CN-122022458-A - Cable fire risk determination method, device, equipment and storage medium

Abstract

The embodiment of the application provides a cable fire risk determining method, device, equipment and storage medium, and relates to the technical field of electric power facilities. The method comprises the steps of obtaining scene information and monitoring data of a target cable, extracting characteristics of the scene information, determining scene characteristic information of the target cable, carrying out weighted fusion processing on the monitoring data by using a confidence weighting algorithm, determining fusion data corresponding to the monitoring data, determining a cable fire risk level of the target cable according to the scene characteristic information and the fusion data by using a dynamic risk assessment engine, outputting a cable fire disposal strategy corresponding to the cable fire risk level, and processing the weight of the fusion data by using a hierarchical analysis method and a Bayesian network by using the dynamic risk assessment engine. The method is used for achieving the effects of improving scene adaptability and improving risk assessment accuracy.

Inventors

• ZHUANG LEYU
• LIN JIAHUANG
• LIU QINGHE
• CHEN JIEBIN
• WANG TENG
• ZHENG XIAOYUE
• CHEN QI
• GAO XIAN
• WANG XINZHE
• CHEN CANJIE
• ZHENG HUICHUN
• CAI JIANYI
• LIAO XIAOYI
• LIN PEILIANG
• QIU ZENGWEI
• ZHUO MENG
• JIANG JIAXIN
• LIN XIYANG
• ZHANG LIXIAN

Assignees

• 广东电网有限责任公司汕头供电局

Dates

Publication Date

20260512

Application Date

20260115

Claims (10)

1. 1. A method for determining the risk of a cable fire, comprising: acquiring scene information and monitoring data of a target cable, wherein the scene information comprises at least one of a cable topological graph, geospatial data and spatial structure information, and the monitoring data comprises at least one of an environment parameter, a cable state parameter and historical analysis data; Extracting the characteristics of the scene information, determining the scene characteristic information of the target cable, and carrying out weighted fusion processing on the monitoring data by using a confidence weighting algorithm to determine fusion data corresponding to the monitoring data; And determining the cable fire risk level of the target cable through a dynamic risk assessment engine according to the scene characteristic information and the fusion data, and outputting a cable fire disposal strategy corresponding to the cable fire risk level, wherein the dynamic risk assessment engine processes the weight of the fusion data by using a hierarchical analysis method and a Bayesian network.
2. 2. The method according to claim 1, wherein the scene characteristic information at least includes a scene category of the target cable, the weighting fusion processing is performed on the monitored data by using a confidence weighting algorithm, and determining fusion data corresponding to the monitored data includes: Determining a scene confidence coefficient according to the scene category of the target cable; determining the confidence weight of the monitoring data according to the variance of the monitoring data and the scene confidence coefficient; And determining fusion data corresponding to the monitoring data according to the monitoring data and the confidence weight of the monitoring data by using a confidence weighting algorithm.
3. 3. The method of claim 2, wherein the determining the fused data corresponding to the monitored data based on the monitored data and the confidence weights of the monitored data using a confidence weighting algorithm comprises: carrying out standardization processing on the monitoring data, and determining standardized monitoring data; and carrying out weighted summation processing on the standardized monitoring data based on the confidence weight of the monitoring data by using a confidence weighting algorithm to obtain fusion data corresponding to the monitoring data.
4. 4. The method of claim 1, wherein the determining, by a dynamic risk assessment engine, a cable fire risk level of the target cable based on the scene feature information and the fusion data comprises: Calling a dynamic risk assessment engine, constructing initial weights of the fusion data by using the analytic hierarchy process, and correcting the initial weights by using the Bayesian network to obtain corrected weights of the fusion data; Adjusting the correction weight according to the scene characteristic information to obtain an adaptive weight of the fusion data; and determining the cable fire risk level of the target cable according to the adaptive weight of the fusion data and the fusion data.
5. 5. The method according to claim 4, wherein the scene feature information at least includes a scene category of the target cable, and the adjusting the correction weight according to the scene feature information, to obtain the adaptive weight of the fusion data, includes: determining a scene adaptation coefficient according to the scene category of the target cable; And adjusting the correction weight according to the scene adaptation coefficient to obtain the adaptation weight of the fusion data.
6. 6. The method of claim 4, wherein the determining the cable fire risk level of the target cable based on the adaptation weights of the fusion data and the fusion data comprises: carrying out weighted summation processing according to the adaptive weight of the fusion data and the fusion data to obtain a cable fire risk value; and determining the cable fire risk level of the target cable according to a mapping relation between a preset cable fire risk value and the cable fire risk level.
7. 7. The method of any of claims 1-6, wherein prior to outputting a cable fire treatment strategy corresponding to the cable fire risk level, the method further comprises: constructing a three-dimensional scene model of the target cable based on the scene information of the target cable; Based on the monitoring data, performing fire diffusion simulation in the three-dimensional scene model, and determining a fire diffusion simulation result; And optimizing a cable fire disaster treatment strategy corresponding to the cable fire disaster risk level according to the fire disaster spread simulation result.
8. 8. A cable fire risk determination apparatus, comprising: The system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring scene information and monitoring data of a target cable, the scene information comprises at least one of a cable topological graph, geospatial data and spatial structure information, and the monitoring data comprises at least one of an environment parameter, a cable state parameter and historical analysis data; The processing unit is used for extracting the characteristics of the scene information, determining the scene characteristic information of the target cable, carrying out weighted fusion processing on the monitoring data by using a confidence weighting algorithm, and determining fusion data corresponding to the monitoring data; the evaluation unit is used for determining the cable fire risk level of the target cable through a dynamic risk evaluation engine according to the scene characteristic information and the fusion data and outputting a cable fire disposal strategy corresponding to the cable fire risk level, and the dynamic risk evaluation engine processes the weight of the fusion data through a hierarchical analysis method and a Bayesian network.
9. 9. An electronic device is characterized by comprising a memory and a processor; The memory stores computer-executable instructions; The processor executing computer-executable instructions stored in the memory, causing the processor to perform the method of any one of claims 1-7.
10. 10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-7.

