Power transmission line detection method and system based on infrared image of unmanned aerial vehicle

Abstract

The invention relates to the technical field of power transmission line detection, in particular to a power transmission line detection method and system based on an infrared image of an unmanned aerial vehicle. The method comprises the following steps: acquiring an infrared image of a power transmission line through thermal infrared remote sensing equipment, monitoring the load state of the line in real time, and marking an abnormal temperature area to identify the temperature abnormity of an external line; and then, fitting the aging degree of the external wrapping material according to the abnormal regions, and further predicting the safe use period of the power transmission line in the time dimension. And finally, in combination with a preset line maintenance manual, a maintenance suggestion is generated according to the safe use prediction period, and the maintenance suggestion is fed back to the power transmission control center, so that line detection and maintenance can be executed in time. According to the invention, the power transmission line detection technology is optimized, so that the power transmission line detection technology is more perfect.

Description

Technical Field

[0001] This invention relates to the field of power transmission line inspection technology, and in particular to a method and system for power transmission line inspection based on UAV infrared images. Background Technology

[0002] With the development of drone technology and its widespread application in various industries, the potential of drones in power transmission line inspection is gradually emerging. Drones are characterized by their maneuverability and ability to carry various sensors, enabling efficient and accurate power transmission line inspections. In particular, drones equipped with infrared thermal imaging equipment can capture real-time temperature changes of the line under different load conditions, providing accurate thermal images and helping to quickly identify areas of abnormal temperature. Thermal imaging technology can effectively detect potential faults on the line, such as overload, equipment aging, or insulation damage. However, a traditional method of power transmission line inspection based on drone infrared images suffers from inaccurate analysis of the degree of aging under abnormal temperatures, resulting in large errors in the detection of the line's safe service life. Summary of the Invention

[0003] Therefore, it is necessary to provide a method and system for detecting power transmission lines based on UAV infrared images to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for detecting power transmission lines based on UAV infrared images is provided, the method comprising the following steps: Step S1: Acquire infrared images of the transmission line using thermal infrared remote sensing equipment; monitor the load status of the transmission line in real time; mark abnormal temperature areas in the infrared images of the transmission line to obtain abnormal temperature areas of the external line. Step S2: Fit the aging degree of the external wrapping material according to the abnormal temperature area of ​​the line to obtain the material aging fitting degree; predict the safe service life of the transmission line in the time dimension based on the material aging fitting degree to obtain the line safe service life prediction period. Step S3: Using the preset line maintenance manual, generate maintenance and repair suggestions based on the predicted safe use cycle of the line, and feed them back to the transmission control center to perform transmission line testing.

[0005] The present invention also provides a power transmission line detection system based on UAV infrared images, for performing the power transmission line detection method based on UAV infrared images as described above. The power transmission line detection system based on UAV infrared images includes: The abnormal temperature area marking module is used to acquire infrared images of transmission lines through thermal infrared remote sensing equipment; monitor the load status of transmission lines in real time; and mark abnormal temperature areas in the infrared images of transmission lines to obtain abnormal temperature areas of external lines. The safe service life prediction module is used to fit the aging degree of the external wrapping material according to the abnormal temperature area of ​​the line to obtain the material aging fitting degree; based on the material aging fitting degree, the safe service life of the transmission line is predicted in the time dimension to obtain the line safe service life prediction period. The maintenance and repair suggestion generation module is used to generate maintenance and repair suggestions based on the predicted safe service cycle of the line using a preset line maintenance and repair manual, and then feed them back to the transmission control center to perform transmission line inspection.

[0006] The beneficial effects of this invention lie in its ability to accurately detect temperature anomalies caused by factors such as excessive load and equipment aging by acquiring infrared images of transmission lines through thermal infrared remote sensing equipment and monitoring the line load status in real time. This process effectively marks abnormal temperature areas, providing accurate image data for subsequent fault diagnosis. Compared to traditional manual inspections, this method is more efficient and accurate, enabling timely detection of potential overheating problems, providing maintenance personnel with accurate fault location, reducing the risk of human error, and improving line safety. Fitting the aging degree of the external wrapping material based on the marked abnormal temperature areas provides a more accurate assessment of the line's aging status. Material aging is a significant factor causing transmission line faults; pre-analysis of its aging degree can predict the line's service life and provide a scientific basis for subsequent maintenance plans. By predicting the safe operating period over time, power companies can plan reasonable maintenance cycles, avoiding line accidents caused by overuse or delayed maintenance. This process not only improves line utilization efficiency but also reduces the risk of sudden faults. By generating maintenance suggestions using a pre-set line maintenance manual and feeding them back to the transmission control center, efficient execution of line maintenance can be ensured. The safe service life generated based on material aging fitting results provides a reliable basis for maintenance planning, helping to rationally schedule line maintenance and repair timelines and avoiding power supply interruptions due to untimely inspections. Furthermore, the feedback mechanism ensures that relevant departments receive timely detection information, improving information transmission efficiency and maintenance response speed, thereby achieving intelligent management and efficient operation of the lines and further enhancing the stability and security of the power system. Therefore, this invention is an optimization of a traditional transmission line detection method based on UAV infrared images. It solves the problem of inaccurate analysis of aging under abnormal temperatures, leading to large errors in the detection of the safe service life of the lines, thus improving the accuracy of aging analysis under abnormal temperatures and reducing the error in detecting the safe service life of the lines. Attached Figure Description

[0007] Figure 1This is a flowchart illustrating the steps of a power transmission line detection method based on UAV infrared images. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. Detailed Implementation

[0008] Please see Figures 1 to 3 A method for detecting power transmission lines based on UAV infrared images, the method comprising the following steps: Step S1: Acquire infrared images of the transmission line using thermal infrared remote sensing equipment; monitor the load status of the transmission line in real time; mark abnormal temperature areas in the infrared images of the transmission line to obtain abnormal temperature areas of the external line. Step S2: Fit the aging degree of the external wrapping material according to the abnormal temperature area of ​​the line to obtain the material aging fitting degree; predict the safe service life of the transmission line in the time dimension based on the material aging fitting degree to obtain the line safe service life prediction period. Step S3: Using the preset line maintenance manual, generate maintenance and repair suggestions based on the predicted safe use cycle of the line, and feed them back to the transmission control center to perform transmission line testing.

[0009] In this embodiment of the invention, reference Figure 1 The above is a schematic flowchart of the power transmission line detection method based on UAV infrared images according to the present invention. In this example, the power transmission line detection method based on UAV infrared images includes the following steps: Step S1: Acquire infrared images of the transmission line using thermal infrared remote sensing equipment; monitor the load status of the transmission line in real time; mark abnormal temperature areas in the infrared images of the transmission line to obtain abnormal temperature areas of the external line. In this embodiment of the invention, a thermal infrared remote sensing device mounted on the bottom of a drone is used to perform infrared image acquisition of power transmission lines. The drone flies at a constant speed along the axis of the power transmission line, with a flight altitude set at approximately 5 meters above the centerline of the conductor, and a flight speed maintained at 0.5 m / s. The thermal infrared detection resolution of the device is 640×512, and the frame rate is 30 Hz. While acquiring infrared images, the infrared detection device also collects the operating current and voltage data of the power transmission line through an integrated current and voltage sensing module. This data is then superimposed onto the image index after synchronous time series calibration to obtain the load status at the corresponding time. The acquired infrared images are enhanced for contrast using an infrared grayscale distribution linear stretching algorithm, and temperature details are fully displayed through normalized grayscale histogram mapping. Finally, image noise is suppressed by median filtering with a 3×3 window. Subsequently, pixel temperature inversion is performed on the enhanced image, converting infrared radiation values ​​into actual temperature values. A temperature matrix is ​​constructed for the entire image, and threshold segmentation is performed on the temperature matrix. When the temperature value of any pixel in the matrix exceeds 70℃, it is marked as an anomaly. An abnormal connected region is automatically extracted using an eight-neighbor connected region growing algorithm to form an image of the abnormal temperature region of the external line. During the processing, the system continuously records the UAV attitude, imaging angle, and geographic coordinate information and automatically corrects the geometric distortion of the image to ensure that the spatial positioning accuracy of the abnormal region is less than 0.3 meters.

