Power cable joint insulation state intelligent monitoring method based on complex environment

Abstract

The invention provides a power cable joint insulation state intelligent monitoring method based on a complex environment. The method belongs to the technical field of power system monitoring, and comprises the steps that related data are collected through multiple means, and the related data comprise environment temperature data, partial discharge signals and temperature distribution images; collected environment parameters and partial discharge signals are input to an intelligent analysis system, preprocessing and feature extraction are carried out, an insulation state prediction model is established based on a machine learning algorithm, and the aging degree and potential fault points of a cable joint insulation layer are predicted according to historical data and real-time monitoring data. Environment temperature data, partial discharge signals and temperature distribution images are collected through multiple means, and an intelligent analysis system and a machine learning algorithm are combined, so that the aging degree and potential fault points of the cable joint insulating layer can be predicted more accurately.

Description

Technical Field

[0001] The invention proposes an intelligent monitoring method for the insulation state of a power cable joint based on a complex environment, and belongs to the technical field of power system monitoring. Background Art

[0002] In the complex operating environment of the power system, the performance of the insulation material of the cold shrink cable intermediate joint, as a key connecting component, is directly related to the safe and stable operation of the entire system. However, since the cable joint will be affected by various factors such as high temperature, high pressure, and mechanical damage during operation, the insulation layer is prone to aging and cracking, and even causes partial discharge and breakdown, leading to power failures and safety accidents. Although traditional cable insulation monitoring methods, such as partial discharge monitoring, temperature measurement, and insulation resistance testing, can detect insulation defects to a certain extent, they often have problems such as low monitoring efficiency, insufficient accuracy, and inability to monitor in real time. Therefore, it is particularly important to develop an innovative intelligent monitoring method to realize real-time monitoring and early warning of the insulation status of power cable joints in complex environments. Summary of the invention

[0003] The present invention provides an intelligent monitoring method for the insulation status of a power cable joint based on a complex environment, which is used to solve the problems mentioned in the above background technology:

[0004] The present invention proposes an intelligent monitoring method for the insulation status of a power cable joint based on a complex environment, the method comprising:

[0005] S1. Collect relevant data by multiple means, wherein the relevant data includes ambient temperature data, partial discharge signals and temperature distribution images;

[0006] S2. Input the collected environmental parameters and partial discharge signals into the intelligent analysis system for preprocessing and feature extraction. Based on the machine learning algorithm, an insulation state prediction model is established to predict the aging degree and potential fault points of the insulation layer of the cable joint according to historical data and real-time monitoring data.

[0007] S3. Using infrared thermal imaging data and image processing technology, identify temperature abnormality areas and calculate temperature gradients, and evaluate the thermal stability of the insulation layer based on the temperature gradient calculation results;

[0008] S4. According to the output results of the insulation status prediction model, set the warning threshold. When the prediction result exceeds the threshold, trigger the warning mechanism; combine the partial discharge monitoring data and infrared thermal imaging results to accurately locate the location of the insulation defect and give the fault type and degree;

[0009] S5. Send the early warning information and fault location results to the operation and maintenance personnel in real time. According to the monitoring data and early warning results, intelligently optimize the structural design of the cold shrink cable intermediate joint, regularly inspect and maintain the cable joints, and formulate targeted maintenance plans based on the monitoring data.

[0010] Furthermore, the S1 includes:

[0011] S11. Real-time collection of ambient temperature is performed through a high-precision temperature sensor, the humidity level around the connector is monitored through a humidity sensor, and the electric field distribution around the cable connector is monitored through an electric field strength sensor;

[0012] S12, capturing the partial discharge signal in the cable insulation layer through a high-frequency current sensor, and filtering and noise reduction processing the collected partial discharge signal;

[0013] S13. Use a high-resolution infrared thermal imaging camera to regularly capture temperature distribution images of the insulation layer of the cable joint, and use an image enhancement algorithm to enhance the collected temperature distribution images.

[0014] Furthermore, the S2 includes:

[0015] S21, preprocessing the collected environmental parameters and partial discharge signals;

[0016] S22. Based on statistical analysis and machine learning algorithms, feature selection is performed on the preprocessed data to identify characteristic variables that have an impact on insulation status prediction;

[0017] S23, extracting features from the preprocessed data through time domain, frequency domain and time-frequency domain analysis techniques, and fusing the extracted feature variables to form a comprehensive feature vector;

[0018] S24, dividing the preprocessed data set into a training set and a validation set, using the training set to train the built-in machine learning model, and using the validation set to evaluate the performance of the model;

[0019] S25. Evaluate the performance of the model through cross-validation technology, adjust the model's hyperparameters and optimize the model structure based on the validation results;

[0020] S26. Use the trained machine learning model to predict the test set, output the insulation status prediction result, and evaluate the uncertainty of the prediction result;

[0021] S27. Design early warning strategies based on the uncertainty of the prediction results. When the uncertainty of the prediction results is high, trigger a higher level of early warning mechanism.

[0022] S28. Compare the predicted results with the actual situation, evaluate the accuracy and reliability of the model, iteratively optimize the model based on the evaluation results, and regularly collect new monitoring data based on the continuous learning and updating mechanism to update and optimize the model.

[0023] Furthermore, the S23 includes:

[0024] The mean and variance of each characteristic variable after preprocessing are calculated, and the volatility and stability of the data are evaluated based on the evaluation record; the trend line of the time series data is calculated through the sliding window technology to identify the changing trends of environmental parameters and partial discharge signals over time;

[0025] Identify and record the maximum and minimum values ​​of each characteristic variable within a time period, analyze abnormal fluctuations, evaluate the correlation of time series data at different time intervals, and reveal the time dependence within the data;

[0026] Convert the time domain signal into frequency domain representation, analyze the spectral components of each characteristic variable, and identify the main frequency components and their energy distribution;

[0027] Calculate and plot power spectrum density to evaluate signal energy at different frequencies and identify potential periodic or random features; calculate the entropy of the spectrum to measure the complexity of the signal frequency distribution;

[0028] Perform Fourier transform on the signal within the time window to obtain the spectrum information that changes with time, capture the instantaneous frequency characteristics of the partial discharge signal, decompose the signal using wavelet functions of different scales, and extract the time-frequency characteristics at multiple scales;

[0029] Decompose the complex signal into a series of intrinsic mode functions, each IMF represents a different frequency component of the signal; standardize all extracted features;

[0030] Based on correlation analysis, the most critical feature subset for insulation status prediction is further screened, and the screened features are fused into a comprehensive feature vector using weighted average.

[0031] And through random forest, the contribution of each feature in the comprehensive feature vector is evaluated.

[0032] Furthermore, the S3 includes:

[0033] S31. Based on high-resolution infrared thermal imaging technology, obtain the temperature distribution image of the cable joint insulation layer, and apply image processing algorithms to preliminarily identify the temperature abnormality area;

[0034] S32, performing detailed analysis on the initially identified temperature anomaly area, and removing noise and artifacts using morphological processing technology;

[0035] S33, calculating geometric features such as area, perimeter, shape factor, and statistical features of the temperature anomaly region;

[0036] S34, setting a plurality of temperature gradient calculation points around the temperature anomaly region, calculating the temperature gradient between each calculation point and its adjacent points, and forming a temperature gradient distribution diagram;

[0037] S35. Analyze the thermal conductivity of the insulation layer according to the temperature gradient distribution diagram to determine whether there is a thermal conduction barrier or heat accumulation phenomenon;

[0038] S36. Combine the thermal properties of the insulation material to evaluate the thermal stability of the insulation layer and predict the risk of thermal failure;

[0039] S37, establishing a thermal stability evaluation model, and quantitatively evaluating the thermal stability of the insulation layer based on the geometric characteristics, statistical characteristics, and temperature gradient distribution of the temperature anomaly area;

[0040] S38. Based on the results of the thermal stability assessment model, combined with historical data and industry standards, set the warning threshold, and dynamically adjust the warning threshold based on the aging degree of the insulation layer and changes in the operating environment.

[0041] Furthermore, the S35 includes:

[0042] On the temperature gradient distribution map, the areas where the temperature gradient changes significantly are identified, and the areas where the temperature gradient exceeds or falls below the threshold are marked as potential heat conduction obstacle areas by setting the gradient change threshold;

[0043] Using the connected domain analysis in image processing technology, the points on the temperature gradient map are connected into lines to form a heat conduction path network;

[0044] According to the thermal conductivity characteristics of the insulating material, a weight is assigned to each path, and the weight is inversely proportional to the thermal conductivity of the material, and the thermal conduction efficiency of the entire insulating layer is calculated;

[0045] Evaluate the deviation of heat transfer efficiency from normal values, identify areas where heat transfer efficiency is significantly reduced, analyze local high or low temperature concentration areas in the temperature gradient distribution map, and evaluate the rate and degree of heat accumulation by calculating the temperature gradient change rate of the area;

[0046] Combining the results of temperature gradient anomaly identification and heat conduction efficiency evaluation, the heat conduction obstacle points in the insulation layer are located, and the heat conduction obstacles are classified according to the temperature gradient characteristics of the obstacle points, the position on the heat conduction path, and possible material defects;

[0047] Using time series analysis technology, we compare historical data of temperature gradient distribution diagrams, analyze the trend of thermal conductivity over time, identify areas where thermal conductivity continues to decline, and predict possible thermal failure risks in the future.

[0048] Furthermore, the S4 includes:

[0049] S41. According to the historical analysis of power system operation data, combined with industry standards and practical experience, different levels of warning thresholds are set. Based on the warning trigger mechanism, when the monitoring data reaches or exceeds the set threshold, the corresponding level of warning is automatically triggered; and a warning information transmission channel is established.

[0050] S42, using monitoring equipment to collect real-time data, combining historical data and industry standards, extracting fault features, wherein the fault features include abnormal fluctuations, mutation points, and frequency components; and preprocessing the extracted fault features;

[0051] S43, based on the fault characteristics and in combination with the fault location algorithm, locate the fault location; and perform simulation testing and optimization on the fault location algorithm;

[0052] S44. Based on the results obtained by the fault location algorithm, combined with on-site inspection and data analysis, diagnose the fault and determine the fault type and cause; establish a fault diagnosis knowledge base to record the fault characteristics, location methods and treatment measures of common faults;

[0053] S45. Based on the results of fault location and diagnosis, combined with changes in the operating status of the power system, the warning threshold is dynamically adjusted, and the performance of the warning mechanism is regularly tested and evaluated. The warning threshold and trigger mechanism are optimized and improved based on the test results.

