Resistance value-based high-voltage cable lead seal defect monitoring system

Abstract

The invention discloses a high-voltage cable lead seal defect monitoring system based on a resistance value, and relates to the field of cable monitoring, and the system comprises an instruction management module which is used for connecting a transmission power grid of a cable, obtaining the collection authority of power supply operation state data, and editing and sending control instructions of all function modules; the model construction module is used for collecting operation parameters of a plurality of lead sealing nodes and associated cables in a specified cable section in real time, integrating the operation parameters into power supply operation state data and constructing a lead sealing three-dimensional model; the trend monitoring module is used for inputting the real-time power supply operation state data of the current cable section into the lead sealing three-dimensional model and outputting the resistance change trend of a plurality of lead sealing nodes; by capturing and marking abnormal states of a plurality of nodes of a specified cable section, an efficient detection path is generated, the path planning efficiency is improved, the detection time is shortened, the maximum detection coverage is ensured, external environment interference is avoided, and crack propagation is predicted through a more accurate model.

Description

Technical Field

[0001] The invention relates to the technical field of cable monitoring, and in particular to a high-voltage cable lead seal defect monitoring system based on resistance value. Background Art

[0002] High-voltage cables are widely used in power transmission and distribution systems, especially in urban power grids, industrial parks and large-scale infrastructure. They are usually buried underground or laid along structures such as buildings and bridges. As the high-voltage cables age, material aging or external environmental factors such as water intrusion, soil corrosion, mechanical damage, etc. may cause lead seal defects. These defects may lead to reduced cable insulation performance, and then cause safety accidents such as short circuits, power outages and even fires. Since the reliability and safety of the power system are crucial, an effective monitoring system is needed to promptly detect and evaluate cable defects.

[0003] Traditional monitoring methods, such as eddy current monitoring or infrared monitoring, are not sensitive enough when detecting tiny defects, resulting in early defects not being discovered in time. They are unable to respond quickly after defects occur, causing defects to expand to an irreversible stage. They only analyze single data and have high limitations.

[0004] For daily node monitoring, the existing node selection relies on manual experience, the node evaluation is one-sided, the detection path is inefficient and time-consuming, the defect prediction is rough and has large errors, and it is impossible to accurately simulate crack growth. Summary of the invention

[0005] (I) Technical problems to be solved: In view of the above-mentioned shortcomings of the prior art, the present invention provides a high-voltage cable seal defect monitoring system based on resistance value, which can effectively solve the problems of the prior art.

[0006] (II) Technical solution: To achieve the above objectives, the present invention is implemented through the following technical solution. The present invention discloses a high-voltage cable seal defect monitoring system based on resistance value, including: an instruction management module, which is used to connect to the transmission power grid of the cable, obtain the collection authority of the power supply operation status data, and edit and send the control instructions of each functional module.

[0007] The model building module is used to collect the operating parameters of several seal nodes and associated cables in a specified cable segment in real time, integrate them into power supply operation status data, and build a three-dimensional seal model.

[0008] The trend monitoring module is used to input the real-time power supply operation status data of the current cable segment into the lead seal three-dimensional model, output the resistance change trends of several lead seal nodes, set the early warning threshold, and determine whether there is a resistance fluctuation beyond the preset range in the resistance change trend of a certain node.

[0009] The detection planning unit is used to capture the key nodes in the cable section with abnormal resistance fluctuations, generate infrared detection points based on the key nodes, and generate the best defect detection path.

[0010] The defect assessment module is used to perform defect detection on the lead seal area through the optimal defect detection path, obtain multi-modal defect data and fuse and classify it, and evaluate the defect performance coefficients of several nodes.

[0011] The fission prediction module is used for the coherent fusion of defect manifestation coefficients in time series and predicts the future cycle growth process of cracks based on the fusion results.

[0012] The fission verification module is used to reversely inject the prediction results of the fission prediction module into the three-dimensional model of the lead seal, simulate and verify the crack propagation path, and automatically update the verified parameters to each functional module. The prediction results submitted by the fission prediction module are integrated within a preset number of cycles as a monitoring report.

