Constant temperature control device, method and equipment for air-cooled heat preservation box, medium and product

Abstract

The invention discloses a constant temperature control device, method and equipment for an air-cooled heat preservation box, a medium and a product, and relates to the field of air-cooled heat preservation box temperature control methods, and the device comprises a plurality of temperature sensors, a wind speed sensor and Raspberry Pi 4B; the temperature sensor is used for acquiring the temperature of a plurality of acquisition points in the air-cooled heat preservation box; the wind speed sensor is used for collecting the current rotating speed of an evaporator fan in the air-cooled heat preservation box. And the Raspberry Pi 4B is used for determining an optimized control value according to the temperatures of the plurality of acquisition points by using an improved GA-HHO algorithm and a PID control algorithm so as to drive a motor driver and adjust the current rotating speed of an evaporator fan to reach the optimal rotating speed of the fan, so that the temperature in the air-cooled heat preservation box reaches the set temperature. According to the method, the advantages of the two optimization algorithms are fused, the PID control parameters are dynamically optimized and adjusted, and the control precision of the temperature of the air-cooled heat preservation box is improved.

Description

Technical Field

[0001] This application relates to the field of temperature control methods for air-cooled insulated boxes, and in particular to a constant temperature control device, method, equipment, medium, and product for air-cooled insulated boxes. Background Technology

[0002] With the continuous improvement of the national economic level, the demand for cold chain logistics continues to grow. Air-cooled insulated boxes, due to their environmentally friendly and efficient characteristics, have become an important tool for cold chain transportation. However, in actual operation, the temperature control of air-cooled insulated boxes faces many challenges. Factors such as fluctuations in external ambient temperature, changes in cargo loading, and the nonlinear characteristics of the refrigeration system make it difficult for traditional fixed-parameter PID control methods to achieve precise temperature control, resulting in high energy consumption and directly affecting the quality of refrigerated goods and the economic efficiency of transportation. Summary of the Invention

[0003] The purpose of this application is to provide a constant temperature control device, method, equipment, medium, and product for an air-cooled insulated box, so as to improve the control accuracy of the temperature of the air-cooled insulated box.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] In a first aspect, this application provides a constant temperature control device for an air-cooled insulated box, comprising: multiple temperature sensors, a wind speed sensor, and a Raspberry Pi 4B; all of the multiple temperature sensors and the wind speed sensor are connected to the Raspberry Pi 4B;

[0006] The temperature sensor is used to collect the temperature at multiple points inside the air-cooled insulated box.

[0007] The wind speed sensor is used to collect the current rotational speed of the evaporator fan inside the air-cooled insulation box;

[0008] The Raspberry Pi 4B is used to determine an optimized control value based on the temperatures of multiple sampling points using an improved AGA-HHO algorithm and a PID control algorithm. This value drives the motor driver to adjust the current speed of the evaporator fan to the optimal fan speed, thereby bringing the temperature inside the air-cooled insulation box to the set temperature. The Raspberry Pi 4B includes a PID parameter optimization module and a PID control module. The PID parameter optimization module uses the improved AGA-HHO algorithm to determine the optimal PID parameters. The PID control module uses the optimal PID parameters and an error signal to determine the optimized control value using a PID control algorithm. The improved AGA-HHO algorithm is determined based on the AGA algorithm and the HHO algorithm. The optimal PID parameters include the optimal proportional gain coefficient, the optimal integral time coefficient, and the optimal derivative time coefficient. The error signal is the difference between the temperatures of the multiple sampling points and the corresponding set temperature. The optimized control value is the optimal fan speed.

[0009] In one embodiment, it further includes: a wireless communication module;

[0010] The wireless communication module is used to transmit the current rotation speed and the temperature of the multiple acquisition points to the Raspberry Pi 4B.

[0011] In one embodiment, the Raspberry Pi 4B further includes: a data processing module;

[0012] The data processing module is used to use a threshold algorithm to remove outliers from the temperatures of multiple collection points and to perform normalization processing to obtain the processed temperatures of multiple collection points.

