CN107742162B - Multidimensional feature association analysis method based on allocation monitoring information - Google Patents
Multidimensional feature association analysis method based on allocation monitoring information Download PDFInfo
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- CN107742162B CN107742162B CN201710998399.0A CN201710998399A CN107742162B CN 107742162 B CN107742162 B CN 107742162B CN 201710998399 A CN201710998399 A CN 201710998399A CN 107742162 B CN107742162 B CN 107742162B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
- G06F16/3344—Query execution using natural language analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention provides a multidimensional feature association analysis method based on allocation monitoring information, which comprises the following steps: and acquiring each piece of fault information in the allocation monitoring log, establishing a fault information analysis rule, establishing a fault association analysis model and analyzing fault risks existing in the electrical equipment by utilizing the general rule. According to the multidimensional feature association analysis method, equipment information, weather information, human factors and the like are associated with faults through analysis of fault feature points, reasons and trigger factors possibly generated by the faults are intuitively given out, a function of rapidly analyzing daily reports is realized, objectivity and efficiency of analysis are improved, a certain preventive alarm instruction can be given, and some unknown factors are prepared in advance.
Description
Technical Field
The invention relates to a power grid fault analysis method, in particular to a multidimensional feature association analysis method based on allocation monitoring information.
Background
In the current environment, a large amount of distribution and adjustment daily report information exists in the distribution network monitoring of the power grid system, and fault records in the record information are text information recorded according to habits of different personnel at the same time, so that the method is most convenient and easy to operate for construction maintenance personnel and personnel at the same time, a large amount of work flows are reduced, quick work is facilitated, but the defects are obvious, management analysis is not facilitated, useful records are difficult to analyze from the management analysis to better serve the operation of the power grid system, and therefore a method for better structurally analyzing the fault information in the daily report is required to be provided under the condition that the original recording mode is not changed.
Disclosure of Invention
The invention aims at: under the condition of not changing the original circuit system structure, a multidimensional feature association analysis method is provided, and fault information in daily reports is better structurally analyzed.
In order to achieve the above object, the present invention provides a multidimensional feature association analysis method based on allocation monitoring information, comprising the following steps:
step 1, classifying the allocation monitoring log to obtain each fault information in the allocation monitoring log;
step 2, word segmentation processing is carried out on the fault information, and a fault information analysis rule is established;
step 3, acquiring equipment model information and weather information, respectively associating the equipment model information and the weather information through a fault information analysis rule, and establishing a fault association analysis model;
and 4, obtaining a general rule of the fault event according to the fault association analysis model, and analyzing the fault risk of the electrical equipment under the current meteorological condition by utilizing the general rule.
As a further limiting scheme of the present invention, in step 1, the fault information includes a location area, an occurrence time, a fault class and a fault handling content, the fault class includes a device cause fault and a weather cause fault, and hierarchical regular carding is sequentially performed according to the order of the location area, the occurrence time, the fault class and the fault handling content.
In step 2, the ICTCLAS word segmentation system is utilized to segment the fault information after the hierarchical regular carding, so that the keywords of each fault category are obtained from the fault processing content.
In step 2, the specific steps of establishing the fault information analysis rule are that the fault categories are corresponding to the keywords, the word frequencies of the keywords are counted, the ranking threshold value of the keywords is set, the first five digits exceeding the ranking threshold value are marked as the feature words of each fault category, the equipment cause fault category is corresponding to the equipment fault feature words, and the weather cause fault category is corresponding to the weather fault feature words.
In step 3, equipment model information is obtained from the power grid D5000 database, weather information is obtained from the local weather base information, equipment fault feature words are associated with the equipment model information in the power grid D5000 database by using a fault information analysis rule, and weather fault feature words are associated with the weather information in the local weather base information, so that a complete fault association analysis model is formed.
As a further limiting scheme of the present invention, in step 4, the general rule of the fault event is the probability that each station device in the location area to be analyzed will have each type of fault in a specified period of time.
The invention has the beneficial effects that: the method for analyzing the daily report by the fault information in a layering manner divides the information in the daily report, correlates the equipment information and the weather information by analyzing the fault characteristic points, intuitively gives out the possible reasons and the possible initiating factors of the fault, realizes a function of rapidly analyzing the daily report, improves the objectivity and the efficiency of analysis, can give out a certain preventive alarm instruction, and prepares some unknown factors in advance.
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FIG. 1 is a schematic flow chart of the method of the invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
As shown in fig. 1, the multidimensional feature association analysis method based on the dispatching monitoring information disclosed by the invention comprises the following steps:
step 1, classifying the allocation monitoring log to obtain each fault information in the allocation monitoring log;
step 2, word segmentation processing is carried out on the fault information, and a fault information analysis rule is established;
step 3, acquiring equipment model information and weather information, respectively associating the equipment model information and the weather information through a fault information analysis rule, and establishing a fault association analysis model;
and 4, obtaining a general rule of the fault event according to the fault association analysis model, and analyzing the fault risk of the electrical equipment under the current meteorological condition by utilizing the general rule.
In step 1, the fault information includes a location area, an occurrence time, a fault class and a fault processing content, the fault class includes a device cause fault and a weather cause fault, and hierarchical regular carding is sequentially performed according to the sequence of the location area, the occurrence time, the fault class and the fault processing content.
