Multi-parameter fusion driven cable full life cycle monitoring method

Abstract

The invention discloses a multi-parameter fusion driven cable full life cycle monitoring method, which relates to the technical field of cable monitoring, and comprises the following steps: acquiring risk data of the surrounding environment of a cable and historical operation data of the cable, and performing calculation and analysis to obtain a fault risk prediction index; the fault risk prediction index is used for reflecting the possibility of cable fault risk; dividing the priority of cable maintenance according to a comparative analysis result of the fault risk prediction index of the cable and a preset fault risk upper limit threshold and a fault risk lower limit threshold; according to the comparison result of the fault risk prediction index and the preset fault risk threshold value, the maintenance priority is divided into the emergency maintenance level, the planned maintenance level and the maintenance-free level, the emergency maintenance situation is immediately alarmed and dispatched for maintenance, the planned maintenance is processed after a scheme is formulated within the specified time, normal operation is kept if maintenance is not needed, and the maintenance efficiency is improved. Operation and maintenance resource distribution is optimized, unnecessary shutdown is reduced, and the operation reliability and the service life of the cable are improved.

Description

Technical Field

[0001] This invention relates to the field of cable monitoring technology, and in particular to a multi-parameter fusion-driven method for monitoring the entire life cycle of cables. Background Technology

[0002] A cable is an electrical energy or signal transmission device, typically composed of several or groups of conductors (each group containing at least two conductors) twisted together in a rope-like manner. Each group of conductors is insulated from each other and often twisted around a central conductor, with the entire cable covered by a highly insulating outer layer. Cables are characterized by being internally energized and externally insulated. As the core carrier of electrical energy transmission in power systems, cables bear the crucial connection function from the generation end to the user end. Cables are widely used in urban power grids, industrial parks, and new energy projects. However, during long-term operation, cables are susceptible to environmental corrosion, mechanical stress, and electrical aging, which may lead to potential faults such as decreased insulation performance and increased partial discharge. Given the ever-increasing demands for power supply reliability in modern power grids, conducting full-process condition monitoring of cables is of great significance.

[0003] However, traditional cable operation status monitoring often relies on manual periodic collection and monitoring of single parameters of the cable, such as monitoring the cable sheath temperature only through temperature sensors or obtaining discharge signals through partial discharge detectors. This isolated monitoring mode is difficult to fully reflect the true health status of the cable and is prone to misjudgment or omission.

[0004] To address the aforementioned technical deficiencies, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to address the problem that traditional cable operation status monitoring often relies on manual periodic collection and monitoring of a single parameter of the cable, such as monitoring the cable sheath temperature only through a temperature sensor or obtaining discharge signals through a partial discharge detector. This isolated monitoring mode is difficult to fully reflect the true health status of the cable and is prone to misjudgment or omission.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a multi-parameter fusion-driven method for monitoring the entire life cycle of cables, comprising the following steps: Step 1: By deploying a sensor array in the cable line, real-time data on cable status and risk data of the surrounding environment are collected. Historical operating data of the cable is also collected through the cable operation and maintenance database. The collected status and risk data are then preprocessed, and a temperature correction coefficient is analyzed based on the collected data. Step 2: Obtain cable status data and perform calculations and analysis to obtain the cable health status index, and then determine the cable status based on the health status index; Step 3: Obtain risk data of the environment surrounding the cable and historical operating data of the cable, and perform calculation and analysis to obtain the cable fault risk prediction index. The fault risk prediction index is used to reflect the probability of cable failure. Step 4: Based on the comparative analysis results of the cable fault risk prediction index with the preset upper and lower thresholds of fault risk, the priority of cable maintenance is divided.

[0007] Furthermore, the status data includes cable temperature, partial discharge signal strength of the cable, and grounding current data of the cable; the risk data includes temperature, humidity, abnormal gas concentration, and water level data of the surrounding environment of the cable; and the historical operation data includes the number of historical cable failures and the cable's service life data.

[0008] Furthermore, in step one, the analysis process for the temperature correction coefficient is as follows: Collect historical cable temperature measurement data from multiple cables operating at different ambient temperatures to form a dataset. Randomly divide the dataset into a training set and a test set. Label each cable in the training set with its actual temperature as a label. Then, construct a temperature correction model based on a convolutional neural network. Train the temperature correction model using the training set and test it using the test set to obtain a qualified temperature correction model. Finally, input the cable ambient temperature and cable measurement temperature data into the qualified temperature correction model to determine the cable's temperature correction coefficient. .

