CN112446601B

CN112446601B - Method and system for diagnosing data of uncomputable area

Info

Publication number: CN112446601B
Application number: CN202011310264.9A
Authority: CN
Inventors: 邵雪松; 陈霄; 张德进; 周玉; 蔡奇新; 李悦; 季欣荣; 徐鸣飞; 崔高颖
Original assignee: State Grid Jiangsu Electric Power Co ltd Marketing Service Center; State Grid Jiangsu Electric Power Co Ltd
Current assignee: State Grid Jiangsu Electric Power Co ltd Marketing Service Center; State Grid Jiangsu Electric Power Co Ltd
Priority date: 2020-11-20
Filing date: 2020-11-20
Publication date: 2024-02-20
Anticipated expiration: 2040-11-20
Also published as: CN112446601A

Abstract

The embodiment of the invention provides a method and a system for diagnosing uncomplculable area data, which realize continuous analysis of abnormal work order factor details of an abnormal work order by carrying out iterative diagnosis on the abnormal file in target line loss file data, and carry out fusion on area line loss factors and corresponding line loss factor influence factor information to configure a preset decision model, so that the decision precision of the configured preset decision model is higher, the data processing efficiency is greatly improved, and the decision analysis efficiency is further improved.

Description

Method and system for diagnosing data of uncomputable area

Technical Field

The invention relates to the technical field of power data management, in particular to a method and a system for diagnosing data of an uncomputable area.

Background

The line loss archive data corresponding to the construction process of the uncomputable area can be recorded in real time at the area monitoring centers of all the areas.

In the traditional diagnosis decision scheme of the data of the uncomplculable area, the data diagnosis can be carried out by a manual screening observation mode, so that the area line loss factors and the line loss factor influence factor information are decided.

However, the research of the inventor of the present invention shows that the manual screening cost is extremely high, and the screening speed is difficult to achieve the expected effect, so that the decision accuracy and the analysis efficiency of the decision are greatly affected.

Disclosure of Invention

In order to overcome the defects in the prior art at least, the invention aims to provide the method and the system for diagnosing the data of the uncompritable area, which can realize continuous analysis of the abnormal work order factor details of the abnormal work order by carrying out iterative diagnosis on the abnormal file in the target line loss file data, and fuse the line loss factors of the area and corresponding line loss factor influence factor information to configure a preset decision model, so that the decision precision of the configured preset decision model is higher, the data processing efficiency is greatly improved, and the decision analysis efficiency is further improved.

In a first aspect, the present invention provides a method for diagnosing data of a non-computable area, applied to a server, where the server is communicatively connected to a plurality of area monitoring centers, the method comprising:

acquiring target line loss archive data, and performing abnormal archive diagnosis and abnormal work order diagnosis on the target line loss archive data to obtain a corresponding target abnormal archive diagnosis data sequence, wherein the target line loss archive data is line loss archive data corresponding to an uncomputable platform area construction process of the platform area monitoring center in a preset time period;

Performing diagnosis analysis on the target abnormal file diagnosis data sequence to obtain an abnormal file diagnosis node sequence and a corresponding past ammeter reporting event, and determining the abnormal file diagnosis node sequence of which the past ammeter reporting event meets a preset matching rule as a target ammeter reporting operation unit;

traversing the target abnormal file diagnosis data sequence according to the target ammeter report operation unit, circularly analyzing the abnormal work order factor detail of the abnormal work order, adding an acquisition system curve for the target abnormal file diagnosis data sequence conforming to the target ammeter report operation unit, and extracting the station area line loss factor and corresponding line loss factor influence factor information in the target abnormal file diagnosis data sequence added with the acquisition system curve;

configuring a preset decision model according to the line loss factors of the transformer area, the line loss factor influence factor information and the acquisition system curve to obtain the configured preset decision model, and performing decision analysis processing on the target abnormal file diagnosis data sequence based on the configured preset decision model.

In a possible implementation manner of the first aspect, the step of performing diagnostic analysis on the target abnormal archive diagnostic data sequence to obtain an abnormal archive diagnostic node sequence and a corresponding past ammeter reporting event, and determining the abnormal archive diagnostic node sequence that the past ammeter reporting event meets a preset matching rule as the target ammeter reporting operation unit includes:

Performing diagnosis analysis on the target abnormal file diagnosis data sequence to obtain a corresponding abnormal file diagnosis node sequence;

acquiring a first target ammeter report event of an abnormal work order and a global ammeter report event of the abnormal work order contained in each abnormal file diagnosis node sequence;

determining a corresponding first past ammeter reporting event according to the matching relation between the first target ammeter reporting event and the global ammeter reporting event;

and determining the abnormal file diagnosis node sequence of the first past ammeter reporting event matched with the preset parameter characteristics as a target ammeter reporting operation unit.

In a possible implementation manner of the first aspect, the step of performing an abnormal archive diagnosis and an abnormal worksheet diagnosis on the target line loss archive data to obtain a corresponding target abnormal archive diagnosis data sequence includes:

performing segmentation and abnormal file diagnosis operations on the target line loss file data to obtain a corresponding abnormal file diagnosis data sequence;

acquiring an abnormal work order factor detail of an abnormal work order, and determining the abnormal work order factor detail in the abnormal file diagnosis data sequence;

and diagnosing the corresponding abnormal worksheets for the abnormal worksheet factor details in the abnormal archive diagnostic data sequence to obtain a corresponding target abnormal archive diagnostic data sequence.

