Cable life dynamic evaluation system based on multi-physical field coupling

Abstract

The application discloses a cable life dynamic evaluation system based on multi-physical field coupling and particularly relates to the field of industrial automation and control systems, comprising a multi-physical field perception module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, wherein through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of the cable are collected in real time, multi-physical field characteristics are matched with a cable defect database in real time by using a cross-scale dynamic correlation algorithm, a damage state is evaluated, a time sequence neural network architecture is used to predict the remaining life and model self-correction is realized through digital twin comparison, and interference is suppressed by combining an environment-physical field coupling compensation matrix, so that the perception, evaluation, life prediction and continuous optimization of the model of the cable health state are realized, and the efficiency of the cable life evaluation is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial automation and control systems, and more particularly, to a cable life dynamic evaluation system based on multi-physical field coupling. BACKGROUND

[0002] With the continuous expansion of the scale of the power system and the increase of the service life of the cable, the aging and failure of the cable are increasingly prominent, which threatens the safe and stable operation of the power grid. As a key equipment for power transmission, the monitoring and life evaluation of the running state of the cable are of great significance to ensure the reliability of power supply.

[0003] The prior art mainly uses a single physical field monitoring combined with a statistical model or a simple algorithm to evaluate the cable life. For example, by laying temperature sensors to collect the cable conductor temperature, combining a thermal aging model to calculate the remaining life, detecting the partial discharge signal of the insulation layer, and evaluating the cable health state according to the correlation between the discharge quantity and the defect degree to evaluate the cable life

[0004] However, in actual use, there are still some shortcomings, such as relying on single physical field information, which is difficult to fully reflect the complex failure mechanism of the multi-field coupling of "heat-electricity-magnetism-force" of the cable, which easily leads to inaccurate evaluation, secondly, the fixed threshold lacks dynamic adaptability to the cable operating conditions and environmental interference, the evaluation result accuracy is poor, the simple linear life model cannot depict the nonlinear time-varying characteristics of defect evolution, the prediction accuracy is limited, the system lacks self-learning and optimization ability, the model parameters cannot be updated and corrected according to real-time data, and it is difficult to realize accurate dynamic evaluation. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a cable life dynamic evaluation system based on multi-physical field coupling, which solves the problems raised in the above background art through the following scheme.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a cable life dynamic evaluation system based on multi-physical field coupling, comprising a multi-physical field perception module, the cable to be evaluated is recorded as a target cable, and the temperature field, electric field, magnetic field and mechanical stress field data of the target cable during operation are collected in real time;

[0007] A coupling analysis engine module receives the data of the multi-physical field perception module and matches the multi-physical field characteristics with a pre-established cable defect database in real time through a cross-scale dynamic correlation algorithm, and outputs the target cable damage state evaluation result to a dynamic life evaluation module.

[0008] a dynamic life assessment module, based on the damage state assessment result output by the coupling analysis engine module, using a multi-physical field coupling time sequence neural network architecture for life prediction, and synchronizing the life prediction result to the digital twin interaction module;

[0009] a digital twin interaction module, receiving actual measurement data and life prediction results, comparing the simulation data of the digital twin body in real time, triggering a self-correction signal when the difference exceeds the dynamic threshold, and feeding back the signal to the coupling analysis engine module for model parameter optimization;

[0010] an environmental interference suppression module, interconnected with the multi-physical field sensing module, dynamically correcting the original measurement data through an environment-physical field coupling compensation matrix, and inputting the corrected data to the coupling analysis engine module.

[0011] Preferably, the multi-physical field sensing module includes a distributed optical fiber temperature sensor array, a capacitive electric field sensor group, and a magnetostrictive stress sensor, wherein the distributed optical fiber temperature sensor is arranged in a spiral winding manner on the surface of the cable conductor, the capacitive electric field sensor is embedded in the cable insulation shielding layer in the form of an orthogonal array, and the magnetostrictive stress sensor is arranged at equal intervals along the axial direction of the cable.

