A distribution network abnormal data processing method and device

Abstract

The application discloses a kind of processing method and device of abnormal data of distribution network, the method includes: the target research and judgment data corresponding to target power grid is collected, according to the abnormal screening model determined, the preset abnormal determination operation is executed to target research and judgment data, and initial abnormal data is obtained;According to the preset distribution network data analysis model, analyze initial abnormal data, and obtain the data detail report corresponding to initial abnormal data;According to template abnormal data and abnormal screening model, the abnormal matching degree screening operation is executed to the detail data corresponding to each abnormal parameter in data detail report, and target abnormal data is obtained, to execute preset abnormal processing operation to target abnormal data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid data processing, and particularly relates to a processing method and device for abnormal data of distribution network. BACKGROUND

[0002] With the rapid development of power grid technology, the domestic power grid structure is also increasingly improved and tends to be complex. At the same time, with the comprehensive popularization of the Internet and automation, the data of the domestic power grid is developing towards automation, resulting in an explosive increase in the amount of data of the power grid, which requires power grid enterprises to improve the processing efficiency of the explosive increase in automated data while building more complex power grid structures.

[0003] Among them, in the process of processing massive automated data, the occurrence of data anomalies in automated data is one of the most common problems. Initially, when the amount of data of automated data is small, the processing speed of abnormal data can be relatively fast and accurate, but with the explosive growth of data volume, the complexity and concealment of abnormal data types, the difficulty of abnormal processing of power grid automation data is increased, the abnormal processing efficiency is reduced, and abnormal data cannot be solved in a timely and accurate manner, which reduces the power supply reliability and user evaluation of power grid enterprises to a certain extent. Therefore, it is particularly important to provide a method for improving the abnormal processing efficiency of power grid abnormal data. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a processing method and device for abnormal data of distribution network, which can intelligently locate abnormal data in massive target research data and determine the abnormal information of the abnormal data, thereby improving the determination efficiency and accuracy of abnormal data of distribution network.

[0005] To solve the above technical problems, the present application discloses a processing method for abnormal data of distribution network, which comprises:

[0006] Collecting target research data corresponding to a target power grid, the target research data comprising abnormal data requiring a preset abnormal processing operation;

[0007] Performing a preset abnormal determination operation on the target research data according to the determined abnormal screening model to obtain initial abnormal data, the abnormal screening model comprising template abnormal data that has been determined as abnormal data, and the initial abnormal data comprising data with a data matching degree reaching a preset matching degree threshold with the template abnormal data;

[0008] According to a preset network configuration data analysis model, the initial abnormal data is analyzed to obtain a data detail report corresponding to the initial abnormal data, the data detail report including an abnormal parameter used to determine whether the initial abnormal data is abnormal data;

[0009] According to the template abnormal data and the abnormal screening model, an abnormal matching degree screening operation is performed on detail data corresponding to each abnormal parameter in the data detail report to obtain target abnormal data, and the preset abnormal processing operation is performed on the target abnormal data, the target abnormal data being data with an abnormal matching degree greater than a preset matching degree threshold.

[0010] As an optional implementation, in the first aspect of the present application, according to the abnormal parameter and the template abnormal data, an abnormal type corresponding to the target abnormal data is determined, the abnormal type including a known abnormal type recorded in a preset abnormal database or a pending abnormal type other than the known abnormal type;

[0011] When the abnormal type corresponding to the target abnormal data is the known abnormal type, a historical processing record matched with the known abnormal type is called, and a new abnormal processing scheme for processing the target abnormal data is generated according to an abnormal processing scheme corresponding to the historical processing record, and the target abnormal data is processed according to the new abnormal processing scheme;

[0012] When the abnormal type corresponding to the target abnormal data is the pending abnormal type, an abnormal similarity screening operation is performed in the abnormal database according to the abnormal type and the target abnormal data to obtain a screening result;

[0013] According to the screening result, an abnormal processing scheme for the target abnormal data is determined, and the target abnormal data is processed according to the abnormal processing scheme.

[0014] As an optional implementation, in the first aspect of the present application, when the abnormal type corresponding to the target abnormal data is the pending abnormal type, the determination of the abnormal processing scheme for the target abnormal data according to the screening result includes:

[0015] It is determined whether the screening result indicates that there is a control abnormal data in the abnormal database, the control abnormal data being data with an abnormal similarity reaching a preset similarity threshold with the target abnormal data;

[0016] When it is determined that the screening result indicates that there is no control abnormal data in the abnormal database, the target abnormal data is collected in a determined pending judgment data set, the data collected in the pending judgment data set being data that needs to perform a preset judgment operation.

[0017] According to the abnormal parameter, the target abnormal data, determine remaining investigation data, the remaining investigation data is the data in the target abnormal data except the data corresponding to the abnormal parameter;

[0018] According to the preset research and judgment model, the preset research and judgment operation is performed on the remaining investigation data, and an abnormal research and judgment processing scheme is obtained as an abnormal processing scheme for the target abnormal data;

[0019] When it is judged that the screening result indicates that the comparison abnormal data exists in the abnormal database, an abnormal processing scheme corresponding to the comparison abnormal data is analyzed to obtain an analysis result, and a processing scheme matched with the target abnormal data is generated according to the analysis result as an abnormal processing scheme for the target abnormal data.

[0020] As an optional implementation, in the first aspect of the application, before the method further comprises:

[0021] According to the data detail report, determine the data type corresponding to the initial abnormal data, the data type includes line data type of distribution network line and / or device data type corresponding to distribution network operation equipment;

[0022] According to the abnormal parameter and the data type, perform the collection and classification operation on the data detail report to obtain a collection and classification result;

[0023] And the abnormal matching degree screening operation is performed on the detail data corresponding to each abnormal parameter in the data detail report according to the template abnormal data and the abnormal screening model to obtain the target abnormal data, comprising:

[0024] According to the template abnormal data and the abnormal screening model, the abnormal matching degree screening operation is performed on the detail data corresponding to each abnormal parameter in the collection and classification result to obtain the target abnormal data.

[0025] As an optional implementation, in the first aspect of the application, before the method further comprises:

[0026] detecting whether an abnormal point processing instruction for the target research data is received, when it is detected that the abnormal point processing instruction is not received, performing the operation of performing a preset abnormality determination operation on the target research data according to the determined abnormality screening model to obtain initial abnormal data, the abnormal point processing instruction including abnormal information corresponding to the determined abnormal data;

[0027] when it is detected that the abnormal point processing instruction is received, determining corresponding point abnormal data in the target research data and a point processing scheme for the point abnormal data according to the abnormal information;

[0028] extracting the point abnormal data and performing a point processing operation on the point abnormal data according to the point processing scheme to obtain point corrected data;

[0029] updating the target research data to normal distribution network data which does not need to perform the preset abnormality processing operation according to the point corrected data, and recording the point abnormal data and the point processing scheme as support data for performing the preset abnormality processing operation on other research data except the target research data in the future.

