10kV cable joint temperature inversion correction method based on environment heat exchange condition

Abstract

The invention relates to the technical field of power equipment monitoring, in particular to a 10kV cable joint temperature inversion correction method based on environmental heat exchange conditions, which comprises the following steps: measuring surface temperature data through a sensor array, and analyzing material layering characteristics in combination with X-ray imaging; then, establishing an electromagnetic-thermal-fluid field coupling model, and calculating a dynamic thermal resistance coefficient and a heat flow distortion index; thirdly, optimizing surface temperature distribution and iteratively updating thermal resistance parameters by utilizing a bidirectional feedback correction strategy, and finally generating a high-precision internal temperature field; through adaptive temperature compensation and multi-source data fusion, the accuracy of temperature prediction is improved, long-term monitoring errors are reduced, the method can be widely applied to intelligent operation and maintenance and fault early warning of cable joints, and the safety and stability of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and particularly to a method for inversely correcting the temperature of a 10 kV cable joint based on environmental heat exchange conditions. Background Art

[0002] In the power transmission and distribution system, 10 kV cables are widely used in urban power grids, industrial parks, and power supply networks of critical infrastructure. Their operation safety is directly related to the stability and reliability of the power system. As the most vulnerable key part in the cable line, the cable joint is affected by factors such as current load, environmental temperature change, and material aging for a long time, and is prone to abnormal temperature rise, which may further lead to local overheating, insulation deterioration, and even short - circuit faults. Therefore, real - time monitoring of the temperature of the cable joint and accurate prediction of the evolution of the internal temperature field based on a physical model are of great significance for cable operation state assessment and fault warning.

[0003] The existing cable joint temperature monitoring technologies mainly rely on thermal imaging, fiber Bragg grating temperature sensors, or infrared thermometers to obtain surface temperature data, and calculate the internal temperature through empirical formulas or heat conduction models. However, these methods have various technical limitations in practical applications. First, the monitoring methods based solely on surface temperature cannot fully consider the influence of factors such as material stratification, air gap distribution, and environmental heat exchange on the internal temperature, resulting in a large error in the calculated internal temperature. Second, some methods use fixed thermal resistance parameters for calculation and fail to perform adaptive correction according to the actual operating environment changes. After long - term use, the calculation accuracy of the model gradually decreases, affecting the monitoring reliability. In addition, the existing methods lack effective dynamic compensation strategies when facing sudden changes in environmental temperature (such as increased wind speed, humidity change, and load fluctuation), which are prone to false alarms or missed alarms in abnormal identification. Summary of the Invention

[0004] The present invention provides a method for inversely correcting the temperature of a 10 kV cable joint based on environmental heat exchange conditions, which combines a temperature inversion and correction method with environmental heat exchange characteristics, dynamic thermal resistance optimization, and adaptive compensation to improve the accuracy of cable joint temperature prediction and the stability of long - term monitoring, thereby reducing the operation risk of cable equipment.

[0005] A method for inversely correcting the temperature of a 10 kV cable joint based on environmental heat exchange conditions includes the following steps: S1: Real - time collect a multi - source monitoring data set of the target cable joint, including surface temperature distribution data, cable operating current data, environmental heat exchange parameter set, and joint material stratification parameters; The environmental heat exchange parameter set includes environmental temperature, wind speed, air humidity, and transient heat flux density; S2: Based on the material layering parameters and the environmental heat exchange parameter set, construct a multi-physical field coupling model for the cable joint. The multi-physical field coupling model synchronously correlates the electromagnetic field - thermal field - fluid field, and outputs the dynamic thermal resistance coefficient and the layered heat flux distortion index of the cable joint; S3: Input the dynamic thermal resistance coefficient and the layered heat flux distortion index along with the surface temperature distribution data into the phase change compensation algorithm to generate the transient compensation factor for the internal temperature of the cable joint; The phase change compensation algorithm triggers the compensation logic through the sudden change of the environmental heat flux density to correct the internal temperature distortion caused by the material layering and the transient change of the environmental parameters; S4: Based on the transient compensation factor, perform two-way feedback correction on the surface temperature distribution data, including: Forward correction: Adjust the spatial weight distribution of the surface temperature according to the transient compensation factor; Reverse correction: Input the corrected internal temperature value back into the multi-physical field coupling model to iteratively optimize the dynamic thermal resistance coefficient; Finally, output the internal temperature field distribution of the cable joint.

