Cable health condition evaluation system and method based on edge calculation

Abstract

The invention relates to the technical field of cable monitoring, in particular to a cable health condition assessment system and method based on edge computing, and the system comprises a cable data collection module, a monitoring reference analysis module, a trigger condition setting module and a health assessment management module. Historical operation data and historical monitoring data of a cable are acquired through a cable data acquisition module; the method comprises the following steps: preprocessing cable data by using an edge calculation processor through a monitoring reference analysis module, forming a training sample by the preprocessed data, and establishing a monitoring reference model of a cable; a trigger condition setting module analyzes and sets a trigger condition for fracture monitoring of the cable according to the monitoring reference model; the health assessment management module carries out fracture monitoring on the cable and assesses the health state of the cable when the trigger condition is satisfied, and sends an alarm signal to the monitoring terminal when the health state of the cable is abnormal through assessment, thereby reducing the monitoring cost while guaranteeing the safe operation of the cable, and improving the cable monitoring efficiency.

Description

Technical Field

[0001] The present invention relates to the field of cable monitoring technology, and specifically to a cable health status assessment system and method based on edge computing. Background Art

[0002] Cables are the main transport medium for electric energy and voltage in power systems and are the carriers of electric energy. Considering safety, cables are wrapped with a high insulation layer. In special circumstances, such as certain accidents, some cables may be overloaded. If cables are overloaded for a long time, due to the thermal effect of the current, the high temperature will accelerate the aging of the insulation and even cause the insulation to be broken, resulting in cable breakage. In this case, it is necessary to monitor the cable breakage fault, evaluate the health of the cable, and promptly discover and maintain the fault problem, thereby ensuring the safe operation of the cable. However, when monitoring cable break faults, the existing technology generally adopts real-time monitoring and transmission of monitoring data. Most of the time, the monitored cables may not have break faults, and the appropriate time for cable break monitoring is not selected: cable break monitoring and cable health status assessment are not performed when there may be hidden dangers of cable breakage. There will be a large amount of invalid data transmission process, which cannot reduce monitoring costs and improve cable monitoring efficiency while ensuring the safe operation of the cable.

[0003] Therefore, people need a cable health status assessment system and method based on edge computing to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a cable health status assessment system and method based on edge computing to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a cable health status assessment system based on edge computing, the system comprising: a cable data acquisition module, a monitoring reference analysis module, a trigger condition setting module and a health assessment management module; The output end of the cable data acquisition module is connected to the input end of the monitoring reference analysis module, the output end of the monitoring reference analysis module is connected to the input end of the trigger condition setting module, and the output end of the trigger condition setting module is connected to the input end of the health assessment management module; The cable data acquisition module is used to collect the historical operation data and historical monitoring data of the cable; the monitoring reference analysis module uses the edge computing processor to preprocess the cable data, and the preprocessed data is combined into training samples and a cable monitoring reference model is established; the trigger condition setting module is used to set the trigger conditions for cable break monitoring based on the monitoring reference model analysis; the health assessment management module is used to monitor the cable for break and assess the health status of the cable when the trigger conditions are met, and an alarm signal is sent to the monitoring terminal when the health status of the cable is assessed to be abnormal.

[0006] Furthermore, the cable data acquisition module includes a historical operation data acquisition unit, an environmental information acquisition unit and a fault information acquisition unit; the historical operation data acquisition unit is used to collect the operation load current data of the cable in the previous operation process and the current value data that the cable can withstand, the operation load current represents the current value actually borne by the monitored cable, and the operation load current is measured by a current sensor; the environmental information acquisition unit is used to collect the ambient temperature data of the cable measured when the cable operation load current was monitored in the past, and the temperature data is measured by a temperature sensor; the fault information acquisition unit is used to collect the operation data of the cable when it was overloaded in the past and the duration data of the cable operation overload. The cable may break due to being in an operation overload state for a long time. The operation data of the cable when it was overloaded in the past refers to the operation overload value, and the operation overload value is represented by the absolute value of the difference between the current value actually borne by the cable monitored when overloaded and the current value that the cable can withstand. The fault information acquisition unit is also used to collect the installation time information of the cable when the cable was overloaded in the past, and transmit all the collected data to the edge computing processing unit for data preprocessing.