[0010] Step S2: Fit the aging degree of the external wrapping material according to the abnormal temperature area of ​​the line to obtain the material aging fitting degree; predict the safe service life of the transmission line in the time dimension based on the material aging fitting degree to obtain the line safe service life prediction period. In this embodiment of the invention, firstly, the pixel set of the abnormal temperature region of the external line is read, and the average temperature gradient change rate of the region is calculated. A surface temperature fluctuation curve is formed through comparative analysis of time-series temperature curves. Combining the thermal conductivity, specific heat capacity, and coefficient of thermal expansion data of the line's surface coating material, a time-step iterative model of the temperature field is established using the finite volume method. The thermal stress caused by temperature changes is discretized and solved to obtain the peak value of the surface thermal effect. For the internal heating state caused by this peak thermal effect, an energy balance analysis model based on the heat conduction equation is constructed to calculate the internal heat density distribution range. A creep cyclic stochastic process simulation is performed on the mechanical strength change within the internal heat density range of the conductor. The withstand limit data of the conductor material is statistically analyzed, and the mechanical strength decay rate is calculated based on the cumulative thermal energy effect. Combining the internal strength degradation results of the conductor with the surface aging effect, the polymer chain degradation rate increment index of the coating material is numerically fitted. The slope of the degradation rate curve over time is calculated using a stepwise regression algorithm. This index is correlated with thermal characteristic factors to obtain the interface weakening degree parameter, and an aging degree fitting model of the coating material is established based on the relationship between the weakening degree and the degradation rate. Finally, based on the correlation between the degree of aging and the degree of decline in internal mechanical strength over time, the continuous-time degradation analysis method is applied to deduce the safe service life of the circuit and output the predicted safe service life value of the circuit.

[0011] Step S3: Using the preset line maintenance manual, generate maintenance and repair suggestions based on the predicted safe use cycle of the line, and feed them back to the transmission control center to perform transmission line testing.

[0012] In this embodiment of the invention, the obtained predicted service life of the transmission line is subjected to lifespan reduction cross-validation. First, a historical period sample database is constructed, and conductor operating temperature, aging index, and mechanical strength data for each stage are extracted on a monthly basis. A lifespan reduction curve model is established using a sliding window algorithm. Through multi-dimensional vector space resampling, the predicted period and historical data are gradually converged and validated, outputting lifespan reduction validation data. Subsequently, a hierarchical normalization algorithm is used to standardize the data under different temperature ranges and load conditions, forming a multi-scale normalized matrix. This matrix is ​​input into a preset transmission line maintenance manual data module, and the corresponding maintenance threshold range is matched in the manual based on the line type, climate conditions, and load level parameters. The maintenance suggestion generation module maps the normalized data to the maintenance level range through a rule matching algorithm, automatically generating an operation suggestion list including structural inspection, conductor replacement, insulation cleaning, and connection tightening. Finally, the generated maintenance suggestions are automatically uploaded to the transmission control center server in the form of data packets, triggering detection task instructions through interface protocols to achieve closed-loop execution of the entire remote detection process of the transmission line.

[0013] Step S1 includes the following steps: During high-temperature inspections in summer, drones use thermal infrared remote sensing equipment to collect infrared images of power transmission lines at a distance of 5 meters, along the line in a horizontal direction at a speed of 0.5 m / s; and monitor the load status of the power transmission lines in real time. Contrast optimization processing is performed on infrared images of power transmission lines to generate infrared contrast-adjusted images; The infrared contrast-adjusted image is processed by median filtering to obtain the infrared filtered image; Based on infrared filtered images, when the temperature of the transmission line in the image is greater than 70℃, abnormal temperature areas are marked to obtain the abnormal temperature areas of the external lines.

[0014] In this embodiment of the invention, contrast optimization processing is performed on the acquired infrared images. First, a grayscale matrix is ​​constructed for each frame of the image, with a grayscale range of 0 to 255. Histogram statistics are performed on the matrix to obtain the frequency distribution of grayscale occurrences. Then, a cumulative distribution function is calculated based on an equalization algorithm, and grayscale redistribution is performed to expand the temperature gradient difference region. During the processing, a sliding window technique is used to correct the brightness shift between consecutive frames. The window width is 5 frames, and each pixel undergoes local mean shift adjustment in the time dimension to eliminate the overall brightness unevenness caused by changes in infrared radiation. In the spatial dimension, a bilinear grayscale interpolation function is used to achieve smooth transitions between pixels, preventing the loss of edge sharpness. After contrast adjustment, the grayscale difference between the high-temperature and mid-temperature regions of the infrared image is increased to 1.5 times the original state, while the low-temperature background maintains balanced grayscale. The processed infrared contrast-adjusted image is output, with enhanced grayscale details and temperature feature discrimination.

[0015] The infrared contrast-adjusted image is input into a median filtering algorithm. The filtering process uses a 3×3 neighborhood window to locally process each pixel to eliminate random interference noise from the sensor. The algorithm operates pixel-by-pixel, extracting the nine gray values ​​surrounding the pixel at each processing step, sorting them in ascending order, and then replacing the center pixel's gray value with the gray value at the middle position, thus completing noise suppression. To further maintain the stability of the conductor temperature profile boundary, double threshold detection is applied to the boundary region in the filtered result to ensure that gradient information in areas of abrupt temperature changes is not lost through smoothing. The entire filtering operation sequentially covers the entire image in a sliding manner, and after each frame is processed, an effective pixel ratio detection is performed to remove abnormal gray points. This process generates an infrared filtered image with a noise removal rate of over 90% while preserving the integrity of temperature change edges.

[0016] Anomaly temperature identification and region labeling are performed based on the obtained infrared filtered images. The infrared filtered images are converted into a temperature matrix using a temperature inversion function, and temperature values ​​are stored in a two-dimensional matrix with pixel coordinates as row and column indices. A threshold temperature of 70℃ is defined, and point-by-point temperature comparisons are performed on all elements in the matrix. When the temperature of a pixel exceeds 70℃, that pixel is marked as an anomaly. An eight-neighbor connectivity algorithm is used to cluster adjacent anomaly pixels, establishing a topological connectivity matrix for the anomaly pixel group. Connected regions in the matrix are tracked, and boundary contours are extracted. Further, the area, average temperature, and maximum temperature value of each connected region are calculated, and each region is numbered and stored. Finally, the anomaly temperature regions are visualized in pseudo-color on the infrared filtered image. The center point of each anomaly temperature region is located in latitude and longitude in the UAV coordinate system. Accurate geographic mapping is achieved through flight control data combined with a geocoding system, outputting an external line anomaly temperature region information file, providing input data for subsequent conductor material performance analysis and anomaly point maintenance.

[0017] Specifically, the technical logic between the above steps presents a hierarchical progression from data enhancement and noise suppression to anomaly identification. First, a temperature information enhancement mechanism is constructed through contrast optimization processing, which effectively amplifies the grayscale differences in the temperature distribution of conductors in the infrared image. High-temperature details are enhanced at both the visual and numerical levels, providing a foundation for the accurate identification of discrete temperature points. Subsequently, medium-mean-law filtering is applied to remove noise from the enhanced infrared image, eliminating random interference from the sensor and environmental thermal radiation noise, ensuring the smoothness and continuity of the temperature gradient boundary, and making the filtered image data more stable in spatial features, providing reliable input conditions for subsequent quantitative threshold identification. Then, based on the clear thermal image after noise reduction, a temperature matrix is ​​established and a threshold judgment is performed. When the pixel temperature exceeds 70℃, anomaly points are identified. Clustering identification and boundary extraction of abnormal temperature areas are achieved through connected component algorithms. The data flow and algorithm logic between these three steps are strictly connected, forming a closed-loop analysis path from image enhancement → noise removal → temperature area clustering. Ultimately, high-resolution extraction of infrared temperature information and accurate location of abnormal areas are achieved, giving the detection data high reliability and stability in both spatial and temperature dimensions.

