A method for controlling the temperature of a heat preservation and semen collection environment

Abstract

The present invention discloses a method for controlling the temperature of a sperm preservation environment, belonging to the technical field of LADRC optimization. A simulation model is established for the temperature control system of the sperm preservation environment, including an LADRC controller model, a performance evaluation index model, and a sperm preservation temperature regulation model. A mathematical model of an improved sperm swarm optimization algorithm (ISSO) is established, and a non-linear inertia weight ω and a sperm empowerment mechanism are introduced to improve the sperm swarm optimization algorithm. The ISSO algorithm is used to optimize the first-order LADRC controller, and during the operation of the temperature control system in the sperm preservation environment, the K p and b 0, ω0 parameters of the LADRC controller are automatically optimized, improving the performance of the LADRC controller, keeping the temperature of the sperm preservation environment stable, ensuring that the sperm maintains a suitable environment during storage, and improving the storage quality and fertility success rate.

Description

Technical Field

[0001] The invention belongs to the technical field of LADRC control optimization, and in particular relates to a method for controlling the temperature of a heat-insulating and sperm-collecting environment. Background Art

[0002] With the development of reproductive technology, especially in the fields of artificial insemination, in vitro fertilization, and frozen sperm banks, sperm preservation technology has become an important part of assisted reproduction. Sperm preservation is not only helpful for fertility treatment, but can also be used for gene preservation, genetic research, and improving the reproductive efficiency of genetic species.

[0003] During the storage process, sperm is very sensitive to the external temperature. Slight temperature fluctuations may lead to a decline in sperm quality or even death. Therefore, temperature control plays a vital role in the sperm storage environment. PID control methods are widely used in general temperature control, but in the temperature control of the heat preservation and sperm collection environment, PID control methods cannot provide sufficiently accurate control, resulting in unstable system response, large overshoot or excessive steady-state error.

[0004] LADRC (Linear Active Disturbance Rejection Control) is a linear anti-disturbance control technology in Chinese. It can overcome many defects of traditional PID control methods and is more suitable for temperature control of the semen collection environment with high precision and high dynamic performance requirements. The sperm swarm optimization algorithm (SSO) is a new meta-heuristic optimization algorithm inspired by the sperm fertilization process. The algorithm simulates the behavior of sperm in the process of swimming through the cervix to the egg and has strong global search ability and robustness. Combining the improved SSO algorithm and LADRC controller can effectively improve the accuracy and robustness of the temperature control of the semen collection environment. Summary of the invention

[0005] The object of the present invention is to provide a method for controlling the temperature of a heat preservation and sperm collection environment, by optimizing the LADRC controller through an improved sperm swarm optimization algorithm (ISSO), and automatically optimizing the K of the LADRC controller during the operation of the temperature control system in the heat preservation and sperm collection environment. p , b0, ω0 parameters are adjusted to improve the anti-disturbance, self-adaptation and precise temperature control capabilities of the LADRC controller, so that the temperature of the thermal insulation and sperm collection environment can maintain temperature stability without the need for frequent manual adjustment; the control accuracy of the thermal insulation and sperm collection environment temperature control system is greatly enhanced, ensuring that sperm maintains a suitable environment during storage, ultimately improving storage quality and fertility success rate.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for controlling the temperature of a heat preservation and sperm collection environment, comprising: an improved sperm swarm optimization algorithm (ISSO) and a first-order LADRC control algorithm, and the specific steps are:

[0007] S1. Establish a simulation model for the temperature control system of the heat preservation and collection environment, wherein the simulation model includes a LADRC controller model, a performance evaluation index model and a heat preservation and collection temperature adjustment model.

[0008] S2. In the simulation model, a performance evaluation index model is established, and the performance evaluation index of the control system is used as the objective function of the ISSO algorithm.

[0009] S3. In the simulation model, in order to simulate the actual situation of the temperature regulation of the indoor environment of the thermal insulation and precision collection, a thermal insulation and precision collection temperature regulation model is established.

[0010] S4, introduce nonlinear inertia weight and sperm empowerment mechanism to improve sperm group optimization algorithm, the specific improvements are: Sa1, introduce nonlinear factors to improve the speed formula of sperm update;

[0011] Sa2. Introduce sperm empowerment mechanism to improve the position update formula of the algorithm.

