Air conditioning control method and system

Abstract

The present invention relates to the field of refrigeration equipment control technology, specifically an air-conditioning control method and system. First, the present invention establishes a refrigerant flow prediction model by combining the advantages of LSTM and XGBoost, accurately captures time series and static characteristics, improves prediction accuracy and system adaptability, provides reliable data support for intelligent regulation, optimizes refrigerant distribution, improves energy efficiency and reduces energy consumption; secondly, the present invention uses a multivariable collaborative control algorithm to feedforward control the electronic expansion valve opening and compressor frequency, accurately matches load and flow, improves system efficiency and ensures coordinated operation of various components, reduces energy waste, and optimizes stability and comfort; finally, the present invention combines PID feedback control to fine-tune the electronic expansion valve and compressor frequency in real time, ensures optimal energy efficiency and stability of the system, realizes closed-loop control, further improves temperature control accuracy, reduces fluctuations and optimizes energy efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigeration equipment control, and in particular to an air-conditioning control method and system. Background Art

[0002] Multi-split air conditioners are widely used in large commercial buildings, hotels, office buildings, and other locations. Their efficient cooling and heating capabilities and flexible installation methods have made them a mainstream choice in modern air conditioning technology. These systems independently adjust the operating status of each indoor unit based on the temperature requirements of different zones, achieving a high degree of energy efficiency optimization and comfort control. Efficient multi-split air conditioning control systems not only help save energy costs, but also reduce environmental pollution and improve overall building comfort, becoming a core component of smart buildings and green environmental protection concepts.

[0003] However, existing multi-split air conditioning control systems still face certain technical challenges in practical applications. Traditional control methods rely on simple PID control or fixed preset parameters, making it difficult to cope with dynamic changes in system load and complex environmental changes. The inability to accurately predict and adjust the required flow of each indoor unit results in uneven refrigerant distribution, leading to low energy efficiency, poor temperature control accuracy, frequent system starts and stops, and even overheating or overcooling, affecting system stability and comfort. In addition, traditional control methods lack intelligent adaptive capabilities, making it difficult to achieve refined control and effectively reduce energy consumption. Therefore, it is necessary to develop an air conditioning control system that is more intelligent, precise, and adaptable to complex operating conditions.

[0004] To this end, an air conditioning control method and system are proposed. Summary of the Invention

[0005] This invention provides an air conditioning control method and system. By combining an LSTM-based and XGBoost-based refrigerant flow prediction model, this method improves prediction accuracy and system adaptability, optimizes refrigerant distribution, enhances energy efficiency, and reduces energy consumption. A multivariable collaborative control algorithm is used to precisely adjust the electronic expansion valve opening and compressor frequency, ensuring load-flow matching, improving system efficiency, and reducing energy waste. Furthermore, PID feedback control adjusts system parameters in real time, achieving closed-loop control and enhancing temperature control accuracy and overall energy efficiency.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An air conditioning control method, comprising:

[0008] Real-time collection of status parameters of each indoor unit in the multi-split system; the status parameters include evaporator inlet and outlet temperature and pressure, condenser inlet and outlet temperature and pressure, ambient temperature and humidity, and the current opening of the electronic expansion valve;

[0009] Based on the state parameters, the required flow of each indoor unit is calculated using a pre-trained refrigerant flow prediction model, and a global refrigerant allocation strategy is generated in combination with the total system load;

[0010] According to the global refrigerant distribution strategy, a multivariable coordinated control algorithm is used to feedforward control the opening of the electronic expansion valve and synchronously adjust the compressor frequency to match the system load with the flow rate;

[0011] Monitor the evaporator outlet superheat deviation and compressor frequency deviation, and control the electronic expansion valve opening and compressor frequency through PID controller feedback.

[0012] Furthermore, the establishment of the refrigerant flow prediction model includes:

[0013] Acquire historical data records of the indoor unit and preprocess the historical data records to obtain preprocessed data records; the historical data records include historical state parameters and historical demand flow;

[0014] The preprocessed data records are divided into a training set and a validation set in a ratio of 7:3, the training set is used to train the LSTM combined with XGBoost fusion model, and the fusion model is optimized by a loss function;

[0015] When the loss function converges, the fusion model is verified using a validation set; if the validation error is greater than an error threshold, the fusion model is retrained; otherwise, a refrigerant flow prediction model is obtained.

