System control method based on central air conditioner control target

Abstract

The invention relates to a system control method based on a central air conditioner control target, and relates to the field of central air conditioner control systems.The system control method based on the central air conditioner control target comprises the following steps that S1, an electric device model is established; s2, a system mathematical model is established, specifically, the system mathematical model is established according to the actual central air-conditioning system layout and the electric device connection relation to analyze the influence of the electric devices on the system; s3, searching for an optimal control strategy: searching for a control parameter with the maximum output power of the system under a constraint condition by using an optimization algorithm; and S4, optimization control parameter application: dynamic adjustment and optimization of the central air conditioning system are realized by using the control parameters, efficient cooperative work, quick response to environmental change, reduction of manual intervention and fine management of the central air conditioning system are realized through mathematical modeling and an optimization algorithm, energy consumption and operation cost are remarkably reduced, and the method is suitable for large-scale popularization and application. And the stability and the performance of the system are improved.

Description

Technical Field

[0001] The present application relates to the field of central air-conditioning control systems, and in particular to a system control method based on a central air-conditioning control target. Background Art

[0002] With the development of modern building technology and the improvement of people's living standards, central air-conditioning systems have become an indispensable part of large public buildings, commercial centers, office buildings and other places. Central air-conditioning systems provide a comfortable indoor environment for the entire building through centralized cooling. However, the energy consumption problem of central air-conditioning systems has become increasingly prominent and has become one of the main sources of building energy consumption. Traditional central air-conditioning control methods mainly rely on locally deployed control strategies, which usually adjust the operating status of the air conditioner based on simple rules or preset temperature set points. For example, when the indoor temperature is higher than the set value, the air-conditioning system will increase the cooling capacity; otherwise, it will reduce the cooling capacity.

[0003] However, traditional control methods have the following problems: 1. Traditional control methods often only focus on the control of a single device or a local area, while ignoring the coordinated work of the entire central air-conditioning system. This may cause some equipment to overwork while other equipment is idle, thereby reducing overall energy efficiency; 2. Due to the need to collect and process relevant data such as temperature in real time, the response speed of traditional control systems may be slow, and it is impossible to adjust the operating status of the equipment in time to adapt to environmental changes; 3. Traditional control systems usually require professionals to conduct regular inspections and maintenance, which not only increases operating costs, but may also affect the normal operation of the system; 4. Traditional control systems have poor adaptability to different usage scenarios and load changes, making it difficult to achieve refined management.

[0004] In view of the above-mentioned related technologies, a solution is now proposed. Summary of the invention

[0005] The purpose of this application is to provide a system control method based on central air conditioning control targets to solve the technical problems of lack of global optimization, slow response speed, high maintenance cost and poor adaptability in the prior art.

[0006] The system control method based on the central air conditioning control target provided in this application adopts the following technical solutions: The system control method based on the central air conditioning control target includes the following steps: S1. Establishing electrical component model: Establishing mathematical models of electrical components of central air conditioners and actual connection relationships of electrical components; S2. Establishing a system mathematical model: establishing a system mathematical model according to the actual central air-conditioning system layout and the connection relationship of the electrical components to analyze the influence of the electrical components on the system; S3. Find the optimal control strategy: Use the optimization algorithm to find the control parameters that maximize the system output power under the constraints; S4. Application of optimized control parameters: using the control parameters to achieve dynamic adjustment and optimization of the central air-conditioning system.

[0007] By adopting the above technical solution, we first start with establishing an electrical device model. This step involves in-depth analysis of each electrical device in the central air-conditioning system, such as compressors, fans, cooling towers, etc., and constructing their mathematical models. These models not only reflect the operating characteristics of each electrical device itself, but also provide a basis for subsequent system analysis based on their connection relationship in the actual system. Next, in the stage of establishing the system mathematical model, according to the layout of the actual central air-conditioning system and the connection relationship between the electrical devices, the mathematical models of each electrical device are integrated to form a comprehensive system mathematical model. This model can analyze in detail the impact of each device on the performance of the entire system, including output power, energy consumption, response speed and other aspects. Through this model, we can more intuitively understand the dynamic changes within the system and provide a theoretical basis for finding the optimal control strategy. In the process of finding the optimal control strategy, advanced optimization algorithms are used to search for the control parameter combination with the maximum output power under various constraints such as temperature, humidity, energy consumption, etc. Finally, in the stage of optimizing the control parameter application, the above-mentioned optimal control parameters are applied to the actual central air-conditioning system to achieve dynamic adjustment and optimization of the system. This includes real-time monitoring of the system's operating status and dynamically adjusting control parameters based on the monitoring data to ensure that the system always operates in the optimal state.

[0008] Preferably, in step S1, the establishment of the mathematical model first collects information to obtain the main parameters, working conditions and limiting factors of each electrical component of the central air conditioner, and uses a general framework to express it. The general framework formula is as follows: Dynamic process: ; Output relationship: ; Boundary conditions: ; Wherein, x(t) represents the input variable; P represents the parameter; f represents the dynamic equation; g represents the output function; and b represents the boundary condition. The above formulas are combined to describe the input, output, internal dynamics, and boundary conditions with other equipment of each electrical component of the central air conditioner.