In a possible implementation manner of the first aspect, the step of traversing the target abnormal file diagnosis data sequence according to the target electric meter reporting operation unit and circularly analyzing the abnormal work order factor details of the abnormal work order includes:

determining an abnormal archive sequence matched with an abnormal archive diagnostic node sequence of the target ammeter reporting operation unit in the target abnormal archive diagnostic data sequence;

acquiring a second target ammeter report event of an abnormal work order and a global ammeter report event of the abnormal work order contained in each abnormal file sequence, and determining a corresponding second past ammeter report event according to a matching relationship between the second target ammeter report event and the global ammeter report event;

determining an abnormal file sequence of the second past ammeter reporting event which is larger than a second preset past ammeter reporting event threshold value as a target abnormal file sequence, performing abnormal work order diagnosis on an abnormal file in the target abnormal file sequence according to the target ammeter reporting operation unit, and analyzing abnormal work order factor details of an abnormal work order;

and executing the step of obtaining a second target ammeter report event of the abnormal worksheet and a global ammeter report event of the abnormal worksheet contained in each abnormal file sequence again, carrying out abnormal worksheet diagnosis on the abnormal files in the target abnormal file sequence in an iterated manner, and analyzing the abnormal worksheet factor details of the abnormal worksheet until the iteration times meet a preset iteration threshold.

In a possible implementation manner of the first aspect, the step of performing, according to the target ammeter report operation unit, an abnormal worksheet diagnosis on an abnormal archive in the target abnormal archive sequence, and analyzing an abnormal worksheet factor specification of the abnormal worksheet includes:

acquiring a diagnosis rule for carrying out abnormal work order diagnosis on each debugging process archive in the target ammeter reporting operation unit;

and according to the diagnosis rule, archiving the abnormal files in the target abnormal file sequence according to a debugging flow to perform abnormal work order diagnosis, and analyzing the abnormal work order factor details of the abnormal work order.

In a possible implementation manner of the first aspect, the step of extracting the line loss factor of the platform area and the corresponding line loss factor influence factor information in the target abnormal archive diagnostic data sequence of the added acquisition system curve includes:

determining a line loss factor of a platform region of the target abnormal archive diagnostic data sequence added with the acquisition system curve;

and calculating line loss factor influence factor information of the target abnormal archive diagnostic data sequence added with the acquisition system curve.

In a possible implementation manner of the first aspect, the step of calculating line loss factor influence factor information of the target abnormal profile diagnostic data sequence of the added acquisition system curve includes:

Acquiring the occupation degree of a target abnormal file in a target abnormal file diagnosis data sequence added with an acquisition system curve, and acquiring the total acquisition system curve occupation ratio appearing in the target line loss file data;

determining corresponding acquisition metering key point information according to the matching relation between the occupancy degree of the target abnormal file and the total acquisition system curve occupancy rate;

acquiring the global line loss archive element quantity in the target line loss archive data, and acquiring the target service line loss archive element quantity containing the target abnormal archive;

calculating a target matching relation between the global line loss archives element quantity and the target business line loss archives element quantity, and calculating the logarithm of the target matching relation to obtain a corresponding logarithm result;

and multiplying the acquired metering key point information by a logarithmic result to obtain the importance degree of the target abnormal file, and combining the importance degrees corresponding to the abnormal files in the same target abnormal file diagnosis data sequence to generate line loss factor influence factor information.

In a possible implementation manner of the first aspect, the line loss profile data is obtained by:

acquiring at least one high-negative-loss uncomputable area list from the area monitoring center in the construction process of the uncomputable area in a preset time period, wherein each high-negative-loss uncomputable area signature in each high-negative-loss uncomputable area list belongs to the same area grid, and each high-negative-loss uncomputable area signature corresponds to an acquisition metering rule under the affiliated area grid;

Carrying out electricity stealing factor identification on the high-negative-loss uncomputable platform region list based on each acquisition metering rule under the grid of the affiliated platform region to obtain electricity stealing factor characteristics and corresponding electricity stealing factor levels of each high-negative-loss uncomputable platform region list;

determining the line loss abnormality reason of the grid of the corresponding station area corresponding to each high-negative-loss uncomputable station area signature according to the electricity stealing factor characteristics and the corresponding electricity stealing factor level;

according to the line loss abnormal reasons of the area grids corresponding to the high-negative-loss uncomputable area signatures, determining line loss archive record items corresponding to each area grid by adopting an artificial intelligent model, generating line loss archive establishment features of a line loss archive establishment process corresponding to the uncomputable area construction process according to the line loss archive record items corresponding to each area grid, and generating corresponding line loss archive data according to the line loss archive establishment features of the line loss archive establishment process.

In a possible implementation manner of the first aspect, the step of determining, according to the characteristics of the electricity stealing factors and the corresponding levels of the electricity stealing factors, a line loss anomaly cause of the grid of the area to which each high negative loss uncomputable area signature corresponds includes:

Screening and obtaining key electricity stealing factor characteristics larger than a preset electricity stealing factor level from the electricity stealing factor characteristics according to the electricity stealing factor characteristics and the corresponding electricity stealing factor level;

acquiring a first file error data point list corresponding to a first electricity stealing factor object and a second file error data point list corresponding to a second electricity stealing factor object on a key electricity stealing factor feature, wherein the first file error data point list comprises a plurality of file error generation flows for generating file errors for related information integration regions in the key electricity stealing factor feature by the first electricity stealing factor object, the second file error data point list comprises a plurality of file error generation flows for generating file errors for the related information integration regions in the key electricity stealing factor feature by the second electricity stealing factor object, and each file error generation flow comprises a plurality of file error generation flow components;

based on a preset file error generation flow category, performing Sqoop extraction on a plurality of file error generation flows in the first file error data point list to obtain a first file error data point list after Sqoop extraction; the preset file error generation flow category belongs to the type corresponding to the file error generation flow components;

