Electric control cabinet heat dissipation structure parameter optimization method and system based on genetic algorithm

Abstract

The invention belongs to the technical field of electric control cabinet heat dissipation structure data processing, and particularly relates to an electric control cabinet heat dissipation structure parameter optimization method and system based on a genetic algorithm, and the method comprises the steps: obtaining the multi-source dynamic thermal load data of a to-be-optimized electric control cabinet in a complete process period, and constructing a transient heating power function; constructing a transient thermal resistance network model containing flow field coupling characteristics, defining a to-be-optimized structure parameter vector, and determining flow field thermal resistance coupling efficiency; based on the transient thermal resistance network model, utilizing a genetic algorithm to carry out iterative optimization on the structure parameter vector, calculating a transient thermal shock response index of the key component, and constructing a fitness function according to the transient thermal shock response index to evaluate individual advantages and disadvantages; and when the genetic algorithm meets a preset termination condition, outputting an optimal structure parameter combination, and generating a heat dissipation structure model based on the optimal structure parameter combination. According to the invention, transient thermal shock and dustproof requirements can be effectively balanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing of electric control cabinet heat dissipation structure. More particularly, the present application relates to a genetic algorithm-based electric control cabinet heat dissipation structure parameter optimization method and system. BACKGROUND

[0002] In the tire manufacturing industry, the vulcanization process usually includes a clamping stage, a vulcanization stage, and an opening stage, which causes the power consumption of core heating elements such as main frequency converters, servo drivers, and contactors in the electric control cabinet of the vulcanizing machine to present a sharp periodic pulse fluctuation. The heating peaks of each core heating element are often asynchronous on the time axis, which poses a very high requirement on the stability of the heat dissipation system.

[0003] The existing heat dissipation design of the electric control cabinet of the vulcanizing machine usually adopts a static maximum method, i.e., simply assuming that all core heating elements are simultaneously in a rated maximum power state and using a genetic algorithm to find heat dissipation structure parameters with a single objective of the lowest steady-state temperature.

[0004] However, this design approach has obvious limitations. On the one hand, it ignores the influence of transient thermal shock, and the heat dissipation structure parameters obtained by optimization tend to one-sidedly pursue ventilation volume, while ignoring the use of heat capacity of heat dissipation structure materials to buffer transient pulse heat, which causes the surface temperature of core heating elements to easily exceed the safety threshold at current peak moments. On the other hand, in order to reduce the calculated steady-state temperature, the genetic algorithm tends to infinitely increase the inlet opening rate, which directly leads to a large amount of carbon black-rich conductive dust invading the electric control cabinet of the vulcanizing machine, seriously reducing the protection level and easily causing short circuit failure. SUMMARY

[0005] In order to solve the problem that the existing heat dissipation design cannot balance transient heat buffering and dust prevention, the present application provides a genetic algorithm-based electric control cabinet heat dissipation structure parameter optimization method and system, which can effectively balance transient thermal shock and dust prevention requirements.

[0006] In a first aspect, the present application provides a genetic algorithm-based parameter optimization method for a heat dissipation structure of an electric control cabinet, comprising: obtaining multi-source dynamic thermal load data of a to-be-optimized electric control cabinet in a complete process cycle, and constructing a transient heat generation power function according to the multi-source dynamic thermal load data; constructing a transient thermal resistance network model containing flow field coupling characteristics, defining a structure parameter vector to be optimized, and determining a flow field thermal resistance coupling efficiency according to the structure parameter vector; based on the transient thermal resistance network model, iteratively optimizing the structure parameter vector by using a genetic algorithm, calculating a transient thermal shock response index of a key component in the iteration process, and constructing a fitness function according to the transient thermal shock response index to evaluate the individual.

[0007] The present application breaks the limitations of the traditional static maximum value design method by constructing a transient heat generation power function through obtaining multi-source dynamic thermal load data, combining a transient thermal resistance network model containing flow field coupling characteristics, introducing a transient thermal shock response index by using a genetic algorithm, and constructing a fitness function for iterative optimization, and can screen out a structure parameter combination that effectively utilizes material heat capacity to suppress temperature fluctuations, thereby optimizing the air inlet structure while ensuring heat dissipation efficiency, and achieving the dual goals of considering transient heat dissipation stability and high-level protection requirements under periodic pulse working conditions.

[0008] Preferably, the constructing a transient heat generation power function comprises: collecting real-time current and real-time voltage of a heat generating element at a current time; obtaining a heat loss efficiency factor of the heat generating element; multiplying the real-time current, the real-time voltage and the heat loss efficiency factor to obtain the transient heat generation power of the heat generating element at the current time.

