CN110008254B - Transformer equipment standing book checking processing method - Google Patents
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Abstract
The application discloses a checking and processing method of a transformer equipment standing book, which comprises the steps of firstly, acquiring data to be recorded of the transformer equipment standing book; checking and identifying whether the data to be recorded of the transformer equipment ledger is abnormal ledger data or not according to a database; if the data to be recorded in the transformer equipment ledger is abnormal ledger data, correcting the abnormal ledger data; and recording the corrected standing book data to be recorded in the database. And the automatic checking and processing of the abnormal standing book data of the power transformation equipment are realized, and the identification and statistics of the error and missing data are carried out. Compared with an artificial checking mode, the transformer equipment ledger checking processing method saves a great amount of time and cost, greatly improves the quality of the ledger basic data, ensures the uniqueness, the integrity and the accuracy of the equipment basic information, the technical parameters and other data, and provides support for promoting the improvement of the production management lean level.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a transformer equipment standing book checking processing method.
Background
The equipment account is a main basis for grasping the equipment condition of an enterprise and reflecting the possession of various equipment, equipment classification and variation conditions of the enterprise, and is a foundation for developing production, transportation and equipment management. Because the types and the quantity of the equipment related to the power system are large, the factory nameplate information of the equipment is incomplete, the equipment is migrated and replaced, and errors and omission are caused by manually inputting data, the problem that the quality of equipment account data is generally low is caused, and the effective development of equipment operation and maintenance management work and equipment state evaluation work is seriously influenced. The account setting data management is carried out by means of the traditional manual mode, and the defects that the working efficiency is low, the achievement is difficult to copy, the effectiveness of manual correction measures is poor, and errors are easy to repeatedly appear exist.
Disclosure of Invention
The application provides a transformer equipment account checking processing method, which aims to solve the problems that the existing transformer equipment account checking processing method is low in working efficiency, difficult to duplicate in achievements, poor in effectiveness of manual correction measures and easy to repeatedly occur in errors.
The application provides a checking processing method for a transformer equipment standing book, which comprises the following steps:
acquiring data to be recorded of a transformer equipment ledger;
checking and identifying whether the data to be recorded of the transformer equipment ledger is abnormal ledger data or not according to a database;
if the data to be recorded in the transformer equipment ledger is abnormal ledger data, correcting the abnormal ledger data;
and recording the corrected standing book data to be recorded in the database.
Preferably, the abnormal ledger data includes: unique abnormal ledger data, integrity abnormal ledger data and accuracy ledger data.
Preferably, the checking whether the data to be entered of the standing book of the power transformation equipment is abnormal standing book data according to the database includes:
uniqueness checking: judging whether the database has the data to be recorded of the transformer equipment ledger or not;
if the database does not have the data to be input of the transformer equipment ledger, integrity checking is carried out;
if the database is stored in the transformer equipment ledger to be recorded, re-acquiring the transformer equipment ledger to be recorded, and then carrying out uniqueness test;
integrity check: judging whether each index item of the transformer equipment ledger data to be recorded contains data or not;
if each index item of the transformer equipment ledger to be recorded with data contains data, performing accuracy check;
if the index item of the data to be input by the transformer equipment ledger lacks data, filling the data, and then checking the accuracy;
and (3) checking accuracy: judging whether the data of the transformer equipment ledger to be recorded data are correct or not;
if the data of the data to be recorded of the transformer equipment ledger is correct, the data to be recorded of the transformer equipment ledger is recorded;
and if the data of the data to be recorded of the transformer equipment ledger is wrong, correcting the abnormal ledger data.
Preferably, the uniqueness check includes:
inserting the data to be input of the transformer equipment ledger into the database to obtain an insertion database;
sequencing the inserted database by using a sequencing and merging algorithm, and comparing the data to be recorded of the transformer equipment ledger with the data adjacent to the data to be recorded of the transformer equipment ledger;
and if the data to be recorded by the transformer equipment ledger is the same as the data adjacent to the data to be recorded by the transformer equipment ledger, merging the data to be recorded by the transformer equipment ledger with the data adjacent to the data to be recorded by the transformer equipment ledger.
