CN114118245A - Automatic restoration system and method for abnormal data of asset account of power grid equipment - Google Patents
Automatic restoration system and method for abnormal data of asset account of power grid equipment Download PDFInfo
- Publication number
- CN114118245A CN114118245A CN202111376656.XA CN202111376656A CN114118245A CN 114118245 A CN114118245 A CN 114118245A CN 202111376656 A CN202111376656 A CN 202111376656A CN 114118245 A CN114118245 A CN 114118245A
- Authority
- CN
- China
- Prior art keywords
- abnormal
- data
- value
- module
- power grid
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 262
- 238000000034 method Methods 0.000 title claims description 28
- 230000008439 repair process Effects 0.000 claims abstract description 49
- 238000004458 analytical method Methods 0.000 claims abstract description 30
- 238000013523 data management Methods 0.000 claims abstract description 19
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 15
- 238000007726 management method Methods 0.000 claims description 34
- 230000005856 abnormality Effects 0.000 claims description 23
- 238000013500 data storage Methods 0.000 claims description 18
- 238000012384 transportation and delivery Methods 0.000 claims description 17
- 238000012546 transfer Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 7
- 238000003058 natural language processing Methods 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 3
- 238000003064 k means clustering Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 12
- 238000013461 design Methods 0.000 abstract description 2
- 230000005540 biological transmission Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000007635 classification algorithm Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000004883 computer application Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Economics (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
An automatic restoration system for abnormal data of an asset account of power grid equipment comprises a power grid asset data management module, an abnormal data positioning and marking module, an abnormal data clustering analysis module and an abnormal data automatic restoration module, wherein when the system is operated, the power grid asset data management module acquires the original data information of the power grid equipment assets from an external system, the abnormal data positioning and marking module identifies and marks abnormal fields in the original data, and determining the data abnormal type and the associated field corresponding to each abnormal field, then constructing a weighted clustering algorithm model by the abnormal data clustering analysis module based on the marked abnormal field, carrying out clustering analysis on the marked abnormal field according to the data abnormal type to form a plurality of abnormal classified data sets, and finally adopting a corresponding repairing scheme to carry out abnormal data repairing by the abnormal data automatic repairing module aiming at each abnormal classified data set. The design realizes rapid, accurate and reliable positioning detection and repair of the abnormal data of the asset ledger of the power grid equipment.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a system and a method for automatically repairing abnormal data of an asset ledger of power grid equipment.
Background
With the continuous perfection of the modern equipment management system of the power grid enterprise, the real object assets of the equipment are put into operation and increased, the data volume of operation and maintenance is gradually huge, and some abnormal conditions, such as field loss, abnormal coding, abnormal numerical value, abnormal equipment state and the like, can not be avoided in the ledger statistical data. In addition, since data is sourced from multiple management systems, data inconsistency between different data sources often occurs, such as the number of devices not matching the total value of assets. These data anomaly problems directly affect the accuracy of grid asset statistics, thereby further affecting the scientificity of decisions. Therefore, accurate positioning and repairing of abnormal data of the power grid ledger have important practical significance for power grid planning development and operation decision.
In the early stage, a field investigation method is mostly adopted for abnormal data of the power grid equipment ledger, manpower and material resources are extremely consumed, and the efficiency is low. With the development of detection technology, people make some improvements on the method, but the technology is relatively backward and has the problem of low detection accuracy. In recent years, relevant researches on power grid equipment data abnormity detection are carried out by relevant scholars. The document "improved data anomaly detection method based on Isolation Forest" (yodong, king petro-man, mengyong, et al. [ J ] computer science, 2018, 45 (10): 155-. The document "a time series abnormity detection method based on frequent pattern discovery" (Li Hailin, Wuli. [ J ]. computer application, 2018, 38 (11): 3204-. The document "new energy power data restoration technology based on big data analysis" (Tiankun [ J ] town construction, 2018 (11): 375-. The document 'research on a power grid transmission capacity abnormal data detection method based on a parallel classification algorithm' (bear learning, Zhang culvert, Rongkuo, etc. [ J ]. electronic design engineering, 2020, 28 (24): 91-94, 99.) extracts transmission capacity abnormal data characteristics based on information entropy, adopts a wireless mesh network structure to collect power grid transmission capacity data, realizes the detection of power grid equipment abnormal data through the parallel classification algorithm, and has certain errors in detection accuracy. Therefore, how to rapidly and accurately realize the comprehensive detection and repair of the abnormal data of the ledger of the power grid equipment is a great problem which needs to be urgently solved in the management of the power grid assets.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a system and a method for automatically repairing abnormal asset ledger data of power grid equipment, which have high accuracy and reliability.
