A method and system for online monitoring and intelligent analysis of cable damage

Abstract

The application discloses an online monitoring and intelligent analysis method and system for cable damage, and relates to the technical field of power cable state monitoring and intelligent operation and maintenance; by constructing a full-element digital twin and a multi-physical field coupling simulation model, real-time monitoring and accurate evaluation of the cable state are realized; the method and the system can not only discover potential faults in advance and avoid unplanned shutdown, but also generate targeted operation and maintenance disposal decision suggestions based on damage evolution trends and residual life evaluation results, so that a full-closed-loop management of monitoring-prediction-early warning-operation is formed.

Description

Technical Field

[0001] This invention relates to the field of power cable condition monitoring and intelligent operation and maintenance technology, specifically to an online monitoring and intelligent analysis method and system for cable damage. Background Technology

[0002] With the rapid development of the power industry, cables, as an important carrier of power transmission, are directly related to the reliability of the power grid and the quality of power supply through their safe and stable operation. However, during long-term operation, cables are inevitably affected by various factors such as material aging, mechanical damage, and electrical degradation, which can lead to a decline in insulation performance and even cause faults, resulting in power outages and economic losses.

[0003] Traditional cable monitoring methods primarily rely on periodic inspections and offline testing. These methods are not only time-consuming and labor-intensive, but also fail to reflect the actual operating status of cables in real time. Periodic inspections often only detect relatively obvious damage, failing to capture early, minor defects; while offline testing requires power outages, affecting the normal operation of the power grid, and the test conditions differ from actual operating conditions, leading to inaccurate assessment results. Furthermore, traditional methods lack the ability to accurately assess the remaining lifespan of cables, making it difficult to formulate scientific and reasonable operation and maintenance strategies based on the actual condition of the cables. Operation and maintenance work is often reactive, inefficient, and costly.

[0004] In view of the many shortcomings of traditional cable monitoring methods, the online monitoring and intelligent analysis method and system for cable damage proposed in this invention is particularly important. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an online monitoring and intelligent analysis method and system for cable damage. By constructing a full-element digital twin and a multi-physics coupled simulation model, it achieves real-time monitoring and accurate assessment of cable conditions. This method and system can not only detect potential faults in advance and avoid unplanned downtime, but also generate targeted operation and maintenance decision-making suggestions based on damage evolution trends and remaining life assessment results, forming a closed-loop management system of monitoring, prediction, early warning, and operation and maintenance. This innovative solution significantly improves the reliability and safety of cable operation, reduces operation and maintenance costs, and provides strong support for the intelligent development of the power industry.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, an online monitoring and intelligent analysis method for cable damage, the specific steps of which are as follows: Step S1: Construct a full-element digital twin of the cable body and its operating environment. The digital twin covers the cable's geometric structure, intrinsic material properties, laying information, historical operation and maintenance data, as well as the geological, meteorological, and electromagnetic environment in which the cable is located, completing the digital mirror mapping of the physical cable. Step S2: Collect real-time online monitoring data and environmental data during cable operation. After data preprocessing, synchronize the data to the digital twin in real time through the time-series synchronization mapping interface to achieve real-time dynamic synchronization of the operating status and environmental conditions between the digital twin and the physical cable. Step S3: Construct a multi-physics field coupled simulation model of cable material aging, mechanical damage, and electrical degradation. Deeply couple the real-time running data synchronized in the digital twin with the multi-physics field coupled simulation model in real time to reproduce the current damage state of the cable and simulate the dynamic evolution process and development trend of cable damage under different working conditions. Step S4: Based on the damage evolution simulation results, combined with the critical threshold for cable material deterioration and failure, historical full-cycle data and a database of similar cable failure cases, the remaining service life of the cable body and key joints is accurately assessed through damage accumulation theory and machine learning correction algorithms. Step S5: Based on the damage evolution trend and remaining life assessment results, classify and provide early warnings for potential cable faults. At the same time, combine cable operation and maintenance procedures and on-site conditions to generate targeted operation and maintenance decision-making suggestions, and output them to the cable operation and maintenance management platform to form a closed-loop management system of monitoring, prediction, early warning and operation and maintenance.