[0030] As an optional implementation, in the first aspect of the present application, the operation of performing a preset abnormality determination operation on the target research data according to the determined abnormality screening model to obtain initial abnormal data comprises:

[0031] calling an abnormal record stored in an abnormal database corresponding to the target power grid, the abnormal record including a high-frequency abnormal record of the target power grid, the high-frequency abnormal record including a plurality of high-frequency abnormal items, the high-frequency abnormal item being a data item in the distribution network data corresponding to the target power grid whose frequency of data abnormality is higher than a preset frequency threshold;

[0032] performing a preset abnormality determination operation on the target research data according to all the high-frequency abnormal items and the determined abnormality screening model to obtain sub-research data corresponding to the high-frequency abnormal items in the target research operation as initial abnormal data.

[0033] As an optional implementation, in the first aspect of the present application, after the operation of determining an abnormality processing scheme for the target abnormal data according to the screening result, the method further comprises:

[0034] Based on the identified anomaly type corresponding to the target anomaly data and the anomaly handling plan for the target anomaly data, the anomaly database is updated and a summary report for the target anomaly data is generated. The summary report includes at least one of the anomaly type corresponding to the target anomaly data, the cause of the anomaly corresponding to the target anomaly data, and the anomaly handling plan for the target anomaly data. The summary report is submitted to the reviewer responsible for reviewing the summary report.

[0035] The system detects whether a disqualification instruction indicating that the summary content corresponding to the summary report has failed the review is received within a preset feedback period. If no disqualification instruction is received within the preset feedback period, the system determines that the summary report is correct and aggregates the summary report.

[0036] When the disqualification instruction is detected within the preset feedback period, the summary report is updated and aggregated according to the correction data included in the disqualification instruction.

[0037] A second aspect of the present invention discloses a processing apparatus for abnormal distribution network data, the apparatus comprising:

[0038] The acquisition module is used to acquire target assessment data corresponding to the target power grid, including abnormal data that requires the execution of preset abnormality handling operations;

[0039] An anomaly detection module is used to perform a preset anomaly detection operation on the target analysis data according to the determined anomaly screening model to obtain initial anomaly data. The anomaly screening model includes template anomaly data that has been determined to be anomaly data, and the initial anomaly data includes data whose data matching degree with the template anomaly data reaches a preset matching degree threshold.

[0040] The analysis module is used to analyze the initial abnormal data according to the preset distribution network data analysis model, and obtain a data detail report corresponding to the initial abnormal data. The data detail report includes abnormal parameters used to determine whether the initial abnormal data is abnormal data.

[0041] An anomaly filtering module is used to perform an anomaly matching degree filtering operation on the detailed data corresponding to each anomaly parameter in the data detail report according to the template anomaly data and the anomaly filtering model to obtain target anomaly data, so as to perform the preset anomaly processing operation on the target anomaly data, wherein the target anomaly data is data whose anomaly matching degree is greater than the preset matching degree threshold.

[0042] As an optional implementation, in a second aspect of the invention, the apparatus further includes:

[0043] The first determination module is configured to determine an abnormality type corresponding to the target abnormality data according to the abnormality parameter and the template abnormality data, wherein the abnormality type comprises a known abnormality type recorded in a preset abnormality database or a pending abnormality type other than the known abnormality type.

[0044] The calling module is configured to call a historical processing record matched with the known abnormality type when the abnormality type corresponding to the target abnormality data is the known abnormality type.

[0045] The generating module is configured to generate a new abnormality processing scheme for processing the target abnormality data according to the abnormality processing scheme corresponding to the historical processing record called by the calling module, and perform abnormality processing on the target abnormality data according to the new abnormality processing scheme.

[0046] The abnormality screening module is further configured to perform an abnormality similarity screening operation in the abnormality database according to the abnormality type and the target abnormality data when the abnormality type corresponding to the target abnormality data is the pending abnormality type, and obtain a screening result.

[0047] The first determination module is further configured to determine an abnormality processing scheme for the target abnormality data according to the screening result, and perform abnormality processing on the target abnormality data according to the abnormality processing scheme.

[0048] As an optional implementation, in the second aspect of the present application, the manner in which the first determination module determines the abnormality processing scheme for the target abnormality data according to the screening result specifically comprises:

[0049] When the abnormality type corresponding to the target abnormality data is the pending abnormality type, it is determined whether the screening result indicates that there is a control abnormality data in the abnormality database, wherein the control abnormality data is data having an abnormality similarity reaching a preset similarity threshold with the target abnormality data.

[0050] When it is determined that the screening result indicates that there is no control abnormality data in the abnormality database, the target abnormality data is collected in a determined pending judgment data set, wherein the data collected in the pending judgment data set is data that needs to perform a preset judgment operation.

[0051] Remaining investigation data is determined according to the abnormality parameter and the target abnormality data, wherein the remaining investigation data is data other than the data corresponding to the abnormality parameter in the target abnormality data.

[0052] A preset judgment model is used to perform the preset judgment operation on the remaining investigation data to obtain an abnormality judgment processing scheme as an abnormality processing scheme for the target abnormality data.

[0053] When it is judged that the screening result indicates that the control abnormality data exists in the abnormality database, an abnormality processing scheme corresponding to the control abnormality data is analyzed to obtain an analysis result, and a processing scheme matched with the target abnormality data is generated as an abnormality processing scheme for the target abnormality data according to the analysis result.

[0054] As an optional implementation, in the second aspect, the apparatus further comprises:

[0055] The second determination module is configured to, before the abnormality screening module performs the abnormality matching degree screening operation on the detailed data corresponding to each of the abnormality parameters in the data detailed report according to the template abnormality data and the abnormality screening model to obtain the target abnormality data, determine a data type corresponding to the initial abnormality data according to the data detailed report, wherein the data type comprises a line data type of a line of a distribution network and / or a device data type corresponding to a running device of the distribution network.

[0056] The collection module is configured to perform a collection classification operation on the data detailed report according to the abnormality parameters and the data type to obtain a collection classification result.

[0057] The abnormality screening module performs the abnormality matching degree screening operation on the detailed data corresponding to each of the abnormality parameters in the collection classification result according to the template abnormality data and the abnormality screening model to obtain the target abnormality data.

[0058] The abnormality screening module performs the abnormality matching degree screening operation on the detailed data corresponding to each of the abnormality parameters in the collection classification result according to the template abnormality data and the abnormality screening model to obtain the target abnormality data.

[0059] As an optional implementation, in the second aspect, the apparatus further comprises:

[0060] The first detection module is configured to, before the first determination module performs the preset abnormality determination operation on the target research and judgment data according to the determined abnormality screening model to obtain the initial abnormality data, detect whether an abnormality point processing instruction for the target research and judgment data is received, and when it is detected that the abnormality point processing instruction is not received, trigger the first determination module to perform the operation of performing the preset abnormality determination operation on the target research and judgment data according to the determined abnormality screening model to obtain the initial abnormality data, wherein the abnormality point processing instruction comprises abnormality information corresponding to the determined abnormality data.

[0061] The second determining module is further configured to, when the first detecting module detects that the abnormal fixed-point processing instruction is received, determine, according to the abnormal information, corresponding fixed-point abnormal data in the target research and judgment data and a fixed-point processing scheme for the fixed-point abnormal data.

[0062] The correction processing module is configured to extract the fixed-point abnormal data and perform a fixed-point processing operation on the fixed-point abnormal data according to the fixed-point processing scheme to obtain fixed-point correction data.

[0063] The first updating module is configured to update the target research and judgment data to normal distribution network data that does not need to perform the preset abnormal processing operation according to the fixed-point correction data obtained by the correction processing module.

