Intelligent energy-saving regulation and control method for air conditioner room based on simulation modeling and integer programming

Abstract

The invention provides an intelligent energy-saving regulation and control method for an air conditioner room based on simulation modeling and integer programming, and the method mainly comprises the following two parts: building a room environment simulation model based on Sysplorer under an MWORKS platform, and obtaining time sequence data such as temperature, humidity and energy consumption through simulation modeling; and then setting a reasonable objective function, an optimization object and a constraint condition through an integer programming method, and obtaining a final intelligent decision result through an optimization method. The method has the advantages of smooth intelligent decision result and high accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent air-conditioning energy-saving control, and in particular to an intelligent energy-saving control method for an air-conditioning room based on simulation modeling and integer programming. Background Art

[0002] MWORKS, a domestically developed Matlab alternative, has been adopted in various fields. Its functionality already meets the needs of some users, particularly in system modeling and simulation, where it demonstrates impressive performance. By utilizing this domestic software, enterprises can achieve more refined energy management, improve equipment efficiency, and reduce energy waste. Meanwhile, domestic researchers are actively exploring simulation modeling and intelligent control strategies based on this domestic software, aiming to match or even surpass the performance of foreign software.

[0003] With the development of domestic industrial software, it will play an increasingly important role in the industrial ecosystem of intelligent manufacturing and high-end manufacturing. In the context of intelligent operation and maintenance of air-conditioning rooms, current data center and computer room air conditioning systems consume high energy. Traditional fixed rules and threshold control methods are difficult to adapt to complex and dynamic operating environments, resulting in significant energy waste. While air cooling systems offer a simple structure and easy maintenance, they are energy-inefficient. While water cooling systems offer high energy efficiency, they are complex and require higher levels of operation and maintenance. As data centers expand, fluctuations in room temperature and humidity, IT load variations, and environmental factors impact the efficiency of traditional control methods, making it difficult to accurately match real-time demand. This results in frequent air conditioning system starts and stops, excessive energy consumption, and even impacts stable equipment operation. Furthermore, traditional energy management methods lack intelligent predictive capabilities and rely solely on statically set operating parameters, unable to dynamically adjust based on real-time data. This results in high PUE (power usage effectiveness) and escalating operation and maintenance costs. More advanced energy-saving optimization technologies are urgently needed.

[0004] Existing, outdated energy-saving algorithms primarily rely on simple PID control, empirical threshold settings, and limited logic control strategies, failing to fully account for the complex thermal environment and energy consumption characteristics of computer rooms. These methods typically start and stop air conditioners based on fixed temperature setpoints and fail to accurately optimize based on dynamic environmental factors, resulting in over-operation or under-responsiveness of the air conditioners. Furthermore, while some computer rooms have introduced limited automatic adjustment strategies, such as timed switching and load balancing, these still lack adaptive capabilities and are unable to cope with IT load fluctuations, environmental changes, and the demands of coordinated multi-device operation. This static, rule-based control approach not only has limited energy savings but can also lead to localized overcooling or overheating due to delayed response. Therefore, with the increasing demand for intelligent data centers, the limitations of outdated energy-saving algorithms are becoming increasingly apparent. There is an urgent need to introduce data-driven, intelligent energy-saving optimization technologies to achieve more precise and efficient air conditioner energy management.

[0005] Therefore, this paper focuses on intelligent energy-saving optimization for air-conditioning rooms. This involves physical modeling of the room environment and intelligent temperature control using optimization methods, particularly integer programming. Through these methods, the research aims to construct an intelligent air-conditioning control system that dynamically optimizes air-conditioning operating parameters, ensuring that temperature and humidity meet target values ​​while minimizing energy consumption. Summary of the Invention

[0006] This paper proposes an intelligent energy-saving control system for air-conditioning rooms, constructs a "simulation-decision-making" closed-loop control architecture, and relies on the MWORKS scientific computing and system modeling and simulation platform to complete the simulation environment construction, algorithm development, model training, testing and tuning, integrated application and other processes of the air-conditioning energy-saving algorithm, thereby supporting the development of the domestic scientific computing and system modeling and simulation ecosystem, and realizing the iterative upgrade of the original air-conditioning energy-saving algorithm.