[0012] Preferably, the temperature field data includes the surface temperature of the target cable conductor, the temperature at different depths of the insulation layer, and the surface temperature of the shielding layer, the electric field data includes the instantaneous field strength of the target cable terminal, the voltage value between the conductor and the shielding layer, the magnetic field data includes the magnetic induction intensity of the center point of the target cable conductor surface, the load current in the cable line, the induced current of the metal shielding layer, and the ground circulating current, and the mechanical stress field data includes the instantaneous axial / radial tension of the target cable and the instantaneous bending strain force.

[0013] Preferably, the cross-scale dynamic correlation algorithm uses cross-scale feature extraction technology to perform feature dimension reduction and parameter processing on the multi-physical field data collected by the multi-physical field sensing module, to obtain a feature vector that can reflect the overall operating state of the cable, and simultaneously calls a pre-established cable defect database to perform real-time calculation on the correlation between the feature vector and the cable defects.

[0014] Preferably, the cable defect database includes defect types and corresponding feature vector data, corresponding multi-physical field response characteristics, and defect evolution rules, and is dynamically updated based on experimental basic data and actual operating cable defect detection case data.

[0015] Preferably, the damage state assessment result includes the damage type, damage location, and damage degree corresponding to the defect, and is output to the dynamic life assessment module using a standardized data interface and a hierarchical identifier.

[0016] Preferably, the timing neural network architecture input end adopts a multi-channel parallel structure, embeds a coupling layer, accesses a bidirectional long-term memory network layer, calculates an evolution function of a damage state over time and a time-dependent coefficient, is trained in combination with historical damage data of the cable and a service life attenuation curve of a similar cable, and outputs a residual service life prediction value of the target cable.

[0017] Preferably, the model parameter optimization is directed to a difference source of digital twin simulation data and actual measurement data, locates and optimizes a multi-physical field coupling coefficient and a feature matching threshold in a cross-scale correlation algorithm, and an adaptive particle swarm algorithm is used in combination with historical optimization records and parameter calibration experience of a similar cable to dynamically adjust the optimization process.

[0018] Preferably, the environment-physical field coupling compensation matrix is a mathematical model for environment interference suppression, quantifies the coupling influence relationship between environmental factors and each physical field in the form of a multi-dimensional matrix, the row dimension of the matrix corresponds to each item of data of a temperature field, an electric field, a magnetic field and a mechanical stress field, the column dimension corresponds to environmental interference factors including temperature, humidity, air pressure and wind speed, and the matrix elements are coupling coefficients calibrated through experiments and actual measurement data, wherein the matrix coefficients are periodically updated according to cable laying scenes and seasonal changes.

[0019] Technical effects and advantages of the present application:

[0020] The present application realizes multi-faceted perception and comprehensive analysis of the cable operation state through the multi-physical field coupling technology, compared with the traditional single parameter monitoring method, simultaneously collects temperature field, electric field, magnetic field and mechanical stress field data, comprehensively reflects the operation health status of the cable through multi-dimensional information fusion, and effectively improves the integrity and accuracy of the state monitoring;

[0021] The present application significantly improves the real-time performance and adaptability of the evaluation by introducing a dynamic life prediction and self-optimization mechanism, uses a timing neural network architecture for life prediction, dynamically corrects the model parameters by combining digital twin technology, continuously optimizes the evaluation model according to actual operation data, adapts to complex working conditions and environmental changes, and greatly improves the accuracy and reliability of life prediction;

[0022] The present application compares the actual data with the simulation data in real time, automatically triggers parameter optimization when the difference exceeds a threshold, adjusts the coupling coefficient and the matching threshold by combining the adaptive particle swarm algorithm and historical experience, effectively reduces the influence of environmental interference and model error, and ensures that the system always maintains high-precision evaluation capability in complex and variable operating environments. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 It is a schematic diagram of the overall structure of the present application;

[0024] Figure 2A schematic diagram for target cable defect determination of the present application;

[0025] Figure 3 A schematic diagram for target cable life prediction of the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0027] As shown in Figs. 1 to 3, the cable life dynamic evaluation system based on multi-physical field coupling includes a multi-physical field perception module. Figure 1 , a distributed optical fiber temperature sensor array, a capacitive electric field sensor group and a magnetostrictive stress sensor. Figure 2 and a distributed optical fiber temperature sensor array, a capacitive electric field sensor group and a magnetostrictive stress sensor. Figure 3 The multi-physical field perception module includes a distributed optical fiber temperature sensor array, a capacitive electric field sensor group and a magnetostrictive stress sensor.