[0007] Further, the monitoring reference analysis module includes an edge computing processing unit and a monitoring reference model building unit; The input end of the edge computing processing unit is connected to the output ends of the environmental information acquisition unit, the historical operation data acquisition unit and the fault information acquisition unit, and the output end of the edge computing processing unit is connected to the input end of the monitoring reference model establishment unit; The edge computing processing unit uses an edge computing processor to perform data preprocessing: analyze the cable abnormal operation coefficient according to the cable operation overload value, the installation time information, and the duration of the cable operation at a constant operation overload value, retrieve the ambient temperature data around the cable when it was in different abnormal operation states in the past, and count the number of cable breakage failures when the cable was in different abnormal operation states and the ambient temperature was different. Different abnormal operation states refer to different abnormal operation coefficients of the cable. The effectiveness of cable monitoring is analyzed based on the statistical results, and the cable abnormal operation coefficient, the ambient temperature around the cable, and the effectiveness of cable monitoring are combined into training sample data, and the training sample data is transmitted to the monitoring reference model establishment unit; the monitoring reference model establishment unit establishes a monitoring reference model of the cable based on the training sample data; The collected data is preferentially transmitted to the edge computing processor for preprocessing, which speeds up the data analysis and processing.

[0008] Further, the trigger condition setting module includes an impact data input unit and a monitoring trigger condition setting unit; The input end of the impact data input unit is connected to the output end of the monitoring reference model establishment unit, and the output end of the impact data input unit is connected to the input end of the monitoring trigger condition setting unit; The monitoring effectiveness threshold is set through the influencing data input unit. When the cable is currently overloaded, the threshold and the monitored current cable ambient temperature are input into the monitoring reference model; the cable abnormal operation coefficient when the monitoring effectiveness reaches the threshold is output through the monitoring trigger condition setting unit, and the duration threshold of the cable running at the current operation overload value is calculated based on the output cable abnormal operation coefficient, and the duration threshold is set as the cable break monitoring trigger value.

[0009] Further, the health assessment management module includes a cable monitoring unit and a health status assessment unit; The input end of the cable monitoring unit is connected to the output end of the monitoring trigger condition setting unit, and the output end of the cable monitoring unit is connected to the input end of the health status assessment unit; When the cable monitoring unit detects that the duration of the cable running at the current operating overload value reaches a trigger value, the cable is monitored for breakage; when the health status assessment unit detects that the cable is broken, the cable health status index is assessed as 1; when it is detected that the cable is not broken, the cable health status index is assessed as 0; when the cable health status index is assessed to be 1, a cable break fault alarm signal is sent to the monitoring terminal.

[0010] A cable health status assessment method based on edge computing includes the following steps: S1: Collect historical operation data and historical monitoring data of the cable; S2: Use the edge computing processor to pre-process the cable data and form the pre-processed data into training samples; S3: Establish a monitoring reference model for the cable and set the triggering conditions for cable break monitoring; S4: When the triggering conditions are met, the cable is monitored for breakage and the health status of the cable is evaluated, and an early warning is issued when the health status of the cable is found to be abnormal.

[0011] Further, in S1: the operating load current data of the cable in the past during operation is collected, and the current value that the cable can withstand is obtained as I, and the ambient temperature of the cable measured when the cable operating load current was monitored in the past is collected, and the operating load current set of the cable when it was overloaded in the past is collected as I ’ ={I1 ’ , I2 ’ ,…,I m ’}, the set of cable operating overload values ​​in the past when the cable was overloaded is obtained as A={ , , …, }, the duration of the cable running at the operating overload value in set A is T={T1, T2, …, T m}, T1 indicates that the cable is running at an overload value The duration of operation is collected from the cable installation time set when the cable was monitored to be overloaded in the past, which is t={t1, t2, …, t m}, where m represents the number of times the cable has been overloaded in the past.

[0012] Furthermore, in S2: According to the formula Calculate the cable abnormal operation coefficient W when the cable is overloaded once in the past j ,in, It represents the operating load current of the cable when it was overloaded for the jth time in the past. Indicates the cable is operating at an overload value Duration of the run, represents the installation time of the cable when the cable was monitored to be overloaded for the jth time in the past, and the abnormal operation coefficient set of the cable when it was overloaded in the past is W={W1, W2, …, W j , …, W m}, the ambient temperature set around the cable in different abnormal operating states is retrieved as C={C1, C2, …, C m}, the previous abnormal operation coefficient of the cable is W j And the ambient temperature is C jThe number of cable break failures is L j , we get the number of cable break failures in different abnormal operating states and different ambient temperatures as L={L1, L2, …, L j , …, L m}, the cable monitoring effectiveness set is calculated as R={R1, R2, …, R j , …, R m}={ , , …, , …, }, forming the training sample data {(W1, C1, R1), (W2, C2, R2), …, (W j , C j , R j ),…,(W m , C m , R m )}.