Combining each file error generation flow component corresponding to each preset file error generation flow category in a preset file error generation flow category list in the first file error data point list after the Sqoop extraction into a first initial file error generation flow list;

performing de-duplication on the first initial file error generation flow list to obtain a first file error generation flow list, thereby obtaining a first file error generation flow list corresponding to the preset file error generation flow category list;

combining each file error generation flow component in the first file error generation flow list into a first file error generation flow component list corresponding to the first electricity stealing factor object, wherein the first file error generation flow component list corresponds to the preset file error generation flow category list, and the preset file error generation flow category type is a list formed by file error generation flow categories for performing file error crawling;

extracting each file error generation flow component corresponding to each preset file error generation flow category in the preset file error generation flow category list from the second file error data point list, and combining the file error generation flow components into a second file error generation flow component list corresponding to the second electricity stealing factor object, wherein the second file error generation flow component list corresponds to the preset file error generation flow category list, and the first file error generation flow component list and the second file error generation flow component list are lists formed by file error generation flow components extracted from the corresponding file error data point list respectively;

Determining a file error record axis of the same file error generation flow component between the first file error generation flow component list and the second file error generation flow component list, and obtaining a file error representation vector from a record axis interval corresponding to the file error record axis;

when the file error representation vector is larger than a preset description value threshold, determining that the first electricity stealing factor object and the second electricity stealing factor object are file error calling partitions;

taking any two electricity stealing factor elements in the key electricity stealing factor characteristics as a first electricity stealing factor object and a second electricity stealing factor object to perform file error crawling until the matching of the electricity stealing factor elements in the key electricity stealing factor characteristics is completed, and obtaining a file error calling partition list with file error generation behaviors in the key electricity stealing factor characteristics;

taking a file error record shaft of the electricity stealing factor element in the file error calling partition list as a target file error calling partition file error record shaft;

taking the file error record axis of the electricity stealing factor element corresponding to the key electricity stealing factor characteristic as the file error record axis of the target total electricity stealing factor element;

Calculating record axis units of the record axis of the record error of the target file error calling partition and the record axis of the record error of the target total electricity stealing factor element, and obtaining record axis unit characteristics corresponding to the key electricity stealing factor characteristics;

and when the record axis unit characteristics meet the preset segmentation length, determining the origin factor formed by the record axis unit characteristics corresponding to the key electricity stealing factor characteristics as the line loss abnormality reason of the grid of the corresponding station area of each high-negative-loss uncomputable station area signature.

In a second aspect, an embodiment of the present invention further provides a device for diagnosing data of a non-computable area, which is applied to a server, where the server is communicatively connected to a plurality of area monitoring centers, and the device includes:

the system comprises an acquisition module, a calculation module and a calculation module, wherein the acquisition module is used for acquiring target line loss archive data, and performing abnormal archive diagnosis and abnormal work order diagnosis on the target line loss archive data to obtain a corresponding target abnormal archive diagnosis data sequence, wherein the target line loss archive data is line loss archive data corresponding to an uncomputable platform area construction process of the platform area monitoring center in a preset time period;

the diagnosis module is used for carrying out diagnosis analysis on the target abnormal file diagnosis data sequence to obtain an abnormal file diagnosis node sequence and a corresponding past ammeter reporting event, and determining the abnormal file diagnosis node sequence of which the past ammeter reporting event meets a preset matching rule as a target ammeter reporting operation unit;

The extraction module is used for traversing the target abnormal file diagnosis data sequence according to the target electric meter reporting operation unit, circularly analyzing the abnormal work order factor detail of the abnormal work order, adding an acquisition system curve for the target abnormal file diagnosis data sequence which accords with the target electric meter reporting operation unit, and extracting the line loss factors of the platform region and the corresponding line loss factor influence factor information in the target abnormal file diagnosis data sequence added with the acquisition system curve;

the decision module is used for configuring a preset decision model according to the line loss factors of the transformer area, the line loss factor influence factor information and the acquisition system curve to obtain the configured preset decision model, and carrying out decision analysis processing on the target abnormal file diagnosis data sequence based on the configured preset decision model.

In a third aspect, an embodiment of the present invention further provides a system for diagnosing data of a non-computable area, where the system for diagnosing data of a non-computable area includes a server and a plurality of area monitoring centers communicatively connected to the server;

the server is used for acquiring target line loss archive data, and performing abnormal archive diagnosis and abnormal work order diagnosis on the target line loss archive data to obtain a corresponding target abnormal archive diagnosis data sequence, wherein the target line loss archive data is line loss archive data corresponding to an uncomputable platform area construction process of the platform area monitoring center in a preset time period;

The server is used for carrying out diagnosis and analysis on the target abnormal file diagnosis data sequence to obtain an abnormal file diagnosis node sequence and a corresponding past ammeter reporting event, and determining the abnormal file diagnosis node sequence of which the past ammeter reporting event meets a preset matching rule as a target ammeter reporting operation unit;

the server is used for traversing the target abnormal file diagnosis data sequence according to the target electric meter reporting operation unit, circularly analyzing the abnormal work order factor detail of the abnormal work order, adding an acquisition system curve for the target abnormal file diagnosis data sequence conforming to the target electric meter reporting operation unit, and extracting the line loss factors of the platform region and the corresponding line loss factor influence factor information in the target abnormal file diagnosis data sequence added with the acquisition system curve;

the server is used for configuring a preset decision model according to the line loss factors of the transformer area, the line loss factor influence factor information and the acquisition system curve to obtain the configured preset decision model, and performing decision analysis processing on the target abnormal file diagnosis data sequence based on the configured preset decision model.

In a fourth aspect, embodiments of the present invention further provide a server, where the server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected by a bus system, where the network interface is used to communicatively connect to at least one platform monitoring center, where the machine-readable storage medium is used to store a program, an instruction, or a code, and where the processor is used to execute the program, the instruction, or the code in the machine-readable storage medium to perform the method for diagnosing data in an uncompritable platform in the first aspect or any one of the possible designs of the first aspect.

In a fifth aspect, embodiments of the present invention provide a computer-readable storage medium having instructions stored therein, which when executed, cause a computer to perform the method of diagnosing non-computable site data in the above-described first aspect or any one of the possible designs of the first aspect.