[0054] Furthermore, the S43 includes:

[0055] S431. Based on the fault characteristics, preliminarily apply the fault location algorithm to the fault characteristic data set, preliminarily locate the fault location, and evaluate the basic performance and accuracy of the algorithm;

[0056] S432. Fuse the fault features extracted from the multi-source monitoring data to form a comprehensive feature vector, build a power system fault simulation model, simulate different types of fault scenarios, and generate corresponding fault feature data sets.

[0057] S433. Apply the optimized fault location algorithm to the simulation data set to verify the location accuracy and stability of the algorithm under different fault types and different operating conditions.

[0058] Furthermore, the S432 includes:

[0059] Extract fault features from multi-source monitoring data through signal processing technology and data analysis methods, and pre-process the extracted fault features;

[0060] The extracted features are fused based on the preset feature fusion strategy, and a fault simulation model is constructed according to the structure and operation characteristics of the power system;

[0061] Based on the fault simulation model, we generate fault feature data sets under different fault types and operating conditions, apply the fused comprehensive feature vectors to the fault location algorithm, and evaluate the effect of feature fusion on the performance of the algorithm.

[0062] According to the evaluation results of feature fusion effect, the simulation model and feature fusion strategy are iteratively optimized.

[0063] Furthermore, the S5 includes:

[0064] S51. Based on the early warning information push mechanism, the early warning information and fault location results are sent to the mobile device or workstation of the operation and maintenance personnel in real time;

[0065] S52. Analyze the structural design and insulation material performance of the cold shrink cable intermediate joint according to the monitoring data and early warning results, and put forward improvement suggestions based on the analysis results;

[0066] S53. Formulate a targeted maintenance plan based on the monitoring data and early warning results, wherein the maintenance plan includes detection frequency, detection method and maintenance measures;

[0067] S54. Carry out regular inspection and maintenance, record the inspection results, compare and analyze with the monitoring data, evaluate the maintenance effect, and continuously optimize the maintenance plan.

[0068] The beneficial effects of the present invention are as follows: by collecting ambient temperature data, partial discharge signals and temperature distribution images through multiple means, combined with intelligent analysis systems and machine learning algorithms, the aging degree and potential fault points of the insulation layer of the cable joint can be predicted more accurately; by using infrared thermal imaging data and image processing technology, temperature abnormality areas can be identified and the thermal stability of the insulation layer can be evaluated, so as to set a reasonable early warning threshold and improve the timeliness and accuracy of fault early warning; by combining partial discharge monitoring data and infrared thermal imaging results, the location of insulation defects can be accurately located, and the type and degree of fault can be given, which is conducive to rapid response and handling of faults; according to the monitoring data and early warning results, the structural design of the cable joint can be intelligently optimized, regular inspection and maintenance can be carried out, and targeted maintenance plans can be formulated to improve maintenance efficiency and effectiveness; by sending early warning information and fault location results to operation and maintenance personnel in real time, timely measures can be taken Measures can be taken to reduce power outages caused by faults and improve the reliability and stability of the power system; through intelligent monitoring and early warning systems, unnecessary maintenance work can be reduced, excessive maintenance can be avoided, and thus maintenance costs can be reduced; the method can adapt to complex environments and has good adaptability and robustness for cable joint insulation status monitoring under different environmental conditions; through time domain, frequency domain and time-frequency domain analysis techniques, as well as feature extraction and fusion, the monitoring data can be understood and processed more comprehensively, and the depth and breadth of data processing can be improved; the entire monitoring method realizes the intelligent management of data collection, analysis, early warning and maintenance, reduces the interference of human factors, and improves the scientificity and systematicness of management; through continuous learning and updating mechanisms, new monitoring data are regularly collected, and the model is updated and optimized, so that the monitoring method can continuously adapt to new operating conditions and environmental changes, and maintain its advancement and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a step diagram of the method described in the present invention. DETAILED DESCRIPTION

[0070] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0071] One embodiment of the present invention, as Figure 1 As shown, a method for intelligently monitoring the insulation status of a power cable joint based on a complex environment, the method comprising:

[0072] S1. Use high-precision sensor equipment to collect real-time environmental parameters around the cold-shrink cable intermediate joint, including temperature, humidity and electric field strength; use partial discharge monitoring technology to collect partial discharge signals in the cable insulation layer, including discharge amplitude, discharge frequency, etc.; use infrared thermal imaging technology to obtain the temperature distribution image of the cable joint insulation layer, and intuitively display potential temperature abnormality areas.

[0073] S2. Input the collected environmental parameters and partial discharge signals into the intelligent analysis system for preprocessing and feature extraction. Based on the machine learning algorithm, an insulation state prediction model is established to predict the aging degree and potential fault points of the insulation layer of the cable joint according to historical data and real-time monitoring data.

[0074] S3. Using infrared thermal imaging data and image processing technology, identify temperature abnormality areas and calculate temperature gradients, and evaluate the thermal stability of the insulation layer based on the temperature gradient calculation results;

[0075] S4. According to the output results of the insulation status prediction model, set the warning threshold. When the prediction result exceeds the threshold, trigger the warning mechanism; combine the partial discharge monitoring data and infrared thermal imaging results to accurately locate the location of the insulation defect and give the fault type and degree;

[0076] S5. Send early warning information and fault location results to operation and maintenance personnel in real time. According to the monitoring data and early warning results, intelligently optimize the structural design of the cold shrink cable intermediate joint to improve the high temperature and high voltage resistance of the insulation material; regularly inspect and maintain the cable joints, and formulate targeted maintenance plans based on the monitoring data.

[0077] The working principle of the above technical solution is: using high-precision sensors to measure the temperature, humidity and electric field strength around the intermediate joint of the cold shrink cable in real time; using partial discharge monitoring technology to capture the local discharge phenomenon inside the cable insulation layer, including the amplitude and frequency of the discharge, which can reflect the damage inside the insulation layer; using infrared thermal imaging technology to obtain the temperature distribution image of the cable joint insulation layer, and intuitively display the possible temperature abnormality areas; input the collected environmental parameters and partial discharge signals into the intelligent analysis system, and first perform preprocessing, such as denoising and standardization, to improve data quality; then perform feature extraction to extract feature information useful for insulation status prediction from the original data; based on machine learning algorithms (such as support vector machines, neural networks, etc.), combined with historical data and real-time monitoring data, train and establish an insulation status prediction model to predict the aging degree and potential fault points of the cable joint insulation layer; using infrared thermal imaging data, identify temperature anomalies through image processing technology area, and calculate the temperature gradient, that is, the rate of change of temperature with spatial position; based on the temperature gradient calculation results, evaluate the thermal stability of the insulation layer, and determine whether there is an overheating risk or a thermal stress concentration area; according to the output results of the insulation state prediction model, set a reasonable warning threshold, and when the prediction result exceeds the threshold, automatically trigger the warning mechanism and notify relevant personnel; combine partial discharge monitoring data and infrared thermal imaging results, use multi-source information fusion technology to accurately locate the location of insulation defects, and determine the type and severity of the fault based on data analysis; send warning information and fault location results to operation and maintenance personnel in real time for rapid response and processing; based on monitoring data and warning results, intelligently optimize the structural design of cold shrink cable intermediate joints, such as improving the formulation of insulating materials to improve their high temperature and high voltage resistance; regularly inspect and maintain cable joints, and formulate targeted maintenance plans based on monitoring data, such as increasing the inspection frequency, replacing aging parts, etc., to ensure the long-term safe operation of cable joints.

[0078] The effects of the above technical solutions are as follows: through high-precision sensors and partial discharge monitoring technology, key environmental parameters around the intermediate joints of cold-shrink cables and partial discharge signals inside the insulation layer can be collected in real time to realize continuous monitoring of the insulation status of the cable joints; the insulation status prediction model established in combination with the machine learning algorithm can accurately predict the aging degree and potential fault points of the insulation layer of the cable joint, and timely set and trigger the early warning mechanism to effectively avoid the occurrence or expansion of faults; using infrared thermal imaging technology and image processing technology, the temperature distribution image of the insulation layer of the cable joint can be intuitively displayed, and the temperature abnormality area can be identified, and the thermal stability of the insulation layer can be evaluated in combination with the temperature gradient calculation results; combined with the partial discharge monitoring data and infrared thermal imaging results, the location of the insulation defect can be accurately located, and the fault type and degree can be given, providing detailed fault diagnosis information for operation and maintenance personnel; according to the monitoring data and early warning results, intelligent optimization The structural design of the cold-shrink cable intermediate joint improves the high temperature and high voltage resistance of the insulating material, and improves the insulation performance of the cable joint from the source; regularly inspect and maintain the cable joints, and formulate targeted maintenance plans based on the monitoring data to ensure the long-term stable operation of the cable joints and reduce power outages and maintenance costs caused by faults; this technical solution realizes intelligent monitoring and early warning of the insulation status of the cable joints, reduces the frequency and intensity of manual inspections, and improves operation and maintenance efficiency; at the same time, through precise fault location and intelligent optimization, it reduces safety risks in the operation and maintenance process and ensures the life safety of operation and maintenance personnel; the application of this technical solution not only improves the insulation monitoring level of power cable joints, but also provides strong support for the intelligent and digital transformation of the power industry; with the continuous development and improvement of technology, this technical solution is expected to be more widely used and promoted in the power industry, promoting the progress and development of the entire industry.

[0079] In one embodiment of the present invention, the S1 includes:

[0080] S11. Use a high-precision temperature sensor to collect the ambient temperature in real time, and monitor the performance changes of the insulation material caused by the temperature change based on the collected ambient temperature; use a humidity sensor to monitor the humidity level around the connector, and use an electric field strength sensor to monitor the electric field distribution around the cable connector;

[0081] S12, capturing the local discharge signal in the cable insulation layer through a high-frequency current sensor, including the discharge amplitude and the discharge frequency, and filtering and noise reduction processing the collected local discharge signal;

[0082] S13. Use a high-resolution infrared thermal imaging camera to regularly capture temperature distribution images of the insulation layer of the cable joint, and use an image enhancement algorithm to enhance the collected temperature distribution images.