[0013] Secondly, this application provides a method for constant temperature control of an air-cooled insulated box. The method is implemented using the aforementioned air-cooled insulated box temperature control device, and includes:

[0014] The temperature at multiple sampling points inside the air-cooled insulation box and the current rotation speed of the evaporator fan inside the air-cooled insulation box are obtained.

[0015] Based on the temperatures at multiple collection points, an optimized control value is determined using an improved AGA-HHO algorithm and a PID control algorithm. This value drives the motor driver to adjust the current speed of the evaporator fan to the optimal fan speed, thereby bringing the temperature inside the air-cooled insulation box to the set temperature. The optimized control value is the optimal fan speed.

[0016] In one embodiment, based on the temperatures of the multiple acquisition points, an optimized control value is determined using an improved AGA-HHO algorithm and a PID control algorithm, specifically including:

[0017] A threshold algorithm is used to remove outliers from the temperatures of multiple data collection points, and then the data is normalized to obtain the processed temperatures of the multiple data collection points.

[0018] The optimal PID parameters are determined using an improved AGA-HHO algorithm; the improved AGA-HHO algorithm is based on the AGA algorithm and the HHO algorithm.

[0019] Based on the optimal PID parameters and the error signal, the optimized control value is determined using a PID control algorithm; the optimal PID parameters include the optimal proportional gain coefficient, the optimal integral time coefficient, and the optimal derivative time coefficient; the error signal is the difference between the processed temperature of multiple acquisition points and the corresponding set temperature.

[0020] In one embodiment, the optimal PID parameters are determined using an improved AGA-HHO algorithm, specifically including:

[0021] The AGA algorithm is used to perform a coarse-grained global search to determine the optimal solution region.

[0022] The HHO algorithm is used to perform a fine search in the optimal solution region to determine the optimal PID parameters.

[0023] In one embodiment, based on the optimal PID parameters and the error signal, an optimized control value is determined using a PID control algorithm, specifically including:

[0024] Using formula Determine the optimized control value; where u(t) is the optimized control value; e(t) is the error signal; K p K is the optimal proportional gain coefficient. i K is the optimal integration time coefficient; d This is the optimal differential time coefficient.

[0025] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the constant temperature control method for the air-cooled insulated box described in any one of the above-mentioned methods.

[0026] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the constant temperature control method for the air-cooled insulated box described above.

[0027] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the constant temperature control method for the air-cooled insulated box described above.

[0028] According to the specific embodiments provided in this application, this application has the following technical effects:

[0029] This application provides a constant temperature control device, method, equipment, medium, and product for an air-cooled insulated box. The device includes multiple temperature sensors, a wind speed sensor, and a Raspberry Pi 4B. The temperature sensors are used to collect the temperature at multiple points within the air-cooled insulated box. The wind speed sensor is used to collect the current rotational speed of the evaporator fan within the air-cooled insulated box. The Raspberry Pi 4B is used to determine optimized control values ​​based on the temperatures at multiple points using an improved AGA-HHO algorithm and a PID control algorithm. These optimized control values ​​drive a motor driver to adjust the current rotational speed of the evaporator fan to achieve the optimal fan speed, thereby ensuring the temperature inside the air-cooled insulated box reaches the set temperature. This application integrates the advantages of two optimization algorithms to achieve dynamic optimization and adjustment of the PID control parameters, effectively solving the problems of insufficient temperature control accuracy and excessive energy consumption caused by fluctuations in ambient temperature and changes in cargo loading, thus improving the temperature control accuracy of the air-cooled insulated box. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A schematic diagram of a constant temperature control device for an air-cooled insulated box provided in an embodiment of this application;

[0032] Figure 2 A system framework diagram of a constant temperature control device for an air-cooled insulated box provided in an embodiment of this application;

[0033] Figure 3 A flowchart illustrating the operation of a constant temperature control device for an air-cooled insulated box provided in an embodiment of this application;

[0034] Figure 4 This is a distribution diagram of temperature and wind speed sensors provided in an embodiment of this application;

[0035] Figure 5 This is a control logic diagram of a PID controller provided in an embodiment of this application;

[0036] Figure 6 A temperature field circulation diagram provided for one embodiment of this application;

