CN114093145A - Visual-auditory cooperative power equipment inspection system and method - Google Patents
Visual-auditory cooperative power equipment inspection system and method Download PDFInfo
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- CN114093145A CN114093145A CN202111341914.0A CN202111341914A CN114093145A CN 114093145 A CN114093145 A CN 114093145A CN 202111341914 A CN202111341914 A CN 202111341914A CN 114093145 A CN114093145 A CN 114093145A
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02G—INSTALLATION OF ELECTRIC CABLES OR LINES, OR OF COMBINED OPTICAL AND ELECTRIC CABLES OR LINES
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Abstract
A power equipment inspection system and method with visual and auditory coordination comprises a multi-source data acquisition module, a data intelligent analysis module and a visual and auditory coordination monitoring module; the multi-source data acquisition module is used for acquiring visual and auditory multi-source data and sending the visual and auditory multi-source data to the data intelligent analysis module; the data intelligent analysis module receives the data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends the equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module; and the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results. The inspection system and the inspection method fully apply the multi-source data monitored by the visual and auditory sensors and other sensors, comprehensively improve the state active perception and the attribute cooperative cognition of the power equipment, and improve the inspection efficiency and accuracy of the power equipment.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to a visual and auditory collaborative power equipment inspection system and method.
Background
The high-efficiency operation and maintenance of the smart power grid is significant for improving the reliability and the service level of the power grid, in recent years, the problems that the contradiction between the scale expansion of the power grid and the relative tension of operation and maintenance personnel is increasingly prominent, risk factors influencing the safety of the power grid exist for a long time, the traditional operation and maintenance mode is difficult to adapt to the rapid development requirement of the power grid and the like exist mainly in the operation and maintenance of the power grid.
At present, artificial intelligence technologies such as image processing, target detection and recognition, voice recognition, intelligent robots and the like are rapidly developed, and a new way and a new method are provided for solving the problems. The new-generation artificial intelligence technology can be deeply fused and applied with the operation and maintenance of the smart power grid, multi-source data such as visual, auditory and other sensor monitoring are fully utilized, active state perception and attribute cooperative cognition of power equipment are comprehensively improved, quality improvement, efficiency improvement and transformation upgrading of electrical equipment in China are led and promoted, and a new mode and a new state are explored for an operation and maintenance system of the smart power grid.
Disclosure of Invention
Based on the above situation in the prior art, the invention aims to provide a visual and auditory collaborative power equipment inspection system and method, so that equipment fault identification and defect early warning of multi-source data are realized, and inspection efficiency of power equipment is greatly improved.
The invention provides a visual and auditory collaborative power equipment inspection system, which comprises a multi-source data acquisition module, a data intelligent analysis module and a visual and auditory collaborative monitoring module;
the multi-source data acquisition module is used for acquiring visual and auditory multi-source data and sending the visual and auditory multi-source data to the data intelligent analysis module;
the data intelligent analysis module receives the data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends the equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module;
and the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results.
Further, multisource data acquisition module includes unmanned aerial vehicle, removes patrolling and examining robot, fixed control, security protection sensor, gas sensor, vocal print sensor and/or intelligent wearing equipment.
Furthermore, the data fusion is based on a device fault recognition deep learning algorithm of multi-mode data, and data information collected in the whole process of the power device is fused; and performing multi-source data enhancement on the power equipment based on the technologies of generation countermeasure and sample synthesis, and performing data annotation.
Further, extracting the characteristics of the processed multi-source data by adopting a directional gradient histogram, a bidirectional gating circulation network and/or a Mel frequency cepstrum coefficient characteristic extraction algorithm;
and training a deep learning target detection model which is suitable for power equipment inspection and integrates multi-source data based on a deep neural network.
Furthermore, the intelligent analysis is based on a CNN deep learning algorithm, the characteristic information of each state of the power equipment is extracted, and the equipment fault identification and defect early warning results are obtained through correlation analysis and cluster analysis of a deep learning target detection model of multi-source data and the characteristic information of each state based on a Hilbert-Schmidt independent criterion.
Furthermore, the audio-visual cooperative monitoring module comprises a data display module and a linkage control module.
Furthermore, the data display module receives analysis result data of the data intelligent analysis module and is used for displaying equipment fault identification and defect early warning results.
Furthermore, the linkage control module gives the cause probability and treatment suggestion of the equipment abnormity and carries out linkage control on the abnormal equipment.
