Constant air volume control method and system of air conditioner fan system

Abstract

The invention belongs to the field of system control, and provides a constant air volume control method and system of an air conditioner fan system, a first machine learning model is used for predicting environmental parameters, and the environmental parameters comprise temperature, humidity and air duct pressure; determining PID controller parameters based on the predicted environmental parameters; dynamically adjusting air conditioner control parameters based on the current system error and the predicted PID controller parameters; the system state is monitored in real time, a feedback mechanism adjusts a control signal, and a set air volume target is maintained. According to the scheme, constant air volume control of the air conditioner fan system can be efficiently carried out.

Description

Technical Field

[0001] The present invention belongs to the field of system control, and in particular relates to a constant air volume control method and system for an air conditioning fan system. Background Art

[0002] In the air conditioning fan system, constant air volume control is a key technology to keep the air supply volume stable within the set value range. The main goal of constant air volume control is to ensure that the air conditioning system can always supply air according to the predetermined air volume under various environmental conditions, so as to meet the indoor temperature, humidity and air quality requirements.

[0003] The operation of the air conditioning system is directly affected by external environmental conditions, especially changes in factors such as temperature, humidity and duct pressure. Fluctuations in these environmental parameters will cause changes in the load of the air conditioning system, thereby affecting the air supply volume. Traditional PID control methods have difficulty maintaining system stability in these complex and changing environments due to their fixed control parameters. When the temperature rises, the air conditioning system needs to increase the air volume for cooling; changes in humidity will change the dehumidification requirements; fluctuations in duct pressure affect the resistance of air flow, increasing the requirements for fan speed regulation.

[0004] Air conditioning fan systems need to have good adaptive adjustment capabilities under different loads and environments. However, traditional control methods are slow to respond to environmental changes and usually require human intervention or excessive parameter adjustments during design to cope with different working conditions, which makes the system adjustment inflexible and inefficient. In scenarios with frequent load changes and complex environments, traditional control methods are difficult to meet the needs of high-precision and high-efficiency air volume control. Summary of the invention

[0005] In order to solve the problems in the prior art, the present invention provides a constant air volume control method for an air conditioning fan system, the method comprising the following steps:

[0006] Predicting environmental parameters using a first machine learning model, the environmental parameters including temperature, humidity, and air duct pressure;

[0007] Determining PID controller parameters based on predicted environmental parameters;

[0008] Dynamically adjust air conditioning control parameters based on current system errors and predicted PID controller parameters;

[0009] The system status is monitored in real time, and the feedback mechanism adjusts the control signal to maintain the set air volume target.

[0010] Another aspect of the present invention further provides a constant air volume control system for an air conditioning fan system, the system comprising the following modules:

[0011] A prediction module, configured to predict environmental parameters using a first machine learning model, wherein the environmental parameters include temperature, humidity, and air duct pressure;

[0012] A first calculation module, used to determine PID controller parameters based on predicted environmental parameters;

[0013] A second calculation module is used to dynamically adjust the air conditioning control parameters based on the current system error and the predicted PID controller parameters;

[0014] The circulation module is used to monitor the system status in real time, and the feedback mechanism adjusts the control signal to maintain the set air volume target.

[0015] The constant air volume control method of the air conditioning fan system of the present invention has the following beneficial effects by adaptively adjusting the PID controller parameters and combining the machine learning model to predict the environmental parameters:

[0016] The present invention can automatically adjust the parameters of the PID controller according to real-time changes in temperature, humidity, air duct pressure, etc., so that the system always maintains the set air volume target, reduces air volume fluctuations caused by environmental changes, and significantly improves the control accuracy of the system.

[0017] By introducing machine learning models to predict environmental parameters (such as temperature, humidity, and air duct pressure), the system can predict changes in the external environment in advance and make corresponding adjustments, enhancing the system's adaptive capabilities and enabling it to operate stably in complex and changing environments.