[0037] Figure 7 A schematic flowchart illustrating a method for constant temperature control of an air-cooled insulated box according to an embodiment of this application;

[0038] Figure 8 A flowchart illustrating the practical application of the constant temperature control method for an air-cooled insulated box provided in an embodiment of this application;

[0039] Figure 9 A flowchart of the improved AGA-HHO algorithm provided in an embodiment of this application;

[0040] Figure 10 A schematic diagram illustrating the principle of temperature and airflow regulation inside an air-cooled insulated box according to an embodiment of this application;

[0041] Figure 11 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] This application belongs to the field of intelligent control technology for air-cooled insulated box temperature control systems, specifically relating to an air-cooled insulated box temperature control device and method, applicable to the temperature optimization problem of air-cooled insulated box temperature control systems in cold chain logistics transportation, in order to reduce energy consumption and enhance temperature control stability.

[0044] Metaheuristic optimization algorithms (such as genetic algorithms and Harris Eagle algorithms) are used to optimize the temperature control system of air-cooled insulated boxes, thanks to their unique global search capabilities and efficient local optimization characteristics. They can quickly converge to the optimal solution.

[0045] One existing method for high-value consumable cabinet temperature control utilizes an improved Harris Eagle algorithm to optimize fuzzy PID parameters, reducing unsatisfactory simulation results caused by over-reliance on expert experience in parameter setting. Another existing method is a PID parameter tuning approach based on an adaptive particle swarm optimization (PSO) genetic algorithm. A simulation model of the temperature control system is built using a Matlab / Simulink platform, and an improved PSO algorithm is employed to optimize the PID control parameters. The optimized K... p K i K d The three parameters are assigned to the PID controller, and by adjusting the on / off state of the relay, the temperature of the system is effectively controlled, the overshoot of the system is reduced and the system adjustment speed is improved, so as to achieve more accurate and faster temperature control.

[0046] Given that metaheuristic algorithms can effectively optimize nonlinear systems, this application designs an improved Adaptive Genetic Algorithm (AGA) and Harris Hawks Optimization (HHO) method for optimizing the temperature of an air-cooled insulated box.

[0047] The technical problem this application aims to solve is to overcome the shortcomings of traditional PID control parameters and poor adaptability in existing temperature control systems for air-cooled insulated boxes, as well as the slow convergence speed and insufficient optimization accuracy of existing intelligent optimization algorithms under complex operating conditions. This application provides a temperature control method for air-cooled insulated boxes based on an improved adaptive genetic algorithm and Harris Eagle optimization. This method integrates the advantages of two optimization algorithms to achieve dynamic optimization and adjustment of PID control parameters, effectively solving the problems of insufficient temperature control accuracy and excessive energy consumption caused by fluctuations in ambient temperature and changes in cargo loading. Simultaneously, it improves the response speed and stability of the control system, meeting the stringent requirements of precise temperature control in cold chain logistics.

[0048] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] This application specifically addresses the energy consumption optimization and temperature fluctuation optimization problems of constant temperature systems in air-cooled insulated boxes. Through optimization and improvement of the crossover operator, mutation operator, and fitness function, the convergence accuracy and speed of the algorithm are significantly improved. Applying this to the PID parameter tuning of the evaporator fan in air-cooled insulated boxes enables highly stable temperature control and optimized energy consumption. Furthermore, this application combines edge real-time computing with cloud data fusion technology to construct a complete cold chain intelligent computing system, providing a systematic solution for the energy-efficient operation of air-cooled insulated boxes.

[0050] In one exemplary embodiment, such as Figure 1 As shown, a constant temperature control device for an air-cooled insulated box is provided, including: multiple temperature sensors, a wind speed sensor, and a Raspberry Pi 4B; the multiple temperature sensors and the wind speed sensor are all connected to the Raspberry Pi 4B.

[0051] The temperature sensor is used to collect the temperature at multiple points inside the air-cooled insulated box.

[0052] The wind speed sensor is used to collect the current rotational speed of the evaporator fan inside the air-cooled insulation box.