Furthermore, the linkage control module establishes an intelligent linkage control model by utilizing the existing abnormal data and historical analysis results of the internal and external power equipment through a machine learning algorithm, gives the cause probability and treatment suggestion of equipment abnormality by combining the analysis results of the data intelligent analysis module, and performs linkage control on abnormal equipment under the authorization of inspection personnel.
A second aspect of the present invention provides a visual-auditory cooperative power equipment inspection method, which is executed by the visual-auditory cooperative power equipment inspection system, and includes the following steps:
the multi-source data acquisition module acquires visual and auditory multi-source data and sends the visual and auditory multi-source data to the data intelligent analysis module;
the data intelligent analysis module receives the data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends the equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module;
and the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results.
In summary, the invention provides a visual and auditory collaborative power equipment inspection system and a method thereof, wherein the inspection system comprises a multi-source data acquisition module, a data intelligent analysis module and a visual and auditory collaborative monitoring module; the multi-source data acquisition module is used for acquiring visual and auditory multi-source data and sending the visual and auditory multi-source data to the data intelligent analysis module; the data intelligent analysis module receives the data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends the equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module; and the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results. The inspection system and the inspection method fully apply the multi-source data monitored by the visual and auditory sensors and other sensors, comprehensively improve the state active perception and the attribute cooperative cognition of the power equipment, and improve the inspection efficiency and accuracy of the power equipment.
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FIG. 1 is a block diagram of a visual and auditory cooperative inspection system for power equipment in an embodiment of the invention;
fig. 2 is a schematic flow chart of a visual and auditory cooperative power equipment inspection method in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The invention provides a visual and auditory cooperative power equipment inspection system, which is used for acquiring multi-source data such as visual, auditory and other sensor monitoring, performing multi-mode information fusion and artificial intelligence analysis on the acquired data, and realizing equipment fault identification and defect early warning of the multi-source data, thereby greatly improving inspection efficiency of power equipment. As shown in fig. 1, the inspection system includes a multi-source data acquisition module, a data intelligent analysis module and an audio-visual cooperative monitoring module.
The multi-source data acquisition module is used for acquiring visual and auditory multi-source data and sending the visual and auditory multi-source data to the data intelligent analysis module. The multi-source data acquisition system can acquire monitoring data of various visual, auditory and other sensors through an advanced equipment perception technology and send the acquired data to the data intelligent analysis system. Wherein, advanced equipment perception technique includes information access and interconnection intercommunication technique of unmanned aerial vehicle, removal inspection robot, fixed control, security protection sensor, gas sensor, vocal print sensor, intelligent wearing equipment etc. multiple equipment.
The data intelligent analysis module receives data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module.
The data fusion technology refers to an equipment fault recognition deep learning algorithm based on multi-mode data, and equipment overall process information such as visual and auditory data, GIS graphic information, meteorological information and online monitoring information is fused. Based on the artificial intelligence technologies such as generation countermeasure and sample synthesis, multisource data enhancement is carried out on the power equipment and key components (insulators, pressing plates, meters, pole number plates, main transformer oil tanks, sleeves and radiating fins) of the power equipment, data labeling work is completed, and the problem of sample imbalance is solved; performing feature extraction on the processed multi-source data based on feature extraction algorithms such as a directional gradient histogram, a bidirectional gating circulation network, a Mel frequency cepstrum coefficient and the like; a deep learning target detection model which is suitable for power equipment inspection and integrates multi-source data is trained based on a Deep Neural Network (DNN).
The intelligent analysis technology is based on deep learning algorithms such as CNN (convolutional neural network) and the like, the characteristic information of each state of the power equipment is extracted, whether the equipment state is normal or not is obtained through correlation analysis and cluster analysis of a multi-source data target detection model and the characteristic information of each state based on Hilbert-Schmidt independent criterion, and therefore identification and early warning of equipment components, appearance defects, environmental conditions and the like are achieved.
And the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results. The audio-visual cooperative monitoring system comprises a data display module and a linkage control module, and is used for displaying identification and early warning information of equipment parts, appearance defects, environmental conditions and the like, giving cause probability and treatment suggestions of equipment abnormity and performing linkage control on the abnormal equipment. The data display module can receive analysis result data of the data intelligent analysis system and visually display the analysis result data through means such as a graph and a table; the linkage control module can fully utilize the existing abnormal data and historical analysis results of the internal and external power equipment through a machine learning algorithm to establish an intelligent linkage control model, the model can be continuously optimized through human intervention, finally, the cause probability and treatment suggestion of equipment abnormality are given by combining the analysis results of the data intelligent analysis system, and the abnormal equipment is subjected to linkage control under the authorization of inspection personnel.
