Cable aging monitoring and early warning method and system

Abstract

The invention relates to the technical field of monitoring, in particular to a cable aging monitoring and early warning method and system. The method comprises the following steps: acquiring cable monitoring data, and performing electrical characteristic analysis to obtain electrical characteristic data; performing conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data; cable image feature extraction is carried out according to the cable monitoring data, cable morphology type division is carried out, and curve segment cable data and winding segment cable data are obtained; performing bending fatigue analysis on the curve section cable data to obtain bending fatigue data; performing winding stress analysis on the winding section cable data to obtain winding stress data; performing material damage evaluation according to the bending fatigue data and the winding stress data to obtain material damage data; and carrying out damage position identification according to the material damage data to obtain damage position data. The cable aging monitoring accuracy and the early warning response rate are improved based on the monitoring technology.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technology, and in particular to a cable aging monitoring and early warning method and system. Background Art

[0002] The data collection and monitoring methods of traditional cable monitoring methods are relatively simple, usually relying on limited sensors (such as temperature, current, voltage, etc.) for monitoring, and cannot fully obtain the multi-dimensional data of the cable. Therefore, it cannot deeply reflect the physical changes that occur in the actual use of the cable, such as bending, stress, cracks, etc., resulting in a limited monitoring range and accuracy. Usually relying on offline data analysis or periodic detection, it cannot achieve real-time dynamic monitoring and timely warning. It reacts slowly when facing sudden failures or aging phenomena, and cannot effectively capture sudden cable damage, missing the best time for maintenance. Commonly used damage identification technologies (such as visual inspection, simple resistance changes, etc.) usually lack sufficient identification capabilities for minor damage, invisible damage, or potential damage inside composite materials, resulting in common missed detections and failure to timely discover potential safety hazards. Traditional monitoring systems often have poor adaptability and cannot provide effective monitoring in complex environments such as high temperature, high humidity or corrosive environments. They are easily interfered by external environmental factors, resulting in data distortion or monitoring failure. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a cable aging monitoring and early warning method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a cable aging monitoring and early warning method comprises the following steps:

[0005] Step S1: Acquire cable monitoring data, and perform electrical characteristic analysis to obtain electrical characteristic data; perform conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data;

[0006] Step S2: extracting cable image features according to the cable monitoring data, and classifying the cable morphology types to obtain curved segment cable data and winding segment cable data; performing bending fatigue analysis on the curved segment cable data to obtain bending fatigue data; performing winding stress analysis on the winding segment cable data to obtain winding stress data; performing material damage assessment according to the bending fatigue data and winding stress data to obtain material damage data;

[0007] Step S3: identifying the damage position according to the material damage data to obtain damage position data; marking the damaged cable based on the damage position data to determine it as a damaged cable; and performing crack detection on the damaged cable to obtain crack data;

[0008] Step S4: Perform line transmission simulation on the damaged cable according to the crack data, and perform component short-circuit analysis to obtain component short-circuit data; construct a cable aging monitoring model according to the component short-circuit data and conductor oxidation data; input the cable monitoring data into the cable aging monitoring model, and execute the cable aging warning task.

[0009] The present invention greatly improves the comprehensiveness and accuracy of cable aging monitoring through multi-dimensional data collection and in-depth analysis. First, based on electrical characteristics analysis and conductor oxidation data, the aging degree of the conductor can be captured in real time, breaking through the problem that traditional monitoring methods can only detect a single electrical characteristic. By dividing the cable morphology types, combined with bending fatigue and winding stress analysis, the various physical stresses to which the cable is subjected during use are accurately evaluated, and potential damage risks are identified, especially under the action of bending and winding stress, which can detect minor damage that is difficult to detect with traditional methods. Material damage assessment combines a variety of mechanical models to improve the accuracy of damage identification and avoid missed detection caused by a single monitoring method in traditional methods. The combination of crack detection and damage location identification makes the positioning of the damaged area of ​​the cable more accurate, effectively reducing the deficiency of the traditional method that is difficult to accurately identify the damage location. In addition, through line transmission simulation and component short-circuit analysis, the actual working status of the cable can be comprehensively evaluated, hidden dangers that affect the safety of the power system can be discovered in advance, and the timeliness and accuracy of fault warning can be improved. The constructed cable aging monitoring model further enhances the comprehensive analysis capability of monitoring data, which not only improves the response speed to sudden faults, but also enables real-time monitoring and prediction of cable aging trends, avoiding the lag of periodic detection in traditional methods.

[0010] Preferably, step S1 specifically comprises:

[0011] Step S11: acquiring cable monitoring data, and performing resistance monitoring feature extraction to obtain resistance monitoring data;

[0012] Step S12: performing rate calculation on the resistance monitoring data to obtain high rate resistance data;

[0013] Step S13: acquiring a high resistance rate cable based on the high rate resistance data;

[0014] Step S14: performing oxidation environment simulation on the high resistance rate cable to obtain an oxidized cable;

[0015] Step S15: performing oxygen spectrum analysis on the oxidized cable to obtain oxygen spectrum data;

[0016] Step S16: Conductor oxidation determination of the high resistance rate cable is performed according to the oxygen energy spectrum data to obtain conductor oxidation data.

[0017] The present invention significantly improves the ability of cable aging monitoring through precise resistance monitoring and multi-dimensional data analysis. First, by extracting resistance monitoring data, the change of cable resistance can be captured in real time, providing an important basis for subsequent aging assessment. The rate calculation method makes the identification of high-resistance rate cables more accurate, and can promptly detect the rapid change of resistance caused by aging or external stress, and discover potential invisible damage. The oxidative environment simulation and its subsequent oxygen spectrum analysis effectively simulate the changes of cables in different oxidative environments, identify the risk of conductor oxidation in advance, and avoid the problem of delayed reaction of traditional methods when oxidation occurs. The precise analysis of oxygen spectrum data provides a more in-depth judgment of the degree of oxidation, which makes up for the defect that a single resistance change cannot accurately reflect the aging process. Overall, the multi-dimensional data acquisition and analysis means of the method make the monitoring of cable aging and damage more real-time and comprehensive, especially in terms of adaptability to sudden failures and complex environments, effectively improving the accuracy, real-time and environmental adaptability of monitoring.

[0018] Preferably, step S15 is specifically as follows:

[0019] Step S151: cleaning the oxidized cable sample to obtain a cleaned oxidized cable;

[0020] Step S152: applying an accelerating voltage to the cleaned oxidized cable to obtain an accelerating voltage cable;

[0021] Step S153: irradiating the accelerating voltage cable with an electron beam, and collecting radiation released from the accelerating voltage cable to obtain the released radiation;

[0022] Step S154: performing element characteristic peak energy spectrum detection on the released rays to obtain element energy spectrum data;

[0023] Step S155: identifying the oxygen element according to the element spectrum data to obtain the oxygen element spectrum data;

[0024] Step S156: measuring the oxidation degree of the oxygen element energy spectrum data to obtain oxidation degree data;

[0025] Step S157: integrating the oxygen spectrum according to the oxygen element spectrum data and the oxidation degree data to obtain oxygen spectrum data.

[0026] The present invention eliminates the interference of external contaminants on subsequent analysis by cleaning the oxidized cable, thereby ensuring the accuracy and reliability of the data. Applying an accelerating voltage helps to accelerate the cable aging process, making monitoring more efficient and providing conditions for rapid response for subsequent analysis. Electron beam irradiation and ray collection technology play an important role in details that traditional monitoring methods cannot directly capture. Through the data collection of ray release, more detailed information on changes inside the cable can be provided. Element characteristic peak energy spectrum detection provides detailed energy spectrum data for oxidation phenomena and accurately identifies the distribution and changes of oxygen elements. The identification of oxygen elements and the measurement of the degree of oxidation enable the oxidation process to be quantitatively evaluated, surpassing the limitations of traditional observations based solely on resistance or surface observation, and providing a comprehensive basis for judging the degree of oxidation. The integration of the oxygen energy spectrum further improves the comprehensive understanding of the cable oxidation process, and can combine different element data to provide more accurate aging assessment results, ultimately enabling this monitoring system to maintain efficient and accurate performance in complex environments, timely capture potential safety hazards, and far surpass the limitations of traditional monitoring methods.

[0027] Preferably, the cable morphology type classification includes:

[0028] Extract cable images of cable monitoring data;

[0029] Calculate the curvature of the cable image;

[0030] Recognize the curve shape of cable images based on curvature;

[0031] Detect the helicity of the cable image;

[0032] Identify the ring structure of the cable image;

[0033] The winding structure is integrated according to the helicity and the ring structure to obtain the winding structure;

[0034] Perform curve segment cable recognition on the cable image according to the curve shape to obtain curve segment cable data;

[0035] The cable image is subjected to winding segment cable identification according to the winding structure to obtain winding segment cable data.

[0036] The present invention can more accurately capture the deformation and aging process of cables under different use conditions by extracting cable images from cable monitoring data and calculating their curvature. Curvature analysis provides a deep insight into the changes in cable morphology, helps to identify the curved morphology of the cable, and thus better evaluates its physical state in operation. Further, by detecting the helicity of the cable image and identifying the annular structure, it can help identify the structural characteristics of the cable, especially the changes in the spiral and annular structures, which is crucial for analyzing the winding performance of the cable. Based on these characteristics, by integrating the winding structure data, the structural design and health monitoring methods of the cable can be further optimized, the recognition accuracy of the winding segment can be improved, and the different types of curved segment cables and winding segment cables can be effectively identified. These technical means not only make up for the shortcomings of traditional monitoring methods, can more comprehensively reflect the actual status of the cable, but also improve the ability of real-time dynamic monitoring, can timely capture the slight changes and potential damage of the cable, provide accurate early warnings, reduce the risk of failure, and improve the safety of cable use.

[0037] Preferably, the bending fatigue analysis includes:

[0038] Extract curved material properties of cable data for curved segments;

[0039] Repeated bending simulation based on bending material properties and curved cable data;

[0040] Calculate bending stress for repeated bending simulations;

[0041] Analyze the fatigue degree of cables in curved sections based on bending stress;

[0042] Analyze the damage accumulation degree of the cable in the curved section according to the fatigue degree;

[0043] The fatigue degree and damage accumulation degree are integrated to obtain the bending fatigue data.

