CN113190537A - Data characterization method for emergency repair site in monitoring area - Google Patents
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Abstract
The invention belongs to the field of power grid field monitoring, and particularly relates to a data characterization method for an emergency repair field in a monitoring area. The method comprises the following steps: collecting monitoring data; dividing data into two types, namely structured data represented by attribute data and unstructured data represented by image data; for two types of different monitoring data, respectively adopting different data representation strategies to represent the data, standardizing the represented coarse data, and then utilizing an object-oriented meta-model to process, model and store the data again; the characterization method effectively avoids data distortion, shortens the time consumption of data processing, and facilitates different data analysis of the stored data by using a big data analysis model based on cloud-edge cooperation to obtain early warning results, emergency repair decisions and other conclusions.
Description
Technical Field
The invention belongs to the field of power grid field monitoring, and particularly discloses a data characterization method for an emergency repair field in a monitoring area.
Background
The disaster site monitoring data based on mobile interconnection comprises power grid and equipment operation data acquired by technical means such as fixed-point monitoring, individual operation tools, intelligent positioning and the like, and is one of important data sources for developing disaster early warning and improving emergency repair efficiency as an important disaster prediction parameter. The data are subjected to correlation analysis and deep excavation, the real-time monitoring capability of the data on the power grid is improved, the method is a key supporting technology for guaranteeing the safe and stable operation of the power grid, and the method has important significance for improving the overall disaster processing efficiency and the management and control capability of the power grid.
At present, in a power grid monitoring system, there are many monitoring devices distributed in different locations, systems and applications, and monitoring data of the monitoring devices have different sources, diversified formats, non-uniform storage mechanisms, ambiguous association modes and lack of effective sharing mechanisms, so that the data utilization rate is low, and an information isolated island is formed. The traditional field monitoring data collection technology needs a large amount of manual work to carry out data sorting, manually extracts effective data, integrates and gathers together, can carry out further utilization and mining analysis, even if the intelligent technology also has the problems of low data characterization efficiency, inaccurate feature extraction, large data redundancy degree and the like, and does not really realize the unified characterization of multi-source data.
Disclosure of Invention
In order to overcome the defects of the prior art in the aspect of monitoring data characterization, the invention provides a data characterization method for an emergency repair site in a monitoring area, which comprises the following steps:
s1, monitoring data acquisition;
s2, dividing the data into two types of structured data represented by attribute data and unstructured data represented by image data;
s3, standardizing the data;
s4, performing dimensionality reduction processing on the structured data by adopting a principal component analysis model to finish the characterization of the structured data;
s5, carrying out artificial intelligence learning on the unstructured data by using an independent component analysis model to obtain a training basis function;
s6, obtaining the characteristic coefficient of the unstructured data in a basis function response mode;
the method carries out modeling processing on the structured data and the unstructured data in a classified mode in a mode matched with the data types, reduces data redundancy, and enables the data to reflect information characteristics of a monitoring site better.
Preferably, in the step S3, for the continuous attribute data in the structured data, mapping to the corresponding value in the interval [0,1] by using a min-max normalization method, and retaining the original state of the discrete attribute data;
further, in step S4, performing dimension reduction on the structured data by using a principal component analysis model, including the following steps:
forming m rows and n columns matrix X by original sample data according to rows and columns0Wherein m represents a record line number, and n represents a column number corresponding to the attribute feature;
computing a transposed matrix X of the matrix X0n*mCarrying out standardization treatment;
calculating covariance matrixes R of the two groups of sample data, and solving characteristic values and corresponding characteristic vectors of the two groups of sample data;
arranging the eigenvectors into a matrix from top to bottom according to the sizes of the corresponding eigenvalues, and taking the first k rows to form an eigenvector matrix Pk*n；
Calculating a data matrix Y reduced to k dimensionk*m＝Pk*nXn*mCalculating the corresponding transpose matrix Y0Obtaining a dimension reduction processing result of the data;
preferably, in step S3, the unstructured data is whitened and centered to obtain sample set data with a uniform format.
Further, step S6 includes the following steps:
and calculating the characteristic response coefficient of the basis function to the input sample X by adopting an independent component analysis model.
And screening the characteristic response coefficients by setting a threshold value to obtain characteristic coefficients.
Compared with the prior art, the invention has the following beneficial effects: the data characterization method provided by the invention realizes centralized processing, effective association and mining analysis of multi-source composite data, improves the utilization rate of data of the emergency repair site, ensures that the corrected data can accurately characterize the state of the monitoring site, is beneficial to unified association and analysis of large data of the disaster site, and improves the emergency repair efficiency of the disaster site.
Drawings
Fig. 1 is a flowchart of a method for characterizing emergency repair site monitoring data according to an embodiment of the present invention.
FIG. 2 is a flow chart of data characterization of structured data in an embodiment of the present invention.
FIG. 3 is a flow chart of dimension reduction of structured data according to an embodiment of the present invention.
FIG. 4 is a flow chart of data characterization of unstructured data in an embodiment of the invention.