[0009] By adopting the above scheme, after the information collection is completed, a general framework is used to express the mathematical model of these electronic devices. This general framework consists of three core parts: dynamic process, output relationship and boundary conditions. Among them, the dynamic process part describes how the electrical device responds to the change of input variables over time, usually expressed in the form of differential equations or difference equations. The output relationship defines the relationship between the output of the electrical device and its internal state, which may be linear or nonlinear. The boundary conditions limit the behavior of the system at different operating points or extreme conditions to ensure that the model can accurately simulate the actual situation. Specifically, in the dynamic process, we use x (t) to represent the input variables, such as voltage, current or temperature setting values; P represents a set of parameters, which may include thermal conductivity, resistance value or other physical properties; f is a function that describes the dynamic behavior of the system, which calculates the state at the next moment based on the current input and historical state.

[0010] Preferably, in step S1, the connection relationship of the electrical components of the central air conditioner is expressed as follows: Series connection relation expression: ; ; Parallel connection relationship expression: ; ; in, Indicates the total voltage; Indicates the total current; Indicates the voltage of each electrical component of the central air conditioner; The current of each electrical component of the central air conditioner is represented. The basic framework of the actual connection relationship of each electrical component of the central air conditioner is constructed through the above formula, thereby establishing an electrical model.

[0011] By adopting the above scheme, it means that in the series circuit, the current is the same everywhere, and the voltage is distributed according to the characteristics of each electrical component of the central air conditioner. This connection method is usually used in scenarios where multiple devices need to be controlled to start or stop in sequence. For example, in an air conditioning system, it may be necessary to ensure that the cooling fan starts before the compressor to avoid overheating of the compressor; In a parallel circuit, the voltage remains the same, while the current varies according to the impedance of each electrical device. This connection method is suitable for situations where the same voltage needs to be provided to multiple devices at the same time. For example, in an air conditioning system, you may want the condenser and evaporator to be able to adjust the temperature independently, but share the same power supply; Through the above formula, we can construct the basic framework of the actual connection relationship between various electrical components. This framework not only helps us understand the interaction between electronic components, but also provides the necessary structural information for subsequent system-level modeling. In actual central air-conditioning systems, the connections between electrical components are often complex, including both series and parallel connections, and may even involve more complex network structures. Therefore, accurately identifying and expressing these connection relationships is an essential step in establishing an accurate and practical mathematical model.

[0012] Preferably, in step S2, the steps of constructing the system mathematical model are as follows: A1. Determine system variables: determine the physical boundaries of the system and identify the mathematical models of the electrical components of the system; A2. Component system equations: The system equations include energy balance equations, fluid mechanics equations and control equations, which enable the central air conditioning system to describe energy conversion, respond to external changes and maintain a stable indoor environment and the flow characteristics of air in the system; A3. Determine model parameters: Determine unknown parameters in the system equations through experiments; A4. Solve the model: use the finite difference method to solve and establish the mathematical model of the central air-conditioning system.

[0013] By adopting the above scheme, the physical boundaries of the system are clarified, which includes identifying all the electrical components in the central air-conditioning system and understanding their working principles and their interrelationships. Through this step, we can determine which variables are critical, which are secondary, and how they are related. For example, temperature, humidity, air flow rate, etc. may be the key variables we need to pay attention to, while the working status of electrical components, energy consumption, etc. may be secondary variables. Next, the component system equation is the core of describing the dynamic behavior of the central air-conditioning system. These equations usually include energy balance equations, fluid mechanics equations, and control equations. The energy balance equation describes the input, output, and conversion process of energy in the system, ensuring that the principle of energy conservation is met. Then, determining the model parameters is a key step to ensure the accuracy of the model. Since the system equations often contain some unknown parameters, we need to determine the specific values ​​of these parameters through experiments. This may involve a series of experimental design and data analysis work to ensure that the parameters we obtain can truly reflect the physical characteristics of the system.

[0014] Preferably, in step A2, the energy balance equation is as follows: ; in, represents the input heat; W represents the work done by the outside world on the system; represents the change in internal energy; It represents the output heat, and the energy conversion and transfer process in the system is described by the equation; The fluid mechanics equation is as follows: ; in, It expresses the physical quantity per unit mass; Indicates density; represents the velocity vector; represents the diffusion coefficient; S represents the source term, The fluid dynamics equations are obtained by appropriately choosing , and S can be used to obtain specific equations describing different physical phenomena, thereby obtaining the flow characteristics of air in the central air conditioning system; The control equation is as follows: ; in, Indicates the performance parameters of the equipment; It represents the rated value of the parameter; a represents the exponential factor, and the value of a depends on the type of equipment. When a=1, it represents a fan; when a=2, it represents a water pump; when a=3, it represents a compressor. By adjusting the operating frequency, the performance parameters of each electrical component in the central air-conditioning system can be changed proportionally.