[0013] Furthermore, the detection planning unit is deployed with sub-modules at the lower level, and the sub-modules include a node identification module, a positioning generation module and a path planning module. The node identification module is interactively connected to the positioning generation module through a wireless network, and the positioning generation module is interactively connected to the path planning module through a wireless network, wherein: the node identification module is used to capture the lead seal nodes with resistance fluctuations exceeding the preset range, and evaluate the impact of the lead seal nodes through the current path and the distribution of cold and hot flows, and mark the lead seal nodes that meet the marking standards and are affected by abnormal fluctuations in the current monitoring period as key nodes.

[0014] The positioning generation module is used to select the optimal infrared detection points for key nodes through the quantum annealing optimization algorithm. The points cover the key nodes and their neighborhoods of preset scales and conform to the direction of the maximum thermal radiation gradient.

[0015] The path planning module is used to generate a preliminary path set based on a number of infrared detection points provided by the positioning generation module, and receive manual active modification to generate a final path set.

[0016] Furthermore, the power supply operation status data collected by the model building module includes: current, voltage, operating temperature of a specified cable segment, cable layer structure, geometric features and physical properties.

[0017] Furthermore, the working logic of the three-dimensional seal model constructed by the model construction module is as follows: the Joule heat distribution is calculated through the current path distribution, the heat source model is corrected in combination with the real-time temperature monitoring data, a dynamic temperature field is generated, and the thermal stress distribution of each node is iteratively updated according to the thermal conductivity coefficient of the cable layer metal sheath, the insulation layer and the seal layer; based on the resistivity temperature coefficient of the seal material, the node resistance value is calculated in real time, the influence coefficient of local deformation on the conductor cross-sectional area and path length is quantified, and the resistance calculation result is updated.

[0018] Furthermore, during the judgment stage, the trend monitoring module initializes the warning threshold based on the material fatigue characteristics and customizes the threshold bandwidth in combination with historical operating data. If the slope exceeds the threshold for three consecutive cycles and the curvature mutation reaches the preset standard mutation, another graded warning is triggered and submitted to the instruction management module.

[0019] Furthermore, during defect detection in the defect assessment module, based on the final path set generated by the detection planning unit, the temperature field distribution on the seal surface is captured, local overheating areas are identified, the cable layer structure is penetrated, the air gap or crack inside the seal is detected, and the resistance fluctuation trend is combined to locate the current leakage or electromagnetic abnormality area. Through timestamp synchronization and spatial coordinate mapping, the infrared hot spot, ultrasonic reflection signal, and electromagnetic field intensity are unified into the three-dimensional model coordinate system of the seal, the confidence of the three types of data is weighted, and the fusion features are extracted. Feature classification is performed based on the fusion features to identify annular cracks, axial cracks, pitting corrosion, closed cracks, open cracks, and delamination defects. The classification results are integrated to output the final defect type label, and a comprehensive evaluation is performed based on the severity coefficient, risk diffusion coefficient, and emergency intervention index to output the defect performance coefficient.

[0020] Furthermore, the defect manifestation coefficient obtained by the defect assessment module is cross-validated with the resistance change trend obtained by the trend monitoring module. When the deviation between the two exceeds 15%, the re-detection protocol is triggered, and the detection planning unit is submitted to trigger the correction instruction. After the correction instruction is passed, the detection path is regenerated and data is collected again.

[0021] Furthermore, the calculation formula for predicting the future periodic growth process of the crack by the fission prediction module is: ; In the above formula, Represents at a point in time The predicted crack growth, represents the time increment, indicating the predicted future time interval, represents the fusion index tensor, represents the norm order, represents the material impedance coefficient, represents any instant in the time process, Represents the prediction starting time point, Represents the defect fusion index F at time The instantaneous rate of change of represents the time differential element, indicating the contribution of the defect state micro-change, represents the activation function, represents the total number of environmental coupling parameters, represents the i-th environmental coupling parameter, indicating the weights of several environmental factors, Represents the i-th environmental impact factor, indicating the time point A quantitative indicator of specific environmental conditions. Represents the exponential function.