Preferably, the integrity check further comprises: and if the index item of the data to be recorded by the transformer equipment ledger lacks data, filling the missing data by adopting a mode filling or mean filling method.
Preferably, the accuracy checking includes checking whether the data to be recorded in the transformer equipment ledger is consistent with the actual value by using an accuracy checking method, and the accuracy checking method includes: a checking method based on a business threshold, a checking method based on discrete point detection of K-means clustering and a checking method based on text similarity.
Preferably, the obtaining the data to be entered of the transformer equipment ledger includes: and reading the data to be recorded of the transformer equipment ledger from the source end of the service system by using a Python tool.
Preferably, the checking processing method further includes: marking the abnormal ledger data and counting the abnormal ledger data.
According to the technical scheme provided by the embodiment of the application, the data to be recorded of the transformer equipment ledger is firstly obtained; checking and identifying whether the data to be recorded of the transformer equipment ledger is abnormal ledger data or not according to a database; if the data to be recorded in the transformer equipment ledger is abnormal ledger data, correcting the abnormal ledger data; and recording the corrected standing book data to be recorded in the database. And the automatic checking and processing of the abnormal standing book data of the power transformation equipment are realized, and the identification and statistics of the error and missing data are carried out. Compared with an artificial checking mode, the transformer equipment ledger checking processing method saves a great amount of time and cost, greatly improves the quality of the ledger basic data, ensures the uniqueness, the integrity and the accuracy of the equipment basic information, the technical parameters and other data, and provides support for promoting the improvement of the production management lean level.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a transformer equipment ledger checking processing method provided by an embodiment of the present application;
fig. 2 is a logic diagram of a method for checking and identifying whether data to be recorded in a standing book of a transformer device is abnormal standing book data according to an embodiment of the present application;
FIG. 3 is a schematic diagram of implementation steps of a K-means algorithm according to an embodiment of the present application;
fig. 4 is a schematic diagram of a process of a text similarity-based verification method according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, an overall flowchart of a transformer equipment ledger checking processing method according to an embodiment of the present application is shown. The checking processing method comprises the following steps:
s101, obtaining data to be input of the transformer equipment ledger.
Wherein, the obtaining the transformer equipment ledger data to be input includes: and reading the data to be recorded of the transformer equipment ledger from the source end of the service system by using a Python tool. According to national network specifications and standards of national power grid company production management system equipment codes, national power grid company production management system equipment parameter specifications, national power grid company production management system data coding specifications and the like, an automatic checking mode of Python programming and curing various check fields of various devices is adopted. The power transformation equipment comprises 18 kinds of power transformation equipment such as a main transformer, a current transformer, a voltage transformer, a circuit breaker, an isolating switch and the like, and the specific reference is made to table 1.
TABLE 1 Equipment Classification Table
The ledger data may include basic information and technical parameters of the power transformation device.
S102, checking and identifying whether the data to be recorded of the transformer equipment ledger is abnormal ledger data according to a database, and if the data to be recorded of the transformer equipment ledger is abnormal ledger data, correcting the abnormal ledger data. If the data are not the three abnormal ledger data, namely the unique abnormal ledger data, the integrity abnormal ledger data and the accuracy ledger data, the data to be input of the transformer equipment ledger are directly input into the database.
Wherein, the unusual ledger data includes: unique abnormal ledger data, integrity abnormal ledger data and accuracy ledger data. Referring to fig. 2, a logic diagram of a method for checking and identifying whether data to be entered of a standing book of a transformer device is abnormal standing book data is provided in an embodiment of the present application. Specifically, checking whether the data to be recorded in the standing book of the power transformation equipment is abnormal standing book data according to the database includes: firstly, carrying out uniqueness checking, secondly, integrity checking and finally, carrying out accuracy checking.
Uniqueness checking: judging whether the database has the data to be recorded of the transformer equipment ledger or not; if the database does not have the data to be input of the transformer equipment ledger, integrity checking is carried out; and if the database stores the data to be recorded in the transformer equipment ledger, re-acquiring the data to be recorded in the transformer equipment ledger, and then carrying out uniqueness test.