[0013] Furthermore, in S3: the cable monitoring reference model is established after fitting the training sample data: ; in, , and represents the fitting coefficient, X represents the first variable referring to the abnormal operation coefficient, Y represents the second variable referring to the ambient temperature, and Z represents the third variable referring to the effectiveness of cable monitoring. The monitoring effectiveness threshold is set to Q. When the cable is currently overloaded, the ambient temperature around the current cable is monitored to be U. The threshold and the monitored ambient temperature around the current cable are input into the monitoring reference model: Let Z=Q, Y=U, and the output cable abnormal operation coefficient when the monitoring effectiveness reaches the threshold is , the current operating overload value of the cable is monitored to be K, and the installation time of the current cable is t ’ , the duration threshold of the cable running at the current overload value is obtained as ,set up It is the trigger value for cable break monitoring; In order to measure how long the cable is prone to breakage when it is overloaded in different environments, big data technology is used to collect the load current data of the cable when it is overloaded in the past. Considering that the temperature of the environment around the cable is also one of the influencing factors causing cable breakage when the cable is overloaded, especially in summer, the ambient temperature is high, and the cable often breaks due to long-term overload operation. Therefore, the ambient temperature data of the cable when it is overloaded is also collected. In addition, the longer the cable is installed, the more serious the cable aging is, and the weaker its insulation layer becomes, which is easy to be broken down, resulting in cable breakage. These data are taken into consideration when coordinating the training samples, and the number of breakage failures in different abnormal operating states and under different environments when the cable was overloaded in the past is divided into groups. The effectiveness of cable monitoring is analyzed. The more times the number of break faults occurs in the corresponding situation, the more necessary it is to monitor the cable for breakage, that is, the higher the monitoring effectiveness. Training samples are generated by combining multi-level data, and the training samples are fitted to establish a cable monitoring reference model. When the current cable is overloaded, a monitoring effectiveness threshold is set. By inputting the data of the current cable, the abnormal operation coefficient of the cable when the monitoring effectiveness reaches the threshold is obtained. When the abnormal operation coefficient of the current cable reaches the output value, the cable is monitored for breakage faults, and the health status of the cable is evaluated. When the appropriate time is selected, cable breakage monitoring and cable health status evaluation are performed when there may be hidden dangers of cable breakage, which reduces the number of transmissions of invalid data, reduces the monitoring cost while ensuring the safe operation of the cable, and improves the efficiency of cable monitoring.

[0014] Furthermore, in S4: when it is monitored that the duration of the cable running at the current operating overload value reaches a trigger value, the cable is monitored for breakage, and if the cable is monitored to be broken, the cable health status index is evaluated to be 1; if the cable is monitored to be not broken, the cable health status index is evaluated to be 0, and when the cable health status index is evaluated to be 1, a cable break fault alarm signal is sent to the monitoring terminal.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are: The present invention uses big data technology to collect the load current data of the cable when it is overloaded in the past. Considering that the temperature of the environment around the cable is also one of the influencing factors causing cable breakage failure when the cable is overloaded, especially in summer, the ambient temperature is high, and the cable often breaks due to long-term overload operation. Therefore, the ambient temperature data around the cable when it is overloaded is collected at the same time. In addition, the longer the cable is installed, the more serious the cable aging is, and the weaker its insulation layer becomes, which is easy to be broken down, resulting in cable breakage. These data are taken into consideration when coordinating the training samples, and the effectiveness of cable monitoring is analyzed according to the number of breakage failures that occurred in different abnormal operating states and in different environments when the cable was overloaded in the past. The more times of breakage failures occur, the more necessary it is to monitor the cable for breakage, that is, the higher the monitoring effectiveness. Training samples are generated by combining multi-level data, and the training samples are fitted to establish a cable monitoring reference model. When the current cable is overloaded, a monitoring effectiveness threshold is set. The abnormal operation coefficient of the cable when the monitoring effectiveness reaches the threshold is obtained by inputting the data of the current cable. When the abnormal operation coefficient of the current cable reaches the output value, the cable is monitored for breakage faults to evaluate the health of the cable. When the appropriate time is selected, cable breakage monitoring and cable health status evaluation are performed when there may be hidden dangers of cable breakage, which reduces the number of transmissions of invalid data, reduces monitoring costs while ensuring the safe operation of the cable, and improves cable monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a structural diagram of a cable health status assessment system based on edge computing of the present invention; Figure 2 It is a flow chart of a cable health status assessment method based on edge computing of the present invention. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0018] Combine the following Figure 1-Figure 2 The present invention is further described with reference to the accompanying drawings and specific embodiments.