FIG. 5 is a diagram of an object-oriented data association analysis model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any new work, are within the scope of the present invention.
Referring to the accompanying fig. 1-5 of the specification, this embodiment is accomplished by the following steps:
the general technical process of the invention is shown in fig. 1, namely: through the monitoring data collection corresponding to various information such as emergency repair site disasters, people, objects, equipment and the like, the unified modeling storage analysis framework of the emergency repair site data is utilized to carry out unified storage on the data, the information transmission efficiency is improved, the data monitoring quality is improved, the emergency site command is further effectively guided, and the disaster repair efficiency is improved. The technical key points are mainly the key technologies of performing unified representation and normalized storage of data. The method is realized by the following steps:
collecting the monitoring data set of the emergency repair site comprises collecting various data such as related meteorological data, disaster data, typical disaster cases, equipment recording data, fault monitoring data, on-line monitoring of a power transmission line and the like of a power grid company in recent years, and forming a monitoring data set taking disasters, people, power equipment, vehicles, materials and the like as the center. Meanwhile, the original storage mode and the expression mode of the data are collected so as to facilitate the understanding of the data type, the original number and the basic characteristics of the emergency disaster site data, and the data are used as the monitoring data support of the unified modeling framework.
The construction of the unified representation of the emergency repair field data comprises the following steps:
the method comprises the steps of firstly classifying and sorting collected data, distinguishing two categories of structured attribute data taking discrete data and continuous data as characteristics and unstructured data such as maps, pictures and videos, and further storing and analyzing the data by adopting different informatization processing modes according to data categories, so that the data have sharing property and popularization property.
And performing data characterization by using a data characterization method based on Principal Component Analysis (PCA) aiming at attribute data represented by weather, temperature, humidity, equipment state (normal/abnormal), fault recording times, disaster frequency, disaster types, monitoring capability, emergency handling capability and the like. The method specifically comprises the following steps:
cleaning the collected data to remove repeated data or repeated attribute characteristics;
completing the missing value by adopting a homogeneous mean interpolation method to obtain a data matrix X of m rows and n columns of the attribute sample data set0＝{xij}(i∈[1,m],j∈[1,n]) Wherein row m represents a record and column n represents an attribute feature;
attribute data can be divided into continuous data and discrete data according to data types. Aiming at continuous data such as air humidity, average wind speed, rainfall and the like, the monitoring numerical value is the coded characteristic value; for discrete attribute data, such as landform, whether a line is reinforced or not, voltage level and the like, unique hot coding is adopted for feature coding, if the line is reinforced or not, a 0-1 coding mode can be adopted for feature coding, 0 represents that the line is not reinforced, and 1 represents that the line is reinforced;
and (6) standardizing data. Obtaining attribute data sample matrix X after feature coding0＝{xij}(i∈[1,m],j∈[1,n]) Mapping each sample data xij to an interval [0,1] by a min-max standardization method]To obtain the mapping value.
And (5) reducing the dimension of the data. Adopting Principal Component Analysis (PCA) to collect data sample X of each monitored object0Performing characteristic dimension reduction to obtain a dimension-reduced data matrix Y0。
And (3) aiming at image data represented by infrared cameras, monitoring cameras and the like, performing image data representation by adopting a data representation method based on an Independent Component Analysis (ICA) method. The method specifically comprises the following steps:
and constructing an image library by using the acquired data.
Directly sampling the image data, preprocessing the video data to obtain image data, and finally forming an image data sample set;
whitening and centralizing the image sample to obtain sample set data with uniform format, and using the sample set data as input data X of a subsequent model;
an objective function is selected. According to ICA analysis model definition, input data X is AS, wherein A is an image feature vector and is a reversible mixing matrix, S is an image independent hidden random variable, and a random variable S is obtained through calculation, wherein W is A-1, namely an objective function estimated by an ICA model;
the non-gaussian nature of the variable S is judged. The non-gaussian property of the random variable S is judged by adopting the negative entropy of the random variable S. In the information theory, entropy is used for describing the information degree contained in the random variable, if randomness is stronger, the entropy is larger, and the entropy value of a Gaussian variable is the largest in the variables with the same variance, so that the non-Gaussian of the random variable can be measured by adopting negative entropy;
and if the variable S is the strongest in non-Gaussian property, the corresponding basis function is the optimal basis function, otherwise, the conversion matrix A is corrected, and the non-Gaussian property judgment is repeatedly carried out until the optimal basis function is obtained and output.
A basis function response is calculated. Calculating a response coefficient S of the basis function to the input sample X according to the formula S ═ WX;
and screening the characteristic response coefficient S. Selecting a proper threshold value to screen the characteristic response coefficient, setting the coefficient smaller than the threshold value as 0, and obtaining a new characteristic response coefficient S' as a main characteristic of the image data;
the image is characterized. And obtaining an estimated value X ' of the image data by using the new characteristic response coefficient S ' and the formula X AS W-1S, and using the estimated value X ' to characterize the image to obtain a characterized image data set.
