A cable health status assessment system and method based on edge computing

Abstract

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, comprising: a cable data acquisition module, a monitoring reference analysis module, a trigger condition setting module and a health assessment management module, wherein the cable data acquisition module collects historical operation data and historical monitoring data of the cable; the monitoring reference analysis module uses an edge computing processor to preprocess the cable data, and the preprocessed data is composed of training samples and a monitoring reference model of the cable is established; the trigger condition setting module analyzes and sets trigger conditions for cable fracture monitoring based on the monitoring reference model; the health assessment management module monitors the cable for fracture and assesses the cable health status when the trigger conditions are met, and sends an alarm signal to the monitoring terminal when the cable health status is assessed to be abnormal, thereby reducing the monitoring cost and improving the cable monitoring efficiency while ensuring the safe operation of the cable.

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 primary transport medium for electric energy and voltage in power systems and are the carriers of electric energy. For safety reasons, cables are often covered with a high-insulation layer. In special circumstances, such as accidents, some cables may passively become overloaded. If cables are overloaded for a long time, the thermal effect of the current and the high temperature will accelerate insulation aging and even cause insulation breakdown, leading to cable breakage. In this case, it is necessary to monitor cable breakage faults, assess the cable's health, and promptly detect and repair faults to ensure safe cable operation.

[0003] However, when monitoring cable break faults, existing technologies generally use 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. A large amount of invalid data will be transmitted, which makes it impossible to reduce monitoring costs and improve cable monitoring efficiency while ensuring the safe operation of the cables.

[0004] Therefore, people need a cable health status assessment system and method based on edge computing to solve the above problems. Summary of the Invention

[0005] 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.

[0006] 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;

[0007] 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;

[0008] The cable data acquisition module collects historical operation data and historical monitoring data of the cable; the monitoring reference analysis module uses an edge computing processor to preprocess the cable data, and the preprocessed data is combined into a training sample to establish a cable monitoring reference model; the trigger condition setting module sets the trigger conditions for cable fracture monitoring based on the monitoring reference model analysis; the health assessment management module monitors the cable fracture and evaluates the cable health status when the trigger conditions are met, and sends an alarm signal to the monitoring terminal when the cable health status is assessed to be abnormal.

[0009] 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 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 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.

[0010] Furthermore, the monitoring reference analysis module includes an edge computing processing unit and a monitoring reference model establishment unit;

[0011] 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;

[0012] The edge computing processing unit uses an edge computing processor to perform data preprocessing: analyzing the cable abnormal operation coefficient based on the cable operation overload value, the installation 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 that occurred when the cable was in different abnormal operation states and the ambient temperature was different. Different abnormal operation states refer to different cable abnormal operation coefficients. The effectiveness of cable monitoring is analyzed based on the statistical results. 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 cable monitoring reference model based on the training sample data;

[0013] The collected data is preferentially transmitted to the edge computing processor for pre-processing, which speeds up the data analysis and processing.

[0014] Furthermore, the trigger condition setting module includes an impact data input unit and a monitoring trigger condition setting unit;

[0015] 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;

[0016] 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 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.

[0017] Furthermore, the health assessment management module includes a cable monitoring unit and a health status assessment unit;

[0018] 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;

[0019] When the cable monitoring unit detects that the duration of the cable operation 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 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 breakage fault alarm signal is sent to the monitoring terminal.

[0020] A cable health assessment method based on edge computing includes the following steps:

[0021] S1: Collect historical operation data and historical monitoring data of the cable;

[0022] S2: Use the edge computing processor to preprocess the cable data and form the preprocessed data into training samples;

[0023] S3: Establish a cable monitoring reference model and set the trigger conditions for cable break monitoring;

[0024] S4: When the trigger condition is met, the cable is monitored for breakage and the health status of the cable is evaluated. If the health status of the cable is found to be abnormal, an early warning is issued.

[0025] Furthermore, in S1: the operating load current data of the cable in the past 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 is 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 cable's operating overload value set in the past is obtained as A={ , ,…, }, the duration of the cable running at the 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 installation time of the cable when the cable is 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.

[0026] Furthermore, in S2: According to the formula Calculate the cable abnormal operation coefficient W when the cable was overloaded once in the past j ,in, Indicates 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, The jth time when the cable was detected to be overloaded is the installation time of the cable. The abnormal operation coefficient set of the cable when it was overloaded is W={W1, W2, ..., W j ,…,Wm}, 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 )}.

