CN113656390A - Power equipment defect label portrait method based on defect equipment - Google Patents
Power equipment defect label portrait method based on defect equipment Download PDFInfo
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Abstract
The application provides a method for portrait of a defect label of power equipment based on defect equipment, which comprises the following steps: the method comprises the steps of obtaining defect data of the electric power equipment, screening the defect data to obtain actual defect equipment data, conducting data mining on the actual defect equipment data based on a data mining method to obtain equipment defect indexes, extracting the defect indexes to obtain characteristic indexes, building a defect equipment label based on the characteristic indexes, building a defect equipment label portrait model according to the defect equipment label, and obtaining a power equipment defect label portrait by combining calculation so as to solve the problems that the defect of the electric power equipment is described by a traditional label and is not fine enough, flexibly, cannot respond timely and can be known comprehensively.
Description
Technical Field
The application relates to the technical field of power equipment management, in particular to a power equipment defect label portrait method based on defect equipment.
Background
The defects of the power equipment are one of the important factors which endanger the safe operation of the power system, and when the power equipment is damaged and has defects, the accidents need to be analyzed and processed.
In the existing image technology, qualitative description of image of equipment is performed by adopting a label mode from one aspect of characteristics, events, evaluation, efficiency and the like of the power equipment, and due to the fact that the types of defects of the power equipment are various and the types of the defects are complex, after a transportation and inspection worker finds the defects of the equipment, the characteristics, classification, evaluation and processing flows of the defects need to be confirmed in person. The existing label portrait technology mainly aims at model portrayal in a certain specific field, lacks quantifiable indexes and comprehensive description, has insufficiently detailed label description and lower flexibility, cannot respond timely when equipment information changes, and is difficult to comprehensively know the overall condition of equipment.
Disclosure of Invention
The application provides an electric power equipment defect label portrait method based on defect equipment, and aims to solve the problems that the defects of the electric power equipment described by a traditional label are not fine enough, the electric power equipment cannot respond timely and the electric power equipment cannot be comprehensively known flexibly.
A method for representing a defect label of a power device based on a defective device comprises the following steps:
acquiring defect data of the electrical equipment;
screening the defect data to obtain actual defect equipment data;
based on a data mining method, performing data mining on the actual defect equipment data to obtain an equipment defect index;
extracting the defect index to obtain a characteristic index;
establishing a defective equipment label based on the characteristic index;
establishing a defective equipment label portrait model according to the defective equipment label;
and calculating to obtain the defect label portrait of the power equipment.
Preferably, the step of screening the defect data includes:
marking the screened data;
cleaning the marked data;
which comprises the following steps: cleaning missing values and removing meaningless information;
format content cleaning and unifying data formats;
and (4) logical error cleaning, and removing repeated and error data.
Preferably, the step of extracting the defect indicator includes:
extracting the name, category, type, defect grade, defect description, defect starting time and defect ending time index data of the defect equipment;
classifying based on the index data;
which comprises the following steps:
time classification, namely setting a time period, and analyzing and comparing defect equipment index data in the time period to obtain defect occurrence time characteristic index data;
classifying defective equipment, namely extracting equipment names, equipment categories and defect descriptions in the index data to obtain frequency data of the defective equipment, and sequencing the frequency data from high to low;
and classifying the defect grade, namely classifying the defect grade index data into general defects, serious defects and critical defects.
Preferably, in the step of establishing a defective device label, the method includes:
and establishing a defective equipment label library, and dividing the defective equipment label library into a label management layer, a label attribute layer and an equipment label layer.
Preferably, the step of establishing a defective device label portrait model according to the defective device label comprises:
presetting a theme category; importing the defective equipment label into the model library, and constructing a defective equipment label portrait analysis model according to a classification algorithm; and classifying the labels of the defective equipment according to the portrait analysis model of the labels of the defective equipment. Preferably, the acquired defect data of the power equipment is acquired by an ETL and data replication method.
According to the technical scheme, the application provides an electric power equipment defect label portrait method based on defect equipment, which comprises the following steps: the method comprises the steps of obtaining defect data of the electric power equipment, screening the defect data to obtain actual defect equipment data, conducting data mining on the actual defect equipment data based on a data mining method to obtain equipment defect indexes, extracting the defect indexes to obtain characteristic indexes, building a defect equipment label based on the characteristic indexes, building a defect equipment label portrait model according to the defect equipment label, and obtaining a power equipment defect label portrait by combining calculation so as to solve the problems that the defect of the electric power equipment is described by a traditional label and is not fine enough, flexibly, cannot respond timely and can be known comprehensively.
Drawings
In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for representing a defect label of a power device based on a defective device.
Detailed Description
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as exemplifications of systems and methods consistent with certain aspects of the application, as recited in the claims.
In the technical solution provided by the present application, a method for drawing a defect label of an electrical device based on a defective device is provided, please refer to fig. one, which includes:
s1: and acquiring defect data of the electrical equipment.