[0009] By using the above technical solution, the real-time current and real-time voltage of the heat generating element at the current time are collected, and the heat loss efficiency factor is combined to construct a transient heat generation power function, a real heat source model that changes with time is established, and the asynchronous fluctuation characteristics of different heat sources in the time dimension can be accurately reflected, providing an accurate excitation source close to the actual working condition for subsequent thermal analysis simulation, and avoiding design redundancy and errors caused by assuming that all elements are in the rated maximum power state at the same time.

[0010] Preferably, the structure parameter vector includes an inclination angle of a guide vane relative to a main air flow and a blade spacing of an air inlet louver; and the transient thermal resistance network model includes a component junction temperature node, a heat sink node, an air inside the cabinet node, a cabinet wall node and an environment node.

[0011] By adopting the technical solution, the structure parameter vector is specifically embodied as the inclination angle of the guide vane relative to the main airflow and the blade spacing of the air inlet louver, the transient thermal resistance network model is refined into key nodes such as component junction temperature nodes, radiator nodes and cabinet air nodes, the mapping relationship between the physical space structure and the thermodynamic topology network is determined, the optimization process can be directly adjusted to the core geometric variables affecting the conflict between heat dissipation and dust prevention, and a model foundation is laid for finding the best balance point in the physical space.

[0012] Preferably, the specific calculation logic of the flow field thermal resistance coupling efficiency is that the numerical value of the flow field thermal resistance coupling efficiency is equal to the quotient of the numerator and the denominator; the numerator is the multiplication product of the air constant pressure specific heat capacity, the air density, the rated air volume of the heat dissipation fan and the guide correction coefficient, wherein the guide correction coefficient is equal to one minus the product of the flow resistance influence coefficient and the sine value of the inclination angle; and the denominator is the arithmetic square root of the flow resistance comprehensive coefficient, wherein the flow resistance comprehensive coefficient is equal to the square of the geometric ratio multiplied by the local resistance coefficient plus the basic flow resistance constant.

[0013] By adopting the technical solution, the specific calculation logic of the flow field thermal resistance coupling efficiency is defined, a nonlinear evaluation index is constructed by using the numerator containing the guide correction coefficient and the denominator containing the flow resistance comprehensive coefficient, the index can represent the comprehensive influence of the structure parameter change on the heat dissipation capacity and the fluid resistance, and especially the sensitivity of the blade spacing to the wind resistance is reflected through the flow resistance comprehensive coefficient, so that the algorithm can automatically find the solution to maintain a small blade spacing under the premise of ensuring heat dissipation, and effectively block the invasion of carbon black dust.

[0014] Preferably, the iterative optimization of the structure parameter vector by the genetic algorithm comprises: setting the population size, the crossover probability and the mutation probability; substituting the structure parameter vector of the individual into the transient thermal resistance network model, combining the transient heat generation power function, and iteratively solving the heat balance differential equation by the Runge-Kutta method to obtain the real-time temperature curve of the key component nodes in the process period.

[0015] By adopting the technical solution, the structure parameter vector of the individual is substituted into the model and the heat balance differential equation is iteratively solved by the Runge-Kutta method, an intermediate proxy solving mechanism with significantly higher calculation efficiency than the traditional computational fluid dynamics is established, the temperature field can be quickly estimated in the huge population size and evolution algebra of the genetic algorithm, and the real-time temperature curve of the key component nodes in the process period is obtained, so that the convergence speed and engineering practicability of the optimization process are greatly improved while ensuring the calculation accuracy.

[0016] Preferably, the calculation logic of the transient thermal shock response index is that the transient thermal shock response index is equal to the value obtained by time-integrating a thermal shock kernel over the entire process cycle; the thermal shock kernel is the product of a temperature rate of change squared term and a temperature normalized logarithm term; the temperature rate of change squared term is the square of the rate of change of the real-time temperature curve over time; and the temperature normalized logarithm term is the natural logarithm value of the real-time temperature of the key component node plus the safety alarm temperature threshold value plus the natural constant.

[0017] Preferably, the calculation logic of the fitness function is that the value of the fitness function is equal to the quotient obtained by dividing a preset normalized scaling coefficient by an evaluation denominator; the evaluation denominator is the sum of the transient thermal shock response index, a weighted temperature extreme value term, and a minimum positive number; and the weighted temperature extreme value term is the product of the absolute maximum value of the node temperature in the process cycle and a preset weight coefficient.