[0006] Optionally, the S1 includes: S11: Collect the surface temperature distribution data of the cable joint through a distributed temperature sensor array. The sensor array is uniformly arranged along the axial and circumferential directions of the cable joint to form a temperature monitoring grid, and output a temperature distribution matrix containing spatial coordinates - temperature values; S12: Synchronously collect the cable operating current data, including the current effective value, current harmonic components, and current mutation time series. Extract the current spectrum characteristics through a high-frequency sampling module, and output the cable operating current data set; S13: Obtain the environmental heat exchange parameter set, and measure the environmental temperature, wind speed, and air humidity in real time through a meteorological sensor; Calculate the transient heat flux density based on the wind speed and air humidity; Output the environmental heat exchange parameter set containing the environmental temperature, wind speed, air humidity, and transient heat flux density; S14: Perform X-ray computed tomography imaging on the cable joint to extract the joint material layering parameters; S15: Perform spatio-temporal alignment and normalization processing on the temperature distribution matrix, the cable operating current data set, the environmental heat exchange parameter set, and the joint material layering parameters, and integrate them into a multi-source monitoring data set.

[0007] Optionally, the S14 specifically includes: S141, X-ray image preprocessing and gray value distribution analysis: Obtain the multi-layer tomographic images of the cable joint through an X-ray computed tomography system, Perform gray value normalization for different material layers (conductor, insulating layer, semiconductive layer) to enhance the contrast of the material interface; Use histogram equalization and adaptive contrast enhancement algorithms to adjust the dynamic range of the image, ensuring that the gray value distribution can reflect the characteristics of the insulating layer thickness change. Analyze the pixel gray values of the preprocessed multi-layer tomographic images, establish a gray value-thickness mapping model, where the change gradient of the gray value is proportional to the insulating layer thickness, extract the boundary gray change points of each layer, and calculate the interlayer thickness distribution, and output the layer thickness distribution curve; S142, Calculation of the insulating layer thickness deviation: Based on the layer thickness distribution curve, use the curve fitting algorithm to smooth the thickness values at different positions, and calculate the thickness mean and standard deviation of each layer as the insulating layer thickness deviation index; S143, Edge detection and segmentation of the interface air gap area: Use the Canny edge detection algorithm to extract the edges of the multi-layer tomographic images, enhance the boundary features between the insulating layer and the conductor and semiconductive layer, combine the gradient direction and intensity information, and remove the pseudo-edge noise through morphological operations (erosion and dilation), and extract continuous edges with a closed structure. Based on the Hough transform, detect possible circular or strip-shaped interface voids to obtain the potential interface air gap area; S144, Calculation of the air gap area ratio and extraction of spatial coordinates: Based on the identified interface air gap area, calculate the area ratio of the air gap; At the same time, calculate the centroid coordinates of each air gap area, establish an air gap distribution matrix, record the distribution coordinates and area ratio of the air gaps at different positions, and output the air gap distribution parameters (including the air gap area ratio and spatial distribution coordinates); S145, Generation of delamination parameters: Combine the insulating layer thickness deviation index and the air gap distribution parameters to construct the joint material delamination parameters.

[0008] Optionally, the S2 includes: S21, Model construction: Based on the joint material delamination parameters, construct a multi-scale geometric model of the cable joint, According to the distribution coordinates of the air gaps, introduce a virtual layer with low thermal conductivity in the geometric model. The thermal conductivity function of the virtual layer is expressed as: , where, is the thermal conductivity of air, is the air gap area, is the total area of the region; To reflect the heat conduction characteristics of the air gap area, finally output a geometric model with defect marks as the solution domain for subsequent multi-physics field calculations.

[0009] S22, Establishment of control equations: Establish the physical field control equations of the electromagnetic field-thermal field-fluid field.

[0010] Optionally, S3 further includes: S23, coupled solution: Based on the geometric model with defect markers, solve the physical field control equations of the electromagnetic field-thermal field-fluid field to generate the dynamic thermal resistance coefficient and the hierarchical heat flux distortion index.

[0011] S24, coupled model verification and error optimization: Perform spatial matching between the temperature distribution matrix output by S11 and the solved temperature field, and calculate the root mean square error between the two. If the root mean square error exceeds the preset threshold, adjust the parameters of the virtual layer thermal conductivity function in S21 and re-perform the multi-physical field solution until the error converges. Finally, output the optimized dynamic thermal resistance coefficient and the hierarchical heat flux distortion index to step S3 to ensure the accuracy of temperature inversion correction.

[0012] Optionally, S3 includes: S31, identify mutation events: Continuously monitor the time series data of the transient heat flux density based on the set of environmental heat exchange parameters output by S13, identify mutation events, and encode the mutation types, and output the heat flux density mutation flag and the mutation type encoding to guide the subsequent temperature gradient calculation and compensation logic matching; S32, temperature gradient deviation calculation: Combine the dynamic thermal resistance coefficient and the hierarchical heat flux distortion index output by S23 to calculate the temperature gradient deviation inside the cable joint to quantify the impact of environmental mutations on the temperature field, perform maximum-minimum normalization on the gradient deviation, and output the standardized temperature gradient deviation matrix, which provides the core input for the phase change compensation strategy.