[0018] Adaptive PID control effectively avoids oscillation or over-adjustment in traditional systems, enables the air conditioning fan system to run smoothly, and ensures the stability of the system under different load and environmental conditions.

[0019] Through the above-mentioned beneficial effects, the present invention significantly improves the energy efficiency and reliability of the system while maintaining a constant air volume of the air-conditioning fan system, and is suitable for air-conditioning fan control in various application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0022] Below, the invention is preferably described in conjunction with the accompanying drawings and specific implementation methods.

[0023] This embodiment solves the above problem through the following steps:

[0024] In one embodiment, reference Figure 1 The present invention provides a constant air volume control method for an air conditioning fan system, which aims to keep the air supply volume stable within a set value range by controlling key parameters in the air conditioning fan system.

[0025] Air conditioning fan system refers to a fan control system used in air conditioning equipment, which is mainly used to adjust the circulation and distribution of air. The fan system controls the flow of air by adjusting the fan speed or the opening of the air valve, thereby adjusting the indoor temperature, humidity and air quality. The system is usually composed of a fan, motor, controller, sensor, air duct, etc., and is widely used in central air conditioning systems, split air conditioning systems, variable air volume air conditioning systems, etc.

[0026] Constant air volume means that the air conditioning fan system maintains a constant air volume during operation to avoid fluctuations in air volume due to changes in the external environment or fluctuations in system load. Constant air volume control technology is used to ensure that the air flow output by the fan can continue to remain within the set target range, thereby ensuring the stability of the indoor environment.

[0027] A first machine learning model is used to predict environmental parameters, where the environmental parameters include temperature, humidity, and air duct pressure.

[0028] In the constant air volume control method of the present invention, a first machine learning model is used to predict environmental parameters, including but not limited to temperature, humidity and air duct pressure. Specifically, the first machine learning model can predict key environmental parameters that affect the operation of the air conditioning fan system in advance by analyzing and learning historical data and real-time collected data of the air conditioning system, thereby providing accurate parameter input for subsequent control strategies.

[0029] The first machine learning model may adopt one or more combinations of multiple machine learning algorithms to improve the accuracy of environmental parameter prediction.

[0030] The first machine learning model can be a regression model or a time series prediction model based on supervised learning or unsupervised learning. Preferably, the model can be a support vector machine regression (SVR), a random forest regression (Random Forest Regression) or a deep learning model based on a long short-term memory network (LSTM).

[0031] The input data of the first machine learning model includes real-time collected data and historical operation data from sensors, specifically including:

[0032] External temperature data (such as measurements from outdoor or indoor temperature sensors) to capture temperature trends at present and over a period of time;

[0033] Air humidity data, used to monitor the humidity level of the air, affecting the dehumidification and air volume adjustment requirements of the air conditioning system;

[0034] The duct pressure data is collected through the pressure difference sensor installed in the duct, reflecting the resistance of air flow in the duct, which in turn affects the load of the air conditioning system.

[0035] The machine learning model is trained using pre-collected historical environmental parameter data. The historical data includes changes in temperature, humidity, and duct pressure over different time periods, supplemented by relevant data such as the actual load and operating time of the air conditioning system. During the model training phase, the model parameters are optimized through supervised learning so that it can provide high-precision parameter prediction values ​​in future prediction processes.

[0036] The machine learning model predicts environmental parameters within a certain period of time in the future based on the input real-time data and historical data, including temperature, humidity and air duct pressure at a certain time or time in the future. The prediction results can be provided to the control module of the air conditioning fan system to adjust the fan operating parameters in advance, such as fan speed, air valve opening, etc.