[0053] The Raspberry Pi 4B is used to determine an optimized control value based on the temperatures of multiple sampling points using an improved AGA-HHO algorithm and a PID control algorithm. This value drives the motor driver to adjust the current speed of the evaporator fan to the optimal fan speed, thereby bringing the temperature inside the air-cooled insulation box to the set temperature. The Raspberry Pi 4B includes a PID parameter optimization module and a PID control module. The PID parameter optimization module uses the improved AGA-HHO algorithm to determine the optimal PID parameters. The PID control module uses the optimal PID parameters and an error signal to determine the optimized control value using a PID control algorithm. The improved AGA-HHO algorithm is determined based on the AGA algorithm and the HHO algorithm. The optimal PID parameters include the optimal proportional gain coefficient, the optimal integral time coefficient, and the optimal derivative time coefficient. The error signal is the difference between the temperatures of the multiple sampling points and the corresponding set temperature. The optimized control value is the optimal fan speed.

[0054] In this embodiment, as Figure 2 and Figure 3 As shown, DS18B20 temperature sensors are deployed in a distributed manner to collect real-time temperature distribution data at multiple points inside the box. Combined with wind speed sensors to monitor fan speed, temperature errors are dynamically generated based on these errors, providing accurate data support for temperature control of the insulation box. The sensor distribution is as follows: Figure 4 As shown, wind speed sensors are deployed at the wind speed measurement points, and temperature sensors are deployed at the temperature measurement points. A Raspberry Pi controller (Raspberry Pi 4B) receives cloud computing at the edge and optimizes the evaporator fan PID parameters using the AGA-HHO algorithm. The PID controller receives the edge optimization signal and, through proportional-integral-derivative adjustment, drives the evaporator fan via a BTS7960, creating a uniform and strong airflow field within the insulation box, accelerating temperature equalization and achieving precise temperature control.

[0055] Edge systems can migrate computing tasks from the cloud to perform real-time calculations on large-scale and complex data. The edge system uses a Raspberry Pi 4B microcomputer as the computing device for intelligent optimization algorithms and PID control algorithms.

[0056] An improved AGA-HHO algorithm control system (PID parameter optimization module) is developed, which utilizes the improved AGA-HHO algorithm to optimize PID parameters in real time.

[0057] The PID control system (PID control module) acquires optimized PID parameters in real time through edge computing nodes. Based on a proportional-integral-derivative coordinated adjustment mechanism, it drives the BTS7960 motor driver to dynamically adjust the evaporator fan speed. The control logic is as follows: Figure 5As shown, precise temperature control is achieved in both the air-cooled insulated box and the refrigerated compartment. Inside the insulated box, the system maintains a stable temperature field through top-supply and bottom-return air circulation. The temperature field circulation diagram for the air-cooled insulated box is shown below. Figure 6 As shown.

[0058] In one embodiment, it further includes a wireless communication module.

[0059] The wireless communication module is used to transmit the current rotation speed and the temperature of the multiple acquisition points to the Raspberry Pi 4B.

[0060] In one embodiment, the Raspberry Pi 4B further includes a data processing module.

[0061] The data processing module is used to use a threshold algorithm to remove outliers from the temperatures of multiple collection points and to perform normalization processing to obtain the processed temperatures of multiple collection points.

[0062] In one exemplary embodiment, such as Figure 7 and Figure 8 As shown, this application provides a method for constant temperature control of an air-cooled insulated box. The method is implemented through the air-cooled insulated box constant temperature control device and includes:

[0063] S1: Obtain the temperature of multiple sampling points inside the air-cooled insulation box and the current speed of the evaporator fan inside the air-cooled insulation box.

[0064] In this embodiment, the temperature and fan speed inside the air-cooled insulation box are collected in real time using a DS18B20 temperature sensor and a wind speed sensor. The temperature and wind speed sensors continuously acquire data on the multi-point temperature distribution and evaporator fan speed within the box. The collected data is transmitted in real time to the Raspberry Pi edge computing node via a low-power LoRa wireless communication module. The Raspberry Pi 4B preprocesses the raw sensor data, using a threshold algorithm to remove outliers, performing fixed-point normalization on valid data, and packaging it into fixed-length data packets. This ensures data reliability and real-time performance, providing accurate feedback signals for subsequent intelligent optimization algorithms.

