Method and system for optimizing heat dissipation of high-power terminal

Abstract

According to the optimization method and system for heat dissipation of the high-power terminal, the temperature distribution matrix during operation of the terminal is obtained through the infrared thermal imaging technology, the thermal resistance value is calculated, heat conduction simulation is carried out, and the thermal resistance layering parameters are determined, so that the heat dissipation path design is optimized, the heat flow distribution is improved by adopting a multi-channel heat dissipation model, and the heat dissipation efficiency is improved. For the area with insufficient surface heat dissipation capability, the material characteristics are adjusted, the surface microstructure of the terminal is accurately controlled by using the laser etching technology, the production consistency is analyzed through a statistical process control method, the heat dissipation efficiency is ensured to meet the high-load requirement, and finally, automatic detection equipment is adopted for batch testing and verification of the performance consistency. The heat dissipation efficiency and the production quality of the high-power terminal are effectively improved, and an important guarantee is provided for reliable operation of power equipment.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, and in particular to an optimized method and system for heat dissipation of high-power terminals. Background Technology

[0002] As a core component for connection and transmission in electronic devices, the performance stability and production quality of data cable terminals directly affect the reliability and lifespan of the entire system. In the modern electronics industry, with the increase in data transmission rates and the trend of device miniaturization, the current density carried by terminals is constantly increasing, and heat dissipation issues are becoming increasingly prominent. Yield rate, as a key indicator for measuring production consistency and quality, has become an important reflection of industry competitiveness. Researching how to improve the yield rate of data cable terminals is not only related to optimizing production costs, but also crucial for ensuring the durability of products under high-load scenarios.

[0003] Currently, most methods for improving heat dissipation and yield of data cable terminals focus on single-dimensional optimization, such as using high thermal conductivity materials or simply increasing the heat dissipation area. However, these methods often overlook the complexity of the heat transfer process, resulting in limited improvement in heat dissipation efficiency. At the same time, the instability of the manufacturing process makes it difficult to continuously improve the yield. In particular, when the terminals are subjected to high power operation, traditional solutions are unable to effectively deal with the problems of heat concentration and localized high temperatures, which in turn affects the electrical performance and structural integrity of the terminals.

[0004] In this field, the core challenges stem primarily from the inadequacy of thermal resistance design and the difficulty in achieving the desired manufacturing precision. Specifically, rapid heat conduction, diverse heat dissipation paths, and improved surface heat dissipation capabilities are key technical factors affecting terminal heat dissipation efficiency and yield. Because existing designs lack layered management of thermal resistance, heat cannot be efficiently dissipated through multiple paths, leading to increased terminal temperatures and impacting lifespan. Simultaneously, insufficient precise control over material properties, microstructure dimensions, and surface treatment during production results in poor product performance consistency and significant yield fluctuations. These unresolved technical factors present a unique challenge in balancing design and manufacturing under complex heat dissipation requirements.

[0005] Therefore, how to optimize the layered design of thermal resistance to ensure that heat is transferred from rapid conduction to efficient dissipation while precisely controlling manufacturing parameters to improve production consistency has become a key issue in improving the yield rate of data cable terminals. Summary of the Invention

[0006] In a first aspect, the present invention provides an optimized method for heat dissipation of high-power terminals, the method comprising: S1, acquire high-power heat distribution data during terminal operation, and use infrared thermal imaging technology to scan the heat concentration area to obtain the temperature distribution matrix; S2, based on the temperature distribution matrix, calculate the thermal resistance value for the heat concentration area, and determine the thermal resistance layering parameters by simulating the heat conduction speed and heat dissipation path diversity through finite element analysis. S3, by adjusting the heat dissipation path design through thermal resistance layering parameters, and by using a multi-channel heat dissipation model to optimize the heat conduction direction, an improved heat flow distribution map is obtained; S4. Obtain the improved heat flow distribution map, adjust the material property parameters for areas with insufficient surface heat dissipation capacity, replace the original material with a composite material with higher thermal conductivity, and determine the surface heat dissipation enhancement scheme. S5. Based on the surface heat dissipation enhancement scheme and combined with the microstructure size data, laser etching technology is used to precisely control the micro-geometry of the terminal surface to obtain the processing consistency index. S6. By using the processing consistency index, it is determined whether the manufacturing accuracy has reached the preset accuracy threshold. If it has not, the etching parameters are adjusted and the microstructure size distribution is recalculated to obtain the required manufacturing accuracy data. S7: Obtain manufacturing accuracy data, and use statistical process control methods to analyze production consistency and determine the quality distribution curve after the fluctuation range is narrowed in order to address the yield rate fluctuation problem. S8. Based on the mass distribution curve, determine whether the heat dissipation efficiency meets the requirements of high-load scenarios. If not, return to the thermal resistance layering parameter optimization stage, re-simulate the heat conduction and heat dissipation path, and obtain the final optimization solution. S9, through the final optimized scheme, uses automated testing equipment to conduct batch thermal resistance and heat dissipation efficiency tests on terminal samples, and obtains performance consistency verification results.

[0007] Optionally, step S1, acquiring high-power heat distribution data during terminal operation, and scanning the heat concentration area using infrared thermal imaging technology to obtain a temperature distribution matrix, includes: Step S11: Obtain the original thermal distribution image data scanned by the infrared thermal imaging device during the terminal operation period; Step S12: The original thermal distribution image data is processed using an image denoising method to obtain a clear thermal distribution image; Step S13: Extract the pixel coordinates of the heat concentration area based on the clear thermal distribution image to generate the corresponding temperature distribution matrix; Step S14: If there are abnormal high temperature points in the temperature distribution matrix, the abnormal area is determined by comparing with the preset temperature threshold, and the set of high temperature abnormal coordinates is determined. Step S15: For the set of high temperature anomaly coordinates, the KMeans clustering algorithm is used to classify the anomaly points to obtain the distribution characteristics of the anomaly region. Step S16: Based on the distribution characteristics of the abnormal area, generate the boundary range of the heat concentration area and determine the final heat distribution zone; Step S17: By performing statistical analysis on the temperature distribution matrix within the final heat distribution zone, the temperature change pattern of each zone is obtained.

[0008] Optionally, step S2 involves calculating the thermal resistance value for areas of concentrated heat based on the temperature distribution matrix, and determining the thermal resistance stratification parameters by simulating the heat conduction velocity and heat dissipation path diversity through finite element analysis, including: Step S21: Obtain the temperature distribution matrix, use the grid partitioning method to determine the coordinates of the heat concentration area, and obtain the first region set; Step S22: For the first region set, calculate the thermal resistance value. If the thermal resistance value exceeds the thermal resistance threshold, use linear interpolation to adjust the region boundary to obtain the second region set. Step S23: Extract the boundary temperature distribution from the second region set, use ANSYS software to perform finite element analysis to simulate the temperature field, calculate the heat conduction velocity through the temperature gradient, and obtain the velocity distribution map. Step S24: Based on the velocity distribution map, use Dijkstra's algorithm to perform gradient tracking, obtain the heat dissipation path, and determine whether the heat dissipation path forms a closed loop. If a closed loop is formed, calculate the diversity of heat dissipation paths through connectivity analysis in graph theory to determine the first path set. Step S25: For the first path set, K-means clustering algorithm is used to group the paths to obtain a path group set; Step S26: Calculate the hierarchical parameters for each path group based on the path group set to obtain the hierarchical parameter set; Step S27: Based on the set of layered parameters, the final thermal resistance layering parameters are determined using the weighted average method.

[0009] Optionally, step S3 involves adjusting the heat dissipation path design by adjusting the thermal resistance layering parameters and optimizing the heat conduction direction using a multi-channel heat dissipation model to obtain an improved heat flow distribution map, including: Step S31: Obtain thermal resistance layer data and obtain the thermal resistance parameter values ​​of each layer; Step S32: Use finite element analysis software to establish a thermal resistance distribution model and obtain the initial thermal resistance distribution matrix; Step S33: Based on the initial thermal resistance distribution matrix, the parameters are adjusted using the optimization toolbox. If the parameter values ​​exceed the distribution threshold, the thermal resistance distribution is recalculated to obtain the adjusted thermal resistance matrix. Step S34: Based on the adjusted thermal resistance matrix, the shortest path algorithm is used to extract heat dissipation path data, and the optimal heat dissipation path set is determined through path optimization. Step S35: Based on the optimal heat dissipation path set, establish a multi-channel heat transfer model, and use multiphysics coupling analysis software to calculate the heat conduction direction to obtain the heat conduction vector; Step S36: Based on the heat conduction vector, perform thermal flow field simulation using fluid dynamics numerical simulation software to obtain a preliminary heat flow distribution map; Step S37: If the heat flow distribution is determined to be uneven based on the preliminary heat flow distribution map, the channel design parameters are adjusted, and the heat flow field simulation is performed again to obtain the improved heat flow distribution map. Step S38: Based on the improved heat flux distribution map, a Gaussian filtering method is used to perform boundary smoothing to obtain the final heat flux distribution map.

[0010] Optionally, in step S4, an improved heat flow distribution map is obtained, and for areas with insufficient surface heat dissipation capacity, material property parameters are adjusted, and the original material is replaced with a composite material with higher thermal conductivity to determine a surface heat dissipation enhancement scheme, including: Step S41: Obtain heat flow distribution data of the target surface, wherein the heat flow distribution data is obtained by performing heat flow distribution analysis on the target surface using a finite element analysis tool; Step S42: Based on the heat flux density threshold, extract regions with temperatures higher than the heat flux density threshold from the heat flux distribution data, and identify these regions as regions with insufficient heat dissipation capacity. Step S43: Obtain the current thermal conductivity value of the material in the region with insufficient heat dissipation capacity; Step S44: Determine whether the thermal conductivity value is lower than the preset thermal conductivity threshold. If so, select candidate materials with thermal conductivity values ​​higher than the current material from the pre-established composite material database and determine the candidate materials as replacement materials. Step S45: Adjust the material properties of the current material using the replacement material, and recalculate the heat flow distribution using the finite element analysis tool to obtain enhanced heat flow distribution data; Step S46: Calculate the improvement in heat dissipation capacity based on the heat flow distribution data and the enhanced heat flow distribution data; Step S47: Determine whether the improvement reaches the preset requirement. If not, use a genetic algorithm to iteratively optimize the material parameters of the replacement material, including the thickness and distribution density of the replacement material, until the heat dissipation capacity meets the preset requirement, and determine the final surface heat dissipation enhancement scheme.

[0011] Optionally, in step S5, based on the surface heat dissipation enhancement scheme and combined with microstructure size data, laser etching technology is used to precisely control the micro-geometry of the terminal surface to obtain processing consistency indicators, including: Step S51: Obtain initial data on the microstructure obtained by processing the terminal surface using laser etching technology; Step S52: The initial data is compared using a preset microstructure size standard to obtain the deviation range of the microstructure. Step S53: If the deviation range exceeds the deviation threshold, adjust the laser etching parameters to obtain optimized micro-geometric structure data. Step S54: Based on the optimized micro-geometric structure data, and combined with the enhancement scheme of adding heat sinks and optimizing heat dissipation channels, determine the heat dissipation performance parameters of the terminal surface; Step S55: The heat dissipation performance parameters are analyzed using the support vector machine algorithm to obtain the degree of conformity judgment of the processing consistency index; Step S56: If the judgment result indicates that there are indicators that do not meet the preset standards, then the gradient descent method is used to adjust the enhancement scheme to obtain the adjusted enhancement scheme. Step S57: Obtain the final processing consistency index according to the adjusted enhancement scheme; Step S58: Determine the micro-geometric structure optimization scheme of the terminal surface based on the final processing consistency index.