In order to achieve the above purpose, the invention provides the following technical scheme:
an abnormal data automatic restoration system for an asset account of power grid equipment comprises a power grid asset data management module and an abnormal data restoration module, wherein the abnormal data restoration module comprises an abnormal data positioning marking module, an abnormal data clustering analysis module and an abnormal data automatic restoration module;
the power grid asset data management module is used for acquiring power grid equipment asset original data information from an external system, wherein the power grid equipment asset original data information comprises various types of equipment asset ledger basic data, equipment asset value scale data, equipment asset financial data, scrapped equipment asset basic data and historical scrapped and retired equipment data;
the abnormal data positioning and marking module is used for identifying and marking abnormal fields in the original data of the power grid equipment assets and determining data abnormal types and associated fields corresponding to the abnormal fields;
the abnormal data clustering analysis module is used for constructing a weighted clustering algorithm model based on the marked abnormal fields, and carrying out clustering analysis on the marked abnormal fields according to the data abnormal types to form a plurality of abnormal classification data sets;
and the abnormal data automatic repairing module is used for repairing the abnormal data by adopting a repairing scheme corresponding to the abnormal type aiming at the abnormal type of each abnormal classified data set.
The system further comprises a repair result management module, wherein the repair result management module comprises a repaired data output display module and a data storage management module, the input end of the repaired data output display module is connected with the output end of the abnormal data automatic repair module, and the output end of the repaired data output display module is connected with the input end of the data storage management module.
A method for automatically repairing abnormal data of an asset account of power grid equipment sequentially comprises the following steps:
a, the power grid asset data management module acquires power grid equipment asset original data information from an external system and sends the information to an abnormal data positioning and marking module;
b, the abnormal data positioning and marking module identifies and marks abnormal fields in the original data of the power grid equipment assets and determines data abnormal types and associated fields corresponding to the abnormal fields;
c, the abnormal data clustering analysis module builds a weighted clustering algorithm model based on the marked abnormal fields, and carries out clustering analysis on the marked abnormal fields according to the data abnormal types to form a plurality of abnormal classification data sets;
and D, the abnormal data automatic repairing module adopts a repairing scheme corresponding to the abnormal type to repair the abnormal data aiming at the abnormal type of each abnormal classified data set.
In step C, the exception types include a limited enumeration exception class, a numerical value exception class and a time exception class, the limited enumeration exception class includes a null value, a non-standard value and an inter-field value logic exception, the numerical value exception class includes a null value, a value less than or equal to 0, an overlarge value and an inter-field value logic exception, and the time exception class includes a null value, a value advanced or too late value and an inter-field value logic exception;
in step D, the repair scheme corresponding to the abnormal type is:
for the conditions that the value in the limited enumeration abnormal class is null, the value in the numerical value abnormal class is null and a default value exists, a scheme of setting the default value or obtaining an accurate value through a related equipment model standard comparison table is adopted for repairing;
for the condition that the value of the limited enumeration abnormal class is a non-standard value, a scheme of obtaining the standard value by associating a standard comparison table is adopted for repairing;
for the condition that the value logic between fields in the limited enumeration exception class is abnormal, repairing by adopting a scheme of related synchronous replacement between fields;
for the conditions that the value in the numerical value abnormal field is null, a default value does not exist, and the value in the numerical value abnormal field is null, the scheme of k-means clustering or data binning algorithm prediction on the same field value of the same type of assets is adopted for repairing;
for the condition that the value logic between the fields is abnormal in the time abnormal class, adopting a scheme of adjusting the sequence or associating redundant information fields by the service data of the multi-source system to repair;
for the time exception type except the inter-field value logic exception, repairing through the scheme of the multi-source system service data correlation redundant information field;
for the fields belonging to both the limited enumeration exception and the numerical exception, when the fields cannot be repaired by the method, the values of the fields involved are clustered by adopting a weighted clustering algorithm, and the cluster center value with the shortest comprehensive distance between the values of the fields and any cluster center is replaced and repaired.