Further, the acquired defect data of the power equipment is acquired through an ETL and data replication mode.
S2: and screening the defect data to obtain actual defect equipment data.
Further, the step of screening the defect data includes:
s21: and identifying the screened data.
S22: and cleaning the marked data.
Which comprises the following steps: and (5) cleaning missing values and removing meaningless information.
Format content cleaning and data format unification.
And (4) logical error cleaning, and removing repeated and error data.
S3: and performing data mining on the actual defect equipment data based on a data mining method to obtain an equipment defect index.
S4: and extracting the defect index to obtain a characteristic index.
Further, the step of extracting the defect index includes:
s41: and extracting the name, the category, the type, the defect grade, the defect description, the defect starting time and the defect ending time index data of the defect equipment.
S42: a classification is made based on the index data,
which comprises the following steps:
and time classification, namely setting a time period, and analyzing and comparing the defect equipment index data in the time period to obtain defect occurrence time characteristic index data.
And classifying the defective equipment, extracting the equipment name, the equipment category and the defect description in the index data to obtain frequency data of the defective equipment, and sequencing the frequency data from high to low.
And classifying the defect grade, namely classifying the defect grade index data into general defects, serious defects and critical defects.
S5: and establishing a defective equipment label based on the characteristic index.
Further, in the step of establishing a defective device label, the method comprises the following steps:
and establishing a defective equipment label library, and dividing the defective equipment label library into a label management layer, a label attribute layer and an equipment label layer.
S6: and establishing a defective equipment label portrait model according to the defective equipment label.
Further, the step of establishing a model library of the label portrait of the defective device according to the label of the defective device comprises the following steps:
s61: a theme category is preset.
S62: and importing the label of the defective equipment into the model library, and constructing an image analysis model of the label of the defective equipment according to a classification algorithm.
S63: and classifying the labels of the defective equipment according to the portrait analysis model of the labels of the defective equipment.
S7: and calculating to obtain the defect label portrait of the power equipment.
In practical application, when acquiring defect data of the power equipment, extracting and collecting structured data from a PMS system are realized by using tools such as ETL (extract transform and load) and data replication. Meanwhile, real-time and non-real-time data acquisition and the like are achieved, and the data are accessed into a data warehouse.
And screening out a related data list of the actual defective equipment in power grid production from the acquired data. Each piece of data is identified through different equipment IDs and contains parameter information of label attributes such as defect grade, defect classification, defect characteristics, defect description, defect evaluation and the like. The defect data of the power equipment comprises a large number of special words, numbers and English words, the recording habits of field scheduling personnel and maintainers are different, and the description words of the defects or faults of the same equipment are possibly different. Synthesizing the characteristics of the defects of the power equipment, and performing data cleaning on the defect data of the power equipment: firstly, cleaning missing values, removing unnecessary contents and supplementing missing contents; secondly, format content cleaning is carried out, a null value or an invalid value needs to be replaced according to business experience, numerical value conversion is carried out on display format inconsistency such as time, date, numerical value, full half angle and the like, and data modification is carried out on the content which is inconsistent with the content of the field; and thirdly, cleaning logic errors, removing the weight and removing unreasonable values. And obtaining defect data with uniform standard after cleaning.
And performing data mining on the actual defect equipment data based on a data mining method to obtain an equipment defect index. Data mining is a complex process of extracting knowledge of mined-out unknown, valuable patterns or laws from large amounts of data. The power equipment accumulates a large amount of historical data in long-term operation and maintenance, information useful for the defect state of the equipment needs to be found from huge defect data, and the state evaluation and the defect of the decision equipment are analyzed by utilizing a data mining technology. Analyzing historical data by using a data mining technology, and mining statistical differences among different types of defects and specific conditions of the defects so as to provide differentiation of various sudden faults of equipment; the transformer oil and the temperatures of various parts are subjected to cluster analysis by using a data mining technology, so that the direct relation between the temperatures of various parts in the transformer and the faults can be obtained under the condition that the defects of the transformer are not known in advance, and a very powerful basis is provided for a decision maker to judge the faults of the transformer.
And extracting the defect index to obtain a characteristic index. Extracting characteristic indexes and carrying out multi-dimensional analysis. And extracting characteristic indexes of the defective equipment, such as equipment name, equipment category, equipment type, defect grade, defect description, starting time, ending time and the like, starting from 3 dimensions of time dimension, defect equipment dimension and defect grade dimension, and performing defect data statistical analysis. And the time dimension can be used for counting the comparison and analysis of the conditions of the defective equipment in a certain time period, and can be carried out by taking a week, a month, a quarter and a year as a period. And judging the frequent time period of the fault through trend analysis, and predicting the time of the future fault. And the defect equipment dimension comprises primary equipment in the station, secondary equipment in the station, line equipment, low-voltage equipment, production auxiliary equipment and the like. And analyzing the defective equipment compared with the example, and counting the high-frequency equipment type faults. And the defect grade dimension and the defects of the power equipment can be defined as general defects, serious defects and critical defects according to the national power grid enterprise standard and the damage degree of the defects. And monitoring the state change of the equipment, and early warning the occurrence of serious defects and critical defects.