[0065] S2: Based on the temperatures of multiple collection points, an optimized control value is determined using an improved AGA-HHO algorithm and a PID control algorithm to drive the motor driver and adjust the current speed of the evaporator fan to achieve the optimal fan speed, so that the temperature inside the air-cooled insulation box reaches the set temperature; the optimized control value is the optimal fan speed.

[0066] In one embodiment, based on the temperatures of the multiple acquisition points, an optimized control value is determined using an improved AGA-HHO algorithm and a PID control algorithm, specifically including:

[0067] S21: A threshold algorithm is used to remove outliers from the temperatures of multiple collection points and then normalize the data to obtain the processed temperatures of the multiple collection points.

[0068] S22: Determine the optimal PID parameters using the improved AGA-HHO algorithm; the improved AGA-HHO algorithm is determined based on the AGA algorithm and the HHO algorithm.

[0069] In one embodiment, S22 specifically includes:

[0070] The AGA algorithm is used to perform a coarse-grained global search to determine the optimal solution region.

[0071] The HHO algorithm is used to perform a fine search in the optimal solution region to determine the optimal PID parameters.

[0072] The improved AGA-HHO algorithm combines the features of adaptive genetic algorithms and Harris Eagle algorithms. The AGA algorithm can quickly locate potential optimal solution regions during the coarse-grained global search; subsequently, the HHO algorithm performs a fine-grained search within these regions, achieving high-precision tuning of PID parameters. This hybrid optimization strategy overcomes the slow convergence speed of traditional genetic algorithms in the later stages and avoids the tendency of the Harris Eagle algorithm alone to get trapped in local optima in complex multimodal problems. A flowchart of the specific algorithm model is shown below. Figure 9 As shown.

[0073] (1) Algorithm initialization: In practical applications, the parameter settings of the improved AGA-HHO algorithm are crucial for optimization results. Parameter settings include population size, maximum number of iterations, variable dimension, upper and lower bounds of the search space, initial escape energy, energy decay coefficient, and Lévy flight index. To improve the algorithm's running speed, this embodiment sets the number of iterations of the improved AGA-HHO algorithm to 100, the population size to 50, and the initial parameters as follows: crossover probability 0.8, mutation probability 0.05, escape energy 0.5, energy decay coefficient 2, and Lévy flight index 1.5.

[0074] (2) Algorithm fitness calculation: The design of the fitness function directly determines the optimization effect. Its core lies in transforming the temperature control target of the air-cooled insulation box into a quantifiable evaluation index, specifically defined as a multi-objective weighted function:

[0075] Fitness = F raw ·[β(t)·G GA +(1-β(t))·H HHO ].

[0076] Among them, F raw Let G be the original objective function value, β(t) be the time-varying weight function, and G be the weighting function. GA For GA diversity; H HHO For HHO containment items.

[0077] G GA It calculates the ratio of standard deviations of a population across all dimensions, reflecting the spatial dispersion. It introduces a sine function to make the diversity weight oscillate and decay over time, avoiding premature maturation caused by monotonic decline.

[0078]

[0079] Where, σ avg σ represents the mean standard deviation of each dimension. max The maximum dimensional standard deviation is T, where t is the current iteration number. max This represents the maximum number of iterations.

[0080] H HHO The Gaussian kernel function is used to measure the distance between individuals and elites, and the variance adapts to energy.

[0081]

[0082] Where E(t) is the energy decay factor, with an initial value of 2, and x is the current position vector of the individual. elite This represents the position of the current optimal individual.

[0083] (3) Determine whether L is odd or even to use a cross-rotation adaptive genetic algorithm or Harris Eagle algorithm. L is a sequence starting from 0 and increasing by integers. Each time a loop is completed, its value is incremented by 1.