[0083] The working principle of the above technical solution is as follows: high-precision temperature sensors are deployed in the surrounding environment of the cold-shrink cable intermediate joint to collect environmental temperature data in real time. The data is input into the intelligent analysis system to monitor the performance changes of the insulation material caused by temperature changes. The performance of the insulation material changes with the temperature. Too high or too low temperature may accelerate the aging process of the insulation material, so real-time monitoring of temperature is essential for evaluating the insulation status; humidity sensors are also installed around the cable joint to monitor the humidity level of the environment. High humidity environment may cause the insulation material to become damp, and moisture will accelerate its aging process and reduce the insulation performance after penetrating into the insulation layer. By real-time monitoring of humidity, it is possible to timely detect and take measures to avoid damage to the insulation material due to moisture; electric field strength sensors are used to monitor the electric field distribution around the cable joint. The electric field concentration area is often the starting point of insulation damage, because these areas may be subjected to higher electric field strength, resulting in local damage to the insulation material. By monitoring the electric field strength, potential electric field concentration areas can be identified, providing an important basis for subsequent insulation status evaluation and fault warning; high-frequency current sensors are installed near the cable insulation layer to capture local discharge signals occurring in the insulation layer. Partial discharge is a discharge phenomenon that occurs at tiny defects or damage inside the insulating material. The discharge signal includes information such as discharge amplitude and discharge frequency. The collected partial discharge signal is first filtered to remove background noise and interference signals to ensure the accuracy of the signal. Then, the signal is further purified through noise reduction processing to provide high-quality data input for subsequent intelligent analysis and insulation status prediction. High-resolution infrared thermal imaging cameras are used to regularly capture temperature distribution images of the insulation layer of the cable joint. Infrared thermal imaging technology can capture infrared radiation emitted from the surface of an object and convert it into a visible temperature distribution image. The collected temperature distribution image may be affected by factors such as ambient lighting and camera performance, resulting in a decrease in image quality. Therefore, the collected image is processed using an image enhancement algorithm to improve the clarity and contrast of the image, making the temperature abnormality area more intuitive and easy to identify.

[0084] The effect of the above technical solution is: through the comprehensive application of high-precision temperature sensors, humidity sensors and electric field strength sensors, the environmental parameters around the intermediate joint of the cold shrink cable, including temperature, humidity and electric field strength, can be fully and real-time monitored. The high-frequency current sensor can accurately capture the partial discharge signal in the cable insulation layer, including the discharge amplitude and discharge frequency. Partial discharge is a discharge phenomenon occurring at tiny defects or damage inside the insulating material, and is an early warning signal of reduced insulation performance. Through filtering and noise reduction processing, the accuracy and reliability of the partial discharge signal can be further improved, providing strong support for subsequent insulation status assessment and fault warning; the high-resolution infrared thermal imaging camera can regularly capture the temperature distribution image of the insulation layer of the cable joint, and enhance the image through the image enhancement algorithm. This makes the temperature abnormal area more intuitive and easy to identify, which helps the operation and maintenance personnel to promptly discover and deal with potential insulation defects. At the same time, infrared thermal imaging technology can also provide the overall temperature distribution of the insulation layer of the cable joint, providing an important basis for evaluating the thermal stability of the insulation layer and formulating targeted maintenance plans; this technical solution realizes real-time monitoring and early warning of the insulation status of the intermediate joint of the cold shrink cable through automated and intelligent monitoring means. Compared with the traditional manual inspection method, this technical solution can significantly improve monitoring efficiency and accuracy and reduce operation and maintenance costs. At the same time, through real-time monitoring and early warning, it can also timely discover and deal with potential insulation defects, avoid the occurrence or expansion of faults, and ensure the safe operation of cable joints; the application of this technical solution not only improves the insulation monitoring level of power cable joints, but also provides strong support for the intelligent and digital transformation of the power industry. With the continuous development and improvement of technology, this technical solution is expected to be more widely used and promoted in the power industry, promoting the intelligent development process of the entire industry.

[0085] In one embodiment of the present invention, the S2 includes:

[0086] S21, preprocessing the collected environmental parameters (temperature, humidity, electric field strength) and partial discharge signals;

[0087] S22. Based on statistical analysis and machine learning algorithms, feature selection is performed on the preprocessed data to identify characteristic variables that have an impact on insulation status prediction;

[0088] S23, extracting features from the preprocessed data through time domain, frequency domain and time-frequency domain analysis techniques, and fusing the extracted feature variables to form a comprehensive feature vector;

[0089] S24, dividing the preprocessed data set into a training set and a validation set, using the training set to train the built-in machine learning model, and using the validation set to evaluate the performance of the model;

[0090] S25. Evaluate the performance of the model through cross-validation techniques, such as K-fold cross-validation. According to the validation results, adjust the hyperparameters of the model and optimize the model structure. The model performance is obtained through the following formula:

[0091]

[0092] Among them, M represents the model, D represents the data set, and K represents the number of cross-validation folds. Represents the accuracy of the k-fold verification;

[0093] S26. Use the trained machine learning model to predict the test set (or real-time monitoring data), output the insulation status prediction result, and evaluate the uncertainty of the prediction result, that is, the confidence or reliability of the prediction result. The uncertainty of the prediction result is calculated by the following method:

[0094]

[0095] in, represents the predicted value of the model for the i-th sample, N represents the number of samples in the test set, represents the average of all predicted values;

[0096] S27. Design early warning strategies based on the uncertainty of the prediction results. When the uncertainty of the prediction results is high, trigger a higher level of early warning mechanism.

[0097] S28. Compare the predicted results with the actual situation, evaluate the accuracy and reliability of the model, and iteratively optimize the model based on the evaluation results, including adjusting feature selection, improving algorithm design, and increasing training data. Based on the continuous learning and updating mechanism, regularly collect new monitoring data to update and optimize the model.

[0098] The working principle of the above technical solution is as follows: the collected environmental parameters (temperature, humidity, electric field strength) and partial discharge signals (discharge amplitude, discharge frequency) are first preprocessed. The main task of the preprocessing stage is to remove outliers and missing values ​​caused by sensor failure, data transmission errors, etc. Based on statistical analysis and machine learning algorithms, feature selection is performed on the preprocessed data. Feature selection is the process of identifying characteristic variables that have a significant impact on the prediction of insulation status. It involves calculating the correlation between features and target variables, using feature importance evaluation methods (such as feature importance scores in random forests, coefficients in Lasso regression, etc.), and model-based feature selection techniques (such as recursive feature elimination); feature extraction of preprocessed data is performed through time domain, frequency domain and time-frequency domain analysis techniques. Time domain analysis focuses on the change of signals over time, frequency domain analysis focuses on the distribution of signals in frequency, and time-frequency domain analysis combines the two to reveal the joint characteristics of signals in time and frequency. The extracted feature variables are combined through fusion techniques (such as principal component analysis, linear discriminant analysis, etc.) to form a comprehensive feature vector to improve the performance of the prediction model; the preprocessed data set is divided into a training set and a validation set. The training set is used to train the built-in machine learning model, while the validation set is used to evaluate the performance of the model. The model training process involves selecting appropriate machine learning algorithms (such as support vector machines, decision trees, neural networks, etc.) and adjusting their parameters to minimize the prediction error; a more comprehensive performance evaluation of the model is performed through cross-validation techniques (such as K-fold cross-validation). Cross-validation divides the data set into K parts, uses K-1 parts as training sets in turn, and the remaining part as a validation set, repeats the experiment K times, and calculates the average performance index. According to the validation results, adjust the model's hyperparameters (such as learning rate, regularization parameters, etc.) and optimize the model structure until the model performs best on the validation set; use the trained machine learning model to predict the test set (or real-time monitoring data) and output the insulation status prediction results. In order to evaluate the uncertainty of the prediction results, that is, the confidence or reliability of the prediction results, the variance, confidence interval or Bayesian method of the prediction results can be calculated; according to the uncertainty of the prediction results, an early warning strategy can be designed. When the uncertainty of the prediction results is high, a higher level of early warning mechanism is triggered to remind the operation and maintenance personnel to take more timely maintenance measures. The design of the early warning strategy should comprehensively consider the accuracy, uncertainty and actual operation and maintenance needs of the prediction results; compare the prediction results with the actual situation to evaluate the accuracy and reliability of the model. According to the evaluation results, the model is iteratively optimized. Optimization measures include adjusting feature selection, improving algorithm design, and increasing training data. Based on the continuous learning and updating mechanism, new monitoring data is collected regularly, and the model is updated and optimized to adapt to the changing working environment and insulation status.

[0099] The effect of the above technical solution is: by removing outliers and missing values ​​caused by sensor failure, data transmission errors and other reasons, the accuracy and completeness of the data are improved. This provides a high-quality data basis for subsequent feature selection, feature extraction and model training, which helps to improve the accuracy and reliability of the prediction results; feature selection based on statistical analysis and machine learning algorithms can identify characteristic variables that have a significant impact on insulation state prediction and reduce the interference of redundant information on model performance. At the same time, feature extraction is performed through time domain, frequency domain and time-frequency domain analysis technology, and the extracted feature variables are fused to form a comprehensive feature vector, which further enhances the sensitivity and prediction ability of the model to the insulation state; the model is trained using the preprocessed data set, and the performance of the model is evaluated through the validation set, which can ensure the accuracy of the model in practical applications. In addition, the performance of the model is evaluated through cross-validation technology, and the hyperparameters of the model are adjusted and the model structure is optimized according to the validation results, which can further improve the prediction ability and generalization performance of the model; the uncertainty of the prediction results is evaluated, that is, the confidence or reliability of the prediction results, which helps to design a more reasonable early warning strategy. When the uncertainty of the prediction results is high, a higher-level early warning mechanism is triggered, which can remind the operation and maintenance personnel to take timely measures to avoid potential safety hazards; compare the prediction results with the actual situation, evaluate the accuracy and reliability of the model, and iteratively optimize the model based on the evaluation results. This includes adjusting feature selection, improving algorithm design, increasing training data, etc. to ensure that the model can adapt to the changing working environment and insulation status. Based on the continuous learning and updating mechanism, new monitoring data is regularly collected to update and optimize the model, which can maintain the effectiveness and accuracy of the model in long-term operation; this technical solution realizes real-time monitoring and prediction of the insulation status of the intermediate joints of cold-shrink cables in an intelligent and automated manner, thereby improving the operation and maintenance efficiency. At the same time, through the design and implementation of early warning strategies, potential safety hazards can be discovered and dealt with in a timely manner, ensuring the safe operation of cable joints and improving the overall safety of the power system.