[0007] Furthermore, the construction process of the full-element digital twin in step S1 is as follows: First, high-precision geometric modeling of the cable body's intermediate joints, terminals, and laying channels is completed using 3D laser scanning and BIM modeling. Then, based on the cable's factory inspection report, the intrinsic physical properties, chemical properties, and degradation constitutive parameters of the insulation, sheath, and conductor materials are entered. Next, based on the laying construction completion data, the entire process data of the cable's burial depth, bending radius, fixed method, and joint installation process are entered. Then, the dynamic boundary condition modeling of the soil geological temperature, humidity, and settlement data along the cable route is completed by connecting with the geographic information system and the regional meteorological platform. Finally, historical operation and maintenance data, defect records, and test data after the cable's commissioning are entered to complete the digital mirror mapping of the physical cable's full-dimensional attributes.

[0008] Furthermore, the implementation process of real-time data acquisition and synchronization mapping in step S2 is as follows: a distributed optical fiber temperature and strain monitoring system is deployed along the entire cable line; UHF partial discharge online monitoring devices are deployed at cable intermediate joints and terminal heads; a high-precision grounding current sensor is deployed in the cable grounding circuit; and environmental temperature, humidity, soil moisture content, and vibration sensors are deployed at 50-meter intervals along the cable laying channel. Real-time load current data of the cable operation is collected synchronously. All collected multi-source heterogeneous data undergoes outlier removal, adaptive wavelet denoising, millisecond-level time sequence alignment, and normalization preprocessing. Then, the preprocessed data is synchronized to the digital twin in real time through a low-latency time sequence synchronization mapping interface to achieve dynamic synchronization between the digital twin and the physical cable operating status, with the synchronization delay controlled within 100ms.

[0009] Furthermore, the deep coupling process between real-time operating data and the multiphysics field coupled simulation model in step S3 involves using the synchronized real-time operating data and environmental data in the digital twin as the dynamic input boundary conditions of the simulation model. First, the heat source input parameters of the thermal field model are corrected using real-time load current and conductor temperature data. Then, the excitation parameters of the electric field model are corrected using real-time electric field strength and partial discharge data. Next, the load parameters of the force field model are corrected using real-time strain and vibration data. Finally, the boundary conditions of the chemical field model are corrected using environmental temperature and humidity and soil parameters, thus completing the real-time dynamic correction of the multiphysics field model. Then, the current insulation degradation mechanical damage and material aging state of the cable are reproduced through finite element iterative calculation. Finally, based on the corrected model, the dynamic evolution process and development trend of cable damage under different load conditions and environmental conditions over the next 12 months, 24 months, and 36 months are simulated.

[0010] Furthermore, in step S3, the multiphysics coupling simulation model adopts the cable damage evolution rate coupling calculation method, and the core calculation formula is: ,in This represents the real-time damage evolution rate of the cable insulation material. The thermal aging damage rate is calculated from the real-time conductor temperature and the material's thermal aging constitutive equation. The electro-aging damage rate is calculated from the real-time electric field strength and partial discharge quantity. The mechanical damage rate is calculated from the real-time strain value and the fatigue accumulation equation. , , The coupling weights for the damage in the three subfields are derived from the contribution results obtained from orthogonal experiments on multiphysics coupling degradation. Values ​​0.4 The value is 0.45. The value is 0.15. The environmental coupling correction coefficient is calculated from the normalized values ​​of real-time environmental humidity, soil moisture content, and salt spray concentration, with a value range of 0.9 to 1.6. This method can accurately reproduce the dynamic evolution process of cable damage under multi-field coupling, and improve the fit and accuracy of simulation.

[0011] Furthermore, the remaining service life assessment in step S4 employs a cable damage cumulative life prediction algorithm, the core formula of which is: ,in The remaining service life of the target section of the cable. The intrinsic failure life threshold for cable insulation materials is derived from the 95% confidence lower limit of the average failure life of 10 parallel tests in the accelerated aging type test of the same batch of cables at the factory. The current cumulative damage equivalent of the cable is obtained by integrating the damage evolution results output from the multiphysics coupled simulation over the interval from cable commissioning to the current time. The real-time operating condition comprehensive correction coefficient is obtained by weighted summation of the normalized values ​​of three core parameters: current load rate, ambient temperature, and grounding current. The weights are derived from Pearson correlation analysis results from a database of similar cable failure cases: load rate weight 0.55, ambient temperature weight 0.25, and grounding current weight 0.2. The nonlinear degradation correction coefficient is obtained by fitting the historical operating data of the cable using the least squares method, with a value ranging from 0.8 to 1.5. This algorithm can accurately match the nonlinear degradation law of the cable, greatly improving the accuracy and adaptability of the remaining life assessment.