[0022] Furthermore, the instruction management module is interactively connected to the allocation module via a wireless network. The allocation module is used to provide identity authentication to the control end of each cable segment in the current cycle. The authenticated control end obtains dynamic allocation instructions for each cable segment from the instruction management module, and submits the feedback data of each control end centrally within a preset time with the instruction management module as the target.

[0023] Furthermore, the instruction management module is interactively connected to the model building module through a wireless network, the model building module is interactively connected to the trend monitoring module through a wireless network, the trend monitoring module is interactively connected to the detection planning unit through a wireless network, the detection planning unit is interactively connected to the defect assessment module through a wireless network, the defect assessment module is interactively connected to the fission prediction module through a wireless network, and the fission prediction module is interactively connected to the fission verification module through a wireless network.

[0024] (III) Beneficial effects: Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects: 1. By constructing a three-dimensional model of the lead seal, the system is allowed to dynamically adjust the model under various operating conditions, making the monitoring more accurate, reflecting the true state of the cable in real time, avoiding misjudgment, improving the accuracy of identifying abnormal resistance fluctuations, giving early warning of potential faults, and reducing the risk of accidents.

[0025] 2. By capturing and marking the abnormal status of several nodes in the specified cable segment, multi-dimensional analysis is provided for the evaluation of key nodes. Even small changes can be sensitively captured, ensuring that the system can promptly identify the nodes that have the greatest impact on the occurrence of defects, thereby conducting targeted monitoring, improving the accuracy of defect warnings, and integrating and evaluating monitoring data. In the case of small samples, the system can maintain high accuracy, effectively integrate thermal imaging, ultrasonic and other data, analyze defect characteristics and change trends, and enhance fault detection capabilities.

[0026] 3. Generate efficient detection paths through optimization algorithms, improve path planning efficiency, shorten detection time, ensure maximum detection coverage, avoid interference from the external environment, improve data reliability, predict crack propagation through more accurate models, and dynamically adjust parameters to significantly improve prediction accuracy, helping maintenance personnel prepare response plans in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a schematic diagram of the framework of the high-voltage cable lead seal defect monitoring system based on resistance value of the present invention.

[0029] Figure 2 It is a schematic diagram of the framework of the detection planning unit in the present invention.

[0030] The numbers in the figure represent, respectively, 1. instruction management module; 2. model building module; 3. trend monitoring module; 4. detection planning unit; 41. node identification module; 42. positioning generation module; 43. path planning module; 5. defect assessment module; 6. fission prediction module; 7. fission verification module; 8. allocation module. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0032] The present invention will be further described below in conjunction with the embodiments.

[0033] Embodiment 1: A high-voltage cable lead seal defect monitoring system based on resistance value in this embodiment, such as Figure 1 and Figure 2As shown, it includes: an instruction management module 1, which is used to connect to the transmission grid of the cable, obtain the collection authority of the power supply operation status data, and edit and send the control instructions of each functional module; the instruction management module 1 is interactively connected with the allocation module 8 through a wireless network, and the allocation module 8 is used to provide identity authentication to the control end of each cable segment in the current cycle, and the authenticated control end obtains the dynamic allocation instruction for each cable segment from the instruction management module 1, and the feedback data of each control end is submitted centrally within a preset time with the instruction management module 1 as the target.

[0034] Model building module 2 is used to collect the operating parameters of several seal nodes and associated cables in the specified cable segment in real time, integrate them into power supply operation status data, and build a three-dimensional seal model; the power supply operation status data includes: current, voltage, operating temperature of the specified cable segment, cable layer structure, geometric characteristics and physical properties.

[0035] The working logic of the lead seal three-dimensional model is: calculate the Joule heat distribution through the current path distribution, correct the heat source model based on the real-time temperature monitoring data, generate a dynamic temperature field, and iteratively update the thermal stress distribution of each node according to the thermal conductivity coefficient of the cable layer metal sheath, insulation layer and lead seal layer.

[0036] Based on the resistivity temperature coefficient of the lead sealing material, the node resistance value is calculated in real time, the influence coefficient of local deformation on the conductor cross-sectional area and path length is quantified, and the resistance calculation results are updated.