Data uniqueness means that certain identification type data must not be repeated in the same data table. And (3) adopting a sequencing and merging algorithm, and sequencing the data and comparing adjacent records so as to find out repeated records. The processing is to combine the duplicate records in the cleaning process after detecting the duplicate data. Meanwhile, in the process of collecting data to the database, constraint can be added to certain identification type fields in the database, and the uniqueness of the data is ensured from the bottom layer. The uniqueness check may specifically include: inserting the data to be input of the transformer equipment ledger into the database to obtain an insertion database; sequencing the inserted database by using a sequencing and merging algorithm, and comparing the data to be recorded of the transformer equipment ledger with the data adjacent to the data to be recorded of the transformer equipment ledger; and if the data to be recorded by the transformer equipment ledger is the same as the data adjacent to the data to be recorded by the transformer equipment ledger, merging the data to be recorded by the transformer equipment ledger with the data adjacent to the data to be recorded by the transformer equipment ledger.
Integrity check: judging whether each index item of the transformer equipment ledger data to be recorded contains data or not;
if each index item of the transformer equipment ledger to be recorded with data contains data, performing accuracy check;
and if the index item of the data to be input by the transformer equipment ledger lacks data, filling the data, and then checking the accuracy.
The data integrity check is to detect the missing of the data item, and if the index item is missing, the missing value can be filled with data by adopting a mode filling or mean filling method.
And (3) checking accuracy: judging whether the data of the transformer equipment ledger to be recorded data are correct or not;
if the data of the data to be recorded of the transformer equipment ledger is correct, the data to be recorded of the transformer equipment ledger is recorded;
and if the data of the data to be recorded of the transformer equipment ledger is wrong, correcting the abnormal ledger data.
The accuracy check may include: and checking whether the data to be recorded of the transformer equipment ledger is consistent with the actual value or not by using an accuracy checking method. Specifically, the accuracy checking method includes: a checking method based on a business threshold, a checking method based on discrete point detection of K-means clustering and a checking method based on text similarity. The accuracy of the data is checked, i.e. the degree to which the data corresponds to the actual value is detected. The data accuracy check adopts different check processing methods according to different types of data, numerical data with a service threshold range adopts check based on a service threshold, numerical data with an undefined or non-existing service threshold range adopts check based on discrete point detection of K-means cluster, and text data such as manufacturer name and the like adopts check processing based on text similarity. After checking the error data, the error data is corrected by adopting mode filling or mean filling.
The checking method based on the business threshold comprises the following steps: the method is based on reasonable data range of indexes given by national power grid 'main network equipment technical parameter specification' and business specialists, and whether the data are accurate or not is judged by using the range. The technical parameter specifications of the equipment account are arranged according to the technical parameter specifications, and part of the contents and the form are shown in table 2.
TABLE 2 parameter Specification Table
The checking method for discrete point detection based on K-means clustering comprises the following steps: for parameters for which some canonical standards are not or are not yet defined, a method based on cluster-based discrete point detection may be employed. Cluster analysis is used to find groups of objects that are strongly correlated locally, while anomaly detection is used to find objects that are not strongly correlated with other objects. Thus, cluster analysis can be used for dispersion detection. The K-Means algorithm, also known as K-average or K-Means, is one of the most widely used classical clustering algorithms. The method takes the average value of all data samples in each cluster subset as the representative point of the class, and the main idea of the algorithm is to divide the data set into different classes through an iterative process, so that a criterion function for evaluating the clustering performance is optimal, and the similarity of objects in the same cluster is higher, and the similarity of objects in different clusters is smaller.
The space requirements of the K-Means algorithm are modest because only the data points and centroids need to be deposited. Specifically, the required storage amount is O ((m+k) n), where m is the number of points and n is the number of attributes. While the time requirement is also modest-substantially linearly dependent on the number of data points. Specifically, the time required is O (IxKxmxn), where I is the number of iterations required for convergence.
The K-Means model generally uses a square error criterion, defined as:where E is the sum of the square errors of all samples in the dataset, p is a point in space, representing a given sample, m i Is cluster C i Average value of (2). This criterion attempts to make the resulting clusters generated as compact and independent as possible.