[0012] Furthermore, after the remaining service life assessment in step S4 is completed, a continuous distribution map of the remaining service life of the entire cable will be generated based on the monitoring points and simulation calculation nodes along the entire cable line. The map will mark the precise remaining service life values ​​of key nodes such as intermediate joints and terminal heads of the cable body. At the same time, it will simulate the fluctuation range of the remaining service life of the cable under three different operating conditions of 80%, 100%, and 120% of the rated load. It will also combine the failure case library of similar cables to rank the failure risk probability of cable sections with a remaining service life of less than 36 months, clarify the failure risk level and core influencing factors of different sections, and provide accurate quantitative data support for subsequent early warning classification and operation and maintenance decisions.

[0013] Furthermore, the implementation process of graded advanced early warning and closed-loop operation and maintenance management in step S5 is as follows: based on the damage evolution trend and remaining life assessment results, potential cable faults are divided into three warning levels. Level 1 warning corresponds to an emergency state where the remaining life is less than 6 months or the damage is about to reach the failure threshold; Level 2 warning corresponds to an important state where the remaining life is 6-24 months or the damage is accelerating; and Level 3 warning corresponds to a general deterioration state where the remaining life is greater than 24 months. For different warning levels, combined with cable operation and maintenance procedures and on-site conditions, corresponding maintenance priorities, maintenance methods, special test projects, load control schemes, and equipment replacement cycle suggestions are generated. Simultaneously, the warning information and operation and maintenance decision suggestions are output to the cable operation and maintenance management platform, and operation and maintenance work orders of the corresponding level are automatically generated. The entire process of work order dispatch, disposal, and acceptance is tracked. After disposal, the relevant data is synchronized to the digital twin to complete the iterative optimization of the model.

[0014] Furthermore, the method also includes a continuous self-optimization process between the digital twin and the multiphysics coupled simulation model. This process involves synchronizing the cable insulation damage detection data obtained from the offline test with the maintenance data to the digital twin after each cable operation and maintenance or offline test. The actual detected damage state is compared with the historical prediction results of the simulation model, and the deviation between the prediction results and the actual detection results is calculated. Based on the deviation value, the degradation constitutive parameters and weights of the multiphysics coupled simulation model are iteratively corrected using a gradient descent algorithm. At the same time, the cable material properties and state parameters in the digital twin are updated. After the correction is completed, the damage evolution simulation and remaining life assessment are performed again to continuously improve the prediction accuracy and adaptability of the model and adapt to the state changes throughout the cable's entire life cycle.

[0015] On the other hand, an online monitoring and intelligent analysis system for cable damage includes: Digital Twin Construction Module: Used to complete the construction, verification, and dynamic updating of a full-element digital twin of the cable body and its operating environment, realizing a full-dimensional digital mirror of the physical cable; Data acquisition and synchronization mapping module: used to connect to various online monitoring devices and environmental monitoring terminals along the cable, complete the real-time data acquisition and preprocessing, and map the data to the digital twin in real time through the time synchronization interface to achieve dynamic synchronization between the digital twin and the physical cable; Multiphysics Coupled Simulation Module: Built-in multiphysics coupled simulation model of cable material aging, mechanical damage and electrical degradation, used to receive real-time synchronous data from digital twin, complete dynamic correction of model boundary conditions, and simulate the dynamic evolution process and development trend of cable damage; Remaining service life assessment module: Based on the damage evolution simulation results, combined with the historical full-cycle data of cable material failure thresholds and the failure case library, it is used to accurately calculate the remaining service life of the cable and generate a full-line distribution map; Advanced warning and operation and maintenance decision-making module: It is used to complete the advanced warning of fault classification based on the damage evolution trend and remaining life assessment results, generate targeted operation and maintenance decision-making suggestions, and output them to the cable operation and maintenance management platform simultaneously.

