Communication cabinet temperature adaptive control method and system

Abstract

The invention discloses a communication cabinet temperature adaptive control method and system, and relates to the technical field of temperature control. Determining a first environment parameter of the target communication cabinet, and performing adaptive optimization on the first environment parameter through an improved firefly algorithm to obtain a target environment parameter; simulating a target environment according to the target environment parameters to perform simulation operation to obtain PID control parameters; optimizing the PID control parameters through an improved sparrow algorithm to obtain target control parameters, and updating the temperature control system according to the target control parameters; the improved firefly algorithm optimizes environmental parameters, and the accuracy and the anti-interference capability are improved; a PID effect is verified based on an accurate parameter simulation environment, and dynamic tuning is realized; the improved sparrow algorithm is combined to optimize control parameters, temperature adaptive control precision and response speed are enhanced, stable and efficient operation of the communication cabinet under different working conditions is ensured, and equipment reliability is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of temperature control, and particularly relates to a method and system for adaptively controlling the temperature of a communication cabinet. Background Art

[0002] With the rapid development of information technology and the increasing complexity of communication networks, as an important part of the communication system, the reliability and security of communication cabinets are particularly crucial. A large number of high-performance communication devices and electronic components are integrated inside the communication cabinet, and these devices will generate a large amount of heat during operation. If heat dissipation is not carried out in a timely and effective manner, the temperature inside the cabinet will rise rapidly, which may cause the equipment to overheat, affect its normal operation, and even lead to safety accidents such as failure shutdown, data loss, and even fire.

[0003] Patent No.: CN119493434A discloses an efficient computer room approach temperature control method, which adaptively adjusts the state of the refrigeration system by combining historical data prediction and real-time sensor data; dynamically optimizes the cabinet layout and air flow path by using thermal imaging technology; optimizes the air flow design by using computational fluid dynamics, and intelligently controls dampers and fans; implements heat recovery and renewable energy utilization to improve energy efficiency; establishes a multi-level redundant refrigeration system to predict and prevent failures; realizes remote monitoring and automated operation and maintenance with the help of the Internet of Things and AI to ensure accurate control of the computer room temperature, reduce energy consumption, and improve operation and maintenance efficiency.

[0004] Although the above technologies solve some problems, there are still some problems, such as: the existing temperature control of communication cabinets has a response lag, unreasonable temperature control leads to high energy consumption, or the temperature control fails to meet expectations, resulting in the frequency reduction of communication equipment. Traditional optimization methods lack accuracy and low optimization efficiency. Summary of the Invention

[0005] The object of the present invention is to solve the problems that the existing temperature control of communication cabinets has a response lag, unreasonable temperature control leads to high energy consumption, or the temperature control fails to meet expectations, resulting in the frequency reduction of communication equipment, and traditional optimization methods lack accuracy and low optimization efficiency, and to propose a method and system for adaptively controlling the temperature of a communication cabinet.

[0006] In the first aspect of the implementation of the present invention, a method for adaptively controlling the temperature of a communication cabinet is first proposed, and the method includes: Determine the first environmental parameters of the target communication cabinet, and adaptively optimize the first environmental parameters through an improved firefly algorithm to obtain target environmental parameters; the first environmental parameters include: specific heat capacity of the communication cabinet, thermal resistance of the communication cabinet, heat generation rate of the communication cabinet, and system time delay of the communication cabinet; Simulate the target environment according to the target environmental parameters and perform simulation operation to obtain PID control parameters; The target control parameters are obtained by optimizing the PID control parameters through an improved sparrow algorithm, and the temperature control system is updated according to the target control parameters.

[0007] Optionally, the target environmental parameters are obtained by adaptively optimizing the first environmental parameters through an improved firefly algorithm, including: Taking the first environmental parameters as fireflies, and initializing the first preset parameters of the improved firefly algorithm; the first preset parameters include: population size, maximum number of iterations, upper and lower bounds, dimension, light intensity absorption coefficient, maximum attraction, and step factor; The fitness is calculated by the position of the target firefly in the population, the target distance between the target firefly and each firefly in the population is calculated, and the relative fluorescence brightness between the target firefly and each firefly in the population is calculated according to the target distance; the fitness represents the maximum fluorescence brightness of the firefly; The attraction between the target firefly and each firefly is calculated according to the target distance, the position update formula is determined according to the number of iterations and the attraction, and the position of the firefly is updated according to the position update formula and the relative fluorescence brightness; Position update formula: x i (t + 1)=ω(t)*x i (t)+β(x j (t)-x i (t)) Adaptive weight formula: ω(t)=sin(πt / 2t max +π)+1 where t is the current number of iterations, t max is the maximum number of iterations, ω(t) is the adaptive weight, x i (t + 1) is the position of firefly i after update at the (t + 1)-th iteration, β( ) is the attraction calculation formula, x j (t) is the position of firefly j at the t-th iteration, x i (t) is the position of firefly i at the t-th iteration; The fitness is calculated according to the updated position of the target firefly in the population. If the maximum number of iterations is reached, the optimal firefly is output; the optimal firefly is the target environmental parameter.