[0100] In one embodiment of the present invention, the S23 includes:

[0101] S231. Calculate the mean and variance of each characteristic variable (such as temperature, humidity, electric field strength and discharge amplitude, etc.) after preprocessing, and evaluate the volatility and stability of the data based on the evaluation record; calculate the trend line of the time series data through the sliding window technology to identify the change trend of environmental parameters and partial discharge signals over time; the mean is obtained by the following method:

[0102]

[0103] Where X = {x 1 …x N} represents the data set of feature variables, where N represents the number of data points;

[0104] The variance is obtained by the following formula:

[0105]

[0106] S232. Identify and record the maximum and minimum values ​​of each characteristic variable within a time period, analyze abnormal fluctuations, evaluate the correlation of time series data at different time intervals, and reveal the time dependence within the data; wherein the correlation is obtained by the following formula:

[0107]

[0108] S233, converting the time domain signal into a frequency domain representation, analyzing the spectrum components of each characteristic variable, and identifying the main frequency components and their energy distribution;

[0109] S234, calculate and draw a power spectrum density diagram, evaluate the signal energy at different frequencies, and identify potential periodic or random characteristics; calculate the entropy value of the spectrum to measure the complexity of the signal frequency distribution; the entropy value of the spectrum is obtained by the following formula:

[0110]

[0111] Where M represents the number of spectral fractions, and P(i) represents the ratio of the power of the i-th spectral component to the total power, which is obtained by the following formula:

[0112]

[0113] Where S(i) represents the power of the i-th spectral component;

[0114] S235, performing Fourier transform on the signal within the time window to obtain spectrum information that changes with time, capturing the instantaneous frequency characteristics of the partial discharge signal, decomposing the signal using wavelet functions of different scales, and extracting time-frequency characteristics at multiple scales;

[0115] S236, decomposing the complex signal into a series of intrinsic mode functions (IMFs), each IMF representing a different frequency component of the signal; and performing standardization on all extracted features;

[0116] S237. Based on the correlation analysis, further screen the feature subset that is most critical to the insulation state prediction, and fuse the screened features into a comprehensive feature vector by using weighted average;

[0117] S238, and evaluate the contribution of each feature in the comprehensive feature vector through random forest.

[0118] The working principle of the above technical solution is: calculate the mean and variance of each characteristic variable (such as temperature, humidity, electric field strength and discharge amplitude, etc.) after preprocessing. The mean reflects the average level of the data, while the variance reveals the degree of dispersion or volatility of the data. Through these statistics, the stability and volatility of the data can be evaluated, providing a basis for subsequent analysis; apply the sliding window technology to the time series data to calculate the trend line in each window. The sliding window technology can capture the trend of data changes over time, help identify the long-term or short-term changes in environmental parameters and partial discharge signals; identify and record the maximum and minimum values ​​of each characteristic variable in each time period, and analyze abnormal fluctuations. At the same time, evaluating the correlation of time series data at different time intervals and revealing the time dependence within the data are crucial to understanding the interaction between characteristic variables and predicting the insulation state; converting the time domain signal into a frequency domain representation, and identifying the main frequency components and energy distribution of each characteristic variable through spectrum analysis. Calculate the power spectrum density diagram, evaluate the signal energy at different frequencies, and identify potential periodic or random characteristics. In addition, the entropy value of the spectrum is calculated to measure the complexity of the signal frequency distribution as an indicator for evaluating the complexity of insulation state changes; the signal is Fourier transformed within the time window to obtain the spectrum information that changes with time and capture the instantaneous frequency characteristics of the partial discharge signal. The signal is decomposed using wavelet functions of different scales to extract time-frequency features at multiple scales. These features can more comprehensively reflect the joint characteristics of the signal in time and frequency; the complex signal is decomposed into a series of intrinsic mode functions (IMFs), each IMF representing a different frequency component of the signal. This decomposition helps to extract the intrinsic characteristics of the signal and improve the accuracy of subsequent analysis. All extracted features are standardized to eliminate the dimensional differences between different features and ensure the comparability between features; based on correlation analysis, the most critical feature subsets for insulation state prediction are further screened. Correlation analysis can reveal the degree of correlation between features and help select the most representative features. The screened features are fused into a comprehensive feature vector using methods such as weighted averaging, which can more comprehensively and accurately reflect the information of the insulation state; the contribution of each feature in the comprehensive feature vector is evaluated using machine learning algorithms such as random forests. Random forests are able to assess the importance of each feature to the model’s predictive performance, helping to understand which features are most critical for insulation status prediction.

[0119] The effects of the above technical solutions are as follows: by calculating the mean and variance, the volatility and stability of the data are evaluated, providing basic information for subsequent insulation state prediction; the trend line of the time series data is identified by the sliding window technology, which can clearly show the changing trend of environmental parameters and partial discharge signals over time, and help capture potential insulation state changes; the maximum and minimum values ​​of each characteristic variable in the time period, as well as the correlation of the time series data, reveal the time dependence within the data, and further enhance the accuracy and comprehensiveness of feature extraction; the time domain signal is converted into a frequency domain representation, and the main frequency components and their energy distribution are identified through spectrum analysis, which helps to identify potential periodic or random characteristics and provides more dimensional information for insulation state prediction; the signal is Fourier transformed within the time window to capture the instantaneous frequency characteristics of the partial discharge signal, and the time-frequency characteristics at multiple scales are extracted in combination with wavelet function decomposition, which can more accurately reflect Changes in insulation state; Based on correlation analysis, the most critical feature subset for insulation state prediction is selected to avoid the interference of redundant information on the prediction results, and improve the prediction efficiency and accuracy; The selected features are merged into a comprehensive feature vector by weighted average, which simplifies the input of the subsequent model and retains key information, which helps to improve the prediction performance of the model; The contribution of each feature in the comprehensive feature vector is evaluated by machine learning algorithms such as random forest, which provides a basis for further optimization and selection of features, and helps to build a more accurate and efficient insulation state prediction model; The comprehensive use of the above technical solutions can more comprehensively extract and integrate feature information related to the insulation state, improve the accuracy and reliability of the prediction model; By optimizing the feature selection and extraction process, the uncertainty of the prediction results is reduced, which provides a strong guarantee for the safe operation of the power system; This technical solution provides strong support for the intelligent operation and maintenance and preventive maintenance of the power system. By real-time monitoring and predicting the changing trend of the insulation state, potential safety hazards can be discovered in time, and corresponding maintenance measures can be taken to avoid accidents and improve the reliability and stability of the power system.

[0120] In one embodiment of the present invention, S3 includes:

[0121] S31. Based on high-resolution infrared thermal imaging technology, obtain the temperature distribution image of the cable joint insulation layer, and apply image processing algorithms (such as threshold segmentation, edge detection, etc.) to preliminarily identify the temperature abnormality area;

[0122] S32, performing detailed analysis on the initially identified temperature anomaly area, and removing noise and artifacts using morphological processing techniques (such as expansion, corrosion, opening operation, closing operation, etc.);

[0123] S33. Calculate the geometric features such as the area, perimeter, shape factor, and statistical features such as the maximum, minimum, average, and standard deviation of the temperature anomaly area; the area is obtained by the following formula:

[0124]

[0125] Where I(i,j) represents the pixel value of the binary image at position (i,j) (1 represents the pixel in the abnormal area, and 0 represents the pixel outside the area), N and M represent the number of rows and columns of the image respectively;

[0126] The circumference is obtained by the following formula:

[0127]

[0128] Among them, (x k ,y k ) represents the continuous points on the boundary, and L represents the number of boundary points;

[0129] The shape factor is obtained by the following formula:

[0130]

[0131] The maximum temperature is obtained by the following formula:

[0132] T max =max(I(i,j))

[0133] The minimum temperature is obtained by the following formula:

[0134] T min =min(I(i,j))

[0135] The average temperature is obtained by the following formula:

[0136]

[0137] Where, T(i,j) represents the temperature value at position (i,j);

[0138] The temperature standard deviation is obtained by the following formula:

[0139]

[0140] S34, setting a plurality of temperature gradient calculation points around the temperature abnormality area, calculating the temperature gradient between each calculation point and its adjacent points, and forming a temperature gradient distribution diagram; the temperature gradient is obtained by the following formula:

[0141]

[0142] in, represents the temperature gradient at position (i, j); as well as Respectively represent the partial derivatives of temperature in x and y, which are approximately calculated by the following difference formula:

[0143]

[0144] Among them, Δ x and Δ y Respectively represent the pixel spacing in the x and y directions of the image;

[0145] S35. Analyze the thermal conductivity of the insulation layer according to the temperature gradient distribution diagram to determine whether there is a thermal conduction barrier or heat accumulation phenomenon;

[0146] S36. Combine the thermal properties of the insulation material (such as thermal conductivity, thermal expansion coefficient, etc.) to evaluate the thermal stability of the insulation layer and predict the possible thermal failure risk;

[0147] S37, establishing a thermal stability evaluation model, and quantitatively evaluating the thermal stability of the insulation layer based on the geometric characteristics, statistical characteristics, and temperature gradient distribution of the temperature anomaly area;

[0148] S38. Based on the results of the thermal stability assessment model, combined with historical data and industry standards, set warning thresholds, including area thresholds for temperature anomaly areas, temperature gradient thresholds, etc., and dynamically adjust the warning thresholds based on the aging degree of the insulation layer and changes in the operating environment.