[0012] S5. A mathematical model is established for the improved sperm swarm optimization algorithm (ISSO). By simulating the movement behavior of sperm in the female reproductive system, the algorithm is iteratively updated in the search space, continuously searching for individual solutions and gradually approaching the optimal solution. After the iteration is terminated, the individual optimal solution is decoded into a control parameter sequence of the LADRC controller, thereby obtaining the optimal ISSO-LADRC control method.

[0013] S6. The ISSO-LADRC method is used to realize closed-loop control of the temperature control of the thermal insulation environment, which can effectively improve the control accuracy and robustness of the flow LADRC controller.

[0014] Furthermore, in S1, a simulation model is established for the temperature control system of the heat preservation and collection environment, and the simulation model includes a LADRC controller model, a performance evaluation index model and a heat preservation and collection temperature adjustment model. In order to improve the accuracy and robustness of the temperature control of the heat preservation and collection environment, the first-order LADRC controller model established includes a linear extended observer (LESO), a linear state error feedback controller (LSEF) and a linear tracking differentiator (LTD). The mathematical model of LESO is:

[0015]

[0016] In formula (1), z1 is the estimated value of the system state, that is, the estimated value of temperature, represents the rate of change of the estimated value of the system state, z2 is the estimated value of the total disturbance, represents the rate of change of the disturbance estimate, β1 and β2 are the observer gains, b0 is the control gain of the system, y is the actual temperature output value of the system, and i is the output control quantity of the first-order LADRC. The mathematical model of β1 and β2 is:

[0017]

[0018] In formula (2), ω0 is the controller bandwidth.

[0019] Furthermore, the mathematical model of LSEF is established based on the state error and disturbance estimation value:

[0020]

[0021] In formula (3), T ref is the target temperature, k p is the controller gain, and b0, z1 and z2 have the same meanings as above.

[0022] Furthermore, LTD is used to generate a smooth signal of the reference input and its differential signal to avoid sudden changes in the reference signal. The mathematical model of LTD is:

[0023]

[0024] In formula (4), v1 is the smoothed signal of the reference input, represents the rate of change of the reference input smooth signal, v2 is the differential signal of the reference input, It represents the rate of change of the reference input differential signal, R is the adjustment parameter of LTD, which determines the tracking speed, T ref is the target temperature.

[0025] Furthermore, in the S2, in the simulation model, a performance evaluation index model is established, and the performance evaluation index of the control system is used as the objective function of the ISSO algorithm. In the temperature control system of the heat preservation and sperm collection environment, in order to ensure the activity of sperm, it is necessary to quickly reach the target temperature, and ITSE is selected as the performance evaluation index model. The mathematical model of ITSE is:

[0026] In formula (5), ITSE is the quantified performance index, e(t) is the tracking error generated by LTD, t is the actual running time of the control system, and T is the total running time of the system.

[0027] Furthermore, in S3, in order to simulate the actual situation of the indoor environment temperature regulation of the heat preservation and collection, a first-order inertia plus pure lag system is established to describe the dynamic characteristics of the heat preservation and collection temperature regulation model, and its mathematical model is:

[0028]

[0029] In formula (6), K is the system gain, which represents the heating or cooling capacity of the temperature control device, τ is the system time constant, which represents the inertia of temperature change, θ is the system lag time, which represents the response lag of the temperature control device, and s is a complex frequency domain variable.

[0030] Furthermore, the inertia characteristics and hysteresis characteristics in the heat preservation and precision temperature regulation model are separated, and the mathematical model of the first-order transfer function is established as follows:

[0031]

[0032] In formula (7), the parameters have the same meaning as above.

[0033] Furthermore, in S4, the sperm group optimization algorithm is improved, and the specific improvement steps are:

[0034] Sa1. Dynamic weights are introduced to improve the mathematical model for sperm velocity update. The improved mathematical model for sperm velocity update is:

[0035] In formula (8), ω is the dynamic weight, v init is the initial sperm velocity, x current_best is the current position of the best sperm individual, x gloabal_best is the global optimal sperm individual position.

[0036] Furthermore, the mathematical model of ω is:

[0037]

[0038] In formula (9), ω is the dynamic weight, α is the weight scaling factor, and Gaussian is the Gaussian function used to adjust the weight according to the current iteration number iter. is the fitness of the position of the i-th sperm individual in the current iteration, f best is the minimum fitness in the current iteration, σ f is a parameter that controls the speed of weight decay, μ is half of the total number of iterations, and σ is a parameter that controls the range of weight adjustment.

