A heat dissipation control system and method based on efficient heat management

Abstract

This invention discloses a heat dissipation control system and method based on efficient thermal management, belonging to the field of power electronic equipment technology. The invention collects and analyzes IGBT temperature data and ambient temperature data inside the chassis using temperature sensors; it then combines these data with a heat dissipation efficiency analysis algorithm to identify the current thermal state and classify heat dissipation demand levels; based on the IGBT temperature data and heat dissipation demand levels, and combined with a heat conduction analysis algorithm, it determines the arrangement of the heat pipe array, including linear, ring, and honeycomb arrangements; by real-time monitoring of the heat distribution data of the heat sink aluminum blocks, it optimizes the heat pipe array arrangement using a random forest algorithm; based on the IGBT temperature data and ambient temperature data inside the chassis, it dynamically adjusts the fan speed using a PID algorithm, employing a dual-fan collaborative heat dissipation strategy; and by combining historical operating data and feedback, it analyzes and optimizes the heat dissipation mode using a support vector machine algorithm.

Description

Technical Field

[0001] This invention relates to the field of power electronic equipment technology, specifically a heat dissipation control system and method based on efficient thermal management. Background Technology

[0002] In modern industry and the energy sector, high-power IGBT devices serve as core components for energy conversion and power control. Managing the heat generated during their operation is crucial for ensuring equipment performance and reliability. With the widespread application of new energy power generation and smart grid technologies, the need for thermal management of IGBT devices is becoming increasingly urgent. However, existing heat dissipation technologies often fail to meet the requirements for efficient and intelligent heat dissipation, leading to low equipment operating efficiency and even potential damage due to overheating.

[0003] Currently, heat dissipation for IGBT devices primarily relies on traditional passive cooling or forced air cooling with a single fan. While these methods can reduce device temperature to some extent, their heat dissipation efficiency is limited and they struggle to cope with complex and variable operating conditions. Especially in high power density and high-temperature environments, traditional cooling methods often fail to effectively control the temperature of IGBT devices, leading to increased thermal stress. Existing cooling systems lack intelligent control and cannot adjust in real time based on the actual operating status of the IGBT devices and the ambient temperature. Furthermore, traditional cooling systems lack effective feedback mechanisms and cannot dynamically optimize based on heat dissipation performance, further impacting heat dissipation efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a heat dissipation control system and method based on efficient thermal management to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a heat dissipation control system and method based on efficient thermal management, comprising:

[0007] Temperature data is collected by temperature sensors, including IGBT temperature data and ambient temperature data inside the chassis, and the temperature data is analyzed and processed.

[0008] Based on IGBT temperature data and combined with heat dissipation efficiency analysis algorithms, the current thermal state is identified; and different heat dissipation requirement levels are classified according to the heat generation situation.

[0009] Based on the analyzed IGBT temperature data and heat dissipation requirements, combined with heat conduction analysis algorithms, the arrangement of the heat pipe array is determined, and the position and combination of the heat pipes within the heat dissipation aluminum block are adjusted, including linear arrangement, ring arrangement and honeycomb arrangement.

[0010] By monitoring the heat distribution data of the heat sink aluminum blocks in real time, the heat dissipation effect of the current arrangement is evaluated. The random forest algorithm is used to analyze and optimize the heat pipe array arrangement, adjust the parameter threshold of the IGBT temperature data, and make adjustments based on the feedback of the heat dissipation effect.

[0011] Based on IGBT temperature data and chassis internal ambient temperature data, the fan speed is dynamically adjusted through a PID algorithm, and a dual-fan collaborative heat dissipation strategy is adopted, dynamically adjusting the main fan and auxiliary fan according to heat dissipation requirements.

[0012] By combining historical operating data and feedback, the heat dissipation mode is analyzed and optimized using the support vector machine algorithm, and adjustments are made based on feedback on the heat dissipation effect.

[0013] In conjunction with the first aspect, in the first implementation of the first aspect of this application, the step of collecting temperature data through a temperature sensor, including IGBT temperature data and ambient temperature data inside the chassis, and analyzing and processing the temperature data, includes:

[0014] Temperature data is collected by a temperature sensor. The IGBT temperature data includes the IGBT junction temperature and the IGBT case temperature.

[0015] The collected temperature data is transmitted to the central processing unit for data cleaning and preprocessing to remove outliers and noise interference.

[0016] The preprocessed temperature data is analyzed using the support vector machine algorithm to obtain the real-time status and trend of the temperature data.

[0017] In conjunction with the first aspect, in the second implementation of the first aspect of this application, the current thermal state is identified based on IGBT temperature data and a heat dissipation efficiency analysis algorithm; according to the heat generation situation, it is divided into different heat dissipation requirement levels, including:

[0018] By collecting IGBT temperature data and analyzing the results, combined with a heat dissipation efficiency analysis algorithm, the current thermal state is evaluated. By analyzing the IGBT temperature data, the rate of temperature change and the uniformity of temperature distribution are calculated.

[0019] Based on the thermal state identification results and the thermal characteristics and safe operating temperature range of the IGBT module, different heat dissipation requirement levels are defined. When the IGBT temperature is within the user-defined first safety threshold a%, it is determined to be a level 1 heat dissipation requirement. When the IGBT temperature is higher than the user-defined first safety threshold a%, it is determined to be a level 2 heat dissipation requirement. When the IGBT temperature is higher than the user-defined second safety threshold b%, it is determined to be a level 3 heat dissipation requirement. When the IGBT temperature is higher than the user-defined third safety threshold c%, it is determined to be a level 4 heat dissipation requirement, and an emergency heat dissipation state is entered.

[0020] In conjunction with the first aspect, in the third implementation of the first aspect of this application, the step of determining the arrangement of the heat pipe array based on the analyzed IGBT temperature data and heat dissipation requirement level, combined with a heat conduction analysis algorithm, and adjusting the position and combination of the heat pipes within the heat dissipation aluminum block, includes linear arrangement, ring arrangement, and honeycomb arrangement, including:

[0021] Under the primary heat dissipation requirement, the heat pipe array adopts a linear arrangement, with the heat pipes evenly distributed along the heat propagation direction to ensure uniform heat distribution.

[0022] The spacing between heat pipes is calculated using a heat conduction analysis algorithm, taking into account the IGBT module's size, power density, and the thermal conductivity of the heat sink. Fourier's law is used to calculate the heat transfer rate within the heat sink, and the heat generation is calculated based on the IGBT module's power density and size. The formula is as follows:

[0023] ;

[0024] Where Q is the heat generation, P is the power density of the IGBT module, and A is the area of ​​the IGBT module;

[0025] The rate of heat conduction in the aluminum heat sink is calculated using Fourier's law, and the formula is:

[0026] ;

[0027] Where q is the heat flux density and k is the thermal conductivity of the aluminum heat sink. For temperature gradient;

[0028] Based on the thermal conductivity of the heat sink aluminum block, the heat transfer task required by the heat pipes is calculated. The optimal spacing between the heat pipes is obtained using a genetic algorithm. By using the spacing between the heat pipes as the optimization variable and aiming at maximizing heat dissipation efficiency, the genetic algorithm parameters, including population size, number of iterations, crossover rate, and mutation rate, are set to generate a set of initial values ​​for the heat pipe spacing, forming an initial population. For each set of heat pipe spacing, the heat transfer task required by the heat sink aluminum block is calculated based on the heat transfer performance of the heat sink aluminum block. The heat dissipation efficiency is used as the fitness value, and the heat pipe spacing with the highest fitness is selected as the optimal spacing value.

[0029] In conjunction with the first aspect, in the fourth implementation of the first aspect of this application, the step of determining the arrangement of the heat pipe array based on the analyzed IGBT temperature data and heat dissipation requirement level, combined with a heat conduction analysis algorithm, and adjusting the position and combination of the heat pipes within the heat dissipation aluminum block, includes linear arrangement, ring arrangement, and honeycomb arrangement, including:

[0030] Under the secondary heat dissipation requirement, the heat pipe array is converted to a ring arrangement, with the heat pipes arranged in a ring around the IGBT module to form a heat enveloping ring.