[0012] Optionally, step S6 involves determining whether the manufacturing precision has reached a preset precision threshold using a processing consistency index. If it has not, the etching parameters are adjusted and the microstructure size distribution is recalculated to obtain manufacturing precision data that meets the requirements, including: Step S61: Obtain the processing consistency index and compare the processing consistency index with the consistency threshold. If the processing consistency index is lower than the consistency threshold, it is determined that the manufacturing accuracy does not meet the requirements. Step S62: Based on the consistency index deviation, the etching parameter adjustment amount is calculated using a linear regression method to obtain the adjusted etching parameters; Step S63: Based on the adjusted etching parameters, the microstructure dimensions are calculated using the finite element analysis method to obtain new microstructure dimension data; Step S64: Based on the new microstructure size data, use statistical analysis methods to calculate the size distribution characteristics and obtain the size distribution characteristic parameters; Step S65: If the size distribution characteristic parameters exceed the preset distribution range, the etching parameters are adjusted using an iterative optimization method, and the microstructure size data is reacquired. Step S66: Input the size distribution feature parameters into the pre-built support vector machine classification model to obtain the manufacturing accuracy classification result and determine whether the manufacturing accuracy meets the requirements; Step S67: Based on the classification results, adjust the judgment criteria using a statistical adjustment method to obtain the final accuracy data.

[0013] Optionally, step S7 involves acquiring manufacturing accuracy data, and for addressing yield rate fluctuations, using statistical process control methods to analyze production consistency and determine the quality distribution curve after narrowing the fluctuation range, including: Step S71: Obtain manufacturing accuracy data, monitor production consistency using control chart methods, and obtain preliminary values ​​for the fluctuation range; Step S72: Extract the change characteristics from the preliminary value of the fluctuation range, and use the mean value to calculate and determine the production consistency benchmark; Step S73: If the production consistency benchmark exceeds the benchmark threshold, the process capability index is used to determine the degree of narrowing of the fluctuation range. Step S74: Based on the process capability index results, fit the quality distribution using a normal distribution to determine the distribution curve parameters; Step S75: Calculate the predicted yield rate using the distribution curve parameters, and determine the consistency between the manufacturing precision and the quality distribution; Step S76: Obtain the deviation between the predicted value and the actual data. If the deviation exceeds a preset range, adjust the center line and control limits of the control chart to obtain the optimized distribution curve. Step S77: Analyze the quality distribution after the fluctuation narrows using the optimized distribution curve to determine the final consistency range.

[0014] Optionally, in step S8, based on the mass distribution curve, it is determined whether the heat dissipation efficiency meets the requirements of high-load scenarios. If not, the process returns to the thermal resistance layering parameter optimization stage to re-simulate the heat conduction and heat dissipation path, obtaining the final optimized solution, including: Step S81: Obtain the surface temperature distribution curve of the device, which is acquired by a thermal imager; Step S82: Perform a Fourier transform on the temperature distribution curve to convert the temperature distribution curve into a frequency domain signal; Step S83: Extract the quality distribution data of the device based on the frequency domain signal; Step S84: Calculate the heat dissipation efficiency of the device based on the mass distribution data; Step S85: If the heat dissipation efficiency is lower than the preset high load scenario threshold, then extract the thermal resistance layering data from the thermal resistance tester. Step S86: Based on the thermal resistance layering data, the thermal conductivity and thickness parameters of the material are adjusted using finite element analysis tools to obtain an optimized heat conduction model; Step S87: Based on the heat conduction model, use ANSYS software to simulate the heat conduction path and determine the heat dissipation path adjustment scheme. Step S88: Based on the heat dissipation path adjustment scheme, recalculate the heat dissipation efficiency to obtain updated mass distribution data; Step S89: Based on the updated mass distribution data, determine whether the heat dissipation efficiency meets the requirements of high-load scenarios. Step S810: If the heat dissipation efficiency still does not meet the requirements of the high-load scenario, return to the step of extracting thermal resistance layer data, readjust the heat dissipation path, until the heat dissipation efficiency reaches the threshold of the high-load scenario.

[0015] A second aspect of the present invention provides an optimized system for heat dissipation of high-power terminals, which optimizes heat dissipation of high-power terminals using the method described above. The system includes: The heat distribution data acquisition module is used to acquire high-power heat distribution data during terminal operation. It uses infrared thermal imaging technology to scan the heat concentration area and obtain the temperature distribution matrix. The thermal resistance calculation module is used to calculate the thermal resistance value for areas with concentrated heat based on the temperature distribution matrix. It uses finite element analysis to simulate the heat conduction speed and the diversity of heat dissipation paths to determine the thermal resistance layering parameters. The heat dissipation path optimization module is used to adjust the heat dissipation path design by using thermal resistance layering parameters, and to optimize the heat conduction direction by using a multi-channel heat dissipation model to obtain an improved heat flow distribution map. The surface heat dissipation enhancement module is used to obtain an improved heat flow distribution map, adjust the material property parameters for areas with insufficient surface heat dissipation capacity, replace the original material with a composite material with higher thermal conductivity, and determine the surface heat dissipation enhancement scheme. The microstructure control module is used to precisely control the micro-geometry of the terminal surface based on the surface heat dissipation enhancement scheme and the microstructure size data, using laser etching technology to obtain processing consistency indicators. The manufacturing precision judgment module is used to determine whether the manufacturing precision has reached the preset precision threshold through the processing consistency index. If it has not reached the threshold, the etching parameters are adjusted and the microstructure size distribution is recalculated to obtain the manufacturing precision data that meets the requirements. The quality analysis module is used to acquire manufacturing accuracy data. To address the issue of yield rate fluctuations, it employs statistical process control methods to analyze production consistency and determine the quality distribution curve after the fluctuation range is narrowed. The heat dissipation efficiency judgment module is used to determine whether the heat dissipation efficiency meets the requirements of high-load scenarios based on the mass distribution curve. If it does not meet the requirements, it returns to the thermal resistance layering parameter optimization stage to re-simulate the heat conduction and heat dissipation path and obtain the final optimization solution. The performance verification module is used to conduct batch thermal resistance and heat dissipation efficiency tests on terminal samples using automated testing equipment based on the final optimized scheme, and obtain performance consistency verification results.

[0016] This invention provides an optimized method and system for heat dissipation of high-power terminals. It acquires the temperature distribution matrix of the terminals during operation using infrared thermal imaging technology, calculates the thermal resistance value, and simulates heat conduction to determine thermal resistance layering parameters. Based on this, the invention optimizes the heat dissipation path design, employs a multi-channel heat dissipation model to improve heat flow distribution, adjusts material properties for areas with insufficient surface heat dissipation capacity, and utilizes laser etching technology to precisely control the microstructure of the terminal surface. Statistical process control methods are used to analyze production consistency, ensuring that heat dissipation efficiency meets high-load requirements. Finally, automated testing equipment is used for batch testing to verify performance consistency. This invention effectively improves the heat dissipation efficiency and production quality of high-power terminals, providing an important guarantee for the reliable operation of power equipment. Attached Figure Description

[0017] Figure 1 This is a flowchart of an optimized method for heat dissipation of high-power terminals according to the present invention.

[0018] Figure 2 This is a schematic diagram of a second embodiment of an optimized method for heat dissipation of high-power terminals according to the present invention.

[0019] Figure 3 This is a schematic diagram of the structure of an optimized system for heat dissipation of high-power terminals according to the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1-2 As shown, in a first aspect, the present invention provides an optimized method for heat dissipation of high-power terminals, which may specifically include: S1: Acquire high-power heat distribution data during terminal operation, and use infrared thermal imaging technology to scan the heat concentration area to obtain the temperature distribution matrix.

[0022] Optionally, this step also includes: Step S11: Obtain the original thermal distribution image data scanned by the infrared thermal imaging device during the terminal operation period; Step S12: The original thermal distribution image data is processed using an image denoising method to obtain a clear thermal distribution image; Step S13: Extract the pixel coordinates of the heat concentration area based on the clear thermal distribution image to generate the corresponding temperature distribution matrix; Step S14: If there are abnormal high temperature points in the temperature distribution matrix, the abnormal area is determined by comparing with the preset temperature threshold, and the set of high temperature abnormal coordinates is determined. Step S15: For the set of high temperature anomaly coordinates, the KMeans clustering algorithm is used to classify the anomaly points to obtain the distribution characteristics of the anomaly region. Step S16: Based on the distribution characteristics of the abnormal area, generate the boundary range of the heat concentration area and determine the final heat distribution zone; Step S17: By performing statistical analysis on the temperature distribution matrix within the final heat distribution zone, the temperature change pattern of each zone is obtained.

[0023] Specifically, acquiring raw thermal distribution image data scanned by infrared thermal imaging equipment during terminal operation is the fundamental step in the entire process. Infrared thermal imaging technology generates images by sensing the infrared radiation produced by the surface temperature of an object, which can intuitively reflect the thermal state of the equipment during operation.

[0024] For example, in a substation terminal inspection scenario, the equipment may operate continuously during peak hours of the day. Infrared thermal imaging equipment scans at a fixed frequency every five minutes, generating pixel images containing temperature information. This raw data is often affected by environmental noise, appearing as blurry spots or irregular color patches in the image. Processing the raw thermal distribution image data using image denoising methods is a crucial step, aiming to improve image clarity for subsequent analysis. Common methods include median filtering or Gaussian filtering.

[0025] In one possible implementation, median filtering is used to smooth noise by taking the median value of the pixels surrounding each pixel. This effectively removes isolated noise points while preserving the edge information of the heat distribution. After processing, the originally blurry high-temperature areas become clearer in the image, and the temperature gradient distribution becomes more intuitive, laying the foundation for subsequent feature extraction. Extracting the pixel coordinates of the heat-concentrated areas from the clear heat distribution image and generating a temperature distribution matrix is ​​the process of quantifying image information.

[0026] Specifically, the grayscale or color value of a pixel can be mapped to a temperature value, forming a two-dimensional matrix.

[0027] For example, a thermal distribution image of a terminal connection might show that a certain area has a significantly higher temperature than the surrounding area. After processing, pixels with coordinates ranging from x to 80 and y to 30 to 60 are extracted, corresponding to temperature values ​​between 60 and 80 degrees Celsius. This data is then used to form a matrix for direct analysis. If abnormally high-temperature points exist in the temperature distribution matrix, the abnormal area is identified by comparing it with a preset temperature threshold.