[0149] The working principle of the above technical solution is as follows: the temperature distribution image of the insulation layer of the cable joint is obtained by using the technology, and the image can intuitively show the temperature difference on the surface of the insulation layer; the temperature distribution image is processed by applying image processing algorithms (such as threshold segmentation, edge detection, etc.) to preliminarily identify the temperature abnormal area. These areas usually appear as hot spots or cold spots with higher or lower temperatures than the surrounding normal areas, which are indicators of potential thermal failure risks; the preliminarily identified temperature abnormal area is refined and analyzed, and morphological processing (such as expansion, corrosion, opening operation, closing operation, etc.) technology is used to remove noise and artifacts to improve the accuracy and reliability of identification; the geometric features such as the area, perimeter, and shape factor of the temperature abnormal area are calculated, and these features can reflect the scale and shape of the abnormal area; the statistical features such as the maximum, minimum, average, and standard deviation of the temperature of the temperature abnormal area are calculated, and these features can reflect the temperature distribution and discreteness of the abnormal area; multiple temperature gradient calculation points are set around the temperature abnormal area, and these points should be evenly distributed to cover the entire abnormal area; the temperature gradient between each calculation point and its adjacent points is calculated to form a temperature gradient distribution map. This graph can reflect the temperature change on the surface of the insulation layer and is an important basis for evaluating the thermal conductivity and thermal stability of the insulation layer. According to the temperature gradient distribution graph, the thermal conductivity of the insulation layer is analyzed. If the temperature gradient increases or decreases abnormally, it may indicate the presence of thermal conduction barriers or heat accumulation, which is an important indicator of thermal failure risk. Combined with the thermal properties of the insulation material (such as thermal conductivity, thermal expansion coefficient, etc.), the thermal stability of the insulation layer is evaluated. These properties can reflect the insulation layer's ability to withstand thermal stress. Based on the thermal stability evaluation results, the possible thermal failure risk is predicted. If the thermal stability of the insulation layer is poor and there are thermal conduction barriers or heat accumulation, the thermal failure risk is high. A thermal stability evaluation model is established to quantitatively evaluate the thermal stability of the insulation layer based on the geometric characteristics, statistical characteristics, and temperature gradient distribution of the temperature anomaly area. The model can give specific evaluation results and scores, which is convenient for objective evaluation of the thermal stability of the insulation layer. Based on the results of the thermal stability evaluation model, combined with historical data and industry standards, warning thresholds are set. These thresholds include the area threshold of the temperature anomaly area, the temperature gradient threshold, etc., which are used to determine whether the insulation layer is in a dangerous state. ; The warning threshold is dynamically adjusted according to the aging degree of the insulation layer and the changes in the operating environment. This can ensure the accuracy and reliability of the warning system and timely detect and deal with potential thermal failure risks.

[0150] The effects of the above technical solutions are as follows: through high-resolution infrared thermal imaging technology, the temperature distribution of the insulation layer of the cable joint can be accurately captured, which has higher monitoring accuracy and efficiency than traditional methods; the application of image processing algorithms, such as threshold segmentation and edge detection, can accurately identify the temperature abnormality area, providing a reliable basis for subsequent detailed analysis; the application of morphological processing technology effectively removes noise and artifacts, and improves the accuracy and reliability of identifying the temperature abnormality area; the calculation of the geometric and statistical characteristics of the temperature abnormality area provides rich data support, which helps to more comprehensively understand the thermal state of the insulation layer; the construction of the temperature gradient distribution map intuitively displays the temperature changes around the temperature abnormality area, and provides a powerful tool for evaluating the thermal conductivity of the insulation layer; by analyzing the temperature gradient distribution map, thermal conduction obstacles or heat accumulation phenomena can be discovered in time, providing a basis for preventing thermal failure Important basis; combining the thermal properties of the insulating material to evaluate the thermal stability of the insulation layer can more accurately predict possible thermal failure risks; the establishment of a thermal stability evaluation model realizes the quantitative evaluation of the thermal stability of the insulation layer, providing a scientific basis for formulating targeted maintenance strategies; based on the results of the thermal stability evaluation model, combined with historical data and industry standards, reasonable early warning thresholds are set, including area thresholds and temperature gradient thresholds for temperature anomaly areas; the dynamic adjustment of the early warning threshold takes into account the aging of the insulation layer and changes in the operating environment, ensuring the accuracy and reliability of the early warning system; this technical solution monitors the temperature distribution and changes of the insulation layer of the cable joint in real time, promptly detects and handles potential thermal failure risks, and helps to ensure the safe operation of the power system; by optimizing the early warning system, the ability to prevent thermal failure risks is improved, and power accidents and losses caused by thermal failure are reduced.

[0151] In one embodiment of the present invention, the S35 includes:

[0152] On the temperature gradient distribution map, the areas where the temperature gradient changes significantly are identified, and the areas where the temperature gradient exceeds or falls below the threshold are marked as potential heat conduction obstacle areas by setting the gradient change threshold;

[0153] Using the connected domain analysis in image processing technology, the points on the temperature gradient map are connected into lines to form a heat conduction path network;

[0154] According to the thermal conductivity characteristics of the insulating material, a weight is assigned to each path, and the weight is inversely proportional to the thermal conductivity of the material, that is, the lower the thermal conductivity, the greater the path weight; based on the reconstructed heat conduction path network, the thermal conduction efficiency of the entire insulating layer is calculated, which is measured by the average value, maximum value or cumulative weight of the path weight on a specific path; the thermal conduction efficiency is calculated by the following formula:

[0155]

[0156] Where N is the total number of all paths in the heat conduction path network, W i represents the weight of the i-th path, L i represents the length of the i-th path, k i represents the thermal conductivity of the material in the i-th path, Indicates the average thermal conductivity of the entire insulation material; represents the weight of a unit length path, and

[0157] Evaluate the deviation of thermal conductivity from normal values, identify areas where thermal conductivity is significantly reduced, which may be areas where thermal conductivity is impeded, and analyze local high or low temperature concentration areas in the temperature gradient distribution map, which may indicate heat accumulation or poor heat dissipation in the insulation layer; evaluate the rate and degree of heat accumulation by calculating the temperature gradient change rate in these areas;

[0158] Combined with the results of temperature gradient anomaly identification and heat conduction efficiency evaluation, the heat conduction obstacle points in the insulation layer are located, and the heat conduction obstacles are classified according to the temperature gradient characteristics of the obstacle points, the position on the heat conduction path, and possible material defects, such as poor contact, material aging, and foreign matter embedding;

[0159] Using time series analysis technology, we compare historical data of temperature gradient distribution diagrams, analyze the trend of thermal conductivity over time, identify areas where thermal conductivity continues to decline, and predict possible thermal failure risks in the future.

[0160] The working principle of the above technical solution is as follows: First, on the temperature gradient distribution map, by comparing the temperature difference of adjacent points, the areas where the temperature gradient changes significantly are identified. These areas may mark abnormal points or thermal resistance positions on the heat conduction path; in order to accurately mark the potential heat conduction obstacle area, a gradient change threshold is set. When the temperature gradient exceeds or falls below the threshold, the corresponding area is marked as a potential heat conduction obstacle area; using the connected domain analysis in image processing technology, the points on the temperature gradient map are connected into lines to form a heat conduction path network. This step helps to intuitively display the heat propagation path in the insulation layer; according to the thermal conductivity characteristics of the insulation material, each path is weighted. The weight is inversely proportional to the thermal conductivity of the material, that is, the lower the thermal conductivity, the greater the path weight. This step reflects the difficulty of heat propagation on different paths; based on the reconstructed heat conduction path network, the thermal conduction efficiency of the entire insulation layer is calculated. This can be measured by the average value, maximum value or cumulative weight of the path weight on a specific path; the calculated thermal conduction efficiency is compared with the normal value to identify areas where the thermal conduction efficiency is significantly reduced. These areas may have thermal conduction obstacles, resulting in heat propagation obstruction; analyze the local high temperature or low temperature concentration areas in the temperature gradient distribution map. These areas may indicate that heat is accumulated or dissipated poorly in the insulation layer; by calculating the temperature gradient change rate in these areas, the rate and degree of heat accumulation are evaluated. This step helps to understand the dynamic changes of heat in the insulation layer; combined with the results of temperature gradient anomaly identification and thermal conduction efficiency evaluation, the thermal conduction obstacle points in the insulation layer are located. These obstacle points may be the key factors that cause heat propagation obstruction; thermal conduction obstacles are classified according to the temperature gradient characteristics of the obstacle points, the position on the heat conduction path, and possible material defects. Common classifications include poor contact, material aging, foreign body embedding, etc.; use time series analysis technology to compare historical data of the temperature gradient distribution map. This step helps to understand the trend of thermal conductivity performance over time; by identifying areas where thermal conductivity performance continues to decline, the risk of thermal failure that may occur in the future is predicted. This step provides a scientific basis for formulating targeted maintenance strategies.

[0161] The effect of the above technical solution is: by identifying the area where the temperature gradient changes significantly on the temperature gradient distribution map and setting the gradient change threshold, the technology can accurately mark the potential heat conduction obstacle area. This method is more intuitive and accurate than the traditional method, which helps to detect and deal with heat conduction problems early; using the connected domain analysis in image processing technology, the points on the temperature gradient map are connected into lines to form a heat conduction path network. This step not only helps to intuitively display the heat propagation path in the insulation layer, but also provides a basis for the subsequent calculation of heat conduction efficiency; according to the thermal conductivity characteristics of the insulating material, each path is weighted, and the thermal conduction efficiency of the entire insulation layer is calculated based on the reconstructed heat conduction path network. This method can comprehensively and scientifically evaluate the thermal conductivity performance of the insulation layer and provide strong support for identifying heat conduction obstacles; analyze the local high temperature or low temperature aggregation areas in the temperature gradient distribution map, calculate the temperature gradient change rate of these areas, and evaluate the rate and degree of heat accumulation. This step helps to gain a deeper understanding of the dynamic changes of heat in the insulation layer and provides an important basis for preventing thermal failures. Combined with the results of temperature gradient anomaly identification and thermal conductivity efficiency evaluation, the thermal conduction obstacle points in the insulation layer are located and classified according to the temperature gradient characteristics of the obstacle points, the position on the thermal conduction path, and possible material defects. This method can accurately identify different types of thermal conduction obstacles and provide guidance for taking corresponding maintenance measures. Using time series analysis technology, the temperature gradient distribution map is compared with historical data to analyze the trend of thermal conductivity performance over time. By identifying areas where thermal conductivity performance continues to decline, this technology can predict the risk of thermal failure that may occur in the future and provide the possibility of taking measures to prevent thermal failure in advance. This technical solution helps to timely discover and deal with potential thermal failure risks by comprehensively and deeply analyzing the thermal conductivity performance and thermal conductivity obstacles of the cable joint insulation layer, thereby improving the safety and reliability of the power system. This is of great significance for ensuring the stable operation of the power system and reducing failures caused by thermal failure.

