CN113992823A - Intelligent primary and secondary equipment fault diagnosis method based on multiple information sources - Google Patents
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Abstract
The invention discloses a primary and secondary equipment fault intelligent diagnosis method based on multiple information sources, which solves the defects of the prior art and comprises the following steps: step 1, generating a rule base according to data of the existing power grid during fault; step 2, the power equipment inspection robot shoots a failed primary and secondary equipment, acquires image information of the primary and secondary equipment, acquires position information of the primary and secondary equipment, and transmits the image information and the position information to a server; step 3, the server receives the image information and the position information, then determines the equipment information of the primary and secondary equipment according to the image information, and carries out matching through the equipment information, the position information and the rule base to identify the fault of the primary and secondary equipment and sense the state of the fault; and 4, the power equipment inspection robot acquires the fault identification, state sensing and positioning information of the primary and secondary equipment from the server and informs the inspection personnel of the fault identification, state sensing and positioning information of the primary and secondary equipment.
Description
Technical Field
The invention relates to the technical field of power equipment fault detection, in particular to a primary and secondary equipment fault intelligent diagnosis method based on multiple information sources.
Background
At present, from the perspective of power grid operation risks, the operation of a power system is difficult to avoid and is challenged by extreme natural conditions and damaged by external environments, various social high-risk events and hidden dangers of emergencies still exist, and power grid accidents also bring serious influence on social and economic operation. Along with the implementation of large operation, the load of Zhejiang is rapidly increased, the scale of a power grid is continuously enlarged, the operation amount of equipment is increased day by day, and the operation amount of total-province dispatching reaches millions of steps every year.
When facing a large amount of protection actions and other related warning information, a controller is difficult to make correct fault judgment quickly, so that the controller is required to have a fault quick auxiliary intelligent positioning function and help the controller to position fault equipment quickly and accurately.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a primary and secondary equipment fault intelligent diagnosis method based on multiple information sources.
The purpose of the invention is realized by the following technical scheme:
a primary and secondary equipment fault intelligent diagnosis method based on multiple information sources comprises the following steps:
step 1, generating a rule base according to data of the existing power grid during fault, wherein the rule base is used for providing data support for intelligent diagnosis of equipment faults;
step 2, the power equipment inspection robot shoots the primary and secondary equipment with faults, image information of the primary and secondary equipment is collected, meanwhile, the power equipment inspection robot positions the position of the primary and secondary equipment and collects position information of the primary and secondary equipment, and the power equipment inspection robot transmits the image information and the position information to a server;
step 3, the server receives the image information and the position information, then determines the equipment information of the primary and secondary equipment according to the image information, and identifies and senses the state of the fault of the primary and secondary equipment by matching the equipment information, the position information and the rule base, so that the fault identification, state sensing and positioning of the primary and secondary equipment are realized;
and 4, the power equipment inspection robot acquires the fault identification, state sensing and positioning information of the primary and secondary equipment from the server, and informs the inspection personnel of the fault identification, state sensing and positioning information of the primary and secondary equipment, so that the inspection personnel can perform accurate inspection.
Preferably, the method for generating the rule base specifically includes the following substeps: the method comprises the following steps that 1, relevant data of power equipment during operation of a robot for routing inspection are collected, and then the relevant data are converted into historical analysis documents to be stored;
the substep 2, collecting primary and secondary equipment target data inspected by the power equipment inspection robot, summarizing the primary and secondary equipment target data inspected by the power equipment inspection robot into power grid event data according to the inspected primary and secondary equipment target data, and storing the power grid event data;
and substep 3, forming a rule base according to the analysis process rules of the power equipment inspection robot according to the power grid event data and the historical analysis document.
Preferably, the related data comprises inspection personnel identity information, inspection historical records, equipment attribute information, equipment operation data, a language and gesture standard library and a three-dimensional feature model.
Preferably, after the inspection personnel finish detecting the failed primary and secondary equipment, the inspection personnel records and uploads the inspection information to the server, and the server receives the inspection information and then inputs the inspection information into the historical analysis document so as to update the rule base.