[0008] Optionally, the target control parameters are obtained by optimizing the PID control parameters through an improved sparrow algorithm, including: Step 1: Taking the PID control parameters as sparrows, initializing the second preset parameters of the improved sparrow algorithm, and initializing the sparrow population position through tent chaotic mapping; the second preset parameters include: group size, maximum number of iterations, upper and lower limits, dimension; Step 2: Calculate the fitness of each individual in the sparrow population, sort them in descending order of fitness to obtain a fitness table, screen the corresponding sparrows in the fitness table according to a preset rule to obtain a screening result, use the screening result as the discoverers, and use the remaining sparrows as the followers; Step 3: Optimize the discoverer position update strategy through the butterfly algorithm, and update the positions of the discoverers according to the discoverer position update strategy; Step 4: Optimize the follower position update strategy through the Cauchy distribution and the Gaussian distribution, and update the followers according to the follower position update strategy; Step 5: Update the global fitness, detect whether the maximum number of iterations is reached. If it is reached, output the optimal sparrow; otherwise, return to Step 3.

[0009] Optionally, calculate the fitness of each individual in the sparrow population. The fitness formula includes: where SY is the fitness, wc(t) is the temperature error, e is the natural constant, P(t) is the air-conditioning working energy consumption, and T is the total control process time.

[0010] Optionally, update the temperature control system according to the target control parameters, including: Send the target control parameters to the temperature control system, and the temperature control system receives the target control parameters and performs temperature control according to the target control parameters; Obtain the second environmental parameters in the target communication cabinet after a preset time period, and determine whether the target communication cabinet is in an abnormal overload state. If it is in an abnormal overload state, update according to the second environmental parameters; otherwise, stop the update.

[0011] In the second aspect of the implementation of the present invention, a temperature adaptive control system for a communication cabinet is proposed, including: an environment determination module, a simulation module, and a control update module: The environment determination module is used to determine the first environmental parameters of the target communication cabinet, and adaptively optimize the first environmental parameters through an improved firefly algorithm to obtain target environmental parameters; the first environmental parameters include: the specific heat capacity of the communication cabinet, the thermal resistance of the communication cabinet, the heat generation rate of the communication cabinet, and the system time delay of the communication cabinet; The simulation module is used to simulate the target environment according to the target environmental parameters and perform simulation operation to obtain PID control parameters; The control update module is used to optimize the PID control parameters through an improved sparrow algorithm to obtain target control parameters, and update the temperature control system according to the target control parameters.

[0012] Optionally, the environment determination module includes: an algorithm initialization module, a fitness calculation module, a firefly calculation module, a firefly update module, and an iterative update module: The algorithm initialization module is used to take the first environmental parameter as a firefly and initialize the first preset parameters of the improved firefly algorithm; the first preset parameters include: population size, maximum number of iterations, upper and lower bounds, dimension, light intensity absorption coefficient, maximum attraction, and step factor; The fitness calculation module is used to calculate the fitness by the position of the target firefly in the population, calculate the target distance between the target firefly and each firefly in the population, and calculate the relative fluorescence brightness between the target firefly and each firefly in the population according to the target distance; the fitness represents the maximum fluorescence brightness of the firefly; The firefly update module is used to calculate the attraction between the target firefly and each firefly according to the target distance, determine the position update formula according to the iteration number and the attraction, and update the position of the firefly according to the position update formula and the relative fluorescence brightness; position update formula: x i (t + 1)=ω(t)*x i (t)+β(x j (t)-x i (t)) Adaptive weight formula: ω(t)=sin(πt / 2t max +π)+1 where t is the current iteration number, t max is the maximum number of iterations, ω(t) is the adaptive weight, x i (t + 1) is the position of the firefly i after update at the (t + 1)-th iteration number, β( ) is the attraction calculation formula, x j (t) is the firefly position of the firefly j at the t-th iteration number, x i (t) is the firefly position of the firefly i at the t-th iteration number; The iterative update module is used to calculate the fitness according to the updated position of the target firefly in the population, and output the optimal firefly if the maximum number of iterations is reached; the best firefly is the target environmental parameter.

[0013] Optionally, the control update module includes: a first execution module, a second execution module, a third execution module, a fourth execution module, and a fifth execution module: The first execution module is used to take the PID control parameter as a sparrow, initialize the second preset parameters of the improved sparrow algorithm, and initialize the sparrow population position through tent chaos mapping; the second preset parameters include: population size, maximum number of iterations, upper and lower limits, dimension; The second execution module is used to calculate the fitness of each individual in the sparrow population, sort them in descending order of fitness to obtain a fitness table, screen the corresponding sparrows in the fitness table according to a preset rule to obtain a screening result, use the screening result as the discoverers, and use the remaining sparrows as the followers; The third execution module is used to optimize the discoverer position update strategy through the butterfly algorithm and update the positions of the discoverers according to the discoverer position update strategy; The fourth execution module is used to optimize the follower position update strategy through the Cauchy distribution and the Gaussian distribution and update the followers according to the follower position update strategy; The fifth execution module is used to update the global fitness, detect whether the maximum number of iterations is reached. If so, output the optimal sparrow; otherwise, return to the third execution module. Optionally, the second execution module is further used for: where SY is the fitness, wc(t) is the temperature error, e is the natural constant, P(t) is the air-conditioning operating energy consumption, and T is the total control process time.

[0014] Optionally, the control update module further includes: a data transmission module and a data judgment module: The data transmission module is used to send the target control parameters to the temperature control system, and the temperature control system receives the target control parameters and performs temperature control according to the target control parameters; The data judgment module is used to obtain the second environmental parameters in the target communication cabinet after a preset time period, and judge whether the target communication cabinet is in an abnormal overload state. If it is in an abnormal overload state, update according to the second environmental parameters; otherwise, stop the update.