[0018] Step S2 includes the following steps: To obtain the properties of the surface material of power transmission lines; Based on the abnormal temperature area of ​​the external line and the load status of the transmission line, assess the internal heating state of the line corresponding to the abnormal temperature area of ​​the external line. The mechanical strength degradation of the conductor under the internal heating state of the corresponding line is analyzed to obtain the mechanical strength degradation data of the internal conductor. Based on the abnormal temperature area of ​​the line and the corresponding internal heating state of the line, the aging degree of the external wrapping material of the transmission line is fitted to obtain the material aging fitting degree. Based on the aforementioned internal conductor mechanical strength degradation data and material aging fitting degree, the safe service life of the transmission line is predicted in the time dimension to obtain the predicted safe service life of the line.

[0019] As an example of the present invention, reference is made to... Figure 2 As shown, step S2 in this example includes: S21: Obtain the properties of the surface material of the transmission line; In this embodiment of the invention, when acquiring the surface material properties of transmission lines, a physical property database of the transmission conductor surface material is established. The database includes basic parameters such as thermal conductivity, specific heat capacity, coefficient of thermal expansion, and the initial tensile strength, elastic modulus, and glass transition temperature of the polymer coating. The database is sourced from factory inspection reports and long-term operational sample data. Based on the monitored line model and production batch number, corresponding reference data is retrieved, and deviations in physical parameters caused by environmental temperature and humidity are corrected. Material property parameters are recorded using a standardized matrix for subsequent numerical calculations. Each element of the matrix represents the order of magnitude of a single property, forming a complete set of surface material properties.

[0020] S22: Based on the abnormal temperature area of ​​the external line and the load status of the transmission line, assess the internal heating state of the line corresponding to the abnormal temperature area of ​​the external line. In this embodiment of the invention, the internal heating condition of the corresponding region is evaluated based on the abnormal temperature area of ​​the external line and the load status of the transmission line. The heat generation per unit length of resistance is calculated using known line current values. Simultaneously, high-temperature points in the infrared imaging results are used as heat source boundaries, and the temperature distribution at these points and the conductor cross-section temperature are decomposed hierarchically. Temperature differences at different depths are calculated and converted into equivalent heat flow directions to determine the degree of internal heating. During the calculation process, the thermal conductivity and cross-sectional dimensions of the material are used to divide the conductor from the surface layer to the core into several segments. The heat transfer value is calculated for each segment, forming a thermal state distribution of the conductor from the outside to the inside. This method clarifies the thermal influence range of each layer of the structure under the abnormal temperature region, providing an input basis for mechanical degradation analysis.

[0021] S23: Perform internal heating state analysis on the corresponding line to obtain internal conductor mechanical strength degradation data; In this embodiment of the invention, the mechanical strength degradation of the conductor is analyzed based on the internal thermal state of the line. First, the internal thermal state distribution matrix is ​​read, and the conductor structure in areas with high heat density is refined and layered. Based on the characteristic parameters of the conductor matrix metal, a time-series thermal creep model is established. The degree of plastic deformation of the metal grains under a non-uniform thermal field is estimated through numerical integration of thermal stress and the dislocation accumulation rate of the metal lattice. Then, the deformation rate is discretized as a time-varying function, and the strain increase is calculated hourly to construct a time-varying curve of the conductor's mechanical strength. The initial strength point and the tolerance limit point are marked on the curve, and the strength loss gradient is obtained. This gradient is then processed using a linear interpolation method to generate internal conductor mechanical strength degradation data, making the mechanical degradation trends of different heat load regions a comparable sequence.

[0022] S24: Fit the aging degree of the external wrapping material to the characteristics of the transmission line surface material based on the abnormal temperature area of ​​the line and the corresponding internal heating state of the line, and obtain the material aging fitting degree. In this embodiment of the invention, the aging degree of the conductor surface coating material is fitted based on the abnormal temperature region and internal heating state of the line. Using the surface temperature change curve as input, the temperature rise rate is differentially solved using a time step Δt to obtain the surface temperature fluctuation curve. The energy accumulation per unit volume of material under cyclic thermal load is calculated using a combination of material thermal conductivity, specific heat capacity, and coefficient of thermal expansion. Then, a regression analysis of heat release is performed on a nonlinear time axis to form a regression function for the heat energy difference between high and low temperature cycling intervals. The degradation rate coefficient of the material molecular chain and the heat energy accumulation function are combined for recursive calculation to obtain the material degradation rate increment index. Based on the numerical relationship between this index and the thermal aging time, the degree of interface weakening is calculated according to the polymer interface stress weakening equation. Finally, the two are coupled and interpolated to obtain a quantitative fitting result of the aging degree of the external coating material.

[0023] S25: Based on the aforementioned internal conductor mechanical strength degradation data and material aging fitting degree, predict the safe service life of the transmission line in the time dimension to obtain the predicted safe service life of the line.

[0024] In this embodiment of the invention, the safe service life of a transmission line is derived based on the fitting degree between conductor mechanical strength degradation data and material aging. A two-parameter attenuation model is established in the time dimension, with the strength degradation rate and aging index as independent variables. An interval superposition algorithm is used to calculate the overall trend of conductor performance degradation. Interpolation is performed on conductor cross-sectional deformation data and material aging functions to obtain the insulation performance attenuation gradient curve. Subsequently, the failure threshold of the conductor is determined based on the slope point of the degradation curve, and the end point of the safe service life is determined. The lifespan of each conductor sample point is calculated iteratively, and the minimum value of the safe service life of all sample points is taken as the overall safe service life prediction period of the transmission line. The final output period results are expressed in time series form, providing a quantitative reference for the long-term operation life assessment and maintenance planning of the line.

[0025] Specifically, the aforementioned technical logic realizes a complete closed-loop chain from material physical properties—thermal effect conduction—mechanical performance degradation—surface aging fitting—life prediction. Its logical connections are as follows: First, by establishing a database of the physical properties of the transmission line surface materials, high-precision basic parameter inputs are provided for all subsequent thermodynamic calculations, enabling the conductor model to have traceable and calibrable physical property support; next, the external abnormal temperature information monitored by infrared sensors is combined with the load state, and heat conduction and energy distribution modeling is used to transform the external apparent temperature information into the actual thermal state distribution inside the conductor, achieving accurate inversion from external detection signals to the internal thermal state; subsequently, based on the internal thermal state, a thermal creep model and lattice dislocation accumulation mechanism are introduced to improve the conductor's thermal performance. The mechanical strength degradation process of the transmission line under a non-uniform thermal field is analyzed temporally to obtain mechanical degradation data at the structural level, providing quantitative support for life modeling. Furthermore, the thermal state is coupled with material parameters, and the aging degree of the external coating material is accurately characterized by fitting nonlinear thermal energy accumulation and molecular degradation rate. This comprehensively reflects the material performance degradation trend from both thermal stress and molecular layer degradation perspectives. Finally, the internal mechanical strength degradation data and the external aging fitting results are coupled with two parameters to establish a time-dimensional performance degradation model, enabling dynamic prediction of the safe service life of the transmission line. The overall logic realizes a continuous derivation from thermo-mechanical-chemical multi-physics field coupling analysis to life prediction decision-making, expanding the detection results from single temperature anomalies to quantifiable life assessments, significantly improving the accuracy, predictability, and engineering practical value of transmission line health status assessment.