[0037] The trend monitoring module 3 is used to input the real-time power supply operation status data of the current cable segment into the lead seal three-dimensional model, output the resistance change trend of several lead seal nodes, set the warning threshold, and judge whether there is a resistance fluctuation beyond the preset range in the resistance change trend of a certain node; in the judgment stage, the trend monitoring module 3 sets the initial warning threshold based on the material fatigue characteristics, and customizes the threshold bandwidth in combination with the historical operating data. If the slope exceeds the threshold for three consecutive cycles and the curvature mutation reaches the preset standard mutation, another graded warning is triggered and submitted to the instruction management module 1; the warning threshold is set based on the material fatigue characteristics and the historical operating data, and can be customized, thereby enhancing the flexibility and reliability of the system under different working conditions. Compared with the traditional monitoring technology with fixed thresholds, this mechanism greatly improves the accuracy of the warning.

[0038] The detection planning unit 4 is used to capture the key nodes in the cable section with abnormal resistance fluctuation, generate infrared detection points according to the key nodes, and generate the best defect detection path.

[0039] The detection planning unit 4 is deployed with submodules at the lower level, which include a node identification module 41, a positioning generation module 42 and a path planning module 43. The node identification module 41 is interactively connected to the positioning generation module 42 through a wireless network, and the positioning generation module 42 is interactively connected to the path planning module 43 through a wireless network, wherein: the node identification module 41 is used to capture the lead seal nodes with resistance fluctuations exceeding the preset range, and evaluate the impact of the lead seal nodes through the current path and the distribution of cold and hot flows, and mark the lead seal nodes that meet the marking standards and are affected by abnormal fluctuations during the current monitoring period as key nodes.

[0040] The positioning generation module 42 is used to select the optimal infrared detection point for the key node through the quantum annealing optimization algorithm. The point covers the key node and its neighborhood of a preset scale and conforms to the direction of the maximum thermal radiation gradient.

[0041] The path planning module 43 is used to generate a preliminary path set based on a number of infrared detection points provided by the positioning generation module 42, and receive manual active modification to generate a final path set.

[0042] The defect assessment module 5 is used to perform defect detection on the lead seal area through the optimal defect detection path, obtain multi-modal defect data and fuse and classify it, and evaluate the defect performance coefficients of several nodes.

[0043] The fission prediction module 6 is used for the coherent fusion of defect manifestation coefficients in the time series, and predicts the future periodic growth process of the crack based on the fusion results.

[0044] The fission verification module 7 is used to reversely inject the prediction results of the fission prediction module 6 into the three-dimensional model of the lead seal, simulate and verify the crack propagation path, and automatically update the verified parameters to each functional module. The prediction results submitted by the fission prediction module 6 are integrated within a preset number of cycles as a monitoring report.

[0045] The instruction management module 1 is interactively connected to the model building module 2 through a wireless network, the model building module 2 is interactively connected to the trend monitoring module 3 through a wireless network, the trend monitoring module 3 is interactively connected to the detection planning unit 4 through a wireless network, the detection planning unit 4 is interactively connected to the defect assessment module 5 through a wireless network, the defect assessment module 5 is interactively connected to the fission prediction module 6 through a wireless network, and the fission prediction module 6 is interactively connected to the fission verification module 7 through a wireless network.

[0046] Compared with the existing technology, the real-time collection of multiple operating parameters of the cable segment and the use of these parameters to construct a three-dimensional model of the seal improves the response speed and accuracy to the cable operating status and enables timely detection of potential defects. By calculating the Joule heat distribution and correcting the heat source model, the system can dynamically generate the temperature field and update the thermal stress distribution, providing a more accurate representation of the thermal dynamic behavior of the cable and its impact, and providing a more comprehensive monitoring capability compared to static models.

[0047] Real-time capture and marking of nodes with abnormal resistance fluctuations ensures a rapid response to potential problems, allowing the system to focus on high-risk areas, thereby reducing the risk of false alarms and missed reports, selecting the best detection points, and ensuring that infrared detection covers key nodes and their neighborhoods, in line with the maximum thermal radiation gradient, optimizing traditional detection methods and improving the efficiency and accuracy of defect detection.