[0015] By adopting the above scheme, the energy balance equation is the basic equation that describes the energy conversion and transfer process in the system. The equation shows that the change in the internal energy of the system is equal to the sum of the input heat and the work done by the outside world on the system minus the output heat. Specifically, the input heat may come from the heat generated by electrical devices such as compressors and heaters, while the work done by the outside world on the system may include mechanical actions such as fan rotation and pumping. The output heat is mainly dissipated to the environment through components such as condensers and cooling towers. This equation not only helps us understand the flow path of energy in the system, but also provides us with a basis for adjusting the working state of the system to optimize energy efficiency. The fluid mechanics equation is the key equation that describes the flow characteristics of air in the central air-conditioning system. The equation takes into account the changes of physical quantities such as temperature, pressure, humidity, etc. per unit mass over time and space, as well as the influence of parameters such as density, velocity vector and diffusion coefficient. By appropriately selecting the source term S, we can obtain specific equations that describe different physical phenomena such as turbulence, laminar flow, heat conduction, etc. These equations can reveal the flow law of air in the system, help us predict and control the air flow, velocity and direction, so as to ensure the uniform distribution of indoor air quality and temperature. The control equation is an equation that describes the working characteristics of the equipment according to its performance parameters. In this equation, represents the performance parameters of the equipment such as power, efficiency, flow, etc., represents the rated value of the parameter, and a represents the exponential factor, whose value depends on the type of equipment.

[0016] Preferably, in step S3, the algorithm steps of the optimization algorithm are as follows: B1. Divide the problem of the central air conditioning system into multiple sub-problems; B2. Use simulated annealing algorithm to find the optimal solution for each sub-problem separately; B3, combining the solutions of each sub-problem to form a solution to the original problem; B4. Use the calculation formula to calculate. The formula is as follows: ; in, ; E represents minimizing total energy consumption; represents the cooling load demand of each area; i represents the area; represents the power consumption of the jth device; m is the total number of devices; represents the cooling capacity of the jth equipment to each i-th area; By using the divide-and-conquer method, the optimization problem of the central air-conditioning system is decomposed into multiple sub-problems, the optimal solution of each sub-problem is solved separately, and then these solutions are combined to obtain the objective function of the entire system .

[0017] By adopting the above scheme, the divide-and-conquer strategy is adopted to refine the complex central air-conditioning system optimization problem into several sub-problems, and each sub-problem is solved independently for a specific part or function of the system. The key to this step is to identify and isolate the key variables and constraints in the system, ensuring that each sub-problem is both solvable and can be effectively integrated in subsequent steps. For each sub-problem, we use the simulated annealing algorithm, a powerful global optimization tool, to seek the optimal solution. The simulated annealing algorithm is particularly suitable in this scenario because it is good at jumping out of the local optimum and exploring the global optimal solution. It simulates the slow cooling mechanism in the physical annealing process and accepts inferior solutions with a certain probability, so that there is a chance to jump out of the local minimum and approach the global optimum. For each sub-problem, the algorithm iteratively executes and continuously evaluates the system performance under different configurations until the approximate optimal solution of the sub-problem is found. Then, in step B3, we merge the optimal solutions of all sub-problems to form an overall solution to the original central air-conditioning system optimization problem. This step requires accurate processing of the interactions and dependencies between the solutions of each sub-problem to ensure that the merged solution remains optimal or close to optimal at the global level. This may require additional coordination mechanisms, such as adjusting the operating parameters of certain equipment to balance the cooling load demand between different areas and avoid conflicts within the system. Finally, a specific calculation formula is used to evaluate and adjust the combined solution to ensure that the final solution meets the goal of minimizing total energy consumption. The formula comprehensively considers the cooling load demand of each area, the power consumption of each equipment and its contribution to the cooling of different areas, aiming to achieve efficient use of energy through fine-tuning.

[0018] Preferably, in step S3, the objective function found by the divide-and-conquer method is Perform iterative calculation to obtain the optimal control parameters. The calculation steps of the iterative calculation are as follows: C1. Assume that the objective function , and the parameter vector x has n components; the gradient It is expressed as: ; The calculation formula of C2 algorithm is: ; in, represents the solution vector of the kth iteration; α represents the learning rate; i represents the number of times; represents the i-th component of the parameter vector at the k-th iteration, and the control parameters of the central air-conditioning system are iteratively updated through the formula to obtain the optimal solution.

[0019] By adopting the above scheme, first, we assume that the objective function has been preliminarily determined by the divide-and-conquer method, and the parameter vector x contains n components, which represent the current values ​​of each controllable variable in the system. In order to guide the iteration process, we need to calculate the gradient of the objective function relative to these parameter components. The gradient is a vector whose direction points to the direction where the function value grows fastest, and the magnitude represents the rate of change in this direction. Next, we use the gradient information and the learning rate α to update the control parameters. The learning rate is a pre-set positive number that determines the step size of the parameter update at each iteration. A suitable learning rate can ensure that the algorithm will neither miss the optimal solution due to a large step size nor fall into a slow convergence process due to a small step size. By repeating the above iterative process, we gradually approach the minimum point of the objective function, thereby finding the optimal control parameter combination for the central air-conditioning system. In this process, each iteration is adjusted based on the most accurate gradient information at present, so the value of the objective function can be effectively reduced until the preset convergence condition is met or the maximum number of iterations is reached. The advantage of this method lies in its simplicity and effectiveness. It does not require the specific form or analytical properties of the objective function to be known in advance, but only requires the ability to calculate the objective function and its gradient. In addition, since each iteration is based on the current optimal estimate, the stability and convergence of the algorithm can be guaranteed. In practical applications, we can also adjust the learning rate and other hyperparameters according to specific circumstances to further improve the performance and adaptability of the algorithm.