[0016] Compared with existing technologies, this online monitoring and intelligent analysis method and system for cable damage has the following advantages: I. This method and system construct a full-element digital twin of the cable body and its operating environment, and combine it with a multi-physics coupled simulation model to accurately reproduce the current damage state of the cable in real time, including material aging, mechanical damage, and electrical degradation. Through deep real-time coupled simulation, the system can simulate the dynamic evolution and development trend of cable damage under different operating conditions. Furthermore, by combining damage accumulation theory and machine learning correction algorithms, it can accurately assess the remaining service life of the cable body and key joints. This precise monitoring and assessment capability helps to detect potential faults in advance, avoid unplanned downtime, and improve the reliability and safety of cable operation.

[0017] Second, this system not only enables real-time monitoring of cable damage and assessment of remaining life, but also provides tiered, proactive warnings for potential cable faults based on these results. Combining cable maintenance procedures with on-site conditions, the system generates targeted maintenance decision-making suggestions and simultaneously outputs them to the cable maintenance management platform, automatically generating maintenance work orders and completing the entire process of work order dispatch, handling, and acceptance. This closed-loop management model achieves proactive and refined maintenance work, significantly improving maintenance efficiency. Furthermore, data-driven decision-making suggestions enhance the scientific rigor and accuracy of maintenance decisions. In addition, the system possesses continuous self-optimization capabilities for digital twins and multiphysics coupled simulation models, continuously improving the model's predictive accuracy and adaptability, further ensuring the safe operation of cables throughout their entire lifecycle.

[0018] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of an online monitoring and intelligent analysis method for cable damage; Figure 2 A flowchart illustrating the cable fault classification, early warning, and closed-loop management process for an online monitoring and intelligent analysis method for cable damage; Figure 3 This is a flowchart of an online monitoring and intelligent analysis system for cable damage. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0022] Example This embodiment applies to a 110kV high-voltage cable tunnel laying line in the core urban area. The line is 2.8km long and uses cable trays within the tunnel throughout. A total of 28 cable joints are installed, and cable terminations are located at both ends. The line has an 8-year service life and is responsible for supplying the main power to the core urban business district and government area, requiring extremely high power supply reliability. The specific implementation process of this embodiment is as follows: Figure 1 As shown.

[0023] The first step is to construct a digital twin of the cable body and its operating environment. This involves first completing millimeter-level high-precision geometric modeling of the cable body, intermediate joints, terminal heads, and tunnel laying channels using 3D laser scanning and BIM modeling. Then, based on the cable's factory inspection report, the intrinsic physical properties, chemical properties, and degradation constitutive parameters of the insulation, sheath, and conductor materials are entered. Next, based on the laying and construction completion data, the entire process data of cable burial depth, bending radius, fixing method, and joint installation technology are entered. Then, by connecting with the geographic information system and regional meteorological platform, dynamic boundary condition modeling of soil geology, temperature, humidity, and settlement data in the tunnel along the cable route is completed. Finally, historical operation and maintenance data, defect records, and test data after the cable is put into operation are entered to complete the digital mirror mapping of the physical cable's full-dimensional attributes, forming a digital twin covering the cable's geometric structure, intrinsic material properties, laying information, historical operation and maintenance data, and the geological, meteorological, and electromagnetic environment in which the cable is located.

[0024] The second step involves collecting real-time online monitoring data and environmental data during cable operation. A distributed fiber optic temperature and strain monitoring system is deployed along the entire cable line. UHF partial discharge online monitoring devices are deployed at all 28 intermediate joints and end terminals of the cable. High-precision grounding current sensors are deployed in the cable grounding circuit. Environmental temperature and humidity, soil moisture content, and vibration sensors are deployed at 50-meter intervals in the cable tunnel laying channel. Real-time load current data of the cable operation is collected simultaneously. All collected multi-source heterogeneous data undergoes outlier removal, adaptive wavelet denoising, millisecond-level time sequence alignment, and normalization preprocessing. The preprocessed data is then synchronized to the constructed digital twin in real time through a low-latency time sequence synchronization mapping interface.