[0084] (4) In the AGA algorithm, the algorithm simulates the biological evolution process, using the basic idea of ​​"survival of the fittest" to iteratively perform crossover, mutation, and selection. The improved AGA algorithm adjusts the crossover and mutation probabilities in real time based on fitness, thereby improving the algorithm's convergence speed and performance. The improved adaptive crossover and mutation probabilities are functions of:

[0085]

[0086] Among them, P c (t,f c P represents the dynamic crossover probability. m (t,f m P represents the dynamic mutation probability; c,min P is the minimum crossover probability. c,max0 Let P be the initial maximum crossover probability value. m,minP is the minimum value of the mutation probability. m,max0 K represents the initial maximum mutation probability value. c0 K is a control parameter for the crossover probability, which controls the steepness of the Logistic function; m0 f is a control parameter for the probability of mutation, which controls the steepness of the Logistic function; c f represents the maximum fitness between the two individuals participating in the crossover. m f represents the maximum fitness between the two individuals involved in the mutation; max f represents the maximum fitness of the current population. avg The average fitness of the current population is given by a value initially set to 0.7. a, b, g, and h are adjustment parameters. An improved adaptive genetic algorithm is employed, which can quickly traverse flat regions in the search space and accurately locate the optimal solution in the later stages of convergence. Its entropy protection mechanism fundamentally suppresses premature convergence, and the introduction of a time-varying function significantly reduces sensitivity to initial parameters, demonstrating breakthrough convergence speed and solution accuracy in complex multimodal optimization problems.

[0087] (5) In the HHO algorithm, which is a metaheuristic algorithm inspired by the predatory behavior of birds of prey in nature, the algorithm simulates the cooperative hunting of rabbits by a flock of Harris eagles during its operation. It mainly includes the exploration phase, the transition phase, and the development phase. The following are the key formulas and concepts in the HHO algorithm:

[0088] Exploration Phase: The Harris Eagle will randomly roam and search, waiting for an opportunity to ambush prey. The specific formula is:

[0089]

[0090] Where X(t) and X(t+1) represent the positions of the eagle in the current iteration and the next iteration, respectively; X rand The eagle's position is randomly selected; X rabbit Let r1, r2, r3, r4, and q be the positions of the prey in the optimal solution; r1, r2, r3, r4, and q are values ​​that vary randomly between [0, 1]; LB and UB are the lower and upper bounds of the search space variables; X m This represents the average position of the eagle population.

[0091]

[0092] Where N is the total number of prey, X i (t) represents the position t of each prey in the iteration.

[0093] Transition Phase: During the energy escape process, the prey's energy decays over time. If |E||>1, the algorithm enters the global exploration phase; if |E|<1, it enters the local development phase. The formula for the energy factor E that determines the search strategy is:

[0094]

[0095] Where t is the current iteration number; E0 = 2 × rand() - 1 is the initial energy, and the random number rand() takes the value [-1, 1].

[0096] Development phase: Combining the rabbit's escape behavior and the hunter's trapping strategy, the HHO algorithm proposes four alternative strategies based on the random number [0, 1] r (simulating the prey's escape probability) and the prey's remaining energy |E|.

[0097] 1) Soft encirclement (|E|≥0.5, r≥0.5; the prey still has the energy to escape).

[0098] The prey escapes through random, deceptive leaps, while the Harris Eagle continues its relentless assault, steadily depleting the prey's energy in order to find the optimal position to strike the rabbit.

[0099] ΔX=X rabbit (txt).

[0100] X(t+1)=ΔX-E|J·X rabbit (t)-X(t)|.

[0101] Where J = 2(1-r5) is the intensity of the prey's random jump.

[0102] 2) Hard encirclement (|E|<0.5, r≥0.5; prey exhausted).

[0103] When the Harris Eagle suddenly chose to surround and hunt, the rabbit was already exhausted and its chances were slim. This indicates a change in the current position during a strong attack.

[0104] X(t+1)=X rabbit (t)-E|ΔX|.

[0105] 3) Gradual dive soft encirclement (|E|≥0.5, r<0.5).

[0106] The prey escapes the encirclement by leaping, using a random, deceptive opportunity, while the Harris Eagle continues its weak encirclement, gradually exhausting the prey's energy. This strategy achieves a gradual, soft encirclement of the prey by incorporating levy flight capabilities.

[0107]

[0108] Y(t) = X rabbit (t)-E|J·X rabbit (txt).