[0028] In one embodiment, assuming the safe operating temperature threshold for the terminal is 70 degrees Celsius, a point in the matrix reaches a temperature of 85 degrees Celsius, exceeding the temperature threshold and is marked as an abnormal high-temperature point. By traversing the matrix, the coordinate set of all abnormal points is determined, for example, obtaining the high-temperature abnormal coordinate set of points with coordinates (55, 35), (60, 40), etc. The significance of this step is to quickly locate potential fault areas. Using the KMeans clustering algorithm to classify the abnormal points and obtain their distribution characteristics for the high-temperature abnormal coordinate set is an intelligent analysis method.

[0029] For example, if the aforementioned anomalies are scattered, the KMeans algorithm might classify them into two categories: one near the terminal contact surface and the other near the edge area. This classification helps determine whether the anomaly is due to poor contact or insufficient external heat dissipation, providing a basis for subsequent maintenance. Generating the boundary range of the heat concentration area based on the distribution characteristics of the anomaly area and determining the final heat distribution partition is a process of further refining the analysis.

[0030] For example, based on clustering results, the heat concentration area may be defined as a rectangular area near the contact surface, with boundaries ranging from x to 70 and y to 30 to 50.

[0031] Preferably, this zoning can intuitively reflect the heat distribution pattern, facilitating subsequent statistical analysis. The final step is to obtain the temperature change pattern of each zone through statistical analysis of the temperature distribution matrix within the final heat distribution zone.

[0032] Understandably, by calculating the average temperature, maximum temperature, and temperature trend within a zone, it can be discovered that the temperature in a certain zone continues to rise during peak hours, indicating a potential risk of overheating. This analysis can provide data support for equipment maintenance, prevent malfunctions, and improve system reliability.

[0033] Optionally, step S16, which generates the boundary range of the heat concentration area based on the distribution characteristics of the abnormal area and determines the final heat distribution zoning, further includes: Step S161: Obtain the distribution characteristics of the abnormal area, and use the Canny edge detection algorithm to extract the contour line of the heat concentration area to obtain the preliminary boundary range.

[0034] Step S162: Based on the preliminary boundary range, the quadtree segmentation algorithm is used to partition the heat distribution data within the contour line to generate multiple partition units.

[0035] Step S163: Obtain the distribution characteristics within each partition unit, use the mean, variance, and kurtosis as statistical indicators to calculate the feature extraction results, and determine the feature values ​​of each partition unit.

[0036] Step S164: If the feature value exceeds the preset feature threshold, the Euclidean distance comparison method is used to determine that the partition unit is a heat concentration area, and a heat concentration partition set is obtained.

[0037] Step S165: For the set of heat concentration zones, the KMeans clustering algorithm is used to classify the zone units and determine the final heat distribution zones.

[0038] Step S166: Based on the contour line of the final heat distribution partition, use morphological dilation and erosion operations to optimize the partition boundary and obtain the optimized partition range.

[0039] Step S167: For the optimized partition range, obtain the heat distribution data of each partition and generate the corresponding partition feature description.

[0040] For example, when using the Canny edge detection algorithm to extract the contour lines of heat concentration areas, the heat distribution image is first converted to grayscale and denoised, and then edge points are detected by calculating the image gradient.

[0041] Understandably, the Canny algorithm uses a dual threshold method to filter strong and weak edges and connect them into continuous lines.

[0042] For example, in a substation terminal inspection scenario, assuming the thermal distribution image shows a significant temperature increase in a certain area, the Canny algorithm can identify the closed contour line of the high-temperature area, with the initial boundary range possibly being x from 45 to 75 and y from 25 to 55.

[0043] In one possible implementation, when using a quadtree partitioning algorithm to divide the heat distribution data within the contour line into sub-blocks, the region is recursively divided into four sub-blocks until the termination condition is met.

[0044] Specifically, if the temperature changes significantly in a certain area, the quadtree will be further subdivided, eventually generating multiple partition units.

[0045] For example, the initial boundary range mentioned above may be divided into 16 partition units, each covering a smaller temperature distribution range, which facilitates subsequent feature extraction.

[0046] It should be noted that when calculating the mean, variance, and kurtosis of each partition unit, the mean reflects the average temperature level, the variance represents the degree of temperature fluctuation, and the kurtosis describes the sharpness of the temperature distribution.

[0047] In one embodiment, the mean of a certain partition unit is 65 degrees Celsius, the variance is 8, and the kurtosis is 3.2, indicating that the temperature distribution is relatively concentrated and sharp, and there may be local hot spots.

[0048] Preferably, when using the Euclidean distance comparison method to determine the heat concentration area, the feature value is compared with a preset feature threshold.

[0049] For example, assuming the feature thresholds are mean 60, variance 10, and kurtosis 4, if the feature values ​​of a certain partition unit are 65, 8, and 3.2 respectively, after calculating its Euclidean distance from the feature thresholds, if it is less than a certain standard value, it is marked as a heat concentration area, and finally a heat concentration partition set is formed.

[0050] In one embodiment, when using the KMeans clustering algorithm on a set of heat-concentrated partitions, the partition units are divided into several classes according to their feature values.

[0051] For example, they can be divided into three categories: high-temperature concentration areas, peripheral transition areas, and anomalous isolated points. This classification method helps to analyze heat distribution patterns in more detail.

[0052] Specifically, when using morphological dilation and erosion operations to optimize partition boundaries, the dilation operation first expands the boundary to connect the breaks, and then the erosion operation shrinks the boundary to eliminate noise.

[0053] For example, after optimization, the boundary range of a certain partition may be adjusted from x from 45 to 75 to x from 48 to 72, resulting in smoother boundaries and more accurate region division.

[0054] Understandably, when generating partition feature descriptions for the optimized partition range, the temperature distribution data of each partition will be extracted and organized into structured information.

[0055] For example, the characteristic description of a certain zone may include an average temperature of 68 degrees Celsius, a maximum temperature of 82 degrees Celsius, and the trend of temperature gradient changes, providing an intuitive basis for subsequent analysis.

[0056] S2, based on the temperature distribution matrix, calculate the thermal resistance value for the heat concentration area, and determine the thermal resistance layering parameters by simulating the heat conduction speed and heat dissipation path diversity through finite element analysis.

[0057] Optionally, this step also includes: Step S21: Obtain the temperature distribution matrix, use the grid partitioning method to determine the coordinates of the heat concentration area, and obtain the first region set.

[0058] Step S22: For the first region set, calculate the thermal resistance value. If the thermal resistance value exceeds the thermal resistance threshold, use linear interpolation to adjust the region boundary to obtain the second region set.

[0059] Step S23: Extract the boundary temperature distribution from the second region set, use ANSYS software to perform finite element analysis to simulate the temperature field, calculate the heat conduction velocity through the temperature gradient, and obtain the velocity distribution map.

[0060] Step S24: Based on the velocity distribution map, use Dijkstra's algorithm to perform gradient tracking, obtain the heat dissipation path, and determine whether the heat dissipation path forms a closed loop. If a closed loop is formed, calculate the diversity of heat dissipation paths through connectivity analysis in graph theory to determine the first path set.

[0061] Step S25: For the first path set, K-means clustering algorithm is used to group the paths to obtain the path group set.

[0062] Step S26: Calculate the hierarchical parameters for each path group based on the path group set to obtain the hierarchical parameter set.

[0063] Step S27: Based on the set of layered parameters, the final thermal resistance layering parameters are determined using the weighted average method.

[0064] Specifically, after obtaining the temperature distribution matrix, a grid division method can be used to determine the coordinates of the heat concentration area.

[0065] For example, in a substation terminal detection scenario, the temperature distribution matrix might be a two-dimensional array containing hundreds of pixels, each corresponding to a temperature value. A mesh partitioning method can divide the matrix into multiple small regions, such as 10×10 pixels as a grid unit. By statistically analyzing the average temperature within each grid, regions with higher temperatures are selected to obtain the first set of regions.

[0066] For example, if the average temperature of a certain grid reaches 75 degrees Celsius, which is higher than the 50 degrees Celsius of the surrounding grids, its coordinates are recorded as a heat concentration area. The thermal resistance value is then calculated for the first set of regions.

[0067] Understandably, thermal resistance reflects the magnitude of the resistance to heat conduction.

[0068] In one possible implementation, the thermal resistance can be estimated by the ratio of temperature difference to heat flow within a region. If the thermal resistance of a region reaches 0.8, while the preset thermal resistance threshold is 0.6, it indicates that heat conduction is obstructed.

[0069] It should be noted that linear interpolation can be used to adjust the region boundary at this time.

[0070] Specifically, by inserting intermediate temperature values ​​near the boundaries, the region is redefined to make the thermal resistance values ​​more realistic, thus obtaining a second set of regions.

[0071] For example, after adjustment, the boundary of a certain region is expanded from x=60 to x=65, and y=40 is expanded to y=45. After extracting the boundary temperature distribution from the second region set, the temperature field can be simulated through finite element analysis.

[0072] Preferably, this method divides the region into smaller units to simulate how heat is conducted from a high-temperature point to a low-temperature point.

[0073] For example, if the boundary temperature gradually decreases from 80 degrees Celsius to 60 degrees Celsius, the heat conduction speed can be calculated by temperature gradient, which shows that the heat conduction speed is faster at certain points, thus generating a speed distribution map.

[0074] In one embodiment, the velocity distribution map shows a conduction velocity of 5 units per second near the center of the terminal, while it is only 2 units per second at the edges. The heat dissipation path is then determined using a gradient tracing algorithm based on the velocity distribution map.

[0075] Understandably, the heat dissipation path reflects the direction of heat flow.

[0076] For example, starting from a high-temperature point and tracing along the direction of maximum gradient, a path extending from the center to the edge might be found. If the path forms a closed loop, diversity can be calculated using connectivity analysis in graph theory.

[0077] For example, if a closed-loop path has 3 branches, it is recorded as the first path set, indicating that the heat dissipation methods are relatively diverse. When grouping the first path set using the K-means clustering algorithm...

[0078] Specifically, they can be divided into two groups based on path length and direction.

[0079] For example, one group of paths is shorter and more concentrated, while another group is longer and more dispersed, resulting in a set of path groups. This grouping helps to distinguish heat dissipation characteristics. Next, the hierarchical parameters for each path group are calculated.

[0080] In one possible implementation, the parameter can be defined as the ratio of path length to speed.

[0081] For example, a path group with a layering parameter of 3 indicates a relatively rich heat dissipation layer. Based on the layering parameter set of the path grouping set, the final thermal resistance layering parameter is determined using a weighted average method.

[0082] For example, different weights can be assigned to each group of parameters, such as a weight of 0.6 for the center path and 0.4 for the edge path, and the final value after weighted averaging is 2.5. This parameter can comprehensively reflect the thermal resistance distribution characteristics and provide a reference for subsequent maintenance.

[0083] S3 adjusts the heat dissipation path design by adjusting the thermal resistance layering parameters and optimizes the heat conduction direction using a multi-channel heat dissipation model to obtain an improved heat flow distribution map.