In the step B, the abnormal field comprises at least one of financial amount of equipment assets, voltage level, quantity scale, technical scale, value scale, equipment model, manufacturer, delivery date, commissioning date and transfer date, wherein the abnormality of the voltage level, the equipment model and the manufacturer is a limited enumeration abnormality class, the abnormality of the financial amount of the equipment assets, the quantity scale, the technical scale and the value scale is a numerical value abnormality class, and the abnormality of the delivery date, the delivery date and the transfer date is a time abnormality class;
for the abnormal field of the voltage level, when the abnormal field of the voltage level cannot be repaired by the method, the voltage level and the equipment name in the system are extracted by adopting a natural language processing technology, so that the abnormal field of the voltage level is repaired.
In the step B, the method for identifying and marking the abnormal field in the original data of the power grid equipment asset is at least one of threshold value inspection, box separation operation and standard value matching.
The system also comprises a repair result management module, wherein the repair result management module comprises a repaired data output display module and a data storage management module, the signal input end of the repaired data output display module is connected with the signal output end of the abnormal data automatic repair module, and the signal output end of the repaired data output display module is connected with the signal input end of the data storage management module;
the method further comprises the following steps:
and E, the repaired data output display module displays the repaired data, compares the repaired data with the data before repair, judges whether the repair is successful, outputs the repaired data to the data storage management module if the repair is successful, and returns to the step B to repair again if the repair is not successful.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to an automatic restoration system for abnormal data of an asset account of power grid equipment, which comprises a power grid asset data management module and an abnormal data restoration module, wherein the abnormal data restoration module comprises an abnormal data positioning marking module, an abnormal data clustering analysis module and an abnormal data automatic restoration module, the power grid asset data management module is used for acquiring original data information of the power grid equipment from an external system, the abnormal data positioning marking module is used for identifying and marking abnormal fields in the original data of the power grid equipment and determining data abnormal types and associated fields corresponding to the abnormal fields, the abnormal data clustering analysis module is used for constructing a weighted clustering algorithm model based on the marked abnormal fields, clustering analysis is carried out on the marked abnormal fields according to the data abnormal types to form a plurality of abnormal classified data sets, and the abnormal data automatic restoration module is used for aiming at the abnormal types of the abnormal classified data sets, the system adopts the abnormal data restoration scheme corresponding to the abnormal type, and adopts different abnormal restoration schemes for different abnormal types after identifying and marking the abnormal field in the data and carrying out cluster analysis, so that the system not only realizes the rapid positioning detection and automatic restoration of the abnormal data of the asset ledger of the power grid equipment, but also has higher accuracy and reliability. Therefore, the method and the device realize the rapid, accurate and reliable positioning detection and automatic repair of the abnormal data of the asset ledger of the power grid equipment.
Drawings
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Referring to fig. 1, an automatic restoration system for abnormal data of an asset account of a power grid device comprises a power grid asset data management module 1 and an abnormal data restoration module 2, wherein the abnormal data restoration module 2 comprises an abnormal data positioning and marking module 21, an abnormal data clustering analysis module 22 and an abnormal data automatic restoration module 23, the input end of the abnormal data positioning and marking module 21 is connected with the output end of the power grid asset data management module 1, and the output end of the abnormal data positioning and marking module 21 is connected with the input end of the abnormal data automatic restoration module 23 through the abnormal data clustering analysis module 22;
the power grid asset data management module 1 is used for acquiring power grid equipment asset original data information from an external system, wherein the power grid equipment asset original data information comprises various types of equipment asset ledger basic data, equipment asset value scale data, equipment asset financial data, scrapped equipment asset basic data and historical scrapped and retired equipment data;
the abnormal data positioning and marking module 21 is used for identifying and marking abnormal fields in the original data of the power grid equipment assets and determining data abnormal types and associated fields corresponding to the abnormal fields;
the abnormal data clustering analysis module 22 is configured to construct a weighted clustering algorithm model based on the marked abnormal fields, and perform clustering analysis on the marked abnormal fields according to the data abnormal types to form a plurality of abnormal classification data sets;
the abnormal data automatic repairing module 23 is configured to, for the abnormal type to which each abnormal classified data set belongs, perform abnormal data repair by using a repairing scheme corresponding to the abnormal type.
