CN117312911A - Intelligent processing method for massive power operation and maintenance data - Google Patents
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Abstract
The invention discloses an intelligent processing method for mass power operation and maintenance data, which comprises the following steps: step one: the operation and maintenance data management system is unified; step two: processing the operation and maintenance data; step three: carrying out operation and dimension feature tag processing; step four: processing operation and maintenance data based on fuzzy association; compared with the prior art, the invention has the advantages that: the invention can classify real-time and non-real-time data, can realize the preprocessing of operation and maintenance data under different standards, and can deeply mine the operation and maintenance data through fuzzy association rules to find the association rule among the data, thereby judging the operation state of the system.
Description
Technical Field
The invention relates to the technical field of power operation and maintenance, in particular to an intelligent processing method for mass power operation and maintenance data.
Background
The traditional mode of processing the huge amount of operation and data cannot meet the requirements and development requirements of the current power system, an intelligent processing system for the huge amount of operation and data based on fuzzy association is researched, and the application of a data tag system in a data center is combined, so that the data intercommunication and interconnection among different systems are realized through analysis of the characteristic values of the data.
The form of the power operation and maintenance data, the volume of the data is changed continuously along with the expansion of the power grid scale, and the processing mode of the massive power operation and maintenance data is an important ring in the operation and maintenance work of the power system. As the number of in-station devices increases, the amount of data in the power operation becomes huge and complex, the load capacity of the existing data processing method is limited, and the load capacity gradually exceeds the upper limit of the capacity of the data processing system, thereby affecting the processing speed.
Therefore, a set of intelligent processing system for massive operation and maintenance data based on fuzzy association processing is researched by combining a data tagging system, real-time and non-real-time data can be classified, the pretreatment of operation and maintenance data under different standards can be realized, the operation and maintenance data is deeply mined through fuzzy association rules, and the association rules among the data are found, so that the operation state of the system is judged.
Disclosure of Invention
The invention aims to overcome the technical defects, and provides an intelligent processing method for massive power operation and maintenance data, which can be used for classifying real-time and non-real-time data, preprocessing operation and maintenance data under different standards, deep mining the operation and maintenance data through fuzzy association rules, finding the association rules among the data, and judging the operation state of a system.
In order to solve the problems, the technical scheme of the invention is an intelligent processing method for mass power operation data: the method comprises the following steps:
step one: the operation and maintenance data management system is unified;
step two: processing the operation and maintenance data;
step three: carrying out operation and dimension feature tag processing;
step four: and processing the operation and data based on fuzzy association.
Furthermore, the operation and maintenance data management system unification standard comprises operation and maintenance data preprocessing, operation and maintenance data unification standard and operation and maintenance data management, wherein the operation and maintenance data management comprises data standard management and data quality management, the data standard management is determined by a data information model and a data standard document, the data quality management is determined by a data quality management check rule and an index model, a central node data area is transmitted to a large data resource area through ETL, and then is transmitted to an application system through data service, and data management is performed through management metadata, technical metadata and business metadata.
Further, the operation and maintenance data preprocessing comprises confirming a data range, counting data dimension and analyzing data relation, and the operation and maintenance data unification standard comprises standard diversity, standard expandability and operation and maintenance data management.
Further, the ETL is transmitted to a target database through a business system database and text files by extracting, converting and loading temporary data.
Further, the operation and data processing technology comprises data acquisition, data integration, data storage and data service.
Further, the operation and maintenance data feature tag processing includes a basic tag and a deep tag, the equipment is clustered according to operation data of the equipment and basic information thereof in a period of time, the equipment is well divided through clustering, characteristics and association of each cluster type are deeply researched, in the basic tag, influence degrees of each feature quantity under different defects are different, and the multidimensional feature quantity is integrated in a weighted mode, so that a prediction tag of a fault defect is obtained, and the generation steps are as follows: the method comprises the steps of inputting an operation state data label, vector conversion generation data set, characteristic parameter discretization, apriori algorithm, fault type weight calculation, fault probability calculation, outputting fault type and occurrence probability thereof, and generating a fault prediction label.
Further, the fuzzy association system is composed of fuzzy association rules and fuzzy reasoning algorithms, the Apriori algorithm is adopted to mine the fuzzy association rules, the Apriori algorithm adopts a recursion method to find frequent item sets, the support degree and the confidence degree of each frequent item set are calculated, a trapezoidal function is selected as a membership function to measure the tag attribute, and the functions are as follows:
wherein: a (x) is a membership function value of the data tag attribute "normal"; x is the value of the numeric class data; a, a 1 90% of the normal threshold; a, a 2 110% of the normal threshold, the membership function with data tag attribute "abnormal" is
B (x) =1-a (x), where: b (x) is a membership function value for the data tag attribute "anomaly".
Compared with the prior art, the invention has the advantages that:
1. the invention takes a data tag system and data association as centers, provides a set of intelligent processing system capable of realizing data association analysis, realizes efficient storage and utilization of power data, and invokes staff of each business department to coordinate work according to basic information of operation and maintenance data and operation data and state data of the system so as to complete interconnection and intercommunication of the multi-source power operation and maintenance big data tag system.