[0007] The research content is divided into two parts. First, a simulation model of the air-conditioning room is established, and the partial differential equations are solved by the historical data of the room to calculate the temperature distribution and air-conditioning energy consumption in the current space. Based on this, a visual simulation system is built to intuitively display the temperature field and energy consumption, providing a basis for subsequent intelligent control. Secondly, operations research optimization methods are used, including but not limited to integer programming, nonlinear programming, and other meta-heuristic algorithms. This patent will take integer programming as an example, and by establishing an objective function based on temperature, humidity, and energy consumption, the system will be able to optimize the air-conditioning operation strategy according to the predicted data, improve energy-saving efficiency, realize intelligent temperature adjustment of the air-conditioning, and form an end-to-end energy-saving optimization closed loop from virtual simulation, intelligent decision-making to physical execution. The specific steps are as follows:

[0008] 1. Establishment of computer room environment simulation model

[0009] The present invention uses Sysplorer under the MWORKS platform, a domestic MATLAB alternative, to build an air-conditioned room model. The purpose is to build the air-conditioning model and connect it to the room model to obtain the temperature and humidity readings of the sensor. Based on this, the objective function is optimized by integer programming to obtain an intelligent decision on the air-conditioning setting temperature.

[0010] First, the present invention uses the closed-loop air conditioning circuit component in the standard library TAThermalSystem (vehicle thermal management model library) in Sysplorer to cool the air in the circuit. The principle is as follows.

[0011] The CompressorR134a component is used to simulate the process of the compressor increasing the pressure and temperature of the refrigerant through mechanical work. At the same time, the power monitoring sensor is used to read the power change of the air conditioner engine over time. The Integrator component is used to integrate the power over time to finally obtain the air conditioner energy consumption. The performance equation of the compressor is described as follows:

[0012] W=∫pdt

[0013]

[0014] h out =h in +Δh comp ,

[0015] Among them, W represents energy consumption, p represents power, t represents time, p out and p in are the inlet and outlet pressures of the compressor, h in 、h out is the specific enthalpy of the refrigerant flowing into / out of the compressor, is the mass flow rate of refrigerant, η is the compressor efficiency, Δh comp is the specific enthalpy increase during the compression process. out 、p in 、 Δh comp Both can be set manually by inputting into the model. The compressor efficiency η is calculated as follows:

[0016] η=η vol ×η isentropic ×η mech ,

[0017] where η vol (Volumetric efficiency), η isentropic (isentropic efficiency), η mech (Mechanical efficiency) is a parameter that can be set and has different emphases depending on the actual situation.

[0018] The Condenser component is then used to simulate the process in the condenser where the refrigerant releases heat and condenses into liquid through heat exchange with the ambient air. The heat exchange equation of the condenser describes the heat transfer between the refrigerant and the air:

[0019]

[0020] where Q hot is the heat exchange capacity, is the mass flow rate of the refrigerant. The heat exchange efficiency of the condenser is usually described by the logarithmic mean temperature difference:

[0021]

[0022] ΔT1=ΔT ref,in -ΔT air,out ,

[0023] ΔT1=ΔT ref,out -ΔT air,in .

[0024] Where ΔT ref,in , ΔT ref,out is the inflow and outflow refrigerant temperature, ΔT air,out , ΔT air,in The inflow and outflow air temperatures.

[0025] High-pressure liquid refrigerant passes through the simpleTXV component, and the simulated expansion valve converts the high-pressure liquid refrigerant into low-pressure liquid refrigerant through throttling. The throttling principle is as follows:

[0026]

[0027] Among them C d is the flow coefficient, A is the effective flow area of ​​the expansion valve, ρ is the density of the refrigerant, and ΔP is the pressure difference before and after the expansion valve. The opening y of the expansion valve is controlled by the superheat ΔT sh :

[0028]

[0029] Where k is the proportional coefficient, T i is the integration time constant.