[0031] The ring arrangement conducts heat from the IGBT module to other areas of the heat sink through a ring-shaped heat flow path, thereby reducing local temperature. The radius of the ring and the number of heat pipes are determined using a heat conduction analysis algorithm. An initial ring radius R is set to 1.5 times the diagonal length of the IGBT module, and the initial number of heat pipes N is calculated based on the heat transfer capacity of a single heat pipe.

[0032] ;

[0033] Where Q represents the total heat generation of the IGBT module. Indicates rounding up;

[0034] The heat generation of the IGBT module, the thermal conductivity and thickness of the heat sink were collected. A heat conduction model was established using the finite element method (FEM). Under initial parameters, the heat distribution of the IGBT module during operation was simulated. The temperature distribution and hot spot locations on the heat sink were recorded. The heat conduction path from the IGBT module through the heat pipe to the heat sink was analyzed, and the heat flow path and heat concentration areas were evaluated. Optimization was performed based on the heat generation of the IGBT module and the heat dissipation capacity of the heat sink. The temperature distribution was calculated using finite element simulation, and the highest temperature T was... hotspot Exceeding the allowed value T max At that time, adjust the parameters and compare the distance of the hot spot from the center point of the IGBT module with the length of the IGBT module's diagonal to determine whether the hot spot is closer to the inside or outside; adjust the number of heat pipes until T hotspot ≤T max The formula is:

[0035] ;

[0036] Where, N new For the new number of heat pipes, N old This represents the current number of heat pipes.

[0037] In conjunction with the first aspect, in the fifth implementation of the first aspect of this application, the step of determining the arrangement of the heat pipe array based on the analyzed IGBT temperature data and heat dissipation requirement level, combined with a heat conduction analysis algorithm, and adjusting the position and combination of the heat pipes within the heat dissipation aluminum block, includes linear arrangement, ring arrangement, and honeycomb arrangement, including:

[0038] Under the three-level heat dissipation requirement, a honeycomb arrangement is adopted, with heat pipes tightly arranged in a honeycomb structure to form multiple heat conduction paths and increase the heat dissipation area. By collecting thermal conductivity and density data of heat pipes and honeycomb structure materials, the honeycomb structure is discretized using the finite element analysis algorithm combined with aerodynamic principles. The influence of honeycomb structures of different sizes and shapes on airflow and heat exchange is simulated. The honeycomb structure is divided into x units, and the heat conduction behavior of each unit is described by the following formula:

[0039] ;

[0040] Where, q i Let k be the heat flux density of the i-th unit, and k1 be the thermal conductivity of the material. The temperature gradient of the i-th unit;

[0041] The effects of honeycomb structures of different sizes and shapes on airflow and heat exchange were simulated. The amount of heat exchange between air and the honeycomb structure, Q1, was calculated using the following formula:

[0042] ;

[0043] Where h is the convective heat transfer coefficient, A1 is the heat exchange area, and T S T represents the surface temperature of the honeycomb structure. ∞ The ambient air temperature unaffected by heat sources;

[0044] The heat transfer efficiency η of the heat pipe under different layouts is calculated using a heat transfer analysis algorithm. The formula is as follows:

[0045] ;

[0046] Where, k t Let A be the thermal conductivity of the heat pipe, L be the length of the heat pipe, and A be the thermal conductivity of the heat pipe. t This refers to the cross-sectional area of ​​the heat pipe.

[0047] The optimal honeycomb structure size, shape, and heat pipe layout are calculated using a genetic algorithm, with the following formula:

[0048] ;

[0049] Among them, minT max To minimize the highest temperature T on the heat sink maxd represents the cell structure size, s represents the cell shape, and l represents the heat pipe layout. By encoding the cell structure size, shape, and heat pipe layout genetically, an initial population is generated. Each individual represents a cell structure size, shape, and heat pipe layout scheme. Based on selection, crossover, and mutation operations, a new population is generated. Finite element analysis, fitness assessment, and genetic operations are repeated until the individual with the highest fitness is extracted from the final population as the optimal cell structure size, shape, and heat pipe layout.

[0050] In conjunction with the first aspect, in the sixth implementation of the first aspect of this application, the step of evaluating the heat dissipation effect of the current arrangement by real-time monitoring of the heat distribution data of the heat sink aluminum block, analyzing and optimizing the heat pipe array arrangement using a random forest algorithm, adjusting the parameter thresholds of the IGBT temperature data, and making adjustments based on feedback on the heat dissipation effect includes:

[0051] The system receives and processes the heat distribution data of the heat sink aluminum block, analyzes it using the random forest algorithm, constructs a random forest model, and uses the bootstrap method to randomly extract multiple subsets from the original data. Each subset is used to train a decision tree. The system splits on the random subsets of the feature set and selects the optimal split point. The constructed random forest model is used to learn from the training data, and the trained random forest model is used to predict new heat distribution data to evaluate the heat dissipation effect under different heat pipe array arrangements. The model's performance is evaluated through cross-validation. Based on the model's prediction results and actual heat dissipation requirements, the parameter thresholds of the IGBT temperature data are adjusted to adapt to the optimized heat pipe array arrangement.

[0052] Implement the adjusted layout scheme, monitor the actual heat dissipation effect, feed the newly collected data into the random forest model for continuous learning and optimization of the model, and adjust the heat pipe array layout and parameter threshold settings.

[0053] In conjunction with the first aspect, in the seventh implementation of the first aspect of this application, the step of dynamically adjusting the fan speed using a PID algorithm based on IGBT temperature data and chassis internal ambient temperature data, and adopting a dual-fan collaborative heat dissipation strategy, dynamically adjusting the main fan and auxiliary fan according to heat dissipation requirements, includes:

[0054] The collected temperature data is input into the PID control algorithm. Based on the deviation between the set temperature target value and the actual temperature value, the fan speed adjustment is calculated. The PID algorithm controls the fan speed by adjusting the proportional, integral and derivative parameters, responding to temperature changes and reducing overshoot.

[0055] Based on the output of the PID algorithm, the main fan speed is dynamically adjusted. When the IGBT module temperature rises, the main fan speed increases; when the temperature decreases, the main fan speed decreases. The auxiliary fan, as an auxiliary cooling method, dynamically adjusts its speed and airflow direction according to the main fan's operating status and cooling requirements. When the main fan speed reaches a threshold, the auxiliary fan starts working to further reduce the temperature of the IGBT module and the inside of the chassis. When the main fan speed decreases, the auxiliary fan stops working or reduces its speed. The dual-fan collaborative cooling strategy includes optimizing the fan airflow direction by adjusting the fan installation angle or using fans with adjustable airflow direction to optimize the airflow path.

[0056] In conjunction with the first aspect, in the eighth implementation of the first aspect of this application, the step of combining historical operating data and feedback to analyze and optimize the heat dissipation mode using a support vector machine algorithm, and adjusting based on feedback on the heat dissipation effect, includes:

[0057] Continuously collect and store key data, including the temperature of the IGBT module, the temperature distribution of the heat sink, the fan speed, the ambient temperature, and the equipment operating status. Use the support vector machine algorithm to analyze the historical operating data, identify the factors and interrelationships that affect the heat dissipation effect, build a heat dissipation effect prediction model, and optimize the heat dissipation mode, including the arrangement of the heat pipe array and the fan speed control strategy.

[0058] Based on the results of algorithm analysis, the parameter thresholds of IGBT temperature data are dynamically adjusted; the operating effect is monitored in real time, the monitoring data is compared with the prediction model, and the heat dissipation mode and parameter thresholds are adaptively adjusted. When the actual heat dissipation effect deviates from the set expectation, the algorithm automatically adjusts the relevant parameters, corrects the deviation and optimizes the heat dissipation performance.