The system further comprises a repair result management module 3, wherein the repair result management module 3 comprises a repaired data output display module 31 and a data storage management module 32, the input end of the repaired data output display module 31 is connected with the output end of the abnormal data automatic repair module 23, and the output end of the repaired data output display module 31 is connected with the input end of the data storage management module 32.
A method for automatically repairing abnormal data of an asset account of power grid equipment sequentially comprises the following steps:
step A, the power grid asset data management module 1 acquires power grid equipment asset original data information from an external system and sends the information to an abnormal data positioning and marking module 21;
step B, the abnormal data positioning and marking module 21 identifies and marks abnormal fields in the original data of the power grid equipment assets, and determines data abnormal types and associated fields corresponding to the abnormal fields;
step C, the abnormal data clustering analysis module 22 builds a weighted clustering algorithm model based on the marked abnormal fields, and carries out clustering analysis on the marked abnormal fields according to the data abnormal types to form a plurality of abnormal classification data sets;
and step D, the abnormal data automatic repairing module 23 adopts a repairing scheme corresponding to the abnormal type to repair the abnormal data according to the abnormal type to which each abnormal classified data set belongs.
In step C, the exception types include a limited enumeration exception class, a numerical value exception class and a time exception class, the limited enumeration exception class includes a null value, a non-standard value and an inter-field value logic exception, the numerical value exception class includes a null value, a value less than or equal to 0, an overlarge value and an inter-field value logic exception, and the time exception class includes a null value, a value advanced or too late value and an inter-field value logic exception;
in step D, the repair scheme corresponding to the abnormal type is:
for the conditions that the value in the limited enumeration abnormal class is null, the value in the numerical value abnormal class is null and a default value exists, a scheme of setting the default value or obtaining an accurate value through a related equipment model standard comparison table is adopted for repairing;
for the condition that the value of the limited enumeration abnormal class is a non-standard value, a scheme of obtaining the standard value by associating a standard comparison table is adopted for repairing;
for the condition that the value logic between fields in the limited enumeration exception class is abnormal, repairing by adopting a scheme of related synchronous replacement between fields;
for the conditions that the value in the numerical value abnormal field is null, a default value does not exist, and the value in the numerical value abnormal field is null, the scheme of k-means clustering or data binning algorithm prediction on the same field value of the same type of assets is adopted for repairing;
for the condition that the value logic between the fields is abnormal in the time abnormal class, adopting a scheme of adjusting the sequence or associating redundant information fields by the service data of the multi-source system to repair;
for the time exception type except the inter-field value logic exception, repairing through the scheme of the multi-source system service data correlation redundant information field;
for the fields belonging to both the limited enumeration exception and the numerical exception, when the fields cannot be repaired by the method, the values of the fields involved are clustered by adopting a weighted clustering algorithm, and the cluster center value with the shortest comprehensive distance between the values of the fields and any cluster center is replaced and repaired.
In the step B, the abnormal field comprises at least one of financial amount of equipment assets, voltage level, quantity scale, technical scale, value scale, equipment model, manufacturer, delivery date, commissioning date and transfer date, wherein the abnormality of the voltage level, the equipment model and the manufacturer is a limited enumeration abnormality class, the abnormality of the financial amount of the equipment assets, the quantity scale, the technical scale and the value scale is a numerical value abnormality class, and the abnormality of the delivery date, the delivery date and the transfer date is a time abnormality class;
for the abnormal field of the voltage level, when the abnormal field of the voltage level cannot be repaired by the method, the voltage level and the equipment name in the system are extracted by adopting a natural language processing technology, so that the abnormal field of the voltage level is repaired.