[0027] Furthermore, in S3: the training sample data is fitted to establish a cable monitoring reference model:

[0028] ;

[0029] 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 and Y = U. 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;

[0030] In order to measure the duration of overload operation of cables in different environments and the probability of cable breakage, big data technology is used to collect the load current data of cables when they are overloaded in the past. Considering that the temperature of the environment around the cable is also one of the factors affecting the cable breakage when the cable is overloaded, especially in summer when the ambient temperature is high, the cable breakage occurs frequently due to long-term overload operation. Therefore, the ambient temperature data of 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, the weaker its insulation layer will become, and it will be easily broken down, resulting in cable breakage. These data are taken into consideration when coordinating the training samples, and the number of breakage failures that occurred in different abnormal operating states and under different environments when the cable was overloaded in the past is divided into groups. Analyze the effectiveness of cable monitoring. 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. Combine multi-level data to generate training samples, fit the training samples to establish a cable monitoring reference model, and set the monitoring effectiveness threshold when the current cable is overloaded. 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. Choose the right time to monitor the cable breakage and evaluate the health status of the cable when there may be hidden dangers of cable breakage, thereby reducing the number of transmissions of invalid data, reducing the monitoring cost while ensuring the safe operation of the cable, and improving the cable monitoring efficiency.

[0031] 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. If the cable is detected to be broken, the cable health status index is evaluated as 1; if the cable is detected not to be broken, the cable health status index is evaluated as 0. When the cable health status index is evaluated to be 1, a cable breakage fault alarm signal is sent to the monitoring terminal.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention uses big data technology to collect the load current data of the cable when it is overloaded in the past. Taking into account 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 breaks frequently due to long-term overload operation, so 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 training samples, and the effectiveness of cable monitoring is analyzed according to the number of breakage failures in different abnormal operating states and different environments when the cable was overloaded in the past. The more the number of cable breakage failures, 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 failures, 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 monitoring costs while ensuring the safe operation of the cable, and improves cable monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] 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:

[0035] Figure 1 This is a structural diagram of a cable health status assessment system based on edge computing in the present invention;

[0036] Figure 2 This is a flow chart of a cable health status assessment method based on edge computing in the present invention. DETAILED DESCRIPTION

[0037] The preferred embodiments of the present invention are described below with reference to 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.

[0038] The following combination Figure 1-Figure 2 The present invention is further described with reference to the accompanying drawings and specific examples.

[0039] Example 1:

[0040] 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 historical operation data and historical monitoring data of the cable; the monitoring reference analysis module uses an edge computing processor to preprocess the cable data, and the preprocessed data is composed of training samples and a cable monitoring reference model is established; the trigger condition setting module analyzes and sets the trigger conditions for cable break monitoring based on the monitoring reference model; the health assessment management module monitors the cable break and evaluates the cable health status when the trigger conditions are met, and sends an alarm signal to the monitoring terminal when the cable health status is assessed to be abnormal.

[0041] 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 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 operation load current was monitored in the past, and the temperature data is measured by the 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.

[0042] The monitoring reference analysis module includes an edge computing processing unit and a monitoring reference model establishment unit. The edge computing processing unit uses the 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 operation 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 in different abnormal operation states and different ambient temperatures is counted. Different abnormal operation states refer to different cable abnormal operation coefficients. 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 cable monitoring reference model based on the training sample data.

[0043] 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 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.

[0044] 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 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.

[0045] Example 2:

[0046] like Figure 2 As shown, this embodiment provides a cable health assessment method based on edge computing, which is implemented based on the health assessment system in the embodiment and specifically includes the following steps:

[0047] 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 cable's operating overload value set in the past is obtained as A={ , ,…, }, the duration of the cable running at the 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 installation time of the cable when the cable is 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;

[0048] 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 was overloaded once in the past j ,in, Indicates 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, The jth time when the cable was detected to be overloaded is the installation time of the cable. The abnormal operation coefficient set of the cable when it was overloaded 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 )};

[0049] S3: Establish a cable monitoring reference model and set the trigger conditions for cable break monitoring: Fit the training sample data to establish the cable monitoring reference model:

[0050] ;

[0051] in, 、 and Represents the fitting coefficient, which can be solved according to the following formulas: 、 and :

[0052] ;

[0053] ;

[0054] ;

[0055] 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;

[0056] 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 overload value reaches the trigger value, the cable is monitored for breakage using a cable fault detector. If the cable is detected to be broken, the health status index of the cable is evaluated to be 1; if the cable is not detected to be 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;

[0057] 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 when the cable is currently overloaded, the ambient temperature around the cable is monitored to be U=31. Input the threshold and the monitored ambient temperature around the 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 current installation time of the 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 a duration of 0.16 hours, the cable fault detector is used to monitor the cable break.