And modeling and storing the monitoring data by adopting an object-oriented data association analysis model to realize organization and management of the monitoring data. Management and connection among different dimensions are researched by taking the ID of the monitoring object as a center, and the association relation between the monitoring data and the object is strengthened on the basis to store the data, as shown in figure 5, so that the monitoring data of the emergency repair site is identified and stored.
And carrying out unified characterization on the data, storing the data, and carrying out next analysis processing on the monitoring data by using a big data analysis model based on edge cloud cooperation to obtain an early warning result and guide field decision.
In conclusion, a unified characterization modeling method suitable for emergency repair field data in a monitoring area is constructed, so that the field monitoring data can be stored and analyzed and processed in a unified manner, the intelligent decision efficiency is improved, and the emergency repair field command strategy generation is assisted in real time, so that the standardization and the intellectualization of disaster emergency repair are realized.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A data characterization method for an emergency repair site in a monitoring area is characterized by comprising the following steps:
s1, monitoring data acquisition;
s2, dividing the data into two types of structured data represented by attribute data and unstructured data represented by image data;
s3, standardizing the data;
s4, performing dimensionality reduction processing on the structured data by adopting a principal component analysis model to finish the characterization of the structured data;
s5, carrying out artificial intelligence learning on the unstructured data by using an independent component analysis model to obtain a training basis function;
s6, obtaining the characteristic coefficient of the unstructured data in a basis function response mode;
and S7, substituting the characteristic coefficients into the training basis functions to calculate an estimated value for representing the unstructured data.
2. The data characterization method for emergency repair sites in the monitored area according to claim 1, wherein in step S3, the structured data with continuous attribute data is mapped to the corresponding value in the interval [0,1] by using a min-max standardization method, and the structured data with discrete attribute data remains in the original state.
3. The method of claim 1, wherein the step S3 whitening and centralizing the unstructured data to obtain sample set data with uniform format.
4. The data characterization method for the emergency repair site in the monitored area according to claim 2, wherein the step S4 of performing dimension reduction processing on the structured data by using a principal component analysis model includes the following steps:
forming m rows and n columns matrix X by original sample data according to rows and columns0Wherein m represents a record line number, and n represents a column number corresponding to the attribute feature;
computing a transposed matrix X of the matrix X0n*mCarrying out standardization treatment;
calculating covariance matrixes R of the two groups of sample data, and solving characteristic values and corresponding characteristic vectors of the two groups of sample data;
arranging the eigenvectors into a matrix from top to bottom according to the sizes of the corresponding eigenvalues, and taking the first k rows to form an eigenvector matrix Pk*n；
Calculating a data matrix Y reduced to k dimensionk*m＝Pk*nXn*mCalculating the corresponding transpose matrix Y0And obtaining the dimension reduction processing result of the data.
5. A data characterisation method for emergency repair sites within a monitored area according to claim 3, wherein said step S6 comprises the following steps:
calculating a characteristic response coefficient of the basis function to the input sample X by adopting an independent component analysis model;
and screening the characteristic response coefficients by setting a threshold value to obtain characteristic coefficients.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113965504A (en) * | 2021-10-25 | 2022-01-21 | 国网福建省电力有限公司检修分公司 | Safety reinforcement acceptance method and system for network equipment of transformer substation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20070096409A (en) * | 2006-03-24 | 2007-10-02 | 주식회사 네이블텍 | A disaster prevention system and method |
| CN107272655A (en) * | 2017-07-21 | 2017-10-20 | 江南大学 | Batch process fault monitoring method based on multistage ICA SVDD |
| CN111639817A (en) * | 2020-06-05 | 2020-09-08 | 山东大学 | Emergency material demand prediction method and system for power grid meteorological disasters |
| CN112507035A (en) * | 2020-11-25 | 2021-03-16 | 国网电力科学研究院武汉南瑞有限责任公司 | Power transmission line multi-source heterogeneous data unified standardized processing system and method |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20070096409A (en) * | 2006-03-24 | 2007-10-02 | 주식회사 네이블텍 | A disaster prevention system and method |
| CN107272655A (en) * | 2017-07-21 | 2017-10-20 | 江南大学 | Batch process fault monitoring method based on multistage ICA SVDD |
| CN111639817A (en) * | 2020-06-05 | 2020-09-08 | 山东大学 | Emergency material demand prediction method and system for power grid meteorological disasters |
| CN112507035A (en) * | 2020-11-25 | 2021-03-16 | 国网电力科学研究院武汉南瑞有限责任公司 | Power transmission line multi-source heterogeneous data unified standardized processing system and method |
Non-Patent Citations (1)
| Title |
|---|
| 吕瑞明: "变电站智能视频监控系统研究", 《中国硕士学位论文全文数据库 信息科技辑》, no. 1, pages 12 - 20 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113965504A (en) * | 2021-10-25 | 2022-01-21 | 国网福建省电力有限公司检修分公司 | Safety reinforcement acceptance method and system for network equipment of transformer substation |
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