[0019] Embodiment 1: like Figure 1As shown, this embodiment provides a cable health status assessment system based on edge computing, and the system includes: a cable data acquisition module, a monitoring reference analysis module, a trigger condition setting module and a health assessment management module. The cable data acquisition module collects the historical operation data and historical monitoring data of the cable; the monitoring reference analysis module uses the edge computing processor to preprocess the cable data, and the preprocessed data is composed of training samples and a monitoring reference model for the cable is established; the trigger condition setting module analyzes and sets the trigger conditions for cable fracture monitoring according to the monitoring reference model; the health assessment management module monitors the cable for fracture and evaluates the health status of the cable when the trigger conditions are met, and sends an alarm signal to the monitoring terminal when the health status of the cable is assessed to be abnormal.

[0020] The cable data acquisition module includes a historical operation data acquisition unit, an environmental information acquisition unit and a fault information acquisition unit; the historical operation data acquisition unit is used to collect the operation load current data of the cable in the previous operation process and the current value data that the cable can withstand. The operation load current represents the current value actually borne by the monitored cable, and the operation load current is measured by a current sensor; the environmental information acquisition unit is used to collect the ambient temperature data of the cable measured when the cable operation load current was monitored in the past, and the temperature data is measured by a temperature sensor; the fault information acquisition unit is used to collect the operation data of the cable when it was overloaded in the past and the duration data of the cable operation overload. The cable will break due to being in an operation overload state for a long time. The operation data of the cable when it was overloaded in the past refers to the operation overload value. The operation overload value is represented by the absolute value of the difference between the current value actually borne by the cable monitored when overloaded and the current value that the cable can withstand. The fault information acquisition unit is also used to collect the installation time information of the cable when the cable was overloaded in the past, and transmit all the collected data to the edge computing processing unit for data preprocessing.

[0021] The monitoring reference analysis module includes an edge computing processing unit and a monitoring reference model establishment unit. The edge computing processing unit uses an edge computing processor to perform data preprocessing: the cable abnormal operation coefficient is analyzed based on the cable operation overload value, the installation time information, and the duration of the cable running at a constant operation overload value, the ambient temperature data around the cable when it was in different abnormal operation states in the past is retrieved, and the number of cable breakage failures that occurred when the cable was in different abnormal operation states and the ambient temperature was different in the past is counted. Different abnormal operation states refer to different abnormal operation coefficients of the cable. The effectiveness of cable monitoring is analyzed based on the statistical results, and the cable abnormal operation coefficient, the ambient temperature around the cable and the effectiveness of cable monitoring are composed of training sample data, and the training sample data is transmitted to the monitoring reference model establishment unit; the monitoring reference model establishment unit establishes a monitoring reference model of the cable based on the training sample data.

[0022] The trigger condition setting module includes an impact data input unit and a monitoring trigger condition setting unit. The monitoring effectiveness threshold is set through the impact data input unit. When the cable is currently overloaded, the threshold and the monitored current cable surrounding environment temperature are input into the monitoring reference model; the cable abnormal operation coefficient when the monitoring effectiveness reaches the threshold is output through the monitoring trigger condition setting unit, and the duration threshold of the cable running at the current operation overload value is calculated based on the output cable abnormal operation coefficient, and the duration threshold is set as the cable break monitoring trigger value.

[0023] The health assessment management module includes a cable monitoring unit and a health status assessment unit. When the cable monitoring unit detects that the duration of the cable running at the current operating overload value reaches a trigger value, the cable is monitored for breakage; when the health status assessment unit detects that the cable is broken, the cable health status index is evaluated as 1; when it is detected that the cable is not broken, the cable health status index is evaluated as 0, and when the cable health status index is evaluated to be 1, a cable break fault alarm signal is sent to the monitoring terminal.