[0018] Preferably, in the process of iteratively optimizing the structure parameter vector by using the genetic algorithm, the process further includes: in each generation evolution, selecting individuals in the population according to the value of the fitness function, and performing crossover and mutation operations on the selected individuals according to a preset crossover probability and a mutation probability, to generate a next generation population containing updated structure parameter vectors.

[0019] Preferably, after outputting the optimal structure parameter combination, the process further includes generating a corresponding temperature response curve and performance analysis chart based on the optimal structure parameter combination, and visually displaying the temperature response curve.

[0020] In a second aspect, the present application provides an electric control cabinet heat dissipation structure parameter optimization system based on a genetic algorithm, comprising a processor and a memory, and the memory stores computer program instructions, which realize the above-mentioned electric control cabinet heat dissipation structure parameter optimization method based on a genetic algorithm when executed by the processor.

[0021] By adopting the above technical solution, the above-mentioned electric control cabinet heat dissipation structure parameter optimization method based on a genetic algorithm is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is manufactured according to the memory and the processor, and convenient use is achieved.

[0022] The beneficial effects of the present application are: The present application constructs a flow field thermal resistance coupling performance formula and uses the flow resistance coupling mechanism in the formula to automatically find a balance point by the algorithm, maintains a smaller blade spacing of the inlet louver under the premise of ensuring the heat dissipation performance, and blocks most carbon black dust under the premise of not sacrificing heat dissipation.

[0023] Further, by introducing the transient thermal shock response index, the screened heat dissipation structure can effectively utilize the material heat capacity and smooth airflow to offset the thermal shock caused by the current pulse, significantly reduce the temperature rise rate of the key component node and prolong the service life of the device, and finally realize the double improvement of heat dissipation stability and protection safety. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flow chart illustrating a genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet in the present application; Figure 2 is a comparison chart illustrating the temperature response of key components under multiple heat source loads; Figure 3 is an iterative convergence curve chart of the thermal shock response index; Figure 4 is a chart illustrating the relationship between the angle of the flow guide structure and the coupling efficiency of the flow field thermal resistance. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments.

[0026] The specific embodiments of the present application will be described in detail below with reference to the drawings.

[0027] The embodiments of the present application disclose a genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet, referring to Figure 1 , comprising steps S1-S4: S1, obtaining the multi-source dynamic thermal load data of the electric control cabinet to be optimized in a complete process cycle, and constructing a transient heat generation power function according to the multi-source dynamic thermal load data.

[0028] In an optional embodiment, when it is necessary to obtain the multi-source dynamic thermal load data of the electric control cabinet of the tire vulcanizing machine to be optimized in a complete process cycle, since the main heat generating elements of the electric control cabinet of the vulcanizing machine are all connected to the common DC bus or AC power supply line, the total output power of the system can be calculated by obtaining the current data of the incoming line end of the electric control cabinet and the voltage data of the bus end of the electric control cabinet. Therefore, in order to obtain the current data of the incoming line end of the electric control cabinet and the voltage data of the bus end of the electric control cabinet, a current sensor needs to be installed at the incoming line end of the electric control cabinet, and a voltage transmitter needs to be installed at the bus end of the electric control cabinet, and then the running state of the PLC is read through the industrial Ethernet.

[0029] It should be noted that the length of a complete vulcanization process cycle is denoted as The electric control cabinet contains a plurality of heating elements, which are set as m in the application, and the current passing through the mth heating element is recorded as The voltage applied to the mth heating element is recorded as Wherein, The current data of the incoming line end of the electric control cabinet, the running state of the mth heating element can be derived, and a special current sensor can be added at the mth heating element, The voltage at the busbar end of the electric control cabinet can be defined as the voltage across the mth heating element, and a voltage sensor can be additionally arranged at both ends of the mth heating element, and finally the heat loss efficiency factor of the mth heating element is obtained through the product manual Finally, the transient heat generation power function of the mth heating element is constructed , The calculation method of .

[0030] In order to more clearly illustrate the calculation process of the transient heat generation power function, the embodiment of the application will be described by example as follows: Suppose that the mth heating element is a frequency converter at this time, and the mth heating element is in the mold closing stage at the 10th second, 50A, 380V, 0.04, then the transient heat generation power of the mth heating element at this time is: .

[0031] In this way, by constructing a dynamic heat load matrix containing all key heat sources, the "this and that" characteristics of different heat sources in the time dimension can be accurately reflected, and data close to the actual working condition are provided for subsequent thermal analysis.