[0009] Furthermore, the sensor array includes a temperature sensor, a partial discharge detection sensor, a water level sensor, a temperature and humidity sensor, a gas analysis sensor, an infrared imaging sensor, and a ground current monitoring sensor.

[0010] Furthermore, the preprocessing of the collected status and risk data involves standardizing the collected temperature, partial discharge signal strength, grounding current environment temperature, humidity, abnormal gas concentration, and water level parameters using the Min-Max standardization method. ,in, These are standardized parameter values. For the first The value of the parameter to be processed. It is the minimum value of parameters of the same type. It represents the maximum value of parameters of the same type.

[0011] Furthermore, the calculation process for the cable's health status index is as follows: S11. Obtain cable temperature, cable partial discharge signal strength, and cable grounding current data, and perform analysis and calculation. S12. Calculate the cable health status index according to the following formula. : in, The number of monitoring samples for cable temperature. For the first The cable temperature value monitored this time. The preset standard cable temperature value, This is the preset maximum temperature that the cable can withstand. The partial discharge signal strength of the cable. The preset maximum allowable partial discharge signal strength for the cable. This is the grounding current value of the cable. This is the preset maximum allowable grounding current value for the cable. The preset standard partial discharge signal strength, The preset standard grounding current value, This is the temperature correction factor for the cable; S13. Obtain the preset health status upper limit threshold. and the lower limit threshold of health status With health status index Comparative analysis, when If this occurs, it indicates a serious cable fault, and personnel will be dispatched immediately to inspect and repair the cable. If the cable is in good condition and requires no maintenance, then it is in good working order. If the cable is in a state of warning, further calculation and analysis of the probability of cable failure is required. S14. When the cable temperature exceeds the cable temperature threshold, when the partial discharge signal strength of the cable exceeds the discharge intensity threshold, and when the grounding current of the cable exceeds the grounding current threshold, an abnormal alarm will be sent to the cable life cycle monitoring terminal, and personnel will be dispatched to inspect and repair the cable.

[0012] Furthermore, the calculation process for the cable fault risk prediction index is as follows: S21. Obtain data on temperature, humidity, abnormal gas concentration, water level, and historical fault count and service life of the cable in the surrounding environment of the cable, and analyze and calculate them in conjunction with the health status index of the cable under early warning status. S22. Calculate the cable fault risk prediction index according to the following formula. : in, This refers to the health status index of cables in a warning state. The service life of the cable. For the design service life of the cable, This represents the number of historical cable faults. The preset number of standard cable faults, The temperature of the surrounding environment of the cable. The preset standard temperature for the cable environment. The humidity of the surrounding environment where the cable is located. The standard humidity of the preset cable environment, The abnormal gas concentration in the surrounding environment of the cable. The preset standard abnormal gas concentration for the cable environment includes carbon monoxide and hydrogen sulfide. The water level in the surrounding environment of the cable. The standard water level of the cable environment is preset. The cable fault risk prediction index is used to reflect the probability of cable failure. The higher the value of the fault risk prediction index, the higher the probability of cable failure. S23. When the temperature of the surrounding environment of the cable exceeds the ambient temperature threshold, the humidity exceeds the humidity threshold, the abnormal gas concentration exceeds the abnormal gas concentration threshold, or the water level exceeds the warning water level threshold, an abnormal alarm will be sent to the cable life cycle monitoring terminal, and personnel will be dispatched to inspect and repair the cable.