[0024] Embodiment 2: like Figure 2 As shown, this embodiment provides a cable health status assessment method based on edge computing, which is implemented based on the health status assessment system in the embodiment and specifically includes the following steps: S1: Collect the historical operation data and historical monitoring data of the cable: collect the operating load current data of the cable in the past operation process, obtain the current value that the cable can withstand as I, collect the ambient temperature of the cable measured when monitoring the operating load current of the cable in the past, and collect the operating load current set of the cable when it was overloaded in the past as I ’ ={I1 ’ , I2 ’ ,…,I m ’}, the set of cable operating overload values ​​in the past when the cable was overloaded is obtained as A={ , , …, }, the duration of the cable running at the operating overload value in set A is T={T1, T2, …, T m}, T1 indicates that the cable is running at an overload value The duration of operation is collected from the cable installation time set when the cable was monitored to be overloaded in the past, which is t={t1, t2, …, t m}, where m represents the number of times the cable has been overloaded in the past; S2: Use the edge computing processor to preprocess the cable data and form the preprocessed data into training samples: According to the formula Calculate the cable abnormal operation coefficient W when the cable is overloaded once in the past j ,in, It represents the operating load current of the cable when it was overloaded for the jth time in the past. Indicates the cable is operating at an overload value Duration of the run, represents the installation time of the cable when the cable was monitored to be overloaded for the jth time in the past, and the abnormal operation coefficient set of the cable when it was overloaded in the past is W={W1, W2, …, W j , …, W m}, the ambient temperature set around the cable in different abnormal operating states is retrieved as C={C1, C2, …, C m}, the previous abnormal operation coefficient of the cable is W j And the ambient temperature is C j The number of cable break failures is L j , we get the number of cable break failures in different abnormal operating states and different ambient temperatures as L={L1, L2, …, L j , …, L m}, the cable monitoring effectiveness set is calculated as R={R1, R2, …, R j , …, R m}={ , , …, , …, }, forming the training sample data {(W1, C1, R1), (W2, C2, R2), …, (W j , C j , R j ),…,(W m , C m , R m )}; S3: Establish a cable monitoring reference model and set the triggering conditions for cable break monitoring: Establish the cable monitoring reference model after fitting the training sample data: ; in, , and Represents the fitting coefficient, and is solved according to the following formulas , and : ; ; ; X represents the first variable referring to the abnormal operation coefficient, Y represents the second variable referring to the ambient temperature, and Z represents the third variable referring to the effectiveness of cable monitoring. The monitoring effectiveness threshold is set to Q. When the cable is currently overloaded, the ambient temperature around the current cable is monitored to be U. The threshold and the monitored ambient temperature around the current cable are input into the monitoring reference model: Let Z=Q, Y=U, and the output cable abnormal operation coefficient when the monitoring effectiveness reaches the threshold is , the current operating overload value of the cable is monitored to be K, and the installation time of the current cable is t ’ , the duration threshold of the cable running at the current overload value is obtained as ,set up It is the trigger value for cable break monitoring; S4: When the trigger condition is met, the cable is monitored for breakage and the health status of the cable is evaluated. When the health status of the cable is abnormal, an early warning is issued: when it is monitored that the duration of the cable running at the current operating overload value reaches the trigger value, the cable fault detector is used to monitor the cable for breakage. If the cable is monitored to be broken, the health status index of the cable is evaluated to be 1; if the cable is monitored to be not broken, the health status index of the cable is evaluated to be 0. When the health status index of the cable is evaluated to be 1, a cable break fault alarm signal is sent to the monitoring terminal; For example, the cable monitoring reference model is established after fitting the training sample data {(W1, C1, R1), (W2, C2, R2), (W3, C3, R3), (W4, C4, R4), (W5, C5, R5)} = {(44, 30, 0.85), (91, 20, 0.86), (10, 21, 0.32), (46, 32, 0.92), (32, 24, 0.55)}: , set the monitoring effectiveness threshold to Q=0.60, and monitor the ambient temperature around the current cable to be U=31 when the cable is currently overloaded. Input the threshold and the monitored ambient temperature around the current cable into the monitoring reference model: Let Z=Q=0.6, Y=U=31, and output the cable abnormal operation coefficient when the monitoring effectiveness reaches the threshold: , the current operating overload value of the cable is monitored to be K=50, and the installation time of the current cable is t ’ =4, unit: year, the duration threshold of the cable running at the current overload value is , set 0.16 as the cable break monitoring trigger value. When it is detected that the cable has been running at an overload value of 50 for 0.16 hours, the cable fault detector is used to monitor the cable break.