Description

Cable fire risk determination method, device, equipment and storage medium Technical Field The application relates to the technical field of electric power facilities, in particular to a cable fire risk determining method, a device, equipment and a storage medium. Background As a key component of power and communication transmission, the safety of the cable directly relates to the stable operation of an infrastructure and the life and property safety of people, so that the cable fire risk assessment becomes an important research topic in the power industry and related technical fields. In the prior art, cable fire risk assessment typically relies on a fixed threshold judgment or a single risk assessment model. These methods are often based on empirical rules, setting a fixed set of parameter thresholds, and determining that there is a fire risk when the monitored data exceeds these thresholds. However, the method has obvious limitation in practical application, is difficult to adapt to different application scenes and environmental conditions, and has the problems of poor scene adaptability, high false alarm rate and the like. Disclosure of Invention The cable fire risk determining method, device, equipment and storage medium provided by the embodiment of the application are used for achieving the effects of improving scene adaptability, improving risk assessment accuracy and the like. In a first aspect, an embodiment of the present application provides a method for determining a risk of fire in a cable, including: acquiring scene information and monitoring data of a target cable, wherein the scene information comprises at least one of a cable topological graph, geospatial data and spatial structure information, and the monitoring data comprises at least one of an environment parameter, a cable state parameter and historical analysis data; Extracting the characteristics of the scene information, determining the scene characteristic information of the target cable, and carrying out weighted fusion processing on the monitoring data by using a confidence weighting algorithm to determine fusion data corresponding to the monitoring data; And determining the cable fire risk level of the target cable through a dynamic risk assessment engine according to the scene characteristic information and the fusion data, and outputting a cable fire disposal strategy corresponding to the cable fire risk level, wherein the dynamic risk assessment engine processes the weight of the fusion data by using a hierarchical analysis method and a Bayesian network. In a possible implementation manner, the scene feature information at least includes a scene category of the target cable, the weighting fusion processing is performed on the monitored data by using a confidence weighting algorithm, and determining fusion data corresponding to the monitored data includes: Determining a scene confidence coefficient according to the scene category of the target cable; determining the confidence weight of the monitoring data according to the variance of the monitoring data and the scene confidence coefficient; And determining fusion data corresponding to the monitoring data according to the monitoring data and the confidence weight of the monitoring data by using a confidence weighting algorithm. In one possible implementation manner, the determining, using a confidence weighting algorithm, fusion data corresponding to the monitoring data according to the monitoring data and confidence weights of the monitoring data includes: carrying out standardization processing on the monitoring data, and determining standardized monitoring data; and carrying out weighted summation processing on the standardized monitoring data based on the confidence weight of the monitoring data by using a confidence weighting algorithm to obtain fusion data corresponding to the monitoring data. In one possible implementation manner, the determining, according to the scene feature information and the fusion data, the cable fire risk level of the target cable through a dynamic risk assessment engine includes: Calling a dynamic risk assessment engine, constructing initial weights of the fusion data by using the analytic hierarchy process, and correcting the initial weights by using the Bayesian network to obtain corrected weights of the fusion data; Adjusting the correction weight according to the scene characteristic information to obtain an adaptive weight of the fusion data; and determining the cable fire risk level of the target cable according to the adaptive weight of the fusion data and the fusion data. In a possible implementation manner, the scene feature information at least includes a scene category of the target cable, and the adjusting the correction weight according to the scene feature information to obtain the adaptive weight of the fusion data includes: determining a scene adaptation coefficient according to the scene category of the target cable; And adjust