[0016] Furthermore, the refrigerant flow prediction model includes:

[0017] Input layer, used to receive the status parameters of the indoor unit;

[0018] The fusion layer includes LSTM units and XGBoost units, which are used to extract and integrate the time series features and static features of the state parameters;

[0019] Prediction layer, used to output prediction results;

[0020] The error feedback layer is used to execute the loss function and reduce the training error during model training;

[0021] The output layer is used to output the required flow.

[0022] Furthermore, the global refrigerant allocation strategy generated in combination with the total system load includes:

[0023] Calculate total system load and compressor target frequency;

[0024] Establishing a global refrigerant allocation constraint function based on real-time monitoring data and the compressor target frequency; the real-time monitoring data includes the set temperature, real-time temperature, real-time flow rate and demand flow rate of the indoor unit;

[0025] Solving the global refrigerant allocation constraint function to obtain a global refrigerant allocation strategy;

[0026] The calculation formula of the global refrigerant allocation constraint function is:

[0027]

[0028] Among them, min represents the minimization function, i represents the index number of the indoor unit, T set,i represents the set temperature of the i-th indoor unit, T real,i represents the real-time temperature of the i-th indoor unit, m i represents the real-time flow of the i-th indoor unit, m pre,i represents the required flow rate of the i-th indoor unit, f comp represents the compressor target frequency; ω1, ω2, and ω3 represent the temperature weight, flow weight, and load weight, respectively.

[0029] Furthermore, the feedforward control includes:

[0030] defining system state variables and system control variables, and establishing state equations based on the system state variables and the system control variables;

[0031] Solving the state equation to obtain an electronic expansion valve opening adjustment amount and a compressor frequency adjustment amount;

[0032] The electronic expansion valve opening and the compressor frequency are adjusted according to the electronic expansion valve opening adjustment amount and the compressor frequency adjustment amount.

[0033] Furthermore, the feedback control includes:

[0034] Calculate the superheat and superheat deviation at the evaporator outlet;

[0035] Adjust the opening of the electronic expansion valve based on PID control to match the refrigerant flow to the load demand and ensure stable superheat;

[0036] Adjust the compressor frequency based on the electronic expansion valve opening feedback to ensure that the refrigerant supply matches the system load demand and avoid frequent compressor starts and stops;

[0037] Continuously monitor system status and optimize gain coefficients.

[0038] The present invention also provides an air conditioning control system, comprising:

[0039] A data acquisition module is used to collect the status parameters of each indoor unit in the multi-split system in real time; the status parameters include the evaporator inlet and outlet temperature and pressure, the condenser inlet and outlet temperature and pressure, the ambient temperature and humidity, and the current opening of the electronic expansion valve;

[0040] a refrigerant flow prediction module, configured to calculate the required flow of each indoor unit based on the state parameters using a pre-trained refrigerant flow prediction model, and generate a global refrigerant allocation strategy in combination with the total system load;

[0041] a feedforward control module for feedforward controlling the opening of the electronic expansion valve using a multivariable coordinated control algorithm according to the global refrigerant distribution strategy, and synchronously adjusting the compressor frequency to match the system load with the flow rate;

[0042] The feedback control module is used to monitor the evaporator outlet superheat deviation and compressor frequency deviation, and control the electronic expansion valve opening and compressor frequency through PID controller feedback.

[0043] The beneficial effects of the present invention are:

[0044] 1. This paper proposes a refrigerant flow prediction model. By combining the advantages of LSTM and XGBoost, it can simultaneously capture the time series characteristics and static characteristics of the refrigerant flow, thereby improving the prediction accuracy and system adaptability. It can more accurately predict the demand flow of each indoor unit, provide reliable data support for the intelligent control of the air-conditioning system, optimize refrigerant distribution, improve overall energy efficiency and reduce energy consumption.