[0025] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A cable health status assessment system based on edge computing, characterized in that: The system includes: a cable data acquisition module, a monitoring reference analysis module, a trigger condition setting module and a health assessment management module; The output end of the cable data acquisition module is connected to the input end of the monitoring reference analysis module, the output end of the monitoring reference analysis module is connected to the input end of the trigger condition setting module, and the output end of the trigger condition setting module is connected to the input end of the health assessment management module; Collect the historical operation data and historical monitoring data of the cable through the cable data acquisition module; The monitoring reference analysis module uses an edge computing processor to preprocess the cable data, composes the preprocessed data into training samples, and establishes a monitoring reference model for the cable; The trigger condition setting module sets the trigger condition for cable break monitoring according to the monitoring reference model analysis; The health assessment management module monitors the cable for breakage and assesses the health status of the cable when a trigger condition is met, and sends an alarm signal to the monitoring terminal when it is assessed that the health status of the cable is abnormal.

2. A cable health status assessment system based on edge computing according to claim 1, characterized in that: The cable data acquisition module includes a historical operation data acquisition unit, an environmental information acquisition unit and a fault information acquisition unit; The historical operation data collection unit collects the operation load current data of the cable in the past operation process and the current value data that the cable can withstand. The operation load current represents the current value actually borne by the monitored cable, and the operation load current is measured by the current sensor; The environmental information acquisition unit is used to collect the ambient temperature data of the cable measured when the cable operating load current was monitored in the past; The fault information collection unit is used to collect the operation data of the cable when it was previously overloaded and the duration data of the cable operation overload. The operation data of the cable when it was previously overloaded refers to the operation overload value. The operation overload value is represented by the absolute value of the difference between the current value actually borne by the cable monitored when it is overloaded and the current value that the cable can withstand. The fault information collection unit is also used to collect the installation time information of the cable when the cable was previously monitored to be overloaded; All collected data are transmitted to the edge computing processing unit for data preprocessing.

3. A cable health status assessment system based on edge computing according to claim 2, characterized in that: The monitoring reference analysis module includes an edge computing processing unit and a monitoring reference model building unit; The input end of the edge computing processing unit is connected to the output ends of the environmental information acquisition unit, the historical operation data acquisition unit and the fault information acquisition unit, and the output end of the edge computing processing unit is connected to the input end of the monitoring reference model establishment unit; The edge computing processing unit uses an edge computing processor to perform data preprocessing: analyzing the cable abnormal operation coefficient according to the cable operation overload value, the installed time information, and the duration of the cable operation at a constant operation overload value, retrieving the ambient temperature data around the cable when it was in different abnormal operation states in the past, and counting the number of cable breakage failures when the cable was in different abnormal operation states and the ambient temperature was different. The different abnormal operation states refer to different abnormal operation coefficients of the cable. The effectiveness of cable monitoring is analyzed based on the statistical results, and the cable abnormal operation coefficient, the ambient temperature around the cable, and the effectiveness of cable monitoring are combined into training sample data, and the training sample data is transmitted to the monitoring reference model establishment unit; The monitoring reference model establishing unit establishes a monitoring reference model of the cable according to the training sample data.

4. A cable health status assessment system based on edge computing according to claim 3, characterized in that: The trigger condition setting module includes an impact data input unit and a monitoring trigger condition setting unit; The input end of the impact data input unit is connected to the output end of the monitoring reference model establishment unit, and the output end of the impact data input unit is connected to the input end of the monitoring trigger condition setting unit; The monitoring effectiveness threshold is set by the impact data input unit, and when the cable is currently overloaded, the threshold and the monitored current ambient temperature of the cable are input into the monitoring reference model; The monitoring trigger condition setting unit outputs the cable abnormal operation coefficient when the monitoring effectiveness reaches the threshold, calculates the duration threshold of the cable running at the current operation overload value based on the output cable abnormal operation coefficient, and sets the duration threshold as the cable break monitoring trigger value.

