The method includes: in response to receiving a user query request, acquiring power terminal data collection data, wherein the power terminal data collection data includes meter reading data, meter file data, and configuration data; the meter reading data includes a meter serial number and the corresponding data content and configuration identifier; the meter file data includes a mapping relationship between the meter serial number and the file address; the configuration data includes a configuration identifier and the configuration information corresponding to the configuration identifier; the user query request includes a target file address, a target configuration identifier, and a target time range; matching the corresponding target meter serial number from the meter reading data based on the target file address and the mapping relationship; matching the corresponding target configuration information from the configuration data based on the target configuration identifier; extracting a target data subset from the meter reading data according to the target time range, the target meter serial number, and the target configuration information; performing anomaly analysis on the target data subset according to the target configuration information and preset power operation parameter rules, and outputting the analysis results.

2. The method for anomaly analysis of power terminal data collection according to claim 1, characterized in that, The configuration information includes a set of meter identifiers corresponding to the configuration identifier. Before extracting the target data subset from the meter reading data based on the target time range, the target meter number, and the target configuration information, the method further includes: in response to the set of meter identifiers corresponding to the target configuration information not containing the target file address, outputting a configuration error message.

3. The method for anomaly analysis of power terminal data collection according to claim 2, characterized in that, The configuration information also includes execution frequency and acquisition plan. The data content includes power operation parameters and acquisition storage timestamp. The step of extracting a target data subset from the meter reading data based on the target time range, the target table number, and the target configuration information includes: using the execution frequency and acquisition plan corresponding to the target time range, the target table number, and the target configuration information as joint filtering conditions, and extracting power operation parameters and acquisition storage timestamps that meet the joint filtering conditions from the meter reading data to construct a target data subset.

4. The method for anomaly analysis of power terminal data collection according to claim 3, characterized in that, The method further includes: The target data subset is parsed and reorganized to generate a structured data table, wherein the structured data table includes a target file address column, a power operation parameter column, and a data acquisition and storage time stamp column, which are used to record the target file address value, the power operation parameter value, and the data acquisition and storage time stamp value, respectively.

5. The method for anomaly analysis of power terminal data collection according to claim 4, characterized in that, The method further includes: generating a visualization chart based on the power operation parameter values ​​and the acquisition and storage time scale values ​​in the structured data table, wherein the visualization chart includes a line chart with the acquisition and storage time scale values ​​on the horizontal axis and the power operation parameter values ​​on the vertical axis.

6. The method for anomaly analysis of power terminal data collection according to claim 5, characterized in that, The preset power operation parameter rules include the changing trends and parameter ranges corresponding to the power operation parameters. The step of performing anomaly analysis on the target data subset based on the target configuration information and the preset power operation parameter rules, and outputting the analysis results, includes: determining whether the power operation parameters conform to the changing trends in the time series; if not, determining that there is a trend anomaly; determining whether the power operation parameters exceed the parameter range; if so, determining that there is an over-limit anomaly; determining the expected number of data records based on the execution frequency, comparing the actual number of records in the target data subset with the expected number of data records; if the actual number of records is less than the expected number of data records, determining that there is a missing record anomaly.

7. The method for anomaly analysis of power terminal data collection according to claim 6, characterized in that, The method further includes: marking outlier values ​​on the structured data table and / or the visualization chart for the target data subset that is determined to have trend anomalies, limit-crossing anomalies, and missing value anomalies.

8. The method for anomaly analysis of power terminal data collection according to claim 3, characterized in that, The configuration data includes task configuration data and scheme configuration data; the task configuration data includes a task identifier as the configuration identifier, the execution frequency corresponding to the task identifier, and a scheme identifier; the scheme configuration data includes a scheme identifier and a set of meter identifiers and a collection plan corresponding to the scheme identifier, wherein the task configuration data and the scheme configuration data form a many-to-many combination relationship through the scheme identifier.

9. A smart device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that, when executed by the at least one processor, implements the anomaly analysis method for power terminal data collection as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the anomaly analysis method for power terminal data collection as described in any one of claims 1 to 8.

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