[0037] In a preferred embodiment, the first machine learning model can be implemented based on a long short-term memory network (LSTM) architecture. The model predicts the temperature, humidity and air duct pressure for a period of time in the future by capturing the implicit time series correlation in the input data. The specific steps are as follows:

[0038] Collect temperature, humidity and air duct pressure data for the past 30 days;

[0039] Use this data to train an LSTM model, which enables the model to identify patterns and trends in parameter changes;

[0040] Obtain the latest temperature, humidity and air duct pressure data in real time and input them into the trained LSTM model;

[0041] Output the predicted temperature, humidity and air duct pressure values ​​for the next 24 hours.

[0042] By using the first machine learning model to predict the environmental parameters of the air-conditioning system, the response speed and control accuracy to environmental changes can be improved, so that the air-conditioning fan system can continuously maintain a constant air volume under complex and changeable environmental conditions, thereby improving the stability and energy efficiency of the system.

[0043] PID controller parameters are determined based on the predicted environmental parameters.

[0044] The system first predicts key environmental parameters through a machine learning model. The environmental parameters include but are not limited to temperature T ext , temperature H ext And the air duct pressure P duct These parameters will affect the operating load of the air conditioning system and the air supply demand of the fan. Assume that at time t, the machine learning model is used to predict the environmental parameters at the future time t+k, and the following is obtained:

[0045] Predicted temperature It represents the predicted value of the ambient temperature at the future time t+k;

[0046] Predicted humidity represents the predicted humidity value at the future time t+k;

[0047] Predicting duct pressure Represents the predicted value of the duct pressure at the future time t+k.

[0048] PID controller (Proportional-Integral-Derivative Controller) is a feedback control system widely used in the field of industrial control. It generates control signals by performing three operations on the system error (the difference between the set value and the actual output value) - proportional (Proportional, P), integral (Integral, I) and derivative (Derivative, D) to adjust the system behavior and make the system output as close to the set target value as possible.

[0049] The PID controller makes corresponding adjustments based on the error of the current system and the change of the error over time. Its control formula is:

[0050]

[0051] in:

[0052] u(t) is the output signal of the controller, which is usually used to adjust the actuators of the system (such as motors, heaters, etc.);

[0053] e(t) = r(t) - y(t) is the error of the system, representing the difference between the set value r(t) and the actual output y(t);

[0054] K p is the proportional coefficient, which indicates the strength of proportional control; proportional control is adjusted according to the size of the current error. The larger the error, the larger the output control signal.

[0055] K iis the integral coefficient, which indicates the strength of the integral control; the integral control is adjusted according to the accumulated value of the error. It can eliminate the steady-state error in the system, that is, the situation where the system cannot reach the set value after a long period of operation.

[0056] K d is the differential coefficient, which indicates the strength of differential control. Differential control is regulated according to the rate of change of the error. It can predict the trend of the error, reduce the oscillation of the system, and improve the stability of the system.

[0057] In the present invention, according to the changes in temperature, humidity and air duct pressure, the various parameters of the PID controller will be dynamically adjusted to ensure that the air conditioning system maintains constant air volume control under different environmental conditions.

[0058] External temperature T ext Affects the load demand of the air conditioning system. Higher temperatures mean that the system needs to increase the air volume for cooling. Therefore, when higher temperatures are predicted in the future, the proportionality factor K needs to be increased. p , in order to speed up the response speed of air volume adjustment. The dynamic adjustment of the proportional coefficient can be expressed by the following formula:

[0059]

[0060] in:

[0061] K p0 is the initial proportionality coefficient of the system at normal temperature;

[0062] is the predicted value of the ambient temperature at the future time t+k

[0063] T ref is the reference temperature (usually the ideal temperature when the system is designed, which may be the median value in the comfortable temperature range);

[0064] α T is the temperature change proportionality coefficient K p The sensitivity coefficient of .

[0065] when Greater than T ref When the proportionality coefficient K p Increase to speed up the response; on the contrary, when the temperature is lower than the reference temperature, K p Reduce and avoid overreaction.