Based on any one of the aspects, the invention performs abnormal file diagnosis and abnormal work order diagnosis by collecting the target line loss file data to obtain a corresponding target abnormal file diagnosis data sequence; calculating a target abnormal file diagnosis data sequence to obtain an abnormal file diagnosis node sequence and a past ammeter reporting event, and determining the abnormal file diagnosis node sequence of which the past ammeter reporting event meets a preset matching rule as a target ammeter reporting operation unit; circularly analyzing the abnormal work order factor details of the abnormal work order according to the target abnormal file diagnosis data sequence by the target ammeter reporting operation unit; adding an acquisition system curve for a target abnormal file diagnosis data sequence conforming to a target ammeter reporting operation unit, and extracting the line loss factors and the line loss factor influence factor information of the transformer area in the target abnormal file diagnosis data sequence added with the acquisition system curve; configuring a preset decision model according to the line loss factors of the transformer area, the line loss factor influence factor information and the acquisition system curve, and performing decision analysis processing on the target abnormal file diagnosis data sequence by the preset configured decision model. In this way, iterative diagnosis of the abnormal work order is carried out on the abnormal file in the target line loss file data, continuous analysis of the abnormal work order factor details of the abnormal work order is realized, and the line loss factors of the platform area and the corresponding line loss factor influence factor information are fused to configure the preset decision model, so that the decision accuracy of the configured preset decision model is higher, the data processing efficiency is greatly improved, and the decision analysis efficiency is further improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of an application scenario of a non-computable region data diagnosis system according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method for diagnosing data in a non-computable area according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of functional modules of a device for diagnosing data of a non-computable area according to an embodiment of the present invention;

fig. 4 is a schematic block diagram of a server for implementing the above-mentioned method for diagnosing data of a non-computable area according to an embodiment of the present invention.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It should be appreciated that "system," "apparatus," "unit," and/or "module" as used in this specification is a method for distinguishing between different components, elements, parts, portions, or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

FIG. 1 is a schematic diagram of the interaction of a non-computable site data diagnostic system 10 provided in one embodiment of the present invention. The non-computable site data diagnostic system 10 may include a server 100 and a site monitoring center 200 communicatively coupled to the server 100. The non-computable site data diagnostic system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the non-computable site data diagnostic system 10 may include only a portion of the components shown in fig. 1 or may include other components as well.

In this embodiment, the server 100 and the platform monitoring center 200 in the non-computable platform data diagnosis system 10 may cooperatively perform the non-computable platform data diagnosis method described in the following method embodiments, and the detailed description of the method embodiments may be referred to for the execution steps of the server 100 and the platform monitoring center 200.

In order to solve the foregoing technical problems in the background art, fig. 2 is a flowchart of a method for diagnosing uncompatible area data according to an embodiment of the present invention, and the method for diagnosing uncompatible area data according to the embodiment may be executed by the server 100 shown in fig. 1, and the method for diagnosing uncompatible area data is described in detail below.

Step S110, obtaining target line loss archive data, and performing abnormal archive diagnosis and abnormal work order diagnosis on the target line loss archive data to obtain a corresponding target abnormal archive diagnosis data sequence.

And step S120, performing diagnosis analysis on the target abnormal file diagnosis data sequence to obtain an abnormal file diagnosis node sequence and a corresponding past ammeter reporting event, and determining the abnormal file diagnosis node sequence of which the past ammeter reporting event meets a preset matching rule as a target ammeter reporting operation unit.

Step S130, traversing the target abnormal file diagnosis data sequence according to the target ammeter report operation unit, circularly analyzing the abnormal work order factor detail of the abnormal work order, adding an acquisition system curve for the target abnormal file diagnosis data sequence which accords with the target ammeter report operation unit, and extracting the station area line loss factor and the corresponding line loss factor influence factor information in the target abnormal file diagnosis data sequence added with the acquisition system curve.

Step S140, configuring a preset decision model according to the line loss factors of the area, the line loss factor influence factor information and the acquisition system curve to obtain the configured preset decision model, and performing decision analysis processing on the target abnormal archive diagnostic data sequence based on the configured preset decision model.

In this embodiment, the target line loss archival data may be line loss archival data corresponding to the process of constructing an uncomputable area in the preset time period by the area monitoring center 200. For example, the site monitoring center 200 may provide services throughout different location areas.

In this embodiment, the abnormal file may refer to a file index for measuring each abnormal recording condition generated in a specific platform area metering process, and the abnormal work order may refer to work order recording information for reflecting each abnormal event in the specific platform area metering process.

In this embodiment, each abnormal archive diagnostic node in the abnormal archive diagnostic node sequence may be used to represent a diagnostic basis specifically referred to later in the decision process, and the corresponding past ammeter report event may refer to the characteristic information of various events reported before the abnormal archive diagnostic node as the diagnostic object of the later diagnosis.

In this embodiment, the abnormal work order factor details may refer to abnormal work order contents contained in abnormal file information in a subsequent abnormal diagnosis process, each abnormal work order content may correspond to an acquisition system curve, a line loss factor of a platform region may be used to represent a characteristic of a line loss source of the platform region in the subsequent abnormal diagnosis process, and corresponding line loss factor influence factor information may be used to represent an influence weight occupied by the characteristic of the information source.

In this embodiment, in step S140, the preset decision model may be configured according to the line loss factor of the cell and the information of the influence factor of the line loss factor, so as to obtain the configured preset decision model. For example, the line loss factor of the area, the information of the line loss factor influence factor and the acquisition system curve can be input into a preset decision model to be trained to obtain a corresponding information acquisition system curve list, the information acquisition system curve list is compared with the acquisition system curve, model parameters of the preset decision model are continuously analyzed according to comparison differences, and decision analysis processing can be performed on the target abnormal archive diagnostic data sequence based on the configured preset decision model until the training termination condition is reached. For example, a corresponding target acquisition system curve can be obtained for the target abnormal archive diagnostic data sequence based on the configured preset decision model, and then decision analysis processing is performed based on curve data information related to the target acquisition system curve.