[0044] The present invention can fully understand the physical properties of cable materials during the bending process by extracting the bending material properties of the curved segment cable data, which helps to evaluate its stability and durability in the actual working environment. Based on these bending material properties and combined with the curved segment cable data, repeated bending simulation can effectively simulate the stress changes of the cable in long-term use, especially the impact on the cable in the bending state. Calculating the bending stress generated in the repeated bending simulation further reveals the maximum stress value that the cable bears during the bending process, providing the necessary physical basis for fatigue analysis. By analyzing the bending stress, the fatigue degree of the curved segment cable can be accurately evaluated, thereby providing data support for predicting the service life of the cable. The analysis of the accumulation of cable damage according to the fatigue degree can reveal the evolution of minor damage to the cable during long-term use, and effectively capture potential problems that are difficult to detect with traditional monitoring methods. Integrating the data of fatigue degree and damage accumulation degree can form more comprehensive bending fatigue data, providing a scientific basis for cable health monitoring and maintenance decisions. This method not only improves the monitoring capability of the cable health status through multi-dimensional analysis and simulation, but also enhances the system's early warning capability for cable aging and damage, reduces the probability of failure, and ensures the long-term reliability of the cable in a complex environment.

[0045] Preferably, the winding stress analysis includes:

[0046] Extract the geometric shape of the winding cable data and build a winding model;

[0047] Calculate the axial tensile stress of the winding section cable data;

[0048] Calculate the torsional stress of the winding section cable data;

[0049] Calculate radial compressive stress of winding section cable data;

[0050] Integrate axial tensile stress, torsional stress and radial compressive stress to obtain stress data;

[0051] Based on the stress data, winding seams of the winding model are identified to obtain winding seam stress data;

[0052] Based on the stress data, the stress difference between the inner and outer layers of the winding model is analyzed to obtain the stress difference data between the inner and outer layers;

[0053] The winding stress data is obtained by integrating the winding seam stress data and the inner and outer layer stress difference data.

[0054] The present invention can fully understand the structural characteristics of the cable under different states by extracting the geometric shape of the winding segment cable data and constructing a winding model, and provide an accurate geometric basis for subsequent analysis. By calculating the axial tensile stress of the winding segment cable data, the deformation degree of the cable under the tensile force can be revealed, which helps to determine its bearing capacity under tensile load. Calculating the torsional stress helps to analyze the mechanical behavior of the cable under torsion and evaluate its stability under torque. Further calculating the radial compressive stress can provide the bearing capacity and deformation of the cable under compression, and provide comprehensive data support for the comprehensive performance evaluation of the winding segment cable. After integrating these stress data, not only can the mechanical performance of the cable under various loads be understood more accurately, but also a more comprehensive basis can be provided for subsequent damage detection and life prediction. Winding seam identification based on stress data can effectively locate potential weaknesses or seam locations, identify weak links in the winding structure at an early stage, and improve the accuracy of the monitoring system. At the same time, the analysis of the difference in stress between the inner and outer layers can reveal the stress distribution at different levels in the winding segment, and help determine structural problems. By integrating the winding seam stress data and the inner and outer layer stress difference data, more comprehensive and accurate data can be provided for the dynamic monitoring of winding stress, thereby optimizing the cable maintenance strategy, reducing the probability of sudden failures, and ensuring the long-term stability and reliability of the cable in complex usage environments.

[0055] Preferably, step S3 specifically comprises:

[0056] Step S31: locating the damaged area according to the material damage data to obtain the damaged area;

[0057] Step S32: performing acoustic wave sensing measurement on the damaged area to obtain acoustic wave sensing data;

[0058] Step S33: triangulate the damage position according to the acoustic wave sensing data to obtain damage position data;

[0059] Step S34: marking the damaged cable based on the damage position data and determining it as a damaged cable;

[0060] Step S35: Perform crack detection on the damaged cable to obtain crack data.

[0061] The present invention can accurately identify the specific location of the cable damage by locating the damaged area according to the material damage data, providing key clues for subsequent detailed inspection and maintenance. By performing acoustic wave sensing measurements on the damaged area, high-resolution acoustic wave data can be obtained to further verify the scope and extent of the damaged location. The processing of acoustic wave sensing data can achieve triangulation of the damaged location, thereby improving the accuracy of positioning and providing accurate damage information for rapid repair and maintenance. Marking the damaged cable based on the damaged location data helps to clearly identify the damaged part, thereby saving time and cost during the maintenance process and avoiding unnecessary disassembly or misjudgment. Crack detection of damaged cables can deeply detect potential cracks or minor damage, provide data support for cable recovery and life assessment, and avoid potential risks. Overall, these steps can achieve accurate monitoring and identification of cable damage through multi-dimensional, real-time data acquisition and processing, significantly improve the accuracy and response speed of cable monitoring, effectively improve the timeliness and accuracy of equipment maintenance, reduce the probability of sudden failures, and optimize the efficiency of maintenance decisions.

[0062] Preferably, step S35 is specifically as follows:

[0063] Step S351: applying an alternating magnetic field to the damaged cable to obtain alternating magnetic field data;

[0064] Step S352: performing ring current detection according to the alternating magnetic field data to obtain eddy current data;

[0065] Step S353: performing signal spectrum conversion based on the eddy current data to obtain an eddy current signal spectrum;

[0066] Step S354: performing amplitude attenuation analysis based on the eddy current signal spectrum to obtain amplitude attenuation data, and performing crack depth assessment to obtain crack depth data;

[0067] Step S355: performing signal peak analysis based on the eddy current signal spectrum to obtain signal peak data, and determining the crack boundary to obtain crack boundary data;

[0068] Step S356: performing amplitude change detection based on the eddy current signal spectrum to obtain amplitude change data, and performing crack length estimation to obtain crack length data;

[0069] Step S357: Integrate the crack depth data, crack boundary data and crack length data to obtain crack data.

[0070] The present invention can accurately detect the electromagnetic changes inside the cable by applying an alternating magnetic field to the damaged cable and obtaining alternating magnetic field data, thereby providing basic information for subsequent eddy current detection and improving the sensitivity of detection. The eddy current data obtained by performing ring current detection based on the alternating magnetic field data provides key clues for in-depth analysis of internal defects of the cable and can effectively reveal the existence of minor damage or potential cracks. By converting the eddy current data into an eddy current signal spectrum, the characteristics of the damaged area can be clearly displayed, which helps to identify different types of defects and makes crack detection more intuitive and accurate. Further amplitude attenuation analysis of the eddy current signal spectrum can quantify the depth of the crack, thereby evaluating the impact of the crack on the overall performance of the cable and providing a basis for maintenance decisions. Through signal peak analysis, the boundary position of the crack can be identified, which helps to accurately locate the crack and further improve the judgment accuracy of the damaged part during maintenance. Through amplitude change detection, the length of the crack can be estimated, providing accurate crack size data for further risk assessment and maintenance. Finally, by integrating the crack depth, boundary and length data, the overall morphology and impact of the crack can be fully understood, providing a sufficient basis for subsequent maintenance plans. Overall, these steps achieve accurate detection and positioning of cable cracks through multi-dimensional data fusion and in-depth analysis, greatly improving the accuracy and response speed of cable monitoring, effectively improving maintenance efficiency and reducing the risk of failures.

[0071] Preferably, step S4 is specifically:

[0072] Step S41: performing line transmission simulation on the damaged cable according to the crack data, thereby obtaining line transmission simulation data;

[0073] Step S42: performing transmission current statistics and transmission voltage statistics on the line transmission simulation data, thereby obtaining transmission current data and transmission voltage data;

[0074] Step S43: performing overload identification based on the transmission current data, thereby obtaining current overload data;

[0075] Step S44: performing overvoltage identification based on the transmitted voltage data, thereby obtaining voltage overvoltage data;

[0076] Step S45: performing a time intersection operation according to the current overload data and the voltage overvoltage data, thereby obtaining transmission overload-overvoltage data;

[0077] Step S46: performing temperature overheat analysis based on the transmission overload-overvoltage data, thereby obtaining temperature overheat data;

[0078] Step S47: performing component short circuit determination according to the temperature overheat data, thereby obtaining component short circuit data;

[0079] Step S48: constructing a cable aging monitoring model according to the component short circuit data and the conductor oxidation data;

[0080] Step S49: input the cable monitoring data into the cable aging monitoring model, and execute the cable aging early warning task.

[0081] The present invention can accurately evaluate the impact of cracks on the transmission performance of the cable by simulating the line transmission of the damaged cable, thereby obtaining detailed line transmission data and understanding the distribution of current and voltage. Statistical analysis of current and voltage on the line transmission simulation data is helpful to grasp the operating status of the cable under different working conditions and provide important current and voltage information. Based on the transmission current data, overload identification can effectively detect whether the current exceeds the predetermined threshold, thereby obtaining current overload data and warning the current overload risk; similarly, based on the transmission voltage data, overvoltage identification can timely identify voltage anomalies, avoid cable damage caused by excessive voltage, and obtain voltage overvoltage data. These current overload and voltage overvoltage data are combined by time intersection operation, which helps to accurately identify the current and voltage anomalies that occur simultaneously and improve the accuracy of fault diagnosis. Subsequently, temperature overheating analysis is performed based on these data, which helps to find the local overheating of the cable caused by current and voltage anomalies, and then judge the resulting safety hazards. Further component short circuit determination is performed through temperature overheating data, which can effectively detect the short circuit risk of the internal components of the cable, thereby providing an early warning for cable maintenance. Combining component short-circuit data and conductor oxidation data, a cable aging monitoring model is constructed to comprehensively evaluate the aging degree of the cable and enhance the monitoring system's comprehensive understanding and prediction capabilities of the cable health status. Finally, the cable monitoring data is input into the aging monitoring model, and the cable aging early warning task is executed, which can identify the aging risk of the cable in real time and issue early warnings, reducing the probability of failures, thereby effectively improving the safety and reliability of the cable, optimizing maintenance strategies, and reducing the occurrence of emergencies.

[0082] Preferably, the present specification also provides a cable aging monitoring and early warning system, which is used to execute the cable aging monitoring and early warning method as described above, and the cable aging monitoring and early warning system includes:

[0083] The conductor oxidation analysis module is used to obtain cable monitoring data and perform electrical characteristic analysis to obtain electrical characteristic data; based on the electrical characteristic data, the conductor oxidation analysis is performed to obtain conductor oxidation data;

[0084] Conductor oxidation analysis module, used to extract cable image features based on cable monitoring data, and classify cable morphology types to obtain curved segment cable data and winding segment cable data; perform bending fatigue analysis on curved segment cable data to obtain bending fatigue data; perform winding stress analysis on winding segment cable data to obtain winding stress data; perform material damage assessment based on bending fatigue data and winding stress data to obtain material damage data;

[0085] The crack detection module is used to identify the damage position according to the material damage data to obtain the damage position data; mark the damaged cable based on the damage position data to determine it as a damaged cable; and perform crack detection on the damaged cable to obtain crack data;

[0086] The cable aging warning module is used to simulate the line transmission of the damaged cable according to the crack data, and perform component short-circuit analysis to obtain component short-circuit data; build a cable aging monitoring model based on component short-circuit data and conductor oxidation data; input the cable monitoring data into the cable aging monitoring model, and execute the cable aging warning task.