[0039] Sa2. Introduce sperm empowerment mechanism to improve the position update formula of the algorithm, so that some sperm individuals can obtain stronger adaptability, thereby improving the algorithm's global search ability and the ability to jump out of local optimality. When the algorithm is in two adjacent iterations, the minimum fitness f in the current iteration best When the error does not exceed 1%, the sperm empowerment mechanism is introduced to empower some individuals with smaller fitness. The empowered individuals have stronger search capabilities. The mathematical model of the sperm empowerment mechanism is:

[0040] In formula (10), is the location of the i-th sperm individual in the current iteration, i = 1, ..., N, N is the population size of the algorithm, iter is the current iteration number, is the updated position of the i-th sperm individual, is the velocity of the i-th sperm individual in the current iteration, is the fitness of the i-th sperm individual in the current iteration, f prc is the fitness threshold for sperm empowerment in the current iteration, x gbest is the location of the sperm individual with the smallest fitness in the total number of iterations, δ is the control mutation intensity factor, U(-1, 1) is a uniform random perturbation, x mean is the average position of all sperm individuals in the population, x mean The mathematical model is:

[0041] x mean =(x worst -x best ) (11);

[0042] In formula (11), x worst is the location of the individual with the highest fitness in the current iteration, x best is the location of the individual with the smallest fitness in the current iteration.

[0043] Furthermore, in said S5, a mathematical model is established for the improved sperm swarm optimization algorithm (ISSO), and the specific steps are: Sb1, initializing the upper bound and lower bound [ub, lb] of the individual solution, initializing the total number of iterations MaxIter and the population size N of the improved sperm swarm optimization algorithm (ISSO), and initializing the dimension dim of the individual solution, that is, the number of parameters to be optimized of the first-order LADRC controller;

[0044] Sb2. Initialize the positions of all sperm individuals. The mathematical model for initializing the positions of sperm individuals is:

[0045] x init =lb+r×(ub-lb) (12);

[0046] In formula (12), x init is the initial position of the randomly generated sperm individual, r is a random number between [0, 1], ub and lb are the upper and lower bounds of the individual solution;

[0047] Sb3, calculate the fitness value of each sperm through the objective function, sort the fitness of all individuals, and select the minimum fitness as f best , the individual position with the minimum fitness is x best , the maximum fitness is f worst , the individual position with the maximum fitness is x worst ;

[0048] Sb4. Calculate the initial velocity of individual sperm. The mathematical model of the initial velocity of sperm is:

[0049] v init =D×v0×log 10 (pH_R1) (13);

[0050] In formula (13), D is a random number with a value between [0, 1], representing the velocity damping factor, v0 is the initial sperm velocity parameter, and pH_R1 is a random number with a value between [7, 14], representing the pH value;

[0051] Sb5. Calculate the current best individual sperm position of the sperm population. The mathematical model for calculating the current best individual sperm position is:

[0052]

[0053] In formula (14), x current_best is the updated individual optimal position, pH_R2 is a random number between [7, 14], representing the pH value, Temp_R1 is a random number between [35.1, 38.5], x best is the individual position with the minimum fitness in the current iteration, is the position of the i-th sperm individual in the current iteration;

[0054] Sb6. Calculate the global optimal sperm individual position of the sperm population. The mathematical model for calculating the global optimal sperm individual position is:

[0055]

[0056] In formula (15), x global_best is the updated global optimal sperm individual position, pH_R3 is a random number between [7, 14], representing the pH value, Temp_R2 is a random number between [35.1, 38.5], x gbest is the individual position with the minimum fitness in the total iteration, is the position of the i-th sperm individual in the current iteration;

[0057] Sb7, updating the sperm velocity through the improved sperm velocity updating mathematical model, the improved sperm velocity updating mathematical model is the same as step Ss1;

[0058] Sb8. Divide the population into three parts and perform different mutation operations on each part, including: non-uniform mutation, uniform mutation and no mutation;

[0059] Sb9. Perform non-uniform mutation operation on the population in the first part. The mathematical model of non-uniform mutation is:

[0060]

[0061] In formula (16), r1 is a random number between [0, 1], iter is the current iteration number, and MaxIter is the total iteration number;

[0062] Sb10. Perform uniform mutation operation on the population in the second part. The mathematical model of uniform mutation is:

[0063]

[0064] In formula (17), r2 is a random number with a value between [0, 1], and the other parameters have the same meanings as above;

[0065] Sb11, check whether the positions of all individuals in the population are out of bounds. If so, assign the upper and lower bounds [ub, lb] to the individual positions;

[0066] Sb12, check whether the current number of iterations iter is greater than the total number of iterations MaxIter. If so, the individual position with the lowest fitness is output as the optimal solution, and the optimal solution is decoded into the optimal controller parameter sequence of the LADRC controller. If not, return to step Sb3 to continue iterative optimization.