[0032] S2, a transient thermal resistance network model containing flow field coupling characteristics is constructed, a structure parameter vector to be optimized is defined, and a flow field thermal resistance coupling performance is determined according to the structure parameter vector.

[0033] In an optional embodiment, the application establishes an intermediate proxy model, i.e. a flow field thermal resistance coupling performance index , and then quickly estimates the temperature field in the multiple iteration process of the genetic algorithm. Specifically, first, the lumped parameter method is used to discretize the electric control cabinet system into a plurality of thermal nodes including component junction temperature nodes, radiator nodes, cabinet air nodes, cabinet wall nodes and environment nodes; then a structure parameter vector to be optimized is defined The structure parameter vector includes the inclination angle of the guide vane relative to the main airflow The blade spacing of the air inlet shutter and the coordinate offset of the fan installation position and other data; and finally, a flow field thermal resistance coupling performance index is constructed to represent the comprehensive heat dissipation and flow capacity of the current structure parameter under unit driving force , the calculation method of the flow field thermal resistance coupling performance index is as follows: ; wherein, is the flow field thermal resistance coupling performance, is the specific heat capacity of air at constant pressure, is the air density, which is 1.205 kg / m³ in the present application; is the rated air volume of the heat dissipation fan, which is derived from the PQ curve parameters of the heat dissipation fan; is the guide plate inclination angle, which is in the range of 0° to 90° in the present application; is the flow resistance influence coefficient, which is in the range of 0.1 to 0.5 in the present application, and is used to represent the attenuation rate of the effective air volume caused by the change of the guide plate inclination angle; is the local resistance coefficient along the path; is the characteristic length of the air duct; is the louver spacing; is the basic flow resistance constant, which is set to 1 in the embodiment of the present application.

[0034] In order to more clearly illustrate the role and calculation process of the flow field thermal resistance coupling performance index, the embodiment of the present application will be described by examples as follows: First, it is assumed that the specific heat capacity of air at constant pressure is 1005; the rated air volume of the heat dissipation fan is 0.5; the flow resistance influence coefficient is 0.2; the local resistance coefficient along the path is 1.5; the basic flow resistance constant is 1; the characteristic length of the air duct is 2; the guide plate inclination angle is 30°; and the louver blade spacing is 0.02 m.

[0035] Therefore, .

[0036] In this way, when decreases in the flow field thermal resistance coupling performance formula, the denominator increases, resulting in decreases, which conforms to the physical law that "the wind resistance is greater when the mesh is denser", thereby accurately reflecting the nonlinear influence of the structure change on the heat dissipation capacity on the mathematical model.

[0037] S3. Based on the transient thermal resistance network model, the genetic algorithm is used to iteratively optimize the structural parameter vector. During the iteration process, the transient thermal shock response index of key components is calculated, and a fitness function is constructed based on the transient thermal shock response index to evaluate the individual's performance.

[0038] In an optional embodiment, after obtaining the flow field thermal resistance coupling performance index, the optimal solution is searched in the parameter space by a genetic algorithm, and the population size is set to 50 to 100, the crossover probability is 0.8, and the mutation probability is 0.1.

[0039] In each generation of the population, the structural parameter vector of the individual is substituted into the transient thermal resistance network model that includes the flow field coupling characteristics. Combined with the constructed transient heating power function, the heat capacity of the component junction temperature node, heat sink node, cabinet air node and cabinet wall node is determined according to the material and mass properties of the components. The contact thermal resistance, convection thermal resistance and conduction thermal resistance are determined according to the heat transfer mode between the nodes. The value of the convection thermal resistance between the heat sink node and the cabinet air node is the reciprocal of the flow field thermal resistance coupling efficiency.

[0040] The Runge-Kutta method is then used to iteratively solve the thermal equilibrium differential equations. This method is existing technology and readily understood by those skilled in the art. To clarify the construction process of the thermal equilibrium differential equations, this invention provides an illustrative explanation: The product of the rate of temperature change over time at a component junction node and its heat capacity equals the transient heating power function minus the heat flow from that node to the heat sink node. The product of the rate of temperature change over time at the heat sink node and its heat capacity equals the heat flow from the component junction node minus the heat flow to the air node inside the cabinet. The product of the rate of temperature change over time at the air node inside the cabinet and its heat capacity equals the heat flow from the heat sink node minus the heat flow through the cabinet wall to the environmental node and the heat flow discharged through the vents. By simultaneously solving the above equations, the real-time temperature curves of the key component nodes throughout the complete process cycle are obtained. .