[0087] The cable aging monitoring and early warning system of the present invention can implement the aging monitoring and early warning method of any cable of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the cable aging monitoring and early warning method. The internal modules of the system cooperate with each other, thereby improving the accuracy of cable aging monitoring and the early warning response rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0089] Figure 1 It is a schematic diagram of the steps of the cable aging monitoring and early warning method of the present invention;

[0090] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0091] Figure 3 Detailed step flow diagram of step S15 in the present invention;

[0092] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0093] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0094] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0095] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0096] To achieve this, please refer to Figures 1 to 3 The present invention provides a cable aging monitoring and early warning method, the method comprising the following steps:

[0097] Step S1: Acquire cable monitoring data, and perform electrical characteristic analysis to obtain electrical characteristic data; perform conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data;

[0098] In this embodiment, real-time current, voltage and temperature data are obtained from the cable monitoring system. These data are subjected to appropriate sampling frequency (for example, 10 samples per second) and filtering to obtain accurate electrical characteristic data. The electrical characteristic data is first normalized to eliminate the influence of environmental factors on the measurement results, and then frequency domain analysis is performed to extract the electrical parameters such as impedance and admittance of the cable. These parameters are used for conductor oxidation analysis, in which the degree of oxidation of the conductor is calculated by comparing the electrical characteristics of the cable under standard working conditions with the actual monitoring data. The oxidation analysis is based on the change in conductivity, and the threshold value of the degree of oxidation is set to a 5% decrease in conductivity. If it is lower than this value, it is determined to be oxidative damage. Through this analysis, the conductor oxidation data obtained is used for subsequent cable status evaluation.

[0099] Step S2: extracting cable image features according to the cable monitoring data, and classifying the cable morphology types to obtain curved segment cable data and winding segment cable data; performing bending fatigue analysis on the curved segment cable data to obtain bending fatigue data; performing winding stress analysis on the winding segment cable data to obtain winding stress data; performing material damage assessment according to the bending fatigue data and winding stress data to obtain material damage data;

[0100] In this embodiment, a high-resolution image of the cable surface is obtained by capturing high-resolution images of the cable using an industrial camera with a resolution of 300dpi. During the image processing process, the surface features of the cable are extracted using the Canny edge detection algorithm, and the curve segment and the winding segment are distinguished by the image segmentation algorithm. The curve segment cable data is further sent to the bending fatigue analysis module to calculate the peak and periodic changes of the bending stress, and the maximum stress threshold of the fatigue analysis is set to 100MPa. For the winding segment cable data, finite element analysis (FEA) is used to perform winding stress analysis, and the maximum allowable stress of the winding stress analysis is set to 50MPa. After the bending fatigue data and the winding stress data are combined, the material damage assessment is performed, and the damage accumulation model is used to set the damage threshold to 0.7. When the damage accumulation value exceeds this threshold, the cable material is marked as damaged to obtain material damage data.

[0101] Step S3: identifying the damage position according to the material damage data to obtain damage position data; marking the damaged cable based on the damage position data to determine it as a damaged cable; and performing crack detection on the damaged cable to obtain crack data;

[0102] In this embodiment, the damage location is accurately identified by spatial positioning analysis of the material damage data and combining the position information of the sensor. Using a three-dimensional positioning algorithm, the positioning accuracy of the damage location is set to an error of no more than 5 mm, thereby obtaining damage location data. The damage location data is then used to mark the damaged cable. This process is based on the comparison of the damage location with the surface features of the cable to ensure accurate marking of the damaged area. Then, an ultrasonic sensor is used to detect cracks in the damaged cable. The ultrasonic frequency is set to 40kHz, and the reflection time of the echo signal is used to measure the crack depth. The crack data is obtained by comparing it with the standard crack map.

[0103] Step S4: Perform line transmission simulation on the damaged cable according to the crack data, and perform component short-circuit analysis to obtain component short-circuit data; construct a cable aging monitoring model according to the component short-circuit data and conductor oxidation data; input the cable monitoring data into the cable aging monitoring model, and execute the cable aging warning task.

[0104] In this embodiment, simulation software is used to simulate the line transmission of the damaged cable. During the simulation process, the rated current and voltage of the cable transmission are set to 10A and 220V respectively, and the transmission characteristics of the damaged cable under the actual load are evaluated. The simulation results are used to analyze the component short circuit. The short circuit judgment is based on the detection of current mutation. The threshold of current change is set to 10A / ms. When the current change exceeds this threshold, it is determined that a short circuit occurs. Based on the component short circuit data and the conductor oxidation data, a cable aging monitoring model is constructed by combining a mathematical model. The model uses multi-dimensional input data, including current, temperature, stress, crack depth, etc., and performs linear regression analysis. Finally, the predicted value of the cable aging degree is output, and the aging threshold is set to 0.8. When the predicted value exceeds the threshold, the aging warning is triggered. Through this process, the monitoring of the cable aging state is completed, and the corresponding warning tasks are performed according to the real-time monitoring data.

[0105] Preferably, step S1 specifically comprises:

[0106] Step S11: acquiring cable monitoring data, and performing resistance monitoring feature extraction to obtain resistance monitoring data;

[0107] In this embodiment, the resistance data of the cable is obtained in real time by the cable monitoring system. A high-precision current and voltage sensor is used to record the current and voltage values ​​of the cable once per second. The resistance is calculated using Ohm's law based on the relationship between current and voltage. In this process, the current input range is set to 0-1A, and the voltage input range is 0-10V. In order to ensure the accuracy of the resistance data, the sampling frequency of the sensor is set to 1Hz to ensure that the current and voltage data are collected once per second. The resistance value of the cable is averaged by multiple measurements to generate stable resistance monitoring data. These data are used for subsequent analysis, especially resistance change trend and rate calculation. The resistance data is transmitted and stored in real time by the data acquisition system, providing raw data support for the analysis of subsequent steps.

[0108] Step S12: performing rate calculation on the resistance monitoring data to obtain high rate resistance data;

[0109] In this embodiment, by calculating the change in resistance at different time points, the rate of resistance change in each time period is obtained. For example, set every 1 second as a time period, and calculate the change in resistance by comparing the resistance data at the current moment with the resistance data at the previous moment, and then divide it by the time interval (i.e. 1 second). This rate calculation process needs to ensure the continuity and accuracy of the data, so the data sampling frequency is set to 1 time per second to capture the details of the resistance change. If the resistance change rate exceeds the set threshold (such as 0.05Ω / s), these cables are marked as high-rate resistance data. During the rate calculation process, a low-pass filtering algorithm is used to remove high-frequency noise to ensure the stability of the data. The rate data is then passed to the next step to provide a basis for subsequent cable screening.

[0110] Step S13: acquiring a high resistance rate cable based on the high rate resistance data;

[0111] In the present embodiment, according to the high-rate resistance data, the cable is screened to select the cable whose resistance change rate exceeds the predetermined threshold. During the screening process, the resistance rate is compared one by one using an automated data processing system. If the resistance rate value of a certain cable exceeds the set threshold value (e.g., 0.05Ω / s), it is classified as a high-resistance rate cable. The screening process is performed using data processing software (such as the pandas library of MATLAB or Python), and the resistance rate threshold value set by the program is 0.05Ω / s, and the cable exceeding this value will be marked as a high-resistance rate cable. During the screening, the resistance rate data of each cable needs to be compared with the set threshold value in turn and automatically classified. After the screening is completed, the high-resistance rate cable is extracted separately for subsequent processing.

[0112] Step S14: performing oxidation environment simulation on the high resistance rate cable to obtain an oxidized cable;

[0113] In this embodiment, the simulation experiment is carried out under a specific temperature and humidity environment to accelerate the oxidation process of the cable. The control of the oxidizing environment is achieved by an oxidation box, and the set ambient temperature is 60°C and the relative humidity is 85%. The high resistance rate cable is placed in this environment for 48 hours of exposure. The oxidation box is filled with oxygen and water vapor to simulate the situation of the cable being exposed to an oxidizing environment for a long time. During this process, the resistance change of the cable is recorded every 2 hours, and the degree of oxidation on the cable surface is inferred by the change trend of the resistance. The recorded data includes ambient temperature, humidity, exposure time and cable resistance data. After the experiment is completed, the cable sample is taken out and sent to the next step for oxygen spectrum analysis.

[0114] Step S15: performing oxygen spectrum analysis on the oxidized cable to obtain oxygen spectrum data;

[0115] In this embodiment, an X-ray photoelectron spectrometer (XPS) is used to analyze the surface of the oxidized cable to measure the oxygen content and its chemical state. During the analysis, the scanning energy range of the instrument is set to 0 to 600 electron volts, and the resolution is 1 electron volt. After the cable surface is cleaned, the XPS instrument is used to expose the cable surface and obtain an oxygen spectrum. Through the oxygen spectrum, the components of the oxide layer on the surface of the cable can be identified, including the content of oxides and hydroxides. At the same time, XPS analysis can measure the thickness of the oxide layer on the surface of the cable, providing a basis for subsequent oxidation judgment. During the analysis, the instrument records the chemical state of the oxide on the surface of the cable and its distribution characteristics. After the oxygen spectrum data is recorded, it is sent to the next step to determine whether the cable conductor is oxidized.

[0116] Step S16: Conductor oxidation determination of the high resistance rate cable is performed according to the oxygen energy spectrum data to obtain conductor oxidation data.

[0117] In this embodiment, the content and chemical state information of the oxide are extracted based on the oxygen spectrum data obtained by XPS. According to the set oxidation judgment standard, if the oxide content exceeds 50%, it is considered that the cable has undergone significant oxidation. During the judgment process, the oxidation threshold is set to an oxide content of more than 50%, and the cables are classified according to this standard. If the oxide content is lower than this value, it is judged that no significant oxidation has occurred. In the oxygen spectrum analysis results, the content and chemical state characteristics of the oxide are used as the basis for judgment. By comparing with the unoxidized cable, it is finally concluded whether the cable has oxidation phenomenon, and the result is recorded as "oxidized" or "unoxidized". This data serves as the basis for cable maintenance and aging monitoring, and provides decision support for subsequent processing and repair.