[0026] Analysis of the degradation of conductor mechanical strength under internal heating conditions includes: Obtain the basic characteristics of the internal conductors; evaluate the internal heat density range of the corresponding line's internal heating state to obtain the internal heat density range; Extract the coefficient of thermal expansion and initial mechanical strength from the basic properties of the internal conductors; simulate the nonlinear time-varying characteristics of the internal thermal density range; Based on the nonlinear time-varying characteristics, a power-law creep cyclic stochastic process simulation is performed on the thermal expansion coefficient and initial mechanical strength, and the creep cyclic process data of the conductor is output. The tolerance limit is inferred from the creep cycle process data to obtain the wire tolerance limit data; Based on the conductor withstand limit data, the mechanical strength degradation of the conductor under internal heating conditions is analyzed to obtain the internal conductor mechanical strength degradation data.

[0027] In this embodiment of the invention, when obtaining the basic characteristics of the internal conductors, relevant parameters are obtained through structural design and material testing data provided by the manufacturer. The technical documentation provided by the conductor manufacturer includes a table of physical properties of the conductor's metal core and stranded layers, with key items including density, thermal conductivity, specific heat capacity, coefficient of thermal expansion, initial tensile strength, yield strength, and modulus of elasticity. To ensure the parameters are applicable to the current operating line, information is matched according to the line installation batch and the manufacturer's certificate number, and factory testing records for the same batch of conductors are retrieved from the manufacturer's database. The manufacturer's testing data are obtained through standardized tests, including high-temperature tensile tests, thermal diffusivity analysis, and isothermal mechanical property testing, and include environmental reference temperature, humidity, and testing time, with clear metrological certification codes. After obtaining this data, the parameters are allocated to the core, inner strand, and outer strand regions according to the conductor structure hierarchy, and recorded in the internal basic characteristic data table to form a complete basic characteristic dataset for thermal density assessment.

[0028] The coefficient of thermal expansion and initial mechanical strength are extracted from the basic properties of the internal conductor and correlated with the internal heat density range obtained in the previous step to establish a thermal stress change analysis sequence. Using the distribution of the heat density range, the difference in expansion between different regions of the conductor due to heating is determined. Subsequently, the time dimension is introduced into the calculation, using external load fluctuation periods as the time input condition to simulate the temperature rise and recovery process under alternating high and normal temperatures. Each temperature change node in the time series corresponds to a mechanical strain change, thereby obtaining the nonlinear time-varying characteristics of the internal heat density range. Through this method, the staged distribution law of stress and deformation of the conductor under continuous heating is clarified, forming time-series data for subsequent degradation analysis.

[0029] The nonlinear time-varying characteristics obtained in the previous step are input into the creep cyclic process calculation. Based on the thermal expansion rate and initial strength of the conductor in different temperature ranges, the strain difference caused by heating in each stage is sequentially accumulated, and the instantaneous recovery value is extracted at the end of each time period. The strain increment is repeatedly accumulated using heating and cooling cycles as the period, gradually establishing the creep cyclic process data. This process consists of multiple heating and cooling stages, with the length increment and corresponding plastic deformation ratio calculated once for each stage. By comparing the strain values ​​of each layer, the direction of internal thermal stress migration and the depth of influence are determined. After complete calculation, a continuous time series of creep cyclic process data is generated, containing the cumulative trend of strain development in the conductor under thermal cycling.

[0030] The tolerance limit is inferred from the creep cycle data. First, the ratio of the total strain amplitude to the initial elongation of the conductor within each cycle is statistically calculated, accumulating the unit deformation ratio under stress. Based on the historical tensile strength limit of the conductor's base material, the deformation ratio is compared with a set safety factor to progressively determine the conductor's tolerance threshold. Then, by comparing the strain values ​​of different conductor sections, the point of accelerated strength degradation is identified. When the strain rate increase exceeds a critical proportion for three consecutive cycles, this point is marked as the occurrence of the tolerance limit. Finally, the conductor's tolerance limit data is output, recording the corresponding temperature, time, and strength values, providing direct boundary parameters for mechanical degradation derivation.

[0031] Internal mechanical strength degradation analysis is performed based on conductor withstand limit data. The withstand value of each thermal zone is compared with the current strength value to calculate the strength attenuation ratio, and the results are incorporated into a time series. Data from different thermal zones are then integrated sequentially to form the overall conductor strength degradation distribution. Using the calculated strength attenuation curves, the periods and regions with the most significant strength reduction are identified, determining the location of the weakened layer in the internal metal structure. The final output of internal conductor mechanical strength degradation data includes three-dimensional information of time, temperature, and degradation ratio, providing a quantitative analytical basis for the thermal stress life assessment of transmission lines.

[0032] Specifically, in this series of technical processes, by comprehensively utilizing fundamental material properties, thermal density assessment, creep cycle simulation, and mechanical strength degradation analysis, accurate prediction of the thermal stress and mechanical strength of conductors was gradually achieved. First, by acquiring and adjusting the fundamental characteristic data of the conductors to adapt to the actual operating environment, the accuracy and real-time nature of the data were ensured. This data provided a solid foundation for thermal density assessment. Furthermore, through the interaction between temperature changes and mechanical strain, the nonlinear time-varying characteristics of the conductors under thermal cycling were established. This process accurately captured the stress distribution of the conductors during thermal expansion and cooling cycles, and through creep simulation, the creep cycle process data of the conductors was gradually derived. The creep process simulation accumulated strain in multiple thermal cycles, revealing the thermal stress migration and its depth of influence within the conductor's internal zones. By further inferring the tolerance limits from the data during the creep process, and combining the tensile strength and safety factor of the conductors, the tolerance limits of the conductors were obtained. These limit values ​​provided clear boundary parameters for subsequent mechanical strength degradation analysis, thereby achieving accurate quantification of the strength decay process. Through the logical connection of these technologies, the quantitative analysis of conductor mechanical strength degradation was successfully completed, providing an efficient and accurate predictive tool for the thermal stress life assessment of transmission lines.

[0033] Fitting the aging degree of the external wrapping material includes: Based on the abnormal temperature region of the line, the surface temperature fluctuation curve is plotted to obtain the surface temperature fluctuation curve of the line; the thermal conductivity, specific heat capacity and thermal expansion coefficient of the surface material properties of the transmission line are extracted. The peak thermal effect over time is predicted based on the surface temperature fluctuation curve of the line, and the peak surface thermal effect is obtained. Nonlinear thermal energy release regression analysis is performed on the internal heating state of the corresponding line, and nonlinear thermal energy release regression data is output. Based on the surface peak thermal effect and nonlinear heat release regression data, the thermal conductivity, specific heat capacity and thermal expansion coefficient of the surface material of the circuit are analyzed by the polymer chain degradation rate increment index to obtain the degradation rate increment index. The degree of weakening of the coupling interface is correlated with the incremental degradation rate index; The aging degree of the external coating material is fitted based on the degradation rate increment index and the degree of interface weakening to obtain the material aging fitting degree.