2. According to the invention, through the deep application framework of the built big data tag system, the operation and maintenance data is placed between the big data center and the regulation platform after being subjected to data preprocessing, so that the data processing capability is enhanced, the data processing efficiency is improved, the connection between the service and the data architecture is enhanced, and the application of the data preprocessing technology of the operation and maintenance data tag system in the aspect of electric power operation and maintenance is further studied in depth.
Drawings
FIG. 1 is a diagram of a data management model of the intelligent processing method for mass power operation data.
Fig. 2 is an ETL architecture diagram of the intelligent processing method of the mass power operation data of the present invention.
FIG. 3 is a basic tag data classification diagram of the intelligent processing method of the mass power operation data.
FIG. 4 is a flow chart of fault prediction label generation for the intelligent processing method of the mass power operation data.
Fig. 5 is a block diagram of a fuzzy inference system of the intelligent processing method of the mass power operation data of the invention.
Detailed Description
In order to make the contents of the present invention more clearly understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in fig. 1-5, the intelligent processing method for the mass power operation data comprises the following steps:
step one: the operation and maintenance data management system is unified;
step two: processing the operation and maintenance data;
step three: carrying out operation and dimension feature tag processing;
step four: and processing the operation and data based on fuzzy association.
1. Operation and maintenance data management system unification
1. Operation and maintenance data preprocessing
(1) Validating a data range
Currently, the information operation profession has excavated a large amount of operation and maintenance data through different ways, and before data management, business is firstly divided, and the data management range is determined. For example, system data management, equipment data management and customer service data management are further subdivided into monitoring index data management, standing account data management, personnel information data management and the like.
(2) Statistical data dimension
In sorting the data dimensions, the data may be partitioned from a plurality of different dimensions. According to the purpose of the data, the data can be divided into attribute description, activity records and information statistics; the data may be classified into user data, system data, device data, and the like according to the object of the data description.
(3) Analyzing data relationships
The data elements are applied to various business scenes, and the business scenes have association relations. Therefore, in a normal case, there is always a certain association relationship between data.
2. Operation and maintenance data unification standard
By analyzing the actual business, the method discovers that the quality and the compliance of the data are required to be managed by making a perfect standard system, so that the potential value of the data can be better exerted. The standard system covers the full life cycle of data and is defined in standardization in aspects of data acquisition, storage, delivery, management, application and the like.
(1) Diversity of standards
And researching and formulating data standard standardization definition conforming to actual business scene. Due to the diversity of the service scenario itself, the corresponding standards are also more abundant. With the increase of service types and the continuous expansion of the original service range, the existing standard system is required to be continuously enriched, and more service data are defined.
(2) Standard extensibility
With the continuous development of business, corresponding data standards will also change, and thus, revisions, extensions or revocation of outdated standards are required. In this process, attention should be paid to the influence of the variation of the standard system on the data management process, and rules for controlling the standard version should be formulated in the early stage of management, including standard data definition, standard calculation dimension, standard statistical caliber, etc. When the standard is changed, a strict auditing process is executed according to the data cleaning rule brought along with the standard, so that the stability of the upstream and downstream data interfaces is ensured.
3. Fortune dimension management
When managing the operation and maintenance data, a definite data management department or responsible post should be set, and the problem of data quality appears clearly to people. When the data center makes a data plan, the data contradiction should be avoided, and the unified standard of the data is determined. When the quality problem occurs to the data, the processing optimization is immediately carried out, so that the quality problem closed loop of the operation and maintenance data is realized. The management center establishes an effective data management and control mechanism, performs quality inspection on historical data stored in the system, and confirms the data quality in time when new operation and maintenance data appear in the system.
2. Fortune dimension processing
The method comprises 4 steps of data acquisition, data integration, data storage, data service and the like.
1. And (3) data acquisition: the distributed data acquisition mode has the capability of extracting interfaces for different types of data sources.
2. Data integration: the real-time streaming data integration technology is utilized to realize real-time processing of mass streaming data, including loading, checking, cleaning, decomposing, completing, structuring, merging, quoting, abnormal marking and the like, real-time processing and real-time output of the data, and real-time application support is provided for online service.
3. And (3) data storage: and the management and monitoring of the monitoring data transmission assembly are carried out, and various monitoring data are required to be sent to a monitoring task for processing and warehousing through the unified transmission assembly. And the configuration management, the online capacity expansion and the transmission state monitoring of the data transmission assembly are realized.
4. Data service: the method for constructing the universal data service component abstracts data in various data sources into a service model for intelligent operation and maintenance analysis and data visualization scenes. The method has the advantages of cross-data source association query, automatic generation of query sentences and flexible management capability on the data model.
3. Method for processing operation and maintenance data characteristic labels
1. Basic label
In the service processing of the power grid, different electrical equipment have different ID codes, the coding modes are different, so that the interconnection and intercommunication of information data between systems cannot be realized, and part of data still need to be matched manually, so that the primary task of the power operation and maintenance data after pretreatment is to carry out intelligent matching through data characteristics, and then the matched result is transmitted to the system.