[0067] The present invention proposes a method for controlling the temperature of a heat preservation and sperm collection environment, which automatically optimizes the b0 and k of the LADRC controller during the operation of the temperature control system in the heat preservation and sperm collection environment by improving the sperm swarm optimization algorithm (ISSO). p , ω0 parameters, compared with the prior art, the beneficial effects of the present invention are:

[0068] M1. By introducing nonlinear inertia weight and sperm empowerment mechanism, the algorithm has a larger search range in the early stage and can quickly explore the global solution space; while in the later stage, the search range is gradually reduced to improve the local search accuracy and avoid falling into the local optimum;

[0069] M2. By improving the sperm group optimization algorithm to optimize the first-order LADRC controller in the heat preservation and sperm collection environment, the improved SSO algorithm can more accurately optimize the control parameters of the LADRC, so that the LADRC has better dynamic response and anti-interference capabilities, improves the control accuracy of the LADRC, and enables the LADRC to maintain a high control performance in the complex environment of heat preservation and sperm collection;

[0070] M3. The optimized LADRC controller can control the temperature of the heat preservation and sperm collection environment more accurately, reduce the impact of temperature fluctuations on the heat preservation and sperm collection environment, and ensure the survival rate and quality of sperm. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The overall technical optimization framework diagram for the temperature control of the thermal insulation environment.

[0072] Figure 2 Flowchart for optimizing the parameters of the first-order LADRC controller for an improved sperm swarm optimization algorithm.

[0073] Figure 3 Comparison of evolution curves for optimizing the first-order LADRC controller using the improved sperm swarm optimization algorithm and the common sperm swarm optimization algorithm.

[0074] Figure 4 Evolutionary graph of the best control parameters for optimizing the first-order LADRC controller using the improved sperm swarm optimization algorithm.

[0075] Figure 5 Comparison chart of the responses of the first-order LADRC controller optimized by the improved sperm swarm optimization algorithm and the ordinary sperm swarm optimization algorithm. DETAILED DESCRIPTION

[0076] The following will further illustrate the content of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention, but it should not be understood as limiting the present invention. Without departing from the spirit and essence of the present invention, modifications or replacements made to the method, steps or conditions of the present invention all belong to the protection scope of the present invention.

[0077] See also Figure 1-Figure 5 The present invention provides a method for controlling the temperature of a heat preservation and sperm collection environment. The method improves the sperm swarm optimization algorithm (ISSO) by introducing nonlinear inertia weight and sperm empowerment mechanism, and optimizes the LADRC controller parameters in the temperature control system of the heat preservation and sperm collection environment by using the improved sperm swarm optimization algorithm, thereby enhancing the control accuracy and stability of the temperature control system of the heat preservation and sperm collection environment. The implementation process includes two parts: ISSO algorithm modeling and temperature control system simulation. Figure 1 As shown, the specific steps are S1 to S5.

[0078] S1. Establish a simulation model for the temperature control system of the heat preservation and collection environment through Simulink. The simulation model includes a LADRC controller model, a performance evaluation index model and a heat preservation and collection temperature adjustment model. The specific steps of establishing the LADRC controller model are as follows:

[0079] Sf1. Establish the linear extended observer (LESO) of the first-order LADRC controller model. The mathematical model of LESO is:

[0080]

[0081] In formula (1), z1 is the estimated value of the system state, that is, the estimated value of temperature, represents the rate of change of the estimated value of the system state, z2 is the estimated value of the total disturbance, represents the rate of change of the disturbance estimate, β1 and β2 are observer gains, b0 is the control gain of the system, y is the actual temperature output value of the system, and u is the output control quantity of the first-order LADRC. The mathematical models of β1 and β2 are:

[0082]

[0083] In formula (2), ω0 is the controller bandwidth;

[0084] Sf2. Establish a linear state error feedback controller (LSEF) of the first-order LADRC controller model. The mathematical model of LSEF is:

[0085]

[0086] In formula (3), T ref is the target temperature, k p is the controller gain, b0, z1 and z2 have the same meaning as above;

[0087] Sf3. Establish the linear tracking differentiator (LTD) of the first-order LADRC controller model. The mathematical model of LTD is:

[0088]

[0089] In formula (4), v1 is the smoothed signal of the reference input, represents the rate of change of the reference input smooth signal, v2 is the differential signal of the reference input, It represents the rate of change of the reference input differential signal. R is the adjustment parameter of LTD, which determines the tracking speed. It is set to 5. T ref is the target temperature.