[0034] In this embodiment of the invention, when plotting the surface temperature fluctuation curve based on the abnormal temperature region of the transmission line, the pixel temperature data sequence within the abnormal region is first extracted from the infrared image. Each frame of the image is numbered chronologically, and the temperature values ​​of pixels at the same location are concatenated to form a time series. A three-dimensional data table is constructed with the frame time interval as the horizontal axis and the temperature value as the vertical axis, and missing points are smoothly filled in using an interpolation algorithm. Subsequently, the average value of each time series is calculated to obtain the surface temperature fluctuation curve of the transmission line. The curve includes three stages: a temperature rise segment, a stable segment, and a temperature fall segment, reflecting the continuous heating changes of the surface material during actual operation. After plotting, the physical property parameters of the surface material corresponding to the transmission line are extracted from the basic data file of the transmission line, including thermal conductivity, specific heat capacity, and coefficient of thermal expansion. This data is provided by the manufacturer and has undergone on-site temperature correction processing. After parameter extraction, the parameters are recorded in the thermal parameter database for subsequent temperature transfer and energy accumulation calculations. This step ultimately generates the surface temperature fluctuation curve and its corresponding material thermophysical properties, constituting the initial dataset for surface thermal behavior analysis.

[0035] The peak thermal effect over time is predicted based on the surface temperature fluctuation curve. Using the fluctuation curve as a basis, differential calculations are performed on the temperature change rate to extract the variation of the temperature rise rate along the time axis. The temperature change rates are grouped and statistically analyzed, and the heating trends within different time periods are compared. A local peak identification algorithm is used to determine the location of the maximum thermal effect. Then, the ambient temperature curve is compared to correct for the influence of environmental factors, yielding the temperature peak purely caused by the conductor's electrical load. Subsequently, the peak thermal effect range is calculated using the temperature rise and duration as parameters. To ensure the stability of the peak data, a moving average operation is performed on the temperature change data of three adjacent periods to exclude abnormal transient points, ensuring the continuity of the peak curve. Finally, peak thermal effect data of the conductor surface under high-temperature conditions is obtained, providing input for internal thermal energy analysis.

[0036] Nonlinear thermal energy release regression analysis was performed on the internal heating state of the conductor. First, heating data calculated from the internal temperature distribution and the peak surface thermal effect were synchronized over time to form a complete thermal energy input sequence. Then, the thermal energy release rate for each time period in the sequence was statistically classified, and the delay in heat transfer to different depths was treated as an independent variable to construct a heat correspondence table. Next, the cumulative heat release ratio at each depth was determined based on the time-varying trend, revealing the heat decay process in different sections of the conductor. After the analysis, the thermal energy release rate and corresponding temperature changes for each time period were output as a structured data table, forming nonlinear thermal energy release regression data. This data describes the temporal pattern and decay characteristics of internal thermal energy release, providing quantitative data support for subsequent aging degree fitting and conductor life assessment.

[0037] Based on the peak thermal effect and nonlinear heat release regression data of the surface layer, an incremental index analysis of the polymer chain degradation rate of the surface material of the electrical circuit was performed. First, the upper limit temperature of the material's heating capacity was determined based on the peak thermal effect, and the corresponding cumulative heat energy and duration of action were extracted from the regression data. Then, the heat input per unit volume of the surface material was compared with the thermal diffusion rate to calculate the internal thermal stress distribution. By comparing the trend of thermal conductivity changes over different time periods, the magnitude of the decrease in thermal conductivity with temperature fluctuations was determined. Simultaneously, the change ratio of the coefficient of thermal expansion and the gradient of the specific heat capacity were detected, and the weighted difference of these three indicators in the time series was calculated to obtain a comprehensive rate of change reflecting the stability of the polymer chain. Subsequently, this rate of change sequence was correlated point-by-point with the heat release rate sequence to calculate the cumulative increment of the chain structure breakage rate, which was then converted into a degradation rate increment index to characterize the difference in molecular-level degradation rate of the material under high-load thermal cycling. This process meticulously records the correspondence between the thermal performance decay process and the chemical bond breaking rate, forming accurate degradation rate increment data.

[0038] The degree of interfacial weakening is correlated with the degradation rate increment index. First, the interfacial layer is delineated along the region of the internal material contacting the metal surface of the conductive wire at the polymer coating profile. The change in molecular chain end-group density due to thermal aging is calculated by the correlation between the degradation rate index and time step. The relative attenuation ratio of interfacial bonding force is calculated and defined as the decrease in interfacial shear strength. Then, the incremental ratio of the damaged area of ​​the interface during temperature cycles is evaluated by the degradation rate difference ratio at different temperature zones. Simultaneously, the variation law of interfacial strain over time is established, and the formation trend of interfacial microcracks is judged by matching temperature exceedance points. Combining the above parameters, a quantitative value of the degree of interfacial weakening is obtained. This value reflects the relative decrease in the adhesion performance and load transfer capacity between the polymer and the metal conductive wire, serving as the input parameter for subsequent aging fitting calculations of the external coating material.

[0039] The aging degree of the external cladding material is fitted based on the degradation rate increment index and the degree of interfacial weakening. Using time as the main axis, the material degradation rate sequence and the interfacial adhesion degradation sequence are superimposed at the same sampling interval to construct a complete aging process. For each time period, the weighted average of the cumulative rate of molecular chain breakage and the rate of attenuation of interfacial shear strength is calculated and continuously summed on the time axis to form a material aging process curve. Then, based on the total duration of thermal action and the frequency of high temperatures, linear extrapolation is used to determine when the material properties will reach a specified proportion of their initial strength. This yields the numerical value of the aging degree of the cladding material, serving as a comprehensive reflection of the material's performance degradation within the operating cycle. The results are output in the form of time-series data, indicating the degradation proportion corresponding to the material's structural stability at each moment, providing a basis for predicting the lifespan of transmission line cladding materials.

[0040] Specifically, a continuous derivation logic was formed, from dynamic modeling of the temperature field to identification of thermal effects, regression of energy release, analysis of molecular chain degradation, quantitative analysis of interface weakening, and prediction of aging fitting. The overall process begins by structuring the time-series temperature data obtained from infrared detection of abnormal temperature zones and plotting it as a surface temperature fluctuation curve to dynamically quantify the thermal behavior of the conductor surface. Subsequently, differential analysis is performed on the temperature change rate over time to extract the peak thermal effect, accurately characterizing the transient features of the thermal load. Next, this peak thermal effect is linked with internal heating data, and a time-series model of heat transfer and release at different depths of the conductor is established using a nonlinear regression algorithm, revealing the accumulation and decay patterns of internal thermal energy in the conductor. Based on this, dynamic parameter regression analysis is further performed on the thermal conductivity, specific heat capacity, and coefficient of thermal expansion of the surface material, driven by thermal energy distribution and peak effect, to calculate the incremental index of polymer chain degradation rate, quantifying the rate of thermal degradation at the molecular level. This degradation index is then correlated with changes in the material-metal interface bonding force, reflecting the weakening process of adhesion between the coating layer and the conductor under thermal cycling. Finally, by coupling and fitting molecular degradation and interface weakening data on the time axis, an overall material aging curve is constructed, enabling accurate prediction of the coating layer's lifetime and performance degradation. This logical system enables the closed-loop analysis of thermal, mechanical, and chemical effects, which not only transforms infrared temperature anomalies into microscopic degradation patterns, but also significantly improves the accuracy and traceability of aging assessment of transmission line cladding materials, achieving multi-scale fusion analysis from macroscopic thermal response to microscopic structural deterioration.

[0041] Predicting the safe service life of transmission lines over a time dimension includes: The thermal effect cross-sectional deformation degree of the internal conductor was simulated based on the mechanical strength degradation data of the internal conductor to obtain the degree of conductor cross-sectional deformation. The degree of conductor cross-sectional deformation is deduced and predicted by the change in strand porosity, and the data on the change in strand porosity are output. Based on the data on changes in strand porosity, the probability of strand breakage risk in the conductor is simulated and estimated. Aging trend fitting is performed based on the degree of material aging fitting to obtain aging trend fitting data; The insulation performance degradation gradient is derived from the data fitted based on the aging trend. Based on the probability of strand breakage risk and the gradient of insulation performance degradation, the safe service life of the transmission line is predicted over time to obtain the predicted safe service life of the line.