[0015] Advantages of the present invention: The present invention proposes a method for adaptive temperature control of a communication cabinet. By determining the first environmental parameters of the target communication cabinet, the first environmental parameters are adaptively optimized through an improved firefly algorithm to obtain target environmental parameters; the target environment is simulated according to the target environmental parameters, and PID control parameters are obtained through simulation operation according to the target environment; the PID control parameters are optimized through an improved sparrow algorithm to obtain target control parameters, and the temperature control system is updated according to the target control parameters; the improved firefly algorithm optimizes the environmental parameters, improving accuracy and anti-interference ability; the PID effect is verified based on the accurate parameters to simulate the simulation environment, realizing dynamic tuning; combined with the improved sparrow algorithm to optimize the control parameters, enhancing the temperature adaptive control accuracy and response speed, ensuring the stable and efficient operation of the communication cabinet under different working conditions, and guaranteeing the reliability of the equipment. Description of the Drawings

[0016] The present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 It is a flowchart of a method for adaptively controlling the temperature of a communication cabinet provided by an embodiment of the present invention; Figure 2 It is a framework diagram of a system for adaptively controlling the temperature of a communication cabinet provided by an embodiment of the present invention. Specific implementation manners

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The term "and / or" in this article is only a description of the associated relationship of the associated objects, indicating that there can be three relationships. For example, A and B can represent: A exists alone, A and B exist simultaneously, and B exists alone. These three situations. In addition, the descriptions such as "first" and "second" in the present invention are only for the purpose of description, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on what can be achieved by those of ordinary skill in the art. When the combination of the technical solutions appears to be contradictory or unable to be achieved, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0019] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0020] An embodiment of the present invention provides a method for adaptively controlling the temperature of a communication cabinet. Refer to Figure 1 , Figure 1 It is a flowchart of a method for adaptively controlling the temperature of a communication cabinet provided by an embodiment of the present invention. The method includes the following steps: S101, determine the first environmental parameters of the target communication cabinet, and adaptively optimize the first environmental parameters through an improved firefly algorithm to obtain the target environmental parameters; S102, simulate the target environment according to the target environmental parameters, and perform simulation operation according to the target environment to obtain the PID control parameters; S103, optimize the PID control parameters through an improved sparrow algorithm to obtain the target control parameters, and update the temperature control system according to the target control parameters.

[0021] The first environmental parameters include: the specific heat capacity of the communication cabinet, the thermal resistance of the communication cabinet, the heat generation rate of the communication cabinet, and the time delay of the communication cabinet system; Based on a temperature adaptive control method provided by an embodiment of the present invention, the firefly algorithm is improved to optimize environmental parameters, enhancing accuracy and anti-interference ability; the PID effect is verified based on accurate parameters through simulation of the environment to achieve dynamic optimization; the control parameters are optimized by combining with an improved sparrow algorithm to enhance the temperature adaptive control accuracy and response speed, ensuring the stable and efficient operation of the communication cabinet under different working conditions and guaranteeing the reliability of the equipment.

[0022] In one implementation, the first environmental parameters are, for example: the specific heat capacity of the communication cabinet (the ability of the communication cabinet as a whole to store thermal energy), the thermal resistance of the communication cabinet (the heat dissipation resistance between the communication cabinet and the environment), the heat generation rate of the communication cabinet (the total heat generation power of the communication cabinet), and the system time delay of the communication cabinet system (the system response delay includes: sensor delay, actuator delay, heat propagation delay); the acquisition of the first environmental parameters is all carried out inside the communication cabinet; the acquisition methods of the first environmental parameters are as follows: the specific heat capacity of the communication cabinet is regarded as the specific heat capacity of the air inside the communication cabinet, the thermal resistance of the communication cabinet is measured by a thermocouple, a thermistor or an infrared temperature sensor (the temperature sensors need to be evenly distributed), the heat generation rate of the communication cabinet is obtained by an intelligent electricity meter and a Hall effect sensor (installed at the power input end of the cabinet to monitor the device power consumption in real time), and the system time delay of the communication cabinet system is the delay between the control signal of the communication cabinet system and the actual response time of the corresponding module (for example, the time from when the control signal is sent until the refrigeration system actually starts refrigerating after receiving the control signal).

[0023] In one implementation, the target environment is simulated according to the target environmental parameters. The target environment is a simulation environment (such as Matlab) simulated by the target environmental parameters. The simulation environment is used to simulate the environment inside the communication cabinet. The purpose of the simulation is to verify the effect of the PID control parameters. If the execution effect of the PID control parameters is not ideal, optimization is carried out again, and temperature adaptive control is achieved by continuously optimizing the PID control parameters.

[0024] In one implementation, the first environmental parameters are optimized by a first preset algorithm to obtain the target environmental parameters. The first preset algorithm is an improved firefly algorithm (1. Adaptive inertia weight: dynamically balance global search and local exploitation to avoid premature convergence; 2. Enhanced noise robustness: suppress noise interference through a Gaussian perturbation term); the PID control parameters are optimized by a second preset algorithm to obtain the target control parameters. The second preset algorithm is an improved sparrow algorithm (1. Tent chaos mapping initialization: evenly distribute the initial population to avoid local aggregation; 2. Butterfly algorithm global search: the discoverer quickly moves towards the optimal solution to accelerate convergence; 3. Cauchy-Gaussian mutation: early Cauchy mutation expands the search, and later Gaussian mutation conducts fine optimization) In one implementation, the first environmental parameters of the communication cabinet are optimized by an improved firefly algorithm, which can dynamically balance global search and local development, avoid premature convergence, and enhance noise robustness at the same time. The noise interference is suppressed by a Gaussian perturbation term. This makes the obtained target environmental parameters more accurate and stable, provides a more reliable environmental basis for subsequent temperature control, and thus improves the adaptability and reliability of the temperature control system of the communication cabinet.