Based on the steps, in the embodiment, by performing iterative diagnosis on the abnormal work order in the target line loss archive data, continuous analysis on the abnormal work order factor details of the abnormal work order is realized, and the line loss factors of the transformer area and corresponding line loss factor influence factor information are fused to configure the preset decision model, so that the decision accuracy of the configured preset decision model is higher, the data processing efficiency is greatly improved, and the decision analysis efficiency is further improved.

In one possible implementation, for step S120, this may be achieved by the following exemplary sub-steps, described in detail below.

In the substep S121, diagnostic analysis is performed on the target abnormal archive diagnostic data sequence to obtain a corresponding abnormal archive diagnostic node sequence.

In the substep S122, a first target meter reporting event of the abnormal worksheet and a global meter reporting event of the abnormal worksheet included in each abnormal archive diagnosis node sequence are obtained.

And step S123, determining a corresponding first past ammeter report event according to the matching relation between the first target ammeter report event and the global ammeter report event.

And step S124, determining the abnormal file diagnosis node sequence of the first past ammeter report event matched with the preset parameter characteristics as a target ammeter report operation unit.

In one possible implementation, for step S110, this may be achieved by the following exemplary sub-steps, which are described in detail below.

And S111, performing segmentation and abnormal file diagnosis operation on the target line loss file data to obtain a corresponding abnormal file diagnosis data sequence.

Sub-step S112, obtaining the abnormal work order factor details of the abnormal work order, and determining the abnormal work order factor details in the abnormal file diagnosis data sequence.

And S113, diagnosing the corresponding abnormal worksheet for the abnormal worksheet factor detail in the abnormal archive diagnostic data sequence to obtain a corresponding target abnormal archive diagnostic data sequence.

In one possible implementation manner, for step S130, in the process of traversing the target abnormal archive diagnostic data sequence according to the target electricity meter reporting job unit, the process of circularly resolving the abnormal work order factor details of the abnormal work order may be implemented through the following exemplary sub-steps, which are described in detail below.

In the substep S131, an abnormal archive sequence matching with the abnormal archive diagnostic node sequence of the target ammeter report operation unit in the target abnormal archive diagnostic data sequence is determined.

And a sub-step S132, wherein the second target ammeter report event of the abnormal worksheet and the global ammeter report event of the abnormal worksheet contained in each abnormal file sequence are obtained, and the corresponding second past ammeter report event is determined according to the matching relationship between the second target ammeter report event and the global ammeter report event.

And S133, determining an abnormal file sequence of which the second past ammeter report event is greater than a second preset past ammeter report event threshold value as a target abnormal file sequence, diagnosing an abnormal work order according to the abnormal file in the target abnormal file sequence by the target ammeter report operation unit, and analyzing the abnormal work order factor detail of the abnormal work order.

For example, a diagnostic rule for performing abnormal work order diagnosis for each debug flow archive in the target ammeter report work unit may be obtained. And then, according to the diagnosis rules, archiving the abnormal files in the target abnormal file sequence according to the debugging flow to diagnose the abnormal work orders, and analyzing the abnormal work order factor details of the abnormal work orders.

And a sub-step S134, wherein the step of obtaining the second target ammeter report event of the abnormal worksheet and the global ammeter report event of the abnormal worksheet contained in each abnormal file sequence is re-executed, the abnormal worksheet diagnosis is carried out on the abnormal files in the target abnormal file sequence in an iterated manner, and the abnormal worksheet factor details of the abnormal worksheet are analyzed until the iteration times meet the preset iteration threshold.

In one possible implementation manner, in the process of extracting the line loss factor of the platform region and the corresponding line loss factor influence factor information in the target abnormal archive diagnostic data sequence to which the acquisition system curve is added, for step S130, the following exemplary substeps may be implemented, which are described in detail below.

Sub-step S135, determining a line loss factor of the region of the target abnormal archive diagnostic data sequence to which the acquisition system curve is added.

In sub-step S136, line loss factor influence factor information of the target abnormal archive diagnostic data sequence added with the acquisition system curve is calculated.

For example, the occupancy level of the target abnormal profile in the target abnormal profile diagnostic data sequence to which the acquisition system profile is added may be obtained, and the total acquisition system profile occupancy rate that occurs in the target line loss profile data may be obtained. And then, corresponding acquisition metering key point information is determined according to the matching relation between the occupancy degree of the target abnormal file and the total acquisition system curve occupancy rate. On the basis, the global line loss archive element quantity in the target line loss archive data is obtained, the target service line loss archive element quantity containing the target abnormal archive is obtained, the target matching relationship between the global line loss archive element quantity and the target service line loss archive element quantity is calculated, the logarithm of the target matching relationship is calculated, and the corresponding logarithm result is obtained. And then, multiplying the acquired metering key point information by a logarithmic result to obtain the importance degree of the target abnormal file, and combining the importance degrees corresponding to the abnormal files in the same target abnormal file diagnosis data sequence to generate line loss factor influence factor information.

In one possible implementation, for step S110, the line loss profile data may be obtained through the following steps, which are described in detail below.

Step S101, at least one high negative loss uncomputable station area list is obtained from a station area monitoring center in the construction process of the uncomputable station area in a preset time period.

Step S102, identifying electricity stealing factors of the high-negative-loss uncomputable platform area list based on each acquisition and measurement rule under the grid of the affiliated platform area, and obtaining the characteristics of the electricity stealing factors of each high-negative-loss uncomputable platform area list and the corresponding electricity stealing factor level.

And step S103, determining the line loss abnormality reasons of the grids of the corresponding areas corresponding to the high-negative-loss uncomputable area signatures according to the characteristics of the electricity stealing factors and the corresponding electricity stealing factor levels.