[0020] Preferably, in step S4, after obtaining the control parameter, the following steps are included: reconfiguring a controller of the central air conditioning system using the control parameters; The electrical components are precisely controlled based on the modified control program.

[0021] By adopting the above solution, first, we need to reconfigure the controller of the central air-conditioning system. This step involves inputting new control parameters into the controller to ensure that the controller can adjust the operating state of the system according to these parameters. During the reconfiguration process, we need to ensure that the communication between the controller and each electrical component is normal so that the controller can accurately send control instructions and receive feedback information. After completing the reconfiguration of the controller, we can accurately control each electrical component in the central air-conditioning system based on the modified control program. By adjusting the working state of the electrical components such as switch state, operating frequency, etc., we can achieve fine control of the entire system.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. By establishing a mathematical model of electrical components and systems, the coordinated work of various parts of the central air-conditioning system is achieved. This method can effectively avoid the situation where some equipment is overworked while other equipment is idle, thereby improving overall energy efficiency; 2. The use of optimization algorithms and methods of dynamically adjusting control parameters speeds up the system's response to environmental changes. This rapid response capability enables the system to adjust the operating status of the equipment in a timely manner to adapt to different environmental requirements; 3. The control method based on mathematical models and optimization algorithms enables the system to be finely managed according to different usage scenarios and load changes. This flexibility enables the system to maintain efficient operation under different conditions; 4. By finding the optimal control strategy, the goal of maximizing the output power under the constraints is achieved. This directly leads to a significant reduction in system energy consumption, thereby reducing the overall energy consumption of the central air-conditioning system; 5. Use the divide-and-conquer method and iterative calculation to continuously optimize control parameters to ensure that the system always operates in the optimal state. This continuous optimization process further improves the long-term operating efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of the system control method based on the central air conditioning control target of the present application; Figure 2 It is a flow chart for constructing the mathematical model of the application system; Figure 3 It is a flowchart of the algorithm steps of the optimization algorithm of this application. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1 - Attachment Figure 3 , further details of this application are given.

[0025] The embodiment of the present application discloses a system control method based on a central air-conditioning control target.

[0026] Reference Figure 1 , a system control method based on a central air conditioning control target comprises the following steps: S1. Establishing electrical component model: Establishing mathematical models of electrical components of central air conditioners and actual connection relationships of electrical components; S2. Establishing a system mathematical model: establishing a system mathematical model according to the actual central air-conditioning system layout and the connection relationship of the electrical components to analyze the influence of the electrical components on the system; S3. Find the optimal control strategy: Use the optimization algorithm to find the control parameters that maximize the system output power under the constraints; S4. Application of optimized control parameters: using the control parameters to achieve dynamic adjustment and optimization of the central air-conditioning system.

[0027] Specifically, it is necessary to first model each electrical component in the central air-conditioning system. This step includes establishing mathematical models of each electrical component, which can describe the working characteristics and behavior of the electrical components. At the same time, it is also necessary to integrate these models according to the actual connection relationship of the electrical components to form an overall electrical component model. This model will serve as the basis for subsequent analysis and optimization. According to the layout of the actual central air-conditioning system and the connection relationship of the above electrical components, a system-level mathematical model will be established. This system model will be used to analyze the impact of each electrical component on the entire central air-conditioning system. Through this model, we can better understand the operating mechanism of the system and the interaction between different electrical components. After establishing the system model, the next step is to find the optimal control strategy. This requires the use of optimization algorithms to find control parameters that can maximize the system output power under the premise of meeting certain constraints. These constraints may include energy consumption limits, equipment life, comfort requirements, etc. Through the application of optimization algorithms, a set of optimal control parameters can be obtained. These parameters can optimize energy efficiency while ensuring the stable operation of the system. The last step is to apply the obtained optimal control parameters to the actual central air-conditioning system. This needs to be achieved through dynamic adjustment and optimization. Specifically, it is to adjust the control parameters in real time according to the current system status and external environmental conditions to adapt to different operating requirements. This dynamic adjustment can not only improve the energy efficiency of the system, but also extend the service life of the equipment and improve the user's comfort experience.