[0058] Finally, it should be noted that the above description is merely a preferred embodiment 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 will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A cable health assessment system based on edge computing, characterized by: 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; Collecting 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 cable monitoring reference model; 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 the health status of the cable is abnormal; Collect the cable's previous operating load current data, and obtain the current value that the cable can withstand as I. Collect the ambient temperature of the cable measured when monitoring the cable's operating load current, and collect the cable's previous operating load current when it was overloaded as I. ’ ={I1 ’ , I2 ’ ,…,I m ’ }, the cable's operating overload value set in the past is obtained as A={ , ,…, }, the duration of the cable running at the 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 installation time of the cable when the cable is 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; According to the formula Calculate the cable abnormal operation coefficient W when the cable was overloaded once in the past j ,in, Indicates 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, The jth time when the cable was detected to be overloaded is the installation time of the cable. The abnormal operation coefficient set of the cable when it was overloaded 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 )}; After fitting the training sample data, a cable monitoring reference model is established: ； in, 、 and represents the fitting coefficient, X represents the first variable indicating the abnormal operation coefficient, Y represents the second variable indicating the ambient temperature, and Z represents the third variable indicating the effectiveness of cable monitoring.

2. The 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 monitoring reference analysis module includes an edge computing processing unit and a monitoring reference model establishment unit; The historical operation data acquisition unit is used to collect 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 collects the ambient temperature data of the cable surroundings measured when monitoring the cable operating load current 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 was overloaded and the current value that the cable can withstand. The fault information collection unit is also used to collect the installation duration 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. The cable health status assessment system based on edge computing according to claim 2, characterized in that: 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 based on the cable operation overload value, the installation 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 that occurred when the cable was in different abnormal operation states and the ambient temperature was different. Different abnormal operation states refer to different cable abnormal operation coefficients. The effectiveness of cable monitoring is analyzed based on the statistical results. 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 establishing unit establishes a monitoring reference model of the cable based on the training sample data.

4. The 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; Setting a monitoring effectiveness threshold through the impact data input unit, and inputting the threshold and the monitored current ambient temperature of the cable into the monitoring reference model when the cable is currently overloaded; 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. The 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 detects 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 assessment method based on edge computing, characterized by: 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 form training samples from the preprocessed data; S3: Establish a cable monitoring reference model and set the trigger conditions for cable break monitoring; S4: When the trigger conditions are met, the cable is monitored for breakage and the health status of the cable is evaluated. If the health status of the cable is abnormal, an early warning is issued. In S1: the operating load current data of the cable in the past is collected, and the current value that the cable can withstand is obtained as I. The ambient temperature of the cable is collected when the cable operating load current is monitored in the past, 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 cable's operating overload value set in the past is obtained as A={ , ,…, }, the duration of the cable running at the 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 installation time of the cable when the cable is 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; In S2: According to the formula Calculate the cable abnormal operation coefficient W when the cable was overloaded once in the past j ,in, Indicates 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, The jth time when the cable was detected to be overloaded is the installation time of the cable. The abnormal operation coefficient set of the cable when it was overloaded 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 )}; 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 indicating the abnormal operation coefficient, Y represents the second variable indicating the ambient temperature, and Z represents the third variable indicating the effectiveness of cable monitoring.

7. The cable health status assessment method based on edge computing according to claim 6, characterized in that: Set the monitoring effectiveness threshold to Q, and when the cable is currently overloaded, monitor the ambient temperature around the current cable to U. Input the threshold and the monitored ambient temperature around the current cable into the monitoring reference model: Let Z = Q, Y = U, 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, 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.

8. The cable health status assessment method based on edge computing according to claim 7, characterized in that: In S4: when it is monitored that the duration of the cable running at the current operating overload value reaches the trigger value, the cable is monitored for breakage. If the cable is detected to be broken, the cable health status index is evaluated as 1; if the cable is detected not to be broken, the cable health status index is evaluated as 0. When the cable health status index is evaluated to be 1, a cable breakage fault alarm signal is sent to the monitoring terminal.

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