[0064] The recording module is configured to record the fixed-point abnormal data and the fixed-point processing scheme determined by the second determining module as support data for performing the preset abnormal processing operation on other research and judgment data in addition to the target research and judgment data in the future.

[0065] As an optional implementation, in the second aspect of the present application, the manner in which the abnormal determining module performs a preset abnormal determining operation on the target research and judgment data according to the determined abnormal screening model to obtain initial abnormal data specifically includes:

[0066] The abnormal record stored in the abnormal database corresponding to the target power grid is called, the abnormal record including high-frequency abnormal records of the target power grid, the high-frequency abnormal records including a plurality of high-frequency abnormal items, the high-frequency abnormal item being a data item in the distribution network data corresponding to the target power grid, the frequency of data abnormality of which is higher than a preset frequency threshold;

[0067] The preset abnormal determining operation is performed on the target research and judgment data according to all the high-frequency abnormal items and the determined abnormal screening model, and sub-research and judgment data corresponding to the high-frequency abnormal items in the target research and judgment operation are obtained as initial abnormal data.

[0068] As an optional implementation, in the second aspect of the present application, the device further includes:

[0069] a second updating module, configured to, after the first determining module determines the abnormality processing scheme for the target abnormal data according to the screening result, update the abnormality database according to the determined abnormality type corresponding to the target abnormal data and the abnormality processing scheme for the target abnormal data, and generate a summary report for the target abnormal data, the summary report including at least one of the abnormality type corresponding to the target abnormal data, the abnormality occurrence cause corresponding to the target abnormal data and the abnormality processing scheme for the target abnormal data, the summary report being used to be submitted to an auditing personnel responsible for auditing the summary report;

[0070] a second detecting module, configured to detect whether a disqualification instruction indicating that the summary content corresponding to the summary report fails to pass the auditing is received within a preset feedback period, and determine that the summary report is correct and collect the summary report when it is detected that the disqualification instruction is not received within the preset feedback period;

[0071] a data processing module, configured to, when it is detected by the second detecting module that the disqualification instruction is received within the preset feedback period, update the summary report according to the correction data included in the disqualification instruction and collect the summary report.

[0072] A third aspect of the present application discloses another device for processing abnormal data in network configuration, the device comprising:

[0073] a memory storing executable program codes;

[0074] a processor coupled with the memory;

[0075] the processor invokes the executable program codes stored in the memory to execute the method for processing abnormal data in network configuration disclosed in the first aspect of the present application.

[0076] A fourth aspect of the present application discloses a computer storage medium storing computer instructions, the computer instructions being invoked to execute the method for processing abnormal data in network configuration disclosed in the first aspect of the present application.

[0077] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0078] In the embodiment of the present application, a processing method of power distribution network abnormal data is provided, which comprises: collecting target research and judgment data corresponding to a target power grid, the target research and judgment data comprising abnormal data requiring to perform a preset abnormal processing operation; performing a preset abnormal determination operation on the target research and judgment data according to a determined abnormal screening model to obtain initial abnormal data, the abnormal screening model comprising template abnormal data that has been determined as abnormal data, and the initial abnormal data comprising data having a data matching degree with the template abnormal data reaching a preset matching degree threshold; analyzing the initial abnormal data according to a preset power distribution data analysis model to obtain a data detail report corresponding to the initial abnormal data, the data detail report comprising abnormal parameters for judging whether the initial abnormal data is abnormal data; performing an abnormal matching degree screening operation on detail data corresponding to each abnormal parameter in the data detail report according to the template abnormal data and the abnormal screening model to obtain target abnormal data, so as to perform the preset abnormal processing operation on the target abnormal data, the target abnormal data being data having an abnormal matching degree greater than the preset matching degree threshold. It can be seen that the embodiment of the present application can preliminarily screen out abnormal data in the target research and judgment data through the abnormal screening model, thereby reducing the abnormal data required to be processed when determining specific abnormal information of the abnormal data; and can also perform detailed abnormal data analysis on the initial abnormal data according to the power distribution data analysis model, thereby accurately locating the abnormal data in the target research and judgment data, improving the accuracy and reliability of the determined abnormal data, and improving the accuracy of the processing result when performing subsequent abnormal data processing. BRIEF DESCRIPTION OF DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0080] Figure 1 is a flowchart of a processing method of power distribution network abnormal data disclosed by the embodiment of the present application;

[0081] Figure 2 is a flowchart of another processing method of power distribution network abnormal data disclosed by the embodiment of the present application;

[0082] Figure 3 is a structural diagram of a processing device of power distribution network abnormal data disclosed by the embodiment of the present application;

[0083] Figure 4 is a structural diagram of another processing device of power distribution network abnormal data disclosed by the embodiment of the present application;

[0084] Figure 5This is a schematic diagram of the structure of another distribution network abnormal data processing device disclosed in an embodiment of the present invention. Detailed Implementation

[0085] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0087] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0088] This invention discloses a method and apparatus for processing abnormal data in a distribution network. It can initially filter out abnormal data in the target assessment data using an anomaly screening model, reducing the amount of abnormal data that needs to be processed when determining the specific anomaly information. Furthermore, it can perform refined anomaly analysis on the initial abnormal data based on a distribution network data analysis model, thereby accurately locating abnormal data in the target assessment data. This improves the accuracy and reliability of the identified abnormal data, and to a certain extent, enhances the accuracy of the processing results obtained during subsequent anomaly data processing. Detailed descriptions follow.

[0089] Example 1

[0090] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for processing abnormal data in a distribution network, as disclosed in an embodiment of the present invention. Figure 1The described power distribution network abnormal data processing method can be applied to a power distribution network abnormal data processing device, and embodiments of the present application are not limited. As shown in Figure 1 The power distribution network abnormal data processing method can include the following operations:

[0091] 101. Collect target research and judgment data corresponding to the target power grid.

[0092] In embodiments of the present application, the target research and judgment data includes abnormal data that needs to perform a preset abnormal processing operation. The abnormal data included in the target research and judgment data can include known abnormal data that has determined abnormal data information, or unknown abnormal data that has completely unknown abnormal data information, and embodiments of the present application are not limited.

[0093] 102. According to the determined abnormal screening model, perform a preset abnormal determination operation on the target research and judgment data to obtain initial abnormal data.

[0094] In embodiments of the present application, the abnormal screening model includes template abnormal data that has been determined as abnormal data, and the initial abnormal data includes data that has a data matching degree with the template abnormal data reaching a preset matching degree threshold. To facilitate understanding, the following describes the application of the data matching degree:

[0095] Suppose that the initial abnormal data corresponds to a trouble ticket, and the data abnormality exists in the trouble ticket number and the trouble time corresponding to the trouble ticket. The template abnormal data corresponds to the determined abnormality of the trouble ticket number and the abnormality of the trouble time in the historical record. By comparing the work order information corresponding to the currently researched trouble ticket with the template abnormal data, if the number data corresponding to the trouble ticket number matches the abnormality of the trouble ticket number in the historical record, it is determined that the abnormality of the trouble ticket number is the abnormality of the trouble ticket number. The specific application is not limited in embodiments of the present application.

[0096] 103. According to the preset power distribution data analysis model, analyze the initial abnormal data to obtain a data detail report corresponding to the initial abnormal data.