[0013] S33, match compensation strategy: Based on the mutation type encoding and the standardized temperature gradient deviation matrix, match the corresponding compensation strategy and generate the initial compensation factor.

[0014] Optionally, S3 further includes: S34, generate transient compensation factor: Perform timeliness correction on the initial compensation factor to generate the transient compensation factor; S35, verification and optimization: Verify the effectiveness of the generated transient compensation factor and optimize the calculation rules.

[0015] Optionally, S4 includes: S41, forward correction: Perform forward correction, adjust the spatial weights of the surface temperature distribution data according to the output transient compensation factor, and output the corrected surface temperature matrix; S42, reverse correction: Based on the corrected surface temperature matrix as the boundary condition of the coupled model, re-solve the temperature field and optimize the dynamic thermal resistance coefficient, and output the updated dynamic thermal resistance coefficient and the hierarchical heat flux distortion index.

[0016] Optionally, S4 further includes: S43, two-way correction verification: Based on the corrected surface temperature matrix and the recalculated temperature field, calculate the error residual between the two to determine whether the correction converges. If the residual does not converge, reduce the learning rate and re-execute the optimization calculation in S42; S44: Data fusion: After the correction converges, fuse data from different sources to generate the final internal temperature field distribution.

[0017] Advantages of the present invention: In the present invention, based on the environmental heat exchange characteristics, a multi-source data fusion and physical field coupling modeling method is constructed. By collecting the surface temperature of the cable joint, the operating current of the cable, environmental parameters, and material stratification characteristics in real time, a multi-physical field calculation model covering factors such as electromagnetism, heat flow, and air convection is established. Aiming at influencing factors such as air gaps and insulation layer thickness deviations, the present invention adopts a transient compensation algorithm to correct the influence of environmental changes on the temperature field, and combines forward temperature correction and reverse thermal resistance optimization to improve the internal temperature prediction accuracy. Experiments show that compared with traditional methods, the present invention can significantly reduce the temperature calculation error, make the predicted temperature closer to the actual operating state, and improve the reliability of the safety assessment of the cable joint.

[0018] The present invention proposes a two-way feedback temperature correction strategy, which combines surface temperature optimization and dynamic thermal resistance iterative adjustment to achieve adaptive optimization of the temperature field calculation. In the forward correction process, the transient compensation factor is used to optimize the surface temperature distribution and reduce the local temperature deviation caused by air gaps and environmental changes. In the reverse correction process, the dynamic thermal resistance is recalculated based on the optimized surface temperature, and the temperature compensation parameters can be continuously adjusted according to the actual operating environment through iterative solution of the multi-physical field model. The present invention uses a residual calculation method to verify the correction convergence, and optimizes the correction accuracy through dynamic learning rate adjustment to ensure that the system can automatically adapt to the temperature field changes during long-term operation and improve the stability of temperature monitoring.

[0019] The present invention proposes a hierarchical compensation algorithm based on dynamic thermal resistance optimization and temperature gradient correction for the heat flow distortion caused by cable joint material stratification, interface air gaps, and external environmental changes. The insulation layer thickness deviation and air gap distribution are extracted by high-precision X-ray tomography, and combined with the virtual layer thermal conductivity calculation method to accurately quantify the heat conduction characteristics of the air gap region. In the temperature correction process, the optimized surface temperature, thermal resistance parameters, and transient compensation factor are fused by three-dimensional interpolation method to generate an internal temperature field that more conforms to the heat conduction characteristics of the cable joint. Compared with traditional fixed compensation methods, the present invention can accurately adapt to the actual operating conditions of different cable joints, improve the accuracy of temperature anomaly identification, reduce the false alarm rate, and further improve the long-term operation safety of cable equipment. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only for the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention; Figure 2 It is a schematic flowchart of the S3 process according to an embodiment of the present invention. Specific embodiments

[0022] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the accompanying drawings are only for more specifically describing the embodiments and are not intended to specifically limit the present invention.

[0023] It should be pointed out that in the specification, when referring to "an embodiment", "embodiments", "exemplary embodiments", "some embodiments", etc., it indicates that the described embodiments may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. Additionally, when combining embodiments to describe specific features, structures or characteristics, achieving such features, structures or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0024] Generally, terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather, at least in part depending on the context, allowing for the existence of other factors that may not be explicitly described.