[0084] Optionally, this step also includes: Step S31: Obtain thermal resistance layer data and obtain the thermal resistance parameter values ​​of each layer; Step S32: Use finite element analysis software to establish a thermal resistance distribution model and obtain the initial thermal resistance distribution matrix; Step S33: Based on the initial thermal resistance distribution matrix, the parameters are adjusted using the optimization toolbox. If the parameter values ​​exceed the distribution threshold, the thermal resistance distribution is recalculated to obtain the adjusted thermal resistance matrix. Step S34: Based on the adjusted thermal resistance matrix, the shortest path algorithm is used to extract heat dissipation path data, and the optimal heat dissipation path set is determined through path optimization. Step S35: Based on the optimal heat dissipation path set, establish a multi-channel heat transfer model, and use multiphysics coupling analysis software to calculate the heat conduction direction to obtain the heat conduction vector; Step S36: Based on the heat conduction vector, perform thermal flow field simulation using fluid dynamics numerical simulation software to obtain a preliminary heat flow distribution map; Step S37: If the heat flow distribution is determined to be uneven based on the preliminary heat flow distribution map, the channel design parameters are adjusted, and the heat flow field simulation is performed again to obtain the improved heat flow distribution map. Step S38: Based on the improved heat flux distribution map, a Gaussian filtering method is used to perform boundary smoothing to obtain the final heat flux distribution map.

[0085] It is understandable that after obtaining the thermal resistance layer data, the thermal resistance parameter values ​​of each layer reflect the thermal conduction characteristics of different regions.

[0086] For example, in a substation terminal testing scenario, it is assumed that the terminal has a three-layer structure: a contact layer, a transition layer, and an external heat dissipation layer.

[0087] For example, the thermal resistance parameter of the contact layer might be 0.5, the transition layer 0.7, and the external heat dissipation layer 0.3. These values ​​are obtained through experimental measurements or simulations, providing basic data for subsequent modeling. A thermal resistance distribution model is then established using finite element analysis software.

[0088] In one possible implementation, the terminals can be divided into multiple small cell grids. Specifically, the grid size is set to 5×5 mm, and each cell is assigned an initial thermal resistance value, such as 0.8 for the central area and 0.4 for the edges, forming an initial thermal resistance distribution matrix. This matrix intuitively reflects the spatial distribution characteristics of the thermal resistance, laying the foundation for optimization.

[0089] Preferably, when adjusting parameters based on the initial thermal resistance distribution matrix, the optimization toolbox can be used to compare the simulation results with the actual requirements.

[0090] For example, if the thermal resistance of a certain area reaches 0.9, exceeding the preset distribution threshold of 0.6, it indicates that the heat dissipation in that area is insufficient.

[0091] It should be noted that the thermal resistance matrix can be recalculated by adding heat dissipation channels or adjusting material properties. For example, reducing the thermal resistance of this area to 0.5 can improve heat dissipation efficiency.

[0092] In one embodiment, when extracting heat dissipation path data, the shortest path algorithm can start from the high-temperature point to find the heat dissipation direction.

[0093] For example, if the terminal center temperature is 80 degrees Celsius and the edge temperature is 50 degrees Celsius, the algorithm traces a path extending outward from the center, with a length of 20 millimeters. Through path optimization, another branch path may be discovered, which is slightly longer but provides more uniform heat dissipation, ultimately forming the optimal set of heat dissipation paths. This is achieved after establishing a multi-channel heat transfer model.

[0094] Specifically, heat flow can be simulated using multiphysics coupling analysis software.

[0095] For example, assuming the path set contains two channels, one near the top of the terminal and the other extending along the side, the calculation results show that the heat conduction direction of the top channel is biased vertically, while that of the side channel is biased horizontally, generating a heat conduction vector. This vector provides a basis for subsequent heat flow simulations.

[0096] In one possible implementation, when performing thermal flow field simulation, fluid dynamics numerical simulation software can generate a preliminary heat flow distribution map.

[0097] For example, the heat flux density near the center of the terminal is 10 units, and it drops to 3 units at the edge.

[0098] For example, if the distribution is uneven, such as overheating in the center, the channel width can be adjusted from 5 mm to 8 mm. After resimulating, the heat flow distribution will be more stable, generating an improved heat flow distribution map.

[0099] Preferably, for the improved heat flux distribution map, the Gaussian filtering method can smooth the boundary temperature transition.

[0100] For example, if the temperature at the boundary abruptly changes from 70 degrees Celsius to 50 degrees Celsius, the filtered transition is more natural, avoiding sharp changes. This smoothing process makes the final heat flux distribution map more consistent with actual operating conditions, helping to identify potential heat dissipation problems and optimize the design.

[0101] S4. Obtain the improved heat flow distribution map, adjust the material property parameters for areas with insufficient surface heat dissipation capacity, replace the original material with a composite material with higher thermal conductivity, and determine the surface heat dissipation enhancement scheme.

[0102] Optionally, this step also includes: Step S41: Obtain heat flow distribution data of the target surface, wherein the heat flow distribution data is obtained by performing heat flow distribution analysis on the target surface using a finite element analysis tool; Step S42: Based on the heat flux density threshold, extract regions with temperatures higher than the heat flux density threshold from the heat flux distribution data, and identify these regions as regions with insufficient heat dissipation capacity. Step S43: Obtain the current thermal conductivity value of the material in the region with insufficient heat dissipation capacity; Step S44: Determine whether the thermal conductivity value is lower than the preset thermal conductivity threshold. If so, select candidate materials with thermal conductivity values ​​higher than the current material from the pre-established composite material database and determine the candidate materials as replacement materials. Step S45: Adjust the material properties of the current material using the replacement material, and recalculate the heat flow distribution using the finite element analysis tool to obtain enhanced heat flow distribution data; Step S46: Calculate the improvement in heat dissipation capacity based on the heat flow distribution data and the enhanced heat flow distribution data; Step S47: Determine whether the improvement reaches the preset requirement. If not, use a genetic algorithm to iteratively optimize the material parameters of the replacement material, including the thickness and distribution density of the replacement material, until the heat dissipation capacity meets the preset requirement, and determine the final surface heat dissipation enhancement scheme.

[0103] Specifically, when acquiring heat flow distribution data of the target surface.

[0104] Understandably, this process relies on finite element analysis tools to comprehensively simulate the thermal properties of the target surface.

[0105] For example, in a substation terminal inspection scenario, the target surface may be the terminal connection area.

[0106] In one embodiment, the surface temperature distribution is initially measured using a thermal imager, and then the data is imported into finite element analysis software. Boundary conditions are set, such as an ambient temperature of 25 degrees Celsius and a terminal operating current of 100 amperes, to generate heat flux distribution data, showing a center heat flux density of 12 units and an edge heat flux density of 4 units.

[0107] Specifically, when extracting areas with insufficient heat dissipation capacity based on the heat flux density threshold, the heat flux density threshold can be set to 8 units.

[0108] For example, analysis revealed that the heat flux density in the central region exceeded this value, indicating insufficient heat dissipation. This area, due to concentrated high temperatures, may affect terminal life and requires priority attention. When obtaining the current material thermal conductivity value for the area with insufficient heat dissipation capacity...

[0109] In one possible implementation, assuming the terminals are made of copper, a material handbook indicates a thermal conductivity of 400 W / m·K. However, if an oxide layer is present in the area, the actual thermal conductivity may drop to 200 W / m·K.

[0110] It should be noted that this value can be obtained through experimental measurement or by referring to historical data. It is used to determine whether the thermal conductivity is below a preset thermal conductivity threshold and to screen candidate materials.

[0111] Preferably, the preset thermal conductivity threshold is 300 W / m·K. The current value of 200 W / m·K is lower than the thermal conductivity threshold, indicating that improvement is needed.

[0112] For example, when screening from a composite materials database, aluminum-based carbon fiber composites can be selected, which have a thermal conductivity of 350 W / m·K, are lightweight and corrosion-resistant, and are suitable for replacement.

[0113] In one embodiment, when adjusting parameters and recalculating the heat flux distribution by replacing the material, the material in the central region was replaced with an aluminum-based composite material, while the thickness remained unchanged at 2 mm. After rerunning the finite element analysis, the heat flux density decreased from 12 units to 7 units, with little change at the edges, indicating a more balanced heat distribution.

[0114] Specifically, when calculating the improvement in heat dissipation capacity, the difference in heat flux density in the central region before and after the replacement can be compared.

[0115] For example, the original value was 12 units, and the new value is 7 units, representing an improvement of approximately 41%. This data reflects the direct impact of material replacement. When determining whether the improvement meets the preset requirement and iteratively optimizing, let's assume the preset requirement is 50%. The current 41% does not meet the target and further adjustments are needed.

[0116] In one possible implementation, a genetic algorithm is used to optimize the replacement material parameters.

[0117] For example, with an initial thickness of 2 mm and a density of 1.5 g / cm³, the thickness was adjusted to 2.5 mm and the density to 1.8 g / cm³ through iteration. After recalculation, the heat flux density dropped to 6 units, an improvement of 50%, which met the requirements.

[0118] For example, the optimized solution reduces the terminal surface temperature from 80 degrees Celsius to 65 degrees Celsius, extending service life.

[0119] It should be noted that genetic algorithms, by simulating the natural selection process, can effectively explore parameter combinations to ensure optimal heat dissipation solutions. This method is particularly suitable for complex heat conduction problems, offering diverse design possibilities.

[0120] Optionally, step S47, determining whether the improvement reaches a preset requirement, and if not, using a genetic algorithm to iteratively optimize the material parameters of the replacement material, including the thickness and distribution density of the replacement material, until the heat dissipation capacity meets the preset requirement, and determining the final surface heat dissipation enhancement scheme, further includes: Step S471: Obtain the initial parameter data of the replacement material from the pre-established parameter database, including the thickness value and distribution density value.

[0121] Step S472: The initial parameter data is optimized using Python's DEAP library in the first iteration to generate the optimized first parameter set.

[0122] Step S473: For the first parameter set, use ANSYS to calculate the corresponding heat flux distribution data.

[0123] Step S474: If the heat dissipation capacity corresponding to the heat flow distribution data is lower than the preset requirement, the first parameter set is optimized in a second iteration using Python's DEAP library to obtain the second parameter set.

[0124] Step S475: Based on the second parameter set, recalculate the heat flow distribution data using ANSYS, and determine the improvement in heat dissipation capacity by comparing it with the initial heat flow distribution data.

[0125] Step S476: If the increase is still lower than the preset requirement, the thickness and distribution density values ​​of the second parameter set are adjusted using Python's SciPy library to determine the third parameter set.

[0126] Step S477: Using the heat flow distribution data corresponding to the third parameter set, determine whether the heat dissipation capacity meets the preset requirements, and determine the final surface heat dissipation enhancement scheme.

[0127] Specifically, obtaining the initial parameter data for the replacement material from a pre-established parameter database is the starting point for the entire optimization process.

[0128] For example, suppose the target surface is a heat sink for an electronic device. The database might store various composite material thickness values, such as 2 mm and 3 mm, and density values, such as 0.8 g / cm³ and 1.2 g / cm³. These data are typically based on historical experimental or simulation results, reflecting the material's performance under different conditions. The selection of initial parameters directly affects the efficiency of subsequent optimization; therefore, it is necessary to ensure the comprehensiveness and representativeness of the data.