[0109] Z(t) = Y(t) + S × LF(D).

[0110] Where F describes the fitness function, D represents the solution dimension, S represents a D-dimensional random vector, LF is the Levy flight function, Y(t) is the candidate position guided by the "rabbit" during the asymptotic dive soft siege, and Z(t) is the candidate position obtained by the "jump" strategy during the asymptotic dive soft siege.

[0111] 4) Gradual dive attack (|E|<0.5, r<0.5).

[0112] As the prey's ability to escape diminishes, it gains a greater chance of breaking out of the encirclement. The Harris Eagle, under appropriate circumstances, gradually depletes the prey's strength, choosing to charge at a faster pace and launch a sudden attack on the hard surroundings.

[0113]

[0114] Y'(t)=X rabbit (t)-E|J·X rabbit (txt).

[0115] Z'(t)=Y'(t)+S×LF(D).

[0116] Y'(t) represents the candidate positions guided by the "rabbit" during the progressive dive hard siege; Z'(t) represents the candidate positions obtained by the "jump" strategy during the progressive dive hard siege.

[0117] (6) Once the maximum number of iterations is reached, output the optimal K. p K i and K d K p K i and K d This refers to the three-dimensional components of the optimal chromosome position obtained by the AGA algorithm or the optimal eagle position obtained by the HHO algorithm after the AGA and HHO algorithms have converged. Where K... p K i and K d These are the optimal proportional gain coefficient, optimal integral time coefficient, and optimal derivative time coefficient tuned using the improved AGA-HHO algorithm.

[0118] S23: Based on the optimal PID parameters and the error signal, the optimized control value is determined using a PID control algorithm; the optimal PID parameters include the optimal proportional gain coefficient, the optimal integral time coefficient, and the optimal derivative time coefficient; the error signal is the difference between the processed temperature of multiple acquisition points and the corresponding set temperature.

[0119] In one embodiment, S23 specifically includes:

[0120] Using formula Determine the optimized control value; where u(t) is the optimized control value; e(t) is the error signal; K p K is the optimal proportional gain coefficient. i K is the optimal integration time coefficient; d This is the optimal differential time coefficient.

[0121] Running an improved AGA-HHO algorithm on a Raspberry Pi 4B to optimize PID parameters enables distributed sensing and real-time edge processing of environmental parameters inside the air-cooled insulation box, effectively improving system response speed and reducing cloud computing load. The edge device (Raspberry Pi 4B) is the central layer of the architecture, capable of migrating cloud computing tasks to handle large-scale, complex data in real time. The Raspberry Pi connects to temperature and wind speed sensors via a low-power LoRa wireless communication module to acquire real-time temperature and wind speed values ​​inside the air-cooled insulation box. The Raspberry Pi connects to the evaporator fan controller via serial communication, transmitting the optimized control value u(t) to the evaporator fan controller, which outputs a PWM signal to control the BTS7960 motor drive module. This controls the evaporator fan speed, achieving intelligent temperature control of the air-cooled insulation box.

[0122] The application process of the constant temperature control device for air-cooled insulated boxes is as follows: Figure 10 As shown, the entire working process is a closed-loop control system. By improving the AGA-HHO algorithm, the parameters of the PID controller are dynamically adjusted to determine whether the target temperature is met in real time. The optimized PID parameters are used by the controller to dynamically adjust the evaporator fan speed, thereby controlling the temperature and wind speed inside the air-cooled insulation box more accurately and efficiently.

[0123] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method for constant temperature control of an air-cooled insulated box.

[0124] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method for constant temperature control of an air-cooled insulated box.

[0125] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method for constant temperature control of an air-cooled insulated box.

[0126] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 11As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for temperature control of an air-cooled insulated box.