[0025] The third step involves constructing a multi-physics coupled simulation model for cable material aging, mechanical damage, and electrical degradation. This model deeply couples real-time operating data synchronized in the digital twin with the multi-physics coupled simulation model. The synchronized real-time operating data and environmental data in the digital twin are used as dynamic input boundary conditions for the simulation model. Real-time load current and conductor temperature data are used to correct the heat source input parameters of the thermal field model; real-time electric field strength and partial discharge data are used to correct the excitation parameters of the electric field model; real-time strain and vibration data are used to correct the load parameters of the force field model; and environmental temperature, humidity, and soil parameters are used to correct the boundary conditions of the chemical field model. This completes the real-time dynamic correction of the multi-physics model. Finite element iterative calculations are then used to reproduce the current insulation degradation, mechanical damage, and material aging state of the cable. Based on the corrected model, a cable damage evolution rate coupled calculation method is employed. The core calculation formula is: ,in This represents the real-time damage evolution rate of the cable insulation material. For thermal aging damage rate, For electrical aging damage rate, For mechanical damage rate, , , The coupling weights for the damage in the three subfields are: The environmental coupling correction coefficient is used to simulate the dynamic evolution and development trend of cable damage over the next 12, 24, and 36 months under different load conditions and environmental conditions.

[0026] The fourth step involves using the damage evolution simulation results, combined with the critical threshold for cable material degradation and failure, historical full-cycle data, and a database of similar cable failure cases, to predict the cumulative lifespan of cable damage. The core formula of this algorithm is: ,in The remaining service life of the target section of the cable. This refers to the intrinsic failure lifetime threshold of the cable insulation material. This represents the current cumulative damage equivalent of the cable. This is a comprehensive correction factor for real-time operating conditions. To mitigate the degradation of the nonlinear correction coefficient and accurately assess the remaining service life of the cable body and key joints, a continuous distribution map of the remaining service life of the entire cable is generated based on the monitoring points and simulation calculation nodes along the entire cable line after the assessment. The map marks the precise remaining service life values ​​of key nodes such as the cable body, intermediate joints, and terminal heads. At the same time, the remaining service life fluctuation range of the cable is simulated under three different operating conditions of 80%, 100%, and 120% of the rated load. In addition, based on the failure case library of similar cables, the failure risk probability of cable sections with a remaining service life of less than 36 months is ranked, and the failure risk level and core influencing factors of different sections are identified.

[0027] The fifth step involves classifying and issuing early warnings for potential cable faults based on damage evolution trends and remaining life assessment results. Figure 2 As shown, by combining cable operation and maintenance procedures with the on-site conditions in the tunnel, targeted operation and maintenance decision-making suggestions are generated and simultaneously output to the cable operation and maintenance management platform. Potential cable faults are divided into three warning levels: Level 1 warning corresponds to an emergency state where the remaining life is less than 6 months or the damage is about to reach the failure threshold; Level 2 warning corresponds to an important state where the remaining life is 6-24 months or the damage is accelerating; and Level 3 warning corresponds to a general deterioration state where the remaining life is greater than 24 months. For different warning levels, combined with cable operation and maintenance procedures and on-site conditions, corresponding maintenance priorities, maintenance methods, special test items, load control schemes, and equipment replacement cycle suggestions are generated. The warning information and operation and maintenance decision-making suggestions are simultaneously output to the cable operation and maintenance management platform, automatically generating operation and maintenance work orders of the corresponding level, completing the entire process of work order dispatch, handling, and acceptance tracking. After the handling is completed, the relevant data is synchronized to the digital twin to complete the iterative optimization of the model.

[0028] The process of the cable damage online monitoring and intelligent analysis system based on this embodiment is as follows: Figure 3 As shown in the figure. In this embodiment, the continuous self-optimization process of the digital twin and the multiphysics coupled simulation model is executed simultaneously. After each cable operation and maintenance or offline test is completed, the cable insulation status and damage detection data obtained from the offline test are synchronized with the operation and maintenance data to the digital twin. The actual detected damage status is compared with the historical prediction results of the simulation model, and the deviation value between the prediction result and the actual detection result is calculated. Based on the deviation value, the degradation constitutive parameters and weights of the multiphysics coupled simulation model are iteratively corrected using the gradient descent algorithm. At the same time, the cable material properties and status parameters in the digital twin are updated. After the correction is completed, the damage evolution simulation and remaining life assessment are performed again.