[0042] In this embodiment of the invention, when simulating the degree of thermal effect cross-sectional deformation of the internal conductor's mechanical strength degradation data, the numerical values ​​reflecting the strength changes of each radial layer of the conductor in the degradation data are input as basic parameters. Based on the actual structure of the conductor, the cross-section is divided into three regions: the central core layer, the inner strand layer, and the outer strand layer. The corresponding decrease in bearing capacity is calculated based on the degradation ratio of each layer. Then, the thermal expansion of each layer is calculated by combining the temperature rise data, accumulating the radial and tangential strain distributions. The deformation is obtained by superimposing the strength attenuation values ​​and thermal strain of the three regions. The obtained data are overlaid and analyzed to determine the degree of non-uniform deformation of the overall cross-section. The relative shrinkage values ​​of the material cross-sectional area between the highest and lowest temperature points are then compared to determine the range of conductor roundness variation. Continuous weighted calculations are performed on the micro-elongation in the axial direction, ultimately forming a comprehensive quantitative result of the conductor cross-sectional deformation degree. The radius variation and deformation difference of each layer are recorded, providing input for the subsequent derivation of the strand structure gap changes.

[0043] After obtaining the results of the conductor cross-sectional deformation, the change in strand porosity is deduced and predicted. First, the radial variation in the cross-sectional deformation data is decomposed into the increase or decrease in the spacing between each strand, and the relative displacement difference of each layer of strands is calculated. Based on the conductor stranding structure parameters, the geometric distribution of the interlaced strands is determined, and the comparison between the center distance of adjacent strands after deformation and the original parameters is calculated. Then, the proportion of increase or decrease in the area of ​​the gaps formed between strands within each cross-sectional region is statistically analyzed, and radial averaging is performed in conjunction with the overall circumferential deformation trend of the conductor to derive the change in porosity. This process calculates each strand node within the cross-section to obtain dynamic porosity distribution data under thermal conditions. The results show the changes in the contact state between strands during continuous thermal expansion and contraction cycles, providing a stable input for strand breakage risk analysis.

[0044] This study simulates and estimates the probability of strand breakage risk in conductors based on data on changes in strand porosity. First, points exceeding a threshold in porosity changes are extracted and designated as areas of significantly weakened support. The distribution ratio of these areas along the conductor's circumference is statistically analyzed, and the local load concentration trend is determined by combining this with the relative slippage of adjacent strands. Then, based on the porosity changes and load concentration in each segment, the corresponding degree of stress unevenness is calculated, and areas of uneven stress are considered potential strand breakage sensitive points. Next, the number and distribution density of sensitive points per unit length of conductor are statistically analyzed, and the probability of strand breakage is calculated using an interval-weighted method. Finally, the probability values ​​are recorded synchronously with the conductor's operating time and temperature rise, outputting conductor strand breakage risk probability data to achieve a quantitative assessment of the structural integrity of the stranded wire.

[0045] When performing aging trend fitting based on the material aging fitting degree, the previously obtained material aging fitting degree data are first arranged in chronological order to establish a sequence of aging degree changes with operating time. The aging degree values ​​in this sequence are then smoothed, and a sliding interval weighted average method is used to eliminate the influence of periodic fluctuations. The corrected data are grouped by time period, and the difference in aging rate changes at each stage is compared to extract the incremental trend. The correspondence between aging rate and upper temperature limit and heat exposure duration is calculated at each stage, and the change trend is recursively extrapolated using a differencing method, updating the aging rate change value step by step. A trend fitting curve is established with time as the independent variable and aging degree as the dependent variable. The change in the curve's growth rate is used to determine the direction of continuous degradation of material performance with operating time. During the calculation process, the same fitting process is performed on the coating layer, insulation layer, and protective coating, and the results from multiple layers are weighted and integrated to obtain a comprehensive trend reflecting the aging development of the entire coating system, outputting the aging trend fitting data.

[0046] The insulation performance degradation gradient is derived based on aging trend fitting data. The time-varying rate of insulation performance degradation is defined according to the difference in aging rates between the surface material and the insulation layer in the aging trend. The insulation performance degradation value for each time period is obtained by calculating the slope of the aging trend curve, and these values ​​are summed sequentially to construct an insulation strength change sequence. Then, based on the number and thickness of the covering material structure, the insulation performance distribution is transformed into a gradient form. Spatial interpolation is then performed on the gradient values ​​to obtain the distribution changes of insulation resistance or breakdown strength in different regions. Gradient comparisons are performed separately in high-temperature load areas and mechanical stress concentration areas to identify the sections with the most significant insulation performance degradation, forming insulation degradation gradient data along the line direction. The gradient values ​​at each location are sorted according to the time axis to form a three-dimensional time series table, providing a quantitative basis for performance degradation estimation of the safe life of transmission lines.

[0047] Based on the probability of strand breakage risk and the insulation performance degradation gradient, the safe service life prediction of transmission lines is performed in the time dimension. First, the strand breakage risk probability data and insulation performance gradient data are synchronized and calibrated to a unified time axis. For each moment, a corresponding risk probability value and insulation performance degradation ratio are matched to calculate a comprehensive failure risk index. These are then accumulated and superimposed along the time axis to form a risk growth curve. The growth rate of the curve is calculated segment by segment to find the point where the risk index first reaches a set safety threshold. This point is used as the cutoff time for the line's safe service life prediction. The prediction results for each conductor segment are then taken as the minimum time value to obtain the overall prediction cycle for the entire transmission line. This result is output in days and bound to the line number and geographical coordinates for subsequent maintenance planning and service life management, completing the entire process of time-dimensional safe service life prediction for transmission lines.

[0048] Specifically, a systematic logical chain was constructed, encompassing thermal deformation analysis, structural void evolution, strand breakage risk assessment, material aging evolution, insulation performance degradation, and lifetime prediction decision-making. The core idea is to perform multi-scale coupled analysis of the mechanical degradation of conductors under thermal conditions and material aging behavior. First, by quantifying the non-uniform deformation in the radial and tangential directions through thermo-mechanical coupling simulation of the conductor cross-section, the true deformation response of the internal metal strands under continuous thermal stress is revealed. Then, based on the deformation results, the change in porosity between the strands is deduced, establishing a mapping relationship between microstructural geometric mismatch and macroscopic mechanical integrity, thereby capturing the dynamic trend of relaxation in the conductor's internal structure. On this basis, through coupled calculation of porosity distribution and local load concentration, a probabilistic assessment of strand breakage risk is formed, realizing the quantitative transformation from physical degradation to failure risk. Simultaneously, for the external cladding and insulation system, a time evolution model of material properties is constructed using an aging fitting sequence, and the decay rate change of the multilayer structure under thermal cycling and environmental stress is inferred through trend curves. Subsequently, an insulation performance attenuation gradient is established based on the difference in aging rates between different layers, achieving a dynamic characterization of the spatial distribution of insulation layer strength. Finally, the mechanical strand breakage risk and the insulation attenuation gradient are synchronously integrated on the time axis to derive the growth trajectory of the comprehensive failure risk over time, accurately determining the safe service life of the transmission line. This technology system achieves full-chain dynamic coupling between multiple physical fields such as heat, force, and insulation, enabling life prediction to no longer rely on a single parameter, but rather to be based on two-dimensional reasoning of structural evolution and material degradation, thereby significantly improving the scientific nature, interpretability, and long-term prediction accuracy of transmission line safety assessment.

[0049] The simulation estimates the probability of strand breakage risk in the conductor, including: Calculate the relative slip of the strands based on the data of changes in strand porosity; Based on the aforementioned data on changes in stock line gap ratio and relative slippage of stock lines, the support weakening index is estimated; The force imbalance coefficient is calculated based on the relative slippage of the stock line and the support weakening index. The probability of conductor breakage risk is estimated by simulation based on the support weakening index and the force imbalance coefficient.