[0025] As Figure 1 - Figure 2 shown, a temperature inversion correction method for a 10 kV cable joint based on environmental heat exchange conditions includes the following steps: S1: Real-time collect a multi-source monitoring data set of the target cable joint, including surface temperature distribution data, cable operating current data, environmental heat exchange parameter set, and joint material delamination parameters; The environmental heat exchange parameter set includes environmental temperature, wind speed, air humidity, and transient heat flux density; S2: Based on the material stratification parameters and the environmental heat exchange parameter set, construct a multi-physical field coupling model for the cable joint. The multi-physical field coupling model synchronously correlates the electromagnetic field - thermal field - fluid field, and outputs the dynamic thermal resistance coefficient and the stratified heat flux distortion index of the cable joint; S3: Input the dynamic thermal resistance coefficient, the stratified heat flux distortion index, and the surface temperature distribution data into the phase change compensation algorithm to generate the transient compensation factor for the internal temperature of the cable joint; The phase change compensation algorithm triggers the compensation logic through the sudden change of the environmental heat flux density, and corrects the internal temperature distortion caused by the material stratification and the transient change of environmental parameters; S4: Based on the transient compensation factor, perform two-way feedback correction on the surface temperature distribution data, including: Forward correction: Adjust the spatial weight distribution of the surface temperature according to the transient compensation factor; Reverse correction: Input the corrected internal temperature value back into the multi-physical field coupling model to iteratively optimize the dynamic thermal resistance coefficient; Finally, output the internal temperature field distribution of the cable joint.

[0026] S1 includes: S11: Collect the surface temperature distribution data of the cable joint through a distributed temperature sensor array. The sensor array is evenly arranged along the axial and circumferential directions of the cable joint to form a temperature monitoring grid, and output a temperature distribution matrix containing spatial coordinates - temperature values; S12: Synchronously collect the cable operating current data, including the current effective value, current harmonic components, and current mutation time series. Extract the current spectrum characteristics through a high-frequency sampling module, and output the cable operating current data set; S13: Obtain the environmental heat exchange parameter set, and measure the environmental temperature, wind speed, and air humidity in real time through meteorological sensors; Calculate the transient heat flux density based on the wind speed and air humidity. The steps are as follows: First, calculate the dynamic convective heat transfer coefficient through the wind speed and air humidity, expressed as: ; Among them, is the wind speed, is the air humidity, is the dynamic convective heat transfer coefficient, is the heat transfer model coefficient fitted based on experimental data; Next, use the heat flux density formula to calculate the transient heat flux density , expressed as: ; Among them, is the temperature of the cable joint surface, obtained through the temperature distribution matrix, is the environmental temperature; Output an environmental heat exchange parameter set including ambient temperature, wind speed, air humidity, and transient heat flux density; S14: Conduct X-ray computed tomography imaging on the cable joint and extract the joint material stratification parameters; S15: Perform spatio-temporal alignment and normalization processing on the temperature distribution matrix, cable operating current data set, environmental heat exchange parameter set, and joint material stratification parameters, and integrate them into a multi-source monitoring data set.

[0027] S14 specifically includes: S141, X-ray image preprocessing and gray value distribution analysis: Obtain multi-layer tomographic images of the cable joint through the X-ray computed tomography system, Perform gray value normalization processing for different material layers (conductor, insulation layer, semi-conductive layer) to enhance the contrast of the material interface; Use histogram equalization and adaptive contrast enhancement algorithms to adjust the dynamic range of the image, ensuring that the gray value distribution can reflect the characteristics of the insulation layer thickness change. Perform pixel gray value analysis on the preprocessed multi-layer tomographic images, establish a gray value-thickness mapping model, where the change gradient of the gray value is proportional to the insulation layer thickness, extract the boundary gray change points of each layer, and calculate the interlayer thickness distribution, and output the layer thickness distribution curve; S142, Insulation layer thickness deviation calculation: Based on the layer thickness distribution curve, use the curve fitting algorithm to smooth the thickness values at different positions, and calculate the thickness mean and standard deviation of each layer as the insulation layer thickness deviation index. The specific calculation formula is as follows: ; ; Among them, is the thickness mean, is the standard deviation, represents the insulation layer thickness corresponding to the th pixel point, is the total number of sampling points. Finally, output the insulation layer thickness deviation index and its spatial distribution matrix for analyzing the uniformity of the insulation layer thickness; S143, Edge detection and segmentation of the interface air gap region: Use the Canny edge detection algorithm to extract the edges of the multi-layer tomographic images, enhance the boundary features between the insulation layer and the conductor and semi-conductive layer, combine the gradient direction and intensity information, remove the pseudo-edge noise through morphological operations (erosion and dilation), and extract continuous edges with a closed structure. Detect possible circular or strip-shaped interface voids based on the Hough transform to obtain the potential interface air gap region; S144, Calculation of the air gap area ratio and extraction of spatial coordinates: Based on the identified interface air gap region, calculate the area ratio of the air gap , expressed as: ; Wherein, is the total area of the detected air gap region, is the total cross-sectional area of the cable joint; Meanwhile, calculate the centroid coordinates of each air gap region , and establish an air gap distribution matrix to record the distribution coordinates and area ratios of air gaps at different positions, and output air gap distribution parameters (including air gap area ratios and spatial distribution coordinates) as important inputs for subsequent thermal resistance calculation and temperature correction; S145, generation of layering parameters: Combine the insulation layer thickness deviation index and air gap distribution parameters to construct the joint material layering parameters. This parameter, as the key structural feature information of the cable joint, is provided to S15 for normalization processing of the multi-source monitoring data set and further used for the construction of the multi-physical field coupling model of the cable joint.