[0048] The fusion and classification of data collected from multiple detection methods helps to form a more comprehensive defect manifestation coefficient assessment, integrate the advantages of different types of data, and provide more accurate detection results. By fusing the defect manifestation coefficient in time series and predicting the growth process of future crack cycles, dynamic monitoring of the cable health status is achieved, allowing operation and maintenance personnel to take measures in advance to reduce downtime and maintenance costs.

[0049] Embodiment 2: In other aspects, this embodiment also provides a calculation formula for predicting the future cycle growth process of a crack. The calculation formula for predicting the future cycle growth process of a crack is: ; In the above formula, Represents at a point in time The predicted crack growth, usually the length or depth of the crack, represents the time increment, indicating the predicted future time interval, Represents the fusion index tensor, which contains three-dimensional features of spatial position, defect intensity, and propagation tendency. represents the norm order, represents the material impedance coefficient, represents any instant in the time process, Represents the prediction starting time point, Represents the defect fusion index F at time The instantaneous rate of change of represents the time differential element, indicating the contribution of the defect state micro-change, Represents the activation function, which is used for nonlinear adjustment and output range conversion. represents the total number of environmental coupling parameters, represents the i-th environmental coupling parameter, indicating the weights of several environmental factors, Represents the i-th environmental impact factor, indicating the time point A quantitative indicator of specific environmental conditions. represent exist The modulus or length under the norm reflects the intensity and comprehensive impact of the current defect performance. Represents an exponential function, which indicates the nonlinear characteristics of the crack length or depth growth process.

[0050] Crack growth Based on the current state and environmental influences to predict future crack growth states using Calculate the performance of the current defect under a specific norm in order to quantify the strength of the impact, material impedance coefficient Dividing by the modulus is to take into account the characteristics of the material, which affect the ability of crack propagation. The exponential integral part describes the time evolution of the defect performance and models the impact process of future crack propagation. The environmental coupling parameter and impact factor The model is adapted to the crack growth behavior occurring under different environmental conditions.

[0051] Embodiment 3: In this embodiment, a defect detection process is provided, specifically: based on the final path set generated by the detection planning unit 4, a mobile detection device equipped with an infrared thermal imager, an ultrasonic flaw detector, and an electromagnetic sensor is controlled to scan along key points of the path such as the direction of the maximum thermal radiation gradient, capture the temperature field distribution on the surface of the seal, identify the local overheating area, penetrate the cable layer structure, detect the air gap or crack inside the seal, and locate the current leakage or electromagnetic abnormal area in combination with the resistance fluctuation trend. Through timestamp synchronization and spatial coordinate mapping, the infrared hot spot spatial resolution is 0.5mm, and the ultrasonic reflection signal accuracy is ±0. .1mm, and electromagnetic field intensity sensitivity 1μT are unified into the three-dimensional model coordinate system of the lead seal, and the confidence of the three types of data are weighted to extract fusion features, specifically the coupling index of temperature gradient and ultrasonic attenuation rate, the phase difference between resistance fluctuation rate and magnetic field distortion, and the degree of deviation of multiple parameters from normal working conditions calculated based on Mahalanobis distance. Feature classification is performed based on the fusion features to identify annular cracks, axial cracks, pitting corrosion, closed cracks, open cracks and delamination defects. The classification results are integrated to output the final defect type label, and a comprehensive evaluation is performed based on the severity coefficient, risk diffusion coefficient and emergency intervention index to output the defect performance coefficient.

[0052] The severity coefficient is the standardized weighted Z-score of crack depth, temperature anomaly value, and resistance offset, with a weight ratio of 4:3:3; the risk diffusion coefficient is calculated based on the fractal dimension to calculate the crack edge roughness, and combined with thermal flow simulation to predict the expansion tendency; the emergency intervention index is balanced through the NSGA-II multi-objective optimization algorithm. The maintenance cost and failure probability are output, and a standardized value of 0-1 is output to ensure that the entire process from data collection to coefficient evaluation is completed within 5 minutes, a typical value of a single monitoring cycle.