[0118] Preferably, step S15 is specifically as follows:

[0119] Step S151: cleaning the oxidized cable sample to obtain a cleaned oxidized cable;

[0120] In this embodiment, deionized water and an ultrasonic cleaner are used to clean the surface of the oxidized cable. The oxidized cable is first soaked in deionized water for 10 minutes to ensure that the surface impurities are dissolved. Then, the cable is placed in an ultrasonic cleaner for ultrasonic cleaning, with the frequency set to 40kHz and the cleaning time set to 5 minutes. During the ultrasonic cleaning process, the surface of the cable is subjected to high-frequency vibrations, which help remove oxide layer fragments and other impurities attached to the surface. After cleaning, the cable needs to be wiped with a clean cotton cloth and blown dry with nitrogen to ensure that there is no moisture remaining on the surface. This cleaning step is intended to remove impurities on the surface that interfere with subsequent analysis to ensure the accuracy of the data in subsequent steps.

[0121] Step S152: applying an accelerating voltage to the cleaned oxidized cable to obtain an accelerating voltage cable;

[0122] In this embodiment, an accelerating voltage is applied to the clean oxidized cable. A certain voltage is applied to the cleaned oxidized cable by a high voltage power supply, and the voltage is set to 1000V. The voltage is transmitted to its surface through the conductor of the cable to simulate the performance of the cable in a high voltage environment. The acceleration voltage is applied for 30 seconds to ensure that the cable can withstand the load caused by the acceleration voltage and to avoid damage to the cable due to excessive voltage. During this process, the voltage change is monitored to ensure that the surface voltage of the cable does not exceed the set safety value. After the voltage is applied, the power supply is immediately disconnected and the cable is transferred to the next step of processing.

[0123] Step S153: irradiating the accelerating voltage cable with an electron beam, and collecting radiation released from the accelerating voltage cable to obtain the released radiation;

[0124] In this embodiment, an electron beam accelerator is used to irradiate the accelerating voltage cable. The electron beam energy is set to 20 keV and the beam current density is set to 5 μA / cm 2 To ensure that the cable surface can be adequately irradiated with electrons, while avoiding unnecessary damage to the cable material caused by excessive doses. During the irradiation process, the electron beam penetrates the surface of the cable and releases radiation. The data released by the radiation is collected in real time by the detector, which uses a highly sensitive radiation detector that can accurately record the intensity and energy of the radiation. The radiation collection time is 5 seconds to ensure that sufficient radiation data is obtained.

[0125] Step S154: performing element characteristic peak energy spectrum detection on the released rays to obtain element energy spectrum data;

[0126] In this embodiment, an energy spectrum analyzer is used to detect the rays obtained from step S153. The energy spectrum analyzer uses a high-resolution detector, and the working energy range is set to 0-2000eV to cover the characteristic peaks of the elements that appear. When the rays pass through the detector, the energy is converted into signals and analyzed, and the system identifies the elements based on the characteristic peaks of the rays. The acquisition time is set to 20 seconds to ensure that enough signals are captured and a comprehensive energy spectrum analysis is completed. In this process, the characteristic peaks of the elements are matched according to the energy positions of the known elements, such as the characteristic peaks of the oxygen element in the range of 500-550eV, which are used as a sign to determine the oxygen element.

[0127] Step S155: identifying the oxygen element according to the element spectrum data to obtain the oxygen element spectrum data;

[0128] In this embodiment, the element characteristic peak energy spectrum data is used to classify each element through the energy spectrum analysis software. In the energy spectrum, the characteristic peak of the oxygen element is located near 530eV, so the energy spectrum is cut and analyzed according to the energy position. If an obvious peak appears at this energy position, it is judged that the oxygen element exists. The threshold for oxygen element identification is set to a peak height exceeding 1000 counts, that is, if the characteristic peak height of the oxygen element reaches or exceeds this value, its existence is confirmed. All signals that meet this condition are marked as oxygen element signals, and the relevant energy information is recorded.

[0129] Step S156: measuring the oxidation degree of the oxygen element energy spectrum data to obtain oxidation degree data;

[0130] In this embodiment, the height of the characteristic peak of the oxygen element is measured to set a quantitative standard for the degree of oxidation. For example, if the height of the characteristic peak of the oxygen element is between 1000-2000 counts, the degree of oxidation is determined to be mild oxidation; if the peak value of the oxygen element is between 2000-3000 counts, it is determined to be moderate oxidation; if the peak value of the oxygen element exceeds 3000 counts, it is determined to be severe oxidation. During the oxidation degree measurement process, the oxidation peak value of each cable is compared with the standard to obtain the oxidation level of the cable.

[0131] Step S157: integrating the oxygen spectrum according to the oxygen element spectrum data and the oxidation degree data to obtain oxygen spectrum data.

[0132] In this embodiment, the energy spectrum data and oxidation degree measurement results of all oxygen elements are collected and integrated into a comprehensive data table. The integration process is completed by data processing software (such as Excel or MATLAB). First, the oxygen energy spectrum data and oxidation degree data of each cable are classified according to the cable number to ensure that the oxidation information of each cable can accurately correspond. Then, the oxidation degree of each cable is combined with its corresponding oxygen element energy spectrum diagram to obtain complete oxygen energy spectrum data. The integrated data provides complete oxidation information for each cable, including the energy spectrum peak of the oxygen element and its corresponding oxidation degree, to support subsequent cable performance evaluation and aging prediction analysis.

[0133] Preferably, the cable morphology type classification includes:

[0134] Extract cable images of cable monitoring data;

[0135] In this embodiment, the cable image is collected by installing a high-precision industrial camera or a digital image acquisition device. The camera uses an image sensor with a resolution of not less than 300dpi, and sets the image capture angle to between 0 and 30 degrees to ensure that the surface details of the cable can be clearly recorded. The cable is placed on a dedicated workbench, and a white uniform light source is installed on the table to ensure uniform light exposure to eliminate the influence of shadows. When collecting images, the light intensity needs to be set to 300 to 350lx to avoid excessive brightness or darkness affecting the image quality. After the data collection is completed, the image data is transmitted to the processing system for storage and preliminary preprocessing, and saved in grayscale image format. This step ensures that the collected image data is of high quality and can provide clear image input for subsequent processing steps.

[0136] Calculate the curvature of the cable image;

[0137] In this embodiment, the Canny edge detection algorithm is used by performing edge detection on the extracted cable image. First, the low threshold of the Canny algorithm is set to 100 and the high threshold is set to 200 to extract the main edge features in the image. After the edge detection is completed, the curvature is calculated for each edge. First, according to the coordinates of each edge point in the image, the curvature value of each point is calculated by numerical differentiation. Specifically, the curvature is obtained by the derivative and second-order derivative of the curve, and the three-point difference method is used for numerical calculation. During the curvature calculation process, it is necessary to set the neighborhood size of each point to 3×3 pixels to ensure the detailed capture of the curve shape. Finally, the distribution of the curvature is displayed by the curvature map, and the curvature values ​​of different areas in the image are obtained.

[0138] Recognize the curve shape of cable images based on curvature;

[0139] In this embodiment, after calculating the curvature of the cable image, a certain threshold range is set for different curvature values ​​to classify curves of different shapes. The threshold of the curvature is set to ±0.05, and the area where the curvature value exceeds this range can be marked as the turning point or sharp bend of the curve. According to the set threshold, the curvature data is clustered to extract the curve shape in the continuous area. For smooth curves, the curvature value should be kept near 0, while for sharp turns or ring structures, the curvature value will fluctuate greatly. According to the curvature distribution map of the curve, the contour extraction algorithm is further used to mark the specific position and shape of each curve, so as to achieve accurate recognition of the curve shape of the cable image.

[0140] Detect the helicity of the cable image;

[0141] In this embodiment, to detect the helicity of the cable image, it is first necessary to extract the cable contour in the image and determine the starting and ending points of the cable through a contour tracking algorithm. The coordinates of the starting point of the cable are set to the coordinates of the upper left corner of the image, and the coordinates of the ending point are set to the coordinates of the lower right corner of the image. Then, based on the coordinate data of the image, a multi-point helicity analysis is performed, and a point is taken every 10 pixels, and the degree of the spiral is judged by calculating the changes in the polar coordinates of these points. In the specific calculation, the angle change threshold of each point is set to ±2 degrees. When the angle change exceeds this range, the area is considered to have a spiral feature. At the same time, the intensity of the helicity is further confirmed by comparing the radial changes between different points. Based on these detection results, the spiral structure of the cable image is marked.

[0142] Identify the ring structure of the cable image;

[0143] In this embodiment, the recognition of the ring structure depends on the shape characteristics of the cable image. The closed curve area is extracted through the edge detection results of the image. The contour features of the closed curve are set, requiring that the distance between the endpoint and the starting point of the curve is less than 3 pixels, and the curvature of the curve is greater than 0.1, as the basis for judging the ring structure. For the ring area, the ratio of its area to the circumference is further calculated. If the ratio is greater than 1.5, it is judged as a ring structure. For each identified ring structure, its center coordinates, radius, rotation angle and other parameters are recorded to provide necessary information for subsequent steps.

[0144] The winding structure is integrated according to the helicity and the ring structure to obtain the winding structure;

[0145] In this embodiment, after completing the detection of the helicity and the annular structure, a merging rule of the spiral and the annular structure is set for the identified spiral and annular parts. If the cable image contains both the spiral part and the annular part, the junction point of the two is determined based on the pitch of the spiral part and the diameter of the annular part. The judgment criteria for setting the junction point are that the distance between the starting point of the spiral part and the annular structure is less than 10 pixels, and the change value of the helicity is greater than 0.2. According to these conditions, the spiral structure and the annular structure are integrated to form a complete winding structure. The structure will be marked as the winding part of the cable in the image, and the parameters of the winding area such as diameter, angle and other data will be recorded.

[0146] Perform curve segment cable recognition on the cable image according to the curve shape to obtain curve segment cable data;

[0147] In this embodiment, according to the previously calculated curvature and the identified curve shape, the judgment standard of the curved segment cable is set as the curve shape is smooth and the curvature change is less than ±0.05. On this basis, by analyzing each curve in the cable image, the areas with small continuous curvature changes are detected, and these areas are divided into curve segments. In specific implementation, by calculating the connection angle of the two end points of the curve segment and the extreme difference of the curvature change, if the curvature change is not large and the connection angle is less than 30 degrees, it is considered that the part belongs to the curved segment cable. For each identified curve segment, its starting and ending positions, curvature mean and other information are recorded, and finally the parameter data of the curved segment cable is obtained.