[0028] It needs to be specifically explained that the multi-physical field perception module includes a distributed optical fiber temperature sensor array, a capacitive electric field sensor group and a magnetostrictive stress sensor.

[0029] The temperature field data includes the surface temperature of the target cable conductor, the temperature at different depths of the insulation layer, and the surface temperature of the shielding layer.

[0030] It needs to be further explained that the reason for using different arrangement methods of the sensors includes:

[0031] The distributed optical fiber temperature sensor is arranged on the surface of the conductor in a spiral winding manner, which maximizes the temperature perception range of the conductor in the axial and circumferential directions through the spiral path, and the elastic laying method of the spiral winding does not hinder the normal thermal expansion and contraction of the cable.

[0032] The capacitive electric field sensor is embedded in the insulating shielding layer in a quadrature array form, the electric field distribution in the cable insulation layer has a composite characteristic of radial main and axial auxiliary, the quadrature arranged sensors respectively perceive radial and axial electric field components, the embedded shielding layer ensures the close contact of the sensor and the electric field sensitive area, and the protection of the shielding layer reduces external electromagnetic interference.

[0033] The magnetostrictive stress sensor is arranged at equal intervals along the axial direction, the mechanical stress of the cable in operation is distributed in a gradient in the axial direction, the equal interval arrangement judges the stress concentration area through the stress difference value of adjacent sensors, directly converts the mechanical deformation into a magnetic permeability change signal, and high sensitivity monitoring of a small stress fluctuation is realized.

[0034] It needs to be further explained that the reasons for selecting the temperature field, the electric field, the magnetic field and the mechanical stress field as the multi-physical field sensing object include:

[0035] The temperature field, the electric field, the magnetic field and the mechanical stress field jointly constitute the core physical process that is mutually related and influenced in the operation of the cable, comprehensively reflect the working state and health degree of the cable, the cable is essentially an electric energy transmission carrier, the temperature field is the result of the balance between energy loss in electric energy transmission and external environment heat dissipation, temperature abnormity is often an early signal of failure, the electric field is the basis for the existence and transmission of electric energy, the distribution state directly determines the safety of the insulation system, the magnetic field is generated by the current and is closely related to the load size, the conductor structure and the shielding mode, the mechanical stress field is derived from the mechanical action of installation, thermal expansion and contraction and external environment, and directly affects the mechanical integrity and structural stability of the cable, the temperature field, the electric field, the magnetic field and the mechanical stress field have mutual action, for example, overloading will increase the current, cause the magnetic field to be enhanced and the temperature to be increased, and then cause thermal expansion to generate additional mechanical stress, meanwhile, high temperature will accelerate the insulation aging and change the electric field distribution, the multi-physical field sensing of the target cable "thermal-electric-magnetic-force" is helpful to the integrity of the failure and damage state evaluation and the life prediction of the target cable.

[0036] The coupling analysis engine module receives the multi-physical field sensing module data and performs real-time matching of the multi-physical field characteristics and the pre-established cable defect database through a cross-scale dynamic correlation algorithm, and outputs the target cable damage state evaluation result to the dynamic life evaluation module.

[0037] It needs to be specifically explained that the cross-scale dynamic correlation algorithm performs feature dimension reduction and parameter processing on the multi-physical field data collected by the multi-physical field sensing module through a cross-scale feature extraction technology, obtains a feature vector that can reflect the overall operation state of the cable, and simultaneously calls the pre-established cable defect database to perform real-time calculation on the correlation of the feature vector and the cable defect.

[0038] The cable defect database comprises defect types and corresponding feature vector data, corresponding multi-physical field response features and defect evolution rules, and the cable defect database is dynamically updated based on experimental basic data and actual operation cable defect detection case data.