Preferably, the method for shooting the failed primary and secondary equipment by the power equipment inspection robot is to shoot by a three-dimensional camera, and the server determines the equipment information of the primary and secondary equipment by comparing and identifying the acquired image information with a pre-stored three-dimensional characteristic model.
Preferably, the specific process of comparing and identifying the acquired image information and the prestored three-dimensional feature model by the server is as follows: decomposing acquired image information into six-face graphs to form six pieces of two-dimensional image information, then performing feature extraction on each piece of two-dimensional image information, comparing and matching the six-face graphs of the three-dimensional feature models preset in the server through the feature extraction, and selecting the six-face graphs of the three-dimensional feature models stored in advance with the highest confidence coefficient as matching six-face graphs of the two-dimensional image information; and if the matched six pictures of the acquired image information are all six pictures of the same prestored three-dimensional characteristic model, successfully matching the acquired image information with the three-dimensional characteristic model, and finishing comparison and identification. The design greatly improves the accuracy of image recognition of primary and secondary equipment.
Preferably, if the matched six-face images of the acquired six-face images of the image information are not the same six-face images of the prestored three-dimensional feature model, performing a secondary matching process, firstly determining one three-dimensional feature model as a closest matching model, wherein the three-dimensional feature model contains the largest number of matched six-face images, secondly calculating confidence degrees of the remaining six-face images of the image information and the six-face images of the three-dimensional feature model, if all the confidence degrees are greater than or equal to a set threshold value, judging that the acquired image information is successfully matched with the three-dimensional feature model, and completing comparison and identification; and if at least one confidence coefficient is smaller than the set threshold value, judging that the matching of the acquired image information and the three-dimensional characteristic model fails and reminding inspection personnel. In the actual operation process of the primary and secondary equipment, the actual three-dimensional image and the preset three-dimensional image may slightly differ due to the influence of objective factors, so that the scheme can further ensure the accuracy of image recognition.
The invention has the beneficial effects that: the power equipment intelligent inspection robot has the functions of equipment information inquiry, remote guidance and automatic fault identification, can assist power personnel to carry out regular inspection work, is applied to the power equipment intelligent inspection robot to carry out online inspection in inspection, can assist the power equipment intelligent inspection robot to carry out manual inspection, can improve the intelligent level of inspection to a greater extent, and reduces the labor cost of inspection links.
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FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples.
Example (b):
a primary and secondary equipment fault intelligent diagnosis method based on multiple information sources is disclosed, as shown in FIG. 1, and comprises the following steps:
step 1, generating a rule base according to data of the existing power grid during fault, wherein the rule base is used for providing data support for intelligent diagnosis of equipment faults;
step 2, the power equipment inspection robot shoots the primary and secondary equipment with faults, image information of the primary and secondary equipment is collected, meanwhile, the power equipment inspection robot positions the position of the primary and secondary equipment and collects position information of the primary and secondary equipment, and the power equipment inspection robot transmits the image information and the position information to a server;
step 3, the server receives the image information and the position information, then determines the equipment information of the primary and secondary equipment according to the image information, and identifies and senses the state of the fault of the primary and secondary equipment by matching the equipment information, the position information and the rule base, so that the fault identification, state sensing and positioning of the primary and secondary equipment are realized;
and 4, the power equipment inspection robot acquires the fault identification, state sensing and positioning information of the primary and secondary equipment from the server, and informs the inspection personnel of the fault identification, state sensing and positioning information of the primary and secondary equipment, so that the inspection personnel can perform accurate inspection.
The method for generating the rule base specifically comprises the following substeps: the method comprises the following steps that 1, relevant data of power equipment during operation of a robot for routing inspection are collected, and then the relevant data are converted into historical analysis documents to be stored;
the substep 2, collecting primary and secondary equipment target data inspected by the power equipment inspection robot, summarizing the primary and secondary equipment target data inspected by the power equipment inspection robot into power grid event data according to the inspected primary and secondary equipment target data, and storing the power grid event data;
and substep 3, forming a rule base according to the analysis process rules of the power equipment inspection robot according to the power grid event data and the historical analysis document.