[0028] S2 includes: S21, model construction: Based on the joint material layering parameters, construct a multi-scale geometric model of the cable joint, Based on the material layering parameters output by S14, reconstruct the internal geometric structure of the cable joint in 3D modeling software and construct geometric entities that conform to the actual defect characteristics. Specifically, based on the insulation layer thickness deviation index, define the thickness variation of different regions in the model to simulate the non-uniformity of the cable insulation layer; according to the distribution coordinates of the air gaps, introduce a virtual layer with low thermal conductivity in the geometric model, and the thermal conductivity function of the virtual layer is expressed as: Wherein, is the thermal conductivity of air, is the air gap area, is the total area of the region; to reflect the heat conduction characteristics of the air gap region, and finally output a geometric model with defect marks as the solution domain for subsequent multi-physical field calculations.

[0029] S22, establishment of control equations: Establish physical field control equations for the electromagnetic field - thermal field - fluid field, and the steps are as follows: Electromagnetic field: Use the cable operating current data set obtained by S12, extract the current harmonic components, and calculate the Joule heat distribution inside the cable joint conductor based on Maxwell's equations, so as to generate an electromagnetic loss density matrix as the subsequent heat source term.

[0030] Thermal field: Take the electromagnetic loss density matrix as the heat source term, and combine the transient heat flux density to construct an unsteady heat conduction equation: ; Wherein, The electromagnetic loss density matrix from electromagnetic field solution The environmental heat flux density are the volume density and specific heat capacity of the insulating layer Fluid field: According to the environmental wind speed and air humidity, calculate the convective heat transfer coefficient of the air flow on the surface of the cable joint and embed it into the boundary conditions of the heat field equation

[0031] Finally, output the complete coupled control equations of the electromagnetic-thermal-fluid field, providing a basis for the next step of solution

[0032] S3 also includes S23, coupled solution: Based on the geometric model with defect markers, solve the physical field control equations of the electromagnetic field-thermal field-fluid field to generate the dynamic thermal resistance coefficient and the stratified heat flux distortion index First, combine the temperature gradient distribution of the heat conduction equation to calculate the dynamic thermal resistance coefficient of the cable joint : ; where is the volume of the calculation region, covering the insulating layer and air gap regions of the cable joint is defined by the defective geometry model to ensure that the thermal properties of the air gap region are accurately simulated. In addition, extract the heat flux density vector field of the air gap region from the calculated temperature field and calculate its deviation from the ideal defect-free model to obtain the stratified heat flux distortion index , and the temperature field is obtained by solving the unsteady heat conduction equation , where is the stratified heat flux distortion index is the heat flux density in the defect region is the heat flux density in the defect-free model is the number of calculation grid points After normalization, generate the stratified heat flux distortion index to quantify the influence of the air gap on heat flux transmission. Finally, output the dynamic thermal resistance coefficient and the stratified heat flux distortion index, providing inputs for temperature inversion correction

[0033] S24, coupled model verification and error optimization: Spatially match the temperature distribution matrix output by S11 with the temperature field solved by S23 and calculate the root mean square error , expressed as ; where is the actually measured temperature data The temperature value calculated for the model. If the root mean square error exceeds the preset threshold, the parameters of the virtual layer thermal conductivity function in S21 are adjusted, and the multi-physics solution is re-executed until the error converges. Finally, the optimized dynamic thermal resistance coefficient and the hierarchical heat flux distortion index are output to step S3 to ensure the accuracy of temperature inversion correction.

[0034] S3 includes: S31, identifying mutation events: Based on the set of environmental heat exchange parameters output by S13, continuously monitor the time series data of the transient heat flux density, identify mutation events, and encode the mutation types, including: When the change rate of the heat flux density exceeds the threshold a heat flux density mutation flag is generated, indicating that there is a significant change in the heat exchange of the cable joint by the external environment. To further classify the mutation types, the mutation types are distinguished according to the change directions of wind speed and humidity (such as sudden cooling caused by heavy rain or rapid heat dissipation caused by strong wind), and the mutation type code is output. For example, sudden cooling caused by heavy rain (code 01) or rapid heat dissipation caused by strong wind (code 02). Finally, the heat flux density mutation flag and the mutation type code are output to guide the subsequent temperature gradient calculation and compensation logic matching; S32, calculating the temperature gradient deviation: Combining the dynamic thermal resistance coefficient and the hierarchical heat flux distortion index output by S23, calculate the temperature gradient deviation inside the cable joint to quantify the impact of environmental mutations on the temperature field.