[0028] Reference Figure 1 In step S1, the mathematical model is established by first collecting information to obtain the main parameters, working conditions and limiting factors of each electrical component of the central air conditioner, and expressing them using a general framework. The general framework formula is as follows: Dynamic process: ; Output relationship: ; Boundary conditions: ; Among them, x(t) includes the dynamic characteristic variables of the electrical components, such as refrigerant pressure, temperature, enthalpy, air flow rate, etc.; P represents the physical parameters of the equipment, such as heat exchange area, efficiency, heat transfer coefficient, etc., which are determined by experiments or equipment calibration; f represents the dynamic equation; g represents the output function, which is used to describe the observed quantities of the equipment, such as heat exchange capacity, refrigerant flow, outlet temperature of the evaporator and condenser, etc.; b represents the boundary conditions; u(t) represents the control input of the system, such as compressor power, fan speed, expansion valve opening, etc. The above formulas are used to describe the input, output, internal dynamics, and boundary conditions with other devices of each electrical component of the central air conditioner. This expression allows each electrical component of the central air conditioner to be regarded as a subsystem, and each subsystem is described by the above formulas. Its input, output, internal dynamics, and boundary conditions with other devices. Through numerical simulation tools, these models can be coupled as a complete system for analysis.

[0029] Specifically, in order to establish the mathematical model of each electrical component of the central air conditioner, detailed information collection is required first. The goal of this stage is to obtain the main parameters, working conditions, and possible limiting factors of each electrical component. This information is essential for building an accurate and practical mathematical model. With the collected data, we can use a general framework to express the characteristics and behaviors of each electrical component, such as the compressor model, which is as follows: ; ; in, Indicates the refrigerant mass flow rate; represents the compressor efficiency. This general framework includes three aspects: dynamic process, output relationship and boundary conditions. Specifically, the dynamic process describes how the electrical device responds to the change of input variables over time; the output relationship defines the impact of the electrical device on the external environment or other equipment; and the boundary conditions define the safety or performance standards that must be followed when the electrical device operates. In this framework, we use x(t) to represent the input variable, which is a function that changes with time and reflects the changes in the external environment or control instructions; P represents a series of parameters, such as temperature set points, wind speed, etc., which are key factors affecting the behavior of the electrical device; f is a method to describe the dynamic behavior inside the electrical device. g represents the output function, which converts the internal state of the electrical device into an observable output signal, such as cooling capacity, energy consumption and other indicators; b represents the boundary conditions, which ensure that the operation of the electrical device does not exceed the design range to avoid damage or failure; by combining the above formulas, we can comprehensively describe the input, output, internal dynamic changes of each electrical device in the central air-conditioning system, and its interaction with other equipment; such a model not only helps to understand the working principle of a single electrical device, but also reveals the synergy and potential problems at the system level.

[0030] Reference Figure 1 In step S1, the connection relationship of the electrical components is expressed as follows: Series connection relation expression: ; ; Parallel connection relationship expression: ; ; in, Indicates the total voltage; Indicates the total current; Indicates the voltage of each electrical component; The current of each electrical device is represented, and the basic framework of the actual connection relationship of the electrical devices is constructed through the above formula, so as to establish the electrical model.

[0031] Specifically, in a series connection, electrical components are connected in sequence, which means that the current will flow to the next electrical component after passing through one electrical component. The characteristic of this connection method is that the total voltage is equal to the sum of the voltages of each electrical component, while the total current remains unchanged. At the same time, since the current is constant in a series circuit, the total current is equal to the current of any one electrical component; The electrical components in a parallel connection are connected in parallel, which means that all electrical components are directly connected to the power supply and share the same voltage. In this connection mode, the total current is equal to the sum of the currents of each electrical component, while the total voltage remains unchanged. At the same time, since the voltage is constant in a parallel circuit, the total voltage is equal to the voltage of any one electrical component; Through the above formula, we can build a basic framework of the actual connection relationship of each electrical component. Under this framework, we can further analyze the impact of each electrical component on the system and how they work together to achieve the overall control goal. For example, in a series connection, if an electrical component fails or its performance degrades, it may affect the operating efficiency of the entire system; while in a parallel connection, even if an electrical component has a problem, other electrical components can still continue to work, thereby improving the reliability and flexibility of the system.

[0032] Reference Figure 2 ,In step S2, the steps of constructing the system mathematical model are as follows: A1. Determine system variables: determine the physical boundaries of the system and identify the mathematical models of the electrical components of the system; A2. Component system equations: The system equations include energy balance equations, fluid mechanics equations and control equations, which enable the central air conditioning system to describe energy conversion, respond to external changes and maintain a stable indoor environment and the flow characteristics of air in the system; A3. Determine model parameters: Determine unknown parameters in the system equations through experiments; A4. Solve the model: use the finite difference method to solve and establish the mathematical model of the central air-conditioning system.