[0045] 2. This invention utilizes a multivariable coordinated control algorithm to feedforward control the opening of the electronic expansion valve and simultaneously adjust the compressor frequency. This method accurately matches system load with refrigerant flow requirements, thereby achieving efficient system operation. This strategy not only improves cooling efficiency and reduces energy consumption, but also ensures the coordinated operation of various components, reducing unnecessary energy waste. It also optimizes the stability and comfort of the air conditioning system, improving overall system performance.

[0046] 3. This invention monitors the evaporator outlet superheat deviation and compressor frequency deviation, and, in conjunction with PID controller feedback, fine-tunes the electronic expansion valve opening and compressor frequency in real time, ensuring optimal system efficiency and stability during operation. Combined with feedforward control, the PID feedback mechanism provides more refined dynamic adjustment, enabling closed-loop control of the multi-split air conditioning system. This further improves temperature control accuracy, reduces fluctuations, and optimizes refrigerant distribution, ensuring precise matching of load and flow, enhancing system responsiveness and overall energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 This is a flow chart of an air conditioning control method provided by the present invention;

[0049] Figure 2 It is a structural schematic diagram of a refrigerant flow prediction model provided by the present invention;

[0050] Figure 3 This is a structural diagram of an air-conditioning control system provided by the present invention. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0052] Example 1

[0053] An air conditioning control method, such as Figure 1 Shown, including:

[0054] S100: Real-time collection of status parameters of each indoor unit in the multi-split system; the status parameters include evaporator inlet and outlet temperature and pressure, condenser inlet and outlet temperature and pressure, ambient temperature and humidity, and the current opening of the electronic expansion valve;

[0055] S200: Based on the state parameters, calculate the required flow of each indoor unit using a pre-trained refrigerant flow prediction model, and generate a global refrigerant allocation strategy in combination with the total system load;

[0056] Furthermore, the establishment of the refrigerant flow prediction model includes:

[0057] Acquire historical data records of the indoor unit and preprocess the historical data records to obtain preprocessed data records; the historical data records include historical state parameters and historical demand flow;

[0058] The preprocessed data records are divided into a training set and a validation set in a ratio of 7:3, the training set is used to train the LSTM combined with XGBoost fusion model, and the fusion model is optimized by a loss function;

[0059] When the loss function converges, the fusion model is verified using a validation set; if the validation error is greater than an error threshold, the fusion model is retrained; otherwise, a refrigerant flow prediction model is obtained.

[0060] Specifically, the preprocessing process includes outlier detection, missing value filling, and data normalization. Outlier detection is used to remove sensor error data (such as negative temperature and unreasonable EEV opening), missing value filling is used to fill missing data using interpolation or KNN, and data normalization is used to normalize data of different dimensions to improve the model convergence speed.

[0061] Furthermore, the refrigerant flow prediction model is as follows: Figure 2 Shown, including:

[0062] Input layer, used to receive the status parameters of the indoor unit;

[0063] The fusion layer includes LSTM units and XGBoost units, which are used to extract and integrate the time series features and static features of the state parameters;

[0064] Prediction layer, used to output prediction results;

[0065] The error feedback layer is used to execute the loss function and reduce the training error during model training;

[0066] The output layer is used to output the required flow.

[0067] Specifically, the input layer is used to receive the state parameters of the indoor unit; the fusion layer includes an LSTM unit and an XGBoost unit, wherein the LSTM unit includes three LSTM sub-units connected in series, which are used to extract the time series features of the state parameters, and the XGBoost unit includes an XGBoost sub-unit (excluding the prediction part), which is used to extract the static features of the state parameters. The time series features and the static features are fused in the fusion layer through the SUM function; the prediction layer includes a RELU activation function and a fully connected neural network, and the RELU activation function is used to improve the nonlinear fitting ability of the fusion features. The fully connected neural network receives the activated fusion features and outputs the prediction results; the error feedback layer is used to execute the loss function. In this embodiment, the loss function is not limited, and is preferably the mean square error; the output layer is used to output the demand flow. When the loss value of the loss function in 10 epochs is less than the loss threshold, the demand flow is output. In this embodiment, the loss threshold is preferably 0.17.