The related data comprises inspection personnel identity information, inspection historical records, equipment attribute information, equipment operation data, a language and gesture standard library and a three-dimensional characteristic model.
After the inspection personnel finish detecting the failed primary and secondary equipment, the inspection personnel record the inspection information and upload the inspection information to the server, and the server receives the inspection information and then inputs the inspection information into the historical analysis document so as to update the rule base. And after the inspection personnel finish detecting the fault equipment, the inspection personnel fills in the inspection record, the inspection record comprises the attribute information of the fault equipment, the equipment operation data, the equipment fault information, the equipment fault reason information and the identity information of the inspection personnel, and then the inspection personnel fills in the inspection record into the historical analysis document. When the polling records are filled in by polling personnel, the polling time and weather need to be noted, and then the failure rule of the equipment is summarized through the failure time and weather of the equipment, so that whether the failure reason of the equipment is an external factor or an internal factor is determined.
The routing inspection history, the primary and secondary equipment attribute information and the equipment operation data of the same equipment are linked, and when the equipment is replaced, the routing inspection history, the equipment attribute information and the equipment operation data of new equipment can be conveniently and quickly used for covering the data of the replaced equipment.
The method for shooting the failed primary and secondary equipment by the power equipment inspection robot is characterized in that shooting is performed through a three-dimensional camera, and the server compares and identifies acquired image information with a pre-stored three-dimensional characteristic model to determine equipment information of the primary and secondary equipment.
The specific process of comparing and identifying the acquired image information and the prestored three-dimensional characteristic model by the server is as follows: decomposing acquired image information into six-face graphs to form six pieces of two-dimensional image information, then performing feature extraction on each piece of two-dimensional image information, comparing and matching the six-face graphs of the three-dimensional feature models preset in the server through the feature extraction, and selecting the six-face graphs of the three-dimensional feature models stored in advance with the highest confidence coefficient as matching six-face graphs of the two-dimensional image information; and if the matched six pictures of the acquired image information are all six pictures of the same prestored three-dimensional characteristic model, successfully matching the acquired image information with the three-dimensional characteristic model, and finishing comparison and identification.
If the matched six-face images of the acquired six-face images of the image information are not the six-face images of the same prestored three-dimensional feature model, performing a secondary matching process, firstly determining one three-dimensional feature model as the closest matched model, wherein the three-dimensional feature model contains the largest number of matched six-face images, secondly calculating confidence degrees of the remaining six-face images of the image information and the six-face images of the three-dimensional feature model, if all the confidence degrees are greater than or equal to a set threshold value, judging that the acquired image information is successfully matched with the three-dimensional feature model, and completing comparison and identification; and if at least one confidence coefficient is smaller than the set threshold value, judging that the matching of the acquired image information and the three-dimensional characteristic model fails and reminding inspection personnel.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units. The components shown as modules or units may or may not be physical units, i.e. may be located in one place or may also be distributed over a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the wood-disclosed scheme. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
While there have been shown and described the fundamental principles and essential features of the invention and advantages thereof, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof; the present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A primary and secondary equipment fault intelligent diagnosis method based on multiple information sources is characterized by comprising the following steps:
step 1, generating a rule base according to data of the existing power grid during fault, wherein the rule base is used for providing data support for intelligent diagnosis of equipment faults;
step 2, the power equipment inspection robot shoots the primary and secondary equipment with faults, image information of the primary and secondary equipment is collected, meanwhile, the power equipment inspection robot positions the position of the primary and secondary equipment and collects position information of the primary and secondary equipment, and the power equipment inspection robot transmits the image information and the position information to a server;
step 3, the server receives the image information and the position information, then determines the equipment information of the primary and secondary equipment according to the image information, and identifies and senses the state of the fault of the primary and secondary equipment by matching the equipment information, the position information and the rule base, so that the fault identification, state sensing and positioning of the primary and secondary equipment are realized;
and 4, the power equipment inspection robot acquires the fault identification, state sensing and positioning information of the primary and secondary equipment from the server, and informs the inspection personnel of the fault identification, state sensing and positioning information of the primary and secondary equipment, so that the inspection personnel can perform accurate inspection.