[0162] In one embodiment of the present invention, the S4 includes:

[0163] S41. According to the historical analysis of power system operation data, combined with industry standards and practical experience, different levels of warning thresholds are set, including minor warning, medium warning and severe warning; based on the warning trigger mechanism, when the monitoring data reaches or exceeds the set threshold, the corresponding level of warning is automatically triggered; the warning information includes key information such as warning level, warning time, warning location, possible impact range and recommended response measures, and establish warning information transmission channels, including SMS, email, APP push and other methods, to ensure that the warning information can be quickly and accurately delivered to relevant personnel;

[0164] Differentiated early warning response strategies are formulated for different levels of early warning, including on-site inspections, remote monitoring, equipment shutdown and other measures to control the development of faults and reduce losses.

[0165] Establish an early warning response team, clarify the responsibilities and tasks of team members, and ensure that early warning response measures can be implemented quickly and effectively.

[0166] S42, using the real-time data of current, voltage, temperature, etc. collected by the monitoring equipment, combined with historical data and industry standards, to extract fault features, wherein the fault features include abnormal fluctuations, mutation points, and frequency components; and preprocessing the extracted fault features;

[0167] S43. Based on the fault characteristics, combined with the fault location algorithm, including advanced algorithms such as wavelet transform, neural network, support vector machine, etc., the fault location is located; and the fault location algorithm is simulated, tested and optimized;

[0168] S44. Based on the results obtained by the fault location algorithm, combined with on-site inspection and data analysis, diagnose the fault and determine the fault type and cause; establish a fault diagnosis knowledge base to record the fault characteristics, location methods and treatment measures of common faults;

[0169] S45. Based on the results of fault location and diagnosis, combined with changes in the operating status of the power system, the warning threshold is dynamically adjusted, and the performance of the warning mechanism is regularly tested and evaluated. The warning threshold and trigger mechanism are optimized and improved based on the test results.

[0170] The working principle of the above technical solution is as follows: according to the historical analysis of the power system operation data, combined with industry standards and practical experience, different levels of warning thresholds (minor warning, medium warning, severe warning) are set; these thresholds are used to determine whether the current state of the power system deviates from the normal range, thereby triggering the corresponding warning; when the data collected by the monitoring equipment (such as current, voltage, temperature, etc.) reaches or exceeds the set warning threshold, the corresponding level of warning is automatically triggered; the warning information includes key information such as warning level, warning time, warning location, possible impact range and recommended response measures; establish a variety of warning information transmission channels (such as SMS, email, APP push, etc.) to ensure that the warning information can be quickly and accurately delivered to relevant personnel; formulate differentiated warning response strategies (such as on-site inspection, remote monitoring, equipment shutdown, etc.) for different levels of warning; establish a warning response team, clarify the responsibilities and tasks of team members, and ensure that the warning response measures can be implemented quickly and effectively; use monitoring equipment to collect current, voltage, temperature and other data of the power system in real time; combine historical data and industry standards to extract fault features from the collected data, including abnormal fluctuations, mutation points and frequency components; pre-process the extracted fault features, such as denoising, filtering , normalization, etc., to improve the accuracy of subsequent fault location and diagnosis; select appropriate fault location algorithms (such as wavelet transform, neural network, support vector machine and other advanced algorithms); based on the extracted fault features, apply the fault location algorithm to locate the fault position; simulate and test the fault location algorithm to evaluate its accuracy and robustness under different fault types and operating environments; optimize and improve the algorithm according to the test results to improve the accuracy and reliability of fault location; based on the results obtained by the fault location algorithm, combined with on-site inspection and data analysis, diagnose the fault and determine the fault type and cause; establish a fault diagnosis knowledge base to record the fault characteristics, location methods and treatment measures of common faults; the knowledge base can be used to assist fault diagnosis and provide treatment suggestions to improve the efficiency and accuracy of fault diagnosis; according to the results of fault location and diagnosis, combined with the changes in the operating status of the power system, the warning threshold is dynamically adjusted; this helps to ensure that the warning mechanism can maintain sensitivity and accuracy under different operating conditions; regularly test and evaluate the performance of the warning mechanism, including warning trigger accuracy, information transmission speed, warning response effect, etc.; optimize and improve the warning threshold and trigger mechanism according to the test results to improve the overall performance and reliability of the warning mechanism.

[0171] The effects of the above technical solutions are as follows: by setting warning thresholds based on historical data and industry standards, combined with real-time monitoring data, warnings of corresponding levels can be accurately triggered to ensure the accuracy and timeliness of warning information; the establishment of multiple warning information delivery channels (such as text messages, emails, APP push, etc.) ensures that warning information can be quickly and accurately delivered to relevant personnel, shortening the response time; using advanced algorithms (such as wavelet transform, neural network, support vector machine, etc.) to locate faults, improving the accuracy and efficiency of fault location; combining on-site inspection and data analysis to diagnose faults, accurately determine the type and cause of faults, and provide strong support for fault handling; formulate differentiated warning response strategies for different levels of warnings, including on-site inspection, remote monitoring, equipment shutdown and other measures to effectively control the development of faults and reduce losses; establish a warning response team and clarify the team The responsibilities and tasks of members should be strengthened to ensure that early warning response measures can be implemented quickly and effectively; a fault diagnosis knowledge base should be established to record the fault characteristics, location methods and treatment measures of common faults, provide a convenient reference for fault handling, and improve the efficiency and quality of fault handling; according to the results of fault location and diagnosis, combined with the changes in the operating status of the power system, the early warning threshold should be dynamically adjusted to ensure the sensitivity and accuracy of the early warning mechanism; the performance of the early warning mechanism should be tested and evaluated regularly, and the early warning threshold and trigger mechanism should be optimized and improved according to the test results to achieve continuous optimization and improvement of the early warning mechanism; through the implementation of the above technical solutions, potential faults in the power system can be discovered and handled in a timely manner, the occurrence and development of faults can be effectively prevented, and the safety and reliability of the power system can be enhanced; this is of great significance to ensure the stable operation of the power system, reduce power outages and ensure the safety of users' electricity use.

[0172] In one embodiment of the present invention, the S43 includes:

[0173] S431. Based on the fault characteristics, preliminarily apply the fault location algorithm to the fault characteristic data set, preliminarily locate the fault location, and evaluate the basic performance and accuracy of the algorithm;

[0174] S432. Fault features extracted from multi-source monitoring data such as current, voltage, and temperature are integrated to form a comprehensive feature vector, and a power system fault simulation model is constructed to simulate different types of fault scenarios, including short circuit, open circuit, overload, etc., and generate corresponding fault feature data sets.

[0175] S433. Apply the optimized fault location algorithm to the simulation data set to verify the location accuracy and stability of the algorithm under different fault types and different operating conditions.

[0176] The working principle of the above technical solution is as follows: based on the real-time data of current, voltage, temperature, etc. collected from the monitoring equipment, combined with historical data and industry standards, fault characteristics are extracted, including abnormal fluctuations, mutation points, and frequency components; the extracted fault characteristic data set is input into the preliminarily selected fault location algorithm to preliminarily locate the fault location; the basic performance and accuracy of the algorithm are evaluated, including indicators such as positioning accuracy, calculation speed, and stability; the evaluation results will be used to guide the optimization and improvement of subsequent algorithms; the fault characteristics extracted from multi-source monitoring data such as current, voltage, and temperature are fused to form a comprehensive feature vector; the fused feature vector can more comprehensively reflect the characteristic information of the fault and improve the accuracy of fault location; according to the structure and characteristics of the power system, a power system fault simulation model is constructed ; The simulation model can simulate different types of fault scenarios, including short circuit, open circuit, overload, etc., and generate corresponding fault feature data sets; by running the simulation model, fault feature data sets under different fault types and different operating conditions are generated; these data sets will be used for optimization and verification of subsequent algorithms; based on the results of preliminary application and performance evaluation, the fault location algorithm is optimized and improved; the optimization may include adjustment of algorithm parameters, improvement of algorithm structure, etc.; the optimized fault location algorithm is applied to the simulation data set to verify the algorithm's positioning accuracy and stability under different fault types and different operating conditions; the verification results will be used to evaluate the optimization effect of the algorithm and guide further improvement of the subsequent algorithm; based on the verification results, the algorithm is iteratively optimized until satisfactory positioning accuracy and stability are achieved.

[0177] The effect of the above technical solution is: by preliminarily applying the fault location algorithm to the actual fault feature data set, the fault location can be preliminarily located. This step not only provides a preliminary direction for subsequent fault processing, but also through the evaluation of the basic performance and accuracy of the algorithm, it can timely discover possible problems in the algorithm, laying the foundation for subsequent optimization work; by fusing the fault features extracted from multi-source monitoring data such as current, voltage, and temperature, a comprehensive feature vector is formed. This step not only improves the comprehensiveness of the fault features, so that the algorithm can more accurately capture the essential characteristics of the fault, but also generates a rich fault feature data set by constructing a power system fault simulation model and simulating different types of fault scenarios. This provides strong data support for the optimization and verification of the subsequent algorithm; by applying the optimized fault location algorithm to the simulation data set, the positioning accuracy and stability of the algorithm under different fault types and different operating conditions are verified. This step not only ensures the reliability and effectiveness of the algorithm in practical applications, but also through continuous iterative optimization, the performance of the algorithm can be further improved, so that it can better adapt to the complexity and diversity of the power system; through the implementation of the above technical solution, the performance of the algorithm has been significantly improved. This not only makes fault location more accurate and faster, but also reduces the time and manpower costs of fault handling, and improves the operating efficiency and reliability of the power system; through accurate fault location and timely fault handling, the development and expansion of faults can be effectively avoided, and power outages and safety accidents in the power system can be reduced. This not only ensures the safe and stable operation of the power system, but also improves the user's electricity satisfaction and the economic benefits of power companies.