[0030] The low-temperature, low-pressure liquid refrigerant is evaporated into gas after passing through the evaporator group EvaporatorR134a, absorbing the heat of the surrounding environment to achieve a cooling effect. The evaporator also uses the heat transfer equation and calculates the superheat ΔT sh , providing key parameters for controlling the expansion valve opening:

[0031] ΔT sh =ΔT ref,out -T sat ,

[0032] Where T sat is the saturation temperature of the refrigerant.

[0033] After all components are connected, the set parameters can be changed by the components and transmitted between components to realize the circulation of the air conditioning circuit.

[0034] Air conditioned by the air conditioner flows out through the air_out1 interface and into the fan's air inlet a. The fan then sends the air through outlet b to the sensor pTSensorAir. The sensor monitors the air temperature and pressure and sends the air to the air resistance device airResist. The air resistance device simulates flow resistance and sends the air to the room's air inlet airPortIn. After circulating through the room, the air returns to the air conditioner's air inlet air_in1 through the air outlet airPortOut.

[0035] In this process, the present invention controls the speed of the air conditioner through a PID controller and converts it into power:

[0036]

[0037] Where u(t) is the control output (air conditioner speed), e(t) is the error signal (the difference between the set value and the measured value), k p 、k i 、k d are the proportional, integral, and differential coefficients.

[0038] The air conditioner speed is converted from the output of the PID controller to the actual speed through the gain module:

[0039]

[0040] The air flow rate and pressure are determined by the characteristics of the fan and air resistance, as well as the built-in functions of the fan component:

[0041]

[0042] Next, by further building a room model, we can simulate the temperature changes after the air conditioner sends air into the room.

[0043] To build the room model, first use the airVolumes component in the TYBase library to define the air volume, and define the parameters len, hei, and wid as the length, height, and width of the room. The volume parameter V of the component is tot =len×wid×hei.

[0044] The PlaneWall component in the TYThermoFluidSys library is used to define the walls of the room. The parameters of the wall, such as solid (material), th (thickness), and surfaceArea (surface area), are set according to the actual situation. PlaneWall and airVolumes perform heat exchange to simulate heat conduction between the wall and the air.

[0045] The heat exchange between air and wall satisfies the equation:

[0046] Q conv =h×A×(T air -T wall )

[0047] Among them, Q conv is the heat exchange between air and wall, h is the heat transfer coefficient, A is the wall surface area, T air is the air temperature T wall is the wall temperature.

[0048] The HeatCapacitor component in TYBase is used as the heat capacity to connect to the wall, and the C (heat capacity) and T start The initial temperature is set to simulate the heat storage capacity of the wall. The Adiabatic component in TYThermoFluidSys is used as an adiabatic boundary, connecting each of the six walls to prevent excessive heat dissipation from the boundary.

[0049] Finally, we use the heatTransfer component from the TAThermalSystem library and the HeatCapacitor component from TYBase to construct a heat transfer system connected to airVolumes. This represents the heat transfer process between the room and the outside world, with the HeatCapacitor simulating the external heat capacity. The heatTransfer component's Aheatloss (heat transfer area) and k (thermal conductivity) can be set based on actual conditions.

[0050] The airVolumes component, used to simulate the room's air volume, uses the airPortIn and airPortOut air flow interfaces to simulate air entering and exiting the room, respectively. This means that air is exchanged with the outside world through these ports. These two ports connect to the indoor air conditioning model to form a computer room environment simulation model. Once all components are connected, indoor temperature fluctuations can be simulated.

[0051] The heat conduction of the wall satisfies the equation:

[0052]

[0053] Among them, Q cond is the heat flow through the wall, k is the thermal conductivity, A is the wall surface area, d is the wall thickness, T in With T out is the temperature inside and outside the room, and th is the wall thickness, which can be used to calculate the heat transfer between the room and the outside world.