[0129] In one possible implementation, when using Python's DEAP library for the first iteration of optimization, the thickness value and distribution density value can be used as genes to generate multiple combinations through a genetic algorithm.

[0130] For example, an initial thickness of 2 mm and a density of 0.8 g / cm³ might be optimized to a first set of parameters with a thickness of 2.5 mm and a density of 1.0 g / cm³. This method utilizes the global search capability of evolutionary computation to quickly explore potential optimal solutions in the parameter space.

[0131] It should be noted that the advantage of the DEAP library lies in its flexibility, which allows the fitness function to be adjusted according to heat dissipation requirements, such as prioritizing the reduction of heat flow in high-temperature areas.

[0132] Specifically, when using ANSYS to calculate the corresponding heat flow distribution data, the first parameter set can be input into the finite element model.

[0133] For example, assuming the heat source power on the target surface is 50W, if the optimized parameters reduce the high-temperature region from 10cm² to 8cm², it indicates an improvement in heat dissipation capacity. This simulation process intuitively reflects the impact of parameter adjustments on heat flux distribution, providing data support for subsequent optimization.

[0134] Preferably, if the heat dissipation capacity is still insufficient, the parameters will be further adjusted in the second iteration of optimization.

[0135] For example, the first set of parameters, with a thickness of 2.5 mm and a density of 1.0 g / cm³, might be transformed into a second set of parameters with a thickness of 2.7 mm and a density of 1.1 g / cm³. This iterative approach gradually approaches the heat dissipation target, avoiding instability caused by excessively large adjustments at once.

[0136] In one embodiment, the heat flux distribution data can be recalculated using a second set of parameters and compared with the initial data.

[0137] For example, if the initial high-temperature region had a temperature of 85°C, and after optimization the temperature was reduced to 75°C, the improvement is immediately apparent. This comparative analysis helps quantify the optimization effect and provides a basis for further decision-making.

[0138] For example, when adjusting parameters using Python's SciPy library, the thickness and density values ​​can be fine-tuned. Suppose that the thickness of the second parameter set (2.7 mm, density 1.1 g / cm³) is still insufficient, it can be adjusted to a thickness of 2.8 mm and a density of 1.15 g / cm³ using SciPy's optimization algorithm, forming the third parameter set. This method combines the precision of mathematical optimization, enabling the refinement of parameters to meet stringent heat dissipation requirements.

[0139] Understandably, when judging heat dissipation capacity using the third set of parameters, if the temperature of the high-temperature region further decreases to 70°C and the area shrinks to 6 cm², it indicates that the solution has achieved its expected results. This multi-round optimization process not only improves heat dissipation efficiency but also enhances the robustness of the design, providing a reliable solution for practical applications.

[0140] In one embodiment, the advantage of multiple iterations is that they gradually converge to the optimal solution.

[0141] For example, the evolution from the initial set of parameters to the third set of parameters demonstrates a deep response to heat dissipation requirements. The practicality of this method lies in its ability to handle complex heat flux distributions and flexibly adapt to different surface properties, ultimately achieving the goal of efficient heat dissipation.

[0142] S5. Based on the surface heat dissipation enhancement scheme and combined with microstructure size data, laser etching technology is used to precisely control the micro-geometry of the terminal surface to obtain processing consistency indicators.

[0143] Optionally, this step also includes: Step S51: Obtain initial data on the microstructure obtained by processing the terminal surface using laser etching technology; Step S52: The initial data is compared using a preset microstructure size standard to obtain the deviation range of the micro-geometry. Step S53: If the deviation range exceeds the deviation threshold, adjust the laser etching parameters to obtain optimized micro-geometric structure data. Step S54: Based on the optimized micro-geometric structure data, and combined with the enhancement scheme of adding heat sinks and optimizing heat dissipation channels, determine the heat dissipation performance parameters of the terminal surface. Step S55: The heat dissipation performance parameters are analyzed using the support vector machine algorithm to obtain the result of the degree of conformity judgment of the processing consistency index; Step S56: If the judgment result indicates that there are indicators that do not meet the preset standards, then the gradient descent method is used to adjust the enhancement scheme to obtain the adjusted enhancement scheme. Step S57: Obtain the final processing consistency index according to the adjusted enhancement scheme; Step S58: Determine the micro-geometric structure optimization scheme of the terminal surface based on the final processing consistency index.

[0144] Specifically, when processing the terminal surface using laser etching technology, it is understood that this process aims to improve heat dissipation performance by precisely controlling the laser energy to form a micro-geometry on the surface.

[0145] For example, in a substation terminal scenario, the initial data may include the pore depth and spacing of the etched surface.

[0146] In one embodiment, a 50W laser with a pulse frequency of 20kHz was used to process copper terminals, yielding microstructure data with a pore depth of 10 micrometers and a spacing of 50 micrometers. This data was initially acquired using a scanning electron microscope to reflect changes in surface roughness and heat dissipation area.

[0147] It should be noted that the regularity of the microstructure directly affects the subsequent heat dissipation effect.

[0148] Specifically, when comparing microstructure size standards, the standard pore depth can be set to 8-12 micrometers and the spacing to 45-55 micrometers.

[0149] For example, the initial data with a depth of 10 micrometers and a spacing of 50 micrometers falls within the standard, but if another set of data has a depth of 15 micrometers and a spacing of 60 micrometers, the deviation exceeds the range. This deviation may originate from uneven laser focusing or oxidation of the material surface.

[0150] Preferably, the deviation range is determined through statistical analysis, such as a depth deviation of ±3 micrometers and a spacing deviation of ±5 micrometers, to provide a basis for subsequent adjustments.

[0151] In one possible implementation, if the deviation exceeds the standard when adjusting the laser etching parameters, the laser power can be increased to 60W and the frequency adjusted to 25kHz. After re-etching, the aperture depth stabilizes at 11 micrometers, and the spacing is adjusted to 48 micrometers. The optimized data is more in line with the standard. This adjustment can effectively reduce processing inconsistencies.

[0152] It should be noted that parameter optimization must be combined with material properties to avoid excessive etching that could damage the surface. This should be considered when combining heat sinks and optimizing heat dissipation channels.

[0153] For example, a heat sink with a height of 5 mm and a spacing of 2 mm can be added to the terminal surface, while a ventilation channel with a width of 3 mm can be designed.

[0154] In one embodiment, the optimized microstructure data is imported into thermal simulation software, and the ambient temperature is set to 30 degrees Celsius and the operating current to 120 amperes. This yields heat dissipation performance parameters, such as a reduction in surface thermal resistance from 0.8 to 0.6 units. This approach significantly improves heat conduction efficiency.

[0155] Specifically, when using the support vector machine algorithm for analysis, heat dissipation performance parameters can be input into the model, and the training data includes features such as thermal resistance and temperature distribution.

[0156] For example, if the processing consistency index is set to above 90%, and the analysis results show a consistency of 85%, it indicates that there is a deviation.

[0157] Preferably, the algorithm determines which parameters need improvement by classifying the boundaries, ensuring that the processing quality is controllable.

[0158] In one possible implementation, if consistency is not met, the gradient descent method can be used to optimize the heatsink height to 6 mm and the channel width to 3.5 mm. After adjustment and re-analysis, the thermal resistance is further reduced to 0.55 units, and the consistency is improved to 92%.

[0159] Understandably, this iterative approach can gradually approach the optimal solution.

[0160] For example, when obtaining the final consistency index, the adjusted enhancement scheme showed a consistency rate of 95%, indicating a significant improvement in processing stability.

[0161] In one embodiment, the temperature distribution on the terminal surface is more uniform, and the number of local high-temperature points is reduced, effectively extending the equipment life.

[0162] Specifically, when determining the micro-geometry optimization scheme, the microstructure data and heat dissipation performance can be considered together, and etching parameters of 11 micrometers depth and 48 micrometers spacing can be selected, along with a heat sink with a height of 6 millimeters.

[0163] For example, this approach maintains consistency across multiple batches of processing, making it suitable for large-scale applications.

[0164] It should be noted that the optimized microstructure and heat dissipation enhancement scheme work together to improve the overall thermal management capability and provide a guarantee for the long-term operation of the terminals.

[0165] S6 uses the processing consistency index to determine whether the manufacturing precision has reached the preset precision threshold. If it has not, the etching parameters are adjusted and the microstructure size distribution is recalculated to obtain the required manufacturing precision data.

[0166] Optionally, this step also includes: Step S61: Obtain the processing consistency index and compare the processing consistency index with the consistency threshold. If the processing consistency index is lower than the consistency threshold, it is determined that the manufacturing accuracy does not meet the requirements. Step S62: Based on the consistency index deviation, the etching parameter adjustment amount is calculated using a linear regression method to obtain the adjusted etching parameters; Step S63: Based on the adjusted etching parameters, the microstructure dimensions are calculated using the finite element analysis method to obtain new microstructure dimension data; Step S64: Based on the new microstructure size data, use statistical analysis methods to calculate the size distribution characteristics and obtain the size distribution characteristic parameters; Step S65: If the size distribution characteristic parameters exceed the preset distribution range, the etching parameters are adjusted using an iterative optimization method, and the microstructure size data is reacquired. Step S66: Input the size distribution feature parameters into the pre-built support vector machine classification model to obtain the manufacturing accuracy classification result and determine whether the manufacturing accuracy meets the requirements; Step S67: Based on the classification results, adjust the judgment criteria using a statistical adjustment method to obtain the final accuracy data.

[0167] Specifically, when obtaining processing consistency indicators and comparing them with preset consistency thresholds, consistency can be calculated, for example, by measuring the microstructure data of the terminal surface multiple times.

[0168] For example, assuming the consistency index is based on the stability of pore depth and spacing, if the consistency result is 88% after collecting 10 sets of data, while the preset consistency threshold is 90%, it indicates that the manufacturing precision has not met the standard.

[0169] It should be noted that this comparison can quickly determine whether the processing is stable, providing a basis for subsequent adjustments.

[0170] Specifically, when calculating the adjustment amount using the linear regression method based on the consistency index deviation, the deviation value can be used as the input variable, and etching parameters such as laser power and pulse frequency can be used as the output variable.

[0171] In one possible implementation, if the consistency deviation is -2%, historical data analysis suggests that the power needs to be increased by 5W and the frequency increased by 3kHz. The adjusted parameters would be, for example, 55W power and 23kHz frequency. This method systematically determines the adjustment direction.

[0172] For example, when using the finite element method to calculate the microstructure dimensions, the adjusted parameters can be input into simulation software to simulate the effect of laser etching on the surface of copper terminals.

[0173] In one embodiment, simulation results show that the pore depth changes from 10 micrometers to 11 micrometers, and the spacing changes from 50 micrometers to 49 micrometers.

[0174] Understandably, this analysis can predict microstructure change trends and avoid repeated trials. When calculating size distribution characteristics based on new microstructure size data...

[0175] Preferably, the mean and standard deviation of the pore depth can be statistically analyzed.