[0059] We continuously collect new operational data and regularly retrain and optimize the heat dissipation effect prediction model.

[0060] Secondly, the present invention provides a heat dissipation control system based on high-efficiency thermal management, comprising:

[0061] The data acquisition module includes a temperature sensor unit, a data preprocessing unit, and an analysis unit. The temperature sensor unit is responsible for acquiring IGBT temperature data and the ambient temperature inside the chassis. The data preprocessing unit cleans and formats the acquired data, and the analysis unit analyzes the data.

[0062] Thermal status identification module: includes a heat dissipation efficiency analysis unit and a thermal status classification unit; wherein, the heat dissipation efficiency analysis unit evaluates the current heat dissipation efficiency based on IGBT temperature data and combined with heat dissipation efficiency analysis algorithm; the thermal status classification unit classifies heat dissipation requirements into different levels according to the analysis results;

[0063] The heat pipe array layout optimization module includes a heat conduction analysis unit, a layout determination unit, and a layout adjustment unit. The heat conduction analysis unit calculates the optimal heat pipe layout based on IGBT temperature data and heat dissipation requirements, using a heat conduction analysis algorithm. The layout determination unit selects layout methods such as linear, ring, or honeycomb arrangement based on the calculation results. The layout adjustment unit dynamically optimizes and adjusts the heat pipe layout using a random forest algorithm based on real-time monitored heat distribution data of the heat dissipation aluminum blocks.

[0064] The heat dissipation effect evaluation and optimization module includes a heat distribution monitoring unit, a random forest analysis unit, and a parameter adjustment unit. The heat distribution monitoring unit monitors the heat distribution data of the heat sink aluminum block in real time. The random forest analysis unit uses the random forest algorithm to analyze the monitoring data and evaluate the heat dissipation effect of the current arrangement. The parameter adjustment unit adjusts the parameter thresholds of the IGBT temperature data based on the analysis results to optimize the heat dissipation performance.

[0065] The fan control module includes a PID control unit, a dual-fan collaborative control unit, and a fan speed adjustment unit. The PID control unit calculates the target fan speed using a PID algorithm based on IGBT temperature data and the ambient temperature inside the chassis. The dual-fan collaborative control unit controls the collaborative working mode of the main fan and the auxiliary fan according to the heat dissipation requirements. The fan speed adjustment unit dynamically adjusts the actual fan speed based on the output of the PID control unit.

[0066] Historical data analysis and decision optimization module: includes a data storage unit, a support vector machine analysis unit, and an optimization unit; the data storage unit continuously collects and stores key operational data; the support vector machine analysis unit uses the support vector machine algorithm to analyze historical data and build a heat dissipation effect prediction model; the optimization unit optimizes the algorithm model and the results of the algorithm analysis, and adjusts the parameter thresholds and heat dissipation mode of the IGBT temperature data.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] 1. This invention collects IGBT temperature data and chassis internal ambient temperature data through temperature sensors, and combines multiple algorithms to achieve intelligent control of the heat dissipation system. It dynamically adjusts the heat dissipation strategy based on real-time data, including the arrangement of the heat pipe array and the fan speed.

[0069] 2. This invention optimizes the arrangement of the heat pipe array by combining heat conduction analysis algorithms, based on the size, power density, and thermal conductivity of the IGBT module and the heat dissipation aluminum block. The arrangement includes linear, ring, and honeycomb arrangements, and is dynamically adjusted according to actual heat dissipation requirements.

[0070] 3. This invention dynamically adjusts the fan speed through a PID algorithm and adopts a dual-fan collaborative heat dissipation strategy. Based on IGBT temperature data and the ambient temperature data inside the chassis, the speed and airflow of the main fan and the auxiliary fan are adjusted in real time. Attached Figure Description

[0071] Figure 1 This is a schematic diagram illustrating the steps of a heat dissipation control method based on efficient thermal management according to the present invention.

[0072] Figure 2 This is a schematic diagram of the structure of a heat dissipation control system based on high-efficiency thermal management according to the present invention;

[0073] Figure 3 This is a schematic diagram illustrating the classification steps of heat pipe array arrangement in a heat dissipation control method based on high-efficiency thermal management according to the present invention.

[0074] Figure 4 This is a schematic diagram illustrating the steps of adjusting dual-fan cooling using a PID algorithm in a heat dissipation control method based on efficient thermal management according to the present invention. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Example: Figures 1-4 As shown, the present invention provides a technical solution.

[0077] like Figure 1 A schematic diagram illustrating the steps of a heat dissipation control method based on efficient thermal management is shown. This invention provides a heat dissipation control method based on efficient thermal management, comprising:

[0078] Step S100: Collect temperature data through temperature sensors, including IGBT temperature data and ambient temperature data inside the chassis, and analyze and process the temperature data; based on the IGBT temperature data and combined with the heat dissipation efficiency analysis algorithm, identify the current thermal state; and classify it into different heat dissipation requirement levels according to the heat generation situation.

[0079] Specifically, temperature data is collected through temperature sensors, including IGBT junction temperature and IGBT case temperature.

[0080] The collected temperature data is transmitted to the central processing unit for data cleaning and preprocessing to remove outliers and noise interference.

[0081] The preprocessed temperature data is analyzed using the support vector machine algorithm to obtain the real-time status and trend of the temperature data.

[0082] Furthermore, by combining the collected IGBT temperature data and the analysis results of the IGBT temperature data with the heat dissipation efficiency analysis algorithm, the current thermal state is evaluated, and the temperature change rate and temperature distribution uniformity are calculated by analyzing the IGBT temperature data.

[0083] Based on the thermal state identification results and the thermal characteristics and safe operating temperature range of the IGBT module, different heat dissipation requirement levels are defined. When the IGBT temperature is within the user-defined first safety threshold a%, it is determined to be a level 1 heat dissipation requirement. When the IGBT temperature is higher than the user-defined first safety threshold a%, it is determined to be a level 2 heat dissipation requirement. When the IGBT temperature is higher than the user-defined second safety threshold b%, it is determined to be a level 3 heat dissipation requirement. When the IGBT temperature is higher than the user-defined third safety threshold c%, it is determined to be a level 4 heat dissipation requirement, and an emergency heat dissipation state is entered.

[0084] In one specific embodiment, an energy router system of a wind power converter in a factory was selected as the test environment. Temperature sensors were installed on each IGBT module to collect real-time data on IGBT junction temperature, IGBT case temperature, and the ambient temperature inside the chassis. During the data preprocessing stage, data cleaning, outlier removal, and noise filtering methods were used to process the raw data.

[0085] By analyzing IGBT temperature data, the rate of temperature change and temperature distribution uniformity were calculated. Based on the thermal state identification results and the thermal characteristics and safe operating temperature range of the IGBT module, the heat dissipation requirements were divided into four levels. The safe temperature threshold for the IGBT module was set at 70℃, with classification parameters a=15, b=30, and c=45. Measurements were taken from a selected operating IGBT module. The temperature sensor collected real-time data showing a junction temperature near the collector of 88.3℃, a module base plate temperature of 72.1℃, and an ambient chassis temperature of 45.6℃. After cleaning the raw data, the central processing unit calculated that the temperature exceeded the set safe temperature threshold by 26%, classifying it as a level three heat dissipation requirement.