[0054] The room model and indoor air conditioning model can be connected via an air flow interface to form a computer room environment simulation model. After simulating the indoor temperature changes, multidimensional time series data including temperature, humidity, and energy consumption can be obtained. This data serves as an important basis for designing objective functions and establishing constraints in subsequent integer programming optimization models, providing data support for intelligent regulation and energy-saving optimization of the air conditioning system.

[0055] 2. Intelligent Decision-making of Air Conditioning Temperature Based on Integer Programming

[0056] After completing the simulation modeling of the computer room environment, the system can obtain and update key parameter information including ambient humidity, temperature and energy consumption of air-conditioning equipment in real time. In order to realize the intelligent adjustment of the air-conditioning system, improve energy efficiency and ensure the environmental stability of the computer room operation, it is proposed to introduce a variety of optimization algorithms to dynamically optimize the air-conditioning temperature setting. The optimization methods adopted include but are not limited to integer programming, nonlinear programming and various meta-heuristic algorithms (such as genetic algorithms, particle swarm optimization, simulated annealing, etc.) to fully adapt to the control requirements and optimization goals in different scenarios. For the sake of convenience, this patent will take the integer programming model as an example to elaborate on the modeling ideas and solution methods of the air-conditioning temperature adjustment strategy.

[0057] ●The optimization goal of integer programming

[0058] This patent aims to solve the optimal temperature setting value of the air-conditioning system through optimization methods, so as to minimize energy consumption and improve overall operating efficiency while meeting the environmental control requirements of the computer room. Given that actual air-conditioning equipment usually uses integer form when setting the temperature, and the temperature adjustment is discretized with a step size of 1°C, this patent selects integer programming as the optimization modeling method. The optimization model uses the set temperature of the air conditioner as the decision variable, and the temperature value is discretely enumerated at intervals of 1°C within a reasonable range; by constructing constraints between energy consumption and environmental parameters, the global optimal solution for the temperature setting is solved to ensure that the obtained control strategy achieves a balance between energy saving and environmental stability.

[0059] ●Design of objective function

[0060] This method sets the ideal temperature of the computer room to T ideal =25℃, ideal humidity is H ideal =40%, the objective function is set to minimize the distance between the actual temperature and humidity and the ideal temperature and humidity, while hoping to exhaust the lowest possible value, which is set as:

[0061]

[0062] Among them, α, β, γ are the undetermined weight coefficients, which will be determined later through the hierarchical analysis method; number is the number of sensors; T sensor,i (T),H sensor,i (T) is the power value obtained by fitting the function through the current set temperature of the air conditioner in the simulation modeling part, and then the simulated room temperature and humidity are obtained; P(T), P(T) max and P(T) min It is the value obtained by integrating the power in the simulation modeling part. Its maximum and minimum values ​​are the maximum and minimum values ​​of energy consumption within the specified time range.

[0063] At the same time, each of the temperature, humidity, and energy consumption is normalized to measure the impact of each indicator on the objective function more equally.

[0064] ● Determine the weight coefficient by analytic hierarchy process

[0065] 1. Determine the decision criteria

[0066] The objective function includes three metrics:

[0067] (1) Temperature error term

[0068] (2) Humidity error term

[0069] (3) Energy consumption item (P(T)-P(T)) min ) / (P(T) max -P(T) min )

[0070] The priority of the three indicators considered in the present invention is: first ensure that the temperature and humidity adjustment effects in the computer room reach ideal conditions, and then try to reduce energy consumption. Therefore, the weight coefficients of the three items meet the "temperature adjustment > humidity adjustment > energy consumption" requirement, where temperature is "moderately important" than humidity; humidity is "slightly important" than energy consumption; and temperature is "obviously important" than energy consumption.