[0025] In one implementation, a simulation environment is simulated according to the optimized target environmental parameters, and the PID control parameters are obtained through simulation operation. This process can effectively verify the effect of the PID control parameters. If the execution effect is not ideal, optimization can be carried out again, realizing the accurate simulation and optimization of the temperature control of the communication cabinet, improving the adaptability and effectiveness of the control parameters, and further enhancing the adaptive ability of the temperature control system to ensure that the communication cabinet can maintain a good temperature state under different environmental conditions.

[0026] In one implementation, an improved sparrow algorithm is used to optimize the PID control parameters (in addition to the sparrow algorithm, it can also be a particle swarm optimization algorithm, a grey wolf algorithm, etc.), including Tent chaos mapping initialization, butterfly algorithm global search, and Cauchy-Gaussian mutation. These improvement measures can evenly distribute the initial population, avoid local aggregation, accelerate convergence, and achieve extensive search in the early stage and fine optimization in the later stage during the optimization process. The finally obtained target control parameters can more accurately guide the update of the temperature control system, thereby improving the accuracy and response speed of the temperature control of the communication cabinet and effectively ensuring the stable operation of communication equipment.

[0027] In one embodiment, the first environmental parameters are adaptively optimized by an improved firefly algorithm to obtain target environmental parameters, including: Taking the first environmental parameters as fireflies, initializing the first preset parameters of the improved firefly algorithm; the first preset parameters include: population size, maximum number of iterations, upper and lower bounds, dimension, light intensity absorption coefficient, maximum attraction, and step factor; Calculating the fitness from the position of the target firefly in the population, calculating the target distance between the target firefly and each firefly in the population, and calculating the relative fluorescence brightness between the target firefly and each firefly in the population according to the target distance; the fitness represents the maximum fluorescence brightness of the firefly; Calculating the attraction between the target firefly and each firefly according to the target distance, determining the position update formula according to the number of iterations and the attraction, and updating the position of the firefly according to the position update formula and the relative fluorescence brightness; Position update formula: x i (t + 1)=ω(t)*x i (t)+β(xj (t)-x i (t)) Adaptive weight formula: ω(t)=sin(πt / 2t max +π)+1 where t is the current iteration number, t max is the maximum iteration number, ω(t) is the adaptive weight, x i (t + 1) is the position of firefly i after update at the (t + 1)-th iteration number, β() is the attraction calculation formula, x j (t) is the position of firefly j at the t-th iteration number, x i (t) is the position of firefly i at the t-th iteration number; Calculate the fitness based on the position of the target firefly after update in the population. If the maximum iteration number is reached, output the optimal firefly; the best firefly is the target environmental parameter.

[0028] In one implementation, the first preset parameters are as follows: population size: The population size refers to the number of fireflies in the algorithm, that is, the number of individuals participating in the optimization process. Maximum number of iterations: The maximum number of iterations refers to the maximum number of loops in the algorithm's operation, that is, the upper limit of the number of times the algorithm attempts to optimize the objective function; setting the maximum number of iterations can prevent the algorithm from running indefinitely and control the running time and computational resource consumption of the algorithm. A larger number of iterations may improve the optimization accuracy but also increase the computational time. Upper and lower bounds: The upper and lower bounds refer to the value range of each firefly position variable, that is, the minimum and maximum values of each dimension; the upper and lower bounds limit the position search range of the fireflies, prevent the search process from deviating from the feasible solution space of the actual problem, and ensure the stability and effectiveness of the algorithm. Dimension: The dimension refers to the number of dimensions of each firefly position vector, that is, the number of variables in the optimization problem; the dimension reflects the complexity of the optimization problem. For example, the position of a firefly in a two-dimensional optimization problem can be represented by two variables, while a high-dimensional problem requires more variables. Light intensity absorption coefficient: The light intensity absorption coefficient is a parameter used to describe the attenuation of light intensity, indicating the degree to which light intensity is absorbed during propagation; the light intensity absorption coefficient affects the calculation of the relative fluorescence brightness between fireflies. A larger light intensity absorption coefficient will cause the light intensity to decay faster, thereby affecting the attraction and movement direction between fireflies. Maximum attraction: The maximum attraction refers to the maximum attraction value that can be achieved between fireflies; the maximum attraction determines the maximum movement distance between fireflies and affects the search ability and convergence speed of the algorithm. A larger maximum attraction can make the fireflies approach the optimal solution faster. Step size factor: The step size factor is a parameter used to control the movement step size of fireflies; the step size factor affects the movement distance of fireflies in each iteration. A larger step size factor can make the fireflies move faster in the search space but may lead to an unstable search process; a smaller step size factor can make the search process more refined but may reduce the convergence speed.