[0086] Step S200: Based on the analyzed IGBT temperature data and heat dissipation requirements, and combined with the heat conduction analysis algorithm, determine the arrangement of the heat pipe array, and adjust the position and combination of the heat pipes in the heat dissipation aluminum block, including linear arrangement, ring arrangement and honeycomb arrangement;

[0087] Specifically, under the primary heat dissipation requirement, the heat pipe array adopts a linear arrangement, with the heat pipes evenly distributed along the heat propagation direction to ensure uniform heat distribution;

[0088] The spacing between heat pipes is calculated using a heat conduction analysis algorithm, taking into account the IGBT module's size, power density, and the thermal conductivity of the heat sink. Fourier's law is used to calculate the heat transfer rate within the heat sink, and the heat generation is calculated based on the IGBT module's power density and size. The formula is as follows:

[0089] ;

[0090] Where Q is the heat generation, P is the power density of the IGBT module, and A is the area of ​​the IGBT module;

[0091] The rate of heat conduction in the aluminum heat sink is calculated using Fourier's law, and the formula is:

[0092] ;

[0093] Where q is the heat flux density and k is the thermal conductivity of the aluminum heat sink. For temperature gradient;

[0094] Based on the thermal conductivity of the heat sink aluminum block, the heat transfer task required by the heat pipes is calculated. The optimal spacing between the heat pipes is obtained using a genetic algorithm. By using the spacing between the heat pipes as the optimization variable and aiming at maximizing heat dissipation efficiency, the genetic algorithm parameters, including population size, number of iterations, crossover rate, and mutation rate, are set to generate a set of initial values ​​for the heat pipe spacing, forming an initial population. For each set of heat pipe spacing, the heat transfer task required by the heat sink aluminum block is calculated based on the heat transfer performance of the heat sink aluminum block. The heat dissipation efficiency is used as the fitness value, and the heat pipe spacing with the highest fitness is selected as the optimal spacing value.

[0095] Under the secondary heat dissipation requirement, the heat pipe array is converted to a ring arrangement, with the heat pipes arranged in a ring around the IGBT module to form a heat enveloping ring.

[0096] The ring arrangement conducts heat from the IGBT module to other areas of the heat sink through a ring-shaped heat flow path, thereby reducing local temperature. The radius of the ring and the number of heat pipes are determined using a heat conduction analysis algorithm. An initial ring radius R is set to 1.5 times the diagonal length of the IGBT module, and the initial number of heat pipes N is calculated based on the heat transfer capacity of a single heat pipe.

[0097] ;

[0098] Where Q represents the total heat generation of the IGBT module. Indicates rounding up;

[0099] The heat generation of the IGBT module, the thermal conductivity and thickness of the heat sink were collected. A heat conduction model was established using the finite element method (FEM). Under initial parameters, the heat distribution of the IGBT module during operation was simulated. The temperature distribution and hot spot locations on the heat sink were recorded. The heat conduction path from the IGBT module through the heat pipe to the heat sink was analyzed, and the heat flow path and heat concentration areas were evaluated. Optimization was performed based on the heat generation of the IGBT module and the heat dissipation capacity of the heat sink. The temperature distribution was calculated using finite element simulation, and the highest temperature T was... hotspot Exceeding the allowed value T max At that time, adjust the parameters and compare the distance of the hot spot from the center point of the IGBT module with the length of the IGBT module's diagonal to determine whether the hot spot is closer to the inside or outside; adjust the number of heat pipes until T hotspot ≤T max The formula is:

[0100] ;

[0101] Where, N new For the new number of heat pipes, N old This represents the current number of heat pipes.

[0102] Under the three-level heat dissipation requirement, a honeycomb arrangement is adopted, with heat pipes tightly arranged in a honeycomb structure to form multiple heat conduction paths and increase the heat dissipation area. By collecting thermal conductivity and density data of heat pipes and honeycomb structure materials, the honeycomb structure is discretized using the finite element analysis algorithm combined with aerodynamic principles. The influence of honeycomb structures of different sizes and shapes on airflow and heat exchange is simulated. The honeycomb structure is divided into x units, and the heat conduction behavior of each unit is described by the following formula:

[0103] ;

[0104] Where, q i Let k be the heat flux density of the i-th unit, and k1 be the thermal conductivity of the material. The temperature gradient of the i-th unit;

[0105] The effects of honeycomb structures of different sizes and shapes on airflow and heat exchange were simulated. The amount of heat exchange between air and the honeycomb structure, Q1, was calculated using the following formula:

[0106] ;

[0107] Where h is the convective heat transfer coefficient, A1 is the heat exchange area, and T S T represents the surface temperature of the honeycomb structure. ∞ The ambient air temperature unaffected by heat sources;

[0108] The heat transfer efficiency η of the heat pipe under different layouts is calculated using a heat transfer analysis algorithm. The formula is as follows:

[0109] ;

[0110] Where, k t Let A be the thermal conductivity of the heat pipe, L be the length of the heat pipe, and A be the thermal conductivity of the heat pipe. t This refers to the cross-sectional area of ​​the heat pipe.

[0111] The optimal honeycomb structure size, shape, and heat pipe layout are calculated using a genetic algorithm, with the following formula:

[0112] ;

[0113] Among them, minT max To minimize the highest temperature T on the heat sink max d represents the cell structure size, s represents the cell shape, and l represents the heat pipe layout. By encoding the cell structure size, shape, and heat pipe layout genetically, an initial population is generated. Each individual represents a cell structure size, shape, and heat pipe layout scheme. Based on selection, crossover, and mutation operations, a new population is generated. Finite element analysis, fitness assessment, and genetic operations are repeated until the individual with the highest fitness is extracted from the final population as the optimal cell structure size, shape, and heat pipe layout.

[0114] In one specific embodiment, the IGBT module is 5cm long, 3cm wide, has a power density P=400W / cm², and the heat sink aluminum block has a thermal conductivity k=200W / (m・K), a length of 10cm, a width of 8cm, and a height of 3cm.

[0115] Under the first-level heat dissipation requirement, according to the formula Calculate the heat output Q of the IGBT module, where A is the area of ​​the IGBT module, A = 5 × 3 = 15 cm² = 0.0015 m², then Q = 400 × 0.0015 = 600 W. Calculate the heat transfer rate in the heat sink using Fourier's law, first calculating the heat flux density q. At 17:00:00 on 2025-04-26, the temperature at one end of the heat sink is T1 = 50℃, and the temperature at the other end is T2 = 30℃. What is the temperature gradient? =200K / m, according to the formula We can obtain the heat flux density q = -40000 W / m², where the negative sign indicates the direction of heat transfer.

[0116] The spacing between heat pipes is calculated using a heat conduction analysis algorithm combined with the above data. The genetic algorithm parameters are set as follows: population size is 50, iteration count is 100, crossover rate is 0.7, and mutation rate is 0.1. Let the spacing between heat pipes be d (in cm). An initial population of heat pipe spacing values ​​is generated, which may be 2cm, 3cm, 4cm, etc. Assuming a heat pipe spacing of d = 3cm, the number of heat pipes that can be arranged along the length of the heat sink aluminum block is n1 = 10 ÷ 3 ≈ 3, and the number of heat pipes that can be arranged along the width is n2 = 8 ÷ 3 ≈ 2. Therefore, the total number of heat pipes that can be arranged is n = n1 × n2 = 6. The average heat Q carried by each heat pipe is... avg =Q÷n=100W. After multiple iterations of calculation and evaluation, the optimal spacing between heat pipes was finally determined to be 3cm, ensuring uniform heat distribution and achieving efficient heat dissipation.

[0117] When the cooling requirement is at level two, the heat pipe array is converted to a ring arrangement, with the heat pipes arranged in a ring around the IGBT module. The initial ring radius R is set to 1.5 times the diagonal length of the IGBT module, i.e., R = 5.83 × 1.5 = 8.745 cm. The initial number of heat pipes N is calculated based on the heat transfer capacity of a single heat pipe, assuming a heat transfer capacity of 500W per heat pipe. According to the formula... With N=12 wires, a heat conduction model was established using the finite element method (FEM). Under initial parameters, the heat distribution of the IGBT module during operation was simulated, and the temperature distribution and hotspot locations on the heat sink were recorded. The simulation results show that, under the initial parameters, the highest temperature T... hotspot =120℃, permissible value T max =100℃, reassess the heat handling capacity of a single heat pipe based on the new ring radius. Assuming the original radius R1 was 8.715cm, the heat handling capacity Q of a single heat pipe was... avg1 Given 50W, increase R to 12cm and calculate Q. avg3 ≈26.58W, N new =23 roots, and the temperature distribution was calculated again using finite element simulation to obtain the highest temperature T. hotspot =98℃, at this time T hotspot ≤T max It meets the heat dissipation requirements.