[0053] Working principle: The instruction management module 1 is responsible for connecting to the power grid and authority management, the model building module 2 collects data, the trend monitoring module 3 analyzes the resistance trend, the detection planning unit 4 handles abnormal nodes, the defect assessment module 5 detects defects, the fission prediction module 6 predicts time series, the fission verification module 7 simulates verification, and the allocation module 8 is responsible for identity verification.

[0054] By collecting key operating parameters such as current and temperature in real time and establishing a three-dimensional model of the lead seal, dynamic monitoring of the cable status and abnormal warning are achieved, improving the response speed and accuracy of the system. Secondly, by introducing the quantum annealing optimization algorithm, the system can efficiently select infrared detection points to ensure the comprehensiveness and accuracy of defect detection.

[0055] In addition, the defect manifestation coefficient is used for time series coherence fusion to predict the future growth process of cracks, which enhances the system's preventive maintenance capabilities and reduces the risk of potential damage. The system's intelligent parameter update and monitoring report integration functions improve data processing efficiency, provide a reliable basis for subsequent decision-making, and promote the safe operation of high-voltage cables.

[0056] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high-voltage cable seal defect monitoring system based on resistance value, characterized in that: include: The command management module is used to connect to the transmission grid of the cable, obtain the collection authority of the power supply operation status data, and edit and send the control commands of each functional module; The model building module is used to collect the operating parameters of several seal nodes and associated cables in a specified cable segment in real time, integrate them into power supply operation status data, and build a three-dimensional seal model; The trend monitoring module is used to input the real-time power supply operation status data of the current cable segment into the lead seal three-dimensional model, output the resistance change trend of several lead seal nodes, set the early warning threshold, and determine whether there is a resistance fluctuation beyond the preset range in the resistance change trend of a certain node; the detection planning unit is used to capture the key nodes in the cable segment with abnormal resistance fluctuations, generate infrared detection points according to the key nodes, and generate the best defect detection path; Defect assessment module, used to detect defects in the lead seal area through the optimal defect detection path, obtain multi-modal defect data and fuse and classify them, and evaluate the defect performance coefficients of several nodes; The fission prediction module is used for the coherent fusion of defect manifestation coefficients in time series, and predicts the future cycle growth process of cracks based on the fusion results; the fission verification module is used to reversely inject the prediction results of the fission prediction module into the three-dimensional model of the lead seal, simulate and verify the crack propagation path, and automatically update the verified parameters to each functional module. The prediction results submitted by the fission prediction module are integrated within a preset number of cycles as a monitoring report.

2. A high-voltage cable lead seal defect monitoring system based on resistance value according to claim 1, characterized in that: The detection planning unit is deployed with submodules at the lower level, and the submodules include a node identification module, a positioning generation module and a path planning module. The node identification module is interactively connected with the positioning generation module through a wireless network, and the positioning generation module is interactively connected with the path planning module through a wireless network, wherein: the node identification module is used to capture the lead seal nodes with resistance fluctuations exceeding the preset range, and to evaluate the impact of the lead seal nodes through the current path and the distribution of cold and hot flows, and to mark the lead seal nodes that meet the marking standards due to abnormal fluctuations during the current monitoring period as key nodes; the positioning generation module is used to select the optimal infrared detection points for the key nodes through a quantum annealing optimization algorithm, and the points cover the neighborhood of the key nodes and their preset scales and conform to the direction of the maximum thermal radiation gradient; the path planning module is used to generate a preliminary path set based on a number of infrared detection points provided by the positioning generation module, and to receive manual active modifications to generate a final path set.

3. A high-voltage cable lead seal defect monitoring system based on resistance value according to claim 1, characterized in that: The power supply operation status data collected by the model building module includes: current, voltage, operation temperature of a specified cable segment, cable layer structure, geometric features and physical properties.