[0029] In one implementation, the key factors of the improved firefly algorithm lie in brightness and attraction. Brightness reflects the quality of the firefly's position and determines its movement direction, while attraction determines the distance the firefly moves. Through the continuous update of brightness and attraction, the algorithm achieves the optimization of the goal. In one implementation, the relative fluorescence brightness of a firefly is defined as: I(r)=I0e -γr , where I0 is the maximum fluorescence brightness of the firefly, e is the natural constant, γ is the light intensity absorption coefficient, and r is the Cartesian distance between any two fireflies; the distance between fireflies is defined as: , r ij is the Cartesian distance between any two fireflies i and j, d is the spatial dimension, and k is the k-th component of firefly i in the d-dimensional space coordinates; the attraction of the firefly (attraction calculation formula) is defined as: β(r)=β0e -γ(r×r), where β0 is the maximum attraction; determine the position update formula according to the number of iterations and attraction: x i (t + 1)=ω(t)*x i (t)+β(x j (t)-x i (t)), ω(t)=sin(πt / 2t max +π)+1, t is the current number of iterations, t max is the maximum number of iterations, ω(t) is the adaptive weight, x i (t + 1) is the position of firefly i after update (at the (t + 1)-th iteration), x j (t) is the position of firefly j at the t-th iteration, x i (t) is the position of firefly i at the t-th iteration; when the adaptive weight is larger, the movement step of the previous generation of fireflies has a greater impact on the current movement, and the attraction between fireflies has a relatively smaller impact, thus enhancing the global search ability of the algorithm. On the contrary, when the adaptive weight is smaller, the local search ability of the algorithm is strengthened, and it can perform fine search near the optimal solution, accelerating the convergence speed. In one implementation, by initializing the first preset parameters of the firefly algorithm and dynamically adjusting the positions of fireflies during the iteration process, the algorithm can effectively explore the solution space. The introduction of brightness and attraction enables fireflies to approach the better solution. When the adaptive weight is larger in the initial stage of iteration, the global search ability of the algorithm is enhanced, enabling fireflies to widely explore different regions and avoid falling into local optima. As the number of iterations increases, the adaptive weight gradually decreases, and the local search ability of the algorithm is strengthened. At this time, fireflies can perform fine search near the optimal solution and quickly converge to the global optimal solution. This mechanism of dynamically balancing global search and local search significantly improves the convergence speed of the algorithm. By continuously updating the positions and brightness of fireflies, the finally output optimal firefly position is the target environmental parameter. This process can accurately optimize the first environmental parameter of the communication cabinet, provide high-quality input for subsequent temperature control, and thus improve the performance of the entire temperature control system.

[0030] In one embodiment, the target control parameters are obtained by optimizing the PID control parameters through an improved sparrow algorithm, including: Step 1, take the PID control parameters as sparrows, initialize the second preset parameters of the improved sparrow algorithm, and initialize the sparrow population position through tent chaos mapping; the second preset parameters include: population size, maximum number of iterations, upper and lower limits, dimension; Step 2: Calculate the fitness of each individual in the sparrow population, sort them in descending order of fitness to obtain a fitness table, screen the corresponding sparrows in the fitness table according to a preset rule to obtain a screening result, use the screening result as the discoverers, and use the remaining sparrows as the followers; Step 3: Optimize the position update strategy of the discoverers through the butterfly algorithm, and update the positions of the discoverers according to the position update strategy of the discoverers; Step 4: Optimize the position update strategy of the followers through the Cauchy distribution and the Gaussian distribution, and update the followers according to the position update strategy of the followers; Step 5: Update the global fitness, detect whether the maximum number of iterations is reached. If it is reached, output the optimal sparrow. Otherwise, return to Step 3.

[0031] In one implementation, the second preset parameter, the population size refers to the number of individuals in the sparrow population; the maximum number of iterations refers to the maximum number of loops for the algorithm to run; the upper and lower limits refer to the value range of each sparrow position variable; the dimension refers to the number of dimensions of each sparrow position vector, that is, the number of variables in the optimization problem. In one implementation, in the traditional sparrow algorithm, the random initialization of the population will cause a large number of sparrow individuals to gather together, resulting in uneven distribution. To increase the complexity and diversity of the initial state of the sparrow population, improve the quality of sparrow individuals, and prevent premature convergence of the algorithm resulting in local optimality, a tent chaotic mapping function operator is introduced in the sparrow population initialization stage to ensure that the algorithm can generate different initial solutions in the search space; ,z i is the i-th population, LS is the number of particles in the chaotic sequence, and rand(0,1) is a random number with a value between [0,1].

[0032] In one implementation, sort from largest to smallest according to the fitness value (descending order), screen the corresponding sparrows in the fitness table according to a preset rule to obtain a screening result. The preset rule is that 20%-30% of the individuals are used as discoverers (discoverers are usually individuals with higher fitness values in the current population), and the remaining individuals are used as followers (the remaining individuals are classified as followers, and their fitness values are relatively low). Among them, the discoverers are responsible for widely exploring new areas in the search space to find potential optimal solutions; while the followers perform local searches around the positions of the discoverers to optimize the currently found solutions. In one implementation, in the traditional discoverer position update formula, as the number of iterations increases, the distribution of the population becomes more uniform, which improves the convergence speed of the algorithm to a certain extent. As the dimension of the discoverers decreases and gradually converges to zero, its global search ability decreases; optimize the discoverer position update formula through the global strategy of the butterfly algorithm, enhance the ability of the sparrow algorithm to identify the optimal region and avoid local optimality, while improving the convergence speed, expanding the search space, and enhancing the global search and optimization ability of the algorithm; , f i is the fitness of the i-th sparrow, g * is the optimal solution in the current iteration, SJ is a random number taking values in [0, 1], is the position of the i-th sparrow in the j-th dimension, t represents the current iteration number, q is a random number following a normal distribution, p is a 1×d matrix where each element is 1, YJ ∈ [0, 1] represents the warning value, and AQ ∈ [0.5, 1] represents the safety threshold. In one implementation, the Cauchy distribution is a continuous probability distribution that can bring greater interference to individuals in the early stage of iteration. A Cauchy mutation operator is introduced in the follower update formula to perturb the positions of individuals in the sparrow population, thereby expanding the search range of the algorithm, helping to enhance the global search function of the algorithm, and preventing premature convergence; introducing a Gaussian operator in the follower update formula helps to provide more targeted exploration in the later stage of iteration, enhancing the local search ability of the algorithm and improving its convergence accuracy; combining these two mutation strategies during the update of followers ensures both the global search ability of the algorithm and the detailed search ability near the optimal region. For example: , is the position of the i-th sparrow in the j-th dimension at the (t + 1)-th iteration, KS(0, 1) is the Cauchy distribution function, GS(0, 1) is the Gaussian distribution function, X best (t) is the global optimal position, and ⊗ represents the meaning of multiplication. In one embodiment, the fitness of the individuals in the sparrow population is calculated, and the fitness formula includes: where SY is the fitness, wc(t) is the temperature error, e is the natural constant, P(t) is the air conditioner working energy consumption, and T is the total time of the control process.