[0118] Under the three-level heat dissipation requirement, a honeycomb arrangement is adopted, with heat pipes tightly arranged in a honeycomb structure. Thermal conductivity and density data of the heat pipes and honeycomb structure materials are collected, and the thermal conductivity k of the heat pipes is obtained. t =300W / (m・K), heat pipe length L=10cm=0.1m, heat pipe cross-sectional area A t=1cm²=0.0001m². The thermal conductivity of the honeycomb structure material is k1=150W / (m・K). The honeycomb structure is discretized into x=50 units. Its thermal conduction behavior is given by the formula... Description: At 21:30:00 on 2025-04-29, the surface temperature T of the honeycomb structure was... S =60℃, ambient air temperature T unaffected by heat sources ∞ =25℃, convective heat transfer coefficient h=30W / (m²・K), heat exchange area A=20cm²=0.002m². According to the formula The heat exchange between air and the honeycomb structure is calculated as Q1 = 2.1W. Using a heat conduction analysis algorithm, the heat transfer efficiency η of the heat pipes under different layouts is calculated, based on the formula... The calculated value is η = 300,000. An initial population is generated by genetically encoding the size, shape, and heat pipe layout of the cellular structure, with each individual representing a specific cellular structure size, shape, and heat pipe layout scheme. New populations are generated through selection, crossover, and mutation operations, and finite element analysis, fitness evaluation, and genetic operations are repeated. The goal is to minimize the highest temperature T on the heat sink aluminum block. max With the goal of achieving this, the above operation is repeated multiple times until the individual with the highest fitness is extracted from the final population as the optimal honeycomb structure size, shape, and heat pipe layout.

[0119] Step S300: By monitoring the heat distribution data of the heat sink aluminum block in real time, evaluate the heat dissipation effect of the current arrangement, use the random forest algorithm to analyze and optimize the heat pipe array arrangement, adjust the parameter threshold of the IGBT temperature data, and make adjustments based on the feedback of the heat dissipation effect.

[0120] Specifically, the system receives and processes the heat distribution data of the heat sink aluminum block, analyzes it using the random forest algorithm, constructs a random forest model, and uses the bootstrap method to randomly extract multiple subsets from the original data. Each subset is used to train a decision tree. The system splits on the random subsets of the feature set and selects the optimal split point. The constructed random forest model is used to learn from the training data, and the trained random forest model is used to predict new heat distribution data to evaluate the heat dissipation effect under different heat pipe array arrangements. The model's performance is evaluated through cross-validation. Based on the model's prediction results and actual heat dissipation requirements, the parameter thresholds of the IGBT temperature data are adjusted to adapt to the optimized heat pipe array arrangement.

[0121] Implement the adjusted layout scheme, monitor the actual heat dissipation effect, feed the newly collected data into the random forest model for continuous learning and optimization of the model, and adjust the heat pipe array layout and parameter threshold settings.

[0122] In one specific embodiment, the random forest algorithm was used to process the data. A bootstrap method was used to randomly extract 100 subsets from the original data, each containing 500 data points, to train 100 decision trees. The feature sets were split on these random subsets, and the optimal split point was selected to construct the random forest model. Through cross-validation, the model's prediction accuracy reached 95.2%. Based on the model's prediction results, the heat dissipation effect under the current heat pipe array arrangement was found to be poor. The parameter thresholds for IGBT temperature data were adjusted, setting a, b, and c to 10%, 20%, and 30% respectively, to optimize the heat pipe array arrangement. Implementing the adjusted arrangement, monitoring showed that the maximum temperature of the IGBT module decreased by 15°C, and the heat dissipation efficiency improved by 25%. Newly collected data was fed back into the random forest model for continuous learning and optimization, further adjusting the heat pipe array arrangement and parameter threshold settings to ensure the stability and reliability of the energy router system during high-power operation.

[0123] Step S400: Based on the IGBT temperature data and the ambient temperature data inside the chassis, the fan speed is dynamically adjusted using a PID algorithm. A dual-fan collaborative heat dissipation strategy is adopted, and the main fan and auxiliary fan are dynamically adjusted according to the heat dissipation requirements.

[0124] Specifically, the collected temperature data is input into the PID control algorithm. Based on the deviation between the set temperature target value and the actual temperature value, the fan speed adjustment is calculated. The PID algorithm controls the fan speed by adjusting the proportional, integral, and derivative parameters, responding to temperature changes and reducing overshoot.

[0125] Based on the output of the PID algorithm, the main fan speed is dynamically adjusted. When the IGBT module temperature rises, the main fan speed increases; when the temperature decreases, the main fan speed decreases. The auxiliary fan, as an auxiliary cooling method, dynamically adjusts its speed and airflow direction according to the main fan's operating status and cooling requirements. When the main fan speed reaches a threshold, the auxiliary fan starts working to further reduce the temperature of the IGBT module and the inside of the chassis. When the main fan speed decreases, the auxiliary fan stops working or reduces its speed. The dual-fan collaborative cooling strategy includes optimizing the fan airflow direction by adjusting the fan installation angle or using fans with adjustable airflow direction to optimize the airflow path.

[0126] In one specific embodiment, in the energy router system of a power electronics equipment manufacturing company, a dual-fan coordinated cooling strategy based on a PID algorithm is implemented. Temperature data is collected in real time by temperature sensors installed inside the IGBT module and the chassis, including a current IGBT module temperature of 85°C and an ambient temperature inside the chassis of 40°C. This data is input into the PID control algorithm, setting the target temperature for the IGBT module to 75°C, and calculating a temperature deviation of 10°C. The PID algorithm adjusts the proportional parameter K... p =2.0, integration parameter K i =0.5, differential parameter K d =1.0, calculating the fan speed adjustment. Based on the PID algorithm output, the main fan speed is dynamically adjusted from the initial 1500 RPM to 2000 RPM to cope with the increase in IGBT module temperature. When the main fan speed reaches the threshold of 1800 RPM, the auxiliary fan starts working at 1000 RPM to further reduce the temperature of the IGBT module and the inside of the chassis. When the main fan speed drops below 1700 RPM, the auxiliary fan stops working. Fan airflow is optimized by adjusting the installation angle of the main fan to 30° with the horizontal plane and using an adjustable auxiliary fan to make the airflow path more reasonable. Implementing this dual-fan collaborative cooling strategy reduces the maximum temperature of the IGBT module by 12°C and the internal ambient temperature of the chassis by 5°C, improving the heat dissipation performance and stability of the power router system.

[0127] Step S500: Combining historical operating data and feedback, the heat dissipation mode is analyzed and optimized using the support vector machine algorithm, and adjustments are made based on feedback on the heat dissipation effect.

[0128] Specifically, key data is continuously collected and stored, including the temperature of the IGBT module, the temperature distribution of the heat sink, the fan speed, the ambient temperature, and the equipment operating status. The historical operating data is analyzed using the support vector machine algorithm to identify the factors affecting the heat dissipation effect and their interrelationships, build a heat dissipation effect prediction model, and optimize the heat dissipation mode, including the arrangement of the heat pipe array and the fan speed control strategy.

[0129] Based on the results of algorithm analysis, the parameter thresholds of IGBT temperature data are dynamically adjusted; the operating effect is monitored in real time, the monitoring data is compared with the prediction model, and the heat dissipation mode and parameter thresholds are adaptively adjusted. When the actual heat dissipation effect deviates from the set expectation, the algorithm automatically adjusts the relevant parameters, corrects the deviation and optimizes the heat dissipation performance.

[0130] We continuously collect new operational data and regularly retrain and optimize the heat dissipation effect prediction model.