2. Depth label
Clustering the equipment according to the operation data and the basic information of the equipment in a period of time, well dividing the equipment by clustering, and deeply researching the characteristics and the association of each clustering type. In the basic label, the influence degree of each characteristic quantity under different defects is different, and the multidimensional characteristic quantity is integrated in a weighting mode, so that a predictive label of the fault defect is obtained, and the generation step is shown in fig. 4.
4. Fuzzy correlated operation and data processing
The fuzzy association system consists of fuzzy association rules and a fuzzy inference algorithm, the performance of the fuzzy inference algorithm is not greatly influenced on the overall performance of the system, the performance of the fuzzy inference algorithm mainly depends on the quality of the association rules, the Apriori algorithm is adopted for mining the fuzzy association rules, the Apriori algorithm adopts a recursion method for finding frequent item sets, the support degree and the confidence degree of each frequent item set are calculated, and the applied fuzzy association system is shown in figure 5.
A trapezoidal function is selected as a membership function to measure the tag attribute, the function is as follows:
wherein: a (x) is a membership function value of the data tag attribute "normal"; x is the value of the numeric class data; a_1 is 90% of the normal threshold; a_2 is 110% of the normal threshold, and the membership function of the data tag attribute "abnormal" is B (x) =1-a (x), where: b (x) is a membership function value for the data tag attribute "anomaly".
Aiming at massive large data of power grid operation and maintenance, a set of intelligent processing system capable of realizing data association analysis is provided by taking a data tag system and data association as centers, so that efficient storage and utilization of power data are realized. And according to the basic information of the operation and maintenance data, the operation data and the state data of the system are combined, and the staff of each business department are called to coordinate to finish the interconnection of the multi-source power operation and maintenance big data label system.
Through the deep application framework of the built big data tag system, operation and maintenance data are placed between the big data center and the regulation and control platform after being subjected to data preprocessing, so that the data processing capacity is enhanced, the data processing efficiency is improved, the connection between the service and the data architecture is enhanced, and the application of the data preprocessing technology of the operation and maintenance data tag system in the aspect of electric power operation and maintenance is further studied in depth.
The invention and its embodiments have been described above without limitation. In summary, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical solution should not be creatively devised without departing from the gist of the present invention.
Claims (7)
1. The intelligent processing method for the mass power operation and maintenance data is characterized by comprising the following steps of: the method comprises the following steps:
step one: the operation and maintenance data management system is unified;
step two: processing the operation and maintenance data;
step three: carrying out operation and dimension feature tag processing;
step four: and processing the operation and data based on fuzzy association.
2. The intelligent processing method for mass power operation data according to claim 1, wherein: the operation and maintenance data management system unification standard comprises operation and maintenance data preprocessing, operation and maintenance data unification standard and operation and maintenance data management, wherein the operation and maintenance data management comprises data standard management and data quality management, the data standard management is determined by a data information model and a data standard document, the data quality management is determined by a data quality management check rule and an index model, a central node data area is transmitted to a large data resource place through ETL, and then is transmitted to an application system through a data service, and the data management is performed through management metadata, technical metadata and business metadata.
3. The intelligent processing method for mass power operation data according to claim 2, wherein: the operation and maintenance data preprocessing comprises the steps of confirming a data range, counting data dimension and analyzing data relation, and the operation and maintenance data unification standard comprises standard diversity, standard expandability and operation and maintenance data management.
4. The intelligent processing method for mass power operation data according to claim 2, wherein: the ETL is used for extracting, converting and loading temporary data and transmitting the temporary data to a target database through a service system database and a text file.
5. The intelligent processing method for mass power operation data according to claim 1, wherein: the operation and maintenance data processing technology comprises data acquisition, data integration, data storage and data service.
6. The intelligent processing method for mass power operation data according to claim 1, wherein: the operation and maintenance data feature tag processing comprises a basic tag and a deep tag, the equipment is clustered according to operation data and basic information of the equipment in a period of time, the equipment is well divided through clustering, characteristics and association of each clustering type are deeply researched, in the basic tag, influence degrees of feature quantities under different defects are different, and the multi-dimensional feature quantities are integrated in a weighted mode, so that a prediction tag of a fault defect is obtained, and the generation steps are as follows: the method comprises the steps of inputting an operation state data label, vector conversion generation data set, characteristic parameter discretization, apriori algorithm, fault type weight calculation, fault probability calculation, outputting fault type and occurrence probability thereof, and generating a fault prediction label.
7. The intelligent processing method for mass power operation data according to claim 1, wherein: the fuzzy association system consists of fuzzy association rules and fuzzy reasoning algorithms, the fuzzy association rules are mined by adopting an Apriori algorithm, frequent item sets are found by adopting a recursion method, the support degree and the confidence degree of each frequent item set are calculated, and a trapezoidal function is selected as a membership function to measure the attribute of the tag, wherein the functions are as follows:
wherein: a (x) is a membership function value of the data tag attribute "normal"; x is the value of the numeric class data; a, a 1 90% of the normal threshold; a, a 2 For 110% of the normal threshold, the membership function for data tag attribute "abnormal" is B (x) =1-a (x), where: b (x) is a membership function value for the data tag attribute "anomaly".
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