[0090] S2. In the simulation model, a performance evaluation index model is established, and the performance evaluation index of the control system is used as the objective function of the ISSO algorithm. ITSE is selected as the performance evaluation index model. The mathematical model of ITSE is:

[0091]

[0092] In formula (5), ITSE is the quantified performance index, e(t) is the tracking error generated by LTD, t is the actual running time of the control system, and T is the total running time of the system.

[0093] S3. In the simulation model, in order to simulate the actual situation of the indoor temperature regulation of the thermal insulation collection, a first-order inertia plus pure lag system is established to describe the dynamic characteristics of the thermal insulation collection temperature regulation model. Its mathematical model is:

[0094]

[0095] In formula (6), K is the system gain, which represents the heating or cooling capacity of the temperature control device, τ is the system time constant, which represents the inertia of temperature change, θ is the system lag time, which represents the response lag of the temperature control device, which is set to 2 sampling times, and S is a complex frequency domain variable;

[0096] Furthermore, the inertia characteristics and hysteresis characteristics in the heat preservation temperature regulation model are separated, K is set to 1.5, τ is set to 10, and the mathematical model of the first-order transfer function is established as follows:

[0097]

[0098] In formula (7), the parameters have the same meaning as above.

[0099] S4. Introducing nonlinear inertia weight and sperm empowerment mechanism to improve sperm swarm optimization algorithm. The specific improvement steps are as follows:

[0100] Sa1. Dynamic weights are introduced to improve the mathematical model for sperm velocity update. The improved mathematical model for sperm velocity update is:

[0101]

[0102] In formula (8), ω is the dynamic weight, v init is the initial sperm velocity, x current_best is the current best individual sperm position in the current iteration, x gloabal_best is the position of the global best sperm individual;

[0103] Furthermore, the mathematical model of ω is:

[0104]

[0105] In formula (9), ω is the dynamic weight, α is the weight scaling factor, which is set to 1, and Gaussian is the Gaussian function used to adjust the weight according to the current iteration number iter. is the fitness of the position of the i-th sperm individual in the current iteration, f best is the minimum fitness in the current iteration, σ f is the parameter that controls the weight decay speed, which is set to 5, μ is half of the total number of iterations, and σ is the parameter that controls the weight adjustment range, which is set to one-quarter of the total number of iterations.

[0106] Sa2. The position update formula of the improved algorithm of sperm empowerment mechanism is introduced. The mathematical model of sperm empowerment mechanism is:

[0107] In formula (10), is the location of the i-th sperm individual in the current iteration, i = 1, ..., N, N is the population size of the algorithm, iter is the current iteration number, is the updated position of the i-th sperm individual, is the velocity of the i-th sperm individual in the current iteration, is the fitness of the i-th sperm individual in the current iteration, f pre is the fitness threshold for sperm empowerment in the current iteration, x gbest is the position of the sperm individual with the smallest fitness in the total number of iterations, δ is the control mutation intensity factor, which is set to 0.03, U(-1, 1) is a uniform random perturbation with a value between [-1, 1], and x mean is the average position of all sperm individuals in the population, x mean The mathematical model is:

[0108] x mean =(x worst -x best ) (11);

[0109] In formula (11), x worst is the location of the individual with the highest fitness in the current iteration, x best is the location of the individual with the smallest fitness in the current iteration.