[0131] In one specific embodiment, a support vector machine (SVM) algorithm is used to analyze these historical operating data, selecting data from the past month as the training set. Input features include IGBT module temperature, temperatures at different locations on the heat sink, fan speed, ambient temperature, and device operating status. The performance is measured by whether the highest temperature on the heat sink exceeds a preset safety threshold (70°C), with heat dissipation effect as the output target. Through training and analysis using the SVM algorithm, factors affecting heat dissipation effect and their interrelationships are identified. For example, when the IGBT module temperature exceeds 60°C and the ambient temperature is above 23°C, if the fan speed is below 1500 RPM, the highest temperature on the heat sink will exceed the preset safety threshold, indicating poor heat dissipation. A heat dissipation effect prediction model is constructed, and the heat dissipation mode is optimized based on this model.

[0132] Regarding the fan speed control strategy, when the IGBT module temperature is between 50℃ and 60℃ and the ambient temperature is between 22℃ and 23℃, the fan speed is maintained at 2000 RPM; when the IGBT module temperature rises to between 60℃ and 70℃ and the ambient temperature remains unchanged, the fan speed is automatically increased to 2500 RPM; if the IGBT module temperature exceeds 70℃, the fan speed is increased to 3000 RPM.

[0133] The system monitors the operational performance in real time, compares the monitoring data with the prediction model every minute, and continuously observes the subsequent operation to correct deviations and optimize heat dissipation performance.

[0134] like Figure 2 The system structure diagram of a heat dissipation control system based on high-efficiency thermal management is shown. This invention provides a heat dissipation control system based on high-efficiency thermal management, comprising:

[0135] The data acquisition module includes a temperature sensor unit, a data preprocessing unit, and an analysis unit. The temperature sensor unit is responsible for acquiring IGBT temperature data and the ambient temperature inside the chassis. The data preprocessing unit cleans and formats the acquired data, and the analysis unit analyzes the data.

[0136] Thermal status identification module: includes a heat dissipation efficiency analysis unit and a thermal status classification unit; wherein, the heat dissipation efficiency analysis unit evaluates the current heat dissipation efficiency based on IGBT temperature data and combined with heat dissipation efficiency analysis algorithm; the thermal status classification unit classifies heat dissipation requirements into different levels according to the analysis results;

[0137] The heat pipe array layout optimization module includes a heat conduction analysis unit, a layout determination unit, and a layout adjustment unit. The heat conduction analysis unit calculates the optimal heat pipe layout based on IGBT temperature data and heat dissipation requirements, using a heat conduction analysis algorithm. The layout determination unit selects layout methods such as linear, ring, or honeycomb arrangement based on the calculation results. The layout adjustment unit dynamically optimizes and adjusts the heat pipe layout using a random forest algorithm based on real-time monitored heat distribution data of the heat dissipation aluminum blocks.

[0138] The heat dissipation effect evaluation and optimization module includes a heat distribution monitoring unit, a random forest analysis unit, and a parameter adjustment unit. The heat distribution monitoring unit monitors the heat distribution data of the heat sink aluminum block in real time. The random forest analysis unit uses the random forest algorithm to analyze the monitoring data and evaluate the heat dissipation effect of the current arrangement. The parameter adjustment unit adjusts the parameter thresholds of the IGBT temperature data based on the analysis results to optimize the heat dissipation performance.

[0139] The fan control module includes a PID control unit, a dual-fan collaborative control unit, and a fan speed adjustment unit. The PID control unit calculates the target fan speed using a PID algorithm based on IGBT temperature data and the ambient temperature inside the chassis. The dual-fan collaborative control unit controls the collaborative working mode of the main fan and the auxiliary fan according to the heat dissipation requirements. The fan speed adjustment unit dynamically adjusts the actual fan speed based on the output of the PID control unit.

[0140] Historical data analysis and decision optimization module: includes a data storage unit, a support vector machine analysis unit, and an optimization unit; the data storage unit continuously collects and stores key operational data; the support vector machine analysis unit uses the support vector machine algorithm to analyze historical data and build a heat dissipation effect prediction model; the optimization unit optimizes the algorithm model and the results of the algorithm analysis, and adjusts the parameter thresholds and heat dissipation mode of the IGBT temperature data.

[0141] like Figure 3 A schematic diagram illustrating the classification steps of a heat pipe array arrangement method based on efficient thermal management is provided in this invention. The method includes:

[0142] Analyzing IGBT temperature data determines the heat dissipation requirement level. For Level 1 heat dissipation, a linear arrangement of heat pipes is used. The heat output of the IGBT modules and the heat flux density of the heat sink are calculated, and the optimal heat pipe spacing is determined using a genetic algorithm. For Level 2 heat dissipation, a ring arrangement of heat pipes is used. The initial ring radius and number of heat pipes are calculated based on the diagonal length of the IGBT modules and the total heat output. Finite element analysis is used to simulate heat distribution, and the ring radius and number of heat pipes are adjusted according to hotspot locations. Simulations are repeated until the heat dissipation requirements are met. For Level 3 heat dissipation, a honeycomb arrangement is used. Heat pipe and honeycomb structure material data are collected, the honeycomb structure is discretized, and the heat conduction behavior of each unit is calculated. The impact of the honeycomb structure on airflow and heat exchange is simulated, calculating the heat exchange between air and the honeycomb structure, as well as the heat conduction efficiency of heat pipes under different layouts. A genetic algorithm is used to determine the optimal honeycomb structure size, shape, and heat pipe layout, generating an initial population. Selection, crossover, and mutation operations are performed to generate a new population. Finite element analysis and genetic operations are repeated until the individual with the highest fitness is extracted from the final population to determine the optimal solution.

[0143] like Figure 4 A schematic diagram of a heat dissipation control method based on efficient thermal management, which adjusts the cooling of a dual-fan system using a PID algorithm, is shown. This invention provides a heat dissipation control method based on efficient thermal management, comprising:

[0144] Temperature data from the IGBT module and the internal ambient temperature of the chassis are acquired via temperature sensors. This temperature data is input into the PID control algorithm. The algorithm compares the preset temperature target value with the actual current temperature value to calculate the deviation. By adjusting the proportional, integral, and derivative functions, the fan speed adjustment is calculated according to the formula. The main fan speed is dynamically adjusted: when the IGBT module temperature rises, the main fan speed increases accordingly; when the IGBT module temperature decreases, the main fan speed decreases accordingly. The main fan speed is continuously monitored to determine if a preset threshold has been reached. If the main fan speed reaches this threshold, the auxiliary fan starts working, and its speed and airflow direction are adjusted accordingly. The system dynamically adjusts the operation of the main fan based on its working status and overall cooling requirements. If the main fan speed does not reach the threshold, the auxiliary fan stops working or maintains its current low speed. The installation angle of the fans is adjusted so that the airflow from both fans covers the IGBT module and key heat dissipation areas inside the chassis. Adjustable fans are used, and the airflow direction of the two fans is dynamically adjusted according to the actual cooling situation to ensure that the airflow path is always in the optimal state. The system continues to collect IGBT temperature data and chassis internal ambient temperature data, and then re-enters the input PID algorithm stage, repeating the above steps to form a cyclical dynamic cooling control process that adapts to changes in the IGBT module and chassis internal ambient temperature in real time.