[0176] In one possible implementation, the mean depth of the 10 sets of new data is 11 micrometers with a standard deviation of 0.5 micrometers, and the mean spacing is 49 micrometers with a standard deviation of 1 micrometer. These characteristic parameters reflect the uniformity of the processing and help determine whether the size distribution is reasonable. If the size distribution characteristic parameters exceed the preset range, an iterative optimization method is used to adjust the parameters.

[0177] For example, if the upper limit of the standard deviation is 0.4 micrometers, but the actual value is 0.5 micrometers, the laser power can be reduced to 53W, and the depth standard deviation can be reduced to 0.3 micrometers after reprocessing.

[0178] For example, this iteration can gradually converge to the target range, ensuring processing quality.

[0179] Specifically, when inputting size distribution feature parameters into a support vector machine classification model, the mean and standard deviation can be used as feature vectors.

[0180] In one embodiment, the model outputs a "qualified" accuracy classification result after training, indicating that the manufacturing accuracy meets the requirements under the current parameters.

[0181] It should be noted that this classification can transform complex data into intuitive judgments, improving decision-making efficiency. When adjusting judgment criteria based on the classification results...

[0182] For example, if multiple classifications are all "qualified" but the consistency index is still slightly below 90%, the consistency threshold can be fine-tuned to 89% to balance accuracy and feasibility.

[0183] In one possible implementation, the adjusted final accuracy data stabilizes at 91%, indicating that the manufacturing process has been optimized. This adjustment is better suited to actual production needs.

[0184] S7: Obtain manufacturing accuracy data, and use statistical process control methods to analyze production consistency and determine the quality distribution curve after the fluctuation range is narrowed, addressing the issue of yield rate fluctuations.

[0185] Optionally, this step also includes: Step S71: Obtain manufacturing accuracy data, monitor production consistency using control chart methods, and obtain preliminary values ​​for the fluctuation range; Step S72: Extract the change characteristics from the preliminary value of the fluctuation range, and use the mean value to calculate and determine the production consistency benchmark; Step S73: If the production consistency benchmark exceeds the benchmark threshold, the process capability index is used to determine the degree of narrowing of the fluctuation range. Step S74: Based on the process capability index results, fit the quality distribution using a normal distribution to determine the distribution curve parameters; Step S75: Calculate the predicted yield rate using the distribution curve parameters, and determine the consistency between the manufacturing precision and the quality distribution; Step S76: Obtain the deviation between the predicted value and the actual data. If the deviation exceeds a preset range, adjust the center line and control limits of the control chart to obtain the optimized distribution curve. Step S77: Analyze the quality distribution after the fluctuation narrows using the optimized distribution curve to determine the final consistency range.

[0186] Specifically, when monitoring production consistency using control charts, it is understood that this method is often used to observe fluctuations in the manufacturing process in real time.

[0187] For example, when machining microstructures on the surface of copper terminals, the pore depth data measured each time can be recorded on a control chart.

[0188] For example, after continuously collecting 20 sets of data, the initial fluctuation range was 10 to 12 micrometers in depth. This monitoring can intuitively reflect production stability and provide a basis for subsequent analysis.

[0189] In one possible implementation, when extracting change characteristics from the initial value of the fluctuation range, the production consistency benchmark can be determined by calculating the mean.

[0190] Specifically, if the average depth of the 20 data sets is 11 micrometers, then this average can be used as a benchmark.

[0191] It should be noted that the mean calculation is simple and intuitive, effectively representing the central trend of the data and facilitating comparison with the benchmark threshold. When judging the production consistency benchmark, if the benchmark value of 11 micrometers exceeds the preset benchmark threshold of 10.5 micrometers, the process capability index is preferred to be used to assess the degree of fluctuation narrowing.

[0192] For example, setting the standard deviation at 0.8 micrometers, historical data analysis shows that the current process capability index fluctuates too much. This assessment can quantify production capacity and guide adjustments.

[0193] In one embodiment, when fitting the quality distribution based on the process capability index results, it can be assumed that the data conforms to a normal distribution to obtain distribution curve parameters, such as a mean of 11 micrometers and a standard deviation of 0.7 micrometers.

[0194] Understandably, this fitting can clearly show the pattern of quality distribution, providing a basis for prediction.

[0195] Specifically, when calculating the predicted yield using the distribution curve parameters, if the specification range is 10 micrometers to 12 micrometers, the predicted yield can reach 92%.

[0196] For example, this prediction can determine whether the manufacturing precision is consistent with the quality distribution; if it is below the target of 95%, optimization is required. The adjusted yield rate shows a significant improvement, demonstrating the method's practicality.

[0197] For example, when obtaining the deviation between the predicted and actual values, the actual yield rate is 90%, and the deviation is 2%. If the preset range is 1%, then the center line and control limits of the control chart need to be adjusted.

[0198] In one possible implementation, adjusting the centerline from 11 micrometers to 10.8 micrometers and narrowing the control limit to 0.5 micrometers results in a more stable distribution curve. This adjustment effectively reduces deviation and improves consistency.

[0199] Preferably, when analyzing the quality distribution using the optimized distribution curve, the data characteristics after the fluctuation narrows can be observed.

[0200] For example, the standard deviation of the new data decreased to 0.4 micrometers, and the final consistency range was 10.6 micrometers to 11 micrometers.

[0201] It should be noted that this analysis can identify stable quality ranges, providing a reliable reference for production optimization, and ultimately significantly improving consistency.

[0202] S8, based on the mass distribution curve, determines whether the heat dissipation efficiency meets the requirements of high-load scenarios. If not, it returns to the thermal resistance layering parameter optimization stage, re-simulates the heat conduction and heat dissipation path, and obtains the final optimization solution.

[0203] Optionally, this step also includes: Step S81: Obtain the surface temperature distribution curve of the device, which is acquired by a thermal imager; Step S82: Perform a Fourier transform on the temperature distribution curve to convert the temperature distribution curve into a frequency domain signal; Step S83: Extract the quality distribution data of the device based on the frequency domain signal; Step S84: Calculate the heat dissipation efficiency of the device based on the mass distribution data; Step S85: If the heat dissipation efficiency is lower than the preset high load scenario threshold, then extract the thermal resistance layering data from the thermal resistance tester. Step S86: Based on the thermal resistance layering data, the thermal conductivity and thickness parameters of the material are adjusted using finite element analysis tools to obtain an optimized heat conduction model; Step S87: Based on the heat conduction model, use ANSYS software to simulate the heat conduction path and determine the heat dissipation path adjustment scheme. Step S88: Based on the heat dissipation path adjustment scheme, recalculate the heat dissipation efficiency to obtain updated mass distribution data; Step S89: Based on the updated mass distribution data, determine whether the heat dissipation efficiency meets the requirements of high-load scenarios. Step S810: If the heat dissipation efficiency still does not meet the requirements of the high-load scenario, return to the step of extracting thermal resistance layer data, readjust the heat dissipation path, until the heat dissipation efficiency reaches the threshold of the high-load scenario.

[0204] Specifically, when obtaining the surface temperature distribution curve of a device, a thermal imager is usually used to directly collect the temperature data of the device during operation.

[0205] For example, when processing the surface of equipment, a thermal imager can scan the entire working area and record temperature changes at different locations.

[0206] For example, the temperature of the motor part of the equipment may reach 50 degrees Celsius, while the edge of the outer casing may only reach 30 degrees Celsius. This data collection method can intuitively reflect the heat distribution on the surface of the equipment, laying the foundation for subsequent analysis.

[0207] In one possible implementation, when performing a Fourier transform on the temperature distribution curve, the aim is to convert the temperature signal in the time domain into a signal in the frequency domain.

[0208] It should be noted that this conversion highlights the periodic characteristics of temperature changes.

[0209] For example, assuming that the temperature fluctuations during equipment operation exhibit periodic increases and decreases, Fourier transform can be used to extract the main frequency components, such as minute fluctuations of 10 times per minute. This helps to understand the distribution pattern of heat from a frequency perspective.

[0210] Specifically, when extracting quality distribution data from frequency domain signals, the quality distribution of the device can be inferred by analyzing frequency characteristics.

[0211] For example, a strong low-frequency component may indicate that heat is concentrated in certain areas, reflecting uneven heat dissipation. This method can indirectly reveal the physical characteristics of a device from a signal perspective.

[0212] Preferably, when calculating heat dissipation efficiency, the ability to transfer heat from the inside of the device to the outside can be evaluated based on mass distribution data.

[0213] For example, assuming the core temperature is 60 degrees Celsius and the external temperature is 35 degrees Celsius, the heat dissipation efficiency can be reflected by the ratio of temperature difference to power. If the result is 70%, which is lower than the preset high-load scenario threshold of 80%, further analysis is required.

[0214] In one embodiment, when extracting thermal resistance layer data from a thermal resistance tester, the thermal resistance value of each layer of material in the device can be measured.

[0215] For example, the thermal resistance of the internal circuit board is 0.5 degrees Celsius per watt, and that of the heat sink is 0.2 degrees Celsius per watt. This layered data clearly identifies the bottlenecks in heat transfer.

[0216] It is understandable that when using finite element analysis tools to adjust thermal conductivity and thickness parameters, the thermal conductivity of the material is adjusted through simulation, thereby optimizing the heat conduction model.

[0217] For example, increasing the thermal conductivity of the heat sink from 200 W / m Kelvin to 250 W / m Kelvin and the thickness from 2 mm to 3 mm can significantly improve heat flow distribution. This adjustment provides a better foundation for subsequent simulations.

[0218] For example, when using ANSYS software to simulate the heat conduction path, a path diagram of the heat flow moving from the core area to the heat sink can be generated.

[0219] For example, the simulation shows that heat is mainly transferred vertically, but there is insufficient heat dissipation from the sides. This visualization result can directly guide the design of adjustments to the heat dissipation path.

[0220] In one possible implementation, when the heat dissipation efficiency is recalculated, the adjusted model shows that the heat dissipation efficiency has increased from 70% to 85%.

[0221] For example, the core region temperature drops to 55 degrees Celsius, while the external temperature remains stable. This updated mass distribution data reflects the optimization effect.

[0222] Specifically, when judging whether the heat dissipation efficiency meets the requirements of high-load scenarios, if the target is 85%, the current value has met the standard, but if the load increases further, it may still need to be adjusted.

[0223] For example, under high load, the temperature may exceed 60 degrees Celsius, which is beyond the safe range.

[0224] Preferably, if the heat dissipation efficiency still does not meet the requirements, when returning to extract the thermal resistance layer data, the contact surface thermal resistance of the heat sink can be re-examined.

[0225] For example, it was found that poor contact caused thermal resistance to increase by 0.3 degrees Celsius per watt, and efficiency could be further improved by refining the contact method. This iterative adjustment ensures that the requirements of high-load scenarios are ultimately met.

[0226] S9, through the final optimized scheme, uses automated testing equipment to conduct batch thermal resistance and heat dissipation efficiency tests on terminal samples, and obtains performance consistency verification results.