2. The intelligent diagnosis method for the faults of the primary and secondary equipment based on the multiple information sources as claimed in claim 1, wherein the generation method of the rule base specifically comprises the following substeps: the method comprises the following steps that 1, relevant data of power equipment during operation of a robot for routing inspection are collected, and then the relevant data are converted into historical analysis documents to be stored;
the substep 2, collecting primary and secondary equipment target data inspected by the power equipment inspection robot, summarizing the primary and secondary equipment target data inspected by the power equipment inspection robot into power grid event data according to the inspected primary and secondary equipment target data, and storing the power grid event data;
and substep 3, forming a rule base according to the analysis process rules of the power equipment inspection robot according to the power grid event data and the historical analysis document.
3. The intelligent diagnosis method for the faults of the primary and secondary equipment based on the multiple information sources as claimed in claim 2, wherein the related data comprises inspection personnel identity information, inspection historical records, equipment attribute information, equipment operation data, a language and gesture standard library and a three-dimensional feature model.
4. The intelligent primary and secondary equipment fault diagnosis method based on multiple information sources as claimed in claim 1, wherein after the inspection personnel finishes detecting the faulty primary and secondary equipment, the inspection personnel records and uploads the inspection information to the server, and the server receives the inspection information and then records the inspection information into the historical analysis document to update the rule base.
5. The intelligent primary and secondary equipment fault diagnosis method based on multiple information sources as claimed in claim 1, wherein the method for shooting the faulty primary and secondary equipment by the power equipment inspection robot is to shoot by a three-dimensional camera, and the server determines the equipment information of the primary and secondary equipment by comparing and recognizing the acquired image information with a pre-stored three-dimensional characteristic model.
6. The intelligent diagnosis method for the faults of the primary and secondary equipment based on the multiple information sources as claimed in claim 1, wherein the specific process of comparing and identifying the acquired image information and the prestored three-dimensional characteristic model by the server is as follows: decomposing acquired image information into six-face graphs to form six pieces of two-dimensional image information, then performing feature extraction on each piece of two-dimensional image information, comparing and matching the six-face graphs of the three-dimensional feature models preset in the server through the feature extraction, and selecting the six-face graphs of the three-dimensional feature models stored in advance with the highest confidence coefficient as matching six-face graphs of the two-dimensional image information; and if the matched six pictures of the acquired image information are all six pictures of the same prestored three-dimensional characteristic model, successfully matching the acquired image information with the three-dimensional characteristic model, and finishing comparison and identification.
7. The intelligent diagnosis method for the failure of the primary and secondary equipment based on the multiple information sources as claimed in claim 6, wherein if the matching six-face images of the acquired six-face images of the image information are not the same six-face images of the prestored three-dimensional feature model, a secondary matching process is performed, firstly, one three-dimensional feature model is determined as the closest matching model, the three-dimensional feature model contains the largest number of the matching six-face images, secondly, the confidence degrees of the remaining six-face images of the image information and the six-face images of the three-dimensional feature model are calculated, if all the confidence degrees are greater than or equal to a set threshold value, the acquired image information and the three-dimensional feature model are successfully matched, and the comparison and the recognition are completed; and if at least one confidence coefficient is smaller than the set threshold value, judging that the matching of the acquired image information and the three-dimensional characteristic model fails and reminding inspection personnel.
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Cited By (1)
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| CN114518130A (en) * | 2022-03-03 | 2022-05-20 | 广州皖文电气设备有限公司 | Multifunctional general outdoor function box and monitoring method thereof |
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