[0178] In one embodiment of the present invention, the S432 includes:

[0179] Extract fault features such as abnormal fluctuations, mutation points, frequency components, etc. from multi-source monitoring data such as current, voltage, and temperature through signal processing technology and data analysis methods, and pre-process the extracted fault features;

[0180] The extracted features are fused based on the preset feature fusion strategy, and a fault simulation model is constructed according to the structure and operation characteristics of the power system, including the power grid topology, equipment parameters, fault type, etc.;

[0181] Based on the fault simulation model, we generate fault feature data sets under different fault types and operating conditions, apply the fused comprehensive feature vectors to the fault location algorithm, and evaluate the effect of feature fusion on the performance of the algorithm.

[0182] According to the evaluation results of feature fusion effect, the simulation model and feature fusion strategy are iteratively optimized.

[0183] The working principle of the above technical solution is: collect real-time data from multi-source monitoring equipment such as current, voltage, temperature, etc. of the power system; use signal processing technology and data analysis methods, such as filtering, transformation, statistical analysis, etc., to process the collected data and extract fault characteristics; fault characteristics may include abnormal fluctuations, mutation points, frequency components, etc., which can reflect the abnormal state of the power system; pre-process the extracted fault characteristics, such as denoising, normalization, standardization, etc., to improve the quality of the characteristics and the robustness of the algorithm; according to the preset feature fusion strategy, the extracted fault features are fused to form a comprehensive feature vector; the feature fusion strategy may include weighted average, principal component analysis (PCA), neural network fusion and other methods; analyze the structure and operation characteristics of the power system, such as the power grid topology, equipment parameters, etc., to provide a basis for building a fault simulation model; Design a fault simulation model based on the structure and operating characteristics of the power system; the simulation model should be able to simulate different types of fault scenarios, such as short circuit, open circuit, overload, etc.; generate fault feature data sets under different fault types and operating conditions based on the fault simulation model; these data sets will be used for optimization and verification of subsequent algorithms; apply the fused comprehensive feature vector to the fault location algorithm to evaluate the algorithm's positioning accuracy and stability under different fault types and operating conditions; verify the effectiveness of the feature fusion strategy by comparing the positioning accuracy, stability and other indicators of the algorithm before and after fusion; iteratively optimize the parameters of the simulation model and the fault feature extraction method based on the feature fusion effect evaluation results; continuously optimize the feature fusion strategy to improve the effectiveness of feature fusion and the accuracy of the algorithm; repeat the above steps until satisfactory algorithm performance and feature fusion effect are achieved.

[0184] The effects of the above technical solutions are as follows: through signal processing technology and data analysis methods, fault features such as abnormal fluctuations, mutation points, frequency components, etc. are extracted from multi-source monitoring data such as current, voltage, and temperature, which can more accurately capture the abnormal state of the power system; the extracted fault features are preprocessed, such as denoising and normalization, to improve the quality and reliability of the features, and provide a solid foundation for subsequent feature fusion and algorithm application; based on the preset feature fusion strategy, the extracted fault features are fused to form a comprehensive feature vector. This fusion strategy can comprehensively consider the correlation and complementarity between different fault features and improve the effectiveness and robustness of the features; by comparing the positioning accuracy, stability and other indicators of the algorithm before and after fusion, the effectiveness of the feature fusion strategy can be verified, providing guidance for subsequent optimization; according to the structure and operation characteristics of the power system, a fault simulation model is constructed, including the power grid topology, equipment parameters, fault types, etc. This simulation model can simulate different types of fault scenarios and generate real fault feature data sets; by continuously optimizing the parameters of the simulation model and the fault feature extraction method, the accuracy and practicality of the simulation model can be improved, providing strong support for subsequent algorithm verification and optimization; applying the fused comprehensive feature vector to the fault location algorithm can significantly improve the positioning accuracy and stability of the algorithm; by evaluating the improvement effect of feature fusion on algorithm performance, the structure and parameters of the algorithm can be continuously optimized to better adapt to the complexity and diversity of the power system; through accurate fault location and timely fault handling, the development and expansion of faults can be effectively avoided, and power outages and safety accidents in the power system can be reduced; the application of this technical solution can improve the safe and stable operation level of the power system, ensure users' electricity demand and the economic benefits of power companies; through automated fault feature extraction, fusion and algorithm application, manual intervention and operation and maintenance costs can be reduced; at the same time, the application of this technical solution can improve the efficiency of fault handling, shorten fault recovery time, and improve the reliability and availability of the power system.

[0185] In one embodiment of the present invention, S5 includes:

[0186] S51. Based on the early warning information push mechanism, the early warning information and fault location results are sent to the mobile device or workstation of the operation and maintenance personnel in real time;

[0187] S52. Analyze the structural design and insulation material performance of the cold shrink cable intermediate joint according to the monitoring data and early warning results, and put forward improvement suggestions based on the analysis results;

[0188] S53. Formulate a targeted maintenance plan based on the monitoring data and early warning results, wherein the maintenance plan includes detection frequency, detection method and maintenance measures;

[0189] S54. Carry out regular inspection and maintenance, record the inspection results, compare and analyze with the monitoring data, evaluate the maintenance effect, and continuously optimize the maintenance plan.

[0190] The working principle of the above technical solution is as follows: the monitoring equipment in the power system collects key data such as current, voltage, temperature, etc. of the intermediate joint of the cold shrink cable in real time; through data analysis and algorithm processing, potential faults or abnormal conditions are identified and early warning information is generated; the early warning information push mechanism sends the early warning information and fault location results to the mobile devices (such as mobile phones, tablets) or workstations of the operation and maintenance personnel in real time; the push methods may include SMS, email, APP push, etc., to ensure that the operation and maintenance personnel can obtain the early warning information in the first time; the early warning information contains the fault location results, indicating the specific location where the problem may occur in the intermediate joint of the cold shrink cable; this helps the operation and maintenance personnel to quickly locate the problem and take corresponding measures; collect monitoring data of the intermediate joint of the cold shrink cable , including historical and real-time data of parameters such as current, voltage, and temperature; conduct in-depth analysis of the early warning results to understand the abnormal conditions that occur during the operation of the cold shrink cable intermediate joint; evaluate the structural design and insulation material performance of the cold shrink cable intermediate joint based on the monitoring data and early warning results; analyze whether there are defects in the structural design and whether the insulation material is aged or has degraded; based on the evaluation results, put forward improvement suggestions for the structural design and insulation material of the cold shrink cable intermediate joint; improvement suggestions may include optimizing the design, replacing high-performance materials, and strengthening maintenance; determine the maintenance needs of the cold shrink cable intermediate joint based on the monitoring data and early warning results; formulate a targeted maintenance plan, including detection frequency, detection methods, and maintenance measures. The inspection frequency should be determined based on the operating status and historical fault records of the cold shrink cable intermediate joints; inspection methods may include appearance inspection, electrical performance testing, insulation resistance measurement, etc.; maintenance measures may include cleaning, tightening, replacement of parts, etc.; regular inspection and maintenance shall be carried out according to the maintenance plan to ensure the normal operation of the cold shrink cable intermediate joints; the details of each inspection and maintenance shall be recorded, including the inspection results, maintenance measures and problems encountered; the results of regular inspections shall be compared and analyzed with the monitoring data to evaluate the effectiveness of maintenance measures; comparative analysis can reveal the changing trends and potential problems of the cold shrink cable intermediate joints during operation; based on the results of comparative analysis, the maintenance plan shall be continuously optimized; optimization may include adjusting the inspection frequency, improving the inspection methods, increasing maintenance measures, etc.; the goal is to ensure the long-term safe and stable operation of the cold shrink cable intermediate joints and reduce the failure rate.

[0191] The effects of the above technical solution are: through the early warning information push mechanism, the operation and maintenance personnel can receive the early warning information and fault location results of the cold shrink cable intermediate joints in real time; this enables the operation and maintenance personnel to respond quickly and take timely measures to avoid the expansion of faults, reduce power outage time and economic losses; the analysis of the structural design of the cold shrink cable intermediate joints and the performance of the insulation materials helps to discover potential design defects and material aging problems; the improvement suggestions based on the analysis results can guide manufacturers to optimize product design and improve the reliability and durability of the cold shrink cable intermediate joints; the formulated maintenance plan is tailored according to the monitoring data and early warning results to ensure the pertinence and efficiency of the maintenance work; through reasonable detection frequency, detection methods and maintenance measures, the operation and maintenance personnel can more effectively manage the maintenance work of the cold shrink cable intermediate joints and reduce maintenance Cost, improve maintenance efficiency; regular inspection and maintenance, as well as comparative analysis of inspection results and monitoring data, provide a strong basis for evaluating maintenance effects; continuous optimization of maintenance plans based on evaluation results ensures the long-term stability and reliability of cold shrink cable intermediate joints; this continuous improvement mechanism helps to improve the professional skills of operation and maintenance personnel and improve the overall management level of the power system; through the above series of measures, the safety and stability of cold shrink cable intermediate joints can be significantly improved; this helps to reduce power outages and safety accidents caused by cold shrink cable intermediate joint failures, and ensure the safe and stable operation of the power system; through real-time early warning, targeted maintenance, effect evaluation and continuous optimization and other measures, the operation and maintenance costs can be significantly reduced; at the same time, the power outage time and economic losses caused by failures are reduced, and the economic benefits of the power system are improved.