[0097] In embodiments of the present application, the data detail report includes an abnormal parameter for judging whether the initial abnormal data is abnormal data. Specifically, when the target research and judgment data is a certain trouble ticket corresponding to the target power grid, the abnormal parameter can be a work order parameter corresponding to the trouble ticket, such as a trouble ticket type, a trouble ticket number, a trouble ticket area, etc., and embodiments of the present application are not limited.

[0098] In embodiments of the present application, optionally, before step 103, the power distribution network abnormal data processing method can further include the following operations:

[0099] detecting whether an abnormal point processing instruction for the target research data is received, when it is detected that the abnormal point processing instruction is not received, performing the operation of performing a preset abnormality determination operation on the target research data according to the determined abnormality screening model, to obtain initial abnormal data, the abnormal point processing instruction including abnormal information corresponding to the determined abnormal data;

[0100] When it is detected that the abnormal point processing instruction is received, according to the abnormal information, determining corresponding point abnormal data in the target research data and a point processing scheme for the point abnormal data;

[0101] extracting the point abnormal data, and performing a point processing operation on the point abnormal data according to the point processing scheme to obtain point corrected data;

[0102] According to the point corrected data, the target research data is updated to be a normal distribution network data which does not need to perform the preset abnormal processing operation, and the point abnormal data and the point processing scheme are recorded as support data for subsequent execution of the preset abnormal processing operation on other research data except the target research data.

[0103] It can be seen that in the embodiment of the application, before the abnormality determination is performed on the target research data, if there is an abnormal point processing instruction, the abnormal point processing instruction is preferentially executed, which is beneficial to improve the abnormal positioning efficiency and the abnormal processing efficiency, avoids the situation that the abnormal data can be directly located, but the positioning of the abnormal data is still carried out according to the specified procedure, wasting the program running resources, and at the same time, the abnormality determination efficiency for the target research data is reduced, a kind of abnormality determination method for target research data is provided, which improves the abnormality determination efficiency for target research data and the accuracy of the obtained abnormality determination result to a certain extent.

[0104] 104、According to the template abnormal data and the abnormality screening model, an abnormal matching degree screening operation is performed on the detail data corresponding to each abnormal parameter in the data detail report to obtain target abnormal data, so that a preset abnormal processing operation is performed on the target abnormal data.

[0105] In the embodiment of the application, the target abnormal data is data with an abnormal matching degree greater than a preset matching degree threshold, further, the initial abnormal data is data including abnormal data after the data range of the target research data is reduced after preliminary screening, and the target abnormal data is a data partition in which specific abnormal data is subdivided on the basis of the initial abnormal data.

[0106] Optionally, before the step 104, the power distribution network abnormal data processing method can further include the following operations:

[0107] According to the data detail report, determine the data type corresponding to the initial abnormal data, and the data type includes a line data type of a power distribution line and / or a device data type corresponding to a power distribution operation device;

[0108] According to the abnormal parameters and the data type, perform a collection and classification operation on the data detail report to obtain a collection and classification result;

[0109] The method of performing the abnormal matching degree screening operation on the detail data corresponding to each abnormal parameter in the data detail report according to the template abnormal data and the abnormal screening model to obtain the target abnormal data specifically includes the following steps:

[0110] Performing the abnormal matching degree screening operation on the detail data corresponding to each abnormal parameter in the collection and classification result according to the template abnormal data and the abnormal screening model to obtain the target abnormal data.

[0111] It can be seen that, in the embodiment of the application, before the abnormal matching degree screening operation is performed on the detail data corresponding to each abnormal parameter in the data detail report, the data detail report can be adaptively collected and classified, the calling efficiency and accuracy of the data detail report in subsequent calling are improved, and the accuracy of the subsequently determined target abnormal data is improved to a certain extent.

[0112] It can be seen that, in the embodiment of the application, before the abnormal matching degree screening operation is performed on the detail data corresponding to each abnormal parameter in the data detail report, the data detail report can be adaptively collected and classified, the calling efficiency and accuracy of the data detail report in subsequent calling are improved, and the accuracy of the subsequently determined target abnormal data is improved to a certain extent. Figure 1 The described power distribution network abnormal data processing method can preliminarily screen out abnormal data in the target research and judgment data through the abnormal screening model, reduce the abnormal data required to process the specific abnormal information of the subsequently determined abnormal data, and can also perform detailed abnormal data analysis on the initial abnormal data according to the power distribution network data analysis model, thereby accurately positioning the abnormal data in the target research and judgment data, improving the accuracy and reliability of the determined abnormal data, and improving the accuracy of the processing result obtained when the subsequent abnormal data processing is performed to a certain extent.

[0113] Embodiment two

[0114] Please refer to Figure 2 , Figure 2 is a flowchart of another power distribution network abnormal data processing method disclosed in the embodiment of the application. In the embodiment of the application, Figure 2 The described power distribution network abnormal data processing method can be applied to a power distribution network abnormal data processing device, and the embodiment of the application is not limited. For example, Figure 2As shown, the power distribution network abnormal data processing method can include the following operations:

[0115] 201, collect target research and judgment data corresponding to the target power grid.

[0116] 202, according to the determined abnormal screening model, execute the preset abnormal judgment operation on the target research and judgment data to obtain initial abnormal data.

[0117] 203, according to the preset power distribution data analysis model, analyze the initial abnormal data to obtain a data detail report corresponding to the initial abnormal data.

[0118] 204, according to the template abnormal data and the abnormal screening model, execute an abnormal matching degree screening operation on the detail data corresponding to each abnormal parameter in the data detail report to obtain target abnormal data, and execute a preset abnormal processing operation on the target abnormal data.

[0119] In the embodiment of the application, for other descriptions of steps 201-204, please refer to other specific descriptions of steps 101-104 in embodiment one, and the embodiment of the application will not be repeated here.

[0120] 205, according to the abnormal parameter and the template abnormal data, determine the abnormal type corresponding to the target abnormal data.

[0121] In the embodiment of the application, the abnormal type includes a known abnormal type recorded in the preset abnormal database or a pending abnormal type other than the known abnormal type; when the abnormal type corresponding to the target abnormal data is the known abnormal type, step 206 is executed, and when the abnormal type corresponding to the target abnormal data is the pending abnormal type, step 208 is executed.

[0122] 206, when the abnormal type corresponding to the target abnormal data is the known abnormal type, a historical processing record matched with the known abnormal type is called.

[0123] 207, according to the abnormal processing scheme corresponding to the historical processing record, a new abnormal processing scheme for processing the target abnormal data is generated.

[0124] In the embodiment of the application, the new abnormal processing scheme is used to execute abnormal processing on the target abnormal data.

[0125] 208, when the abnormal type corresponding to the target abnormal data is the pending abnormal type, according to the abnormal type and the target abnormal data, an abnormal similarity screening operation is performed in the abnormal database to obtain a screening result.

[0126] 209, according to the screening result, determine an abnormal processing scheme for the target abnormal data, and execute abnormal processing on the target abnormal data according to the abnormal processing scheme.