[0068] This model combines the strengths of LSTM and XGBoost to simultaneously capture the time series and static characteristics of refrigerant flow, improving prediction accuracy. Through loss function optimization in the error feedback layer, the model adaptively adjusts parameters, reduces training error, and enhances generalization. The refrigerant flow prediction model ensures that after deep feature extraction and error optimization of the input data, it ultimately outputs highly accurate demand flow forecasts, providing precise data support for intelligent control of air conditioning systems, improving energy efficiency and operational stability.

[0069] Furthermore, the global refrigerant allocation strategy generated in combination with the total system load includes:

[0070] Calculate total system load and compressor target frequency;

[0071] Establishing a global refrigerant allocation constraint function based on real-time monitoring data and the compressor target frequency; the real-time monitoring data includes the set temperature, real-time temperature, real-time flow rate and demand flow rate of the indoor unit;

[0072] Solving the global refrigerant allocation constraint function to obtain a global refrigerant allocation strategy;

[0073] The calculation formula of the global refrigerant allocation constraint function is:

[0074]

[0075] Among them, min represents the minimization function, i represents the index number of the indoor unit, T set,i represents the set temperature of the i-th indoor unit, T real,i represents the real-time temperature of the i-th indoor unit, m i represents the real-time flow of the i-th indoor unit, m pre,i represents the required flow rate of the i-th indoor unit, f comp represents the compressor target frequency; ω1, ω2, and ω3 represent the temperature weight, flow weight, and frequency weight, respectively.

[0076] Specifically, the calculation formula for the total system load is:

[0077]

[0078] Among them, Q total Indicates the total system load, i indicates the index number of the indoor unit, T set,i represents the set temperature of the i-th indoor unit, T real,i represents the real-time temperature of the i-th indoor unit, C p represents the specific heat capacity of air, V air Indicates the air mass flow rate passing through the indoor unit heat exchanger per unit time; the calculation formula for the compressor target frequency is:

[0079]

[0080] Among them, f comp Indicates the compressor target frequency, f min Indicates the minimum frequency of the compressor, f max Indicates the maximum frequency of the compressor, Q total Indicates the total system load, Q raterepresents the system rated load; a global refrigerant allocation constraint function is established based on real-time monitoring data and the compressor target frequency, where the real-time monitoring data includes the set temperature, real-time temperature, real-time flow rate, and demand flow rate of the indoor unit; the global refrigerant allocation constraint function is solved to obtain the global refrigerant allocation strategy.

[0081] By calculating the total system load and the target compressor frequency, combined with real-time monitoring data, a global refrigerant allocation constraint function is constructed and solved to optimize the refrigerant allocation strategy. This strategy ensures the cooling needs of each indoor unit while simultaneously coordinating the electronic expansion valve and compressor, dynamically matching the system load with the refrigerant flow rate. This improves energy efficiency, reduces energy consumption, and enhances the overall stability and comfort of the air conditioning system.

[0082] S300: Based on the global refrigerant distribution strategy, a multivariable coordinated control algorithm is used to feedforward control the opening of the electronic expansion valve and synchronously adjust the compressor frequency to match the system load with the flow rate;

[0083] Furthermore, the feedforward control includes:

[0084] defining system state variables and system control variables, and establishing state equations based on the system state variables and the system control variables;

[0085] Solving the state equation to obtain an electronic expansion valve opening adjustment amount and a compressor frequency adjustment amount;

[0086] The electronic expansion valve opening and the compressor frequency are adjusted according to the electronic expansion valve opening adjustment amount and the compressor frequency adjustment amount.