[0035] First, calculate the reference temperature of the defect-free model based on the surface temperature distribution data , and then calculate the superposition of the temperature gradient deviation according to the following formula : ; where, is the dynamic thermal resistance coefficient, is the surface temperature distribution data, is the change amount of the transient heat flux density, is the weight factor of the hierarchical heat flux distortion index. The calculated temperature gradient deviation reflects the internal temperature field distortion caused by the uneven distribution of the material layer and environmental changes. To further standardize the data, the gradient deviation is normalized by the maximum - minimum value, and the standardized temperature gradient deviation matrix is output, providing the core input for the phase change compensation strategy.

[0036] S33, matching the compensation strategy: Based on the mutation type code and the standardized temperature gradient deviation matrix, match the corresponding compensation strategy and generate the initial compensation factor, specifically as follows: Phase change compensation logic: If the mutation type code is rapid cooling (code 01), adjust the compensation weight of the dynamic thermal resistance coefficient to enhance the adaptability of the insulation layer to rapid temperature drop; if the mutation type code is rapid heat dissipation (code 02), first correct the influence of the stratified heat flux distortion index to alleviate the local heat dissipation enhancement effect caused by the air gap structure.

[0037] Compensation factor calculation: Calculate the compensation factor through a weighting function , calculated as: ; Among them, , , is the compensation weight, which is adaptively adjusted according to the mutation type. The calculated initial compensation factor represents the influence degree of the compensation strategy on different physical variables and is used as the preliminary result of transient compensation correction.

[0038] S3 also includes: S34, generating a transient compensation factor: Perform timeliness correction on the initial compensation factor to generate a transient compensation factor, specifically as follows: First, introduce a time decay factor to control the action time of the compensation factor and ensure that the compensation gradually decays after a short-term heat flux mutation, where the decay coefficient is dynamically adjusted according to the mutation type (set to a smaller value in the rapid cooling scenario to maintain a longer compensation timeliness, while set to a larger value in the rapid heat dissipation scenario to quickly reduce the compensation intensity). Second, to ensure the spatial topological consistency of the compensation factor, perform a spatial convolution operation on the compensation factor and the temperature distribution matrix of S11 to make the compensation evenly distributed in the axial and circumferential directions of the cable joint and avoid excessive local compensation. Finally, output the transient compensation factor, providing input for the two-way feedback correction in step S4; S35, verification and optimization: Verify the effectiveness of the generated transient compensation factor and optimize the calculation rules, steps are as follows: Compare the predicted value of the internal temperature after compensation with the measured data of off-line infrared thermal imaging to verify the accuracy of the compensation algorithm, and calculate the relative error between the predicted value and the measured data , expressed as: Among them, is the predicted value of the internal temperature after compensation, + , is the measured data; If the relative error exceeds 5%, feedback to S33 to adjust the compensation weight function , and recalculate the compensation factor. In addition, based on the error analysis results for different mutation types, dynamically update the mutation detection threshold in S31 and the weight factor in S32 , to optimize the subsequent compensation calculation logic and make the response to future mutation events more accurate. Finally, output the optimized compensation factor to the historical database and apply it to the next round of mutation event analysis to enhance the adaptive ability of the compensation algorithm.

[0039] The phase change compensation algorithm refers to the phase change compensation logic and compensation factor calculation in S33.

[0040] S4 includes: S41, forward correction: Perform forward correction, adjust the spatial weight of the surface temperature distribution data according to the output transient compensation factor, and output the corrected surface temperature matrix. The steps are as follows: Optimize and adjust the surface temperature distribution data of the cable joint based on the calculated transient compensation factor to improve the accuracy of the thermal field calculation. First, align the transient compensation factor with the temperature distribution matrix generated by S11 in spatial coordinates to construct a weight mapping function , expressed as: ; where is the transient compensation factor, is the Euclidean distance between the current coordinate and the center of the air gap region, is the weight diffusion coefficient, used to control the spatial influence range of the compensation factor.

[0041] Next, use the weight mapping function to perform weighted correction on the original surface temperature data, expressed as: , is the corrected surface temperature, is the original surface temperature data; Output the corrected surface temperature matrix for subsequent dynamic thermal resistance optimization calculation; S42, reverse correction: Based on the corrected surface temperature matrix as the boundary condition of the coupling model, re-solve the temperature field and optimize the dynamic thermal resistance coefficient, and output the updated dynamic thermal resistance coefficient and the hierarchical heat flux distortion index. The specific method is as follows: Reverse data injection: Take as the external constraint for the thermal field solution, substitute it into the heat conduction equation, and update the dynamic thermal resistance: ; where is the learning rate, is the temperature calculated by the coupling model; Synchronously update the distortion index: Based on the corrected dynamic thermal resistance coefficient , recalculate the hierarchical heat flux distortion index: ; Ensure that the heat flux distortion index is consistent with the updated temperature field.