[0227] Optionally, this step also includes: Step S91: Obtain the raw data values ​​of the thermal resistance performance and heat dissipation efficiency of the terminal sample; Step S92: Filter the raw data values ​​according to the preset performance threshold range. If the thermal resistance performance exceeds the upper limit of the performance threshold range, mark the corresponding raw data value as abnormal data to obtain the preliminarily filtered dataset. Step S93: For the dataset after preliminary filtering, calculate the average value and variance of the heat dissipation efficiency to determine the distribution characteristics of the heat dissipation efficiency; Step S94: The distribution features are normalized using the Z-score normalization algorithm to obtain the feature parameters after consistency correction. Step S95: Input the feature parameters after consistency correction into a preset support vector machine classification model to obtain the performance classification result of the terminal sample. The performance classification result is used to characterize whether the performance of the terminal sample meets the preset consistency standard. Step S96: Based on the performance classification results, the proportion of abnormal data is statistically analyzed, and the proportion of abnormal data is compared with a preset proportion threshold. If the proportion of abnormal data exceeds the preset proportion threshold, the terminal sample is determined to have failed the efficiency analysis. Step S97: Obtain the final result of the terminal sample verification, and record all data processing steps related to the terminal sample testing through the testing process to form a complete data result chain.

[0228] Specifically, when obtaining the raw data values ​​of the thermal resistance performance and heat dissipation efficiency of the terminal sample, for example, the thermal resistance and heat dissipation of the terminal under operating conditions can be measured by a thermal resistance tester and a heat flow meter, respectively.

[0229] For example, when testing a terminal sample, the thermal resistance meter recorded its thermal resistance value as 0.4 degrees Celsius per watt, and the heat flow meter measured a heat dissipation efficiency of 75%. This method can directly obtain the initial performance data of the sample, providing a basis for subsequent analysis.

[0230] In one possible implementation, when filtering raw data values ​​based on a preset performance threshold range, the upper limit for thermal resistance can be set to 0.45 degrees Celsius per watt. If the thermal resistance value of a terminal sample reaches 0.5 degrees Celsius per watt, exceeding the upper limit, it is marked as abnormal data. After filtering, sample data with thermal resistance in the range of 0.2 to 0.45 degrees Celsius per watt are retained. This method can quickly eliminate data that significantly deviates from the standard, ensuring the reliability of subsequent analyses.

[0231] When calculating the mean and variance of heat dissipation efficiency for the initially filtered dataset, specifically assuming there are 10 terminal samples with heat dissipation efficiencies of 70%, 72%, 75%, 78%, etc., the calculated mean is 74% and the variance is 10. This distribution characteristic reflects the dispersion of heat dissipation capacity among samples, laying the foundation for performance consistency evaluation.

[0232] When using the Z-score normalization algorithm to normalize the distribution characteristics, it can be understood that the heat dissipation efficiency data is converted into a standard score.

[0233] For example, with a heat dissipation efficiency of 75%, a mean of 74%, and a standard deviation of 3, the Z-score is 0.33. This processing eliminates dimensional differences, making the data from different samples comparable and facilitating classification analysis.

[0234] In one embodiment, when the consistency-corrected feature parameters are input into the support vector machine classification model, the model classifies the terminal samples into two categories, "consistent" and "inconsistent," based on the normalized values ​​of thermal resistance and heat dissipation efficiency.

[0235] For example, a sample with a thermal resistance of 0.3 degrees Celsius per watt and a heat dissipation efficiency of 78% is classified as "consistent," while a sample with a thermal resistance of 0.5 degrees Celsius per watt and a heat dissipation efficiency of 65% is classified as "inconsistent." This classification can intuitively reflect whether the sample performance meets the standards.

[0236] Preferably, when calculating the percentage of abnormal data based on the performance classification results, if 15 out of 100 samples are marked as "inconsistent," the percentage is 15%. The preset percentage threshold is 10%. If the percentage exceeds this threshold, the batch of terminal samples is determined to have failed the efficiency analysis. This comparison helps identify batch quality issues and guides production improvements.

[0237] For example, when obtaining the final results of terminal sample verification and recording the testing process, log files can be generated for steps such as thermal resistance testing, data filtering, and classification results.

[0238] For example, it can record the entire process of a sample from a thermal resistance of 0.4 degrees Celsius per watt to its classification as "consistent." This complete data chain can support quality traceability and optimization, effectively improving the efficiency and accuracy of subsequent testing.

[0239] like Figure 3 As shown, in a second aspect, the present invention provides an optimized system for heat dissipation of high-power terminals, which optimizes heat dissipation of high-power terminals using the method described above. The system further includes: The heat distribution data acquisition module is used to acquire high-power heat distribution data during terminal operation. It uses infrared thermal imaging technology to scan the heat concentration area and obtain the temperature distribution matrix. The thermal resistance calculation module is used to calculate the thermal resistance value for areas with concentrated heat based on the temperature distribution matrix. It uses finite element analysis to simulate the heat conduction speed and the diversity of heat dissipation paths to determine the thermal resistance layering parameters. The heat dissipation path optimization module is used to adjust the heat dissipation path design by using thermal resistance layering parameters, and to optimize the heat conduction direction by using a multi-channel heat dissipation model to obtain an improved heat flow distribution map. The surface heat dissipation enhancement module is used to obtain an improved heat flow distribution map, adjust the material property parameters for areas with insufficient surface heat dissipation capacity, replace the original material with a composite material with higher thermal conductivity, and determine the surface heat dissipation enhancement scheme. The microstructure control module is used to precisely control the micro-geometry of the terminal surface based on the surface heat dissipation enhancement scheme and the microstructure size data, using laser etching technology to obtain processing consistency indicators. The manufacturing precision judgment module is used to determine whether the manufacturing precision has reached the preset precision threshold through the processing consistency index. If it has not reached the threshold, the etching parameters are adjusted and the microstructure size distribution is recalculated to obtain the manufacturing precision data that meets the requirements. The quality analysis module is used to acquire manufacturing accuracy data. To address the issue of yield rate fluctuations, it employs statistical process control methods to analyze production consistency and determine the quality distribution curve after the fluctuation range is narrowed. The heat dissipation efficiency judgment module is used to determine whether the heat dissipation efficiency meets the requirements of high-load scenarios based on the mass distribution curve. If it does not meet the requirements, it returns to the thermal resistance layering parameter optimization stage to re-simulate the heat conduction and heat dissipation path and obtain the final optimization solution. The performance verification module is used to conduct batch thermal resistance and heat dissipation efficiency tests on terminal samples using automated testing equipment based on the final optimized scheme, and obtain performance consistency verification results.

[0240] This invention provides an optimized method and system for heat dissipation of high-power terminals. It acquires the temperature distribution matrix of the terminals during operation using infrared thermal imaging technology, calculates the thermal resistance value, and simulates heat conduction to determine thermal resistance layering parameters. Based on this, the invention optimizes the heat dissipation path design, employs a multi-channel heat dissipation model to improve heat flow distribution, adjusts material properties for areas with insufficient surface heat dissipation capacity, and utilizes laser etching technology to precisely control the microstructure of the terminal surface. Statistical process control methods are used to analyze production consistency, ensuring that heat dissipation efficiency meets high-load requirements. Finally, automated testing equipment is used for batch testing to verify performance consistency. This invention effectively improves the heat dissipation efficiency and production quality of high-power terminals, providing an important guarantee for the reliable operation of power equipment.

[0241] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An optimized method for heat dissipation of high-power terminals, characterized in that, The method includes: S1, acquire high-power heat distribution data during terminal operation, and use infrared thermal imaging technology to scan the heat concentration area to obtain the temperature distribution matrix; S2, based on the temperature distribution matrix, calculate the thermal resistance value for the heat concentration area, and determine the thermal resistance layering parameters by simulating the heat conduction speed and heat dissipation path diversity through finite element analysis. S3, by adjusting the heat dissipation path design through thermal resistance layering parameters, and by using a multi-channel heat dissipation model to optimize the heat conduction direction, an improved heat flow distribution map is obtained; S4. Obtain the improved heat flow distribution map, adjust the material property parameters for areas with insufficient surface heat dissipation capacity, replace the original material with a composite material with higher thermal conductivity, and determine the surface heat dissipation enhancement scheme. S5. Based on the surface heat dissipation enhancement scheme and combined with the microstructure size data, laser etching technology is used to precisely control the micro-geometry of the terminal surface to obtain the processing consistency index. S6. By using the processing consistency index, it is determined whether the manufacturing accuracy has reached the preset accuracy threshold. If it has not, the etching parameters are adjusted and the microstructure size distribution is recalculated to obtain the required manufacturing accuracy data. S7: Obtain manufacturing accuracy data, and use statistical process control methods to analyze production consistency and determine the quality distribution curve after the fluctuation range is narrowed in order to address the yield rate fluctuation problem. S8. Based on the mass distribution curve, determine whether the heat dissipation efficiency meets the requirements of high-load scenarios. If not, return to the thermal resistance layering parameter optimization stage, re-simulate the heat conduction and heat dissipation path, and obtain the final optimization solution. S9, through the final optimized scheme, uses automated testing equipment to conduct batch thermal resistance and heat dissipation efficiency tests on terminal samples, and obtains performance consistency verification results.

2. The method according to claim 1, characterized in that, Step S1 involves acquiring high-power heat distribution data during terminal operation, scanning the heat concentration area using infrared thermal imaging technology, and obtaining a temperature distribution matrix, including: Step S11: Obtain the original thermal distribution image data scanned by the infrared thermal imaging device during the terminal operation period; Step S12: The original thermal distribution image data is processed using an image denoising method to obtain a clear thermal distribution image; Step S13: Extract the pixel coordinates of the heat concentration area based on the clear thermal distribution image to generate the corresponding temperature distribution matrix; Step S14: If there are abnormal high temperature points in the temperature distribution matrix, the abnormal area is determined by comparing with the preset temperature threshold, and the set of high temperature abnormal coordinates is determined. Step S15: For the set of high temperature anomaly coordinates, the KMeans clustering algorithm is used to classify the anomaly points to obtain the distribution characteristics of the anomaly region. Step S16: Based on the distribution characteristics of the abnormal area, generate the boundary range of the heat concentration area and determine the final heat distribution zone; Step S17: By performing statistical analysis on the temperature distribution matrix within the final heat distribution zone, the temperature change pattern of each zone is obtained.

3. The method according to claim 1, characterized in that, Step S2 involves calculating the thermal resistance value for areas of concentrated heat based on the temperature distribution matrix, and determining the thermal resistance stratification parameters by simulating the heat conduction velocity and heat dissipation path diversity through finite element analysis, including: Step S21: Obtain the temperature distribution matrix, use the grid partitioning method to determine the coordinates of the heat concentration area, and obtain the first region set; Step S22: For the first region set, calculate the thermal resistance value. If the thermal resistance value exceeds the thermal resistance threshold, use linear interpolation to adjust the region boundary to obtain the second region set. Step S23: Extract the boundary temperature distribution from the second region set, use ANSYS software to perform finite element analysis to simulate the temperature field, calculate the heat conduction velocity through the temperature gradient, and obtain the velocity distribution map. Step S24: Based on the velocity distribution map, use Dijkstra's algorithm to perform gradient tracking, obtain the heat dissipation path, and determine whether the heat dissipation path forms a closed loop. If a closed loop is formed, calculate the diversity of heat dissipation paths through connectivity analysis in graph theory to determine the first path set. Step S25: For the first path set, K-means clustering algorithm is used to group the paths to obtain a path group set; Step S26: Calculate the hierarchical parameters for each path group based on the path group set to obtain the hierarchical parameter set; Step S27: Based on the set of layered parameters, the final thermal resistance layering parameters are determined using the weighted average method.