[0087] Specifically, the system state variables and system control variables are defined, where the system state variable X = [e T,1 ,e T,2 ,...,e T,n ,Δm1,Δm2,...,Δm n ,f comp ] T , where e T Indicates room temperature deviation, calculated as e T =T set,i -T real,i , Δm represents the change in refrigerant flow rate, and the calculation formula is Δm=m i -m pre,i ; System control variable U=O EEV,1 ,O EEV,2 ,...,O EEV,n ,f comp ] T , where O EEVIndicates the opening degree of the electronic expansion valve; the calculation formula of the state equation is:

[0088] X(k+1)=AX(k)+BU(k);

[0089] Wherein, k represents the current state, k+1 represents the next state, A represents the system state matrix, which represents the dynamic relationship between temperature, flow rate, and compressor frequency, and B represents the control input matrix, which describes the influence of the electronic expansion valve opening and the compressor frequency on the system state. A and B can be obtained based on the physical modeling method and the data-driven method. The physical modeling method establishes the state equation based on thermodynamics and fluid mechanics, and the data-driven method combines the LSTM training data-driven model and extracts the approximate state matrix from it. In this embodiment, the data-driven method is used to obtain the state matrix, and the process can be expressed as follows:

[0090] X(k+1)=f LSTM (X(k),U(k));

[0091] Among them, f LSTM The LSTM model is represented. The state transition relationship is trained using LSTM. After training, the partial derivative of each variable is calculated from LSTM to extract the approximate values ​​of A and B. The state equation is solved to obtain the electronic expansion valve opening adjustment amount and the compressor frequency adjustment amount. The electronic expansion valve opening and the compressor frequency are adjusted according to the electronic expansion valve opening adjustment amount and the compressor frequency adjustment amount.

[0092] By defining system state variables and control variables, establishing and solving state equations, the adjustment of the electronic expansion valve opening and compressor frequency is made more precise and efficient. This optimization strategy ensures dynamic matching of refrigerant flow and system load while improving control response speed, reducing control deviation, and effectively avoiding problems such as frequent compressor starts and stops and insufficient refrigerant supply, thereby improving the operating stability, energy efficiency, and overall comfort of the air conditioning system.

[0093] S400: Monitors the evaporator outlet superheat deviation and compressor frequency deviation, and controls the electronic expansion valve opening and compressor frequency through PID controller feedback.

[0094] Furthermore, the feedback control includes:

[0095] Calculate the superheat and superheat deviation at the evaporator outlet;

[0096] Adjust the opening of the electronic expansion valve based on PID control to match the refrigerant flow to the load demand and ensure stable superheat;

[0097] Adjust the compressor frequency based on the electronic expansion valve opening feedback to ensure that the refrigerant supply matches the system load demand and avoid frequent compressor starts and stops;

[0098] Continuously monitor system status and optimize gain coefficients.

[0099] Specifically, the superheat SH i =T evap,i -T sat (P evap,i ), where T evap,i represents the outlet temperature of the evaporator of the i-th indoor unit, P evap,i represents the outlet pressure of the evaporator of the i-th indoor unit, T sat (P evap,i ) indicates the pressure P evap,i Saturation temperature and superheat deviation ΔSH under the state i =SH i -SH set , where SH set Indicates setting the superheat; based on PID control, the electronic expansion valve opening is adjusted to match the refrigerant flow to the load demand and ensure a stable superheat. The PID calculation formula is:

[0100] O EEV,i (k+1)=0 EEV,i (k)+K p ·ΔSH i +K i ∑ΔSH i

[0101] +K d ·(ΔSH i -ΔSH i-1 );

[0102] Among them, K p , K i and K d Represents the gain coefficient of the PID controller; the compressor frequency is adjusted according to the feedback of the electronic expansion valve opening. The calculation formula for adjusting the compressor frequency can be:

[0103] f comp (k+1)=f comp (k)+K f ·∑(O EEV,i (k+1)-O EEV,i (k));

[0104] Among them, K f Express the compressor frequency adjustment gain coefficient; continuously monitor the system status and optimize the gain coefficient, wherein the gain coefficient includes the gain coefficient of the PID controller and the compressor frequency adjustment gain coefficient.