[0033] Specifically, we first need to determine the physical boundaries of the system and identify the mathematical model of each electrical component in the system. This step is crucial because it provides us with a basis for analyzing the behavior of the system. By clarifying the physical boundaries of the system, we can define which factors affect the performance of the system and how the system interacts with the external environment. Next, we need to construct the system equations. These equations usually include energy balance equations, fluid mechanics equations, and control equations. The energy balance equation is used to describe the energy conversion process within the system to ensure energy conservation; the fluid mechanics equation is used to simulate the flow characteristics of air in the system, including changes in parameters such as velocity and pressure; the control equation is set according to actual control needs to guide how the system responds to external changes and maintains a stable indoor environment. Together, these equations constitute a complete description of the dynamic behavior of the central air-conditioning system. After determining the system equations, the next step is to determine the unknown parameters in the equations through experiments. These parameters may include heat transfer coefficients, air density, specific heat capacity, etc., which are crucial for accurately predicting system behavior. Through well-designed experiments, we can measure the actual values ​​of these parameters, thereby improving the accuracy and reliability of the model. The last step is to solve the model. At this stage, we use numerical methods, such as the finite difference method, to solve the system equations established previously. The finite difference method is a commonly used numerical computing technique that discretizes continuous partial differential equations and converts them into a series of algebraic equations so that they can be solved on a computer. This method is not only applicable to complex nonlinear systems, but also can control the solution accuracy and computational efficiency by adjusting the grid size and time step.

[0034] Reference Figure 2 , in step A2, the energy balance equation is as follows: ; in, represents the input heat; W represents the work done by the outside world on the system; represents the change in internal energy; It represents the output heat, and the energy conversion and transfer process in the system is described by the equation; The fluid mechanics equation is as follows: ; in, It expresses the physical quantity per unit mass; Indicates density; represents the velocity vector; represents the diffusion coefficient; S represents the source term, The fluid dynamics equations are obtained by appropriately choosing , and S can be used to obtain specific equations describing different physical phenomena, thereby obtaining the flow characteristics of air in the central air conditioning system; The control equation is as follows: ; in, Indicates the performance parameters of the equipment; It represents the rated value of the parameter; a represents the exponential factor, and the value of a depends on the type of equipment. When a=1, it represents a fan; when a=2, it represents a water pump; when a=3, it represents a compressor. By adjusting the operating frequency, the performance parameters of each electrical component in the central air-conditioning system can be changed proportionally.

[0035] Specifically, we define three types of core equations in detail: energy balance equations, fluid dynamics equations, and control equations. These equations together constitute a comprehensive description of the energy conversion and transfer process and air flow characteristics within the system. The energy balance equation is the basis, which expresses the principle of energy conservation within the system, that is, the sum of the input heat and the work done by the outside world on the system is equal to the change in the system's internal energy plus the output heat. The fluid dynamics equation focuses on the flow behavior of the air in the system. Through the Navier-Stokes equations, we can describe the changes of physical quantities (such as velocity, pressure, etc.) per unit mass over time and space. The control equation is a specific implementation of the performance adjustment of each electrical component in the central air-conditioning system. It reflects the relationship between the performance parameters of the equipment and its rated value, and takes into account the impact of the adjustment of the operating frequency on the performance. By accurately defining and applying the energy balance equation, fluid dynamics equation, and control equation, we can construct a detailed mathematical model to describe the energy conversion, transfer process, and air flow characteristics of the central air-conditioning system. This not only provides a solid theoretical basis for the subsequent optimization control strategy, but also ensures that the model can accurately reflect the dynamic behavior of the actual system.

[0036] Reference Figure 3 In step S3, the algorithm steps of the optimization algorithm are as follows: B1. Divide the problem of the central air conditioning system into multiple sub-problems; B2. Use simulated annealing algorithm to find the optimal solution for each sub-problem separately; B3, combining the solutions of each sub-problem to form a solution to the original problem; B4. Use the calculation formula to calculate. The formula is as follows: ; in, ; E represents minimizing total energy consumption; represents the cooling load demand of each area; i represents the area; represents the power consumption of the jth device; m is the total number of devices; represents the cooling capacity of the jth equipment to each i-th area; By using the divide-and-conquer method, the optimization problem of the central air-conditioning system is decomposed into multiple sub-problems, the optimal solution of each sub-problem is solved separately, and then these solutions are combined to obtain the objective function of the entire system .

[0037] Specifically, the complex optimization problem of the entire central air-conditioning system is decomposed into multiple smaller and more manageable sub-problems, each of which optimizes a part of the system. First, we divide the system into several subsystems according to function or area, for example, according to different building areas or different types of electrical devices. In this way, each sub-problem focuses on a specific subsystem or area, which greatly reduces the complexity of the problem and makes the solution process more efficient. Next, for each sub-problem, we apply the simulated annealing algorithm to find its optimal solution. The simulated annealing algorithm is a probabilistic optimization algorithm that gradually approaches the global optimal solution by simulating the slow cooling in the physical annealing process. In each iteration, the algorithm tries to make a small change to the current solution and decides whether to accept this change based on certain probability criteria. This mechanism enables the algorithm to jump out of the local optimum and has a greater probability of finding the global optimal solution. After obtaining the optimal solution for each sub-problem, we need to merge these solutions to form a complete solution to the original problem. This step requires special care because there may be interdependencies and constraints between different sub-problems. Therefore, in the process of merging solutions, we will consider the coordination and consistency between the solutions of each sub-problem to ensure that the final solution is feasible and can meet the control objectives of the entire system. Finally, we use a calculation formula to evaluate the performance of the merged solution. This formula usually includes multiple factors, such as total energy consumption, the degree of satisfaction of the cooling load demand of each area, equipment power consumption, and cooling capacity. By minimizing the total energy consumption E, we can ensure that the efficient use of energy is achieved while meeting the cooling load demand of each area.

