Heat dissipation control method, system and device for adjusting PID controller based on genetic algorithm and medium

Abstract

The invention discloses a heat dissipation control method, system, equipment and medium for adjusting a PID controller based on a genetic algorithm, and the method comprises the steps: selecting a parameter range through three parameter values of # imgabs0 #, # imgabs1 # and # imgabs2 # of the PID controller, and forming a search space; forming an initial population by the search space; a population individual fitness function value is calculated based on the initial population, and based on the fitness function value, a gun line temperature error e # imgabs3 # serves as genetic algorithm input, # imgabs4 #, # imgabs5 # and # imgabs6 # serve as genetic algorithm output, and PID controller parameters are set through the genetic algorithm; and the output of the PID controller is calculated on the basis of the temperature difference value detected by the PID controller and the gun line after parameter setting, so that the opening degree of an electromagnetic valve of an engine is adjusted to control the output of the liquid cooling oil, and the optimal liquid cooling oil flow speed is continuously output through genetic iteration to stabilize the gun line temperature of the charging pile. The liquid cooling oil flow velocity is adjusted in real time according to the gun line temperature detection value, and the gun line heat dissipation requirement is met while the power consumption of the liquid cooling machine is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of power electronics, and relates to a heat dissipation control method, system, equipment and medium for adjusting a PID controller based on a genetic algorithm. Background Art

[0002] With the vigorous development of the new energy vehicle industry and the rapid growth of electric vehicle ownership, the charging pile industry has ushered in unprecedented development opportunities. In this context, intelligence has not only become a key means to improve the operational efficiency of charging piles and optimize user experience, but also an important driving force for the transformation and upgrading of the entire industry. As an important development direction of the future charging pile market, smart charging piles integrate advanced technologies such as the Internet of Things, big data, cloud computing and artificial intelligence, and can realize remote monitoring, fault warning, automatic billing, intelligent scheduling and other functions, greatly improving the convenience and safety of charging services.

[0003] The PID controller adjusts the output signal according to the difference between the current state of the controlled object and the set value, so that the difference approaches zero. The PID controller calculates the output signal according to the weighted combination of the three parts of proportion (P), integral (I), and differential (D), thereby achieving precise control of the controlled object. Proportional (P) control: adjusts the output of the controller according to the size of the current error. When the error is large, the output signal is also large, which speeds up the response speed of the controlled object. Proportional control can make the system approach the set value quickly, but it often causes overshoot and oscillation. Integral (I) control: adjusts the output of the controller according to the amount of error accumulated over time. It can eliminate the steady-state error of the system and ensure that the system eventually stabilizes near the set value. The role of integral control is similar to accumulating errors and continuously adjusting the output, but it is also prone to overshoot and oscillation. Differential (D) control: adjusts the output of the controller according to the rate of error change. It can predict the future state change trend of the system, thereby reducing overshoot and improving the stability of the system. The introduction of differential control can suppress the oscillation of the system, but it will also increase the sensitivity of the system to noise. In the heat dissipation control of charging piles, PID controllers can play an important role. By real-time monitoring of the temperature, current and other parameters inside the charging pile, the PID controller can dynamically adjust the heat dissipation strategy according to the actual situation to achieve on-demand heat dissipation. For example, in the early stage of charging, when the temperature inside the charging pile rises rapidly, the PID controller can automatically increase the fan speed or increase the coolant flow rate to speed up the heat dissipation; in the later stage of charging, as the temperature gradually stabilizes, the PID controller can automatically reduce the heat dissipation intensity to avoid energy waste and equipment loss caused by excessive cooling.

[0004] In the technological innovation of smart charging piles, the design and optimization of efficient heat dissipation systems are particularly critical, which directly affects the operating stability, energy efficiency ratio and service life of charging piles. At present, the mainstream heat dissipation methods of charging piles on the market are mainly divided into two categories: fan heat dissipation and liquid cooling. Fan heat dissipation promotes air flow by installing a fan to take away the heat generated inside the charging pile; while liquid cooling uses circulating coolant (such as liquid cooling oil) to flow in the internal pipes of the charging pile, and takes away the heat and discharges it out of the system through the principle of heat exchange. These two heat dissipation methods have their own advantages. Fan heat dissipation has relatively low cost and simple maintenance, but the heat dissipation efficiency may be limited by ambient temperature and fan performance; liquid cooling has higher heat dissipation efficiency and can better adapt to high-power charging scenarios, but the system complexity and cost are also relatively high.

[0005] However, the heat dissipation strategy currently adopted by most charging piles is still relatively traditional, that is, fixed heat dissipation mode - once the charging pile is started, the fan will continue to run at a preset fixed speed, or the coolant in the liquid cooling system will circulate at a fixed flow rate. This "one-size-fits-all" heat dissipation method often has problems of energy waste and inefficiency in practical applications. Especially in the later stages of the charging process, as the battery power approaches saturation, the charging current decreases significantly, and the heat generated inside the charging pile also decreases accordingly. At this time, continuing to use high-speed fans or high-flow liquid cooling oil for heat dissipation not only increases unnecessary energy consumption, but may also accelerate the wear of fans and pumps and shorten the life of the equipment.

[0006] Therefore, in the prior art, as the internal friction of the charging pile increases and the temperature rises during use, the liquid cooling oil flow rate cannot be adjusted in time to reduce the power consumption of the liquid cooler to meet the heat dissipation requirements. Summary of the invention

[0007] In view of the problems existing in the prior art, the present invention provides a heat dissipation control method, system, device and medium for adjusting the PID controller based on genetic algorithm, which stabilizes the charging pile gun line temperature by continuously outputting the optimal liquid cooling oil flow rate through genetic iteration, and adjusts the liquid cooling oil flow rate in real time according to the gun line temperature detection value, thereby reducing the power consumption of the liquid cooler while meeting the gun line heat dissipation requirements.

[0008] The present invention is achieved through the following technical solutions: A liquid cooling machine heat dissipation control method based on genetic algorithm to adjust PID controller, comprising: Determine the PID controller by trial and error , and The three parameter values ​​are: , and They are the proportional constant, integral constant and differential constant of the PID controller respectively; Select parameter ranges based on the three parameter values ​​and form a search space with the parameter ranges; The search space is divided into multiple subspaces, and n individuals are randomly selected from each space to form the initial population; Calculate the fitness function value of the individual population based on the initial population. Based on the fitness function value, the gun line temperature error e is the genetic algorithm input, , and It is the output of genetic algorithm, and realizes the tuning of PID controller parameters by genetic algorithm; Based on the temperature difference detected at the gun line and the PID controller after parameter adjustment, the output of the PID controller is calculated, and the opening of the engine electromagnetic valve is adjusted according to the output of the PID controller to control the output of the liquid cooling oil.

[0009] Preferably, the PID controller is determined by trial and error. , and The three parameter values ​​are: By changing the integral value, it is determined that the gun line temperature deviation is in a decreasing state, and a preliminary integral value is obtained; By changing the proportional value, it is determined that the gun line temperature deviation continues to be in a decreasing state, and the initial proportional value is obtained; By changing the differential value, when it is determined that the gun line temperature deviation continues to be in a decreasing state, the initial differential value can be output; and around , and The three parameter values ​​of select parameter ranges and form the parameter ranges into a search space; in, , and They are the proportional constant, integral constant and differential constant of the PID controller respectively.

[0010] Preferably, the search space is divided into 8 subspaces, and n individuals are randomly selected from each space to form an initial population of size 8n, which is represented by a decimal number.

[0011] Preferably, the fitness function value of the population individuals is calculated, specifically as follows: Based on the liquid cooling oil control amount, the absolute value of the gun line temperature deviation, the rise time and the output increment as the influencing factors of the objective function, the objective function is selected as follows: J

[0012] In the formula, e is the gun line temperature deviation, is the liquid cooling oil flow rate control quantity, , , , is the weighting coefficient, and , is the rise time, and the output increment is , is the gun line temperature value at the current moment, ) is the gun line temperature value at the previous moment; According to the maximization principle, the inverse of the objective function is selected as the fitness function, specifically:

[0013] In the formula, J Represents the individuals in the current population The performance index value of Expressed as a constant to avoid the denominator being zero, usually set to 0.0001, f Represents the individuals in the current population The fitness value of .

[0014] Preferably, when the genetic algorithm performs selection, crossover and mutation operations on the population, a fuzzy control algorithm is used to adjust the crossover probability and mutation probability of the population.

[0015] Preferably, the liquid cooling oil output of the PID controller is calculated as follows:

[0016] In the formula, e is the temperature difference detected at the current gun line of the PID controller, u is the liquid cooling oil flow rate input value of the PID controller, , and They are the three parameters of the PID controller: proportional constant, integral constant and differential constant; e , and They represent the gun line temperature difference, the gun line temperature primary deviation and the gun line temperature secondary deviation respectively.

[0017] Preferably, the gun line temperature difference e It is a positive deviation or a negative deviation. If the gun line temperature difference is a positive deviation, the engine electromagnetic valve opening is adjusted through the PID controller to increase the liquid cooling oil flow rate; if the gun line temperature difference is a negative deviation, the engine electromagnetic valve opening is adjusted through the PID controller to reduce the liquid cooling oil flow rate.

[0018] Preferably, the parameter determination module is used to determine the PID controller by trial and error. , and The three parameter values ​​of A search space establishment module, for selecting parameter ranges based on three parameter values ​​and forming a search space from the parameter ranges; The initial population establishment module is used to divide the search space into multiple subspaces, and n individuals are randomly selected from each space to form the initial population; A fitness calculation module is used to calculate the fitness function value of the individual population based on the initial population; The parameter tuning module is used to tune the parameters of the PID controller based on the fitness function value after selecting, crossover and mutation operations on the population through the genetic algorithm; The output module of the PID controller is used to calculate the output of the PID controller based on the temperature difference detected between the PID controller after parameter adjustment and the gun line, so as to adjust the opening of the engine electromagnetic valve to control the output of the liquid cooling oil.

[0019] An electronic device, characterized in that it includes a processor and a memory, wherein at least one instruction is stored in the memory, and the instruction stored in the memory is executed to implement the liquid cooler heat dissipation control method based on genetic algorithm adjustment of PID controller as described.

[0020] A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the liquid cooler heat dissipation control method based on genetic algorithm adjustment of PID controller is implemented as described.

[0021] Compared with the prior art, the present invention has the following beneficial technical effects: A liquid cooler heat dissipation control method based on genetic algorithm to adjust the PID controller. This method combines the global search optimization capability of the genetic algorithm and the precise control capability of the PID controller to achieve intelligent control of the liquid cooler heat dissipation system: First, the three parameter values ​​of the proportional constant, integral constant and differential constant of the PID controller are preliminarily determined by the trial and error method. A parameter range is selected based on these three parameter values, and this range is divided into multiple subspaces. n individuals are randomly selected from each subspace to form an initial population. These individuals represent different PID parameter combinations. Calculate the fitness function value of each individual in the initial population. The fitness function value is calculated based on the charging pile gun line temperature error e(k), which reflects the control effect of the PID controller under the current parameter combination. The genetic algorithm takes the temperature error e(k) as input and uses , and As output, the parameters of the PID controller are continuously optimized through genetic operations such as selection, crossover, and mutation. After multiple rounds of genetic iterations, the genetic algorithm will output a set of optimal PID parameter combinations. This set of parameter combinations can keep the temperature of the charging pile gun line within a stable range, and the flow rate of the liquid cooling oil is also optimal. Based on the temperature difference detected at the gun line and the PID controller after parameter tuning, the output of the PID controller can be calculated. This output signal is used to adjust the opening of the engine solenoid valve to control the output flow rate of the liquid cooling oil. By continuously adjusting the opening of the solenoid valve, the flow rate of the liquid cooling oil can be matched with the heat dissipation requirements of the charging pile gun line to achieve stable temperature control.

[0022] The present invention adjusts the parameters of the PID controller through a genetic algorithm, so that the PID controller can more accurately adjust the flow rate of the liquid cooling oil according to the temperature error of the charging pile gun line. This precise flow control can significantly improve the heat dissipation efficiency and reduce the temperature fluctuation inside the charging pile. Since the flow rate of the liquid cooling oil is precisely controlled, unnecessary energy consumption can be avoided. During the charging process, when the gun line temperature is low, the flow rate of the liquid cooling oil can be reduced; when the temperature is high, the flow rate is increased; this on-demand heat dissipation strategy can significantly reduce the energy consumption of the charging pile. The genetic algorithm can find a set of optimal PID parameter combinations so that the charging pile gun line temperature remains within a stable range. This method combines the advantages of the genetic algorithm and the PID controller to realize the intelligent control of the charging pile heat dissipation system. This intelligent control can further improve the reliability and user experience of the charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The block diagram of the fan control system based on genetic algorithm to adjust the PID controller; Figure 2 It is the flowchart of genetic algorithm; DETAILED DESCRIPTION The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0024] In order to enable those skilled in the art to better understand the solution of the present invention, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solution in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, the ordinary technicians in this field have obtained the above-mentioned The liquid cooling machine heat dissipation control method based on genetic algorithm adjustment of PID controller provided by the present invention can adjust the liquid cooling oil flow rate in real time according to the gun line temperature detection value, reduce the power consumption of the liquid cooling machine and meet the gun line heat dissipation requirements.

[0025] The specific process is: Choosing the best PID controller parameters , and Therefore, the gun line temperature error e is selected as the genetic algorithm input. , and is the output of the genetic algorithm. The specific steps for tuning the PID controller parameters using IGA1 are as follows: Figure 2 As shown: (a) Determine the range of each parameter It is known that the genetic algorithm optimization object is the three parameters of the PID controller , and , three parameter values ​​are determined by trial and error. That is, first, by changing the integral value until it is determined that the gun line temperature deviation is in a decreasing state, the initial integral value can be obtained; then, by changing the proportional value, it is determined that the gun line temperature deviation continues to be in a decreasing state, and an initial proportional value can be obtained; finally, by changing the differential value, it is determined that when the gun line temperature deviation continues to be in a decreasing state, the initial differential value can be output. The parameter range is selected around the parameter value, and the parameter range forms a search space, which is recorded as , and .

[0026] (b) Generation of the initial population The search space is divided into 8 subspaces, and n individuals are randomly selected from each space to form an initial population PT0 of size 8n, which is represented by a decimal number.

[0027] (c) Calculate the fitness function value of the individual population The fitness function is an indicator for evaluating the performance of a population, while the objective function is a performance indicator for evaluating the control effect of a system. In order to make the gun line temperature control system have better stability, accuracy and speed, this paper takes the control amount, absolute value of deviation, rise time and overshoot as the influencing factors of the objective function, and selects the objective function as: J

[0028] In the formula, e is the gun line temperature deviation, is the liquid cooling oil flow rate control quantity, , , , is the weighting coefficient, and , is the rise time, and the output increment is . is the gun line temperature value at the current moment, ) is the gun line temperature value at the previous moment.

[0029] The smaller the objective function is, the better the control performance of the system is. According to the maximization principle, the inverse of the objective function is selected as the fitness function, that is:

[0030] In the formula, J Represents the individuals in the current population The performance index value of represents a very small constant to avoid the denominator being zero, usually set to 0.0001, f Represents the individuals in the current population The fitness value of .

[0031] (d) Perform genetic manipulation The population selection operation uses a selection operator that combines elite preservation and roulette, and then uses the selection operator to perform survival of the fittest on individuals in the population. Suppose the population size is individual The fitness is f , then the individual The probability of being selected is P for:

[0032] The population crossover operation uses a single-point crossover operator, and then the two paired chromosomes exchange some genes according to the single-point crossover method, thereby generating two new individuals. The population mutation operation uses a basic bit mutation operator, which replaces the number on the corresponding bit of the replaced individual data with the number on a certain bit of other individuals. The functions of the crossover operator and the mutation operator are both to generate new individuals in the population, but the new individuals are generated in different ways. The former generates new individuals as a result of internal gene exchange, while the latter is generated by external interference gene mutation. The crossover operator determines whether to perform a crossover operation on each individual in the population based on the crossover probability, and the mutation operator determines whether to perform a mutation operation based on the mutation probability. If the crossover probability remains unchanged, the algorithm cannot converge in the later stage. Therefore, the crossover probability should be large in the early stage and small in the later stage, so that the crossover operator will not affect the generation of excellent new individuals and will not lose convergence. The mutation probability should also change with the algorithm search progress. The mutation probability should not be too small in the early stage, otherwise the algorithm will easily fall into the local optimum and lose the multi-directional development of the population individuals. It should not be too large in the later stage, otherwise the algorithm will lose the convergence direction. In order to effectively adjust the algorithm search progress, fuzzy control algorithm or other algorithms can be used to automatically adjust the crossover probability and mutation probability. The gun line heats up seriously during charging. This patent helps the gun line to dissipate heat by intelligently controlling the flow rate of liquid cooling oil. After multiple simulations and measurements, it was found that the crossover probability output value range is mostly within 15%~87%, and the mutation probability output value range is within 13%~70%. At this time, the gun line temperature can converge quickly. Outside the range, the convergence time is longer or even diverges.

[0033] (e) Determine whether the algorithm stops The termination condition is that the number of genetic iterations reaches the set value. If the termination condition is met, the optimal parameters are obtained and the population iteration operation ends. Otherwise, return to step 2 and continue the selection, crossover and mutation process. The specific flowchart is shown in Figure 2 .

[0034] Calculate the output of the PID controller

[0035] In the formula, e is the temperature difference detected at the current gun line of the PID controller, u is the liquid cooling oil flow rate input value of the PID controller, , and They are the three parameters of the PID controller: proportional constant, integral constant and differential constant; e , and They represent the gun line temperature difference, the gun line temperature primary deviation and the gun line temperature secondary deviation respectively. , and The calculation formula is: e y

[0036]

[0037] In the formula, k , k -1 and k -2 represents the current moment, one moment in the past, and two moments in the past. r is the gun line temperature setting value, It is the actual detection value of the gun line temperature.

[0038] The gun line temperature difference is a positive deviation or a negative deviation. If the gun line temperature difference is a positive deviation, the engine electromagnetic valve opening is adjusted through the PID controller to increase the liquid cooling oil flow rate. If the gun line temperature difference is a negative deviation, the engine electromagnetic valve opening is adjusted to the PID controller to reduce the liquid cooling oil flow rate. The detected gun line temperature difference is adjusted in 5°C increments, the minimum liquid cooling oil flow rate is 0, the temperature difference is reduced or increased by one order of magnitude at 0-5°C, the temperature difference is reduced or increased by two orders of magnitude at 5-10°C, the temperature difference is reduced or increased by three orders of magnitude at 10-15°C, and so on.

[0039] Figure 1 This is the block diagram of the fan control system based on genetic algorithm to adjust the PID controller. In the figure, IGA is the genetic algorithm, Gn(s) is the mathematical model of the gun line temperature (the gun line temperature model can be obtained by the manufacturer based on the gun line output current, charging time, charging power, etc. as variables, and tested in different environments to obtain test data), r is the gun line temperature set value, and y is the actual detection value of the gun line temperature. , and are the optimal proportional constant, integral constant and differential constant values ​​inferred by the genetic algorithm.

[0040] A heat dissipation control system for a liquid cooler based on genetic algorithm adjustment of a PID controller, characterized in that: The parameter determination module is used to determine the PID controller through trial and error. , and The three parameter values ​​of A search space establishment module, for selecting parameter ranges based on three parameter values ​​and forming a search space from the parameter ranges; The initial population establishment module is used to divide the search space into multiple subspaces, and n individuals are randomly selected from each space to form the initial population; A fitness calculation module is used to calculate the fitness function value of the individual population based on the initial population; The parameter tuning module is used to tune the parameters of the PID controller based on the fitness function value after selecting, crossover and mutation operations on the population through the genetic algorithm; The output module of the PID controller is used to calculate the output of the PID controller based on the temperature difference detected between the PID controller after parameter adjustment and the gun line, so as to adjust the opening of the engine electromagnetic valve to control the output of the liquid cooling oil.

[0041] A terminal device is provided in one embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0042] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0043] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0044] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0045] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0046] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0047] There are other embodiments, which should all fall within the scope of protection of the present invention.

[0048] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0049] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A liquid cooler heat dissipation control method based on genetic algorithm to adjust PID controller, characterized in that: include, Determine the PID controller by trial and error , and Three parameter values; among them, , and They are the proportional constant, integral constant and differential constant of the PID controller respectively; Select parameter ranges based on the three parameter values ​​and form a search space with the parameter ranges; The search space is divided into multiple subspaces, and n individuals are randomly selected from each space to form the initial population; Calculate the fitness function value of the individual population based on the initial population. Based on the fitness function value, the gun line temperature error e is the genetic algorithm input, , and It is the output of genetic algorithm, which realizes the tuning of PID controller parameters by genetic algorithm; Based on the PID controller after parameter tuning and the gun line temperature difference e , the output of the PID controller is calculated, and the opening of the engine electromagnetic valve is adjusted according to the output of the PID controller to control the output of the liquid cooling oil.

2. The method for controlling heat dissipation of a liquid cooler by adjusting a PID controller based on a genetic algorithm according to claim 1, characterized in that: Determine the PID controller by trial and error , and The three parameter values ​​are: By changing the integral value, it is determined that the gun line temperature deviation is in a decreasing state, and a preliminary integral value is obtained; By changing the proportional value, it is determined that the gun line temperature deviation continues to be in a decreasing state, and the initial proportional value is obtained; By changing the differential value, when it is determined that the gun line temperature deviation continues to be in a decreasing state, the initial differential value can be output; and around , and The three parameter values ​​of select parameter ranges and form the parameter ranges into a search space; in, , and They are the proportional constant, integral constant and differential constant of the PID controller respectively.

3. The method for controlling heat dissipation of a liquid cooler by adjusting a PID controller based on a genetic algorithm according to claim 1, characterized in that: The search space is divided into 8 subspaces, and n individuals are randomly selected from each space to form an initial population of size 8n, which is represented by a decimal number.

4. The method for controlling heat dissipation of a liquid cooler by adjusting a PID controller based on a genetic algorithm according to claim 1, characterized in that: Calculate the fitness function value of the individual population, specifically: Based on the liquid cooling oil control amount, the absolute value of the gun line temperature deviation, the rise time and the output increment as the influencing factors of the objective function, the objective function is selected as follows: J In the formula, e is the gun line temperature deviation, is the liquid cooling oil flow rate control quantity, , , , is the weighting coefficient, and , is the rise time, and the output increment is , is the gun line temperature value at the current moment, ) is the gun line temperature value at the previous moment; According to the maximization principle, the inverse of the objective function is selected as the fitness function, specifically: In the formula, J Represents the individuals in the current population The performance index value of Expressed as a constant to avoid the denominator being zero, usually set to 0.0001, f Represents the individuals in the current population The fitness value of .

5. The method for controlling heat dissipation of a liquid cooler by adjusting a PID controller based on a genetic algorithm according to claim 1, characterized in that: When the genetic algorithm performs selection, crossover and mutation operations on the population, a fuzzy control algorithm is used to adjust the crossover probability and mutation probability of the population.

6. The method for controlling heat dissipation of a liquid cooler by adjusting a PID controller based on a genetic algorithm according to claim 1, characterized in that: Calculate the liquid cooling oil output of the PID controller as: In the formula, e is the temperature difference detected at the current gun line of the PID controller, u is the liquid cooling oil flow rate input value of the PID controller, , and They are the three parameters of the PID controller: proportional constant, integral constant and differential constant; e , and They represent the gun line temperature difference, the gun line temperature primary deviation and the gun line temperature secondary deviation respectively.

7. The method for controlling heat dissipation of a liquid cooler by adjusting a PID controller based on a genetic algorithm according to claim 1, characterized in that: Gun line temperature difference e It is a positive deviation or a negative deviation. If the gun line temperature difference is a positive deviation, the engine electromagnetic valve opening is adjusted through the PID controller to increase the liquid cooling oil flow rate; if the gun line temperature difference is a negative deviation, the engine electromagnetic valve opening is adjusted through the PID controller to reduce the liquid cooling oil flow rate.

8. A liquid cooler heat dissipation control system based on genetic algorithm adjustment of PID controller according to any one of claims 1 to 7, characterized in that: The parameter determination module is used to determine the PID controller through trial and error. , and The three parameter values ​​of A search space establishment module, for selecting parameter ranges based on three parameter values, and forming a search space from the parameter ranges; The initial population establishment module is used to divide the search space into multiple subspaces, and n individuals are randomly selected from each space to form the initial population; A fitness calculation module is used to calculate the fitness function value of the individual population based on the initial population; The parameter tuning module is used to tune the parameters of the PID controller based on the fitness function value after selecting, crossover and mutation operations on the population through the genetic algorithm; The output module of the PID controller is used to calculate the output of the PID controller based on the temperature difference detected between the PID controller after parameter adjustment and the gun line, so as to adjust the opening of the engine electromagnetic valve to control the output of the liquid cooling oil.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein at least one instruction is stored in the memory, and the instruction stored in the memory is executed to implement the liquid cooler heat dissipation control method based on genetic algorithm adjustment of PID controller as described in claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for controlling the heat dissipation of a liquid cooler by adjusting a PID controller based on a genetic algorithm as described in claims 1-7 is implemented.

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