5. A cable health status assessment system based on edge computing according to claim 4, characterized in that: The health assessment management module includes a cable monitoring unit and a health status assessment unit; The input end of the cable monitoring unit is connected to the output end of the monitoring trigger condition setting unit, and the output end of the cable monitoring unit is connected to the input end of the health status assessment unit; When the cable monitoring unit detects that the duration of the cable running at the current overload value reaches a trigger value, the cable is monitored for breakage; When the health status assessment unit monitors that the cable is broken, the health status index of the cable is assessed to be 1; when the cable is not broken, the health status index of the cable is assessed to be 0. When the health status index of the cable is assessed to be 1, a cable break fault alarm signal is sent to the monitoring terminal.

6. A cable health status assessment method based on edge computing, characterized in that: The following steps are involved: S1: Collect historical operation data and historical monitoring data of the cable; S2； Use edge computing processors to preprocess cable data and compose training samples from the preprocessed data; S3: Establish a monitoring reference model for the cable and set the triggering conditions for cable break monitoring; S4: When the triggering conditions are met, the cable is monitored for breakage and the health status of the cable is evaluated, and an early warning is issued when the health status of the cable is found to be abnormal.

7. A cable health status assessment method based on edge computing according to claim 6, characterized in that: In S1: the operating load current data of the cable in the past during operation is collected, and the current value that the cable can withstand is obtained as I, and the ambient temperature of the cable measured when the cable operating load current was monitored in the past is collected, and the operating load current set of the cable when it was overloaded in the past is collected as I ’ ={I1 ’ , I2 ’ ,…,I m ’ }, the set of cable operating overload values ​​in the past when the cable was overloaded is obtained as A={ , , …, }, the duration of the cable running at the operating overload value in set A is T={T1, T2, …, T m }, T1 indicates that the cable is running at an overload value The duration of operation is collected from the cable installation time set when the cable was monitored to be overloaded in the past, which is t={t1, t2, …, t m }, where m represents the number of times the cable has been overloaded in the past.

8. The cable health status assessment method based on edge computing according to claim 7 is characterized in that: In S2: According to the formula Calculate the cable abnormal operation coefficient W when the cable is overloaded once in the past j ,in, It represents the operating load current of the cable when it was overloaded for the jth time in the past. Indicates the cable is operating at an overload value Duration of the run, represents the installation time of the cable when the cable was monitored to be overloaded for the jth time in the past, and the abnormal operation coefficient set of the cable when it was overloaded in the past is W={W1, W2, …, W j , …, W m }, the ambient temperature set around the cable in different abnormal operating states is retrieved as C={C1, C2, …, C m }, the previous abnormal operation coefficient of the cable is W j And the ambient temperature is C j The number of cable break failures is L j , we get the number of cable break failures in different abnormal operating states and different ambient temperatures as L={L1, L2, …, L j , …, L m }, the cable monitoring effectiveness set is calculated as R={R1, R2, …, R j , …, R m }={ , , …, , …, }, forming the training sample data {(W1, C1, R1), (W2, C2, R2), …, (W j , C j , R j ),…,(W m , C m , R m )}.

9. A cable health status assessment method based on edge computing according to claim 8, characterized in that: In S3: After fitting the training sample data, a cable monitoring reference model is established: ； in, , and represents the fitting coefficient, X represents the first variable referring to the abnormal operation coefficient, Y represents the second variable referring to the ambient temperature, and Z represents the third variable referring to the effectiveness of cable monitoring. The monitoring effectiveness threshold is set to Q. When the cable is currently overloaded, the ambient temperature around the current cable is monitored to be U. The threshold and the monitored ambient temperature around the current cable are input into the monitoring reference model: Let Z=Q, Y=U, and the output cable abnormal operation coefficient when the monitoring effectiveness reaches the threshold is , the current operating overload value of the cable is monitored to be K, and the installation time of the current cable is t ’ , the duration threshold of the cable running at the current overload value is obtained as ,set up Cable break monitoring trigger value.

10. A cable health status assessment method based on edge computing according to claim 9, characterized in that: In S4: when it is monitored that the duration of the cable running at the current operating overload value reaches a trigger value, the cable is monitored for breakage. If the cable is monitored to be broken, the cable health status index is evaluated to be 1; if the cable is monitored to be not broken, the cable health status index is evaluated to be 0. When the cable health status index is evaluated to be 1, a cable break fault alarm signal is sent to the monitoring terminal.

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