[0013] Furthermore, the priority allocation process for cable maintenance is as follows: S31. Obtain the preset upper limit threshold for fault risk. and the lower limit threshold of fault risk With the cable fault risk prediction index Comparative analysis; S32, when In such cases, the cable maintenance priority will be classified as emergency maintenance, and an emergency alarm message will be immediately sent to the cable life cycle monitoring terminal, and personnel will be dispatched immediately to inspect and repair the cable. S33, when In such cases, the priority of cable maintenance will be classified as planned maintenance, and a planned alarm message will be immediately sent to the cable life cycle monitoring terminal. A maintenance plan will be prepared within the specified time, and then staff will be dispatched to inspect and repair the cable. S34, when In this case, the cable maintenance priority is classified as maintenance-free, meaning no maintenance is required and the cable continues to operate normally.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This multi-parameter fusion-driven cable lifecycle monitoring method utilizes a sensor array to collect cable status data, environmental risk data, and historical operational data in real time. This addresses the information limitations of traditional monitoring methods that rely on manual, periodic collection of single parameters. Furthermore, the Min-Max standardization method is used to preprocess the collected data, effectively eliminating the influence of differences in parameter dimensions and providing a unified data foundation for subsequent calculations of the health status index and fault risk prediction index. Then, the health status index calculation comprehensively considers key parameters such as cable temperature, partial discharge signal strength, and grounding current, and dynamically determines the cable's operational status based on preset health status thresholds. This enables a quantitative assessment of cable health and allows for immediate triggering of response mechanisms when cable anomalies occur, significantly improving the efficiency of handling faulty cables. Meanwhile, the fault risk prediction index integrates external factors such as ambient temperature, humidity, abnormal gas concentration, and water level with data on the cable's historical fault count and years of operation, and combines this with the cable's health status index under early warning conditions to construct a multi-dimensional risk assessment model. This further enhances the scientific nature of fault prediction. Then, based on the comparison between the fault risk prediction index and the preset fault risk threshold, maintenance priorities are divided into emergency maintenance, planned maintenance, and no-maintenance levels. Emergency maintenance triggers an immediate alarm and dispatches repair personnel. Planned maintenance involves developing a solution within a specified timeframe and then proceeding with the repair. No-maintenance levels maintain normal operation, optimizing the allocation of maintenance resources, reducing unnecessary downtime, and improving the reliability and service life of the cable. Attached Figure Description

[0015] Figure 1 A schematic diagram of the method flow of the present invention is shown. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example: like Figure 1As shown, the multi-parameter fusion-driven cable lifecycle monitoring method first involves deploying a sensor array in the cable line to collect real-time cable status data and risk data of the surrounding environment. The sensor array includes temperature sensors, partial discharge detection sensors, water level sensors, temperature and humidity sensors, gas analysis sensors, infrared imaging sensors, and grounding current monitoring sensors. Historical operating data of the cable is also collected from the cable's maintenance database. The collected status and risk data are then preprocessed, and a temperature correction coefficient is calculated based on the collected data. This coefficient corrects the cable temperature data, preventing the ambient temperature from affecting the actual cable temperature. For example, in winter, high ambient temperatures can cause the measured cable temperature to drop, resulting in a lower measured temperature than the actual cable temperature. The cable temperature data refers to the measured temperature (i.e., the temperature data measured by the temperature sensor). Since the measured temperature is obtained through a temperature sensor, the sensor is affected by the measurement environment, leading to deviations in the actual cable temperature. The temperature correction coefficient corrects these deviations, ensuring the actual cable temperature is the cable's true temperature. It should be noted that the status data includes cable temperature, partial discharge signal strength of the cable, and grounding current data of the cable; the risk data includes temperature, humidity, abnormal gas concentration and water level of the surrounding environment of the cable; and the historical operation data includes the number of historical cable failures and the service life of the cable. The analysis process for the temperature correction coefficient is as follows: Historical cable temperature measurement data at different ambient temperatures are collected as a dataset. This dataset is randomly divided into a training set and a test set. The actual cable temperature is labeled on each cable in the training set as a tag. A temperature correction model is then constructed based on a convolutional neural network. The training set is used to train the temperature correction model, and the test set is used to test it to obtain a qualified temperature correction model. Finally, the cable ambient temperature and cable measurement temperature data are input into the qualified temperature correction model to determine the cable's temperature correction coefficient. ; The preprocessing of the collected status and risk data involves standardizing the collected temperature, partial discharge signal strength, grounding current, ambient temperature, humidity, abnormal gas concentration, and water level parameters using the Min-Max standardization method. ,in, These are standardized parameter values. For the first The value of the parameter to be processed. It is the minimum value of parameters of the same type. It represents the maximum value of parameters of the same type.