[0039] The damage state evaluation result comprises a damage type corresponding to the defect, a damage position and a damage degree, and is output to the dynamic life evaluation module in a standardized data interface and a hierarchical identification.

[0040] It should be further explained that the multi-physical field data collected by the multi-physical field perception module is subjected to feature dimension reduction and parameter processing, and the specific analysis mode comprises:

[0041] The arithmetic mean of the temperatures of multiple sites on the surface of the conductor is calculated and is denoted as the average temperature of the conductor, the ratio of the temperature difference at different depths in the radial direction of the insulation layer to the radial distance is calculated and is denoted as the temperature gradient of the insulation layer, the arithmetic mean of the temperatures of multiple sites on the surface of the shielding layer is calculated and is denoted as the average temperature of the shielding layer, the ratio of the voltage value between the conductor and the shielding layer to the thickness of the insulation layer is calculated and is denoted as the average electric field strength, the ratio of the instantaneous field strength of the target cable terminal to the average electric field strength is calculated and is denoted as the electric field distortion coefficient, the vector sum of the magnetic induction strengths in the x, y and z directions of the center point of the surface of the conductor of the target cable is calculated and is denoted as the resultant magnetic induction strength, the ratio of the difference between the maximum value and the minimum value of the load current in the target cable line in a fixed time period to the average value is calculated and is denoted as the load current fluctuation coefficient, the ratio of the induced current of the metal shielding layer to the current of the conductor is calculated and is denoted as the shielding current ratio, the effective value of the current on the grounding wire is measured and is denoted as the grounding current value, the covariance of the instantaneous axial / radial tension of the target cable is calculated and is denoted as the axial / radial tension coupling coefficient, and the ratio of the difference in the instantaneous bending strain force at different axial positions of the target cable at the same time to the axial distance is calculated and is denoted as the bending strain spatial distribution gradient.

[0042] The above-mentioned average temperature of the conductor, temperature gradient of the insulation layer, average temperature of the shielding layer, average electric field strength, electric field distortion coefficient, resultant magnetic induction strength, load current fluctuation coefficient, shielding current ratio, grounding current value, axial / radial tension coupling coefficient and bending strain spatial distribution gradient are denoted as the feature vector reflecting the overall operation state of the cable.

[0043] Real-time calculation of the relevance of the feature vector to the cable defect comprises: real-time extraction and calculation of the feature vector of the target cable, retrieval of the historical cases with a similarity of the top 20 to the feature vector from the cable defect database by using a K-nearest neighbor algorithm, the similarity is measured by using the Euclidean distance, the distance wherein, is the feature i, is the feature i of the kth historical case, the similarity S threshold is set to 0.85, and if there is a historical case with a similarity If greater than S, it is determined that the target cable has the defect type corresponding to the case, and if the similarity of all cases is lower than S, the evaluation result of 'unidentified high-confidence defect' is output, and the feature vector set is marked as a sample to be analyzed for subsequent database expansion and model optimization.

[0044] The specific steps of dynamically updating the cable defect database include: synchronously collecting standardized multi-physical field data of laboratory simulated defects and operation data of actual defect cases in the field, statistically analyzing and expanding the feature vector interval of known defect types, extracting unique features and defining identifiers for new defect types through cluster analysis, optimizing single-field physical features through correlation analysis, supplementing multi-field coupling rules, iteratively updating defect evolution parameters based on time series data using regression models, correcting biased features through a feedback mechanism after double verification of experimental samples and field cases, and completing dynamic updating of the cable defect database once every quarter.

[0045] The damage type includes common defects and other defects, the damage location is positioned based on the axial and radial coordinate axes of the target cable, and the damage degree includes mild damage, moderate damage, and severe damage.

[0046] The feature vector of the conductor average temperature, insulation layer temperature gradient, shielding layer average temperature, and ground loop current value; axial and radial tension coupling coefficient, and bending strain spatial distribution gradient is used to determine whether there are common defects such as conductor overheating, insulation aging, shielding layer failure, mechanical damage, and abnormal ground system. The average electric field intensity, electric field distortion coefficient; synthetic magnetic induction intensity, load current fluctuation coefficient, and shielding loop current ratio are used to determine whether there are other defects with a single feature vector deviating from the specified threshold.