[0105] By calculating the superheat and its deviation at the evaporator outlet in real time, PID control is used to precisely adjust the electronic expansion valve opening, dynamically matching the refrigerant flow rate to the load demand and ensuring a stable superheat. Furthermore, the compressor frequency is adjusted based on feedback from the electronic expansion valve opening to ensure that the refrigerant supply matches the system load, avoiding increased energy consumption and equipment damage caused by frequent compressor starts and stops. By continuously monitoring the system status and optimizing the gain coefficient, the system possesses stronger adaptive capabilities, further improving the air conditioning system's operational stability, energy efficiency, and overall control accuracy, thereby enhancing equipment life and user comfort.

[0106] Example 2

[0107] A company is upgrading the technology of one of its household multi-split air conditioning systems (one-to-four) and plans to adopt an air conditioning control system provided by the present invention, including:

[0108] A data acquisition module is used to collect the status parameters of each indoor unit in the multi-split system in real time; the status parameters include the evaporator inlet and outlet temperature and pressure, the condenser inlet and outlet temperature and pressure, the ambient temperature and humidity, and the current opening of the electronic expansion valve;

[0109] a refrigerant flow prediction module, configured to calculate the required flow of each indoor unit based on the state parameters using a pre-trained refrigerant flow prediction model, and generate a global refrigerant allocation strategy in combination with the total system load;

[0110] a feedforward control module for feedforward controlling the opening of the electronic expansion valve using a multivariable coordinated control algorithm according to the global refrigerant distribution strategy, and synchronously adjusting the compressor frequency to match the system load with the flow rate;

[0111] The feedback control module is used to monitor the evaporator outlet superheat deviation and compressor frequency deviation, and control the electronic expansion valve opening and compressor frequency through PID controller feedback.

[0112] To evaluate the performance differences between the new and existing systems, a performance test was conducted under high-temperature summer conditions (outdoor temperature 35°C, initial indoor temperature 30°C, target temperature 25°C). The original air conditioning control system was based on a traditional PID controller. Table 1 compares the performance of the original and current control systems. The performance test results show that the current control system significantly outperforms the original system in several key indicators. The energy efficiency ratio (EER) increased by 28.1%, the temperature stabilization time was shortened by 52%, the superheat fluctuation range was significantly reduced by 77%, and the number of compressor starts and stops was reduced by 62.5%. These improvements demonstrate that the current control system, through its optimized global refrigerant allocation strategy and multivariable coordinated control algorithm, not only improves energy efficiency and temperature control accuracy, but also reduces system fluctuations and equipment losses, further enhancing system stability and operational efficiency. Overall, the current control system offers significant advantages in reducing energy consumption, improving comfort, and extending equipment life.

[0113] Table 1 Comparison of indicators between the original control system and the current control system

[0114] index Original control system Current control system Energy Efficiency Ratio (EER) 3.2 4.1 Temperature stabilization time (minutes) 25 12 Superheat fluctuation range (℃) ±3.5 ±0.8 Compressor start and stop times / hour 8 3

[0115] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An air conditioning control method, characterized in that: include: Real-time collection of status parameters of each indoor unit in the multi-split system; the status parameters include evaporator inlet and outlet temperature and pressure, condenser inlet and outlet temperature and pressure, ambient temperature and humidity, and the current opening of the electronic expansion valve; Based on the state parameters, the required flow of each indoor unit is calculated using a pre-trained refrigerant flow prediction model, and a global refrigerant allocation strategy is generated in combination with the total system load; According to the global refrigerant distribution strategy, a multivariable coordinated control algorithm is used to feedforward control the opening of the electronic expansion valve and synchronously adjust the compressor frequency to match the system load with the flow rate; Monitor the evaporator outlet superheat deviation and compressor frequency deviation, and control the electronic expansion valve opening and compressor frequency through PID controller feedback; The global refrigerant allocation strategy generated by combining the total system load includes: Calculate total system load and compressor target frequency; Establishing a global refrigerant allocation constraint function based on real-time monitoring data and the compressor target frequency; the real-time monitoring data includes the set temperature, real-time temperature, real-time flow rate and demand flow rate of the indoor unit; Solving the global refrigerant allocation constraint function to obtain a global refrigerant allocation strategy; The calculation formula of the global refrigerant allocation constraint function is: Among them, min represents the minimization function, i represents the index number of the indoor unit, T set,i represents the set temperature of the i-th indoor unit, T real,i represents the real-time temperature of the i-th indoor unit, m i represents the real-time flow of the i-th indoor unit, m pre,i represents the required flow rate of the i-th indoor unit, f comp represents the compressor target frequency; ω1, ω2, and ω3 represent the temperature weight, flow weight, and load weight, respectively.