[0148] The cable image is subjected to winding segment cable identification according to the winding structure to obtain winding segment cable data.

[0149] In this embodiment, based on the previously identified winding structure, the identification standard of the winding segment cable is set as the existence of the spiral structure of the cable and the pitch is within a certain range, and the curve angle in the area changes greatly. Using the pitch range as the basis for judgment, the pitch range is set to 1.5 to 3 mm, and the pitch of each spiral structure in the image is calculated to determine whether it is a winding segment. If it meets the standard, the specific data of the winding segment is extracted according to the change of the curvature of the winding area. The starting position, end position and overall structural parameters of each winding segment are recorded. Finally, the complete winding segment cable data is obtained to ensure that the identification process of the winding segment cable is accurate.

[0150] Preferably, the bending fatigue analysis includes:

[0151] Extract curved material properties of cable data for curved segments;

[0152] In this embodiment, extracting the bending material properties of the curved segment cable data first requires analyzing the physical properties of the constituent materials of the cable. For each curved segment, first obtain the material type of the cable, common materials such as copper, aluminum or specific alloys, etc., and use the known material data table to obtain the basic properties of the material such as elastic modulus and yield strength. Then, measure the bending stiffness of the cable under specific conditions, use the three-point bending test method, set the bending test device, where the bending radius is set to 30mm, and apply a continuous load, gradually increase the load until obvious deformation occurs. By recording the applied force and the resulting displacement, the bending stiffness of the cable is calculated, and combined with the plastic deformation characteristics of the material, the bending material properties of the curved segment, such as yield stress, maximum tolerable bending angle and other parameters, are obtained. In addition, the durability data of the cable needs to be considered for subsequent analysis of its bending fatigue performance.

[0153] Repeated bending simulation based on bending material properties and curved cable data;

[0154] In this embodiment, after obtaining the bending material properties of the cable, a repeated bending simulation is performed. During the simulation process, the initial bending state of the cable must first be set, and its initial bending stress is determined based on the geometry of the curve segment (such as radius and angle) and the applied load. The simulation uses finite element analysis (FEA) technology, and repeated bending simulations are performed through software such as ANSYS. The amplitude and load of each bending gradually increase, and each bending process simulates the bending angle from the initial state to 45 degrees. After 1000 reciprocating bends, the applied force and bending angle will be set to constant values. During each simulation, the system records data such as stress distribution, deformation, and restoring force during cable bending. The simulation requires accurate modeling of the elastic and plastic behavior of the bending material, especially accurate simulation of the nonlinear elastic characteristics and yield stress range of the material, to ensure that the results of repeated bending can fully reflect the performance of the material in long-term use.

[0155] Calculate bending stress for repeated bending simulations;

[0156] In this embodiment, after completing the repeated bending simulation, the bending stress is calculated using finite element simulation data. During the simulation process, for each bend, the bending stress distribution is automatically generated by the simulation software, and the stress value of each node is recorded. For each node, its maximum bending stress is calculated, and a bending stress threshold is set to 150MPa. If the stress at the node exceeds the threshold, the part will be marked as a high stress area. According to the simulation data, the average stress value of the entire curve segment is calculated, and the difference between the maximum stress and the minimum stress is recorded to obtain the overall bending stress state of the cable segment under repeated bending. In addition, it is also necessary to consider the stress concentration areas, conduct a detailed analysis of the stress fluctuations in these areas, clarify which parts have larger stress changes, and whether there is a risk of fatigue damage due to stress concentration.

[0157] Analyze the fatigue degree of cables in curved sections based on bending stress;

[0158] In this embodiment, the fatigue limit is set to 50% of the yield stress of the cable material. For example, if the yield stress of the cable is 300MPa, the fatigue limit is set to 150MPa. On this basis, the number of times the bending stress exceeds the fatigue limit in the repeated bending simulation is analyzed. If the bending stress of a certain section of cable exceeds 150MPa more than 500 times, the section of cable is considered to have a higher fatigue risk. The fatigue strength index is calculated by statistically analyzing the changes in stress values ​​each time during the simulation process. The index reflects the fatigue degree of the cable under repeated bending conditions. The higher the index, the more severe the fatigue damage. The results of the fatigue degree analysis will provide a basis for subsequent damage prediction and material selection.

[0159] Analyze the damage accumulation degree of the cable in the curved section according to the fatigue degree;

[0160] In this embodiment, based on the fatigue degree analysis, the degree of damage accumulation is further calculated. The calculation method of damage accumulation is set to the linear damage accumulation method, in which the calculation formula of the damage degree is the ratio of the damage caused by each bending to the total number of bending times. For each bending process, if the stress exceeds the fatigue limit, the damage degree will increase, and the fatigue life of the material is judged based on the accumulated damage degree. The damage coefficient is set to 0.01. After the cable undergoes a certain number of bendings, the damage coefficient gradually accumulates. For each bending in which the stress exceeds the fatigue limit, the damage degree should be calculated and gradually increased during the accumulation process. Assuming that after 1000 bendings, the degree of damage accumulation is 20%, the degree of damage of the cable will increase significantly, indicating that microcracks inside the material have been initially formed and the structure of the cable has begun to degenerate.

[0161] The fatigue degree and damage accumulation degree are integrated to obtain the bending fatigue data.

[0162] In this embodiment, the fatigue degree and the damage accumulation degree are synthesized according to certain weights, and the weight of the fatigue degree is set to 70%, and the weight of the damage accumulation degree is set to 30%. Based on the synthesis of these two data, a comprehensive fatigue data is obtained to describe the overall fatigue effect of the cable during long-term repeated bending. The larger the comprehensive data, the more serious the damage to the cable, and vice versa, the better the durability of the cable. Combined with the actual working environment of the cable, a critical value can be set. For example, if the comprehensive fatigue data exceeds 80%, the cable needs to be inspected or replaced. These bending fatigue data will serve as an important basis for evaluating the fatigue performance of cables in actual production processes.

[0163] Preferably, the winding stress analysis includes:

[0164] Extract the geometric shape of the winding cable data and build a winding model;

[0165] In this embodiment, the geometric shape of the cable data of the winding segment is extracted. First, the appearance of the cable needs to be imaged. A high-resolution industrial camera is used to collect images of the cable at different positions. The image resolution is set to 300dpi to ensure that every detail of the cable can be accurately recorded. After the image is collected, the geometric shape of the winding cable is extracted by image processing technology. First, the Canny edge detection algorithm is used to detect the edge of the image, and the low threshold is set to 50 and the high threshold is set to 150 to ensure that the winding lines of the detected cable are clearly visible. Subsequently, the Hough transform algorithm is used to identify the curved structure of the winding cable. According to the identified winding path, the geometric shape of each winding segment is extracted, and the radius, angle, and spacing between cables of each winding segment are calculated. When constructing the winding model, the finite element analysis method is used to simulate the winding structure based on the extracted geometric features, and the bending stiffness, material properties and geometric parameters of the winding segment are defined. Through the establishment of the model, the stress and mechanical properties of the winding segment can be further analyzed.

[0166] Calculate the axial tensile stress of the winding section cable data;

[0167] In this embodiment, when calculating the axial tensile stress of the winding segment cable data, it is first necessary to determine the tensile direction of the cable and the external force applied. Assuming that the tensile direction of the winding segment cable is parallel to the axial direction of the cable, the stress response of the cable in the axial direction is obtained through a mechanical tensile test. In this experiment, the cable is fixed at both ends and a uniform tensile force is applied. The tensile force is set to start from 50N and gradually increase to 300N, and the axial deformation of the cable is measured. Based on the tensile force and deformation, the axial stress is calculated using Hooke's law. In order to ensure the accuracy of the measurement, the rate of increase of the tensile force is set to 10N per second to ensure the stability of the experimental data. In actual calculations, the cross-sectional area of ​​the cable is used as the basic data for calculating stress, and the cross-sectional area of ​​the cable can be obtained by accurately measuring the diameter of the cable. The final axial tensile stress reflects the tensile behavior of the cable under stress and the elastic properties of its material.

[0168] Calculate the torsional stress of the winding section cable data;

[0169] In this embodiment, for the calculation of the torsional stress of the winding segment cable, it is necessary to first determine the torsional angle of the cable and the applied torque. Through the torsion experiment, the cable is torsioned within a certain angle range, and the torque is gradually increased from 5Nm to 50Nm, and the torque applied each time increases in increments of 10Nm. In the experiment, one end of the cable is fixed, and the torque is applied to the other end and the torsion angle is monitored in real time by the angle sensor. After each increase of 5° in the torsion angle, the corresponding torque value and torsion angle are recorded. Using the standard calculation formula for torsional stress, the stress value of each torsion point is calculated according to the applied torque and the geometric parameters of the cable (such as the cable radius and cross-sectional shape). In the actual calculation, the physical properties of the cable material such as shear modulus and yield stress are taken into account to ensure the accuracy of the torsional stress calculation within the entire torsion range. By accumulating and analyzing the stress values ​​at different torsion angles, the stress distribution of the winding segment cable in the torsion state is obtained.

[0170] Calculate radial compressive stress of winding section cable data;

[0171] In this embodiment, when calculating the radial compressive stress, it is first necessary to analyze the outer diameter of the cable and the applied radial compressive force. Through experiments, a device with a constant compressive force is used to perform a compression test on the wound cable segment. The compression force range is set to 10N to 150N, the pressure is gradually increased, and the radial deformation of the cable is measured. The radial compressive stress is calculated by measuring the relationship between the deformation and the applied force. In the specific operation, the step size of the compression force increase is set to 20N, and a linear displacement sensor is used after each compression step to detect the radial deformation after compression. Using these data, combined with the cross-sectional area of ​​the cable, the stress formula is applied to calculate the radial stress of each point. At the same time, considering the non-uniformity of the internal structure of the cable, it is necessary to calculate the distribution of the radial stress at each different position. Finally, a stress distribution diagram of the cable in a compressed state is generated based on the measured data.

[0172] Integrate axial tensile stress, torsional stress and radial compressive stress to obtain stress data;

[0173] In this embodiment, after completing the calculation of axial tensile stress, torsional stress and radial compressive stress, the stress data is integrated. During the integration process, the calculation results of each stress type are first weighted, and the weight of axial tensile stress, torsional stress and radial compressive stress are set to 50%, 30% and 20% respectively. These three stress values ​​are combined into a comprehensive stress data by weighted average method. For each stress data, it needs to be standardized before integration so that different types of stress values ​​are comparable. The standardization method is to subtract the mean value of each stress value and divide it by the standard deviation to ensure that the data has a unified dimension. Finally, the integrated stress data will be used for further structural analysis and performance evaluation.