[0127] Further, when the abnormal type corresponding to the target abnormal data is the pending abnormal type, the manner of determining the abnormal processing scheme for the target abnormal data according to the screening result in step 209 specifically includes:

[0128] determining whether the screening result indicates that there is collation abnormal data in the abnormal database, the collation abnormal data being data with an abnormal similarity to the target abnormal data reaching a preset similarity threshold;

[0129] when it is determined that the screening result indicates that there is no collation abnormal data in the abnormal database, the target abnormal data is collected in the determined pending judgment data set, the data collected in the pending judgment data set being data that needs to perform a preset judgment operation;

[0130] determining remaining investigation data according to the abnormal parameter and the target abnormal data, the remaining investigation data being data in the target abnormal data other than the data corresponding to the abnormal parameter;

[0131] performing a preset judgment operation on the remaining investigation data according to a preset judgment model to obtain an abnormal judgment processing scheme as the abnormal processing scheme for the target abnormal data;

[0132] when it is determined that the screening result indicates that there is collation abnormal data in the abnormal database, analyzing the abnormal processing scheme corresponding to the collation abnormal data to obtain an analysis result, and generating a processing scheme matched with the target abnormal data according to the analysis result as the abnormal processing scheme for the target abnormal data.

[0133] It can be seen that in the embodiment of the application, the abnormal processing strategy for different screening results is provided, wherein for the case that there is no collation abnormal data, the target abnormal data can be automatically collected in the pending judgment data set and the judgment processing is performed on the remaining investigation data, the overall data investigation on the target abnormal data is realized, the case that the abnormal data exists in the remaining investigation data and cannot be investigated is avoided, and the accuracy and reliability of the abnormal processing for the target abnormal data are improved to a certain extent.

[0134] It can be seen that the implementation Figure 2 The described processing method of abnormal data in network distribution can adaptively adjust the processing method of the target abnormal data according to the abnormal type, if the target abnormal data is of a known abnormal type, the processing efficiency and accuracy of processing the target abnormal data are improved by calling the corresponding historical processing record, if the target abnormal data is of a pending abnormal type, the abnormal database can also automatically call the abnormal similarity screening on the target abnormal data, so that the matched abnormal processing scheme is determined according to the screening result, the abnormal data is processed in a targeted manner, and the reliability and accuracy of processing the target abnormal data are improved.

[0135] In an optional embodiment, before the operation of performing the preset abnormality determination operation on the target research data according to the determined abnormality screening model to obtain the initial abnormal data, the processing method of the distribution network abnormal data can further include the following operations: detecting whether an abnormality pinpointing processing instruction for the target research data is received, when it is detected that the abnormality pinpointing processing instruction is not received, performing the operation of performing the preset abnormality determination operation on the target research data according to the determined abnormality screening model to obtain the initial abnormal data, and the abnormality pinpointing processing instruction includes abnormal information corresponding to the determined abnormal data.

[0136] When it is detected that the abnormality pinpointing processing instruction is received, according to the abnormal information, determining corresponding pinpointing abnormal data in the target research data and a pinpointing processing scheme for the pinpointing abnormal data;

[0137] extracting the pinpointing abnormal data, and performing a pinpointing processing operation on the pinpointing abnormal data according to the pinpointing processing scheme to obtain pinpointing correction data;

[0138] According to the pinpointing correction data, updating the target research data to be normal distribution network data that does not need to perform the preset abnormality processing operation, and recording the pinpointing abnormal data and the pinpointing processing scheme as support data for performing the preset abnormality processing operation on other research data in addition to the target research data in the future.

[0139] In this optional embodiment, further optionally, the operation of performing the preset abnormality determination operation on the target research data according to the determined abnormality screening model to obtain the initial abnormal data specifically includes:

[0140] calling abnormality records stored in an abnormality database corresponding to the target power grid, the abnormality records including high-frequency abnormality records of the target power grid, the high-frequency abnormality records including a plurality of high-frequency abnormality items, and the high-frequency abnormality item being a data item in the distribution network data corresponding to the target power grid, the frequency of data abnormality of which is higher than a preset frequency threshold;

[0141] performing the preset abnormality determination operation on the target research data according to all the high-frequency abnormality items and the determined abnormality screening model to obtain sub-research data corresponding to the high-frequency abnormality items in the target research operation as the initial abnormal data.

[0142] It can be seen that in the optional embodiment, another exception handling strategy is provided, and the exception pointing processing instruction is used to accurately locate the pointing exception data. In the case of quickly locating the pointing exception data, the original exception judgment process is not executed, thereby adaptively executing the exception pointing processing instruction in priority, improving the determination efficiency and accuracy of determining the exception data when performing the exception judgment operation on the target research and judgment data. In addition, based on the exception screening model, the initial exception data is assisted in research and judgment through the high-frequency exception record stored in the exception database when performing the exception judgment operation on the target research and judgment data, thereby improving the determination efficiency of the initial exception data to a certain extent.

[0143] In another optional embodiment, after determining the exception handling scheme for the target exception data according to the screening result, the distribution network exception data processing method can further include the following operations:

[0144] According to the determined exception type corresponding to the target exception data and the exception handling scheme for the target exception data, the exception database is updated, and a summary report for the target exception data is generated. The summary report includes at least one of the exception type corresponding to the target exception data, the exception occurrence reason corresponding to the target exception data, and the exception handling scheme for the target exception data. The summary report is used to submit to an auditing personnel responsible for auditing the summary report;

[0145] Detecting whether a disqualification instruction indicating that the summary content corresponding to the summary report fails to pass the audit is received within a preset feedback period. When it is detected that the disqualification instruction is not received within the preset feedback period, it is determined that the summary report is correct and the summary report is collected;

[0146] When it is detected that the disqualification instruction is received within the preset feedback period, the summary report is updated according to the correction data included in the disqualification instruction and the summary report is collected.

[0147] It can be seen that in the optional embodiment, after each target exception data is processed, the exception database can be automatically updated. The iterative update of the exception data serves as auxiliary data for subsequent exception judgment and exception handling, thereby improving the judgment efficiency and accuracy of the subsequent judgment exception data, and improving the exception efficiency of the subsequent processing exception data and the reliability and accuracy of the obtained exception processing result. In addition, the summary report can be updated and / or collected according to the instruction fed back by the auditing personnel, which is beneficial to improving the processing efficiency of the summary report.

[0148] In the optional embodiment, further, when it is detected that the disqualification instruction is received within the preset feedback period, the summary report is updated according to the correction data included in the disqualification instruction and the summary report is collected. The above operations can include the following operations:

[0149] Based on the correction data included in the disqualification instruction, a correction matching operation is performed on the summary report to obtain the matching result, which includes erroneous data in the summary report that is inconsistent with the correction data.

[0150] Based on the analysis of the corrected data, the error data is obtained, and the error report corresponding to the error data is obtained. The error data is then updated with the corrected data, and the updated summary report is obtained. The updated summary report is also collected. The error report corresponding to the error data is used as auxiliary data when tracing back the error cause corresponding to the summary report.

[0151] As can be seen, in this optional embodiment, the error data can be intelligently extracted and the corresponding error report can be generated based on the disqualification instruction, which is beneficial to improving the backtracking efficiency when tracing the cause of the error in the subsequent summary report; it can also automatically update the summary report based on the correction data corresponding to the disqualification instruction, ensuring that the final summary report is the correct summary data, which improves the processing efficiency of the summary report to a certain extent.