And establishing a defective equipment label based on the characteristic index. And establishing a defective device label. The tag is typically a manually specified highly refined signature, a method of visualizing the data. And constructing a defective equipment label library according to the defective equipment feature identification and the label dimension. The tag library contains 3 content layers of tag management, tag attributes and device tags. The label management layer provides label creation, editing, examination and approval, operation, application, evaluation and the like on the basis of the label data elements; the label attribute layer organizes, stores and manages basic information of the defective equipment, equipment name, equipment type, defect grade, defect description, start time, end time and the like; and the equipment label layer organizes, stores and manages equipment defect hidden information, including defect occurrence frequency, defect state, defect early warning and the like. The defect attribute and the defect label form a complete defect equipment panoramic view, and the defects are described in an omnibearing, multilevel and three-dimensional manner.
And establishing a defective equipment label portrait model according to the defective equipment label, and combining calculation to obtain a defective label portrait of the power equipment. And formulating a label rule according to the label characteristic data item and by combining with the actual production requirement, and storing the equipment labels generated in various ways in a structured way to form an equipment label library. And automatically dividing the classified data set by adopting a classification algorithm according to preset theme classes and algorithms to finish classifier construction. In the classification process, a label portrait analysis model of the defect equipment is constructed by the steps of data processing, feature extraction, classifier construction and the like. And combining the classification result to obtain a defect label portrait of the power equipment.
In summary, the application provides a method for drawing a defect label of power equipment based on defect equipment, which takes data service as a foothold and a starting point, follows the latest standard of a national company unified data model (SG-CIM) according to equipment management service requirements and data management requirements, hierarchically and hierarchically defines the defect label of the power equipment by methods of service data induction, data mining and the like, adopts big data frontier technologies such as drawing analysis, data mining and the like, researches a multi-level, multi-angle and omnibearing panoramic image of the defect equipment to obtain defect data of the power equipment of different types, comprehensively analyzes the data, mines the inherent value of the data, predicts the future defect, and realizes the extraction, configuration and integration of the defect data and the deep mining of the defect data. The constructed model is greatly improved in classification, layering, multi-dimension and high precision, and the accuracy and comprehensiveness of image description are remarkably improved.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.
Claims (6)
1. A method for representing a defect label of a power device based on a defective device is characterized by comprising the following steps:
acquiring defect data of the electrical equipment;
screening the defect data to obtain actual defect equipment data;
based on a data mining method, performing data mining on the actual defect equipment data to obtain an equipment defect index;
extracting the defect index to obtain a characteristic index;
establishing a defective equipment label based on the characteristic index;
establishing a defective equipment label portrait model according to the defective equipment label;
and calculating to obtain the defect label portrait of the power equipment.
2. The method for representing the defect label of the power equipment based on the defective equipment as claimed in claim 1, wherein the step of screening the defect data comprises:
marking the screened data;
cleaning the marked data;
which comprises the following steps: cleaning missing values and removing meaningless information;
format content cleaning and unifying data formats;
and (4) logical error cleaning, and removing repeated and error data.
3. The method for representing the defect label of the power equipment based on the defective equipment as claimed in claim 1, wherein the step of extracting the defect index comprises:
extracting the name, category, type, defect grade, defect description, defect starting time and defect ending time index data of the defect equipment;
classifying based on the index data;
which comprises the following steps:
time classification, namely setting a time period, and analyzing and comparing defect equipment index data in the time period to obtain defect occurrence time characteristic index data;
classifying defective equipment, namely extracting equipment names, equipment categories and defect descriptions in the index data to obtain frequency data of the defective equipment, and sequencing the frequency data from high to low;
and classifying the defect grade, namely classifying the defect grade index data into general defects, serious defects and critical defects.
4. The method for representing the defect label of the power equipment based on the defective equipment as claimed in claim 1, wherein in the step of establishing the defect label of the defective equipment, the method comprises:
and establishing a defective equipment label library, and dividing the defective equipment label library into a label management layer, a label attribute layer and an equipment label layer.
5. The method for representing the defect label of the power equipment based on the defective equipment as claimed in claim 1, wherein the step of establishing the defect label representation model of the defective equipment according to the defect label comprises:
presetting a theme category;
importing the defective equipment label into the model library, and constructing a defective equipment label portrait analysis model according to a classification algorithm;
and classifying the labels of the defective equipment according to the portrait analysis model of the labels of the defective equipment.
6. The method as claimed in claim 1, wherein the defect label of the power device is acquired by ETL and data copy.
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