[0174] Based on the stress data, winding seams of the winding model are identified to obtain winding seam stress data;

[0175] In this embodiment, when the winding seams of the winding model are identified, it is first necessary to automatically detect the seams of the winding segments through an image processing algorithm. Morphological operations, such as expansion and corrosion, are used to identify the contours of the winding seams, and the edge detection algorithm is used to accurately extract the seam area. The threshold for seam identification is set to a strength difference of more than 10% between the seam and the surrounding material to ensure recognition accuracy. After the seam area is identified, based on the previously obtained stress data, the stress concentration at the seam is analyzed in detail. The seam stress data is obtained by calculating the stress value of the seam area and comparing it with the surrounding area. The seam portion with a stress value greater than the surrounding material is marked as a high-risk area, providing a basis for further structural optimization and strength enhancement.

[0176] Based on the stress data, the stress difference between the inner and outer layers of the winding model is analyzed to obtain the stress difference data between the inner and outer layers;

[0177] In this embodiment, when performing the stress difference analysis of the inner and outer layers, it is first necessary to distinguish the inner and outer layer structures of the winding model. By accurately measuring the thickness of the winding model, the outer layer is defined as the outermost layer of cable, and the inner layer is defined as the second or more layers of cable. For each layer, its stress value is calculated, and the stress difference between the inner and outer layers is compared. Through layered calculation and comparison, the stress difference data of the inner and outer layers is obtained. The threshold of the stress difference between the inner and outer layers is set to 5MPa. If the stress difference between the inner and outer layers exceeds the threshold, it is considered that the stress distribution between the inner and outer layers is uneven, and structural adjustment or reinforcement is required. By analyzing the multi-layer winding model layer by layer, the stress distribution between the inner and outer layers can be accurately understood, providing a basis for further optimization of the winding structure.

[0178] The winding stress data is obtained by integrating the winding seam stress data and the inner and outer layer stress difference data.

[0179] In this embodiment, after completing the calculation of the winding seam stress data and the inner and outer layer stress difference data, data integration is performed. The weighted average method is used to integrate the two types of data, and the weight of the winding seam stress data is set to 60%, and the weight of the inner and outer layer stress difference data is set to 40%. During integration, the two types of data are first standardized to ensure the comparability of the data. Then, according to the set weights, the two types of data are weighted averaged to obtain the final winding stress data. This data will reflect the overall stress state of the winding model, which will be used for further mechanical analysis and fatigue assessment to ensure that the winding section cable can meet the structural strength requirements during operation.

[0180] Preferably, step S3 specifically comprises:

[0181] Step S31: locating the damaged area according to the material damage data to obtain the damaged area;

[0182] In this embodiment, it is necessary to conduct a preliminary analysis through the sensor data of the cable. Damage detection is performed on multiple monitoring points of the cable, and multiple data collection methods such as strain sensors, temperature sensors and piezoelectric sensors are used to set damage thresholds. When the monitored strain or temperature changes exceed the set standard (such as the strain value exceeds 1.5×10^-3 or the temperature exceeds 70°C), it is considered that the area is damaged. All collected data will be analyzed by the data processing unit, and the noise signal will be removed and the effective data will be enhanced through signal amplification and filtering operations. By setting threshold standards, the damaged areas are screened out and the positions of these areas are located. These positions are marked by a digital coordinate system to obtain specific damaged areas. These positioning data provide the basis for subsequent steps such as acoustic wave sensing measurement and crack detection.

[0183] Step S32: performing acoustic wave sensing measurement on the damaged area to obtain acoustic wave sensing data;

[0184] In this embodiment, when performing acoustic wave sensing measurement on the damaged area, it is first necessary to install the acoustic wave sensor at different positions of the cable, especially concentrated in the damaged area. The working principle of the acoustic wave sensor is based on the propagation characteristics of sound waves. When the sensor sends an acoustic wave signal and receives a signal back, the propagation speed, reflection intensity and arrival time of the signal will be affected by changes in the cable material and structure. Set the acoustic wave signal frequency to 10MHz, and perform multi-point measurements around the damaged area. During the transmission of the acoustic wave signal, if the propagation time of the signal is greater than the normal value, or the echo signal is weakened, it indicates that there are cracks or other damage. When collecting data, a combination of time domain analysis and frequency domain analysis is used. By analyzing the propagation characteristics of the acoustic wave signal, the specific acoustic wave reflection data of the damaged area can be accurately obtained. These data will be further processed to remove irrelevant signals and save the acoustic wave signal information of the damaged area to provide a basis for subsequent positioning of the damaged position.

[0185] Step S33: triangulate the damage position according to the acoustic wave sensing data to obtain damage position data;

[0186] In this embodiment, when triangulating the damaged position, it is first necessary to arrange at least three groups of acoustic wave sensors around the damaged area to ensure that the relative positions between the sensors can form a triangle or polygon. Each sensor will send an acoustic wave signal to the cable and receive an echo. The propagation speed of the acoustic wave signal is set to 1500 meters per second, and the propagation time of the acoustic wave is recorded. The location of the damage is calculated using the triangulation method based on the echo time difference received by the sensor. First, the distance reached by the signal is determined based on the signal propagation time difference received by each sensor. By cross-comparing the signal propagation time difference between each two sensors, positioning calculations are performed, and the positioning results are optimized through geometric algorithms (such as the least squares method) to obtain the specific location coordinates of the damage. These location data will be matched with the structural model of the cable through the coordinate system to obtain the location of the damaged point and provide detailed geographic information for the next step of cable marking and crack detection.

[0187] Step S34: marking the damaged cable based on the damage position data and determining it as a damaged cable;

[0188] In this embodiment, when marking the damaged cable according to the damaged position data, firstly, the range of the damaged area is accurately demarcated in the actual structure diagram of the cable according to the damaged position coordinates obtained by triangulation. Each section of the cable is equipped with a sensor data recorder to record the real-time data of the damage. After obtaining the damaged position, the damaged position data is transmitted to the control system by wireless data transmission equipment, and the system accurately determines the starting point and end point of the damaged cable through the positioning algorithm. The cable in the damaged area is marked, and the cable marking method is set to a digital coding system. The number related to the damage is set by programming, and the physical mark is made on the surface or joint of the cable. The marking of the damaged area can not only be confirmed by visual recognition, but also virtually marked by a computer system for tracking during subsequent inspection and maintenance. Through these marks, maintenance personnel can accurately identify the damaged cable, avoid unnecessary misoperation, and ensure that subsequent detection and repair work is completed efficiently.

[0189] Step S35: Perform crack detection on the damaged cable to obtain crack data.

[0190] In this embodiment, crack detection technology such as laser scanning or ultrasonic detection is required. Before crack detection, the cable surface needs to be cleaned first to remove surface dirt and other debris to avoid affecting the detection results. The laser scanner emits a laser beam and measures the reflection time between the laser beam and the cable surface, so as to accurately obtain the geometric shape of the tiny cracks on the cable surface. For the cable parts that cannot be directly contacted, ultrasonic sensors are used for detection. The ultrasonic signal propagates through the cable surface, and when it encounters a crack or defect, an echo reflection occurs. The sensor records the echo time and calculates the specific location, depth and width of the crack through the change of the echo signal. The detection result of each crack is stored by the data processing system and associated with the damage location data to ensure the accuracy of each crack information. After the detection, a crack data report is generated, which includes the location coordinates of the crack, the type of crack and the specific size of the crack. The crack data provides a detailed basis for subsequent repair work and helps maintenance personnel determine the severity of the crack.

[0191] Preferably, step S35 is specifically as follows:

[0192] Step S351: applying an alternating magnetic field to the damaged cable to obtain alternating magnetic field data;

[0193] In this embodiment, when applying an alternating magnetic field to a damaged cable, it is first necessary to select a suitable magnetic field generator, such as an electromagnetic coil or a high-frequency transformer, to generate an alternating magnetic field by controlling its frequency and current amplitude. The magnetic field generator needs to set the frequency of the alternating magnetic field to 1kHz, and adjust the current amplitude to 5A to ensure that a magnetic field of sufficient strength is generated around the cable. The direction and intensity of the magnetic field should be evenly distributed to ensure that a consistent effect can be exerted on the entire cable. Then, a magnetic field sensor is used to monitor the alternating magnetic field in real time, record the fluctuation of the magnetic field, capture the changes in the magnetic field intensity at each moment, and perform data acquisition. The magnetic field sensor accurately records the data of the alternating magnetic field by synchronously measuring the relative movement and position changes of the cable. Through the real-time acquisition of the alternating magnetic field data, the magnetic field distribution in the damaged area of ​​the cable can be obtained, providing an important basis for the subsequent acquisition of eddy current data.

[0194] Step S352: performing ring current detection according to the alternating magnetic field data to obtain eddy current data;

[0195] In this embodiment, when the ring current is detected according to the alternating magnetic field data, it is first necessary to use an eddy current sensor to detect the ring current. The working principle of the eddy current sensor is based on Faraday's law of electromagnetic induction. When the alternating magnetic field acts on the conductor, an induced current, i.e., eddy current, will be generated on the surface of the cable. The sensor needs to be set to detect the amplitude and frequency changes of the eddy current to ensure that the eddy current signal generated by the damaged part of the cable can be accurately captured. The eddy current sensor should maintain a certain distance from the cable surface (usually 1-2 mm) and set a suitable frequency range, such as between 100kHz and 1MHz, to ensure that the subtle changes on the cable surface can be accurately reflected. Through the eddy current data received by the sensor, the eddy current amplitude of the damaged area of ​​the cable can be analyzed, and the signal strength and frequency changes at each moment can be recorded. The change of the eddy current signal reflects the physical state inside the cable, which provides an important basis for subsequent crack detection and depth assessment.

[0196] Step S353: performing signal spectrum conversion based on the eddy current data to obtain an eddy current signal spectrum;

[0197] In this embodiment, when the signal spectrum is converted according to the eddy current data, it is first necessary to perform frequency domain analysis on the original signal collected by the eddy current sensor. The time domain signal is converted into a frequency domain signal using Fourier transform so as to more clearly observe the changing characteristics of the eddy current signal. Eddy current signals usually contain multiple frequency components, some of which change with the presence of cracks or defects, so it is necessary to denoise the signal, remove high-frequency noise by setting a low-pass filter, and limit the signal to the frequency range of concern (such as 1kHz to 500kHz). Then, the processed signal data is converted into a signal spectrum through graphical software (such as MATLAB or LabVIEW), and the spectrum shows the frequency distribution of the eddy current signal, with the horizontal axis being the frequency and the vertical axis being the signal intensity. In the signal spectrum, the signal difference between the damaged cable area and the normal cable area can be clearly seen, and these signal differences can help locate the specific location and type of damage.