[0042] Output the updated dynamic thermal resistance coefficient and the hierarchical heat flux distortion index , for subsequent temperature correction verification.

[0043] S4 also includes: S43, two-way correction verification: Based on the corrected surface temperature matrix and the recalculated temperature field, calculate the error residual between the two to determine whether the correction converges: ; Among them, represents the two-norm calculation error. If the residual does not converge (such as the continuous 3 - iteration descent rate is less than 5%), then reduce the learning rate , and re - execute the optimization calculation in S42. This process ensures the stability of thermal resistance correction and temperature compensation, and avoids problems such as over - correction or slow convergence; S44: Data fusion: After the correction converges, fuse data from different sources to generate the final internal temperature field distribution. The specific method is as follows: Multi - source data fusion: Input the forward - corrected surface temperature matrix , the reverse - corrected dynamic thermal resistance coefficient and the transient compensation factor into the three - dimensional interpolation algorithm to calculate the internal temperature field , expressed as: ; Outlier suppression: Perform median filtering on the interpolation result to eliminate local temperature jumps that may be caused by uneven weights, and ensure the smoothness and continuity of the temperature field.

[0044] Output the final internal temperature field distribution , for final data storage and application.

[0045] The present invention covers any substitutions, modifications, equivalent methods, and schemes made within the spirit and scope of the present invention. For the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention. However, those skilled in the art can fully understand the present invention without these detailed descriptions. Additionally, well - known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0046] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A temperature inversion correction method for 10kV cable joints based on environmental heat exchange conditions, characterized in that Including the following steps: S1: Collect a multi-source monitoring data set of the target cable joint in real time, including surface temperature distribution data, cable operating current data, environmental heat exchange parameter set and joint material delamination parameters; The environmental heat exchange parameter set includes environmental temperature, wind speed, air humidity and transient heat flux density; S2: Based on the material delamination parameters and the environmental heat exchange parameter set, construct a multi-physics field coupling model of the cable joint. The multi-physics field coupling model synchronously correlates the electromagnetic field-thermal field-fluid field, and outputs the dynamic thermal resistance coefficient and delamination heat flux distortion index of the cable joint; S3: Input the dynamic thermal resistance coefficient, delamination heat flux distortion index and surface temperature distribution data into the phase change compensation algorithm to generate a transient compensation factor for the internal temperature of the cable joint; The phase change compensation algorithm triggers the compensation logic through the sudden change of environmental heat flux density to correct the internal temperature distortion caused by material delamination and transient environmental parameters; S4: Based on the transient compensation factor, perform two-way feedback correction on the surface temperature distribution data, including: Forward correction: Adjust the spatial weight distribution of the surface temperature according to the transient compensation factor; Reverse correction: Input the corrected internal temperature value back into the multi-physics field coupling model to iteratively optimize the dynamic thermal resistance coefficient; Finally, output the internal temperature field distribution of the cable joint.

2. A temperature inversion correction method for 10 kV cable joints based on environmental heat exchange conditions according to claim 1, characterized in that The S1 includes: S11: Collect the surface temperature distribution data of the cable joint through a distributed temperature sensor array. The sensor array is uniformly arranged along the axial and circumferential directions of the cable joint to form a temperature monitoring grid, and output a temperature distribution matrix containing spatial coordinates-temperature values; S12: Synchronously collect the cable operating current data, including the effective value of the current, current harmonic components and current mutation time series, extract the current spectrum characteristics through a high-frequency sampling module, and output the cable operating current data set; S13: Obtain the environmental heat exchange parameter set, and measure the environmental temperature, wind speed and air humidity in real time through a meteorological sensor; Calculate the transient heat flux density based on the wind speed and air humidity; Output the environmental heat exchange parameter set containing environmental temperature, wind speed, air humidity and transient heat flux density; S14: Perform X-ray computed tomography imaging on the cable joint to extract the joint material delamination parameters; S15: Perform spatio-temporal alignment and normalization processing on the temperature distribution matrix, cable operating current data set, environmental heat exchange parameter set and joint material delamination parameters, and integrate them into a multi-source monitoring data set.