[0145] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A heat dissipation control method based on efficient thermal management, characterized by, include: Temperature data is collected by temperature sensors, including IGBT temperature data and ambient temperature data inside the chassis, and the temperature data is analyzed and processed. Based on IGBT temperature data and combined with heat dissipation efficiency analysis algorithms, the current thermal state is identified; and different heat dissipation requirement levels are classified according to the heat generation situation. Based on the analyzed IGBT temperature data and heat dissipation requirements, and combined with heat conduction analysis algorithms, the arrangement of the heat pipe array is determined, and the position and combination of the heat pipes within the heat sink are adjusted, including linear, ring, and honeycomb arrangements. For Level 1 heat dissipation requirements, a linear arrangement of the heat pipe array is used. The heat output of the IGBT module and the heat flux density of the heat sink are calculated, and the optimal heat pipe spacing is determined using a genetic algorithm. For Level 2 heat dissipation requirements, a ring arrangement of the heat pipe array is used. The initial ring radius and number of heat pipes are calculated based on the diagonal length of the IGBT module and the total heat output. Finite element analysis is used to simulate heat distribution, and the ring radius and number of heat pipes are adjusted according to the hotspot locations. The simulation is repeated until the heat dissipation requirements are met. For Level 3 heat dissipation requirements, a honeycomb arrangement is used. Data on the heat pipes and honeycomb structure materials are collected, the honeycomb structure is discretized, the heat conduction behavior of each unit is calculated, the influence of the honeycomb structure on airflow and heat exchange is simulated, the heat exchange between air and the honeycomb structure is calculated, and the heat conduction efficiency of the heat pipes under different layouts is determined. The optimal honeycomb structure size, shape, and heat pipe layout are determined using a genetic algorithm. By monitoring the heat distribution data of the heat sink aluminum blocks in real time, the heat dissipation effect of the current arrangement is evaluated. The random forest algorithm is used to analyze and optimize the heat pipe array arrangement, adjust the parameter threshold of the IGBT temperature data, and make adjustments based on the feedback of the heat dissipation effect. Based on IGBT temperature data and chassis internal ambient temperature data, the fan speed is dynamically adjusted through a PID algorithm, and a dual-fan collaborative heat dissipation strategy is adopted, dynamically adjusting the main fan and auxiliary fan according to heat dissipation requirements. By combining historical operating data and feedback, the heat dissipation mode is analyzed and optimized using the support vector machine algorithm, including the arrangement of the heat pipe array and the fan speed control strategy, and adjustments are made based on feedback on the heat dissipation effect.

2. The heat dissipation control method based on efficient thermal management according to claim 1, characterized in that, The process involves collecting temperature data via temperature sensors, including IGBT temperature data and ambient temperature data inside the chassis, and analyzing and processing the temperature data, including: Temperature data is collected by a temperature sensor. The IGBT temperature data includes the IGBT junction temperature and the IGBT case temperature. The collected temperature data is transmitted to the central processing unit for data cleaning and preprocessing to remove outliers and noise interference. The preprocessed temperature data is analyzed using the support vector machine algorithm to obtain the real-time status and trend of the temperature data.

3. The heat dissipation control method based on efficient thermal management according to claim 1, characterized in that, Based on IGBT temperature data and combined with a heat dissipation efficiency analysis algorithm, the current thermal state is identified; according to the heat generation, it is divided into different heat dissipation requirement levels, including: By collecting IGBT temperature data and analyzing the results, combined with a heat dissipation efficiency analysis algorithm, the current thermal state is evaluated. By analyzing the IGBT temperature data, the rate of temperature change and the uniformity of temperature distribution are calculated. Based on the thermal state identification results and the thermal characteristics and safe operating temperature range of the IGBT module, different heat dissipation requirement levels are defined. When the IGBT temperature is within the user-defined first safety threshold a%, it is determined to be a level 1 heat dissipation requirement. When the IGBT temperature is higher than the user-defined first safety threshold a%, it is determined to be a level 2 heat dissipation requirement. When the IGBT temperature is higher than the user-defined second safety threshold b%, it is determined to be a level 3 heat dissipation requirement. When the IGBT temperature is higher than the user-defined third safety threshold c%, it is determined to be a level 4 heat dissipation requirement, and an emergency heat dissipation state is entered.

4. The heat dissipation control method based on efficient thermal management according to claim 1, characterized in that, Based on the analyzed IGBT temperature data and heat dissipation requirements, and combined with heat conduction analysis algorithms, the arrangement of the heat pipe array is determined, and the position and combination of the heat pipes within the heat sink aluminum block are adjusted. This includes linear, ring-shaped, and honeycomb arrangements. Under the primary heat dissipation requirement, the heat pipe array adopts a linear arrangement, with the heat pipes evenly distributed along the heat propagation direction to ensure uniform heat distribution. The spacing between heat pipes is calculated using a heat conduction analysis algorithm, taking into account the IGBT module's size, power density, and the thermal conductivity of the heat sink. Fourier's law is used to calculate the heat transfer rate within the heat sink, and the heat generation is calculated based on the IGBT module's power density and size. The formula is as follows: ； Where Q is the heat generation, P is the power density of the IGBT module, and A is the area of ​​the IGBT module; The rate of heat conduction in the aluminum heat sink is calculated using Fourier's law, and the formula is: ； Where q is the heat flux density and k is the thermal conductivity of the aluminum heat sink. For temperature gradient; Based on the thermal conductivity of the heat sink aluminum block, the heat transfer task required by the heat pipes is calculated. The optimal spacing between the heat pipes is obtained using a genetic algorithm. By using the spacing between the heat pipes as the optimization variable and aiming at maximizing heat dissipation efficiency, the genetic algorithm parameters, including population size, number of iterations, crossover rate, and mutation rate, are set to generate a set of initial values ​​for the heat pipe spacing, forming an initial population. For each set of heat pipe spacing, the heat transfer task required by the heat sink aluminum block is calculated based on the heat transfer performance of the heat sink aluminum block. The heat dissipation efficiency is used as the fitness value, and the heat pipe spacing with the highest fitness is selected as the optimal spacing value.

5. The heat dissipation control method based on high-efficiency thermal management according to claim 1, characterized in that, Based on the analyzed IGBT temperature data and heat dissipation requirements, and combined with heat conduction analysis algorithms, the arrangement of the heat pipe array is determined, and the position and combination of the heat pipes within the heat sink aluminum block are adjusted. This includes linear, ring-shaped, and honeycomb arrangements. Under the secondary heat dissipation requirement, the heat pipe array is converted to a ring arrangement, with the heat pipes arranged in a ring around the IGBT module to form a heat enveloping ring. The ring arrangement conducts heat from the IGBT module to other areas of the heat sink through a ring-shaped heat flow path, thereby reducing local temperature. The radius of the ring and the number of heat pipes are determined using a heat conduction analysis algorithm. An initial ring radius R is set to 1.5 times the diagonal length of the IGBT module, and the initial number of heat pipes N is calculated based on the heat transfer capacity of a single heat pipe. ； Where Q represents the total heat generation of the IGBT module. Indicates rounding up; The heat generation of the IGBT module, the thermal conductivity and thickness of the heat sink were collected. A heat conduction model was established using the finite element method (FEM). Under initial parameters, the heat distribution of the IGBT module during operation was simulated. The temperature distribution and hot spot locations on the heat sink were recorded. The heat conduction path from the IGBT module through the heat pipe to the heat sink was analyzed, and the heat flow path and heat concentration areas were evaluated. Optimization was performed based on the heat generation of the IGBT module and the heat dissipation capacity of the heat sink. The temperature distribution was calculated using finite element simulation, and the highest temperature T was... hotspot Exceeding the allowed value T max At that time, adjust the parameters and compare the distance of the hot spot from the center point of the IGBT module with the length of the IGBT module's diagonal to determine whether the hot spot is closer to the inside or outside; adjust the number of heat pipes until T hotspot ≤T max The formula is: ； where N new is the number of new hot pipes, N old is the number of current hot pipes.