A second aspect of the present invention provides a visual and audio coordinated power equipment inspection method, which is executed by the visual and audio coordinated power equipment inspection system, and includes the following steps, as shown in fig. 2:
s100, a multi-source data acquisition module acquires visual and auditory multi-source data and sends the visual and auditory multi-source data to a data intelligent analysis module;
step S200, the data intelligent analysis module receives data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends the equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module;
and step S300, the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results.
In summary, the invention provides a visual and auditory collaborative power equipment inspection system and a method thereof, wherein the inspection system comprises a multi-source data acquisition module, a data intelligent analysis module and a visual and auditory collaborative monitoring module; the multi-source data acquisition module is used for acquiring visual and auditory multi-source data and sending the visual and auditory multi-source data to the data intelligent analysis module; the data intelligent analysis module receives the data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends the equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module; and the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results. The inspection system and the inspection method fully apply the multi-source data monitored by the visual and auditory sensors and other sensors, comprehensively improve the state active perception and the attribute cooperative cognition of the power equipment, and improve the inspection efficiency and accuracy of the power equipment.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. A visual-auditory cooperative power equipment inspection system is characterized by comprising a multi-source data acquisition module, a data intelligent analysis module and a visual-auditory cooperative monitoring module;
the multi-source data acquisition module is used for acquiring visual and auditory multi-source data and sending the visual and auditory multi-source data to the data intelligent analysis module;
the data intelligent analysis module receives the data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends the equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module;
and the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results.
2. The visual-auditory collaborative power equipment inspection system according to claim 1, wherein the multi-source data acquisition module comprises an unmanned aerial vehicle, a mobile inspection robot, a stationary monitoring system, a security sensor, a gas sensor, a voiceprint sensor and/or an intelligent wearable device.
3. The visual-auditory collaborative power equipment inspection system according to claim 1, wherein the data fusion is based on a multi-modal data equipment failure recognition deep learning algorithm, and data information collected in the whole process of power equipment is fused; and performing multi-source data enhancement on the power equipment based on the technologies of generation countermeasure and sample synthesis, and performing data annotation.
4. The visual and auditory collaborative power equipment inspection system according to claim 3, wherein feature extraction is performed on the processed multi-source data by using a directional gradient histogram, a bidirectional gated cyclic network and/or a Mel frequency cepstrum coefficient feature extraction algorithm;
and training a deep learning target detection model which is suitable for power equipment inspection and integrates multi-source data based on a deep neural network.
5. The visual-auditory collaborative power equipment inspection system according to claim 4, wherein the intelligent analysis is based on a CNN deep learning algorithm, characteristic information of each state of the power equipment is extracted, and based on Hilbert-Schmidt independent criteria, equipment fault identification and defect early warning results are obtained through correlation analysis and cluster analysis of a deep learning target detection model of multi-source data and the characteristic information of each state.
6. The visual-auditory collaborative power equipment inspection system according to claim 1, wherein the visual-auditory collaborative monitoring module includes a data presentation module and a linkage control module.
7. The visual-auditory collaborative power equipment inspection system according to claim 6, wherein the data display module receives analysis result data of the data intelligent analysis module and is used for displaying equipment fault identification and defect early warning results.
8. The visual-auditory collaborative power equipment inspection system according to claim 6, wherein the linkage control module gives cause probability and treatment suggestion of equipment abnormality and performs linkage control on abnormal equipment.
9. The visual-auditory cooperative power equipment inspection system according to claim 8, wherein the linkage control module establishes an intelligent linkage control model by using existing internal and external power equipment abnormal data and historical analysis results through a machine learning algorithm, gives cause probability and treatment suggestions of equipment abnormality in combination with the analysis results of the data intelligent analysis module, and performs linkage control on abnormal equipment under the authorization of inspection personnel.
10. A vision-hearing cooperative electric power equipment inspection method executed by the vision-hearing cooperative electric power equipment inspection system according to any one of claims 1 to 9, characterized by comprising the steps of:
the multi-source data acquisition module acquires visual and auditory multi-source data and sends the visual and auditory multi-source data to the data intelligent analysis module;
the data intelligent analysis module receives the data acquired by the multi-source data acquisition module, performs overall processing and analysis on the data through data fusion and intelligent analysis, performs equipment fault identification and defect early warning on the multi-source data, and sends the equipment fault identification and defect early warning results to the audio-visual cooperative monitoring module;
and the audio-visual cooperative monitoring module displays the equipment fault identification and defect early warning results.
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