[0033] In one implementation, the smaller the fitness, the better the control performance. The PID control parameters are obtained through simulation running according to the target environment, and the energy consumption (usually for cooling) during the operation of the air conditioner can also be obtained through simulation. e -T decreases as the time T increases or decreases. The system responds more sensitively to early errors, enabling the control system to respond to and compensate for the errors in the time-delay system more quickly, thereby reducing the adjustment time and control amplitude of the system.

[0034] In one implementation, the fitness of the sparrow is calculated through the fitness formula, which comprehensively considers two key factors: temperature error and air conditioner energy consumption. The smaller the fitness, the better the control performance. This fitness calculation method enables the system to simultaneously consider temperature control accuracy and energy consumption optimization during the optimization process. The PID control parameters obtained through simulation running can respond to and compensate for the errors in the time-delay system more quickly, thereby reducing the adjustment time and control amplitude of the system and improving the overall performance and energy efficiency of the temperature control system.

[0035] In one embodiment, updating the temperature control system according to the target control parameter includes: Sending the target control parameter to the temperature control system, and the temperature control system receives the target control parameter and performs temperature control according to the target control parameter; Obtaining the second environmental parameter in the target communication cabinet after a preset time period, and determining whether the target communication cabinet is in an abnormal overload state. If it is in an abnormal overload state, update according to the second environmental parameter; otherwise, stop the update.

[0036] In one implementation, the optimized target control parameter is sent to the temperature control system, so that the temperature control system can adjust the temperature based on these accurate parameters. The control parameter obtained based on the optimization algorithm can effectively cope with the complex thermal environment in the communication cabinet, ensuring that the temperature in the cabinet is always maintained within an appropriate range, thereby providing guarantee for the stable operation of the communication equipment.

[0037] In one implementation, after a preset time period, the second environmental parameter in the target communication cabinet is obtained, and it is determined whether it is in an abnormal overload state (including an increase in equipment power consumption. When the actual power consumption of the equipment exceeds a certain proportion of the rated power consumption, such as more than 80%, or when the overall load rate of the cabinet exceeds the upper limit of the designed load, it can be regarded as an abnormal load; a decrease in equipment performance, such as an increase in data transmission delay, an increase in packet loss rate, and a decrease in throughput). This process enables the temperature control system to monitor the actual operating state of the cabinet in real time and detect abnormal situations in a timely manner. When in an abnormal overload state, the system can be updated according to the latest environmental parameter, dynamically adjust the control strategy, thereby enhancing the self - adaptability of the system and enabling it to better cope with various emergencies and load changes.

[0038] In one implementation, through a precise control and adaptive update mechanism, the temperature control system can avoid over - cooling or over - heating on the premise of ensuring the normal operation of the communication equipment, thereby reducing the energy consumption of the system. At the same time, this optimized control method reduces the possibility of equipment failure due to abnormal temperature, reduces the equipment maintenance cost, extends the service life of the equipment, and improves the operating efficiency and economy of the entire communication system.

[0039] Based on the same inventive concept, an embodiment of the present invention also provides a temperature self - adaptive control system for a communication cabinet. Refer to Figure 2 , Figure 2 which is a schematic structural diagram of a temperature self - adaptive control system for a communication cabinet provided by an embodiment of the present invention, including: an environment determination module, a simulation module, and a control update module: An environment determination module, configured to determine the first environment parameters of the target communication cabinet, and adaptively optimize the first environment parameters through an improved firefly algorithm to obtain the target environment parameters; the first environment parameters include: the specific heat capacity of the communication cabinet, the thermal resistance of the communication cabinet, the heat generation rate of the communication cabinet, and the system time delay of the communication cabinet; A simulation module, configured to simulate the target environment according to the target environment parameters to perform simulation operation to obtain PID control parameters; A control update module, configured to optimize the PID control parameters through an improved sparrow algorithm to obtain the target control parameters, and update the temperature control system according to the target control parameters.

[0040] Based on the communication cabinet temperature adaptive control system provided by the embodiments of the present invention, the environment parameters are optimized through an improved firefly algorithm to improve the accuracy and anti-interference ability; the PID effect is verified by simulating the simulation environment based on accurate parameters to achieve dynamic tuning; the control parameters are optimized by combining an improved sparrow algorithm to enhance the temperature adaptive control accuracy and response speed, ensuring the stable and efficient operation of the communication cabinet under different working conditions and guaranteeing the reliability of the equipment.