[0038] Reference Figure 3 In step S3, the objective function found by the divide-and-conquer method is Perform iterative calculation to obtain the optimal control parameters. The calculation steps of the iterative calculation are as follows: C1. Assume that the objective function , and the parameter vector x has n components; the gradient It is expressed as: ; The calculation formula of C2 algorithm is: ; in, represents the solution vector of the kth iteration; α represents the learning rate; i represents the number of times; represents the i-th component of the parameter vector at the k-th iteration, and the control parameters of the central air-conditioning system are iteratively updated through the formula to obtain the optimal solution.

[0039] Specifically, first, we assume that the objective function is f(x), where the parameter vector x has n components. The objective function f(x) is usually expressed as the total energy consumption of the system or other performance indicators that need to be minimized. In order to perform gradient descent, we need to calculate the gradient of the objective function with respect to the parameter vector x. This gradient vector indicates the direction in which the objective function rises fastest at the current parameter point, and the negative gradient direction is the direction in which the objective function falls fastest. Next, we use an iterative formula to update the parameter vector x to gradually approach the optimal solution. The choice of learning rate is very critical. Too large a learning rate may cause algorithm instability, and too small a learning rate may converge too slowly. In practical applications, the learning rate can be adjusted according to different strategies, such as a fixed value, a decreasing sequence, or an adaptive method based on the gradient size. In addition, in order to avoid oscillation near the local minimum, a momentum term can be introduced or a more advanced optimization algorithm, such as Adam or RMSprop, can be used. Through this iterative process, we continuously update the parameter vector x until a certain stopping criterion is met, such as the norm of the gradient is less than a preset threshold, or the maximum number of iterations is reached. Finally, we will get an optimal parameter vector, which corresponds to the minimum value of the objective function, thereby providing the optimal control parameters for the central air-conditioning system.

[0040] Reference Figure 1 In step S4, after obtaining the control parameter, the following steps are included: reconfiguring a controller of the central air conditioning system using the control parameters; The electrical components are precisely controlled based on the modified control program.

[0041] Specifically, the controller of the central air conditioning system is reconfigured according to the optimal control parameters obtained. This step ensures that the software settings of the controller match the optimized control strategy. Reconfiguration may include adjusting the control logic, updating parameter settings, and calibrating sensors. For example, if the optimization results show that a certain temperature set point or humidity level can more effectively meet the comfort requirements while reducing energy consumption, then these new set values ​​will be input into the controller. In addition, it is necessary to ensure that the hardware interface of the controller is compatible with the electrical components so that the control signals can be accurately transmitted. After the controller is reconfigured, the next step is to accurately control each electrical component based on the modified control program. This means that the control system will adjust the fan speed, water pump flow, compressor operating frequency, etc. according to the new parameters and logic to achieve the optimal indoor environment and energy efficiency ratio. Accurate control requires not only that the controller can accurately issue instructions, but also that the electrical components can respond to these instructions and operate in the expected manner. This may require testing and adjusting the electronic components to ensure that their performance in actual operation is consistent with the design of the control program. During the entire implementation process, the dynamic characteristics of the system and changes in the external environment need to be considered. Therefore, the control system usually contains a feedback mechanism that can monitor key performance indicators in real time and adjust the control parameters as needed. This adaptive capability is critical to maintaining the optimal operating state of the system, especially in the face of uncertain or changing operating conditions.

[0042] The implementation principle of the embodiment of the present application is: according to the actual operation of the central air-conditioning system, a mathematical model of each electrical device is established. These models include the working characteristics and energy consumption relationship of equipment such as compressors, fans, and water pumps. Through detailed modeling, the operating status and energy efficiency performance of each device can be accurately described, and the mathematical model of the entire system can be established based on the actual central air-conditioning system layout and equipment connection relationship. This model should be able to reflect the interaction between the various components in the system and its impact on the overall performance, ensure the accuracy and effectiveness of the optimization algorithm, and use advanced optimization algorithms such as genetic algorithms and particle swarm optimization to solve the established mathematical model. The objective function of optimization is to maximize the output power of the entire system while meeting constraints such as temperature requirements and energy consumption limits. Through iterative calculation, the optimal control parameter combination is found, and then, during the operation of the air-conditioning system, relevant data such as temperature and humidity are collected in real time. Based on these data and the control instructions generated by the optimization algorithm, each component is precisely controlled. By dynamically adjusting the operating status of the equipment, the efficient and energy-saving operation of the system is achieved, and finally the operating status of the central air-conditioning system is continuously monitored, and the control strategy is continuously adjusted and optimized according to the actual operating data. Through the feedback mechanism, the system can ensure that it always maintains the best energy efficiency ratio under different loads and usage conditions, thereby improving energy saving effects and user comfort.