[0018] Then, the cable status data is acquired and calculated and analyzed to obtain the cable health status index, and the cable status is determined based on the health status index. The calculation process for the cable health status index is as follows: S11. Obtain cable temperature, cable partial discharge signal strength, and cable grounding current data, and perform analysis and calculation. S12. Calculate the cable health status index according to the following formula. : in, The number of monitoring samples for cable temperature. For the first The cable temperature value monitored this time. The preset standard cable temperature value, This is the maximum temperature that the cable can withstand (mainly determined by the heat resistance rating of the cable insulation material, such as 70℃ for PVC insulated cables and 90℃ for XLPE insulated cables). The partial discharge signal strength of the cable. The preset maximum allowable partial discharge signal strength for the cable (based on insulation material characteristics, rated voltage level and industry standards (such as IEC60270), the limit is usually 5-10pC for medium and low voltage cables, and more stringent for high voltage cables (≤1pC), to prevent insulation aging and breakdown). This is the grounding current value of the cable. The maximum allowable grounding current value for the cable is preset (based on cable type, rated voltage, grounding system design and safety standards (such as GB 50217), usually limited to within 10A to prevent insulation overheating and personal safety risks). The preset standard partial discharge signal strength, The preset standard grounding current value, This is the temperature correction factor for the cable; S13. Obtain the preset health status upper limit threshold. and the lower limit threshold of health status With health status index Comparative analysis, when If this occurs, it indicates a serious cable fault, and personnel will be dispatched immediately to inspect and repair the cable. If the cable is in good condition and requires no maintenance, then it is in good working order. If the cable is in a state of warning, further calculation and analysis of the probability of cable failure is required. S14. When the cable temperature exceeds the cable temperature threshold, when the partial discharge signal strength of the cable exceeds the discharge intensity threshold, and when the grounding current of the cable exceeds the grounding current threshold, an abnormal alarm will be sent to the cable life cycle monitoring terminal, and personnel will be dispatched to inspect and repair the cable.

[0019] Next, risk data of the environment surrounding the cable and historical operating data of the cable are obtained and analyzed to derive the cable fault risk prediction index, which is used to reflect the probability of cable failure. The calculation process for the cable fault risk prediction index is as follows: S21. Obtain data on temperature, humidity, abnormal gas concentration, water level, and historical fault count and service life of the cable in the surrounding environment of the cable, and analyze and calculate them in conjunction with the health status index of the cable under early warning status. S22. Calculate the cable fault risk prediction index according to the following formula. : in, This refers to the health status index of cables in a warning state. The service life of the cable. For the design service life of the cable, This represents the number of historical cable faults. The preset number of standard cable faults, The temperature of the surrounding environment of the cable. The preset standard temperature for the cable environment. The humidity of the surrounding environment where the cable is located. The standard humidity of the preset cable environment, The abnormal gas concentration in the surrounding environment of the cable. The preset standard abnormal gas concentration for the cable environment includes carbon monoxide and hydrogen sulfide. The water level in the surrounding environment of the cable. The standard water level of the cable environment is preset. The cable fault risk prediction index is used to reflect the probability of cable fault risk. The higher the value of the fault risk prediction index, the higher the probability of cable fault risk. It should be noted that when there is no abnormal gas or water level in the surrounding environment of the cable, the values ​​of "abnormal gas concentration in the surrounding environment of the cable" and "water level in the surrounding environment of the cable" are calculated and analyzed as zero. S23. When the temperature of the surrounding environment of the cable exceeds the ambient temperature threshold, the humidity exceeds the humidity threshold, the abnormal gas concentration exceeds the abnormal gas concentration threshold, or the water level exceeds the warning water level threshold, an abnormal alarm will be sent to the cable life cycle monitoring terminal, and personnel will be dispatched to inspect and repair the cable.

[0020] Finally, based on the comparative analysis results of the cable fault risk prediction index with the preset upper and lower thresholds of fault risk, the priority of cable maintenance is divided. The priority allocation process for cable maintenance is as follows: S31. Obtain the preset upper limit threshold for fault risk. and the lower limit threshold of fault risk With the cable fault risk prediction index Comparative analysis; S32, when In such cases, the cable maintenance priority will be classified as emergency maintenance, and an emergency alarm message will be immediately sent to the cable life cycle monitoring terminal, and personnel will be dispatched immediately to inspect and repair the cable. S33, when In such cases, the priority of cable maintenance will be classified as planned maintenance, and a planned alarm message will be immediately sent to the cable life cycle monitoring terminal. A maintenance plan will be prepared within the specified time, and then staff will be dispatched to inspect and repair the cable. S34, when In this case, the cable maintenance priority is classified as maintenance-free, meaning no maintenance is required and the cable continues to operate normally.