[0071] The comparison matrix is ​​designed as follows:

[0072]

[0073] 2. Constructing matrices and characteristic equations

[0074]

[0075] The characteristic equation is det(A-λI)=0, which can be simplified step by step to obtain

[0076]

[0077] 3. Newton iteration method to solve the maximum eigenvalue

[0078] The Newton iteration formula is as follows:

[0079]

[0080] And the initial value of iteration λ0=3 (because the matrix order n=3) is adopted.

[0081] Iterate until convergence, and finally get

[0082] λ max ≈3.064.

[0083] 4. Calculate the eigenvector

[0084] Substitute λ max = 3.064 to the equation (A-λI)w = 0, we get the linear equation system:

[0085]

[0086] Solve for the non-normalized eigenvector

[0087] w≈[1,0.6,0.25],

[0088] Normalize the weights to get

[0089]

[0090] 5. Consistency test

[0091] The consistency index (CI) is

[0092]

[0093] From the table, we can see that when n=3, RI=0.58, so the random consistency ratio (CR) is

[0094]

[0095] Therefore, the consistency check passes and the matrix is ​​valid.

[0096] 6. Final objective function coefficient

[0097] The three weight coefficients are

[0098] α=0.540, β=0.324, γ=0.135,

[0099] Therefore, the final objective function can be written as

[0100]

[0101] Constraints

[0102] 1. Optimize the target temperature constraint: set the minimum temperature to T min =16℃, the maximum value is T max =30℃, set the constraint range to T min ≤T≤T max , and T is an integer discrete value with an interval of 1°C.

[0103] 2. Power Constraint: Based on historical data, the maximum instantaneous power is set to 0.8KWH. ​​Once the maximum instantaneous power is exceeded, the air conditioner will be turned off for safety reasons and energy saving considerations.

[0104] 3. Temperature adjustment range constraint: Considering the cost of air conditioning adjustment, the maximum adjustment temperature T for each adjustment will also be set. switch ≤2℃

[0105] Finally, software was used to solve the integer programming optimization problem and determine the optimal air conditioner temperature setting. This setting was then fed back into the simulation model to retrieve real-time temperature, humidity, and energy consumption data, achieving closed-loop control.

[0106] The present invention also provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method. Furthermore, the present invention also provides an electronic device comprising a memory and one or more processors, the memory being configured to store one or more programs; and the one or more programs, when executed by the one or more processors, implementing the above method.

[0107] Beneficial effects:

[0108] 1. This invention is based on the domestic product MWORKS to build components for modeling, data prediction and reinforcement learning.

[0109] 2. Sysplorer provides a graphical modeling environment where users can quickly build models by dragging and dropping components, reducing the difficulty of modeling air-conditioned rooms. It has a built-in efficient solver that can quickly process large-scale complex models, improving simulation efficiency. It also shares a work platform with Syslab, making data connection simple and fast.

[0110] 3. The present invention proposes an intelligent air conditioning control method based on integer programming. By taking the air conditioning temperature set value as an integer decision variable and combining it with multi-objective normalized optimization of temperature, humidity and energy consumption, it effectively avoids the distortion caused by the direct addition of indicators of different dimensions, and ensures the objectivity and accuracy of the multi-objective optimization results.

[0111] 4. The method of the present invention adopts the hierarchical analysis method to determine the weight coefficient of each target indicator, objectively reflecting the actual demand between temperature and humidity environmental comfort and energy consumption economy, and significantly improving the scientificity and practicality of the air-conditioning control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0113] Figure 1 Overall flow chart of intelligent operation and maintenance of air-conditioning room;

[0114] Figure 2 Simulation model of the overall environment of the computer room;

[0115] Figure 3 Simulation model of the room's interior environment;

[0116] Figure 4 Simulation model of the internal structure of the air conditioner;

[0117] Figure 5 Flowchart of the integer programming method. DETAILED DESCRIPTION

[0118] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0119] As shown in the figure, the present invention provides an intelligent energy-saving control method for air-conditioning rooms based on simulation modeling and integer programming, comprising the following steps:

[0120] Step 1: Building a computer room environment simulation model

[0121] Step 1.1: Use Sysplorer to build an air-conditioned room model. After the air-conditioned room is built, first set the realexpression parameter value to determine the air-conditioning engine speed and implementation power.