The K-Means algorithm determines which class the sample point belongs to according to the distance from the point to the class centroid, the sample point is divided into the class closest to the class centroid, and the Euclidean distance is generally adopted as the distance function.
x i Is the i-th variable value of sample x, y i Is the i-th variable value of the class centroid y.
K-Means basic algorithm steps
1) Selecting K points as initial centroids;
2) Calculating the distances from each point to K centroids;
3) Dividing all points into K classes according to a distance nearest principle;
4) Calculating the mass centers of K classes;
5) Repeating the steps 2, 3 and 4 until the mass center is unchanged.
The K-means algorithm implementation steps are shown in FIG. 3.
In the transformer equipment ledger data, some numerical judgment without a business threshold value is abnormal, the method can be used for extracting the characteristics from the fields, and the fields are clustered according to the characteristics. In each type of aggregation, correct data is selected, whether other data is 'wrong' is judged by taking the correct data as a standard, and a modification suggestion is provided for the wrong data.
The checking method based on text similarity comprises the following steps: referring to fig. 4, in the text correction, a standard library is established mainly by using a data mining technology, and the similarity between corrected text and text information in the standard library is calculated through text mining, so that the text information is further corrected, and the filling of the text information is standardized. Fields such as manufacturer name, vendor name, etc. can be detected in this way. The whole correction thought of text fields such as equipment manufacturer text and the like:
firstly, a standard manufacturer library is established, and the standard manufacturer library is mainly extracted from a database and is arranged by business staff.
And matching and searching the historical manufacturer name (or the newly input manufacturer field) in a standard manufacturer library by using a text mining means. And calculating cosine similarity with a standard manufacturer library. Cosine similarity is the evaluation of the similarity of two vectors by calculating their angle cosine values. Cosine similarity is the cosine similarity (Cosine Similarity) that evaluates the similarity of two vectors by calculating their included angle cosine value. Let the vector x= (x 1, x2, …, xn), y= (y 1, y2, …, yn), the cosine similarity calculation formula of the vector x, y is as follows:
through calculation, the Yunnan transformer finite company and the Yunnan transformer factory are recommended to users if the cosine similarity is the largest, and the Yunnan transformer factory is corrected to be the Yunnan transformer finite company.
S103, inputting the corrected transformer equipment ledger data to be input into the database. The abnormal ledger data can be marked, the repeat, missing or error conditions of the abnormal ledger field are marked, statistics and summarization are carried out on the repeat, missing or error conditions of the ledger data according to the checking result, and the statistics of the data types with errors are facilitated for users. The counted checking result can be issued to each city company, and special business personnel can check and correct the abnormal account.
According to the technical scheme provided by the embodiment of the application, the data to be recorded of the transformer equipment ledger is firstly obtained; checking and identifying whether the data to be recorded of the transformer equipment ledger is abnormal ledger data or not according to a database; if the data to be recorded in the transformer equipment ledger is abnormal ledger data, correcting the abnormal ledger data; and recording the corrected standing book data to be recorded in the database. And the automatic checking and processing of the abnormal standing book data of the power transformation equipment are realized, and the identification and statistics of the error and missing data are carried out. Compared with an artificial checking mode, the transformer equipment ledger checking processing method saves a great amount of time and cost, greatly improves the quality of the ledger basic data, ensures the uniqueness, the integrity and the accuracy of the equipment basic information, the technical parameters and other data, and provides support for promoting the improvement of the production management lean level.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims. It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (6)
1. The utility model provides a substation equipment standing book checking processing method which is characterized in that the checking processing method comprises the following steps:
acquiring data to be recorded of a transformer equipment ledger;
checking and identifying whether the data to be recorded of the transformer equipment ledger is abnormal ledger data or not according to a database; the abnormal ledger data includes: unique abnormal ledger data, integrity abnormal ledger data and accuracy ledger data;
if the database does not have the data to be input of the transformer equipment ledger, integrity checking is carried out; the integrity check is used for judging whether each index item of the transformer equipment ledger to be recorded with data contains data or not;
if the database is stored in the transformer equipment ledger to be recorded, re-acquiring the transformer equipment ledger to be recorded, and then checking uniqueness; the uniqueness check is to judge whether the database has the data to be input of the transformer equipment ledger or not;
if each index item of the transformer equipment ledger to be recorded with data contains data, performing accuracy check; the accuracy check is to judge whether the data of the transformer equipment ledger to be recorded data are correct or not;
if the index item of the data to be input by the transformer equipment ledger lacks data, filling the data, and then checking the accuracy;
if the data of the data to be recorded of the transformer equipment ledger is correct, the data to be recorded of the transformer equipment ledger is recorded;
if the data of the data to be recorded of the transformer equipment ledger is wrong, correcting the abnormal ledger data;
if the data to be recorded in the transformer equipment ledger is abnormal ledger data, correcting the abnormal ledger data;
and recording the corrected standing book data to be recorded in the database.