[0047] Based on the system, the difference degree is the deviation degree of the actual measurement normal use threshold, the defect degree determination is based on the dynamic threshold, the mild damage refers to the difference degree exceeding the dynamic threshold and being lower than the dynamic threshold + 2%, the moderate damage refers to the difference degree exceeding the dynamic threshold + 2% and being lower than the dynamic threshold + 5%, and the severe damage refers to the difference degree exceeding the dynamic threshold + 5%. The dynamic threshold is adjusted according to the cable operation period, which is 3% for less than 2 years, 5% for 2-5 years, and 8% for more than 5 years.

[0048] The dynamic life assessment module, based on the damage state assessment result output by the coupling analysis engine module, uses a multi-physical field coupled time series neural network architecture for life prediction, and synchronously outputs the life prediction result to the digital twin interaction module.

[0049] It needs to be specifically pointed out that the input end of the time series neural network architecture adopts a multi-channel parallel structure, embeds a coupling layer, accesses a bidirectional long-term memory network layer, calculates the evolution function and time-dependent coefficient of the damage state over time, combines the historical damage data of the cable with the life attenuation curve of similar cables for training, and outputs the remaining life prediction value of the target cable.

[0050] It needs to be further explained that the evolution function of the damage state over time and the specific analysis method of the time-dependent coefficient are as follows: let the damage state vector at time t be , wherein correspond to the normalized damage degree of 5 common defects respectively, the value range is , 1 represents complete failure, and the evolution function is defined as:

[0051]

[0052] , wherein is the damage degree at the initial time , which is taken from historical data or the first detection value, is the basic evolution rate of the defect at time , which is output through a bidirectional LSTM layer, is a multi-physical field coupling coefficient, which is calculated through a coupling layer and reflects the acceleration / slowing effect of temperature, electric field and other parameters on evolution.

[0053] The time-dependent coefficient is defined as:

[0054]

[0055] , wherein is the current time, is the historical time, is a time decay factor, which is optimized through neural network training and takes 30 days, that is, the damage weight of the recent 30 days is higher.

[0056] In the bidirectional LSTM layer, the time-dependent coefficient acts on the historical data through the attention mechanism, and the formula is: , wherein is the weighted real-time evolution rate, so that the model pays more attention to the evolution trend of recent damage.

[0057] Collect historical data of the same type of cable, including multi-physical field monitoring values, damage degree labels and actual failure time throughout the life cycle.

[0058] Digitalize the life attenuation curve of the same type of cable, and fit the reference life model:

[0059]

[0060] , wherein is the average damage degree, and a, b, c are curve parameters.

[0061] The time series neural network is trained by historical data, inputting multi-physical field data, learning damage evolution law through bidirectional LSTM, and outputting damage degree , the loss function is: , the access life prediction head fuses the same type of curve, , wherein, is the predicted value based on historical data, is the weight obtained by training, and the life prediction error optimizes the model, inputs the real-time monitoring data of the target cable, calculates the current average damage degree and evolution trend, and substitutes the reference model to obtain , and outputs the final prediction value after combining the historical trend correction .

[0062] The digital twin interaction module receives actual measurement data and life prediction results, compares the simulation data of the digital twin in real time, triggers a self-correction signal when the difference exceeds a dynamic threshold, and feeds the signal back to the coupling analysis engine module for model parameter optimization.

[0063] It should be specifically noted that the model parameter optimization is aimed at the difference between the simulation data of the digital twin and the actual measurement data, locates the optimization of the multi-physical field coupling coefficient and the feature matching threshold in the cross-scale correlation algorithm, and the optimization process adopts an adaptive particle swarm algorithm and combines historical optimization records and parameter calibration experience of similar cables for dynamic adjustment.