[0021] This invention addresses the limitations of traditional monitoring methods that rely on manual, periodic collection of single parameters, by deploying a sensor array to collect cable status data, environmental risk data, and historical operational data in real time. Furthermore, the use of a Min-Max standardization method for data preprocessing effectively eliminates the influence of differences in parameter dimensions, providing a unified data foundation for subsequent calculations of the health status index and fault risk prediction index. The health status index calculation comprehensively considers key parameters such as cable temperature, partial discharge signal strength, and grounding current, and dynamically determines the cable's operational status based on preset health status thresholds. This enables a quantitative assessment of cable health and allows for immediate triggering of a response mechanism when cable anomalies occur, significantly improving the efficiency of handling faulty cables. Meanwhile, the fault risk prediction index integrates external factors such as ambient temperature, humidity, abnormal gas concentration, and water level with data on the cable's historical fault count and years of operation, and combines this with the cable's health status index under early warning conditions to construct a multi-dimensional risk assessment model. This further enhances the scientific nature of fault prediction. Then, based on the comparison between the fault risk prediction index and the preset fault risk threshold, maintenance priorities are divided into emergency maintenance, planned maintenance, and no-maintenance levels. Emergency maintenance triggers an immediate alarm and dispatches repair personnel. Planned maintenance involves developing a solution within a specified timeframe and then proceeding with the repair. No-maintenance levels maintain normal operation, optimizing the allocation of maintenance resources, reducing unnecessary downtime, and improving the reliability and service life of the cable.

[0022] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0023] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-parameter fusion-driven method for monitoring the entire lifecycle of cables, characterized in that, Includes the following steps: Step 1: By deploying a sensor array in the cable line, real-time data on cable status and risk data of the surrounding environment are collected. Historical operating data of the cable is also collected through the cable operation and maintenance database. The collected status and risk data are then preprocessed, and a temperature correction coefficient is analyzed based on the collected data. Step 2: Obtain cable status data and perform calculations and analysis to obtain the cable health status index, and then determine the cable status based on the health status index; Step 3: Obtain risk data of the environment surrounding the cable and historical operating data of the cable, and perform calculation and analysis to obtain the cable fault risk prediction index. The fault risk prediction index is used to reflect the probability of cable failure. Step 4: Based on the comparative analysis results of the cable fault risk prediction index with the preset upper and lower thresholds of fault risk, the priority of cable maintenance is divided.

2. The multi-parameter fusion-driven cable lifecycle monitoring method according to claim 1, characterized in that, The status data includes cable temperature, partial discharge signal strength of the cable, and grounding current data of the cable. The risk data includes temperature, humidity, abnormal gas concentration, and water level data of the surrounding environment of the cable. The historical operation data includes the number of historical cable failures and the cable's service life data.

3. The multi-parameter fusion-driven cable lifecycle monitoring method according to claim 1, characterized in that, In step one, the analysis process for the temperature correction coefficient is as follows: Collect historical cable temperature measurement data from multiple cables operating at different ambient temperatures to form a dataset. Randomly divide the dataset into a training set and a test set. Label each cable in the training set with its actual temperature as a tag. Then, construct a temperature correction model based on a convolutional neural network. Train the temperature correction model using the training set and test it using the test set to obtain a qualified temperature correction model. Finally, input the cable ambient temperature and cable measurement temperature data into the qualified temperature correction model to determine the cable's temperature correction coefficient. .

4. The multi-parameter fusion-driven cable lifecycle monitoring method according to claim 1, characterized in that, The sensor array includes a temperature sensor, a partial discharge detection sensor, a water level sensor, a temperature and humidity sensor, a gas analysis sensor, an infrared imaging sensor, and a ground current monitoring sensor.