[0041] In one embodiment, the environment determination module includes: an algorithm initialization module, a fitness calculation module, a firefly update module, and an iterative update module: The algorithm initialization module is configured to use the first environment parameters as fireflies and initialize the first preset parameters of the improved firefly algorithm; the first preset parameters include: population size, maximum number of iterations, upper and lower bounds, dimension, light intensity absorption coefficient, maximum attraction, and step factor; The fitness calculation module is configured to calculate the fitness by the position of the target firefly in the population, calculate the target distance between the target firefly and each firefly in the population, and calculate the relative fluorescence brightness between the target firefly and each firefly in the population according to the target distance; the fitness represents the maximum fluorescence brightness of the firefly; The firefly update module is configured to calculate the attraction between the target firefly and each firefly according to the target distance, determine the position update formula according to the number of iterations and the attraction, and update the position of the firefly according to the position update formula and the relative fluorescence brightness; Position update formula: x i (t + 1)=ω(t)*x i (t)+β(x j (t)-x i (t)) Adaptive weight formula: ω(t)=sin(πt / 2t max +π)+1 where t is the current number of iterations, t max is the maximum number of iterations, ω(t) is the adaptive weight, x i(t + 1) is the position of firefly i after update at the (t + 1)-th iteration, β() is the attraction calculation formula, and x j (t) is the position of firefly j at the t-th iteration, and x i (t) is the position of firefly i at the t-th iteration; An iterative update module, which is used to calculate the fitness based on the position of the target firefly updated in the population. If the maximum number of iterations is reached, the optimal firefly is output; the best firefly is the target environmental parameter.

[0042] In one embodiment, the control update module includes: a first execution module, a second execution module, a third execution module, a fourth execution module, and a fifth execution module: The first execution module is used to use the PID control parameters as sparrows, initialize the second preset parameters of the improved sparrow algorithm, and initialize the position of the sparrow population through tent chaotic mapping; the second preset parameters include: population size, maximum number of iterations, upper and lower limits, and dimension; The second execution module is used to calculate the fitness of the individuals in the sparrow population, sort the fitness in descending order to obtain a fitness table, screen the corresponding sparrows in the fitness table according to a preset rule to obtain a screening result, use the screening result as the discoverer, and use the remaining sparrows as followers; The third execution module is used to optimize the position update strategy of the discoverer through the butterfly algorithm, and update the position of the discoverer according to the position update strategy of the discoverer; The fourth execution module is used to optimize the position update strategy of the followers through the Cauchy distribution and the Gaussian distribution, and update the followers according to the position update strategy of the followers; The fifth execution module is used to update the global fitness, detect whether the maximum number of iterations is reached. If it is reached, the optimal sparrow is output; otherwise, it returns to the third execution module. In one embodiment, the second execution module is further used for: where SY is the fitness, wc(t) is the temperature error, e is the natural constant, P(t) is the air-conditioning working energy consumption, and T is the total time of the control process.

[0043] In one embodiment, the control update module further includes: a data transmission module and a data judgment module: The data transmission module is used to send the target control parameter to the temperature control system, and the temperature control system receives the target control parameter and performs temperature control according to the target control parameter; The data judgment module is used to obtain the second environmental parameter in the target communication cabinet after a preset time period, and judge whether the target communication cabinet is in an abnormal overload state. If it is in an abnormal overload state, it is updated according to the second environmental parameter; otherwise, the update is stopped.

[0044] The above has described in detail an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered as defining the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A temperature self - adaptive control method for a communication cabinet, characterized in that, The method includes: Determine the first environmental parameters of the target communication cabinet, and adaptively optimize the first environmental parameters through an improved firefly algorithm to obtain the target environmental parameters; the first environmental parameters include: the specific heat capacity of the communication cabinet, the thermal resistance of the communication cabinet, the heat generation rate of the communication cabinet, and the time delay of the communication cabinet system; Simulate the target environment according to the target environmental parameters to perform simulation operation to obtain PID control parameters; Optimize the PID control parameters through an improved sparrow algorithm to obtain target control parameters, and update the temperature control system according to the target control parameters.

2. The temperature adaptive control method for a communication cabinet according to claim 1, wherein Adapting and optimizing the first environmental parameters through an improved firefly algorithm to obtain target environmental parameters includes: Regarding the first environmental parameters as fireflies, and initializing the first preset parameters of the improved firefly algorithm; the first preset parameters include: population size, maximum number of iterations, upper and lower bounds, dimension, light intensity absorption coefficient, maximum attraction, and step factor; Calculate the fitness based on the position of the target firefly in the population, calculate the target distance between the target firefly and each firefly in the population, and calculate the relative fluorescence brightness between the target firefly and each firefly in the population according to the target distance; the fitness represents the maximum fluorescence brightness of the firefly; Calculate the attraction between the target firefly and each firefly according to the target distance, determine the position update formula based on the number of iterations and the attraction, and update the position of the firefly according to the position update formula and the relative fluorescence brightness; Position update formula: x i (t + 1)= ω(t)*x i (t)+β(x j (t)-x i (t)) Adaptive weight formula: ω(t)=sin(πt / 2t max +π)+1 where t is the current iteration number, t max is the maximum iteration number, ω(t) is the adaptive weight, and x i (t + 1) is the position of firefly i after update at the (t + 1)-th iteration number, β( ) is the attraction calculation formula, and x j (t) is the position of firefly j at the t-th iteration number, and x i (t) is the position of firefly i at the t-th iteration number; the fitness is calculated according to the position of the target firefly after update in the population, and if the maximum iteration number is reached, the optimal firefly is output; the best firefly is the target environmental parameter.

3. A temperature adaptive control method for a communication cabinet according to claim 1, characterized in that, Optimizing the PID control parameters through an improved sparrow algorithm to obtain target control parameters includes: Step 1, regarding the PID control parameters as sparrows, initializing the second preset parameters of the improved sparrow algorithm, and initializing the position of the sparrow population through tent chaotic mapping; the second preset parameters include: group size, maximum number of iterations, upper and lower limits, dimension; Step 2, calculate the fitness of each individual in the sparrow population, sort the fitness in descending order to obtain a fitness table, screen the corresponding sparrows in the fitness table according to a preset rule to obtain a screening result, regard the screening result as the discoverer, and regard the remaining sparrows as followers; Step 3, optimize the position update strategy of the discoverer through the butterfly algorithm, and update the position of the discoverer according to the discoverer position update strategy; Step 4, optimize the position update strategy of the followers through the Cauchy distribution and the Gaussian distribution, and update the followers according to the follower position update strategy; Step 5, update the global fitness, detect whether the maximum number of iterations is reached, if so, output the optimal sparrow, otherwise, return to Step 3.