4. A high-voltage cable lead seal defect monitoring system based on resistance value according to claim 1, characterized in that: The working logic of the three-dimensional seal model constructed by the model construction module is as follows: the Joule heat distribution is calculated through the current path distribution, the heat source model is corrected in combination with the real-time temperature monitoring data, a dynamic temperature field is generated, and the thermal stress distribution of each node is iteratively updated according to the thermal conductivity coefficient of the cable layer metal sheath, the insulation layer and the seal layer; based on the resistivity temperature coefficient of the seal material, the node resistance value is calculated in real time, the influence coefficient of local deformation on the conductor cross-sectional area and path length is quantified, and the resistance calculation result is updated.

5. The high-voltage cable lead seal defect monitoring system based on resistance value according to claim 1 is characterized in that: During the judgment stage, the trend monitoring module initializes the warning threshold based on the material fatigue characteristics and customizes the threshold bandwidth in combination with historical operating condition data. If the slope exceeds the threshold for three consecutive cycles and the curvature mutation reaches the preset standard mutation, another graded warning is triggered and submitted to the instruction management module.

6. The high-voltage cable lead seal defect monitoring system based on resistance value according to claim 1 is characterized in that: During defect detection in the defect assessment module, based on the final path set generated by the detection planning unit, the temperature field distribution on the seal surface is captured, the local overheating area is identified, the cable layer structure is penetrated, the air gap or crack inside the seal is detected, and the current leakage or electromagnetic abnormality area is located in combination with the resistance fluctuation trend. Through timestamp synchronization and spatial coordinate mapping, the infrared hot spot, ultrasonic reflection signal, and electromagnetic field intensity are unified into the three-dimensional model coordinate system of the seal, the confidence of the three types of data is weighted, the fusion features are extracted, and feature classification is performed based on the fusion features to identify annular cracks, axial cracks, pitting corrosion, closed cracks, open cracks, and delamination defects. The classification results are integrated to output the final defect type label, and a comprehensive evaluation is performed based on the severity coefficient, risk diffusion coefficient, and emergency intervention index to output the defect performance coefficient.

7. The high-voltage cable seal defect monitoring system based on resistance value according to claim 1 is characterized in that: The defect manifestation coefficient obtained by the defect assessment module is cross-validated with the resistance change trend obtained by the trend monitoring module. When the deviation between the two exceeds 15%, the re-detection protocol is triggered, and the detection planning unit is submitted to trigger the correction instruction. After the correction instruction is passed, the detection path is regenerated and data is collected again.

8. The high-voltage cable lead seal defect monitoring system based on resistance value according to claim 1 is characterized in that: The calculation formula of the fission prediction module to predict the future cycle growth process of the crack is: ; In the above formula, Represents at a point in time The predicted crack growth, represents the time increment, indicating the predicted future time interval, represents the fusion index tensor, represents the norm order, represents the material impedance coefficient, represents any instant in the time process, Represents the prediction starting time point, Represents the defect fusion index F at time The instantaneous rate of change of represents the time differential element, indicating the contribution of the defect state micro-change, represents the activation function, represents the total number of environmental coupling parameters, represents the i-th environmental coupling parameter, indicating the weights of several environmental factors, Represents the i-th environmental impact factor, indicating the time point A quantitative indicator of specific environmental conditions. Represents the exponential function.

9. The high-voltage cable lead seal defect monitoring system based on resistance value according to claim 1 is characterized in that: The instruction management module is interactively connected to the allocation module via a wireless network. The allocation module is used to provide identity authentication to the control end of each cable segment in the current cycle. The authenticated control end obtains dynamic allocation instructions for each cable segment from the instruction management module, and submits the feedback data of each control end to the instruction management module in a centralized manner within a preset time.

10. The high-voltage cable lead seal defect monitoring system based on resistance value according to claim 1 is characterized in that: The instruction management module is interactively connected to the model building module through a wireless network, the model building module is interactively connected to the trend monitoring module through a wireless network, the trend monitoring module is interactively connected to the detection planning unit through a wireless network, the detection planning unit is interactively connected to the defect assessment module through a wireless network, the defect assessment module is interactively connected to the fission prediction module through a wireless network, and the fission prediction module is interactively connected to the fission verification module through a wireless network.

Images