6. The heat dissipation control method based on high-efficiency thermal management according to claim 1, characterized in that, Based on the analyzed IGBT temperature data and heat dissipation requirements, and combined with heat conduction analysis algorithms, the arrangement of the heat pipe array is determined, and the position and combination of the heat pipes within the heat sink aluminum block are adjusted. This includes linear, ring-shaped, and honeycomb arrangements. Under the three-level heat dissipation requirement, a honeycomb arrangement is adopted, with heat pipes tightly arranged in a honeycomb structure to form multiple heat conduction paths and increase the heat dissipation area. By collecting thermal conductivity and density data of heat pipes and honeycomb structure materials, the honeycomb structure is discretized using the finite element analysis algorithm combined with aerodynamic principles. The influence of honeycomb structures of different sizes and shapes on airflow and heat exchange is simulated. The honeycomb structure is divided into x units, and the heat conduction behavior of each unit is described by the following formula: ； Where, q i Let k be the heat flux density of the i-th unit, and k1 be the thermal conductivity of the material. The temperature gradient of the i-th unit; The effects of honeycomb structures of different sizes and shapes on airflow and heat exchange were simulated. The amount of heat exchange between air and the honeycomb structure, Q1, was calculated using the following formula: ； where h is the convective heat transfer coefficient, A1 is the heat exchange area, T S is the surface temperature of the honeycomb structure, T ∞ is the ambient air temperature unaffected by the heat source; The heat transfer efficiency η of the heat pipe under different layouts is calculated using a heat transfer analysis algorithm. The formula is as follows: ； where k is the thermal conductivity of the heat pipe, L is the length of the heat pipe, A is the cross-sectional area of the heat pipe t t of the heat pipe;​ The optimal honeycomb structure size, shape, and heat pipe layout are calculated using a genetic algorithm, with the following formula: ； Among them, minT max To minimize the highest temperature T on the heat sink max d represents the cell structure size, s represents the cell shape, and l represents the heat pipe layout. By encoding the cell structure size, shape, and heat pipe layout genetically, an initial population is generated. Each individual represents a cell structure size, shape, and heat pipe layout scheme. Based on selection, crossover, and mutation operations, a new population is generated. Finite element analysis, fitness assessment, and genetic operations are repeated until the individual with the highest fitness is extracted from the final population as the optimal cell structure size, shape, and heat pipe layout.

7. The heat dissipation control method based on high-efficiency thermal management according to claim 1, characterized in that, The process involves real-time monitoring of the heat distribution data of the heat sink aluminum blocks to evaluate the heat dissipation effect of the current arrangement, using a random forest algorithm to analyze and optimize the heat pipe array arrangement, adjusting the parameter thresholds of the IGBT temperature data, and making adjustments based on feedback on the heat dissipation effect. This includes: The system receives and processes the heat distribution data of the heat sink aluminum block, analyzes it using the random forest algorithm, constructs a random forest model, and uses the bootstrap method to randomly extract multiple subsets from the original data. Each subset is used to train a decision tree. The system splits on the random subsets of the feature set and selects the optimal split point. The constructed random forest model is used to learn from the training data, and the trained random forest model is used to predict new heat distribution data to evaluate the heat dissipation effect under different heat pipe array arrangements. The model's performance is evaluated through cross-validation. Based on the model's prediction results and actual heat dissipation requirements, the parameter thresholds of the IGBT temperature data are adjusted to adapt to the optimized heat pipe array arrangement. Implement the adjusted layout scheme, monitor the actual heat dissipation effect, feed the newly collected data into the random forest model for continuous learning and optimization of the model, and adjust the heat pipe array layout and parameter threshold settings.

8. The heat dissipation control method based on high-efficiency thermal management according to claim 1, characterized in that, The method involves dynamically adjusting fan speed using a PID algorithm based on IGBT temperature data and the internal ambient temperature data of the chassis, employing a dual-fan collaborative cooling strategy. This strategy dynamically adjusts the main and auxiliary fans according to cooling requirements, including: The collected temperature data is input into the PID control algorithm. Based on the deviation between the set temperature target value and the actual temperature value, the fan speed adjustment is calculated. The PID algorithm controls the fan speed by adjusting the proportional, integral and derivative parameters, responding to temperature changes and reducing overshoot. Based on the output of the PID algorithm, the main fan speed is dynamically adjusted. When the IGBT module temperature rises, the main fan speed increases; when the temperature decreases, the main fan speed decreases. The auxiliary fan, as an auxiliary cooling method, dynamically adjusts its speed and airflow direction according to the main fan's operating status and cooling requirements. When the main fan speed reaches a threshold, the auxiliary fan starts working to further reduce the temperature of the IGBT module and the inside of the chassis. When the main fan speed decreases, the auxiliary fan stops working or reduces its speed. The dual-fan collaborative cooling strategy includes optimizing the fan airflow direction by adjusting the fan installation angle or using fans with adjustable airflow direction to optimize the airflow path.

9. The heat dissipation control method based on high-efficiency thermal management according to claim 1, characterized in that, The process of combining historical operational data and feedback, using a support vector machine algorithm to analyze and optimize the heat dissipation mode, and adjusting based on feedback on the heat dissipation effect includes: Continuously collect and store key data, including the temperature of the IGBT module, the temperature distribution of the heat sink, the fan speed, the ambient temperature, and the equipment operating status. Use the support vector machine algorithm to analyze the historical operating data, identify the factors and interrelationships that affect the heat dissipation effect, build a heat dissipation effect prediction model, and optimize the heat dissipation mode, including the arrangement of the heat pipe array and the fan speed control strategy. Based on the results of algorithm analysis, the parameter thresholds of IGBT temperature data are dynamically adjusted; the operating effect is monitored in real time, the monitoring data is compared with the prediction model, and the heat dissipation mode and parameter thresholds are adaptively adjusted. When the actual heat dissipation effect deviates from the set expectation, the algorithm automatically adjusts the relevant parameters, corrects the deviation and optimizes the heat dissipation performance. We continuously collect new operational data and regularly retrain and optimize the heat dissipation effect prediction model.

10. A heat dissipation control system based on high-efficiency thermal management, using the heat dissipation control method based on high-efficiency thermal management according to any one of claims 1-9, characterized in that, include: The data acquisition module includes a temperature sensor unit, a data preprocessing unit, and an analysis unit. The temperature sensor unit is responsible for acquiring IGBT temperature data and the ambient temperature inside the chassis. The data preprocessing unit cleans and formats the acquired data, and the analysis unit analyzes the data. Thermal status identification module: includes a heat dissipation efficiency analysis unit and a thermal status classification unit; wherein, the heat dissipation efficiency analysis unit evaluates the current heat dissipation efficiency based on IGBT temperature data and combined with heat dissipation efficiency analysis algorithm; the thermal status classification unit classifies heat dissipation requirements into different levels according to the analysis results; The heat pipe array layout optimization module includes a heat conduction analysis unit, a layout determination unit, and a layout adjustment unit. The heat conduction analysis unit calculates the optimal heat pipe layout based on IGBT temperature data and heat dissipation requirements, using a heat conduction analysis algorithm. The layout determination unit selects a linear, ring-shaped, or honeycomb layout based on the calculation results. The layout adjustment unit dynamically optimizes the heat pipe layout using a random forest algorithm based on real-time monitored heat distribution data of the heat dissipation aluminum blocks. The heat dissipation effect evaluation and optimization module includes a heat distribution monitoring unit, a random forest analysis unit, and a parameter adjustment unit. The heat distribution monitoring unit monitors the heat distribution data of the heat sink aluminum block in real time. The random forest analysis unit uses the random forest algorithm to analyze the monitoring data and evaluate the heat dissipation effect of the current arrangement. The parameter adjustment unit adjusts the parameter thresholds of the IGBT temperature data based on the analysis results to optimize the heat dissipation performance. The fan control module includes a PID control unit, a dual-fan collaborative control unit, and a fan speed adjustment unit. The PID control unit calculates the target fan speed using a PID algorithm based on IGBT temperature data and the ambient temperature inside the chassis. The dual-fan collaborative control unit controls the collaborative working mode of the main fan and the auxiliary fan according to the heat dissipation requirements. The fan speed adjustment unit dynamically adjusts the actual fan speed based on the output of the PID control unit. Historical data analysis and decision optimization module: includes a data storage unit, a support vector machine analysis unit, and an optimization unit; the data storage unit continuously collects and stores key operational data; the support vector machine analysis unit uses the support vector machine algorithm to analyze historical data and build a heat dissipation effect prediction model; the optimization unit optimizes the algorithm model and the results of the algorithm analysis, and adjusts the parameter thresholds and heat dissipation mode of the IGBT temperature data.

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