2. An air conditioning control method according to claim 1, characterized in that: The establishment of the refrigerant flow prediction model includes: Acquire historical data records of the indoor unit and preprocess the historical data records to obtain preprocessed data records; the historical data records include historical state parameters and historical demand flow; The preprocessed data records are divided into a training set and a validation set in a ratio of 7:3, the training set is used to train the LSTM combined with XGBoost fusion model, and the fusion model is optimized by a loss function; When the loss function converges, the fusion model is verified using a validation set; if the validation error is greater than an error threshold, the fusion model is retrained; otherwise, a refrigerant flow prediction model is obtained.

3. The air conditioning control method according to claim 2, characterized in that: The refrigerant flow prediction model includes: Input layer, used to receive the status parameters of the indoor unit; The fusion layer includes LSTM units and XGBoost units, which are used to extract and integrate the time series features and static features of the state parameters; Prediction layer, used to output prediction results; The error feedback layer is used to execute the loss function and reduce the training error during model training; The output layer is used to output the required flow.

4. The air conditioning control method according to claim 1, characterized in that: The feedforward control includes: defining system state variables and system control variables, and establishing state equations based on the system state variables and the system control variables; Solving the state equation to obtain an electronic expansion valve opening adjustment amount and a compressor frequency adjustment amount; The electronic expansion valve opening and the compressor frequency are adjusted according to the electronic expansion valve opening adjustment amount and the compressor frequency adjustment amount.

5. The air conditioning control method according to claim 1, characterized in that: The feedback control includes: Calculate the superheat and superheat deviation at the evaporator outlet; Adjust the opening of the electronic expansion valve based on PID control to match the refrigerant flow to the load demand and ensure stable superheat; Adjust the compressor frequency based on the electronic expansion valve opening feedback to ensure that the refrigerant supply matches the system load demand and avoid frequent compressor starts and stops; Continuously monitor system status and optimize gain coefficients.

6. An air conditioning control system, characterized in that: include: A data acquisition module is used to collect the status parameters of each indoor unit in the multi-split system in real time; the status parameters include the evaporator inlet and outlet temperature and pressure, the condenser inlet and outlet temperature and pressure, the ambient temperature and humidity, and the current opening of the electronic expansion valve; a refrigerant flow prediction module, configured to calculate the required flow of each indoor unit based on the state parameters using a pre-trained refrigerant flow prediction model, and generate a global refrigerant allocation strategy in combination with the total system load; a feedforward control module for feedforward controlling the opening of the electronic expansion valve using a multivariable coordinated control algorithm according to the global refrigerant distribution strategy, and synchronously adjusting the compressor frequency to match the system load with the flow rate; Feedback control module, used to monitor the evaporator outlet superheat deviation and compressor frequency deviation, and control the electronic expansion valve opening and compressor frequency through PID controller feedback; The global refrigerant allocation strategy generated by combining the total system load includes: Calculate total system load and compressor target frequency; Establishing a global refrigerant allocation constraint function based on real-time monitoring data and the compressor target frequency; the real-time monitoring data includes the set temperature, real-time temperature, real-time flow rate and demand flow rate of the indoor unit; Solving the global refrigerant allocation constraint function to obtain a global refrigerant allocation strategy; The calculation formula of the global refrigerant allocation constraint function is: Among them, min represents the minimization function, i represents the index number of the indoor unit, T set,i represents the set temperature of the i-th indoor unit, T real,i represents the real-time temperature of the i-th indoor unit, m i represents the real-time flow of the i-th indoor unit, m pre,i represents the required flow rate of the i-th indoor unit, f comp represents the compressor target frequency; ω1, ω2, and ω3 represent the temperature weight, flow weight, and load weight, respectively.

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