[0050] In this embodiment of the invention, the relative slip of the strands is calculated based on the change data of the strand porosity. First, the change value of the porosity of each strand within the conductor cross-section is read, and the change ratio is correlated with the original geometric spacing to obtain the strand spacing change rate. Utilizing the spatial distribution law of the stranded wire structure, the helix angle and circumferential arrangement spacing of each layer of strands are calculated, converting the increase in porosity into the radial displacement increment between strands. Then, combined with the thermal elongation data of the conductor, the axial micro-displacement caused by thermal expansion and contraction is calculated. The radial and axial displacement components are synthesized to obtain the relative displacement of each strand, which is then summarized to form a data sequence of relative slip of the strands.

[0051] The support weakening index is estimated based on the changes in strand porosity and the relative slippage of the strands. The reduction ratio of the contact area is statistically analyzed based on the number of contact points between adjacent strands and the direction of slippage, and the decrease in frictional bearing capacity under stress is calculated. This ratio, normalized and correlated with the porosity change, is defined as the support attenuation ratio. The support relationship between the outer and inner strands is calculated separately using a layer-by-layer summation method to generate the support weakening index. A higher index indicates a more significant decrease in the structural bonding capacity after thermal deformation.

[0052] The stress imbalance coefficient is calculated based on the relative slip of the strands and the support weakening index. First, the stress distribution of the strands in the conductor cross-section is divided into 12 equal parts circumferentially to establish a stress zoning table. For each zone, the angle change between the slip direction and the principal stress direction is calculated, and the deviation is statistically analyzed. Combining the value of the support weakening index, the degree of local load concentration caused by stress deviation and support subsidence is superimposed to calculate the uneven mechanical distribution in each section. The ratio of the maximum value to the average value is defined as the stress imbalance coefficient, which characterizes the stability of load redistribution in the conductor structure.

[0053] The probability of conductor strand breakage risk is simulated and estimated based on the support weakening index and the stress imbalance coefficient. The two parameters obtained in the first two steps are matched according to their time series, and their cumulative growth rate is calculated. A weighted average of the rate of increase within the same time period is calculated to obtain a composite index reflecting the rate of structural embrittlement. This composite index is then compared with a known strand breakage threshold; when it approaches the threshold, a risk growth stage is marked. The percentage of time during which the risk index exceeds the safety limit is statistically analyzed and used as the output of the strand breakage risk probability.

[0054] Specifically, the changes in the porosity between strands and the axial micro-motion caused by thermal expansion and contraction are quantified into the relative displacement of each strand. Through analysis of twisting geometry, helix angle, and circumferential arrangement, radial and axial displacements are synthesized into a precise slip time series. Based on this, the decrease in contact area and frictional bearing capacity is calculated according to the changes in slip direction and the number of contact points and normalized into a support weakening index, thus directly mapping geometric relaxation into a numerical attenuation of structural support capacity. Subsequently, relative slip and support weakening are coupled within the circumferential partitions of the cross-section, calculating the force deviation in each partition and obtaining the force imbalance coefficient in the form of a maximum / mean ratio, characterizing the load redistribution and the degree of local stress concentration. Finally, the time series of support weakening and force imbalance are accumulated and compared with the critical threshold for strand breakage. Based on the compound growth rate, the probability of strand breakage is output and the risk growth segment is marked. Starting from microscopic geometric evolution and integrating contact mechanics and cross-sectional load distribution, this closed loop can transform thermal-deformation-induced structural degradation into a quantifiable strand breakage risk indicator, thereby enabling directional identification of strand breakage locations, risk prioritization, and early warning, significantly improving the sensitivity and decision support capabilities of conductor integrity assessment.

[0055] Step S3 includes the following steps: The remaining safe service life of the line is cross-validated by the prediction period of safe service life to obtain the life reduction verification data. The lifetime decline verification data is subjected to multi-scale normalization processing to output lifetime decline normalized data. The system uses a pre-set line maintenance manual to generate maintenance recommendations based on normalized data of decreasing service life, and then feeds these recommendations back to the power transmission control center for transmission line testing.

[0056] As an example of the present invention, reference is made to... Figure 3 As shown, step S3 in this example includes: S31: Perform cross-validation of the remaining safe service life of the predicted safe service life of the line to obtain the life reduction verification data. In this embodiment of the invention, the remaining safe service life is cross-validated by decreasing cross-validation during the predicted safe service life period of the power line. Using the predicted period data as the core, measured temperature curves, load change sequences, and aging parameter records from the line's operational history are extracted to construct a time series sample set. The predicted period is divided into several continuous time periods, and the operating data and predicted results are compared segment by segment using a sliding window method. The difference in lifespan decrease between the two is calculated, and the root mean square operation is performed on the error sequence for each time period to form a residual distribution. The residual distribution is then iteratively compared to finally output lifespan decrease verification data.

[0057] S32: Perform multi-scale normalization processing on the lifetime decline verification data to output lifetime decline normalized data; In this embodiment of the invention, multi-scale normalization processing is performed on the lifespan decline verification data. First, the lifespan differences across time periods are regrouped according to three scales: day, month, and season. The extreme value range is then independently calculated for each group. Next, a linear interval transformation algorithm is used to map the values ​​at different scales to a unified interval, ensuring consistency in comparisons across different time levels. To prevent a single high value from causing imbalance in the overall results, a piecewise averaging method is applied when calculating the normalized value to normalize the changing trend of the lifespan decline characteristic parameters, forming a multi-scale normalization matrix. After processing, lifespan decline normalized data is output, providing standard input for customized maintenance strategies.

[0058] S33: Using the preset line maintenance manual, maintenance suggestions are generated based on the normalized data of decreasing service life, and then fed back to the transmission control center to perform transmission line testing.

[0059] In this embodiment of the invention, maintenance recommendations are generated from normalized data representing decreasing service life using a pre-set line maintenance manual. First, the standard maintenance thresholds in the manual are read, and the values ​​of each line unit in the normalized data are matched with the corresponding critical levels in the manual. Based on the matching results, the line status is divided into three categories: normal zone, inspection zone, and mandatory repair zone. Then, combined with the line model, years of operation, and load distribution, corresponding maintenance instructions are generated, including specific items such as temperature control detection, conductor replacement, insulation treatment, and connector tightening. Finally, the data interface automatically encapsulates the results into a maintenance task data packet, which is transmitted to the transmission control center via the communication module for the backend system to trigger maintenance scheduling, realizing automatic detection and intelligent maintenance decisions for transmission lines.

[0060] Specifically, by cross-validating the predicted remaining safe service life of the line during its safe service life prediction period, measured data from line operation, including temperature curves, load changes, and aging parameters, are extracted and analyzed. The predicted results and actual operating data are compared segment by segment using the sliding window method to calculate the lifespan reduction difference for each time period. An error sequence is then generated using root mean square (RMS) calculation, ultimately forming a residual distribution. This process provides accurate "lifespan reduction verification data" for subsequent maintenance decisions. Next, these verification data undergo multi-scale normalization. Data is grouped by scales such as day, month, and season, and linear interval transformations are performed to ensure comparability across different time scales. A piecewise averaging method is used to avoid interference from single outliers. This method results in more stable and consistent data normalization, forming a multi-scale normalization matrix and providing a clear and standardized input dataset, offering a solid data foundation for maintenance strategies. Finally, using a pre-defined line maintenance manual, maintenance recommendations are generated based on the normalized lifespan reduction data. The system matches normalized data with standard maintenance thresholds in the maintenance manual, classifies line status into different levels, and generates specific maintenance instructions based on the line's specific characteristics (such as model, years of operation, load distribution, etc.). These instructions are ultimately encapsulated into maintenance task data packets through a data interface and transmitted to the transmission control center, automatically triggering maintenance scheduling and realizing intelligent line detection and maintenance decision-making.