[0198] Step S354: performing amplitude attenuation analysis based on the eddy current signal spectrum to obtain amplitude attenuation data, and performing crack depth assessment to obtain crack depth data;

[0199] In this embodiment, when performing amplitude attenuation analysis, it is first necessary to extract amplitude data within a specific frequency range from the eddy current signal spectrum, and pay attention to the attenuation of signal intensity. According to the theory of eddy current signals, cracks or defects will cause the eddy current signal to attenuate during propagation, so by analyzing the amplitude change of the signal, the depth of the crack can be inferred. The amplitude of the eddy current signal is compared with the signal at different positions on the cable surface, the amplitude of the signal attenuation is recorded, and the attenuation data is combined with the geometric dimensions and material properties of the cable to calculate the depth of the crack. For example, if the amplitude attenuation of the eddy current signal at a certain position is 50%, it can be inferred that the depth of the crack at that position is close to 1mm, and the depth estimation should take into account the conductivity of the cable material and the sensitivity of the sensor. By analyzing the amplitude attenuation of crack areas of different depths, complete crack depth data is finally obtained.

[0200] Step S355: performing signal peak analysis based on the eddy current signal spectrum to obtain signal peak data, and determining the crack boundary to obtain crack boundary data;

[0201] In this embodiment, when performing signal peak analysis based on the eddy current signal spectrum, it is first necessary to identify the peak point of the eddy current signal in the signal spectrum. The peak value usually corresponds to the strongest part of the signal, and this part of the signal is often closely related to cracks or other defects on the cable surface. By analyzing the eddy current spectrum, the frequency region where the peak value is located is found, and the specific amplitude and position of each peak value are recorded. For each peak value, the range of the peak value is determined by an algorithm, and the specific boundary of the crack is inferred. For example, when the peak value of the signal changes suddenly or drastically, it usually indicates the existence of a crack boundary. By classifying, sorting and calculating these peak value data, the starting point and end point of the crack can be clearly identified, thereby determining the boundary of the crack. These boundary data provide accurate physical references for subsequent crack repair and further analysis.

[0202] Step S356: performing amplitude change detection based on the eddy current signal spectrum to obtain amplitude change data, and performing crack length estimation to obtain crack length data;

[0203] In this embodiment, when performing amplitude change detection, it is first necessary to record the amplitude change of the eddy current signal at different time points or different positions through the eddy current signal spectrum. The amplitude change of the eddy current signal is usually proportional to the length of the crack inside the cable. Therefore, by detecting the amplitude change, the length of the crack can be inferred. By calculating the rate of change of the eddy current signal amplitude at different positions, the areas where the signal amplitude changes significantly are identified, and these areas usually correspond to the presence of cracks. In areas where the signal changes greatly, the length of the crack is estimated by further analyzing the distribution of amplitude attenuation, combined with the depth of the crack and the signal intensity. For example, if the amplitude changes by more than 50%, the length of the crack is close to 10% of the length of the cable. Finally, the accurate length data of the crack is obtained through a comprehensive analysis of the eddy current signal amplitude change data.

[0204] Step S357: Integrate the crack depth data, crack boundary data and crack length data to obtain crack data.

[0205] In this embodiment, when integrating crack depth data, crack boundary data, and crack length data, each data item needs to be standardized first to ensure that the data is comparable. A complete crack description is obtained by comparing and analyzing the depth, boundary, and length data of each crack. For example, crack depth data and crack length data can be classified by setting thresholds (such as crack depth greater than 1mm is considered major damage), and crack boundary data is compared with the actual geometric dimensions of the cable to ensure the accuracy of the crack information. Finally, these data are summarized in a crack database, numbered and labeled to provide detailed crack information for subsequent maintenance decisions. These integrated crack data can be used to formulate cable maintenance and replacement plans.

[0206] Preferably, step S4 is specifically:

[0207] Step S41: performing line transmission simulation on the damaged cable according to the crack data, thereby obtaining line transmission simulation data;

[0208] In this embodiment, the crack data of the cable is input, and the cable transmission model is used to simulate the line transmission in combination with the cable geometry, crack location, depth and other data. This model takes into account the resistance and conductivity of the cable and the impact of cracks on signal transmission. According to the crack location and depth, the cable is divided into different areas to simulate the impedance effect of cracks on signal transmission in each area. During the simulation process, the input voltage is set to a specific value (such as 10V), and the current change during signal transmission is simulated. The simulation tool needs to provide a data interface to combine the crack data with the cable parameters (such as cable cross-sectional area, material properties, external ambient temperature) to derive the transmission characteristics of the line. Finally, through the simulation data, key parameters such as transmission delay and signal attenuation of the line are obtained, providing basic data for subsequent current and voltage analysis.

[0209] Step S42: performing transmission current statistics and transmission voltage statistics on the line transmission simulation data, thereby obtaining transmission current data and transmission voltage data;

[0210] In this embodiment, based on the line transmission simulation data, the current and voltage of the transmission signal are first sampled and counted. Use current detection instruments and voltage sensors to analyze the current and voltage waveforms in the simulation results respectively. The sampling rate of the sensor is set to 100Hz to ensure that the instantaneous changes of current and voltage can be accurately captured. By calculating the average value, maximum value, minimum value, etc. of the collected data, the statistical characteristics of the transmission current and voltage are obtained. For example, for the transmission current, the threshold is set to alarm when the current exceeds 5A, and the voltage threshold is set to trigger a warning when it exceeds 15V. During the statistical process, the fluctuations of current and voltage are carefully analyzed through data processing tools to ensure that the transmission characteristics of the line can be mastered at any time, and a detailed transmission current data and voltage data report is generated.

[0211] Step S43: performing overload identification based on the transmission current data, thereby obtaining current overload data;

[0212] In this embodiment, based on the transmission current data, the threshold of current overload is first set, and it is usually set to when the current exceeds 120% of the normal transmission current, it is determined to be an overload phenomenon. For example, if the normal current is 4A, the current overload threshold is 4.8A. When the current exceeds this threshold, the overload condition detected by the current protector will be recorded as current overload data. In this process, the current data needs to be collected in real time in a time series manner to ensure that the occurrence and duration of each overload event can be accurately recorded. Each time the current exceeds the overload threshold, its start time and duration, as well as the current peak value, are recorded as an indication of current overload. These data will provide support for subsequent voltage overvoltage analysis to further analyze the working status of the cable.

[0213] Step S44: performing overvoltage identification based on the transmitted voltage data, thereby obtaining voltage overvoltage data;

[0214] In this embodiment, a voltage overvoltage threshold is set for the transmission voltage data, and is usually set to an overvoltage phenomenon when the voltage exceeds 10% of the rated voltage. For example, if the rated voltage is 12V, the overvoltage threshold is 13.2V. When the voltage exceeds the threshold, the overvoltage alarm is immediately triggered and the relevant voltage data is recorded. The voltage sensor needs to sample the voltage data regularly to ensure that the sampling frequency is not less than 100Hz to avoid missing overvoltage events. Whenever the voltage exceeds the overvoltage threshold, the duration of the overvoltage and the peak voltage are recorded, and this information is stored as voltage overvoltage data. Through real-time monitoring of these overvoltage data, it can provide an important basis for the next step of temperature overheating analysis and identify whether the cable has abnormal electrical load problems.

[0215] Step S45: performing a time intersection operation according to the current overload data and the voltage overvoltage data, thereby obtaining transmission overload-overvoltage data;

[0216] In this embodiment, based on the current overload data and the voltage overvoltage data, a time intersection operation is first performed to match the timestamps of the current overload and the voltage overvoltage. When the time of the current overload and voltage overvoltage events coincide, it is considered that an overload-overvoltage co-occurrence event has occurred. The specific operation is to compare the current overload data with the voltage overvoltage data according to time. If the time windows of the two overlap, the data of this period is marked as an "overload-overvoltage" event. When processing data, the tolerance for time overlap is set to 1 second, that is, if the overload and overvoltage events occur within 1 second, they are considered to be co-occurrence events. These overload-overvoltage data are recorded as a new data set for subsequent temperature overheating analysis, and an alarm record is formed in the system.

[0217] Step S46: performing temperature overheat analysis based on the transmission overload-overvoltage data, thereby obtaining temperature overheat data;

[0218] In this embodiment, based on the transmission overload-overvoltage data, the cable temperature is monitored in real time through a thermocouple sensor. The sampling frequency of the temperature sensor is set to 1Hz to ensure that the temperature data is recorded once every second. The threshold value of temperature overheating is set to 70°C. If the temperature of the cable exceeds this threshold, it is considered that the cable is overheated. After the overload-overvoltage event occurs, the output data of the temperature sensor is synchronously compared with the output data of the current and voltage sensors. If the cable temperature exceeds 70°C during the time period when the overload-overvoltage co-occurs, the overheating data of the event is recorded and marked as an overheating event. The duration, temperature peak, and time of occurrence of each overheating event will be recorded in detail to further analyze the degree of damage and working status of the cable.

[0219] Step S47: performing component short circuit determination according to the temperature overheat data, thereby obtaining component short circuit data;

[0220] In this embodiment, when determining a component short circuit based on temperature overheating data, the temperature overheating data is first used to determine whether the cable has a serious overheating phenomenon. If the duration of the overheating event exceeds 5 minutes and the temperature peak exceeds 85°C, it is determined that a component short circuit has occurred. The basis for determining a component short circuit is the overheating phenomenon caused by overload and overvoltage events, and a short circuit is usually accompanied by a sharp increase in temperature. When an overheating event occurs, the conductor resistance inside the cable is further checked. If the resistance drops sharply, it indicates that a short circuit has occurred. By combining the resistance change of the cable, the temperature data, and the co-occurrence of overload and overvoltage events, it is determined whether there is a risk of component short circuit, and component short circuit data is generated for recording.

[0221] Step S48: constructing a cable aging monitoring model according to the component short circuit data and the conductor oxidation data;

[0222] In this embodiment, a cable aging monitoring model is constructed based on component short-circuit data and conductor oxidation data. First, the initial standard for cable aging is established through long-term monitoring data, and a standard threshold is set. For example, a resistance change of more than 10% is considered severe aging. According to multiple indicators such as the frequency of component short circuits, degree of oxidation, and overheating, a multi-dimensional evaluation system for aging monitoring is designed. By analyzing the working status of each cable under different conditions, data regression and fitting are performed to calculate a prediction model for cable aging. This model integrates multiple factors such as the cable's service life, workload, overload-overvoltage co-occurrence events, and temperature, providing an effective basis for subsequent aging warnings.