[0110] S5. Establish a mathematical model for the improved sperm swarm optimization algorithm (ISSO), the specific steps are:

[0111] Sb1, initialize the upper and lower bounds [ub, lb] of the individual solution to [0, 100], initialize the total number of iterations of the improved sperm swarm optimization algorithm (ISSO) to 20, the population size N to 30, and initialize the dimension dim of the individual solution to 3;

[0112] Sb2. Initialize the positions of all sperm individuals. The mathematical model for initializing the positions of sperm individuals is:

[0113] x init =lb+r×(ub-lb) (12);

[0114] In formula (12), x init is the initial position of the randomly generated sperm individual, r is a random number between [0, 1], ub and lb are the upper and lower bounds of the individual solution;

[0115] Sb3, calculate the fitness value of each sperm through the objective function, sort the fitness of all individuals, and select the minimum fitness as f best , the individual position with the minimum fitness is x best , the maximum fitness is f worst , the individual position with the maximum fitness is x worst ;

[0116] Sb4. Calculate the initial velocity of individual sperm. The mathematical model of the initial velocity of sperm is:

[0117] v init =D×v0×log 10 (pH_R1) (13);

[0118] In formula (13), D is a random number with a value between [0, 1], v0 is the sperm initial velocity parameter, which is set to 1, and pH_R1 is a random number with a value between [7, 14];

[0119] Sb5. Calculate the current best individual sperm position of the sperm population. The mathematical model for calculating the current best individual sperm position is:

[0120]

[0121] In formula (14), x current_best is the updated individual optimal position, pH_R2 is a random number between [7, 14], Temp_R1 is a random number between [35.1, 38.5], x best is the individual position with the minimum fitness in the current iteration, is the position of the i-th sperm individual in the current iteration;

[0122] Sb6. Calculate the global optimal sperm individual position of the sperm population. The mathematical model for calculating the global optimal sperm individual position is:

[0123]

[0124] In formula (15), xglobal_best is the updated global optimal sperm individual position, pH_R3 is a random number between [7, 14], Temp_R2 is a random number between [35.1, 38.5], x gbest is the individual position with the minimum fitness in the total iteration, is the position of the i-th sperm individual in the current iteration;

[0125] Sb7, updating the sperm velocity through the improved sperm velocity updating mathematical model, the improved sperm velocity updating mathematical model is the same as step Ss1;

[0126] Sb8. Divide the population into three parts and perform different mutation operations on each part, including: non-uniform mutation, uniform mutation and no mutation;

[0127] Sb9. Perform non-uniform mutation operation on the population in the first part. The mathematical model of non-uniform mutation is:

[0128]

[0129] In formula (16), r1 is a random number between [0, 1], iter is the current iteration number, and MaxIter is the total iteration number;

[0130] Sb10. Perform uniform mutation operation on the population in the second part. The mathematical model of uniform mutation is:

[0131]

[0132] In formula (17), r2 is a random number with a value between [0, 1], and the other parameters have the same meanings as above;

[0133] Sb11, check whether the positions of all individuals in the population are out of bounds. If so, assign the upper and lower bounds [ub, lb] to the individual positions;

[0134] Sb12, check whether the current number of iterations iter is greater than the total number of iterations MaxIter. If so, the individual position with the lowest fitness is output as the optimal solution, and the optimal solution is decoded into the optimal controller parameter sequence of the LADRC controller. If not, return to step Sb3 to continue iterative optimization.

[0135] S6. Use the ISSO-LADRC method to achieve closed-loop control of the temperature of the heat preservation and collection environment. The specific steps are:

[0136] Sc1. Use Simulink to establish a simulation model for the temperature control system of the heat preservation and precision environment. Set the running time of the control system simulation model to 50s, the sampling time to 0.5s, the initial temperature to 0℃, and the target temperature to 1℃;

[0137] Sc2. Run the improved sperm swarm optimization algorithm mathematical model, output the updated individual solution of the algorithm during the algorithm iteration process, and decode the individual solution into the LADRC controller parameter sequence;

[0138] Sc4. Input the LADRC controller parameter sequence into the controller and run the temperature control system simulation model;

[0139] Sc5, the step signal module outputs the target temperature, and the actual temperature output by the temperature control system is fed back in real time through the feedback loop. The error calculation module calculates the difference between the target temperature and the actual temperature to obtain e(t), and e(t) is input into the ISSO-LADRC controller;

[0140] Sc6, ISSO-LADRC controller outputs the control quantity u(t) to the first-order transfer function, and delays two sampling time points through the delay module to simulate the inertia and hysteresis characteristics of the temperature control system;

[0141] Sc7, performance evaluation index model calculates the performance quantitative index of the temperature control system and passes it to the improved sperm swarm optimization algorithm mathematical model as fitness;

[0142] Sc8. Determine whether the algorithm iteration is terminated. If it is terminated, output the individual optimal solution of the improved sperm swarm optimization algorithm, and decode the individual optimal solution into the optimal LADRC control parameter sequence, such as Figure 3 As shown, the optimal k p , b0, ω0 parameters are: 7.528, 0.409, 8.320.