[0192] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent monitoring method for insulation status of power cable joints based on complex environment, characterized in that: The method comprises: S1. Collect relevant data by multiple means, wherein the relevant data includes ambient temperature data, partial discharge signals and temperature distribution images; S2. Input the collected environmental parameters and partial discharge signals into the intelligent analysis system for preprocessing and feature extraction. Based on the machine learning algorithm, an insulation state prediction model is established to predict the aging degree and potential fault points of the insulation layer of the cable joint according to historical data and real-time monitoring data. S3. Using infrared thermal imaging data and image processing technology, identify temperature abnormality areas and calculate temperature gradients, and evaluate the thermal stability of the insulation layer based on the temperature gradient calculation results; S4. According to the output results of the insulation status prediction model, set the warning threshold. When the prediction result exceeds the threshold, trigger the warning mechanism; combine the partial discharge monitoring data and infrared thermal imaging results to accurately locate the location of the insulation defect and give the fault type and degree; S5. Send the warning information and fault location results to the operation and maintenance personnel in real time, and optimize the structural design of the cold shrink cable intermediate joint based on the monitoring data and warning results.

2. According to claim 1, a method for intelligently monitoring the insulation status of a power cable joint in a complex environment is characterized in that: Said S1 comprises: S11. Real-time collection of ambient temperature is performed through a high-precision temperature sensor, the humidity level around the connector is monitored through a humidity sensor, and the electric field distribution around the cable connector is monitored through an electric field strength sensor; S12, capturing the partial discharge signal in the cable insulation layer through a high-frequency current sensor, and filtering and noise reduction processing the collected partial discharge signal; S13. Use a high-resolution infrared thermal imaging camera to regularly capture temperature distribution images of the insulation layer of the cable joint, and use an image enhancement algorithm to enhance the collected temperature distribution images.

3. According to claim 1, a method for intelligently monitoring the insulation status of a power cable joint in a complex environment is characterized in that: The S2 comprises: S21, preprocessing the collected environmental parameters and partial discharge signals; S22. Based on statistical analysis and machine learning algorithms, feature selection is performed on the preprocessed data to identify characteristic variables that have an impact on insulation status prediction; S23, extracting features from the preprocessed data through time domain, frequency domain and time-frequency domain analysis techniques, and fusing the extracted feature variables to form a comprehensive feature vector; S24, dividing the preprocessed data set into a training set and a validation set, using the training set to train the built-in machine learning model, and using the validation set to evaluate the performance of the model; S25. Evaluate the performance of the model through cross-validation technology, adjust the model's hyperparameters and optimize the model structure based on the validation results; S26. Use the trained machine learning model to predict the test set, output the insulation status prediction result, and evaluate the uncertainty of the prediction result; S27. Design early warning strategies based on the uncertainty of the prediction results. When the uncertainty of the prediction results is high, trigger a higher level of early warning mechanism. S28. Compare the predicted results with the actual situation, evaluate the accuracy and reliability of the model, iteratively optimize the model based on the evaluation results, and regularly collect new monitoring data based on the continuous learning and updating mechanism to update and optimize the model.

4. According to claim 3, a method for intelligently monitoring the insulation status of a power cable joint in a complex environment is characterized in that: The S23 comprises: The mean and variance of each characteristic variable after preprocessing are calculated, and the volatility and stability of the data are evaluated based on the evaluation record; the trend line of the time series data is calculated through the sliding window technology to identify the changing trends of environmental parameters and partial discharge signals over time; Identify and record the maximum and minimum values ​​of each characteristic variable within a time period, analyze abnormal fluctuations, evaluate the correlation of time series data at different time intervals, and reveal the time dependence within the data; Convert the time domain signal into frequency domain representation, analyze the spectral components of each characteristic variable, and identify the main frequency components and their energy distribution; Calculate and plot power spectrum density to evaluate signal energy at different frequencies and identify potential periodic or random features; calculate the entropy of the spectrum to measure the complexity of the signal frequency distribution; Perform Fourier transform on the signal within the time window to obtain the spectrum information that changes with time, capture the instantaneous frequency characteristics of the partial discharge signal, decompose the signal using wavelet functions of different scales, and extract the time-frequency characteristics at multiple scales; Decompose the complex signal into a series of intrinsic mode functions, each IMF represents a different frequency component of the signal; standardize all extracted features; Based on correlation analysis, the most critical feature subset for insulation status prediction is further screened, and the screened features are fused into a comprehensive feature vector using weighted average. And through random forest, the contribution of each feature in the comprehensive feature vector is evaluated.

5. According to claim 1, a method for intelligently monitoring the insulation status of a power cable joint in a complex environment is characterized in that: The S3 includes: S31. Based on high-resolution infrared thermal imaging technology, obtain the temperature distribution image of the cable joint insulation layer, and apply image processing algorithms to preliminarily identify the temperature abnormality area; S32, performing detailed analysis on the initially identified temperature anomaly area, and removing noise and artifacts using morphological processing technology; S33, calculating geometric features such as area, perimeter, shape factor, and statistical features of the temperature anomaly region; S34, setting a plurality of temperature gradient calculation points around the temperature anomaly region, calculating the temperature gradient between each calculation point and its adjacent points, and forming a temperature gradient distribution diagram; S35. Analyze the thermal conductivity of the insulation layer according to the temperature gradient distribution diagram to determine whether there is a thermal conduction barrier or heat accumulation phenomenon; S36. Combine the thermal properties of the insulation material to evaluate the thermal stability of the insulation layer and predict the risk of thermal failure; S37, establishing a thermal stability evaluation model, and quantitatively evaluating the thermal stability of the insulation layer based on the geometric characteristics, statistical characteristics, and temperature gradient distribution of the temperature anomaly area; S38. Based on the results of the thermal stability assessment model, combined with historical data and industry standards, set the warning threshold, and dynamically adjust the warning threshold based on the aging degree of the insulation layer and changes in the operating environment.

6. According to claim 5, a method for intelligently monitoring insulation status of power cable joints in a complex environment is characterized in that: The S35 comprises: On the temperature gradient distribution map, the areas where the temperature gradient changes significantly are identified, and the areas where the temperature gradient exceeds or falls below the threshold are marked as potential heat conduction obstacle areas by setting the gradient change threshold; Using the connected domain analysis in image processing technology, the points on the temperature gradient map are connected into lines to form a heat conduction path network; According to the thermal conductivity characteristics of the insulating material, a weight is assigned to each path, and the weight is inversely proportional to the thermal conductivity of the material, and the thermal conduction efficiency of the entire insulating layer is calculated; Evaluate the deviation of heat transfer efficiency from normal values, identify areas where heat transfer efficiency is significantly reduced, analyze local high or low temperature concentration areas in the temperature gradient distribution map, and evaluate the rate and degree of heat accumulation by calculating the temperature gradient change rate of the area; Combining the results of temperature gradient anomaly identification and heat conduction efficiency evaluation, the heat conduction obstacle points in the insulation layer are located, and the heat conduction obstacles are classified according to the temperature gradient characteristics of the obstacle points, the position on the heat conduction path, and possible material defects; Using time series analysis technology, we compare historical data of temperature gradient distribution diagrams, analyze the trend of thermal conductivity over time, identify areas where thermal conductivity continues to decline, and predict possible thermal failure risks in the future.

7. According to claim 1, a method for intelligently monitoring the insulation status of a power cable joint in a complex environment is characterized in that: The S4 comprises: S41. According to the historical analysis of power system operation data, combined with industry standards and practical experience, different levels of warning thresholds are set. Based on the warning trigger mechanism, when the monitoring data reaches or exceeds the set threshold, the corresponding level of warning is automatically triggered; and a warning information transmission channel is established. S42, using monitoring equipment to collect real-time data, combining historical data and industry standards, extracting fault features, wherein the fault features include abnormal fluctuations, mutation points, and frequency components; and preprocessing the extracted fault features; S43, based on the fault characteristics and in combination with the fault location algorithm, locate the fault location; and perform simulation testing and optimization on the fault location algorithm; S44. Based on the results obtained by the fault location algorithm, combined with on-site inspection and data analysis, diagnose the fault and determine the fault type and cause; establish a fault diagnosis knowledge base to record the fault characteristics, location methods and treatment measures of common faults; S45. Based on the results of fault location and diagnosis, combined with changes in the operating status of the power system, the warning threshold is dynamically adjusted, and the performance of the warning mechanism is regularly tested and evaluated. The warning threshold and trigger mechanism are optimized and improved based on the test results.

8. The intelligent monitoring method for insulation status of power cable joints in complex environments according to claim 7 is characterized in that: The S43 includes: S431. Based on the fault characteristics, preliminarily apply the fault location algorithm to the fault characteristic data set, preliminarily locate the fault location, and evaluate the basic performance and accuracy of the algorithm; S432, fusing the fault features extracted from the multi-source monitoring data to form a comprehensive feature vector, constructing a power system fault simulation model, simulating different types of fault scenarios, and generating corresponding fault feature data sets; S433. Apply the optimized fault location algorithm to the simulation data set to verify the location accuracy and stability of the algorithm under different fault types and different operating conditions.

9. The intelligent monitoring method for insulation status of power cable joints in complex environments according to claim 8, characterized in that: The S432 includes: Extract fault features from multi-source monitoring data through signal processing technology and data analysis methods, and pre-process the extracted fault features; The extracted features are fused based on the preset feature fusion strategy, and a fault simulation model is constructed according to the structure and operation characteristics of the power system; Based on the fault simulation model, we generate fault feature data sets under different fault types and operating conditions, apply the fused comprehensive feature vectors to the fault location algorithm, and evaluate the effect of feature fusion on the performance of the algorithm. According to the evaluation results of feature fusion effect, the simulation model and feature fusion strategy are iteratively optimized.

10. The intelligent monitoring method for insulation status of power cable joints based on complex environment according to claim 1, characterized in that: The S5 comprises: S51. Based on the early warning information push mechanism, the early warning information and fault location results are sent to the mobile device or workstation of the operation and maintenance personnel in real time; S52. Analyze the structural design and insulation material performance of the cold shrink cable intermediate joint according to the monitoring data and early warning results, and put forward improvement suggestions based on the analysis results; S53. Formulate a targeted maintenance plan based on the monitoring data and early warning results, wherein the maintenance plan includes detection frequency, detection method and maintenance measures; S54. Carry out regular inspection and maintenance, record the inspection results, compare and analyze with the monitoring data, evaluate the maintenance effect, and continuously optimize the maintenance plan.

Images