[0152] Example 3

[0153] Please see Figure 3 , Figure 3 This is a schematic diagram of a distribution network anomaly data processing device disclosed in an embodiment of the present invention. The distribution network anomaly data processing device can be a distribution network anomaly data processing terminal, distribution network anomaly data processing equipment, distribution network anomaly data processing system, or distribution network anomaly data processing server. The distribution network anomaly data processing server can be a local server, a remote server, or a cloud server (also known as a cloud server). When the distribution network anomaly data processing server is a non-cloud server, the non-cloud server can communicate with the cloud server; this embodiment of the present invention does not impose limitations. Figure 3 As shown, the device for processing abnormal data in the distribution network may include a data acquisition module 301, an anomaly determination module 302, an analysis module 303, and an anomaly filtering module 304, wherein:

[0154] The acquisition module 301 is used to acquire target assessment data corresponding to the target power grid. The target assessment data includes abnormal data that requires the execution of preset abnormal handling operations.

[0155] The anomaly detection module 302 is used to perform a preset anomaly detection operation on the target analysis data according to the determined anomaly screening model to obtain initial anomaly data. The anomaly screening model includes template anomaly data that has been determined to be anomaly data, and the initial anomaly data includes data whose data matching degree with the template anomaly data reaches a preset matching degree threshold.

[0156] The analysis module 303 is configured to analyze the initial abnormal data according to a preset network data analysis model to obtain a data detail report corresponding to the initial abnormal data, and the data detail report includes an abnormal parameter used to determine whether the initial abnormal data is abnormal data.

[0157] The abnormal screening module 304 is configured to perform an abnormal matching degree screening operation on the detail data corresponding to each abnormal parameter in the data detail report according to the template abnormal data and an abnormal screening model to obtain target abnormal data, and perform a preset abnormal processing operation on the target abnormal data, and the target abnormal data is data with an abnormal matching degree greater than a preset matching degree threshold.

[0158] It can be seen that the implementation Figure 3 The described network abnormal data processing apparatus can preliminarily screen out abnormal data in the target research data through the abnormal screening model, reduce the abnormal data required to process when determining the specific abnormal information of the abnormal data, and can also perform detailed abnormal data analysis on the initial abnormal data according to the network data analysis model, thereby accurately locating the abnormal data in the target research data, improving the accuracy and reliability of the determined abnormal data, and to a certain extent, improving the accuracy of the processing result when performing subsequent abnormal data processing.

[0159] In an optional embodiment, as Figure 4 The network abnormal data processing apparatus can further include a first determination module 305, a calling module 306, and a generation module 307.

[0160] The first determination module 305 is configured to determine an abnormal type corresponding to the target abnormal data screened out by the abnormal screening module 304 according to the abnormal parameter included in the data detail report obtained by the analysis module 303 and the template abnormal data, and the abnormal type includes a known abnormal type recorded in a preset abnormal database or a pending abnormal type other than the known abnormal type.

[0161] The calling module 306 is configured to call a historical processing record matched with the known abnormal type when the abnormal type corresponding to the target abnormal data determined by the first determination module 305 is the known abnormal type.

[0162] The generation module 307 is configured to generate a new abnormal processing scheme for processing the target abnormal data according to the abnormal processing scheme corresponding to the record of the historical processing record called by the calling module 306, and perform abnormal processing on the target abnormal data according to the new abnormal processing scheme.

[0163] The abnormal screening module 304 is further configured to perform an abnormal similarity screening operation in the abnormal database according to the abnormal type and the target abnormal data when the abnormal type corresponding to the target abnormal data is the pending abnormal type to obtain a screening result.

[0164] The first determination module 305 is further configured to determine an exception processing scheme for the target exception data according to the screening result, so as to perform exception processing on the target exception data according to the exception processing scheme.

[0165] It can be seen that, by implementing the described processing apparatus for network configuration exception data, the processing method for processing the determined target exception data can be adaptively adjusted according to the exception type. If the target exception data is of a known exception type, the processing efficiency and accuracy for processing the target exception data can be improved by calling the corresponding historical processing record. If the target exception data is of a pending exception type, the exception database can also be automatically called to perform exception similarity screening on the target exception data, so that a matched exception processing scheme is determined according to the screening result, and the exception data is processed in a targeted manner, thereby improving the reliability and accuracy of processing the target exception data. Figure 4

[0166] In another optional embodiment, the manner in which the first determination module 305 determines the exception processing scheme for the target exception data according to the screening result specifically includes:

[0167] When the exception type corresponding to the target exception data is a pending exception type, it is determined whether the screening result indicates that there is collation exception data in the exception database, the collation exception data being data that has an exception similarity to the target exception data reaching a preset similarity threshold.

[0168] When it is determined that the screening result indicates that there is no collation exception data in the exception database, the target exception data is collected in the determined pending judgment data set, the data collected in the pending judgment data set being data that needs to perform a preset judgment operation.

[0169] According to the exception parameter and the target exception data, remaining investigation data is determined, the remaining investigation data being data in the target exception data other than data corresponding to the exception parameter.

[0170] According to the preset judgment model, a preset judgment operation is performed on the remaining investigation data to obtain an exception judgment processing scheme as the exception processing scheme for the target exception data.

[0171] When it is determined that the screening result indicates that there is collation exception data in the exception database, an exception processing scheme corresponding to the collation exception data is analyzed to obtain an analysis result, and a processing scheme matched with the target exception data is generated according to the analysis result as the exception processing scheme for the target exception data.

[0172] It can be seen that, Figure 4 ​The described distribution network abnormal data processing device provides abnormal processing strategies for different screening results. In the case where there is no reference abnormal data, it can automatically collect the target abnormal data into the dataset to be analyzed and perform analysis processing on the remaining investigation data. This achieves a comprehensive data investigation of the target abnormal data and avoids the situation where data anomalies exist in the remaining investigation data, leading to errors or omissions in the anomaly investigation and thus failing to detect the abnormal data. This improves the accuracy and reliability of anomaly processing for target abnormal data to a certain extent.

[0173] In yet another alternative embodiment, such as Figure 4 As shown, the device for processing abnormal data in the distribution network also includes a second determining module 308 and a collection module 309, wherein:

[0174] The second determining module 308 is used by the anomaly filtering module 304 to perform anomaly matching degree filtering operation on the detailed data corresponding to each anomaly parameter in the data detail report based on the template anomaly data and the anomaly filtering model. Before obtaining the target anomaly data, the anomaly determining module 303 determines the data type corresponding to the initial anomaly data determined by the anomaly judgment module 302 based on the data detail report obtained by the analysis module 303. The data type includes the line data type of the distribution network line and / or the equipment data type corresponding to the distribution network operating equipment.

[0175] The aggregation module 309 is used to perform aggregation and classification operations on the data detail report obtained by the analysis module 303 based on the abnormal parameters and data types, and obtain the aggregation and classification results.

[0176] The anomaly filtering module 304 performs anomaly matching degree filtering operations on the detailed data corresponding to each anomaly parameter in the data detail report based on the template anomaly data and the anomaly filtering model. The specific methods for obtaining the target anomaly data include:

[0177] Based on the template abnormal data and the abnormal screening model, an abnormal matching degree screening operation is performed on the detailed data corresponding to each abnormal parameter in the aggregation and classification results to obtain the target abnormal data.