4. A temperature adaptive control method for a communication cabinet according to claim 3, characterized in that Calculate the fitness of individual sparrows in the population. The fitness formula includes: Among them, SY is the fitness, wc(t) is the temperature error, e is the natural constant, P(t) is the energy consumption of the air conditioner during operation, and T is the total time of the control process.

5. A temperature adaptive control method for a communication cabinet according to claim 1, characterized in that, Updating the temperature control system according to the target control parameters includes: Send the target control parameters to the temperature control system, and the temperature control system receives the target control parameters and performs temperature control according to the target control parameters; Obtain the second environmental parameters inside the target communication cabinet after a preset time period, and determine whether the target communication cabinet is in an abnormal overload state. If it is in an abnormal overload state, update according to the second environmental parameters; otherwise, stop the update.

6. A temperature adaptive control system for a communication cabinet, characterized in that, The system includes: an environment determination module, a simulation module, and a control update module: The environment determination module is configured to determine the first environment parameters of the target communication cabinet, and adaptively optimize the first environment parameters through an improved firefly algorithm to obtain the target environment parameters; the first environment parameters include: the specific heat capacity of the communication cabinet, the thermal resistance of the communication cabinet, the heat generation rate of the communication cabinet, and the time delay of the communication cabinet system; The simulation module is configured to simulate the target environment according to the target environment parameters and perform simulation operation to obtain the PID control parameters; The control update module is configured to optimize the PID control parameters through an improved sparrow algorithm to obtain the target control parameters, and update the temperature control system according to the target control parameters.

7. A temperature adaptive control system for a communication cabinet according to claim 6, wherein, The environment determination module includes: an algorithm initialization module, a fitness calculation module, a firefly update module, and an iterative update module: The algorithm initialization module is configured to use the first environment parameters as fireflies and initialize the first preset parameters of the improved firefly algorithm; the first preset parameters include: population size, maximum number of iterations, upper and lower bounds, dimension, light intensity absorption coefficient, maximum attraction, and step factor; the fitness calculation module is configured to calculate the fitness through the position of the target firefly in the population, calculate the target distance between the target firefly and each firefly in the population, and calculate the relative fluorescence brightness between the target firefly and each firefly in the population according to the target distance; the fitness represents the maximum fluorescence brightness of the firefly. The firefly update module is configured to calculate the attraction between the target firefly and each firefly according to the target distance, determine the position update formula according to the iteration number and the attraction, and update the position of the firefly according to the position update formula and the relative fluorescence brightness; Position update formula: x i (t + 1)= ω(t)*x i (t)+ β(x j (t)- x i (t)) Adaptive weight formula: ω(t)=sin(πt / 2t max +π)+1 where t is the current iteration number, t max is the maximum iteration number, ω(t) is the adaptive weight, x i (t + 1) is the position of firefly i after update at the (t + 1)-th iteration number, β( ) is the attraction calculation formula, x j (t) is the position of firefly j at the t-th iteration number, x i (t) is the position of firefly i at the t-th iteration number; The iterative update module is configured to calculate the fitness according to the updated position of the target firefly in the population, and output the optimal firefly if the maximum number of iterations is reached; the optimal firefly is the target environment parameter.

8. A temperature adaptive control method for a communication cabinet according to claim 6, characterized in that, The control update module includes: a first execution module, a second execution module, a third execution module, a fourth execution module, and a fifth execution module: The first execution module is configured to use the PID control parameters as sparrows, initialize the second preset parameters of the improved sparrow algorithm, and initialize the position of the sparrow population through tent chaotic mapping; the second preset parameters include: group size, maximum number of iterations, upper and lower limits, dimension; the second execution module is configured to calculate the fitness of each individual in the sparrow population, sort the fitness in descending order to obtain a fitness table, screen the corresponding sparrows in the fitness table according to a preset rule to obtain a screening result, use the screening result as the discoverer, and use the remaining sparrows as followers; The third execution module is configured to optimize the discoverer position update strategy through the butterfly algorithm, and update the position of the discoverer according to the discoverer position update strategy; The fourth execution module is configured to optimize the follower position update strategy through the Cauchy distribution and the Gaussian distribution, and update the followers according to the follower position update strategy. The fifth execution module is used to update the global fitness, detect whether the maximum number of iterations is reached. If it is reached, the optimal sparrow is output; otherwise, it returns to the third execution module.

9. A temperature adaptive control method for a communication cabinet according to claim 8, characterized in that, The second execution module is further configured to: where SY is the fitness, wc(t) is the temperature error, e is the natural constant, P(t) is the power consumption of the air conditioner during operation, and T is the total time of the control process.

10. A temperature adaptive control method for a communication cabinet according to claim 6, characterized in that, The control update module further includes: a data transmission module and a data judgment module: The data transmission module is used to send the target control parameter to the temperature control system, and the temperature control system receives the target control parameter and performs temperature control according to the target control parameter; The data judgment module is used to obtain the second environmental parameter in the target communication cabinet after a preset time period, and judge whether the target communication cabinet is in an abnormal overload state. If it is in an abnormal overload state, it is updated according to the second environmental parameter; otherwise, the update is stopped.

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