2. The power transformation equipment ledger checking processing method according to claim 1, characterized in that the uniqueness checking includes:
inserting the data to be input of the transformer equipment ledger into the database to obtain an insertion database;
sequencing the inserted database by using a sequencing and merging algorithm, and comparing the data to be recorded of the transformer equipment ledger with the data adjacent to the data to be recorded of the transformer equipment ledger;
and if the data to be recorded by the transformer equipment ledger is the same as the data adjacent to the data to be recorded by the transformer equipment ledger, merging the data to be recorded by the transformer equipment ledger with the data adjacent to the data to be recorded by the transformer equipment ledger.
3. The power transformation device ledger processing method of claim 1, wherein the integrity check further comprises:
and if the index item of the data to be recorded by the transformer equipment ledger lacks data, filling the missing data by adopting a mode filling or mean filling method.
4. The power transformation equipment ledger checking processing method according to claim 1, characterized in that the accuracy checking includes checking whether the power transformation equipment ledger to-be-entered data is consistent with an actual value or not by using an accuracy checking method, the accuracy checking method including: a checking method based on a business threshold, a checking method based on discrete point detection of K-means clustering and a checking method based on text similarity.
5. The power transformation equipment ledger checking processing method according to claim 1, characterized in that the obtaining the data to be entered of the power transformation equipment ledger includes: and reading the data to be recorded of the transformer equipment ledger from the source end of the service system by using a Python tool.
6. The power transformation equipment ledger processing method according to claim 1, characterized in that the checking processing method further comprises:
marking the abnormal ledger data and counting the abnormal ledger data.
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| CN110866150B (en) * | 2019-10-31 | 2022-09-30 | 南方电网调峰调频发电有限公司 | Method for rapidly generating ledger data map and overhauling defects of pre-control equipment family |
| CN110909525A (en) * | 2019-11-19 | 2020-03-24 | 云南电网有限责任公司信息中心 | System and method for realizing automatic comparison of standing book information |
| CN111221956A (en) * | 2019-12-26 | 2020-06-02 | 国网宁夏电力有限公司中卫供电公司 | PMS distribution network equipment data quality checking method for power management system |
| CN111984638A (en) * | 2020-07-10 | 2020-11-24 | 广州汇通国信科技有限公司 | Distribution transformer heavy overload data management method based on BP neural network algorithm |
| CN112435001A (en) * | 2020-11-13 | 2021-03-02 | 国网冀北电力有限公司智能配电网中心 | Multi-source data checking method and device for power distribution network |
| CN112560797A (en) * | 2020-12-30 | 2021-03-26 | 国网四川省电力公司泸州供电公司 | Substation equipment standing book administers auxiliary system |
| CN112612842A (en) * | 2020-12-30 | 2021-04-06 | 南方电网深圳数字电网研究院有限公司 | Standing book information input method and device, electronic equipment and storage medium |
| CN112948368A (en) * | 2021-04-01 | 2021-06-11 | 哈尔滨理工大学 | Deep anomaly detection method based on data correlation |
| CN113781000A (en) * | 2021-09-18 | 2021-12-10 | 深圳供电局有限公司 | Intelligent data checking power management system and management method |
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