[0122] Step 1.2: Then use Modelica to set the ambient temperature T_Amb, ambient humidity phi_Amb, high-pressure side initial pressure P0_high, low-pressure side initial pressure P0_low, compressor exhaust initial specific enthalpy h0_high, etc. to set the initial state of the system.

[0123] Step 1.3: Set the expansion valve's proportional coefficient k, maximum opening yMax, minimum opening yMin, etc. to adjust the system's operating status.

[0124] Step 1.4: Set the air volume parameter V tot , wall material solid, thickness th, surface area surfaceArea and thermal conductivity and other parameters. Simulation is carried out.

[0125] Step 1.5: After the simulation is complete, you can view the data in the power sensor of the EvaporatorR134a component. Use the integrator component to integrate the time to obtain the energy consumption time series:

[0126] W=∫pdt.

[0127] Among them, W represents energy consumption, p represents power, and t represents time;

[0128] Step 1.6: The temperature and humidity time series are available in the pTSensorAir component.

[0129] Step 2: Construction and solution of integer programming model

[0130] Step 2.1: Define the integer decision variable T for the air conditioner temperature setting and determine the temperature adjustment range of the air conditioner operation;

[0131] Step 2.2: Use the analytic hierarchy process (AHP) to determine the weight coefficients α, β, and γ of the three indicators of temperature deviation, humidity deviation, and energy consumption;

[0132] Step 2.3: Perform Min-Max normalization on the temperature deviation, humidity deviation, and energy consumption indicators respectively to eliminate the dimensional influence between different indicators and establish the normalized comprehensive objective function:

[0133]

[0134] Among them, α, β, γ are the weight coefficients to be determined; number is the number of sensors; T sensor,i (T),H sensor,i (T) is the power value obtained by fitting the function through the current set temperature of the air conditioner in the simulation modeling part, and then the simulated room temperature and humidity are obtained; P(T), P(T) max and P(T)min It is the value obtained by integrating the power in the simulation modeling part. Its maximum and minimum values ​​are the maximum and minimum values ​​of energy consumption within the specified time range;

[0135] Step 2.4: Set the value range constraints for the air conditioning decision variables to ensure that the optimized temperature decision variables are within the allowable range; set the maximum energy consumption threshold constraint for the air conditioning to prevent the power from exceeding the maximum load capacity allowed by the system during operation; set the temperature adjustment range constraint to avoid frequent changes in the air conditioning temperature in a short period of time to prevent equipment loss.

[0136] Step 3: Model solution and dynamic closed-loop control

[0137] Step 3.1: Input the optimization objective function and constraints into the optimization solver and use the integer programming algorithm to solve the problem and obtain the optimal solution for the air conditioning temperature setting value.

[0138] Step 3.2: Feedback the optimal temperature setting value obtained from the solution to the air conditioning control system to adjust the actual operating temperature of the air conditioner;

[0139] Step 3.3: The temperature adjusted by the air conditioner is re-input into the simulation system for simulation modeling, and the temperature, humidity and energy consumption data obtained from the simulation are fed back to the integer programming model for the next step of optimization solution.

[0140] Step 3.4: Repeat steps 3.1 to 3.3 at regular intervals to dynamically implement intelligent air conditioning control.

[0141] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent energy-saving control method for air-conditioning rooms based on simulation modeling and integer programming, characterized in that: The following steps are involved: Step 1: Build a computer room environment simulation model. Create a simulation model of the air-conditioning computer room. Use the computer room's historical data to solve partial differential equations, calculate the current temperature distribution and air conditioning energy consumption, and build a visual simulation system based on this data to intuitively display the temperature field and energy consumption. Step 2: Construction and solution of integer programming model; Through operations research optimization methods, set reasonable objective functions, optimization objects and constraints; Step 3: Model solution and dynamic closed-loop control; obtain the final intelligent decision-making result through optimization method.