In the step 2, the ICTCLAS word segmentation system is utilized to segment the fault information after the hierarchical regular carding, so that the keywords of each fault category are obtained from the fault processing content.
In the step 2, the specific steps of establishing the fault information analysis rule are that each fault category corresponds to a keyword, word frequencies of the keywords are counted, a ranking threshold value of the keywords is set, the first five digits exceeding the ranking threshold value are marked as feature words of each fault category, the equipment cause fault category corresponds to the equipment fault feature words, and the weather cause fault category corresponds to the weather fault feature words.
In step 3, equipment model information is obtained from the power grid D5000 database, meteorological information is obtained from the local area meteorological library information, equipment fault feature words are associated with the equipment model information in the power grid D5000 database by using a fault information analysis rule, and weather fault feature words are associated with the meteorological information in the local area meteorological library information, so that a complete fault association analysis model is formed.
In step 4, the general rule of the fault event is the probability of occurrence of faults of each type in each station device in the position area to be analyzed within a specified time period.
The invention divides the information in the daily report by a method of fault information layering analysis, correlates equipment information, weather information, human factors and the like with faults by analyzing fault characteristic points, intuitively gives out possible reasons and factors for causing the faults, realizes a function of rapidly analyzing the daily report, improves the objectivity and efficiency of analysis, can give out certain preventive alarm guidance, and prepares some unknown factors in advance.
Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention. The foregoing description is only of the preferred embodiments of the invention, and all changes and modifications that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (2)
1. The multidimensional feature association analysis method based on the allocation monitoring information is characterized by comprising the following steps of:
step 1, classifying the allocation monitoring log to obtain each fault information in the allocation monitoring log;
step 2, word segmentation processing is carried out on the fault information, and a fault information analysis rule is established;
step 3, acquiring equipment model information and weather information, respectively associating the equipment model information and the weather information through a fault information analysis rule, and establishing a fault association analysis model;
step 4, obtaining a general rule of a fault event according to a fault association analysis model, and analyzing fault risks of the electrical equipment under the current meteorological conditions by utilizing the general rule;
in the step 1, fault information comprises a location area, occurrence time, fault types and fault processing contents, wherein the fault types comprise equipment cause faults and weather cause faults, and hierarchical regular carding is sequentially carried out according to the sequence of the location area, the occurrence time, the fault types and the fault processing contents;
in the step 2, the ICTCLAS word segmentation system is utilized to carry out word segmentation processing on the fault information after the hierarchical regular carding, so that keywords of each fault category are obtained from the fault processing content;
in the step 2, the specific steps of establishing a fault information analysis rule are that each fault category corresponds to a keyword, word frequencies of the keywords are counted, a ranking threshold value of the keywords is set, the first five digits exceeding the ranking threshold value are marked as feature words of each fault category, equipment cause fault categories correspond to equipment fault feature words, and weather cause fault categories correspond to weather fault feature words;
in the step 3, equipment model information is obtained from a power grid D5000 database, meteorological information is obtained from local area meteorological library information, equipment fault feature words are associated with the equipment model information in the power grid D5000 database by utilizing a fault information analysis rule, and weather fault feature words are associated with the meteorological information in the local area meteorological library information, so that a complete fault association analysis model is formed;
the method for analyzing the fault information in a layering manner divides the information in the daily report, and associates equipment information, weather information, human factors and faults by analyzing fault feature points, so that possible causes and factors for the faults are intuitively given out.
2. The method for multidimensional feature association analysis based on allocation monitoring information according to claim 1, wherein in step 4, the general rule of the fault event is the probability of each type of fault occurring in each station device in the location area to be analyzed within a specified period of time.
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| CN108647791B (en) * | 2018-03-30 | 2020-12-29 | 中国标准化研究院 | Multi-source automobile safety information processing method, device and system |
| CN110428060A (en) * | 2019-06-12 | 2019-11-08 | 南京博泰测控技术有限公司 | A kind of fault information managing method, device and system |
| CN111090973A (en) * | 2019-11-26 | 2020-05-01 | 北京明略软件系统有限公司 | Report generation method and device and electronic equipment |
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| CN103489138A (en) * | 2013-10-16 | 2014-01-01 | 国家电网公司 | Method for analyzing relevancy between power transmission network fault information and line out-of-limit information |
| CN104361500A (en) * | 2014-11-25 | 2015-02-18 | 珠海格力电器股份有限公司 | Air conditioner after-sale failure data processing method and system |
| CN107124291A (en) * | 2017-03-06 | 2017-09-01 | 国网上海市电力公司 | A kind of adjusting device monitoring analysis system and method based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103489138A (en) * | 2013-10-16 | 2014-01-01 | 国家电网公司 | Method for analyzing relevancy between power transmission network fault information and line out-of-limit information |
| CN104361500A (en) * | 2014-11-25 | 2015-02-18 | 珠海格力电器股份有限公司 | Air conditioner after-sale failure data processing method and system |
| CN107124291A (en) * | 2017-03-06 | 2017-09-01 | 国网上海市电力公司 | A kind of adjusting device monitoring analysis system and method based on big data |
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