[0143] like Figure 4 As shown in the figure, the convergence speed of the common algorithm is slow. In the early stage of iteration (the first few iterations), the fitness value drops significantly, but as the number of iterations increases, the rate of decline gradually slows down, and finally stabilizes when the number of iterations reaches about 10. This shows that the common algorithm has a certain optimization ability in the early stage, but it is easy to fall into the local optimum in the later stage, and the convergence speed drops significantly; while the convergence speed of the improved algorithm is significantly faster. In the early stage of iteration, the fitness value drops rapidly, indicating that the improved algorithm can find a better solution faster. Even in the later stage of iteration, the fitness value of the improved algorithm continues to drop until it approaches the optimal solution. This shows that the improved algorithm has achieved a better balance between global search and local optimization, and can converge to a better solution faster.

[0144] like Figure 5As shown in the figure, in the rising stage, the response speed of the improved sperm swarm optimization algorithm is comparable to that of the ordinary sperm swarm optimization algorithm, and both are close to the target temperature of 1°C in a relatively short time. After reaching the target temperature, the overshoot of the improved sperm swarm optimization algorithm is smaller than that of the ordinary sperm swarm optimization algorithm. The output value of the ordinary sperm swarm optimization algorithm once exceeded 15%, showing a large oscillation, and it will take a longer time to reach a stable state. The stabilization time of the improved sperm swarm optimization algorithm is relatively short, and it has stabilized near the target temperature of 1°C in about 10 seconds, while the ordinary sperm swarm optimization algorithm takes 30 seconds to stabilize. Therefore, the improved sperm swarm optimization algorithm is superior to the ordinary sperm swarm optimization algorithm in response speed, overshoot and stabilization time, showing better control performance.

[0145] In summary, the present invention provides a method for controlling the temperature of a heat-insulating sperm collecting environment. The method improves the sperm swarm optimization algorithm (ISSO) by introducing nonlinear inertia weights and sperm empowerment mechanisms, and uses the improved sperm swarm optimization algorithm to optimize the LADRC controller parameters in the temperature control system of the heat-insulating sperm collecting environment, thereby improving the anti-disturbance capability, adaptability and precise temperature control capability of the LADRC controller, so that the temperature of the heat-insulating sperm collecting environment can maintain temperature stability without the need for frequent manual adjustment; the control accuracy of the temperature control system of the heat-insulating sperm collecting environment is greatly enhanced, ensuring that sperm maintains a suitable environment during storage, and ultimately improving storage quality and fertility success rate.

Claims

1. A method for controlling the temperature of a heat preservation and sperm collection environment, characterized in that: Specifically include: S1. Establish a simulation model for the temperature control system of the heat preservation and collection environment, including the LADRC controller model, the performance evaluation index model and the heat preservation and collection temperature adjustment model; S2. Establish a performance evaluation index model, and use the performance evaluation index model of the control system as the objective function of the ISSO algorithm; S3, establish a heat preservation and precision temperature regulation model; S4, introduce nonlinear inertia weight and sperm empowerment mechanism to improve sperm swarm optimization algorithm, specifically: Sa1. Introduce dynamic weight ω to improve the mathematical model of sperm velocity update. The mathematical model of ω is: In formula (1), α is the weight scaling factor, Gaussian is the Gaussian function, is the fitness of the position of the i-th sperm individual in the current iteration, f best is the minimum fitness in the current iteration, σ f is the parameter that controls the weight decay speed, μ is half of the total number of iterations, and σ is the parameter that controls the weight adjustment range; Sa2. Introduce the position update mathematical model of the sperm empowerment mechanism to improve the algorithm. The mathematical model of the sperm empowerment mechanism is: In formula (2), is the location of the i-th sperm individual in the current iteration, i = 1, ..., N, N is the population size of the algorithm, iter is the current iteration number, is the updated position of the i-th sperm individual, is the velocity of the i-th sperm individual in the current iteration, is the fitness of the position of the i-th sperm individual in the current iteration, f prc is the fitness threshold for sperm empowerment selected in the current iteration, x gbest is the location of the sperm individual with the smallest fitness in the total number of iterations, δ is the control mutation intensity factor, U(-1, 1) is a uniform random disturbance, x mean is the average position of all sperm individuals in the population, x mean The mathematical model is: x mean =(x worst -x best ) (3); In formula (3), x worst is the location of the individual with the highest fitness in the current iteration, x best is the location of the individual with the smallest fitness in the current iteration; S5. Establish a mathematical model for the improved sperm swarm optimization algorithm (ISSO). After the iteration is terminated, decode the individual optimal solution found by the algorithm into a control parameter sequence of the LADRC controller to obtain the optimal ISSO-LADRC control method; S6. Use ISSO-LADRC method to realize closed-loop control of the temperature control system of the thermal insulation and precision collection environment.