[0061] The present invention also provides a power transmission line detection system based on UAV infrared images, for performing the power transmission line detection method based on UAV infrared images as described above. The power transmission line detection system based on UAV infrared images includes: The abnormal temperature area marking module is used to acquire infrared images of transmission lines through thermal infrared remote sensing equipment; monitor the load status of transmission lines in real time; and mark abnormal temperature areas in the infrared images of transmission lines to obtain abnormal temperature areas of external lines. The safe service life prediction module is used to fit the aging degree of the external wrapping material according to the abnormal temperature area of ​​the line to obtain the material aging fitting degree; based on the material aging fitting degree, the safe service life of the transmission line is predicted in the time dimension to obtain the line safe service life prediction period. The maintenance and repair suggestion generation module is used to generate maintenance and repair suggestions based on the predicted safe service cycle of the line using a preset line maintenance and repair manual, and then feed them back to the transmission control center to perform transmission line inspection.

[0062] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily 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 invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A power transmission line detection method based on an unmanned aerial vehicle infrared image, characterized in that, The power transmission line detection method based on the unmanned aerial vehicle infrared image comprises the following steps: Step S1: acquiring the infrared image of the power transmission line by the thermal infrared remote sensing device; monitoring the load state of the power transmission line in real time; marking the abnormal temperature area of the power transmission line infrared image to obtain the external line abnormal temperature area; Step S2: fitting the aging degree of the external wrapping material according to the line abnormal temperature area to obtain the material aging fitting degree; predicting the safe use period of the power transmission line in the time dimension based on the material aging fitting degree to obtain the line safe use prediction period; Step S3: generating the maintenance and repair suggestion for the line safe use prediction period by using the preset line maintenance and repair manual, and feeding back to the power transmission control center to execute the power transmission line detection. 2.The power transmission line detection method based on UAV infrared images according to claim 1, characterized in that, Step S1 comprises the following steps: During the summer high-temperature inspection of the unmanned aerial vehicle, the thermal infrared remote sensing device is used to collect the infrared image of the power transmission line at a distance of 5 meters from the power transmission line along the horizontal direction of the power transmission line at a speed of 0.5 m / s; the load state of the power transmission line is monitored in real time; The infrared contrast adjustment image is generated by performing contrast optimization processing on the infrared image of the power transmission line; The infrared filter image is obtained by performing median filter processing on the infrared contrast adjustment image; When the temperature of the power transmission line in the image is greater than 70℃ based on the infrared filter image, the abnormal temperature area is marked to obtain the external line abnormal temperature area. 3.The power transmission line detection method based on UAV infrared images of claim 1, wherein, Step S2 comprises the following steps: Obtain the surface material characteristics of the power transmission line; According to the external line abnormal temperature area and the load state of the power transmission line, the internal heat state of the line corresponding to the external abnormal temperature area is evaluated; The internal heat state of the line corresponding to the internal heat state is analyzed to obtain the internal conductor mechanical strength degradation data; According to the line abnormal temperature area and the internal heat state of the line corresponding to the internal heat state, the external wrapping material aging degree fitting of the surface material characteristics of the power transmission line is performed to obtain the material aging fitting degree; Based on the internal conductor mechanical strength degradation data and the material aging fitting degree, the safe use period of the power transmission line in the time dimension is predicted to obtain the line safe use prediction period. 4.The power transmission line detection method based on UAV infrared images according to claim 3, characterized in that, The internal heat state of the line corresponding to the internal heat state is analyzed to obtain the internal conductor mechanical strength degradation data. The internal heat state of the line corresponding to the internal heat state is analyzed to obtain the internal conductor mechanical strength degradation data. The internal heat state of the line corresponding to the internal heat state is analyzed to obtain the internal conductor mechanical strength degradation data. The internal heat state of the line corresponding to the internal heat state is analyzed to obtain the internal conductor mechanical strength degradation data. ​ ​ 5.The power transmission line detection method based on UAV infrared images of claim 3, wherein, ​ Based on the line abnormal temperature area, a surface layer temperature fluctuation curve is drawn to obtain a line surface layer temperature fluctuation curve; the thermal conductivity, specific heat capacity and thermal expansion coefficient in the surface layer material characteristics of the power transmission line are extracted; Based on the line surface layer temperature fluctuation curve, the peak value of thermal effect in time dimension is predicted to obtain the surface layer peak value of thermal effect; Nonlinear thermal energy release regression analysis is performed on the corresponding internal heat generation state of the line to output nonlinear thermal energy release regression data; According to the surface layer peak value of thermal effect and the nonlinear thermal energy release regression data, the thermal conductivity, specific heat capacity and thermal expansion coefficient in the surface layer material characteristics of the power line are subjected to material polymer chain degradation rate increment index analysis to obtain the degradation rate increment index; According to the degradation rate increment index, the interface weakening degree is associated and coupled; Based on the degradation rate increment index and the interface weakening degree, the aging degree of the external wrapping material is fitted to obtain the material aging fitting degree. 6.The power transmission line detection method based on UAV infrared images of claim 3, wherein, The safety use cycle prediction of the power transmission line in time dimension includes: The thermal effect cross-section deformation degree is simulated based on the internal conductor mechanical strength degradation data to obtain the conductor cross-section deformation degree; The strand void ratio change data is output by deducing and predicting the strand void ratio change based on the conductor cross-section deformation degree; The strand breakage risk probability of the conductor is simulated and estimated based on the strand void ratio change data; The aging trend fitting data is obtained by performing aging trend fitting processing according to the material aging fitting degree; The insulation performance attenuation gradient is deduced according to the aging trend fitting data; The safety use cycle prediction of the power transmission line in time dimension is performed according to the strand breakage risk probability and the insulation performance attenuation gradient to obtain the line safety use prediction cycle. 7.The power transmission line detection method based on UAV infrared images of claim 6, wherein, The strand breakage risk probability of the conductor includes: The strand relative slip amount is calculated based on the strand void ratio change data; The support weakening index is estimated according to the strand void ratio change data and the strand relative slip amount; The stress imbalance coefficient is calculated according to the strand relative slip amount and the support weakening index; The strand breakage risk probability of the conductor is simulated and estimated based on the support weakening index and the stress imbalance coefficient. 8.The power transmission line detection method based on UAV infrared images of claim 1, wherein, Step S3 includes the following steps: The remaining safety use life decrement cross verification is performed on the line safety use prediction cycle to obtain life decrement verification data; The life decrement reduction data is output by performing multi-scale normalization processing on the life decrement verification data; The maintenance and repair suggestions are generated for the life decrement reduction data by using the preset line maintenance and repair manual, and are fed back to the power transmission control center to perform power transmission line detection.

9. A power transmission line detection system based on UAV infrared images, characterized by, The unmanned aerial vehicle infrared image-based power transmission line detection system for executing the unmanned aerial vehicle infrared image-based power transmission line detection method of claim 1 includes: An abnormal temperature area marking module is configured to collect power transmission line infrared images by a thermal infrared remote sensing device, monitor the load state of the power transmission line in real time, and mark the abnormal temperature area of the power transmission line infrared images to obtain the external line abnormal temperature area. The safety use period prediction module is configured to perform fitting of the aging degree of the external wrapping material according to the abnormal temperature area of the line, to obtain a material aging fitting degree; and perform safety use period prediction of the power transmission line in the time dimension based on the material aging fitting degree, to obtain a line safety use prediction period. The maintenance and repair suggestion generation module is configured to generate a maintenance and repair suggestion for the line safety use prediction period by using a preset line maintenance and repair manual, and feed back to the power transmission control center, to perform power transmission line detection.

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