5. The multi-parameter fusion-driven cable lifecycle monitoring method according to claim 1, characterized in that, The preprocessing of the collected status and risk data involves standardizing the collected temperature, partial discharge signal strength, grounding current, ambient temperature, humidity, abnormal gas concentration, and water level parameters using the Min-Max standardization method. ,in, These are standardized parameter values. For the first The value of the parameter to be processed. It is the minimum value of parameters of the same type. It represents the maximum value of parameters of the same type.

6. The multi-parameter fusion-driven cable lifecycle monitoring method according to claim 1, characterized in that, The calculation process for the cable health status index is as follows: S11. Obtain cable temperature, cable partial discharge signal strength, and cable grounding current data, and perform analysis and calculation. S12. Calculate the cable health status index according to the following formula. : in, The number of monitoring samples for cable temperature. For the first The cable temperature value monitored this time. The preset standard cable temperature value, This is the preset maximum temperature that the cable can withstand. The partial discharge signal strength of the cable. The preset maximum allowable partial discharge signal strength for the cable. This is the grounding current value of the cable. This is the preset maximum allowable grounding current value for the cable. The preset standard partial discharge signal strength, The preset standard grounding current value, This is the temperature correction factor for the cable; S13. Obtain the preset health status upper limit threshold. and the lower limit threshold of health status With health status index Comparative analysis, when If this occurs, it indicates a serious cable fault, and personnel will be dispatched immediately to inspect and repair the cable. If the cable is in good condition and requires no maintenance, then it is in good working order. If the cable is in a state of warning, further calculation and analysis of the probability of cable failure is required. S14. When the cable temperature exceeds the cable temperature threshold, when the partial discharge signal strength of the cable exceeds the discharge intensity threshold, and when the grounding current of the cable exceeds the grounding current threshold, an abnormal alarm will be sent to the cable life cycle monitoring terminal, and personnel will be dispatched to inspect and repair the cable.

7. The multi-parameter fusion-driven cable lifecycle monitoring method according to claim 1, characterized in that, The calculation process for the cable fault risk prediction index is as follows: S21. Obtain data on temperature, humidity, abnormal gas concentration, water level, and historical fault count and service life of the cable in the surrounding environment of the cable, and analyze and calculate them in conjunction with the health status index of the cable under early warning status. S22. Calculate the cable fault risk prediction index according to the following formula. : in, This refers to the health status index of cables in a warning state. The service life of the cable. For the design service life of the cable, This represents the number of historical cable faults. The preset number of standard cable faults, The temperature of the surrounding environment of the cable. The preset standard temperature for the cable environment. The humidity of the surrounding environment where the cable is located. The standard humidity of the preset cable environment, The abnormal gas concentration in the surrounding environment of the cable. The preset standard abnormal gas concentration for the cable environment includes carbon monoxide and hydrogen sulfide. The water level in the surrounding environment of the cable. The standard water level of the cable environment is preset. The cable fault risk prediction index is used to reflect the probability of cable failure. The higher the value of the fault risk prediction index, the higher the probability of cable failure. S23. When the temperature of the surrounding environment of the cable exceeds the ambient temperature threshold, the humidity exceeds the humidity threshold, the abnormal gas concentration exceeds the abnormal gas concentration threshold, or the water level exceeds the warning water level threshold, an abnormal alarm will be sent to the cable life cycle monitoring terminal, and personnel will be dispatched to inspect and repair the cable.

8. The multi-parameter fusion-driven cable lifecycle monitoring method according to claim 1, characterized in that, The priority allocation process for cable maintenance is as follows: S31. Obtain the preset upper limit threshold for fault risk. and the lower limit threshold of fault risk With the cable fault risk prediction index Comparative analysis; S32, when In such cases, the cable maintenance priority will be classified as emergency maintenance, and an emergency alarm message will be immediately sent to the cable life cycle monitoring terminal, and personnel will be dispatched immediately to inspect and repair the cable. S33, when In such cases, the priority of cable maintenance will be classified as planned maintenance, and a planned alarm message will be immediately sent to the cable life cycle monitoring terminal. A maintenance plan will be prepared within the specified time, and then staff will be dispatched to inspect and repair the cable. S34, when In this case, the cable maintenance priority is classified as maintenance-free, meaning no maintenance is required and the cable continues to operate normally.

Images