2. A method for controlling the temperature of a heat preservation and sperm collection environment according to claim 1, characterized in that: In said S5, in order to establish a mathematical model for improving the sperm group optimization algorithm, the specific steps are as follows: Sb1, initialize the upper and lower bounds of the individual solution [ub, lb], initialize the total number of iterations MaxIter and the population size N of the improved sperm swarm optimization algorithm (ISSO), and initialize the dimension dim of the individual solution; Sb2, initialize the positions of all sperm individuals; Sb3, calculate the fitness value of each sperm, sort all fitness values, and select the minimum fitness value as f best , the individual position with the minimum fitness is x best , the maximum fitness is f worst , the individual position with the maximum fitness is x worst ; Sb4, calculate the initial velocity of individual sperm, the current best individual sperm position, and the global best individual sperm position; Sb5, updating the sperm velocity through the improved sperm velocity updating mathematical model; Sb6. Divide the population into three parts and perform different mutation operations on each part, including: non-uniform mutation, uniform mutation and no mutation; Sb7, check whether the positions of all individuals in the population are out of bounds, if so, assign the upper and lower bounds [ub, lb] to the individual positions; Sb8. Check whether the current number of iterations iter is greater than the total number of iterations MaxIter. If so, the individual position with the lowest fitness is output as the optimal solution, and the optimal solution is decoded into the optimal controller parameter sequence of the LADRC controller. If not, return to step Sb3 to continue iterative optimization.

3. A method for controlling the temperature of a heat preservation and sperm collection environment according to claim 2, characterized in that: In step Sb2, the mathematical model of initializing the individual sperm positions is: x init =lb+r×(ub-lb) (4); In formula (4), x init is the initial position of the randomly generated sperm individual, r is a random number in [0, 1], ub and lb are the upper and lower bounds of the individual solution.

4. A method for controlling the temperature of a heat preservation and sperm collection environment according to claim 2, characterized in that: In step Sb4, the mathematical model for calculating the initial sperm velocity is: v init =D×v0×log 10 (pH_R1) (5); In formula (5), D is a random number with a value between [0, 1], v0 is the initial sperm velocity parameter, and pH_R1 is a random number with a value between [7, 14]; The mathematical model for calculating the current optimal sperm individual position is: In formula (6), x current_best is the updated individual optimal position, pH_R2 is a random number between [7, 14], Temp_R1 is a random number between [35.1, 38.5], x best is the individual position with the minimum fitness in the current iteration, is the position of the i-th sperm individual in the current iteration; The mathematical model for calculating the global optimal sperm individual position is: In formula (7), x global_best is the updated global optimal sperm individual position, pH_R3 is a random number between [7, 14], Temp_R2 is a random number between [35.1, 38.5], x gbest is the individual position with the minimum fitness in the total iteration, is the position of the i-th sperm individual in the current iteration.

5. A method for controlling the temperature of a heat preservation and sperm collection environment according to claim 2, characterized in that: In step Sb5, the improved sperm velocity updating mathematical model is: In formula (8), ω is the introduced dynamic weight, v init is the initial sperm velocity, x current_best is the current position of the best sperm individual, x gloabal_best is the global optimal sperm individual position.

6. A method for controlling the temperature of a heat preservation and sperm collection environment according to claim 2, characterized in that: The step Sb8 is to perform a non-uniform mutation operation on the population of the first part, and the mathematical model of the non-uniform mutation is: In formula (9), r1 is a random number between [0, 1], iter is the current iteration number, and MaxIter is the total iteration number; The population in the second part is subjected to uniform mutation operation. The mathematical model of uniform mutation is: In formula (10), r2 is a random number with a value between [0, 1], and the other parameters have the same meanings as above.

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