[0029] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for online monitoring and intelligent analysis of cable damage, characterized in that, The specific steps of this method are as follows: Step S1: Construct a full-element digital twin of the cable body and its operating environment. The digital twin covers the cable's geometric structure, intrinsic material properties, laying information, historical operation and maintenance data, as well as the geological, meteorological, and electromagnetic environment in which the cable is located, completing the digital mirror mapping of the physical cable; Step S2: Collect real-time online monitoring data and environmental data during the cable's operation. After data preprocessing, synchronize the data to the digital twin in real time through a time-series synchronization mapping interface; Step S3: Construct a multi-physics field coupled simulation model of cable material aging, mechanical damage, and electrical degradation. Deeply couple the synchronized real-time operating data in the digital twin with the multi-physics field coupled simulation model in real time to reproduce the current damage state of the cable and simulate the dynamic evolution process and development trend of cable damage under different working conditions. Step S4: Based on the damage evolution simulation results, combined with the critical threshold for cable material deterioration and failure, historical full-cycle data, and a database of similar cable failure cases, the remaining service life of the cable body and key joints is accurately assessed using damage accumulation theory and machine learning correction algorithms. Step S5: Based on the damage evolution trend and remaining service life assessment results, potential cable faults are classified and given early warnings. At the same time, combined with cable operation and maintenance procedures and on-site conditions, targeted operation and maintenance decision-making suggestions are generated and simultaneously output to the cable operation and maintenance management platform.

2. The online monitoring and intelligent analysis method for cable damage according to claim 1, characterized in that, The construction process of the full-element digital twin in step S1 is as follows: First, high-precision geometric modeling of the cable body, intermediate joints, terminals, and laying channels is completed at the millimeter level through 3D laser scanning and BIM modeling. Then, the intrinsic physical properties, chemical properties, and degradation constitutive parameters of the insulation, sheath, and conductor materials are entered based on the cable factory inspection report. Next, the entire process data of the cable burial depth, bending radius, fixed method, and joint installation process are entered based on the laying construction completion data. Then, the dynamic boundary condition modeling of soil geological temperature, humidity, and settlement data along the cable route is completed by connecting with the geographic information system and regional meteorological platform. Finally, the historical operation and maintenance data, defect records, and test data after the cable is put into operation are entered to complete the digital mirror mapping of the full-dimensional attributes of the physical cable.

3. The online monitoring and intelligent analysis method for cable damage according to claim 1, characterized in that, The implementation process of real-time data acquisition and synchronous mapping in step S2 is as follows: a distributed optical fiber temperature and strain monitoring system is deployed along the entire cable line; an ultra-high frequency partial discharge online monitoring device is deployed at the cable intermediate joints and terminal heads; a high-precision grounding current sensor is deployed in the cable grounding circuit; and environmental temperature, humidity, soil moisture content, and vibration sensors are deployed at 50-meter intervals along the cable laying channel. Real-time load current data of the cable operation is collected synchronously. All collected multi-source heterogeneous data undergoes outlier removal, adaptive wavelet denoising, millisecond-level time sequence alignment, and normalization preprocessing. The preprocessed data is then synchronized to the digital twin in real time through a low-latency time sequence synchronous mapping interface.

4. The online monitoring and intelligent analysis method for cable damage according to claim 1, characterized in that, The deep coupling process between real-time operating data and the multiphysics field coupled simulation model in step S3 is as follows: the real-time operating data and environmental data synchronized in the digital twin are used as the dynamic input boundary conditions of the simulation model. First, the heat source input parameters of the thermal field model are corrected by real-time load current and conductor temperature data. Then, the excitation parameters of the electric field model are corrected by real-time electric field strength and partial discharge data. Next, the load parameters of the force field model are corrected by real-time strain and vibration data. Then, the boundary conditions of the chemical field model are corrected by environmental temperature and humidity and soil parameters. This completes the real-time dynamic correction of the multiphysics field model. Then, the current insulation degradation mechanical damage and material aging state of the cable are reproduced by finite element iterative calculation. Finally, based on the corrected model, the dynamic evolution process and development trend of cable damage under different load conditions and environmental conditions in the next 12 months, 24 months and 36 months are simulated.

5. The online monitoring and intelligent analysis method for cable damage according to claim 1, characterized in that, In step S3, the multiphysics coupling simulation model adopts the cable damage evolution rate coupling calculation method, and the core calculation formula is: ,in This represents the real-time damage evolution rate of the cable insulation material. For thermal aging damage rate, For electrical aging damage rate, For mechanical damage rate, , , The coupling weights for the damage in the three subfields are: This is the environmental coupling correction coefficient.