[0066] Humidity ext The increase in humidity will increase the dehumidification load of the air conditioning system, and humidity changes generally cause long-term steady-state errors in the system. If the system operates under high humidity conditions for a long time, the steady-state error will accumulate, so the integral coefficient K iIt needs to be increased to eliminate this error more quickly. The adjustment formula of the integral coefficient can be expressed as:

[0067]

[0068] in:

[0069] K i0 is the initial integration coefficient of the system under normal humidity;

[0070] is the predicted future humidity at time t+k;

[0071] H ref is the base humidity;

[0072] α H is the integral coefficient K of humidity change i The sensitivity coefficient of .

[0073] When humidity Higher than H ref When K i increases, helping to eliminate system errors more quickly; when humidity is low, K i It can be appropriately reduced to prevent the integral effect from being too strong and causing system instability.

[0074] Duct pressure P duct It is an important parameter that reflects the system resistance. When the duct pressure increases, it means that the air flow is blocked and the fan load increases. In order to avoid oscillation caused by pressure fluctuations, the differential coefficient K d It needs to be more sensitive to smooth the system response and reduce fluctuations caused by pressure changes. The adjustment formula of the differential coefficient can be expressed as:

[0075]

[0076] in:

[0077] K d0 is the initial differential coefficient of the system at normal duct pressure;

[0078] is the future duct pressure predicted at time t+k;

[0079] P ref is the base pressure of the air duct;

[0080] α P is the differential coefficient K of the duct pressure change d The sensitivity coefficient of .

[0081] when Higher than P ref When the system needs to increase Kd , thereby increasing the sensitivity of the response and reducing the oscillation caused by pressure fluctuations; when the pressure is low, the system can reduce K d to ensure stable operation of the system.

[0082] Through the above formula, the system can adaptively adjust the parameter K of the PID controller when the environmental parameters such as temperature, humidity, and air duct pressure change. p , K i , K d , thereby maintaining constant air volume control of the air conditioning fan system under complex environmental conditions and ensuring the stability and accuracy of the system.

[0083] Based on the current system error and the predicted PID controller parameters, the air conditioning control parameters are dynamically adjusted. The dynamic adjustment process of the control parameters specifically includes the following steps:

[0084] Real-time acquisition of the error between the set air volume and the actual air volume during system operation

[0085] e(t)=Q s (t)-Q m (t)

[0086] e(t) is the current system error;

[0087] Q s (t) is the target air volume set for the system;

[0088] Q m (t) is the actual air volume of the system at the current time t.

[0089] Based on the current error e(t) and the PID controller parameter K predicted in the previous step p (t+k), K i (t+k) and K d (t+k), calculate the control signal u(t) in real time, and dynamically adjust the control parameters (fan speed) of the air-conditioning system.

[0090] The generation formula of the control signal u(t) is:

[0091]

[0092] The system status is monitored in real time, and the feedback mechanism adjusts the control signal to maintain the set air volume target.

[0093] The system collects key parameters related to air volume control in real time through multiple sensors, including:

[0094] Air volume sensor data: used to measure the actual air volume of the air conditioning fan system at time t;

[0095] Air duct pressure sensor data: used to monitor the air pressure inside the air duct and reflect the flow resistance and air volume changes in the air duct;

[0096] Environmental parameter data: Real-time monitoring of external ambient temperature and humidity to ensure that the system can accurately adjust the air volume under different environmental conditions.

[0097] The system uses a closed-loop feedback control mechanism to compare the actual air volume monitored with the set target air volume and generate an error signal. This error signal is used to guide the controller to adjust the operating parameters of the air conditioning system to reduce the error and ensure that the air volume is stable near the set value.

[0098] Based on the error, a control signal is generated by the PID controller, and the control signal is used to adjust the execution unit of the system, such as the fan speed. That is, returning to the previous step again, the control parameters are cyclically determined to perform dynamic control, that is, based on the real-time error and the predicted PID controller parameters, the air conditioning control parameters are dynamically adjusted.