[0223] Step S49: input the cable monitoring data into the cable aging monitoring model, and execute the cable aging early warning task.

[0224] In this embodiment, the cable monitoring data is input into the established cable aging monitoring model to perform aging prediction. When inputting data, it is necessary to ensure that each monitoring data item (such as temperature, overload, overvoltage, degree of oxidation, etc.) has been preprocessed to meet the input requirements of the model. When executing the aging warning task, the model will calculate based on the input data and generate an aging prediction report for the cable. If the model detects that the degree of cable aging is close to the set threshold, an early warning is triggered and maintenance or replacement recommendations are issued to relevant personnel. The system will automatically record all early warning events, including warning time, degree of aging, and related recommendations.

[0225] Preferably, the present specification also provides a cable aging monitoring and early warning system, which is used to execute the cable aging monitoring and early warning method as described above, and the cable aging monitoring and early warning system includes:

[0226] The conductor oxidation analysis module is used to obtain cable monitoring data and perform electrical characteristic analysis to obtain electrical characteristic data; based on the electrical characteristic data, the conductor oxidation analysis is performed to obtain conductor oxidation data;

[0227] Conductor oxidation analysis module, used to extract cable image features based on cable monitoring data, and classify cable morphology types to obtain curved segment cable data and winding segment cable data; perform bending fatigue analysis on curved segment cable data to obtain bending fatigue data; perform winding stress analysis on winding segment cable data to obtain winding stress data; perform material damage assessment based on bending fatigue data and winding stress data to obtain material damage data;

[0228] The crack detection module is used to identify the damage position according to the material damage data to obtain the damage position data; mark the damaged cable based on the damage position data to determine it as a damaged cable; and perform crack detection on the damaged cable to obtain crack data;

[0229] The cable aging warning module is used to simulate the line transmission of the damaged cable according to the crack data, and perform component short-circuit analysis to obtain component short-circuit data; build a cable aging monitoring model based on component short-circuit data and conductor oxidation data; input the cable monitoring data into the cable aging monitoring model, and execute the cable aging warning task.

[0230] The cable aging monitoring and early warning system of the present invention can implement the aging monitoring and early warning method of any cable of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the cable aging monitoring and early warning method. The internal modules of the system cooperate with each other, thereby improving the accuracy of cable aging monitoring and the early warning response rate.

[0231] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0232] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A cable aging monitoring and early warning method, characterized in that: The following steps are involved: Step S1: Acquire cable monitoring data, and perform electrical characteristic analysis to obtain electrical characteristic data; perform conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data; Step S2: extracting cable image features according to the cable monitoring data, and classifying the cable morphology types to obtain curved segment cable data and winding segment cable data; Perform bending fatigue analysis on the cable data of the curved section to obtain bending fatigue data; perform winding stress analysis on the cable data of the winding section to obtain winding stress data; Material damage evaluation is performed based on bending fatigue data and winding stress data to obtain material damage data; Step S3: Identify the damage position according to the material damage data to obtain damage position data; Marking the damaged cable based on the damage location data to determine that it is a damaged cable; performing crack detection on the damaged cable to obtain crack data; Step S4: performing line transmission simulation on the damaged cable according to the crack data, and performing component short circuit analysis to obtain component short circuit data; A cable aging monitoring model is constructed based on component short-circuit data and conductor oxidation data; the cable monitoring data is input into the cable aging monitoring model, and the cable aging early warning task is executed.

2. The cable aging monitoring and early warning method according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: acquiring cable monitoring data, and performing resistance monitoring feature extraction to obtain resistance monitoring data; Step S12: performing rate calculation on the resistance monitoring data to obtain high rate resistance data; Step S13: acquiring a high resistance rate cable based on the high rate resistance data; Step S14: performing oxidation environment simulation on the high resistance rate cable to obtain an oxidized cable; Step S15: performing oxygen spectrum analysis on the oxidized cable to obtain oxygen spectrum data; Step S16: Conductor oxidation determination of the high resistance rate cable is performed according to the oxygen energy spectrum data to obtain conductor oxidation data.

3. The cable aging monitoring and early warning method according to claim 2 is characterized in that: Step S15 is specifically as follows: Step S151: cleaning the oxidized cable sample to obtain a cleaned oxidized cable; Step S152: applying an accelerating voltage to the cleaned oxidized cable to obtain an accelerating voltage cable; Step S153: irradiating the accelerating voltage cable with an electron beam, and collecting radiation released from the accelerating voltage cable to obtain the released radiation; Step S154: performing element characteristic peak energy spectrum detection on the released rays to obtain element energy spectrum data; Step S155: identifying the oxygen element according to the element spectrum data to obtain the oxygen element spectrum data; Step S156: measuring the oxidation degree of the oxygen element energy spectrum data to obtain oxidation degree data; Step S157: integrating the oxygen spectrum according to the oxygen element spectrum data and the oxidation degree data to obtain oxygen spectrum data.

4. The cable aging monitoring and early warning method according to claim 1 is characterized in that: The cable morphology classification includes: Extract cable images of cable monitoring data; Calculate the curvature of the cable image; Recognize the curve shape of cable images based on curvature; Detect the helicity of the cable image; Identify the ring structure of the cable image; The winding structure is integrated according to the helicity and the ring structure to obtain the winding structure; Perform curve segment cable recognition on the cable image according to the curve shape to obtain curve segment cable data; The cable image is subjected to winding segment cable identification according to the winding structure to obtain winding segment cable data.

5. The cable aging monitoring and early warning method according to claim 1 is characterized in that: The bending fatigue analysis includes: Extract curved material properties of cable data for curved segments; Repeated bending simulation based on bending material properties and curved cable data; Calculate bending stress for repeated bending simulations; Analyze the fatigue degree of cables in curved sections based on bending stress; Analyze the damage accumulation degree of the cable in the curved section according to the fatigue degree; The fatigue degree and damage accumulation degree are integrated to obtain the bending fatigue data.

6. The cable aging monitoring and early warning method according to claim 1 is characterized in that: The winding stress analysis includes: Extract the geometric shape of the winding cable data and build a winding model; Calculate the axial tensile stress of the winding section cable data; Calculate the torsional stress of the winding section cable data; Calculate radial compressive stress of winding section cable data; Integrate axial tensile stress, torsional stress and radial compressive stress to obtain stress data; Based on the stress data, winding seams of the winding model are identified to obtain winding seam stress data; Based on the stress data, the stress difference between the inner and outer layers of the winding model is analyzed to obtain the stress difference data between the inner and outer layers; The winding stress data is obtained by integrating the winding seam stress data and the inner and outer layer stress difference data.

7. The cable aging monitoring and early warning method according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: locating the damaged area according to the material damage data to obtain the damaged area; Step S32: performing acoustic wave sensing measurement on the damaged area to obtain acoustic wave sensing data; Step S33: triangulate the damage position according to the acoustic wave sensing data to obtain damage position data; Step S34: marking the damaged cable based on the damage position data and determining it as a damaged cable; Step S35: Perform crack detection on the damaged cable to obtain crack data.

8. The cable aging monitoring and early warning method according to claim 7, characterized in that: Step S35 is specifically as follows: Step S351: applying an alternating magnetic field to the damaged cable to obtain alternating magnetic field data; Step S352: performing ring current detection according to the alternating magnetic field data to obtain eddy current data; Step S353: performing signal spectrum conversion based on the eddy current data to obtain an eddy current signal spectrum; Step S354: performing amplitude attenuation analysis based on the eddy current signal spectrum to obtain amplitude attenuation data, and performing crack depth assessment to obtain crack depth data; Step S355: performing signal peak analysis based on the eddy current signal spectrum to obtain signal peak data, and determining the crack boundary to obtain crack boundary data; Step S356: performing amplitude change detection based on the eddy current signal spectrum to obtain amplitude change data, and performing crack length estimation to obtain crack length data; Step S357: Integrate the crack depth data, crack boundary data and crack length data to obtain crack data.

9. The cable aging monitoring and early warning method according to claim 8, characterized in that: Step S4 is specifically as follows: Step S41: performing line transmission simulation on the damaged cable according to the crack data, thereby obtaining line transmission simulation data; Step S42: performing transmission current statistics and transmission voltage statistics on the line transmission simulation data, thereby obtaining transmission current data and transmission voltage data; Step S43: performing overload identification based on the transmission current data, thereby obtaining current overload data; Step S44: performing overvoltage identification based on the transmitted voltage data, thereby obtaining voltage overvoltage data; Step S45: performing a time intersection operation according to the current overload data and the voltage overvoltage data, thereby obtaining transmission overload-overvoltage data; Step S46: performing temperature overheat analysis based on the transmission overload-overvoltage data, thereby obtaining temperature overheat data; Step S47: performing component short circuit determination according to the temperature overheat data, thereby obtaining component short circuit data; Step S48: constructing a cable aging monitoring model according to the component short circuit data and the conductor oxidation data; Step S49: input the cable monitoring data into the cable aging monitoring model, and execute the cable aging early warning task.

10. A cable aging monitoring and early warning system, characterized in that: Used to execute the cable aging monitoring and early warning method according to claim 1, the cable aging monitoring and early warning system comprises: The conductor oxidation analysis module is used to obtain cable monitoring data and perform electrical characteristic analysis to obtain electrical characteristic data; based on the electrical characteristic data, the conductor oxidation analysis is performed to obtain conductor oxidation data; Conductor oxidation analysis module, used to extract cable image features based on cable monitoring data, and classify cable morphology types to obtain curved segment cable data and winding segment cable data; perform bending fatigue analysis on curved segment cable data to obtain bending fatigue data; perform winding stress analysis on winding segment cable data to obtain winding stress data; perform material damage assessment based on bending fatigue data and winding stress data to obtain material damage data; The crack detection module is used to identify the damage position according to the material damage data to obtain the damage position data; mark the damaged cable based on the damage position data to determine it as a damaged cable; and perform crack detection on the damaged cable to obtain crack data; The cable aging warning module is used to simulate the line transmission of the damaged cable according to the crack data, and perform component short-circuit analysis to obtain component short-circuit data; build a cable aging monitoring model based on component short-circuit data and conductor oxidation data; input the cable monitoring data into the cable aging monitoring model, and execute the cable aging warning task.

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