[0178] It is evident that implementation Figure 4 The described distribution network abnormal data processing device can adaptively collect and classify data detail reports before performing anomaly matching degree filtering operations on the detailed data corresponding to each abnormal parameter in the data detail report. This improves the calling efficiency and accuracy when calling the data detail report later, and to a certain extent improves the accuracy of the target abnormal data subsequently determined.

[0179] In another alternative embodiment, such as Figure 4As shown, the power distribution network abnormal data processing apparatus further includes a first detection module 310, a correction processing module 311, a first updating module 312, and a recording module 313.

[0180] The first detection module 310 is configured to, before the first determination module 305 performs the preset abnormality determination operation on the target research data according to the determined abnormality screening model to obtain the initial abnormal data, detect whether an abnormality pinpointing processing instruction for the target research data is received, and trigger the first determination module 305 to perform the preset abnormality determination operation on the target research data according to the determined abnormality screening model to obtain the initial abnormal data when it is detected that the abnormality pinpointing processing instruction is not received. The abnormality pinpointing processing instruction includes abnormality information corresponding to the determined abnormal data.

[0181] The second determination module 308 is further configured to, when the first detection module 310 detects that the abnormality pinpointing processing instruction is received, determine, according to the abnormality information, the pinpointed abnormal data in the target research data and a pinpointing processing scheme for the pinpointed abnormal data.

[0182] The correction processing module 311 is configured to extract the pinpointed abnormal data determined by the second determination module 308, and perform a pinpointing processing operation on the pinpointed abnormal data according to the pinpointing processing scheme to obtain pinpointed correction data.

[0183] The first updating module 312 is configured to update the target research data collected by the collection module 301 to normal power distribution network data that does not need to perform the preset abnormality processing operation, according to the pinpointed correction data obtained by the correction processing module 311.

[0184] The recording module 313 is configured to record the pinpointed abnormal data and the pinpointing processing scheme determined by the second determination module 308 as support data for performing the preset abnormality processing operation on other research data in addition to the target research data in the future.

[0185] In this optional embodiment, further optionally, the manner in which the abnormality determination module 302 performs the preset abnormality determination operation on the target research data according to the determined abnormality screening model to obtain the initial abnormal data specifically includes:

[0186] calling abnormality records stored in an abnormality database corresponding to the target power grid, the abnormality records including high-frequency abnormality records of the target power grid, the high-frequency abnormality records including a plurality of high-frequency abnormality items, and each high-frequency abnormality item being a data item in the power distribution network data corresponding to the target power grid, the frequency of data abnormality of which is higher than a preset frequency threshold;

[0187] According to all high-frequency abnormal items and the determined abnormal screening model, the target research data is subjected to a preset abnormality determination operation, and sub-research data corresponding to the high-frequency abnormal items in the target research operation is obtained as initial abnormal data.

[0188] As can be seen, Figure 4 The described abnormal data processing device provides another abnormality processing strategy, which realizes accurate positioning of the point abnormal data through the abnormality point processing instruction. In the case of quickly locating the point abnormal data, the original abnormality determination process is not executed, thereby adaptively executing the abnormality point processing instruction in priority, improving the determination efficiency and accuracy of determining abnormal data when performing abnormality determination operation on the target research data. In addition, based on the abnormality screening model, the initial abnormal data is assisted in research by the high-frequency abnormal record stored in the abnormal database when performing abnormality determination operation on the target research data, which improves the determination efficiency of the initial abnormal data to a certain extent.

[0189] In another optional embodiment, as shown in Figure 4 The abnormal data processing device for power distribution network further includes a second update module 314, a second detection module 315, and a data processing module 316.

[0190] The second update module 314 is configured to, after the first determination module 305 determines the abnormality processing scheme for the target abnormal data according to the screening result, update the abnormal database according to the abnormal type corresponding to the target abnormal data determined by the first determination module 305 and the abnormality processing scheme for the target abnormal data, and generate a summary report for the target abnormal data. The summary report includes at least one of the abnormal type corresponding to the target abnormal data, the abnormal occurrence reason corresponding to the target abnormal data, and the abnormality processing scheme for the target abnormal data. The summary report is used to be submitted to an auditor responsible for auditing the summary report.

[0191] The second detection module 315 is configured to detect whether a disqualification instruction indicating that the summary content corresponding to the summary report fails to pass the audit is received within a preset feedback period. When it is detected that the disqualification instruction is not received within the preset feedback period, it is determined that the summary report generated by the second update module 314 is correct and the summary report is collected.

[0192] The data processing module 316 is configured to, when the second detection module 315 detects that the disqualification instruction is received within the preset feedback period, update the summary report according to the correction data included in the disqualification instruction and collect the summary report.

[0193] As can be seen, Figure 4The described processing device of the network configuration exception data can automatically update the exception database after processing each target exception data, and the iterative update of the exception data serves as auxiliary data for subsequent exception determination and exception processing, thereby improving the determination efficiency and accuracy of the subsequent determined exception data, and improving the exception efficiency of the subsequent processed exception data and the reliability and accuracy of the obtained exception processing result. In addition, the matching update of the summary report and / or the operation of collecting the summary report can be performed according to the instructions fed back by the auditors according to the summary report, and the processing efficiency of the summary report is improved.

[0194] Embodiment four

[0195] Please refer to Figure 5 , Figure 5 is another structure diagram of the processing device of the network configuration exception data disclosed by the embodiment of the application. As shown in Figure 5 , the processing device of the network configuration exception data can include:

[0196] a memory 401 storing executable program codes;

[0197] a processor 402 coupled with the memory 401;

[0198] The processor 402 calls the executable program codes stored in the memory 401 to execute the steps in the processing method of the network configuration exception data described in the embodiment one or the embodiment two of the application.

[0199] Embodiment five

[0200] The embodiment of the application discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, the steps in the processing method of the network configuration exception data described in the embodiment one or the embodiment two of the application are executed.

[0201] Embodiment six

[0202] The embodiment of the application discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to make a computer execute the steps in the processing method of the network configuration exception data described in the embodiment one or the embodiment two.

[0203] The apparatus embodiments described above are only illustrative, wherein the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place or distributed to multiple network modules. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0204] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0205] Finally, it should be noted that: the disclosed processing method and device for network exception data disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for processing network configuration exception data, characterized in that, The method comprises: Collecting target research and judgment data corresponding to a target power grid, the target research and judgment data including abnormal data requiring a preset abnormal handling operation to be performed; According to the determined abnormal screening model, performing a preset abnormal determination operation on the target research and judgment data to obtain initial abnormal data, the abnormal screening model including template abnormal data that has been determined to be abnormal data, and the initial abnormal data including data having a data matching degree with the template abnormal data reaching a preset matching degree threshold; According to a preset distribution network data analysis model, analyzing the initial abnormal data to obtain a data detail report corresponding to the initial abnormal data, the data detail report including abnormal parameters for judging whether the initial abnormal data is abnormal data; According to the template abnormal data and the abnormal screening model, performing an abnormal matching degree screening operation on detail data corresponding to each of the abnormal parameters in the data detail report to obtain target abnormal data, so as to perform the preset abnormal handling operation on the target abnormal data, the target abnormal data being data having an abnormal matching degree greater than a preset matching degree threshold.