[0099] On the other hand, the present invention also provides a constant air volume control system for an air conditioning fan system, comprising:

[0100] A prediction module, configured to predict environmental parameters using a first machine learning model, wherein the environmental parameters include temperature, humidity, and air duct pressure;

[0101] A first calculation module, used to determine PID controller parameters based on predicted environmental parameters;

[0102] A second calculation module is used to dynamically adjust the air conditioning control parameters based on the current system error and the predicted PID controller parameters;

[0103] The circulation module is used to monitor the system status in real time, and the feedback mechanism adjusts the control signal to maintain the set air volume target.

[0104] The prior art mentioned in the above background technology section and specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters.

Claims

1. A constant air volume control method for an air conditioning fan system, characterized in that: The method comprises the following steps: Predicting environmental parameters using a first machine learning model, the environmental parameters including temperature, humidity, and air duct pressure; Determining PID controller parameters based on predicted environmental parameters; Dynamically adjust air conditioning control parameters based on current system errors and predicted PID controller parameters; The system status is monitored in real time, and the feedback mechanism adjusts the control signal to maintain the set air volume target.

2. A constant air volume control method for an air conditioning fan system according to claim 1, characterized in that: The machine learning model is a long short-term memory network model.

3. A constant air volume control method for an air conditioning fan system according to claim 1, characterized in that: Determining the PID controller parameters based on the predicted environmental parameters includes: Adjusting the proportional coefficient of the PID controller by predicting the temperature; adjusting the integral coefficient of the PID controller by predicting humidity; The differential coefficient of the PID controller is adjusted by predicting the air duct pressure.

4. A constant air volume control method for an air conditioning fan system according to claim 3, characterized in that: The dynamically adjusting the air conditioning control parameters based on the current system error and the predicted PID controller parameters includes: Substituting the system error and the proportional coefficient, integral coefficient and differential coefficient into the PID control equation, the air conditioning control parameter is obtained.

5. A constant air volume control method for an air conditioning fan system according to claim 3, characterized in that: The real-time monitoring of the system status and the feedback mechanism to adjust the control signal to maintain the set air volume target include: Calculating the real-time error of the system in real time; The air conditioning control parameters are dynamically adjusted based on the real-time error and the predicted PID controller parameters.

6. A constant air volume control system for an air conditioning fan system, characterized in that: The system includes the following modules: A prediction module, configured to predict environmental parameters using a first machine learning model, wherein the environmental parameters include temperature, humidity, and air duct pressure; A first calculation module, used to determine PID controller parameters based on predicted environmental parameters; A second calculation module is used to dynamically adjust the air conditioning control parameters based on the current system error and the predicted PID controller parameters; The circulation module is used to monitor the system status in real time, and the feedback mechanism adjusts the control signal to maintain the set air volume target.

7. A constant air volume control system for an air conditioning fan system according to claim 6, characterized in that: The machine learning model is a long short-term memory network model.

8. A constant air volume control system for an air conditioning fan system according to claim 6, characterized in that: Determining the PID controller parameters based on the predicted environmental parameters includes: Adjusting the proportional coefficient of the PID controller by predicting the temperature; adjusting the integral coefficient of the PID controller by predicting humidity; The differential coefficient of the PID controller is adjusted by predicting the air duct pressure.

9. A constant air volume control system for an air conditioning fan system according to claim 8, characterized in that: The dynamically adjusting the air conditioning control parameters based on the current system error and the predicted PID controller parameters includes: Substituting the system error and the proportional coefficient, integral coefficient and differential coefficient into the PID control equation, the air conditioning control parameter is obtained.

10. A constant air volume control system for an air conditioning fan system according to claim 9, characterized in that: The real-time monitoring of the system status and the feedback mechanism to adjust the control signal to maintain the set air volume target include: Calculating the real-time error of the system in real time; The air conditioning control parameters are dynamically adjusted based on the real-time error and the predicted PID controller parameters.

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