Step S104, according to the line loss abnormal reasons of the corresponding transformer area grids of each high-negative-loss uncomputable transformer area signature, determining the line loss file record items corresponding to each transformer area grid by adopting an artificial intelligent model, and generating the line loss file establishment characteristics of the line loss file establishment process corresponding to the uncomputable transformer area construction process according to the line loss file record items corresponding to each transformer area grid.

In this embodiment, each high-negative-loss uncomputable region signature in each high-negative-loss uncomputable region list belongs to the same region grid, and each high-negative-loss uncomputable region signature corresponds to an acquisition metering rule under the region grid to which the high-negative-loss uncomputable region signature belongs. For example, the high negative loss uncomputable platform region signature of the platform region grid belonging to the same platform region grid can be obtained from the platform region monitoring center in the construction process of the uncomputable platform region in a preset time period, and the high negative loss uncomputable platform region signature belonging to each platform region grid is determined to be a corresponding high negative loss uncomputable platform region list.

For example, multiple high negative loss non-computable region signatures may be included in the non-computable region build process. Similarly, for different high-negative-loss uncomputable region signatures, the types of the region grids corresponding to the different high-negative-loss uncomputable region signatures are different, so that the high-negative-loss uncomputable region signatures can be in one-to-one correspondence with a certain region grid, namely, the region grid can be used for representing the types of the region grid fields.

In this embodiment, the electricity stealing factor may be used to represent a relationship field having a relationship field rule that may represent a region monitoring rule associated therewith, and the electricity stealing factor feature may be used to represent a sequence of relationship fields corresponding to the electricity stealing factor, and the corresponding electricity stealing factor level may be used to represent a level of collection metering keypoints of the relationship field sequence corresponding to the electricity stealing factor having a matching field with the region monitoring rule associated therewith.

In this embodiment, the line loss anomaly cause may be used to represent record information of a time axis of the generated file error generation behavior, so that line loss record items corresponding to each area grid may be determined by using an artificial intelligence model according to the line loss anomaly cause of the area grid to which each high negative loss uncomputable area signature corresponds, where the line loss record items are used to represent frequent record items (for example, items with recording cycle times exceeding twice) in the file error generation process, so that line loss file establishment features of the line loss file establishment process corresponding to the uncomputable area establishment process may be specifically generated according to the line loss record items corresponding to each area grid.

Based on the design, the embodiment extracts the electricity stealing factor characteristics of each high-negative-loss uncomputable platform area list in an electricity stealing factor identification mode, and determines the line loss abnormal reasons of the corresponding platform area grids of each high-negative-loss uncomputable platform area signature based on the electricity stealing factor level, so that each acquisition metering rule is converted into an effective pushing classification basis. According to the line loss abnormal reasons of the area grids corresponding to the high-negative-loss uncomputable area signatures, an artificial intelligent model is adopted to determine line loss file record items corresponding to each area grid, line loss file establishment features of a line loss file establishment process corresponding to the uncomputable area establishment process are generated according to the line loss file record items corresponding to each area grid, and corresponding line loss file data are generated according to the line loss file establishment features of the line loss file establishment process, so that a decision can be made by effectively combining specific types of the area grids according to the uncomputable area establishment process, and the classification accuracy of the decision is improved.

In one possible implementation manner, for step S103, in order to accurately and comprehensively determine the electricity stealing factor element with the archive error generation behavior, thereby improving the coverage rate and accuracy of archive error crawling, and effectively determining the line loss anomaly cause of each high negative loss uncomputable platform area signature corresponding to the grid of the affiliated platform area, the following exemplary substeps may be implemented. The detailed description is as follows.

And step S1031, screening and obtaining key electricity stealing factor characteristics larger than a preset electricity stealing factor level from the electricity stealing factor characteristics according to the electricity stealing factor characteristics and the corresponding electricity stealing factor level.

Sub-step S1032, a first list of case error data points corresponding to the first power stealing factor object and a second list of case error data points corresponding to the second power stealing factor object are obtained on the key power stealing factor feature.

For example, the first file error data point list includes a plurality of file error generation processes in which the first electricity stealing factor object performs file error generation on the relevant information integration region in the key electricity stealing factor feature, the second file error data point list includes a plurality of file error generation processes in which the second electricity stealing factor object performs file error generation on the relevant information integration region in the key electricity stealing factor feature, and each file error generation process includes a plurality of file error generation process components.

In the substep S1033, based on the preset file error generation flow category, the Sqoop extraction is performed on the multiple file error generation flows in the first file error data point list, so as to obtain the first file error data point list after the Sqoop extraction. The preset file error generation flow category belongs to the type corresponding to the file error generation flow components.

In sub-step S1034, each file error generation flow component corresponding to each preset file error generation flow category in the preset file error generation flow category list in the first file error data point list after the Sqoop extraction is combined into a first initial file error generation flow list.

In sub-step S1035, the first initial file error generation flow list is de-duplicated to obtain a first file error generation flow list, so as to obtain a first file error generation flow list corresponding to the preset file error generation flow category list, and each file error generation flow component in the first file error generation flow list is combined into a first file error generation flow component list corresponding to the first electricity stealing factor object.

For example, the first file error generation flow component list corresponds to a preset file error generation flow class list, and the preset file error generation flow class type is a list formed by file error generation flow classes for performing file error crawling.

In sub-step S1036, each file error generation flow component corresponding to each preset file error generation flow category in the preset file error generation flow category list is extracted from the second file error data point list, and combined into a second file error generation flow component list corresponding to the second electricity stealing factor object.

For example, the second file error generation flow component list corresponds to a preset file error generation flow category list, and the first file error generation flow component list and the second file error generation flow component list are respectively lists composed of file error generation flow components extracted from the corresponding file error data point list.