2. The method of claim 1, wherein the method further comprises: The method further comprises: According to the abnormal parameters and the template abnormal data, determining an abnormal type corresponding to the target abnormal data, the abnormal type including a known abnormal type recorded in a preset abnormal database or a pending abnormal type other than the known abnormal type; When the abnormal type corresponding to the target abnormal data is the known abnormal type, calling a historical handling record matched with the known abnormal type, and generating a new abnormal handling scheme for handling the target abnormal data according to an abnormal handling scheme corresponding to the historical handling record, so as to perform abnormal handling on the target abnormal data according to the new abnormal handling scheme; When the abnormal type corresponding to the target abnormal data is the pending abnormal type, performing an abnormal similarity screening operation in the abnormal database according to the abnormal type and the target abnormal data to obtain a screening result; According to the screening result, determining an abnormal handling scheme for the target abnormal data, so as to perform abnormal handling on the target abnormal data according to the abnormal handling scheme.

3. The method of claim 2, wherein the method further comprises: When the abnormal type corresponding to the target abnormal data is the pending abnormal type, the determining of the abnormal handling scheme for the target abnormal data according to the screening result comprises: Judging whether the screening result indicates that there is a comparison abnormal data in the abnormal database, the comparison abnormal data being data having an abnormal similarity reaching a preset similarity threshold with the target abnormal data; When it is judged that the screening result indicates that there is no comparison abnormal data in the abnormal database, collecting the target abnormal data in a determined set of data to be researched and judged, the set of data to be researched and judged collecting data requiring a preset research and judgment operation to be performed; According to the abnormal parameters and the target abnormal data, determining remaining investigation data, the remaining investigation data being data other than data corresponding to the abnormal parameters in the target abnormal data; According to the preset research and judgment model, the preset research and judgment operation is performed on the remaining investigation data, and an abnormal research and judgment processing scheme is obtained as an abnormal processing scheme for the target abnormal data. When it is judged that the screening result indicates that the control abnormal data exists in the abnormal database, an abnormal processing scheme corresponding to the control abnormal data is analyzed to obtain an analysis result, and a processing scheme matched with the target abnormal data is generated according to the analysis result as an abnormal processing scheme for the target abnormal data.

4. The method of claim 1-3, wherein, Before the abnormal matching degree screening operation is performed on the detailed data corresponding to each abnormal parameter in the data detailed report according to the template abnormal data and the abnormal screening model to obtain the target abnormal data, the method further includes: According to the data detailed report, the data type corresponding to the initial abnormal data is determined, and the data type includes line data type of a line of a distribution network and / or device data type corresponding to a running device of the distribution network; According to the abnormal parameter and the data type, a collection and classification operation is performed on the data detailed report to obtain a collection and classification result; And the abnormal matching degree screening operation is performed on the detailed data corresponding to each abnormal parameter in the data detailed report according to the template abnormal data and the abnormal screening model to obtain the target abnormal data, including: According to the template abnormal data and the abnormal screening model, the abnormal matching degree screening operation is performed on the detailed data corresponding to each abnormal parameter in the collection and classification result to obtain the target abnormal data.

5. The method of claim 4, wherein the method further comprises: Before the preset abnormal determination operation is performed on the target research and judgment data according to the determined abnormal screening model to obtain the initial abnormal data, the method further includes: It is detected whether an abnormal point processing instruction for the target research and judgment data is received, when it is detected that the abnormal point processing instruction is not received, the operation of performing the preset abnormal determination operation on the target research and judgment data according to the determined abnormal screening model to obtain the initial abnormal data is performed, and the abnormal point processing instruction includes abnormal information corresponding to the determined abnormal data; When it is detected that the abnormal point processing instruction is received, the corresponding point abnormal data in the target research and judgment data and a point processing scheme for the point abnormal data are determined according to the abnormal information; The point abnormal data is extracted, and a point processing operation is performed on the point abnormal data according to the point processing scheme to obtain point correction data; According to the point correction data, the target research and judgment data is updated to be normal distribution network data which does not need to perform the preset abnormal processing operation, and the point abnormal data and the point processing scheme are recorded as support data for subsequent execution of the preset abnormal processing operation on other research and judgment data except the target research and judgment data.

6. The method of claim 5, wherein the method further comprises: The preset abnormal determination operation is performed on the target research and judgment data according to the determined abnormal screening model to obtain the initial abnormal data, including: Call the abnormal record stored in the abnormal database corresponding to the target power grid, the abnormal record including high-frequency abnormal record of the target power grid, the high-frequency abnormal record including a plurality of high-frequency abnormal items, the high-frequency abnormal item being a data item in the distribution network data corresponding to the target power grid whose data abnormality frequency is higher than a preset frequency threshold; According to all the high-frequency abnormal items and the determined abnormal screening model, the target research and judgment data is executed to a preset abnormal judgment operation, and the sub-research and judgment data corresponding to the high-frequency abnormal item in the target research and judgment operation is obtained as the initial abnormal data.

7. The method of claim 3 or 5 or 6, wherein, After the abnormal processing scheme for the target abnormal data is determined according to the screening result, the method further includes: According to the determined abnormal type corresponding to the target abnormal data, the abnormal processing scheme for the target abnormal data, the abnormal database is updated and the summary report for the target abnormal data is generated, the summary report including at least one of the abnormal type corresponding to the target abnormal data, the abnormal cause corresponding to the target abnormal data and the abnormal processing scheme for the target abnormal data, the summary report being used for submitting to an auditing personnel responsible for auditing the summary report; Detecting whether a disqualification instruction indicating that the summary content corresponding to the summary report fails to pass the audit is received within a preset feedback period, when it is detected that the disqualification instruction is not received within the preset feedback period, it is determined that the summary report is correct and the summary report is collected; When it is detected that the disqualification instruction is received within the preset feedback period, the summary report is updated according to the correction data included in the disqualification instruction and the summary report is collected.

8. A device for processing abnormal data of network distribution, characterized in that, The device includes: The acquisition module is configured to acquire target research and judgment data corresponding to a target power grid, the target research and judgment data including abnormal data that needs to execute a preset abnormal processing operation; The abnormal judgment module is configured to execute a preset abnormal judgment operation on the target research and judgment data according to a determined abnormal screening model, to obtain initial abnormal data, the abnormal screening model including template abnormal data that has been determined to be abnormal data, and the initial abnormal data including data that has a data matching degree reaching a preset matching degree threshold with the template abnormal data; The analysis module is configured to analyze the initial abnormal data according to a preset distribution network data analysis model, to obtain a data detail report corresponding to the initial abnormal data, the data detail report including an abnormal parameter used to determine whether the initial abnormal data is abnormal data; The abnormal screening module is configured to execute an abnormal matching degree screening operation on detail data corresponding to each abnormal parameter in the data detail report according to the template abnormal data and the abnormal screening model, to obtain target abnormal data, and execute the preset abnormal processing operation on the target abnormal data, the target abnormal data being data with an abnormal matching degree greater than a preset matching degree threshold.

9. A device for processing abnormal data of network distribution, characterized in that, The device includes: A memory storing executable program code; A processor coupled with the memory; The processor invokes the executable program code stored in the memory to execute the processing method of the abnormal data in the network configuration as claimed in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which are invoked to execute the processing method of the abnormal data in the network configuration as claimed in any one of claims 1-7.

Images