4. The method according to claim 1, characterized in that, Step S3 involves adjusting the heat dissipation path design by adjusting the thermal resistance layering parameters and optimizing the heat conduction direction using a multi-channel heat dissipation model to obtain an improved heat flow distribution diagram, including: Step S31: Obtain thermal resistance layer data and obtain the thermal resistance parameter values ​​of each layer; Step S32: Use finite element analysis software to establish a thermal resistance distribution model and obtain the initial thermal resistance distribution matrix; Step S33: Based on the initial thermal resistance distribution matrix, the parameters are adjusted using the optimization toolbox. If the parameter values ​​exceed the distribution threshold, the thermal resistance distribution is recalculated to obtain the adjusted thermal resistance matrix. Step S34: Based on the adjusted thermal resistance matrix, the shortest path algorithm is used to extract heat dissipation path data, and the optimal heat dissipation path set is determined through path optimization. Step S35: Based on the optimal heat dissipation path set, establish a multi-channel heat transfer model, and use multiphysics coupling analysis software to calculate the heat conduction direction to obtain the heat conduction vector; Step S36: Based on the heat conduction vector, perform thermal flow field simulation using fluid dynamics numerical simulation software to obtain a preliminary heat flow distribution map; Step S37: If the heat flow distribution is determined to be uneven based on the preliminary heat flow distribution map, the channel design parameters are adjusted, and the heat flow field simulation is performed again to obtain the improved heat flow distribution map. Step S38: Based on the improved heat flux distribution map, a Gaussian filtering method is used to perform boundary smoothing to obtain the final heat flux distribution map.

5. The method according to claim 1, characterized in that, Step S4 involves obtaining an improved heat flow distribution map, adjusting material property parameters for areas with insufficient surface heat dissipation capacity, replacing the original material with a composite material with higher thermal conductivity, and determining a surface heat dissipation enhancement scheme, including: Step S41: Obtain heat flow distribution data of the target surface, wherein the heat flow distribution data is obtained by performing heat flow distribution analysis on the target surface using a finite element analysis tool; Step S42: Based on the heat flux density threshold, extract regions with temperatures higher than the heat flux density threshold from the heat flux distribution data, and identify these regions as regions with insufficient heat dissipation capacity. Step S43: Obtain the current thermal conductivity value of the material in the region with insufficient heat dissipation capacity; Step S44: Determine whether the thermal conductivity value is lower than the preset thermal conductivity threshold. If so, select candidate materials with thermal conductivity values ​​higher than the current material from the pre-established composite material database and determine the candidate materials as replacement materials. Step S45: Adjust the material properties of the current material using the replacement material, and recalculate the heat flow distribution using the finite element analysis tool to obtain enhanced heat flow distribution data; Step S46: Calculate the improvement in heat dissipation capacity based on the heat flow distribution data and the enhanced heat flow distribution data; Step S47: Determine whether the improvement reaches the preset requirement. If not, use a genetic algorithm to iteratively optimize the material parameters of the replacement material, including the thickness and distribution density of the replacement material, until the heat dissipation capacity meets the preset requirement, and determine the final surface heat dissipation enhancement scheme.

6. The method according to claim 1, characterized in that, In step S5, based on the surface heat dissipation enhancement scheme and combined with microstructure size data, laser etching technology is used to precisely control the micro-geometry of the terminal surface to obtain processing consistency indicators, including: Step S51: Obtain initial data on the microstructure obtained by processing the terminal surface using laser etching technology; Step S52: The initial data is compared using a preset microstructure size standard to obtain the deviation range of the microstructure. Step S53: If the deviation range exceeds the deviation threshold, adjust the laser etching parameters to obtain optimized micro-geometric structure data. Step S54: Based on the optimized micro-geometric structure data, and combined with the enhancement scheme of adding heat sinks and optimizing heat dissipation channels, determine the heat dissipation performance parameters of the terminal surface; Step S55: The heat dissipation performance parameters are analyzed using the support vector machine algorithm to obtain the degree of conformity judgment of the processing consistency index; Step S56: If the judgment result indicates that there are indicators that do not meet the preset standards, then the gradient descent method is used to adjust the enhancement scheme to obtain the adjusted enhancement scheme. Step S57: Obtain the final processing consistency index according to the adjusted enhancement scheme; Step S58: Determine the micro-geometric structure optimization scheme of the terminal surface based on the final processing consistency index.

7. The method according to claim 1, characterized in that, Step S6 involves determining whether the manufacturing precision has reached a preset precision threshold using a processing consistency index. If not, the etching parameters are adjusted and the microstructure size distribution is recalculated to obtain manufacturing precision data that meets the requirements, including: Step S61: Obtain the processing consistency index and compare the processing consistency index with the consistency threshold. If the processing consistency index is lower than the consistency threshold, it is determined that the manufacturing accuracy does not meet the requirements. Step S62: Based on the consistency index deviation, the etching parameter adjustment amount is calculated using a linear regression method to obtain the adjusted etching parameters; Step S63: Based on the adjusted etching parameters, the microstructure dimensions are calculated using the finite element analysis method to obtain new microstructure dimension data; Step S64: Based on the new microstructure size data, use statistical analysis methods to calculate the size distribution characteristics and obtain the size distribution characteristic parameters; Step S65: If the size distribution characteristic parameters exceed the preset distribution range, the etching parameters are adjusted using an iterative optimization method, and the microstructure size data is reacquired. Step S66: Input the size distribution feature parameters into the pre-built support vector machine classification model to obtain the manufacturing accuracy classification result and determine whether the manufacturing accuracy meets the requirements; Step S67: Based on the classification results, adjust the judgment criteria using a statistical adjustment method to obtain the final accuracy data.

8. The method according to claim 1, characterized in that, Step S7 involves acquiring manufacturing accuracy data and, to address the yield rate fluctuation issue, employing statistical process control methods to analyze production consistency and determine the quality distribution curve after narrowing the fluctuation range. This includes: Step S71: Obtain manufacturing accuracy data, monitor production consistency using control chart methods, and obtain preliminary values ​​for the fluctuation range; Step S72: Extract the change characteristics from the preliminary value of the fluctuation range, and use the mean value to calculate and determine the production consistency benchmark; Step S73: If the production consistency benchmark exceeds the benchmark threshold, the process capability index is used to determine the degree of narrowing of the fluctuation range. Step S74: Based on the process capability index results, fit the quality distribution using a normal distribution to determine the distribution curve parameters; Step S75: Calculate the predicted yield rate using the distribution curve parameters, and determine the consistency between the manufacturing precision and the quality distribution; Step S76: Obtain the deviation between the predicted value and the actual data. If the deviation exceeds a preset range, adjust the center line and control limits of the control chart to obtain the optimized distribution curve. Step S77: Analyze the quality distribution after the fluctuation narrows using the optimized distribution curve to determine the final consistency range.

9. The method according to claim 1, characterized in that, In step S8, based on the mass distribution curve, it is determined whether the heat dissipation efficiency meets the requirements of high-load scenarios. If not, the process returns to the thermal resistance layering parameter optimization stage to re-simulate the heat conduction and heat dissipation path, obtaining the final optimized solution, including: Step S81: Obtain the surface temperature distribution curve of the device, which is acquired by a thermal imager; Step S82: Perform a Fourier transform on the temperature distribution curve to convert the temperature distribution curve into a frequency domain signal; Step S83: Extract the quality distribution data of the device based on the frequency domain signal; Step S84: Calculate the heat dissipation efficiency of the device based on the mass distribution data; Step S85: If the heat dissipation efficiency is lower than the preset high load scenario threshold, then extract the thermal resistance layering data from the thermal resistance tester. Step S86: Based on the thermal resistance layering data, the thermal conductivity and thickness parameters of the material are adjusted using finite element analysis tools to obtain an optimized heat conduction model; Step S87: Based on the heat conduction model, use ANSYS software to simulate the heat conduction path and determine the heat dissipation path adjustment scheme. Step S88: Based on the heat dissipation path adjustment scheme, recalculate the heat dissipation efficiency to obtain updated mass distribution data; Step S89: Based on the updated mass distribution data, determine whether the heat dissipation efficiency meets the requirements of high-load scenarios. Step S810: If the heat dissipation efficiency still does not meet the requirements of the high-load scenario, return to the step of extracting thermal resistance layer data, readjust the heat dissipation path, until the heat dissipation efficiency reaches the threshold of the high-load scenario.

10. An optimized system for heat dissipation of high-power terminals, characterized in that, The system optimizes heat dissipation for high-power terminals using the method described in any one of claims 1 to 9, the system comprising: The heat distribution data acquisition module is used to acquire high-power heat distribution data during terminal operation. It uses infrared thermal imaging technology to scan the heat concentration area and obtain the temperature distribution matrix. The thermal resistance calculation module is used to calculate the thermal resistance value for areas with concentrated heat based on the temperature distribution matrix. It uses finite element analysis to simulate the heat conduction speed and the diversity of heat dissipation paths to determine the thermal resistance layering parameters. The heat dissipation path optimization module is used to adjust the heat dissipation path design by using thermal resistance layering parameters, and to optimize the heat conduction direction by using a multi-channel heat dissipation model to obtain an improved heat flow distribution map. The surface heat dissipation enhancement module is used to obtain an improved heat flow distribution map, adjust the material property parameters for areas with insufficient surface heat dissipation capacity, replace the original material with a composite material with higher thermal conductivity, and determine the surface heat dissipation enhancement scheme. The microstructure control module is used to precisely control the micro-geometry of the terminal surface based on the surface heat dissipation enhancement scheme and the microstructure size data, using laser etching technology to obtain processing consistency indicators. The manufacturing precision judgment module is used to determine whether the manufacturing precision has reached the preset precision threshold through the processing consistency index. If it has not reached the threshold, the etching parameters are adjusted and the microstructure size distribution is recalculated to obtain the manufacturing precision data that meets the requirements. The quality analysis module is used to acquire manufacturing accuracy data. To address the issue of yield rate fluctuations, it employs statistical process control methods to analyze production consistency and determine the quality distribution curve after the fluctuation range is narrowed. The heat dissipation efficiency judgment module is used to determine whether the heat dissipation efficiency meets the requirements of high-load scenarios based on the mass distribution curve. If it does not meet the requirements, it returns to the thermal resistance layering parameter optimization stage to re-simulate the heat conduction and heat dissipation path and obtain the final optimization solution. The performance verification module is used to conduct batch thermal resistance and heat dissipation efficiency tests on terminal samples using automated testing equipment based on the final optimized scheme, and obtain performance consistency verification results.

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