[0127] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0128] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0129] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0130] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A constant temperature control device for an air-cooled insulated box, characterized in that, include: Multiple temperature sensors, wind speed sensors, and a Raspberry Pi 4B; Multiple temperature sensors and wind speed sensors are connected to the Raspberry Pi 4B; The temperature sensor is used to collect the temperature at multiple points inside the air-cooled insulated box. The wind speed sensor is used to collect the current rotational speed of the evaporator fan inside the air-cooled insulation box; The Raspberry Pi 4B is used to determine an optimized control value based on the temperature of multiple collection points using an improved AGA-HHO algorithm and a PID control algorithm. This value is then used to drive the motor driver and adjust the current speed of the evaporator fan to achieve the optimal fan speed, thereby ensuring that the temperature inside the air-cooled insulation box reaches the set temperature. The Raspberry Pi 4B includes a PID parameter optimization module and a PID control module. The PID parameter optimization module is used to determine the optimal PID parameters using an improved AGA-HHO algorithm; the PID control module is used to determine the optimized control value based on the optimal PID parameters and the error signal using a PID control algorithm; the improved AGA-HHO algorithm is determined based on the AGA algorithm and the HHO algorithm; the optimal PID parameters include the optimal proportional gain coefficient, the optimal integral time coefficient, and the optimal derivative time coefficient; the error signal is the difference between the temperature of multiple sampling points and the corresponding set temperature; the optimized control value is the optimal fan speed.

2. The constant temperature control device for the air-cooled insulated box according to claim 1, characterized in that, Also includes: Wireless communication module; The wireless communication module is used to transmit the current rotation speed and the temperature of the multiple acquisition points to the Raspberry Pi 4B.

3. The constant temperature control device for the air-cooled insulated box according to claim 1, characterized in that, The Raspberry Pi 4B also includes: a data processing module; The data processing module is used to use a threshold algorithm to remove outliers from the temperatures of multiple collection points and to perform normalization processing to obtain the processed temperatures of multiple collection points.

4. A method for constant temperature control of an air-cooled insulated box, characterized in that, The aforementioned method for controlling the constant temperature of an air-cooled insulated box is implemented using the air-cooled insulated box constant temperature control device according to any one of claims 1-3, and the method includes: The temperature at multiple sampling points inside the air-cooled insulation box and the current rotation speed of the evaporator fan inside the air-cooled insulation box are obtained. Based on the temperatures at multiple collection points, an optimized control value is determined using an improved AGA-HHO algorithm and a PID control algorithm. This value drives the motor driver to adjust the current speed of the evaporator fan to the optimal fan speed, thereby bringing the temperature inside the air-cooled insulation box to the set temperature. The optimized control value is the optimal fan speed.

5. The method for constant temperature control of an air-cooled insulated box according to claim 4, characterized in that, Based on the temperatures from multiple data collection points, optimized control values ​​are determined using an improved AGA-HHO algorithm and a PID control algorithm, specifically including: A threshold algorithm is used to remove outliers from the temperatures of multiple data collection points, and then the data is normalized to obtain the processed temperatures of the multiple data collection points. The optimal PID parameters are determined using an improved AGA-HHO algorithm; the improved AGA-HHO algorithm is based on the AGA algorithm and the HHO algorithm. Based on the optimal PID parameters and the error signal, the optimized control value is determined using a PID control algorithm; the optimal PID parameters include the optimal proportional gain coefficient, the optimal integral time coefficient, and the optimal derivative time coefficient; the error signal is the difference between the processed temperature of multiple acquisition points and the corresponding set temperature.

6. The method for constant temperature control of an air-cooled insulated box according to claim 5, characterized in that, The optimal PID parameters are determined using the improved AGA-HHO algorithm, specifically including: The AGA algorithm is used to perform a coarse-grained global search to determine the optimal solution region. The HHO algorithm is used to perform a fine search in the optimal solution region to determine the optimal PID parameters.

7. The method for constant temperature control of an air-cooled insulated box according to claim 4, characterized in that, Based on the optimal PID parameters and error signal, the optimized control value is determined using a PID control algorithm, specifically including: Using formula Determine the optimized control value; where u(t) is the optimized control value; e(t) is the error signal; K p K is the optimal proportional gain coefficient. i K is the optimal integration time coefficient; d This is the optimal differential time coefficient.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the constant temperature control method for the air-cooled insulated box according to any one of claims 4-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the constant temperature control method for the air-cooled insulated box as described in any one of claims 4-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the constant temperature control method for the air-cooled insulated box as described in any one of claims 4-7.

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