[0064] It should be further noted that the digital twin is a simulation model based on the multi-physical field coupling theory, the input is the design parameters of the cable, the real-time operating conditions, and the environmental boundary conditions corrected by the environmental interference suppression module, the model parameter optimization of the digital twin interaction module is implemented according to the process of “difference positioning-adaptive optimization-experience feedback”, the multi-physical field coupling coefficient and the feature matching threshold of the cross-scale dynamic correlation algorithm are locked through residual analysis and sensitivity evaluation, the adaptive particle swarm algorithm is used for optimization, the initial solution space is based on the calibration experience of similar cables, the inertia weight is dynamically adjusted in iteration, the search step of stable adjustment parameters is reduced in combination with historical optimization records, the optimized parameters are fed back to the coupling analysis engine module, and if the difference still exceeds the threshold, the iteration is repeated, and the self-correction signal is triggered if the dynamic threshold requirement (≤5%) is not met for three times.

[0065] The environmental interference suppression module is interconnected with the multi-physical field perception module, dynamically corrects the original measurement data through an environment-physical field coupling compensation matrix, and inputs the corrected data to the coupling analysis engine module.

[0066] It needs to be specifically pointed out that the environment-physical field coupling compensation matrix is a mathematical model for environment interference suppression, which quantifies the coupling influence relationship between environmental factors and various physical fields in the form of a multi-dimensional matrix. The row dimension of the matrix corresponds to the data of the temperature field, electric field, magnetic field and mechanical stress field, and the column dimension corresponds to the environmental interference factors including temperature, humidity, air pressure and wind speed. The matrix elements are coupling coefficients calibrated by experimental and measured data, wherein the matrix coefficients are periodically updated according to the cable laying scene and seasonal changes.

[0067] It needs to be further pointed out that the dynamic modification of the environment-physical field coupling compensation matrix to the original measurement data is as follows: first, a 12-row 4-column compensation matrix M is constructed, wherein the row vectors correspond to 12 core data of the temperature field (target cable conductor surface temperature, temperature at different depths of the insulation layer, shielding layer surface temperature), electric field (target cable terminal instantaneous field strength, voltage between the conductor and the shielding layer), magnetic field (magnetic induction intensity of the center point of the target cable conductor, load current in the cable line, induced current of the metal shielding layer, ground loop current), and mechanical stress field (instantaneous axial / radial tension of the target cable, instantaneous bending strain force), and the column vectors correspond to the environmental factors temperature T, humidity H, air pressure P and wind speed V. The matrix elements are the coupling coefficients of the th physical field data and the th environmental factor (such as represents the influence coefficient of environmental temperature on the conductor surface temperature). When modifying, the deviation value of the real-time environment and the reference environment is collected, and then the correction amount of each physical field is generated through matrix operation, to obtain the corrected data . The matrix coefficients are updated every quarter based on laboratory environment simulation experiments and field measurement data.

[0068] Secondly, in the drawings of the disclosed embodiments, only the structures related to the disclosed embodiments are involved, and other structures can be referred to the usual design. In the case of no conflict, the same embodiment and different embodiments of the present application can be combined with each other.

[0069] Finally, the above only describes the preferred embodiments of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A cable life dynamic assessment system based on multi-physical field coupling, characterized in that, The method comprises the following steps: A multi-physical field perception module, which takes the cable to be evaluated as a target cable, collects temperature field, electric field, magnetic field and mechanical stress field data of the target cable in real time; A coupling analysis engine module receives data from the multi-physical field perception module and matches the multi-physical field characteristics with a pre-established cable defect database in real time through a cross-scale dynamic correlation algorithm, and outputs the target cable damage state evaluation result to a dynamic life assessment module; A dynamic life assessment module, based on the damage state evaluation result output by the coupling analysis engine module, constructs a time series of damage state vectors, adopts a multi-physical field coupled time series neural network architecture, calculates the multi-physical field coupling coefficient through the embedded coupling layer, and learns the evolution law of the damage state through the bidirectional long-term memory network layer. Combined with the historical damage data of the cable and the life attenuation curve of the same type of cable, the remaining life prediction value of the target cable is output, and the life prediction result is synchronized to the digital twin interaction module; A digital twin interaction module receives actual measurement data and life prediction results, compares them with digital twin simulation data in real time, triggers a self-correction signal when the difference exceeds the dynamic threshold, and feeds the signal back to the coupling analysis engine module for model parameter optimization; An environmental interference suppression module is interconnected with the multi-physical field perception module data, and dynamically modifies the original measurement data through an environment-physical field coupling compensation matrix. The modified data is input into the coupling analysis engine module.