6. The online monitoring and intelligent analysis method for cable damage according to claim 1, characterized in that, The remaining service life assessment in step S4 uses a cable damage cumulative service life prediction algorithm, the core formula of which is: ,in The remaining service life of the target section of the cable. This refers to the intrinsic failure lifetime threshold of the cable insulation material. This represents the current cumulative damage equivalent of the cable. This is a comprehensive correction factor for real-time operating conditions. This is a correction coefficient for deteriorating nonlinearity.

7. The online monitoring and intelligent analysis method for cable damage according to claim 6, characterized in that, After the remaining service life assessment in step S4 is completed, a continuous distribution map of the remaining service life of the entire cable will be generated based on the monitoring points and simulation calculation nodes of the entire cable. The map will mark the precise remaining service life values ​​of key nodes such as intermediate joints and terminal heads of the cable body. At the same time, it will simulate the fluctuation range of the remaining service life of the cable under three different operating conditions of 80%, 100%, and 120% of the rated load. It will also combine the failure case library of similar cables to rank the failure risk probability of cable sections with a remaining service life of less than 36 months, and clarify the failure risk level and core influencing factors of different sections.

8. The online monitoring and intelligent analysis method for cable damage according to claim 1, characterized in that, The implementation process of graded early warning and closed-loop operation and maintenance management in step S5 is as follows: Based on the damage evolution trend and remaining life assessment results, potential cable faults are divided into three warning levels. Level 1 warning corresponds to an emergency state where the remaining life is less than 6 months or the damage is about to reach the failure threshold. Level 2 warning corresponds to an important state where the remaining life is 6-24 months or the damage is accelerating. Level 3 warning corresponds to a general deterioration state where the remaining life is greater than 24 months. For different warning levels, combined with cable operation and maintenance procedures and on-site conditions, corresponding maintenance priorities, maintenance methods, special test projects, load control schemes, and equipment replacement cycle suggestions are generated. At the same time, the warning information and operation and maintenance decision suggestions are output to the cable operation and maintenance management platform, and operation and maintenance work orders of the corresponding level are automatically generated. The entire process of work order dispatch, disposal, and acceptance is tracked. After disposal, the relevant data is synchronized to the digital twin to complete the iterative optimization of the model.

9. The online monitoring and intelligent analysis method for cable damage according to claim 1, characterized in that, The method also includes a continuous self-optimization process between the digital twin and the multiphysics coupled simulation model. After each cable operation and maintenance or offline test, the cable insulation damage detection data obtained from the offline test and the operation and maintenance data are synchronized to the digital twin. The actual detected damage state is compared with the historical prediction results of the simulation model, and the deviation value between the prediction result and the actual detection result is calculated. Based on the deviation value, the degradation constitutive parameters and weights of the multiphysics coupled simulation model are iteratively corrected using the gradient descent algorithm. At the same time, the cable material properties and state parameters in the digital twin are updated. After the correction is completed, the damage evolution simulation and remaining life assessment are performed again.

10. An online monitoring and intelligent analysis system for cable damage, applicable to the online monitoring and intelligent analysis method for cable damage as described in any one of claims 1-9, characterized in that, The system includes: a digital twin construction module for constructing, verifying, and dynamically updating a digital twin of the cable body and its operating environment; a data acquisition and synchronization mapping module for connecting to various online monitoring devices and environmental monitoring terminals along the cable route, acquiring and preprocessing real-time data, and mapping the data to the digital twin in real time via a time-series synchronization interface; a multi-physics coupling simulation module with a built-in multi-physics coupling simulation model of cable material aging, mechanical damage, and electrical degradation, receiving real-time synchronization data from the digital twin, dynamically correcting model boundary conditions, and simulating the dynamic evolution and development trend of cable damage; a remaining service life assessment module for accurately calculating the remaining service life of the cable and generating a full-line distribution map based on damage evolution simulation results, combined with historical full-cycle data of cable material failure thresholds and a failure case library; and an advanced early warning and operation and maintenance decision module for performing fault classification and advanced early warning based on damage evolution trends and remaining service life assessment results, generating targeted operation and maintenance decision suggestions, and synchronously outputting them to the cable operation and maintenance management platform.

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