3. A temperature inversion correction method for 10 kV cable joints based on environmental heat exchange conditions according to claim 2, characterized in that, The S14 specifically includes: S141, X-ray image preprocessing and gray value distribution analysis: Obtain the multi-layer tomographic images of the cable joint through an X-ray computed tomography system, and perform gray value normalization processing for different material layers; Use histogram equalization and adaptive contrast enhancement algorithms to adjust the dynamic range of the images, perform pixel gray value analysis on the multi-layer tomographic images, establish a gray value-thickness mapping model, extract the boundary gray change points of each layer, and calculate the interlayer thickness distribution, and output the layer thickness distribution curve; S142. Calculation of insulation layer thickness deviation: Based on the layer thickness distribution curve, a curve fitting algorithm is used to smooth the thickness values at different positions, and the thickness mean and standard deviation of each layer are calculated as the insulation layer thickness deviation index; S143. Edge detection and segmentation of the interface air gap region: The Canny edge detection algorithm is used to extract the edges of the multi-layer tomographic images. Combining the gradient direction and intensity information, pseudo-edge noise is removed through morphological operations, and continuous edges with a closed structure are extracted. The interface voids are detected based on the Hough transform to obtain the potential interface air gap region; S144. Calculation of the air gap area ratio and extraction of spatial coordinates: Based on the identified interface air gap region, the area ratio of the air gap is calculated. At the same time, the centroid coordinates of each air gap region are calculated, and an air gap distribution matrix is established to record the distribution coordinates and area ratio of the air gaps at different positions, and the air gap distribution parameters are output; S145. Generation of lamination parameters: Combining the insulation layer thickness deviation index and the air gap distribution parameters, the lamination parameters of the joint material are constructed.

4. A method for temperature inversion correction of 10 kV cable joints based on environmental heat exchange conditions according to claim 3, characterized in that, The above S2 includes: S21. Model construction: Based on the lamination parameters of the joint material, a multi-scale geometric model of the cable joint is constructed. According to the distribution coordinates of the air gaps, a virtual layer with low thermal conductivity is introduced into the geometric model. The thermal conductivity function of the virtual layer is expressed as: , where is the air thermal conductivity, is the air gap area, is the total area of the region, and output the geometric model with defect marks; S22. Establishment of control equations: The physical field control equations of the electromagnetic field - thermal field - fluid field are established.

5. A temperature inversion correction method for 10 kV cable joints based on environmental heat exchange conditions according to claim 4, characterized in that, The above S3 also includes: S23. Coupled solution: Based on the geometric model with defect markers, the physical field control equations of the electromagnetic field - thermal field - fluid field are solved to generate the dynamic thermal resistance coefficient and the lamination heat flux distortion index; S24. Verification and error optimization of the coupled model: The temperature distribution matrix output by S11 is spatially matched with the temperature field, and the root mean square error between the two is calculated. If the root mean square error exceeds the preset threshold, the parameters of the thermal conductivity function of the virtual layer in S21 are adjusted, and the multi-physical field solution is re-executed until the error converges. Finally, the optimized dynamic thermal resistance coefficient and the lamination heat flux distortion index are output.

6. A temperature inversion correction method for a 10 kV cable joint based on environmental heat exchange conditions according to claim 5, characterized in that, The above S3 includes: S31. Identification of mutation events: Based on the set of environmental heat exchange parameters output by S13, the time series data of the transient heat flux density are continuously monitored to identify mutation events, and the mutation types are encoded, and the heat flux density mutation flag and the mutation type code are output; S32. Calculation of temperature gradient deviation: Combining the dynamic thermal resistance coefficient and the lamination heat flux distortion index output by S23, the temperature gradient deviation inside the cable joint is calculated to quantify the impact of environmental mutations on the temperature field. The gradient deviation is normalized by the maximum - minimum value, and the standardized temperature gradient deviation matrix is output; S33. Matching compensation strategy: Based on the mutation type code and the standardized temperature gradient deviation matrix, the corresponding compensation strategy is matched, and an initial compensation factor is generated.

7. A temperature inversion correction method for a 10 kV cable joint based on environmental heat exchange conditions according to claim 6, characterized in that The above S3 also includes: S34. Generation of transient compensation factor: The initial compensation factor is corrected for timeliness to generate the transient compensation factor; S35. Verification and optimization: The effectiveness of the generated transient compensation factor is verified and the calculation rules are optimized.

8. A temperature inversion correction method for a 10 kV cable joint based on environmental heat exchange conditions according to claim 7, characterized in that The above S4 includes: S41, Forward correction: Perform forward correction, adjust the spatial weights of the surface temperature distribution data according to the output transient compensation factor, and output the corrected surface temperature matrix; S42, Reverse correction: Based on the corrected surface temperature matrix as the boundary condition of the coupling model, re-solve the temperature field, optimize the dynamic thermal resistance coefficient, and output the updated dynamic thermal resistance coefficient and the hierarchical heat flux distortion index.

9. A temperature inversion correction method for a 10 kV cable joint based on environmental heat exchange conditions according to claim 8, characterized in that, S4 further includes: S43, Two-way correction verification: Based on the corrected surface temperature matrix and the re-solved temperature field, calculate the error residuals between the two to determine whether the correction converges. If the residuals do not converge, reduce the learning rate and re-perform the optimization calculation in S42; S44: Data fusion: After the correction converges, fuse data from different sources to generate the final internal temperature field distribution.

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