2. The intelligent energy-saving control method for air-conditioning rooms based on simulation modeling and integer programming according to claim 1 is characterized in that: Step 1 specifically includes the following steps: Step 1.1: Use Sysplorer to build an air-conditioned room model. After the air-conditioned room is built, set the realexpression parameter values ​​to determine the air-conditioning engine speed and power. Step 1.2: Use Modelica to set the ambient temperature T_Amb, ambient humidity phi_Amb, high-pressure side initial pressure P0_high, low-pressure side initial pressure P0_low, and compressor exhaust initial specific enthalpy h0_high to set the initial state of the system. Step 1.3: Set the proportional coefficient k, maximum opening yMax, and minimum opening yMin of the expansion valve to adjust the operating state of the system; Step 1.4: Set the air volume parameter V tot , wall material solid, thickness th, surface area surfaceArea and thermal conductivity parameters are used for simulation; Step 1.5: After the simulation is complete, the data can be viewed in the power sensor of the EvaporatorR134a component. The energy consumption time series can be obtained by integrating the time using the integrator component: W=∫pdt. Where W represents energy consumption, p represents power, and t represents time; Step 1.6: The temperature and humidity time series can be obtained in the pTSensorAir component.

3. The intelligent energy-saving control method for air-conditioning rooms based on simulation modeling and integer programming according to claim 2 is characterized in that: Step 2 specifically includes the following steps: Step 2.1: Define the integer decision variable T for the air conditioner temperature setting and determine the temperature adjustment range of the air conditioner operation; Step 2.2: Use the analytic hierarchy process to determine the weight coefficients α, β, and γ of the three indicators of temperature deviation, humidity deviation, and energy consumption; Step 2.3: Perform Min-Max normalization on the temperature deviation, humidity deviation, and energy consumption indicators respectively to eliminate the dimensional influence between different indicators and establish the normalized comprehensive objective function: Among them, α, β, γ are the weight coefficients to be determined; number is the number of sensors; T sensor,i (T),H sensor,i (T) is the power value obtained by fitting the function through the current set temperature of the air conditioner in the simulation modeling part, and then the simulated room temperature and humidity are obtained; P(T), P(T) max and P(T) min It is the value obtained by integrating the power in the simulation modeling part. Its maximum and minimum values ​​are the maximum and minimum values ​​of energy consumption within the specified time range; Step 2.4: Set the value range constraints for the air conditioning decision variables to ensure that the optimized temperature decision variables are within the allowable range; set the maximum energy consumption threshold constraint for the air conditioning to prevent the power from exceeding the maximum load capacity allowed by the system during operation; set the temperature adjustment range constraint to avoid frequent changes in the air conditioning temperature in a short period of time to prevent equipment loss.

4. The intelligent energy-saving control method for air-conditioning rooms based on simulation modeling and integer programming according to claim 3 is characterized in that: Step 3 specifically includes the following steps: Step 3.1: Input the optimization objective function and constraints into the optimization solver and use the integer programming algorithm to solve the problem and obtain the optimal solution for the air conditioning temperature setting value. Step 3.2: Feedback the optimal temperature setting value obtained from the solution to the air conditioning control system to adjust the actual operating temperature of the air conditioner; Step 3.3: The temperature adjusted by the air conditioner is re-input into the simulation system for simulation modeling, and the simulated temperature, humidity, and energy consumption data are fed back to the integer programming model for the next step of optimization solution; Step 3.4: Repeat steps 3.1 to 3.3 at regular intervals to dynamically implement intelligent air conditioning control.

5. A computer storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

6. An electronic device, characterized in that: The method comprises a memory and one or more processors, wherein the memory is used to store one or more programs; when the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 4 is implemented.

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