In sub-step S1037, a file error record axis of the same file error generating flow component between the first file error generating flow component list and the second file error generating flow component list is determined, a record axis interval corresponding to the file error record axis is obtained to obtain a file error representing vector, and when the file error representing vector is greater than a preset description value threshold, the first electricity stealing factor object and the second electricity stealing factor object are determined to be file error calling partitions.

And step S1038, taking any two electricity stealing factor elements in the key electricity stealing factor characteristics as a first electricity stealing factor object and a second electricity stealing factor object to perform file error crawling until the matching of the electricity stealing factor elements in the key electricity stealing factor characteristics is completed, and obtaining a file error calling partition list with file error generation behaviors in the key electricity stealing factor characteristics.

And step S1039, namely taking a file error record axis of the electricity stealing factor element in the file error calling partition list as a target file error calling partition file error record axis, taking a file error record axis of the electricity stealing factor element corresponding to the key electricity stealing factor characteristic as a target total electricity stealing factor element file error record axis, calculating record axis units of the file error record axis of the target file error calling partition and the file error record axis of the target total electricity stealing factor element, obtaining record axis unit characteristics corresponding to the key electricity stealing factor characteristic, and determining an origin factor formed by the record axis unit characteristics corresponding to the key electricity stealing factor characteristic as a line loss abnormality reason of the grid of the area corresponding to each high-negative-loss uncomplicated area signature when the record axis unit characteristics meet the preset segmentation length.

Based on the steps, since the electricity stealing factor element with the file error generation behavior has the same file error generation flow components when the file error generation is carried out, the corresponding file error generation flows are provided with the same file error generation flow components; therefore, when file error crawling is performed, a file error data point list formed by a plurality of file error generation flow paths of electricity stealing factor elements is obtained, whether the electricity stealing factor elements have file error generation behaviors or not is determined according to the condition of common attributes among the file error generation flow path component lists corresponding to operation lists among the electricity stealing factor elements, and further whether the electricity stealing factor elements are file error calling partitions or not is determined, so that the electricity stealing factor elements with the file error generation behaviors can be accurately and comprehensively determined, the coverage rate and the accuracy rate of file error crawling are improved, and the line loss abnormal reasons of the grid of the station area corresponding to each high-negative-loss uncomputable station area signature are effectively determined.

Fig. 3 is a schematic diagram of functional modules of an uncompatible area data diagnosis apparatus 300 according to an embodiment of the present invention, where the functional modules of the uncompatible area data diagnosis apparatus 300 may be divided according to the method embodiment executed by the server 100, that is, the following functional modules corresponding to the uncompatible area data diagnosis apparatus 300 may be used to execute the method embodiment executed by the server 100. The non-computable area data diagnosis apparatus 300 may include an acquisition module 310, a diagnosis module 320, an extraction module 330, and a decision module 340, and the functions of the respective functional modules of the non-computable area data diagnosis apparatus 300 will be described in detail below.

The obtaining module 310 is configured to obtain target line loss archive data, and perform an abnormal archive diagnosis and an abnormal work order diagnosis on the target line loss archive data to obtain a corresponding target abnormal archive diagnosis data sequence, where the target line loss archive data is line loss archive data corresponding to an uncomputable platform area construction process of the platform area monitoring center 200 in a preset time period. The acquiring module 310 may be configured to perform the step S110, and the detailed implementation of the acquiring module 310 may be referred to the detailed description of the step S110.

The diagnostic module 320 is configured to perform diagnostic analysis on the target abnormal file diagnostic data sequence to obtain an abnormal file diagnostic node sequence and a corresponding past meter reporting event, and determine that the past meter reporting event meets a preset matching rule as a target meter reporting operation unit. Wherein the diagnostic module 320 may be used to perform the step S120 described above, and reference may be made to the detailed description of the step S120 for the detailed implementation of the diagnostic module 320.

The extracting module 330 is configured to traverse the target abnormal archive diagnostic data sequence according to the target electric meter reporting operation unit, circularly analyze the abnormal work order factor details of the abnormal work order, add an acquisition system curve to the target abnormal archive diagnostic data sequence according to the target electric meter reporting operation unit, and extract the line loss factor of the platform region and the corresponding line loss factor influence factor information in the target abnormal archive diagnostic data sequence to which the acquisition system curve is added. Wherein, the extraction module 330 may be used to perform the step S130 described above, and the detailed implementation of the extraction module 330 may be referred to the detailed description of the step S130.

The decision module 340 is configured to configure the preset decision model according to the line loss factor of the area, the line loss factor influence factor information and the acquisition system curve, obtain a configured preset decision model, and perform decision analysis processing on the target abnormal archive diagnostic data sequence based on the configured preset decision model. Wherein, the decision module 340 may be used to perform the above step S140, and the detailed implementation of the decision module 340 may be referred to the above detailed description of step S140.

It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the acquisition module 310 may be a processing element that is set up separately, may be implemented in a chip of the above apparatus, or may be stored in a memory of the above apparatus in the form of program codes, and may be called by a processing element of the above apparatus to execute the functions of the above acquisition module 310. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.

For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more specific integrated circuits (application specific integrated circuit, ASIC), or one or more microprocessors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA), or the like. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general purpose processor, such as a central processing unit (centralprocessing unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

Fig. 4 is a schematic diagram showing a hardware structure of a server 100 for implementing the above-mentioned method for diagnosing non-computable district data according to an embodiment of the present invention, and as shown in fig. 4, the server 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation, at least one processor 110 executes computer-executable instructions (such as the acquisition module 310, the diagnosis module 320, the extraction module 330, and the decision module 340 included in the non-computable region data diagnosis apparatus 300 shown in fig. 3) stored in the machine-readable storage medium 120, so that the processor 110 may perform the non-computable region data diagnosis method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be used to control the transceiver 140 to perform the transceiving actions, so that data may be transceived with the aforementioned region monitoring center 200.