[0041] Real-time temperature profiles of key component nodes throughout the entire process cycle were obtained. Subsequently, the transient thermal shock response index was constructed. The purpose is to screen out structures that can utilize heat capacity to smooth out peaks and fill valleys. The transient thermal shock response index is calculated as follows: ; in, The transient thermal shock response index; The duration of a complete process cycle; The rate of change of temperature over time is given by a squared term used to penalize drastic temperature fluctuations. The safety alarm temperature threshold for the components; The base of the natural logarithm is the base of the natural logarithm in this invention. The value of 2.718 is chosen to ensure that the logarithmic term is greater than 1, thus preventing the weight from becoming zero or negative at low temperatures.

[0042] To more clearly illustrate the role and calculation process of the transient thermal shock response index, the embodiments of the present invention will be explained through examples below: Assume at time... hour, 60 degrees; safety threshold The temperature is 85 degrees; at this point, the rate of temperature change is... It is 2; First, calculate the integrand value of the transient thermal shock response exponent. ; Finally, by integrating over the entire vulcanization process cycle, the transient thermal shock response index can be obtained.

[0043] After obtaining the transient thermal shock response exponent, the fitness function is maximized. Maximize the fitness function The calculation method is as follows: ; in, The fitness function; The preset normalized scaling factor is set as follows in this embodiment of the invention: ; This represents the absolute maximum value of the node temperature within the period; This is a preset weighting coefficient, which is set to 10 in this embodiment of the invention; To prevent extremely small positive numbers with a denominator of 0, it is set as follows in this embodiment of the invention: .

[0044] Suppose that a certain structure is obtained through calculation 500; highest temperature during the cycle The temperature is 75 degrees Celsius; the preset normalization scaling factor is... ; It is 10; but .

[0045] Thus, by constructing the fitness function described above, the algorithm can find the optimal structural parameters while pursuing a low transient thermal shock response exponent and ensuring that the absolute maximum value of the node temperature within the absolute period is not too high.

[0046] S4. When the genetic algorithm meets the preset termination condition, it outputs the optimal combination of structural parameters and generates a heat dissipation structure model based on the optimal combination of structural parameters.

[0047] In an optional embodiment, the genetic algorithm outputs the optimal combination of structural parameters after satisfying a termination condition that includes 50 generations of evolution or when fitness no longer improves. Based on the optimal combination of structural parameters, a three-dimensional structural model, corresponding temperature response curves, and performance analysis charts are generated for engineers to conduct final review.

[0048] Reference Figure 2 The dashed line representing the prior art shows severe sawtooth fluctuations, and the temperature exceeds the 85-degree Celsius safety threshold at peak load moments. The solid line representing the optimized version of this invention is very smooth, exhibiting clear peak-shaving and valley-filling characteristics, and is consistently kept below the safe temperature. This intuitively demonstrates the effectiveness of the introduced... After the index is adjusted, the system's thermal shock resistance is improved.

[0049] Reference Figure 3 The curve representing the exponential convergence of the thermal shock response rapidly declines in the first few generations, and then tends to level off around the 40th generation, indicating that the algorithm has good convergence and can quickly locate the globally optimal structural parameter region.

[0050] Reference Figure 4 The curve showing the relationship between the angle of the flow guiding structure and the coupling effectiveness of the flow field thermal resistance is an inverted U-shaped parabola with a clear peak point. If the angle is too small in the interval to the left of the peak point, the flow resistance will be too large. If the angle is too large in the interval to the right of the peak point, the flow guiding will fail. The middle peak point intuitively shows how the present invention finds the physical optimal balance point through mathematical model.

[0051] In this way, by outputting visual analysis charts, technicians can quickly verify the optimization effect and ensure that the final heat dissipation structure solution not only meets the heat dissipation requirements, but also takes into account dust prevention and structural stability.

[0052] This invention also discloses a system for optimizing the heat dissipation structure parameters of an electrical control cabinet based on a genetic algorithm, comprising a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the method for optimizing the heat dissipation structure parameters of an electrical control cabinet based on a genetic algorithm according to the present invention.

[0053] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0054] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise expressly and specifically defined.

Claims

1. A genetic algorithm-based electric control cabinet heat dissipation structure parameter optimization method, characterized in that, The method comprises: acquiring multi-source dynamic thermal load data of an electric control cabinet to be optimized in a complete process cycle, and constructing a transient heat generation power function according to the multi-source dynamic thermal load data; constructing a transient thermal resistance network model containing flow field coupling characteristics, defining a structure parameter vector to be optimized, and determining flow field thermal resistance coupling efficiency according to the structure parameter vector; based on the transient thermal resistance network model, iteratively optimizing the structure parameter vector by using a genetic algorithm, calculating a transient thermal shock response index of a key component in an iterative process, and constructing a fitness function according to the transient thermal shock response index to evaluate the pros and cons of individuals; when the genetic algorithm meets a preset termination condition, outputting an optimal structure parameter combination, and generating a heat dissipation structure model based on the optimal structure parameter combination.

2. The genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet according to claim 1, characterized in that, The method comprises: collecting real-time current and real-time voltage of a heat generating element at a current time; acquiring a heat loss efficiency factor of the heat generating element; multiplying the real-time current, the real-time voltage and the heat loss efficiency factor to obtain the transient heat generation power of the heat generating element at the current time.

3. The genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet according to claim 1, characterized in that, The structure parameter vector comprises an inclination angle of a flow guide plate relative to a main air flow and a blade spacing of an air inlet louver; and the transient thermal resistance network model comprises a component junction temperature node, a heat sink node, a cabinet air node, a cabinet wall node and an environment node.

4. The genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet according to claim 3, characterized in that, The specific calculation logic for determining the flow field thermal resistance coupling efficiency is: a value of the flow field thermal resistance coupling efficiency is equal to a quotient of a numerator and a denominator; the numerator is a continuous product of air specific heat capacity at constant pressure, air density, rated air volume of a heat dissipation fan and a flow guide correction coefficient, wherein the flow guide correction coefficient is equal to one minus a product of a flow resistance influence coefficient and a sine value of the inclination angle; the denominator is an arithmetic square root of a flow resistance comprehensive coefficient, wherein the flow resistance comprehensive coefficient is equal to a sum of a product of a local resistance coefficient along a path and a square of a geometric ratio and a basic flow resistance constant, and the geometric ratio is a ratio of a characteristic length of an air duct to the blade spacing.

5. The genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet according to claim 1, characterized in that, The iterative optimization of the structure parameter vector by using the genetic algorithm comprises: setting a population size, a crossover probability and a mutation probability; substituting the structure parameter vector of an individual into the transient thermal resistance network model, combining the transient heat generation power function, iteratively solving a heat balance differential equation by using a Runge-Kutta method to obtain a real-time temperature curve of a key component node in a process cycle.

6. The genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet according to claim 5, characterized in that, The calculation logic of the transient thermal shock response index is: the transient thermal shock response index is equal to a value obtained by time integrating a thermal shock core term in the complete process cycle; the thermal shock core term is a product of a temperature change rate square term and a temperature normalized logarithm term; the temperature change rate square term is a square of a change rate of the real-time temperature curve with time; the temperature normalized logarithm term is a natural logarithm value of a sum of a ratio of the real-time temperature of the key component node to a safety alarm temperature threshold and a natural constant.

7. The genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet according to claim 6, characterized in that, The calculation logic of the fitness function is: a value of the fitness function is equal to a quotient of a preset normalized scaling coefficient divided by an evaluation denominator. The evaluation denominator is the sum of the transient thermal shock response index, a weighted temperature extreme value term, and a preset small positive number; The weighted temperature extreme value term is the product of the absolute maximum value of the node temperature in the process cycle and a preset weight coefficient.

8. The genetic algorithm-based parameter optimization method for the heat dissipation structure of an electric control cabinet according to claim 6, characterized in that, In the process of iteratively optimizing the structure parameter vector using the genetic algorithm, the process further includes: In each generation of evolution, the individuals in the population are selected according to the numerical value of the fitness function, and the selected individuals are subjected to crossover and mutation operations according to a preset crossover probability and mutation probability to generate a next generation population containing updated structure parameter vectors.

9. The genetic algorithm-based parameter optimization method for heat dissipation structure of an electric control cabinet according to claim 1, characterized in that, After outputting the optimal structure parameter combination, the process further includes generating a corresponding temperature response curve and performance analysis chart based on the optimal structure parameter combination, and visually displaying the temperature response curve.

10. A genetic algorithm-based electric control cabinet heat dissipation structure parameter optimization system, characterized in that, The method comprises: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement a genetic algorithm-based electrical control cabinet heat dissipation structure parameter optimization method according to any one of claims 1-9.

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