[0043] The embodiments of this specific implementation are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. The same components are represented by the same figure marks. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A system control method based on a central air conditioning control target, characterized in that: The following steps are involved: S1. Establishing electrical component model: Establishing mathematical models of electrical components of central air conditioners and actual connection relationships of electrical components; S2. Establishing a system mathematical model: establishing a system mathematical model according to the actual central air-conditioning system layout and the connection relationship of the electrical components to analyze the influence of the electrical components on the system; S3. Find the optimal control strategy: Use the optimization algorithm to find the control parameters that maximize the system output power under the constraints; S4. Application of optimized control parameters: using the control parameters to achieve dynamic adjustment and optimization of the central air-conditioning system.

2. The system control method based on the central air conditioning control target according to claim 1 is characterized in that: In step S1, the mathematical model is established by first collecting information to obtain the main parameters, working conditions and limiting factors of each electrical component of the central air conditioner, and expressing them using a general framework. The general framework formula is as follows: Dynamic process: ; Output relationship: ; Boundary conditions: ; Wherein, x(t) represents the input variable; P represents the parameter; f represents the dynamic equation; g represents the output function; and b represents the boundary condition. The above formulas are combined to describe the input, output, internal dynamics, and boundary conditions with other equipment of each electrical component of the central air conditioner.

3. The system control method based on central air conditioning control target according to claim 1, characterized in that: In step S1, the connection relationship of the electrical components of the central air conditioner is expressed as follows: Series connection relation expression: ； ； Parallel connection relationship expression: ； ； in, Indicates the total voltage; Indicates the total current; Indicates the voltage of each electrical component of the central air conditioner; The current of each electrical component of the central air conditioner is represented. The basic framework of the actual connection relationship of each electrical component of the central air conditioner is constructed through the above formula, thereby establishing an electrical model.

4. The system control method based on central air conditioning control target according to claim 1, characterized in that: In step S2, the construction steps of the system mathematical model are as follows: A1. Determine system variables: determine the physical boundaries of the system and identify the mathematical models of the electrical components of the system; A2. Component system equations: The system equations include energy balance equations, fluid mechanics equations and control equations, which enable the central air conditioning system to describe energy conversion, respond to external changes and maintain a stable indoor environment and the flow characteristics of air in the system; A3. Determine model parameters: Determine unknown parameters in the system equations through experiments; A4. Solve the model: use the finite difference method to solve and establish the mathematical model of the central air-conditioning system.

5. The system control method based on central air conditioning control target according to claim 4 is characterized in that: In step A2, the energy balance equation is as follows: ； in, represents the input heat; W represents the work done by the outside world on the system; represents the change in internal energy; It represents the output heat, and the energy conversion and transfer process in the system is described by the equation; The fluid mechanics equation is as follows: ； in, It expresses the physical quantity per unit mass; Indicates density; represents the velocity vector; represents the diffusion coefficient; S represents the source term, The fluid dynamics equations are obtained by appropriately choosing , and S can be used to obtain specific equations describing different physical phenomena, thereby obtaining the flow characteristics of air in the central air conditioning system; The control equation is as follows: ； in, Indicates the performance parameters of the equipment; It represents the rated value of the parameter; a represents the exponential factor, and the value of a depends on the type of equipment. When a=1, it represents a fan; when a=2, it represents a water pump; when a=3, it represents a compressor. By adjusting the operating frequency, the performance parameters of each electrical component in the central air-conditioning system can be changed proportionally.

6. The system control method based on central air conditioning control target according to claim 1, characterized in that: In step S3, the algorithm steps of the optimization algorithm are as follows: B1. Divide the problem of the central air conditioning system into multiple sub-problems; B2. Use simulated annealing algorithm to find the optimal solution for each sub-problem separately; B3, combining the solutions of each sub-problem to form a solution to the original problem; B4. Use the calculation formula to calculate. The formula is as follows: ； in, ; E represents minimizing total energy consumption; represents the cooling load demand of each area; i represents the area; represents the power consumption of the jth device; m is the total number of devices; represents the cooling capacity of the jth equipment to each i-th area; By using the divide-and-conquer method, the optimization problem of the central air-conditioning system is decomposed into multiple sub-problems, the optimal solution of each sub-problem is solved separately, and then these solutions are combined to obtain the objective function of the entire system .

7. The system control method based on central air conditioning control target according to claim 6, characterized in that: In step S3, the objective function found by the divide-and-conquer method is Perform iterative calculation to obtain the optimal control parameters. The calculation steps of the iterative calculation are as follows: C1. Assume that the objective function , and the parameter vector x has n components; the gradient It is expressed as: ； The calculation formula of C2 algorithm is: ； in, represents the solution vector of the kth iteration; α represents the learning rate; i represents the number of times; represents the i-th component of the parameter vector at the k-th iteration, and the control parameters of the central air-conditioning system are iteratively updated through the formula to obtain the optimal solution.

8. The system control method based on central air conditioning control target according to claim 7, characterized in that: In step S4, after obtaining the control parameter, the following steps are included: reconfiguring a controller of the central air conditioning system using the control parameters; The electrical components are precisely controlled based on the modified control program.

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