In the step B, the method for identifying and marking the abnormal field in the original data of the power grid equipment asset is at least one of threshold value inspection, box separation operation and standard value matching.
The system also comprises a repair result management module 3, wherein the repair result management module 3 comprises a repaired data output display module 31 and a data storage management module 32, the signal input end of the repaired data output display module 31 is connected with the signal output end of the abnormal data automatic repair module 23, and the signal output end of the repaired data output display module 31 is connected with the signal input end of the data storage management module 32;
the method further comprises the following steps:
and E, the repaired data output display module 31 displays the repaired data, compares the repaired data with the data before repair, and judges whether the repair is successful, if so, the repaired data is output to the data storage management module 32, and if not, the step B is returned to for repairing again.
In the invention, the statistical mode of the service life probability distribution of the scale of the equipment to be scrapped and scrapped is as follows: according to the collected data set, firstly making statistics of the number of recorded data, calculating work-age, then making statistics of the number of recorded data corresponding to each work-age, then making calculation of duty ratio of the number of recorded data corresponding to different work-ages in total recorded data quantity so as to obtain the probability distribution according to work-age.
Example 1:
referring to fig. 1, an automatic restoration system for abnormal data of an asset account of a power grid device comprises a power grid asset data management module 1, an abnormal data restoration module 2 and a restoration result management module 3, wherein the abnormal data restoration module 2 comprises an abnormal data positioning and marking module 21, an abnormal data clustering analysis module 22 and an abnormal data automatic restoration module 23, the restoration result management module 3 comprises a restored data output and display module 31 and a data storage and management module 32, an input end of the abnormal data positioning and marking module 21 is connected with an output end of the power grid asset data management module 1, an output end of the abnormal data positioning and marking module 21 is connected with an input end of the abnormal data automatic restoration module 23 through the abnormal data clustering analysis module 22, an input end of the restored data output and display module 31 is connected with an output end of the abnormal data automatic restoration module 23, the output end of the repaired data output display module 31 is connected with the input end of the data storage management module 32.
The method takes the restoration of abnormal data in the basic information of the PMS system standing account and the asset value scale information of the ERP system as a research object and sequentially comprises the following steps:
1. the power grid asset data management module 1 acquires the original data information of the power grid equipment assets from a PMS system and an ERP system and sends the information to an abnormal data positioning and marking module 21;
2. the abnormal data positioning and marking module 21 performs identification and marking on abnormal fields in the original data of the power grid equipment assets through threshold value checking, binning operation and standard value matching, and determines the data abnormal types and the associated fields corresponding to the abnormal fields, the abnormal fields identified in this embodiment include an original asset value, a net asset value, a voltage class, a delivery date, a commissioning date and a transfer date, wherein the voltage class abnormality is a limited enumeration abnormal class, the voltage class abnormality includes two types of abnormalities with values of null and non-standard values, the auxiliary associated field asset equipment model and the voltage class abnormal value calibration comparison table are provided, the original asset value and the net asset value abnormality are numerical value abnormal classes, the two types of abnormalities with values of 0 and with the net asset value greater than the original asset value exist between the original asset value and the net asset value, and the auxiliary associated field includes the transfer date, the equipment model, the delivery date, The delivery date and the transfer date are abnormal, and the delivery date are abnormal, and the time sequence of the delivery date and the delivery date is abnormal;
3. the abnormal data clustering analysis module 22 builds a weighted clustering algorithm model based on the marked abnormal fields, and performs clustering analysis on the marked abnormal fields according to the data abnormal types to form a plurality of abnormal classification data sets;
4. the abnormal data automatic repairing module 23 repairs the abnormal data by adopting a repairing scheme corresponding to the abnormal type for the abnormal type to which each abnormal classified data set belongs, specifically:
aiming at the condition that the voltage grade value is null or the value is a non-standard value, matching association is carried out on the basis of the equipment model, an accurate value is obtained by an equipment model voltage grade standard comparison table for repairing, if the equipment model does not exist in the equipment model voltage grade standard comparison table, the same equipment of a national network PMS/ERP system is extracted by adopting a natural language processing technology, and voltage grade clustering analysis is carried out by combining the equipment model to obtain the voltage grade with the most consistent characteristics (asset manufacturer and equipment model) for repairing;
aiming at the abnormal original value and net value of the asset, when the original value of the asset is not abnormal, net value calculation restoration is carried out according to depreciation guidelines based on the original value of the asset and the date of transferring the asset or the net value clustering analysis is carried out by combining the similar assets of the national network PMS system/ERP system to obtain reasonable net value for restoration; aiming at the condition that the valuing of the original value and the net value of the asset is 0 or the net value of the asset is greater than the original value of the asset, carrying out clustering analysis and prediction on the net value of the asset based on the PMS/ERP system similar equipment to obtain a reasonable original value and net value for repairing;
aiming at the condition that the values of the delivery date and the commissioning date are empty, replacing and repairing the capital transfer date (the capital transfer date exists in the ERP system and the PMS does not exist) by associating the equipment account basic information and the asset value basic information of the ERP system of the same equipment from the national grid PMS;
and updating the delivery date by taking the transfer date or the commissioning date as a standard according to the condition that the delivery date, the commissioning date and the transfer date time are abnormal in logic sequence.
5. The repaired data output display module 31 displays the repaired data, compares the repaired data with the data before repair, and determines whether the repair is successful, if so, the repaired data is output to the data storage management module 32, and if not, the step 2 is returned to repair again.
Claims (7)
1. The utility model provides a power grid equipment asset ledger abnormal data automatic repair system which characterized in that:
the system comprises a power grid asset data management module (1) and an abnormal data restoration module (2), wherein the abnormal data restoration module (2) comprises an abnormal data positioning and marking module (21), an abnormal data clustering analysis module (22) and an abnormal data automatic restoration module (23), the input end of the abnormal data positioning and marking module (21) is connected with the output end of the power grid asset data management module (1), and the output end of the abnormal data positioning and marking module (21) is connected with the input end of the abnormal data automatic restoration module (23) through the abnormal data clustering analysis module (22);
the power grid asset data management module (1) is used for acquiring power grid equipment asset original data information from an external system, wherein the power grid equipment asset original data information comprises various types of equipment asset ledger basic data, equipment asset value scale data, equipment asset financial data, scrapped equipment asset basic data and historical scrapped and retired equipment data;
the abnormal data positioning and marking module (21) is used for identifying and marking abnormal fields in the original data of the power grid equipment assets and determining the data abnormal type and the associated fields corresponding to each abnormal field;
the abnormal data clustering analysis module (22) is used for constructing a weighted clustering algorithm model based on the marked abnormal fields, and performing clustering analysis on the marked abnormal fields according to the data abnormal types to form a plurality of abnormal classification data sets;
and the abnormal data automatic repairing module (23) is used for repairing the abnormal data by adopting a repairing scheme corresponding to the abnormal type aiming at the abnormal type of each abnormal classified data set.
2. The automatic power grid equipment asset ledger abnormal data repair system according to claim 1, characterized in that:
the system further comprises a repair result management module (3), the repair result management module (3) comprises a repaired data output display module (31) and a data storage management module (32), the input end of the repaired data output display module (31) is connected with the output end of the abnormal data automatic repair module (23), and the output end of the repaired data output display module (31) is connected with the input end of the data storage management module (32).