The specific implementation process of the processor 110 may refer to the above-mentioned method embodiments executed by the server 100, and the implementation principle and technical effects are similar, which are not described herein again.

In the embodiment shown in FIG. 4 described above, it should be appreciated that the processor may be a central processing unit (English: central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (English: digital Signal Processor, DSP), application specific integrated circuits (English: application SpecificIntegrated Circuit, ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.

The machine-readable storage medium 120 may include high-speed RAM memory and may also include non-volatile storage NVM, such as at least one magnetic disk memory.

Bus 130 may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus 130 may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the drawings of the present invention are not limited to only one bus or to one type of bus.

In addition, the embodiment of the invention also provides a readable storage medium, wherein the readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the method for diagnosing the data of the non-computable area is realized.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Such as "one possible implementation," "one possible example," and/or "exemplary" means a particular feature, structure, or characteristic associated with at least one embodiment of the present description. Thus, it should be emphasized and noted that two or more references to "one possible implementation", "one possible example", and/or "exemplary" in this specification at different positions are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, those skilled in the art will appreciate that the various aspects of the specification can be illustrated and described in terms of several patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the specification may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.

The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.

The computer program code necessary for operation of portions of the present description may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python and the like, a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby and Groovy, or other programming languages and the like. The program code may run entirely on the user's computer, or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or medical service platform. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.

Furthermore, the order in which the elements and listings are processed, the use of numerical letters, or other designations in the specification is not intended to limit the order in which the processes and methods of the specification are presented unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing healthcare platform or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. A method of non-computable site data diagnosis, applied to a server communicatively coupled to a plurality of site monitoring centers, the method comprising:

Configuring a preset decision model according to the line loss factors of the transformer area, the line loss factor influence factor information and the acquisition system curve to obtain a configured preset decision model, and performing decision analysis processing on the target abnormal file diagnosis data sequence based on the configured preset decision model;

the step of performing abnormal file diagnosis and abnormal work order diagnosis on the target line loss file data to obtain a corresponding target abnormal file diagnosis data sequence comprises the following steps:

diagnosing corresponding abnormal worksheets for the abnormal worksheet factor details in the abnormal file diagnosis data sequence to obtain a corresponding target abnormal file diagnosis data sequence;

traversing the target abnormal file diagnosis data sequence according to the target ammeter report operation unit, and circularly analyzing the abnormal work order factor details of the abnormal work order, wherein the method comprises the following steps of:

executing the step of obtaining a second target ammeter report event of the abnormal worksheet and a global ammeter report event of the abnormal worksheet contained in each abnormal file sequence again, carrying out abnormal worksheet diagnosis on the abnormal files in the target abnormal file sequence in an iterated manner, and analyzing the abnormal worksheet factor details of the abnormal worksheet until the iteration times meet a preset iteration threshold;

the step of diagnosing the abnormal worksheet according to the abnormal file in the target abnormal file sequence by the target ammeter report operation unit and analyzing the abnormal worksheet factor details of the abnormal worksheet comprises the following steps:

according to the diagnosis rule, archiving the abnormal files in the target abnormal file sequence according to a debugging flow to perform abnormal work order diagnosis, and analyzing the abnormal work order factor details of the abnormal work order;

the step of extracting and adding the platform area line loss factors and corresponding line loss factor influence factor information in the target abnormal archive diagnostic data sequence of the acquisition system curve comprises the following steps:

calculating line loss factor influence factor information of a target abnormal archive diagnostic data sequence added with an acquisition system curve;

the step of calculating and adding the line loss factor influence factor information of the target abnormal archive diagnostic data sequence of the acquisition system curve comprises the following steps:

2. The method for diagnosing uncomplculable area data as recited in claim 1, wherein the step of performing diagnostic analysis on the target abnormal archive diagnostic data sequence to obtain an abnormal archive diagnostic node sequence and a corresponding past meter reporting event, and determining the abnormal archive diagnostic node sequence of which the past meter reporting event satisfies a preset matching rule as a target meter reporting operation unit comprises:

3. The method for diagnosing uncomputable district data as recited in claim 1, wherein the line loss profile data is obtained by:

4. The method for diagnosing data in a non-computable area according to claim 3, wherein the step of determining a line loss abnormality cause of the grid of the area to which each high negative loss non-computable area signature corresponds according to the characteristics of the electricity stealing factors and the corresponding levels of the electricity stealing factors comprises:

5. A non-computable site data diagnostic system, comprising a server and a plurality of site monitoring centers communicatively connected to the server;

the server is used for configuring a preset decision model according to the line loss factors of the transformer area, the line loss factor influence factor information and the acquisition system curve to obtain the configured preset decision model, and performing decision analysis processing on the target abnormal file diagnosis data sequence based on the configured preset decision model;

performing abnormal file diagnosis and abnormal work order diagnosis on the target line loss file data to obtain a corresponding target abnormal file diagnosis data sequence, wherein the method comprises the following steps:

traversing the target abnormal file diagnosis data sequence according to the target ammeter report operation unit, and circularly analyzing the abnormal work order factor details of the abnormal work order, wherein the method comprises the following steps:

Executing again to acquire a second target ammeter report event of the abnormal worksheet and a global ammeter report event of the abnormal worksheet contained in each abnormal file sequence, carrying out abnormal worksheet diagnosis on the abnormal files in the target abnormal file sequence in an iterated manner, and analyzing the abnormal worksheet factor details of the abnormal worksheet until the iteration times meet a preset iteration threshold;

the extracting and adding the platform area line loss factors and corresponding line loss factor influence factor information in the target abnormal archive diagnostic data sequence of the acquisition system curve comprises the following steps:

the calculating and adding line loss factor influence factor information of the target abnormal archive diagnostic data sequence of the acquisition system curve comprises the following steps: