Method for optimizing temperature controller of infant incubator

Abstract

The invention discloses an infant incubator temperature controller optimization method, which belongs to the technical field of PID (Proportion Integration Differentiation) control optimization, and specifically comprises the following steps: step 1, constructing an infant incubator temperature controller system; step 2, improving a sewing training optimization algorithm, wherein a specific improvement strategy is as follows: D1, improving a mathematical model of a training stage of the sewing training optimization algorithm by using a multi-position self-adaptive updating strategy; d2, using a dynamic threshold penalty updating strategy to improve a mathematical model of a practice stage of a sewing training optimization algorithm; 3, optimizing a PID (Proportion Integration Differentiation) controller in the infant incubator temperature controller system by utilizing an improved sewing training optimization algorithm; 4, the values of the control parameters Kp, Ki and Kd obtained through optimization in the step 3 are used for a PID controller in a temperature controller system of the infant incubator; by optimizing the infant incubator temperature controller system through the improved algorithm, the robustness and the control performance of the system are enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of PID control optimization, and in particular relates to an optimization method for a temperature controller of an infant incubator. Background Art

[0002] The temperature controller of the infant incubator is a key device specially designed to maintain the appropriate temperature of the newborn in the incubator. Especially for premature babies and low-birth-weight newborns, their body temperature regulation ability is relatively poor and they are easily affected by changes in ambient temperature. Therefore, the temperature control of the incubator is particularly important. The basic working principle of the controller is to monitor the temperature in the incubator in real time through a high-precision temperature sensor, and compare it with the set target temperature, and automatically adjust the power of the heater to ensure that the temperature in the box remains within a constant and safe range. In order to achieve this goal, a proportional-integral-differential (PID) controller is usually used. This controller can be accurately adjusted according to the current temperature, set temperature and temperature change rate, thereby effectively reducing temperature fluctuations and ensuring the comfort and safety of newborns in the incubator.

[0003] PID controller is a feedback controller widely used in industrial control systems. It aims to adjust the control input so that the system output reaches the set target. It consists of three parts: proportional (P), integral (I) and differential (D). The proportional part is adjusted according to the current error (the difference between the set value and the actual value), the integral part accumulates past errors to eliminate steady-state errors, and the differential part predicts future error changes to improve the response speed of the system. By reasonably adjusting these three parameters, the PID controller can achieve fast and stable system response and is widely used in various control occasions such as temperature, pressure, and flow.

[0004] Sewing Training Optimization Algorithm (STBO) is a new human-based meta-neural algorithm, whose basic inspiration is teaching the sewing process to beginner tailors. It is mathematically modeled in three stages based on theory: training, imitating teacher skills, and practice. The Sewing Training Optimization Algorithm has a high ability to balance search and development. Summary of the invention

[0005] The purpose of the invention is to improve the sewing training optimization algorithm, and use the improved sewing training optimization algorithm to optimize the PID controller in the temperature control system of the infant incubator, so as to improve the temperature control accuracy and response speed of the system, automatically adapt to changes in the external environment, quickly find the optimal control parameters, reduce dependence on manual adjustment, ensure the stability of the temperature in the infant incubator, avoid health risks to the baby caused by excessively high or low temperature, improve the operating efficiency and safety of the equipment, and ultimately ensure the comfort and safety of the baby in the incubator, and improve the reliability and accuracy of medical equipment.

[0006] In order to achieve the above object, the present invention adopts the following technical solution.

[0007] Step 1: construct a baby incubator temperature controller system, which includes: an instruction operation module, a temperature difference calculation module, a PID controller, an improved sewing training optimization algorithm module, a temperature adjustment module, and a temperature sensor module.

[0008] Step 2: Improve the sewing training optimization algorithm. The specific improvements are as follows: D1. A mathematical model of the training phase of the sewing training optimization algorithm is improved using a multi-position adaptive update strategy, wherein the multi-position adaptive update strategy guides the current population individuals through the optimal position and the average position in the population, wherein the average position of the population is adaptively adjusted through the iterative phase of the algorithm, and the current position is adaptively adjusted according to the optimal position and the average position to update the step size, thereby updating the position; D2. A mathematical model of the practice stage of the sewing training optimization algorithm is improved using a dynamic threshold penalty update strategy. The dynamic threshold penalty update strategy dynamically generates a threshold according to the current iteration stage and the fitness value of the individuals in the population. The penalty factor P of the current individual is calculated through the threshold. The penalty factor P affects the quality of the population position update. If the individual position is closer to the optimal position of the current population, its fitness value is rewarded to retain the position. If the individual position deviates from the optimal position in the current population, its fitness value is penalized and the individual position is updated by the penalty factor P.

[0009] Step 3: Use the improved sewing training optimization algorithm to optimize the PID controller in the baby incubator temperature controller system, and obtain the optimal set of control parameters Kp, Ki, and Kd through the improved algorithm optimization.

[0010] Step 4: Use the values ​​of the control parameters Kp, Ki, and Kd optimized in step 3 for the PID controller in the temperature controller system of the infant incubator, and adjust the control amount to achieve the target temperature.

[0011] Preferably, the temperature control system of the infant incubator constructed in the step 1 has a specific execution process as follows: first, the target temperature value is set through the instruction operation module, the target temperature value and the temperature monitored in real time by the temperature sensor are input into the temperature difference calculation module to obtain the temperature difference e(t), the temperature difference e(t) is input into the PID controller, the PID control is optimized by the improved sewing training optimization algorithm module, the control parameters Kp, Ki, Kd are obtained, the control amount u(t) is output according to the optimized control parameters, the temperature adjustment module adjusts the temperature in the incubator according to the control amount u(t), and the mathematical model of the temperature adjustment module is: (1); In formula (1), T represents the temperature in the incubator, C represents the specific heat capacity of the incubator, The heat power of the heater is adjusted by the PID controller, as shown in formula (2). Indicates that the temperature loss is related to the ambient temperature and heat exchange; (2); In formula (2), Kp represents the proportional coefficient, Ki represents the integral coefficient, Kd represents the differential coefficient, I represents the total system operation time, and e(t) represents the real-time temperature difference.

[0012] Preferably, the mathematical model of the training phase of the sewing training optimization algorithm is improved by using a multi-position adaptive update strategy as described in D1, and the improved mathematical model is specifically: (3); In formula (3), X(iter+1) represents the updated individual position, iter represents the current iteration number, X(iter) represents the current individual position, r1 represents a random number between [0,1], Xavg(iter) represents the average position in the population, and the calculation formula is shown in formula (4). Uniform(-1,1) represents the uniform distribution between [-1,1], and Xbest represents the best position in the population. (4); In formula (4), Xavg(iter+1) represents the updated average position, N represents the number of individuals in the population, X(i) represents the position of the i-th individual, a represents the update constant, K(iter) represents the adaptive attenuation coefficient, and the calculation formula is shown in formula (5). Xbest represents the best position in the population, and Xavg(iter) represents the average position under the current number of iterations; (5); In formula (5), K(iter) represents the adaptive attenuation coefficient, k0 represents the initial coefficient, λ represents the adjustment factor, which is a random number between [0.1, 0.5], iter represents the current number of iterations, max_iter represents the maximum number of iterations, and randn(0, 1) represents the random number generated by the standard normal distribution.

[0013] Preferably, by combining the optimal position and average position information, the algorithm can better balance the global search and local search. The adjustment of the adaptive attenuation coefficient and step size enables the algorithm to dynamically adjust the search strategy according to the current iteration stage, improve the convergence speed and accuracy, help the algorithm to jump out of the local optimal solution, and enhance the robustness and stability of the algorithm.

[0014] Preferably, the mathematical model of the practice phase of the sewing training optimization algorithm using a dynamic threshold penalty update strategy described in D2 first generates a dynamic threshold P, and the specific mathematical model is: (6); In formula (6), Thresh(iter) represents the dynamic threshold, t0 represents the initial threshold, iter represents the current number of iterations, max_iter represents the maximum number of iterations, ω represents the threshold change influencing factor, Fbest represents the optimal fitness value under the current number of iterations, and Favg represents the average fitness value under the current number of iterations; The individual penalty factor P is calculated by the value of the threshold Thresh(iter). The specific calculation formula is: (7); In formula (7), μ represents the coefficient factor controlling the penalty intensity, F represents the fitness value of the current individual, and Thresh represents the dynamic threshold, as shown in formula (6); The position of the current individual is updated by the obtained penalty factor P. The specific calculation formula is: (8); In formula (8), X(iter+1) represents the updated individual position, iter represents the current iteration number, X(iter) represents the current individual position, r3 represents a random number between [0,1], lb represents the lower limit of the search space, ub represents the upper limit of the search space, b represents the adjustment factor for controlling the difference in fitness values, F represents the fitness value of the current individual, Fbest represents the optimal fitness value under the current iteration number, and P represents the penalty factor as shown in formula (7); Finally, rewards and punishments are taken for the fitness values ​​of the updated individuals. The specific calculation formula is as follows: (9); In formula (9), F represents the fitness value after individual update, P represents the penalty factor as shown in formula (7), Fbest represents the optimal fitness value under the current number of iterations, and Thresh represents the dynamic threshold as shown in formula (6).

[0015] Preferably, by introducing a dynamic threshold Thresh(iter), the method can adjust the fitness threshold according to the current number of iterations, so as to adaptively balance exploration and development during the optimization process. In the early stage of the algorithm, a larger threshold range provides a larger search space for individuals, encouraging extensive exploration; in the later stage of the optimization process, the threshold gradually decreases, so that the search is more focused on the optimization of local accuracy, avoiding falling into the local optimal solution too early. By calculating the difference between the fitness of each individual and the threshold, combined with a penalty mechanism, the individual fitness value will be punished when it exceeds the threshold. This penalty mechanism effectively reduces the impact of inappropriate individuals on the overall optimization process, and at the same time encourages individuals to gradually move closer to the global optimal solution.

[0016] Preferably, in step three, the PID controller in the temperature controller system of the infant incubator is optimized using an improved sewing training optimization algorithm, and the specific steps are as follows: S1. Initialize the parameters of the improved sewing training optimization algorithm, including the number of individuals in the population N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit ub of the search space, the lower limit lb of the search space, and generate the initial position of the population through the initial parameters; S2. Establish a mapping relationship between the improved sewing training optimization algorithm and the PID controller in the baby incubator temperature controller system, convert the temperature control of the actual system into a mathematical model to be optimized, and correspond the three dimensions of the individual position vector of the algorithm to the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd of the PID controller, that is, X(x1, x2, x3)=(Kp, Ki, Kd). Through the continuous optimization of the algorithm, the individual position vector is continuously updated, thereby dynamically adjusting the PID control parameters, and finally obtaining an optimal set of PID control parameters for the system; S3. Construct the fitness value function of the improved sewing training optimization algorithm, calculate the fitness value of individuals in the population through the fitness value function, select the best individual and the best fitness value, and the specific fitness value function is: (10); In formula (10), J represents the fitness value, L represents the total operation time of the system, and e(t) represents the real-time temperature difference; S4. Update the individual positions through the mathematics of the improved sewing training optimization algorithm. The specific steps are: S41, executing the training phase of the algorithm, the specific mathematical model is shown in formula (3); S42, executing the algorithm's imitation teacher skill phase, generating a collection of imitation variables according to the number of iterations, taking the individual position in the population whose individual fitness value is less than the current individual fitness value as the tutor position to generate a tutor set, each tutor is a three-dimensional vector, each dimension represents a skill, and the updated individual is to select three variables from the imitation variable collection and combine them with the dimensions in the corresponding tutor set to generate a new position as the updated position; S43, the practice phase of executing the algorithm, the specific mathematical model is shown in formula (6) to formula (8); S5. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to search for the optimal solution. If so, output the optimal individual vector in the population as the optimal parameter.

[0017] By adopting the above technical scheme, the beneficial effects of the present invention are as follows: by improving the sewing training optimization algorithm, the adaptability of the algorithm in the optimization process is enhanced, the optimization capabilities of the algorithm in the exploration and development stages are balanced, the convergence speed and optimization accuracy of the algorithm are improved, and better robustness is shown when dealing with complex problems. The improved sewing training optimization algorithm is applied to the PID controller of the temperature control system of an infant incubator, which can improve the control accuracy of the PID controller, thereby improving the control ability and performance of the temperature control system of an infant incubator. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a method for optimizing a temperature controller of an infant incubator.

[0019] Figure 2 This is a model diagram of the temperature control system of an infant incubator.

[0020] Figure 3 A comparison chart of the changes in fitness values ​​during the optimization process of the improved sewing training optimization algorithm and the standard sewing training optimization algorithm.

[0021] Figure 4 Comparison chart of the response of the PID controller optimized for the improved sewing training optimization algorithm and the standard sewing training optimization algorithm. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0023] The present invention provides a technical solution: a method for optimizing a temperature controller of an infant incubator, which specifically comprises the following steps: Figure 1 shown.

[0024] Step 1: Construct a temperature controller system for an infant incubator, the system comprising: an instruction operation module, a temperature difference calculation module, a PID controller, an improved sewing training optimization algorithm module, a temperature adjustment module, and a temperature sensor module. Figure 2 shown.

[0025] Furthermore, the baby incubator temperature control system constructed in the step 1 has a specific execution process as follows: first, the target temperature value is set through the instruction operation module, the target temperature value and the temperature monitored in real time by the temperature sensor are input into the temperature difference calculation module to obtain the temperature difference e(t), the temperature difference e(t) is input into the PID controller, the PID control is optimized by the improved sewing training optimization algorithm module, the control parameters Kp, Ki, Kd are obtained, the control amount u(t) is output according to the optimized control parameters, the temperature adjustment module adjusts the temperature in the incubator according to the control amount u(t), and the mathematical model of the temperature adjustment module is: (1); In formula (1), T represents the temperature in the incubator, C represents the specific heat capacity of the incubator, The heat power of the heater is adjusted by the PID controller, as shown in formula (2). Indicates that the temperature loss is related to the ambient temperature and heat exchange; (2); In formula (2), Kp represents the proportional coefficient, Ki represents the integral coefficient, Kd represents the differential coefficient, I represents the total system operation time, and e(t) represents the real-time temperature difference.

[0026] Step 2: Improve the sewing training optimization algorithm. The specific improvements are as follows: D1. A mathematical model of the training phase of the sewing training optimization algorithm is improved using a multi-position adaptive update strategy, wherein the multi-position adaptive update strategy guides the current population individuals through the optimal position and the average position in the population, wherein the average position of the population is adaptively adjusted through the iterative phase of the algorithm, and the current position is adaptively adjusted according to the optimal position and the average position to update the step size, thereby updating the position; D2. A mathematical model of the practice stage of the sewing training optimization algorithm is improved using a dynamic threshold penalty update strategy. The dynamic threshold penalty update strategy dynamically generates a threshold according to the current iteration stage and the fitness value of the individuals in the population. The penalty factor P of the current individual is calculated through the threshold. The penalty factor P affects the quality of the population position update. If the individual position is closer to the optimal position of the current population, its fitness value is rewarded to retain the position. If the individual position deviates from the optimal position in the current population, its fitness value is penalized and the individual position is updated by the penalty factor P.

[0027] Furthermore, the mathematical model of the training phase of the sewing training optimization algorithm is improved by using a multi-position adaptive update strategy as described in D1. The improved mathematical model is specifically: (3); In formula (3), X(iter+1) represents the updated individual position, iter represents the current iteration number, X(iter) represents the current individual position, r1 represents a random number between [0,1], Xavg(iter) represents the average position in the population, and the calculation formula is shown in formula (4). Uniform(-1,1) represents the uniform distribution between [-1,1], and Xbest represents the best position in the population. (4); In formula (4), Xavg(iter+1) represents the updated average position, N represents the number of individuals in the population, X(i) represents the position of the i-th individual, a represents the update constant, K(iter) represents the adaptive attenuation coefficient, and the calculation formula is shown in formula (5). Xbest represents the best position in the population, and Xavg(iter) represents the average position under the current number of iterations; (5); In formula (5), K(iter) represents the adaptive attenuation coefficient, k0 represents the initial coefficient, λ represents the adjustment factor, which is a random number between [0.1, 0.5], iter represents the current number of iterations, max_iter represents the maximum number of iterations, and randn(0, 1) represents the random number generated by the standard normal distribution.

[0028] Further, the mathematical model of the practice phase of the sewing training optimization algorithm using a dynamic threshold penalty update strategy described in D2 first generates a dynamic threshold P, and the specific mathematical model is: (6); In formula (6), Thresh(iter) represents the dynamic threshold, t0 represents the initial threshold, iter represents the current number of iterations, max_iter represents the maximum number of iterations, ω represents the threshold change influencing factor, Fbest represents the optimal fitness value under the current number of iterations, and Favg represents the average fitness value under the current number of iterations; The individual penalty factor P is calculated by the value of the threshold Thresh(iter). The specific calculation formula is: (7); In formula (7), μ represents the coefficient factor controlling the penalty intensity, F represents the fitness value of the current individual, and Thresh represents the dynamic threshold, as shown in formula (6); The position of the current individual is updated by the obtained penalty factor P. The specific calculation formula is: (8); In formula (8), X(iter+1) represents the updated individual position, iter represents the current iteration number, X(iter) represents the current individual position, r3 represents a random number between [0,1], lb represents the lower limit of the search space, ub represents the upper limit of the search space, b represents the adjustment factor for controlling the difference in fitness values, F represents the fitness value of the current individual, Fbest represents the optimal fitness value under the current iteration number, and P represents the penalty factor as shown in formula (7); Finally, rewards and punishments are taken for the fitness values ​​of the updated individuals. The specific calculation formula is as follows: (9); In formula (9), F represents the fitness value after individual update, P represents the penalty factor as shown in formula (7), Fbest represents the optimal fitness value under the current number of iterations, and Thresh represents the dynamic threshold as shown in formula (6).

[0029] Step 3: Use the improved sewing training optimization algorithm to optimize the PID controller in the baby incubator temperature controller system, and obtain the optimal set of control parameters Kp, Ki, and Kd through the improved algorithm optimization.

[0030] Furthermore, in the step three, the PID controller in the temperature controller system of the infant incubator is optimized using the improved sewing training optimization algorithm, and the specific steps are as follows: S1. Initialize the parameters of the improved sewing training optimization algorithm, including the number of individuals in the population N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit ub of the search space, the lower limit lb of the search space, and generate the initial position of the population through the initial parameters; S2. Establish a mapping relationship between the improved sewing training optimization algorithm and the PID controller in the baby incubator temperature controller system, convert the temperature control of the actual system into a mathematical model to be optimized, and correspond the three dimensions of the individual position vector of the algorithm to the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd of the PID controller, that is, X(x1, x2, x3)=(Kp, Ki, Kd). Through the continuous optimization of the algorithm, the individual position vector is continuously updated, thereby dynamically adjusting the PID control parameters, and finally obtaining an optimal set of PID control parameters for the system; S3. Construct the fitness value function of the improved sewing training optimization algorithm, calculate the fitness value of individuals in the population through the fitness value function, select the best individual and the best fitness value, and the specific fitness value function is: (10); In formula (10), J represents the fitness value, L represents the total operation time of the system, and e(t) represents the real-time temperature difference; S4. Update the individual positions through the mathematics of the improved sewing training optimization algorithm. The specific steps are: S41, executing the training phase of the algorithm, the specific mathematical model is shown in formula (3); S42, executing the algorithm's imitation teacher skill phase, generating a collection of imitation variables according to the number of iterations, taking the individual position in the population whose individual fitness value is less than the current individual fitness value as the tutor position to generate a tutor set, each tutor is a three-dimensional vector, each dimension represents a skill, and the updated individual is to select three variables from the imitation variable collection and combine them with the dimensions in the corresponding tutor set to generate a new position as the updated position; S43, the practice phase of executing the algorithm, the specific mathematical model is shown in formula (6) to formula (8); S5. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to search for the optimal solution. If so, output the optimal individual vector in the population as the optimal parameter.

[0031] Step 4: Use the values ​​of the control parameters Kp, Ki, and Kd optimized in step 3 for the PID controller in the temperature controller system of the infant incubator, and adjust the control amount to achieve the target temperature.

[0032] Furthermore, the mathematical model of the sewing training optimization algorithm was improved through Matlab, the experimental code design and writing were completed, the algorithm parameters were initialized, the maximum number of iterations was 50, the problem dimension was 3, the population size was 100, and the search space range was [200, 0.001]. The simulation model of the infant incubator temperature controller system was constructed through the simulation software Simulink, including the instruction operation module, the temperature difference calculation module, the PID controller, the improved sewing training optimization algorithm module, the temperature adjustment module, and the temperature sensor module. The improved sewing training optimization algorithm model was connected with the Matlab code. Through the optimization of the algorithm mathematical model, the obtained parameters were input into Simulink to complete the control of the system. The mathematical model of the temperature adjustment module was Laplace transformed, and the transformed function was used as the controlled function of the model to simulate the actual operation state of the system. The controlled function is: , s is a complex variable, run the experimental code and model, the experimental results are as follows Figure 3-Figure 4 shown.

[0033] Furthermore, Figure 3 This is a comparison chart of the changes in fitness values ​​during the optimization process of the improved sewing training optimization algorithm and the standard sewing training optimization algorithm. From the figure, it can be seen that the improved algorithm has higher optimization accuracy and the obtained individual fitness value is lower. Figure 4 The response comparison chart of the PID controller optimized by the improved sewing training optimization algorithm and the standard sewing training optimization algorithm is shown. The target value is set to 1 unit. From the figure, it can be seen that the overshoot is smaller and the stability is better when the PID controller is optimized by the improved algorithm. The response curve of the PID controller optimized by the improved algorithm reaches the stable state of the target value at a faster speed.

Claims

1. A method for optimizing a temperature controller of an infant incubator, characterized in that: The specific steps are as follows: Step 1: construct a baby incubator temperature controller system, the system comprising: an instruction operation module, a temperature difference calculation module, a PID controller, an improved sewing training optimization algorithm module, a temperature adjustment module, and a temperature sensor module; Step 2: Improve the sewing training optimization algorithm. The specific improvements are as follows: D1. A mathematical model of the training phase of the sewing training optimization algorithm is improved using a multi-position adaptive update strategy, wherein the multi-position adaptive update strategy guides the current population individuals through the optimal position and the average position in the population, wherein the average position of the population is adaptively adjusted through the iterative phase of the algorithm, and the current position is adaptively adjusted according to the optimal position and the average position to update the step size, thereby updating the position; D2. A mathematical model of the practice phase of the sewing training optimization algorithm is improved by using a dynamic threshold penalty update strategy. The dynamic threshold penalty update strategy dynamically generates a threshold according to the current iteration phase and the fitness value of the individual in the population. The penalty factor P of the current individual is calculated by the threshold. The penalty factor P affects the quality of the population position update. If the individual position is closer to the optimal position of the current population, its fitness value is rewarded to retain the position. If the individual position deviates from the optimal position in the current population, its fitness value is penalized and the individual position is updated by the penalty factor P. Step 3: Optimize the PID controller in the temperature controller system of the baby incubator using the improved sewing training optimization algorithm, and obtain the optimal set of control parameters Kp, Ki, and Kd through the improved algorithm optimization; Step 4: Use the values ​​of the control parameters Kp, Ki, and Kd optimized in step 3 for the PID controller in the temperature controller system of the infant incubator, and adjust the control amount to achieve the target temperature.

2. The method for optimizing the temperature controller of an infant incubator according to claim 1, characterized in that: The baby incubator temperature control system constructed in the step 1 has the following specific execution process: first, the target temperature value is set through the instruction operation module, the target temperature value and the temperature monitored in real time by the temperature sensor are input into the temperature difference calculation module to obtain the temperature difference e(t), the temperature difference e(t) is input into the PID controller, and the PID control is optimized by improving the sewing training optimization algorithm module to obtain the control parameters Kp, Ki, and Kd.

3. The method for optimizing the temperature controller of an infant incubator according to claim 2, characterized in that: In D1, a multi-position adaptive update strategy is used to improve the mathematical model of the training phase of the sewing training optimization algorithm. The improved mathematical model is specifically: (3)； In formula (3), X(iter+1) represents the updated individual position, iter represents the current iteration number, X(iter) represents the current individual position, r1 represents a random number between [0,1], Xavg(iter) represents the average position in the population, and the calculation formula is shown in formula (4). Uniform(-1,1) represents the uniform distribution between [-1,1], and Xbest represents the best position in the population. (4)； In formula (4), Xavg(iter+1) represents the updated average position, N represents the number of individuals in the population, X(i) represents the position of the i-th individual, a represents the update constant, K(iter) represents the adaptive attenuation coefficient, and the calculation formula is shown in formula (5). Xbest represents the best position in the population, and Xavg(iter) represents the average position under the current number of iterations; (5)； In formula (5), K(iter) represents the adaptive attenuation coefficient, k0 represents the initial coefficient, λ represents the adjustment factor, which is a random number between [0.1, 0.5], iter represents the current number of iterations, max_iter represents the maximum number of iterations, and randn(0, 1) represents the random number generated by the standard normal distribution.

4. The method for optimizing the temperature controller of an infant incubator according to claim 3, characterized in that: In D2, a dynamic threshold penalty update strategy is used to improve the mathematical model of the practice phase of the sewing training optimization algorithm. First, a dynamic threshold P is generated. The specific mathematical model is: (6)； In formula (6), Thresh(iter) represents the dynamic threshold, t0 represents the initial threshold, iter represents the current number of iterations, max_iter represents the maximum number of iterations, ω represents the threshold change influencing factor, Fbest represents the optimal fitness value under the current number of iterations, and Favg represents the average fitness value under the current number of iterations; The individual penalty factor P is calculated by the value of the threshold Thresh(iter). The specific calculation formula is: (7)； In formula (7), μ represents the coefficient factor controlling the penalty intensity, F represents the fitness value of the current individual, and Thresh represents the dynamic threshold, as shown in formula (6); The position of the current individual is updated by the obtained penalty factor P. The specific calculation formula is: (8)； In formula (8), X(iter+1) represents the updated individual position, iter represents the current iteration number, X(iter) represents the current individual position, r3 represents a random number between [0,1], lb represents the lower limit of the search space, ub represents the upper limit of the search space, b represents the adjustment factor for controlling the difference in fitness values, F represents the fitness value of the current individual, Fbest represents the optimal fitness value under the current iteration number, and P represents the penalty factor as shown in formula (7); Finally, rewards and punishments are taken for the fitness values ​​of the updated individuals. The specific calculation formula is as follows: (9)； In formula (9), F represents the fitness value after individual update, P represents the penalty factor as shown in formula (7), Fbest represents the optimal fitness value under the current number of iterations, and Thresh represents the dynamic threshold as shown in formula (6).

5. The method for optimizing the temperature controller of an infant incubator according to claim 4, characterized in that: In the step 3, the PID controller in the temperature controller system of the infant incubator is optimized using the improved sewing training optimization algorithm, and the specific steps are as follows: S1. Initialize the parameters of the improved sewing training optimization algorithm, including the number of individuals in the population N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit ub of the search space, the lower limit lb of the search space, and generate the initial position of the population through the initial parameters; S2. Establish a mapping relationship between the improved sewing training optimization algorithm and the PID controller in the baby incubator temperature controller system, convert the temperature control of the actual system into a mathematical model to be optimized, and correspond the three dimensions of the individual position vector of the algorithm to the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd of the PID controller, that is, X(x1, x2, x3)=(Kp, Ki, Kd). Through the continuous optimization of the algorithm, the individual position vector is continuously updated, thereby dynamically adjusting the PID control parameters, and finally obtaining an optimal set of PID control parameters for the system; S3. Construct the fitness value function of the improved sewing training optimization algorithm, calculate the fitness value of individuals in the population through the fitness value function, select the best individual and the best fitness value, and the specific fitness value function is: (10)； In formula (10), J represents the fitness value, L represents the total operation time of the system, and e(t) represents the real-time temperature difference; S4. Update the individual positions through the mathematics of the improved sewing training optimization algorithm. The specific steps are: S41, executing the training phase of the algorithm, the specific mathematical model is shown in formula (3); S42, executing the algorithm's imitation teacher skill phase, generating a collection of imitation variables according to the number of iterations, taking the individual position in the population whose individual fitness value is less than the current individual fitness value as the tutor position to generate a tutor set, each tutor is a three-dimensional vector, each dimension represents a skill, and the updated individual is to select three variables from the imitation variable collection and combine them with the dimensions in the corresponding tutor set to generate a new position as the updated position; S43, the practice phase of executing the algorithm, the specific mathematical model is shown in formula (6) to formula (8); S5. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to search for the optimal solution. If so, output the optimal individual vector in the population as the optimal parameter.

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