3. A method for automatically repairing abnormal data of an asset account of power grid equipment is characterized by comprising the following steps:
the method is carried out by the system of claim 1, comprising the following steps in sequence:
a, the power grid asset data management module (1) acquires power grid equipment asset original data information from an external system and sends the information to an abnormal data positioning and marking module (21);
b, the abnormal data positioning and marking module (21) identifies and marks abnormal fields in the original data of the power grid equipment assets, and determines data abnormal types and associated fields corresponding to the abnormal fields;
c, the abnormal data clustering analysis module (22) builds a weighted clustering algorithm model based on the marked abnormal fields, and carries out clustering analysis on the marked abnormal fields according to the data abnormal types to form a plurality of abnormal classification data sets;
and D, the abnormal data automatic repairing module (23) adopts a repairing scheme corresponding to the abnormal type to repair the abnormal data aiming at the abnormal type of each abnormal classified data set.
4. The automatic restoration method for the asset ledger abnormal data of the power grid equipment according to claim 3, characterized in that:
in step C, the exception types include a limited enumeration exception class, a numerical value exception class and a time exception class, the limited enumeration exception class includes a null value, a non-standard value and an inter-field value logic exception, the numerical value exception class includes a null value, a value less than or equal to 0, an overlarge value and an inter-field value logic exception, and the time exception class includes a null value, a value advanced or too late value and an inter-field value logic exception;
in step D, the repair scheme corresponding to the abnormal type is:
for the conditions that the value in the limited enumeration abnormal class is null, the value in the numerical value abnormal class is null and a default value exists, a scheme of setting the default value or obtaining an accurate value through a related equipment model standard comparison table is adopted for repairing;
for the condition that the value of the limited enumeration abnormal class is a non-standard value, a scheme of obtaining the standard value by associating a standard comparison table is adopted for repairing;
for the condition that the value logic between fields in the limited enumeration exception class is abnormal, repairing by adopting a scheme of related synchronous replacement between fields;
for the conditions that the value in the numerical value abnormal field is null, a default value does not exist, and the value in the numerical value abnormal field is null, the scheme of k-means clustering or data binning algorithm prediction on the same field value of the same type of assets is adopted for repairing;
for the condition that the value logic between the fields is abnormal in the time abnormal class, adopting a scheme of adjusting the sequence or associating redundant information fields by the service data of the multi-source system to repair;
for the time exception type except the inter-field value logic exception, repairing through the scheme of the multi-source system service data correlation redundant information field;
for the fields belonging to both the limited enumeration exception and the numerical exception, when the fields cannot be repaired by the method, the values of the fields involved are clustered by adopting a weighted clustering algorithm, and the cluster center value with the shortest comprehensive distance between the values of the fields and any cluster center is replaced and repaired.
5. The automatic restoration method for the asset ledger abnormal data of the power grid equipment according to claim 4, characterized in that:
in the step B, the abnormal field comprises at least one of financial amount of equipment assets, voltage level, quantity scale, technical scale, value scale, equipment model, manufacturer, delivery date, commissioning date and transfer date, wherein the abnormality of the voltage level, the equipment model and the manufacturer is a limited enumeration abnormality class, the abnormality of the financial amount of the equipment assets, the quantity scale, the technical scale and the value scale is a numerical value abnormality class, and the abnormality of the delivery date, the commissioning date and the transfer date is a time abnormality class;
for the abnormal field of the voltage level, when the abnormal field of the voltage level cannot be repaired by the method, the voltage level and the equipment name in the system are extracted by adopting a natural language processing technology, so that the abnormal field of the voltage level is repaired.
6. The automatic restoration method for the asset ledger abnormal data of the power grid equipment according to claim 3, characterized in that: in the step B, the method for identifying and marking the abnormal field in the original data of the power grid equipment asset is at least one of threshold value inspection, box separation operation and standard value matching.