2. The multi-physics coupling based cable life dynamic assessment system according to claim 1, wherein: The multi-physical field perception module includes a distributed optical fiber temperature sensor array, a capacitive electric field sensor group and a magnetostrictive stress sensor. The distributed optical fiber temperature sensor is arranged in a spiral winding manner on the surface of the cable conductor, the capacitive electric field sensor is embedded in the cable insulation shielding layer in the form of an orthogonal array, and the magnetostrictive stress sensor is arranged at equal intervals along the axial direction of the cable.

3. The multi-physics coupling based cable life dynamic assessment system according to claim 1, wherein: The temperature field data includes the surface temperature of the target cable conductor, the temperature at different depths of the insulation layer, and the surface temperature of the shielding layer. The electric field data includes the instantaneous field strength of the target cable terminal, the voltage between the conductor and the shielding layer. The magnetic field data includes the magnetic induction intensity of the center point of the target cable conductor surface, the load current in the cable line, the induced current of the metal shielding layer, and the ground circulating current. The mechanical stress field data includes the instantaneous axial / radial tension of the target cable and the instantaneous bending strain force.

4. The multi-physics coupling based cable life dynamic assessment system according to claim 1, wherein: The cross-scale dynamic correlation algorithm uses cross-scale feature extraction technology to reduce the dimensionality and process the parameters of the multi-physical field data collected by the multi-physical field perception module, and obtains a feature vector that reflects the overall operating state of the cable. At the same time, the pre-established cable defect database is called to calculate the correlation between the feature vector and the cable defects in real time.

5. The multi-physics coupling based cable life dynamic assessment system according to claim 1, wherein: The cable defect database includes defect types and corresponding feature vector data, corresponding multi-physical field response characteristics and defect evolution law. The cable defect database is dynamically updated based on experimental data and actual operation cable defect detection case data.

6. The multi-physics coupling based cable life dynamic assessment system according to claim 1, wherein: The damage state evaluation result includes the damage type, damage location and damage degree corresponding to the defect, and is output to the dynamic life assessment module in a standardized data interface and hierarchical identification.

7. The multi-physics coupling based cable life dynamic assessment system according to claim 1, wherein: The timing neural network architecture input end adopts a multi-channel parallel structure, embeds a coupling layer, accesses a bidirectional long-term memory network layer, calculates an evolution function and a time-dependent coefficient of the damage state over time, is trained in combination with historical damage data of the cable and a similar cable life attenuation curve, and outputs a residual life prediction value of the target cable.

8. The multi-physics coupling based cable life dynamic assessment system of claim 1, wherein: The model parameter optimization is aimed at the difference sources of the digital twin simulation data and the actual measurement data, locates the optimization of the multi-physical field coupling coefficients and the feature matching threshold in the cross-scale correlation algorithm, and the optimization process adopts an adaptive particle swarm algorithm and dynamically adjusts in combination with historical optimization records and parameter calibration experience of similar cables.

9. The multi-physics coupling based cable life dynamic assessment system of claim 1, wherein: The environment-physical field coupling compensation matrix is a mathematical model for environment interference suppression, quantifies the coupling influence relationship between environmental factors and each physical field in the form of a multi-dimensional matrix, the row dimension of the matrix corresponds to each item of data of the temperature field, the electric field, the magnetic field and the mechanical stress field, the column dimension corresponds to the environmental interference factors including temperature, humidity, air pressure and wind speed, and the matrix elements are coupling coefficients calibrated through experiments and actual measurement data, wherein the matrix coefficients are periodically updated according to the cable laying scene and seasonal changes.

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