7. The automatic restoration method for the asset ledger abnormal data of the power grid equipment according to claim 3, characterized in that:
the system also comprises a repair result management module (3), wherein the repair result management module (3) comprises a repaired data output display module (31) and a data storage management module (32), the signal input end of the repaired data output display module (31) is connected with the signal output end of the abnormal data automatic repair module (23), and the signal output end of the repaired data output display module (31) is connected with the signal input end of the data storage management module (32);
the method further comprises the following steps:
and E, the repaired data output display module (31) displays the repaired data, compares the repaired data with the data before repair, judges whether the repair is successful, outputs the repaired data to the data storage management module (32) if the repair is successful, and returns to the step B to repair again if the repair is not successful.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111376656.XA CN114118245A (en) | 2021-11-19 | 2021-11-19 | Automatic restoration system and method for abnormal data of asset account of power grid equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111376656.XA CN114118245A (en) | 2021-11-19 | 2021-11-19 | Automatic restoration system and method for abnormal data of asset account of power grid equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114118245A true CN114118245A (en) | 2022-03-01 |
Family
ID=80398198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111376656.XA Pending CN114118245A (en) | 2021-11-19 | 2021-11-19 | Automatic restoration system and method for abnormal data of asset account of power grid equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114118245A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756136A (en) * | 2023-08-16 | 2023-09-15 | 深圳市明心数智科技有限公司 | Automatic data processing method, device, equipment and medium for fishpond monitoring equipment |
| CN117332857A (en) * | 2023-09-19 | 2024-01-02 | 上海聚数信息科技有限公司 | Multi-source data-based power grid data automatic management system and method |
-
2021
- 2021-11-19 CN CN202111376656.XA patent/CN114118245A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756136A (en) * | 2023-08-16 | 2023-09-15 | 深圳市明心数智科技有限公司 | Automatic data processing method, device, equipment and medium for fishpond monitoring equipment |
| CN116756136B (en) * | 2023-08-16 | 2023-10-31 | 深圳市明心数智科技有限公司 | Automatic data processing method, device, equipment and medium for fishpond monitoring equipment |
| CN117332857A (en) * | 2023-09-19 | 2024-01-02 | 上海聚数信息科技有限公司 | Multi-source data-based power grid data automatic management system and method |
| CN117332857B (en) * | 2023-09-19 | 2024-04-02 | 上海聚数信息科技有限公司 | Multi-source data-based power grid data automatic management system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106780121B (en) | Power consumption abnormity identification method based on power consumption load mode analysis | |
| CN114118245A (en) | Automatic restoration system and method for abnormal data of asset account of power grid equipment | |
| CN110008254B (en) | Transformer equipment standing book checking processing method | |
| CN111709361B (en) | Method for processing inspection data of unmanned aerial vehicle of power transmission line | |
| CN111738462B (en) | Fault first-aid repair active service early warning method for electric power metering device | |
| CN106327062A (en) | State evaluation method of power distribution network equipment | |
| CN110927654B (en) | Batch running state evaluation method for intelligent electric energy meters | |
| CN103810533A (en) | Cloud-model-based power distribution network fault risk identification method | |
| CN116073372A (en) | Intelligent electricity utilization-based safety monitoring management system and method | |
| CN110688389B (en) | Cloud management system for defects of secondary equipment of transformer substation | |
| CN110826228B (en) | Regional power grid operation quality limit evaluation method | |
| CN117035513A (en) | Intelligent monitoring and management system and method for energy operation of intelligent industrial park | |
| CN112949874B (en) | Power distribution terminal defect characteristic self-diagnosis method and system | |
| CN110489404A (en) | A kind of comprehensive data method for quality control of the field of distribution network based on three-tier architecture | |
| CN108446842B (en) | Power management and distribution wind control management method and system | |
| CN115330404A (en) | System and method for electric power marketing inspection | |
| CN116032003A (en) | Power grid inspection method and system based on power private network data processing | |
| CN113657747B (en) | Intelligent assessment system for enterprise safety production standardization level | |
| CN110781206A (en) | Method for predicting whether electric energy meter in operation fails or not by learning meter-dismantling and returning failure characteristic rule | |
| CN111105218A (en) | Power distribution network operation monitoring method based on holographic image technology | |
| Ya’An | Application of artificial intelligence in computer network technology in the era of big data | |
| CN108335231A (en) | A kind of power distribution network data diagnosis method of Auto-matching | |
| CN114168662A (en) | Power distribution network problem combing and analyzing method and system based on multiple data sources | |
| CN110703183A (en) | Intelligent electric energy meter fault data analysis method and system | |
| CN115693692A (en) | Voltage qualification rate improving method based on power distribution network voltage data analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |