Expansion valve control method, device, electronic device and storage medium

Abstract

The embodiments of the present application relate to a control method, device, electronic device and storage medium for an expansion valve, the method comprising: obtaining first data of an air-conditioning system; inputting the first data into an adaptive model for data adaptive processing to obtain estimated parameters of the adaptive model; inputting the estimated parameters and the first data into the adaptive model for data prediction processing to obtain a first exhaust temperature of the adaptive model; determining a first opening sequence of the expansion valve according to the first exhaust temperature, and controlling the expansion valve opening using the first opening sequence. The exhaust temperature and the expansion valve opening are adaptively controlled by the adaptive model, so that the air conditioner stably maintains the indoor temperature within the user-set range. In this way, the accuracy and energy efficiency issues in the control of the electronic expansion valve of a household air conditioner can be achieved, with the technical effects of improving control accuracy, optimizing energy efficiency performance, and optimizing user experience.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart home technology, and in particular to a control method, device, electronic device, and storage medium for an expansion valve. Background Art

[0002] In residential air conditioning systems, the control of electronic expansion valves is crucial for cooling performance and energy efficiency. Traditional control methods cannot accurately predict system response and dynamic changes, resulting in reduced cooling system performance and energy waste.

[0003] Traditional air conditioning system control methods are often unable to accurately predict and control exhaust temperature when faced with system dynamic changes and uncertainties, resulting in reduced cooling system performance, increased energy consumption, and a suboptimal user experience.

[0004] Therefore, how to improve the control of the electronic expansion valve has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, in order to solve the technical problem of the above-mentioned electronic expansion valve and the dynamic change of exhaust temperature, the embodiments of the present application provide a control method, device, electronic device and storage medium for an expansion valve.

[0006] In a first aspect, an embodiment of the present application provides a method for controlling an expansion valve, comprising:

[0007] Acquire first data of the air conditioning system, where the first data is used to represent the exhaust temperature of the air conditioning system in a real-time environment;

[0008] Inputting the first data into an adaptive model for data adaptive processing, and then performing parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model, wherein the adaptive model is used to characterize dynamic changes in the data and a mapping relationship between the exhaust temperature and the opening of the expansion valve of the air-conditioning system, and the estimated parameters carry the set exhaust temperature data of the air-conditioning system and the set opening of the expansion valve;

[0009] inputting the estimated parameter and the first data into the adaptive model respectively for data prediction processing to obtain a first exhaust temperature of the adaptive model;

[0010] A first opening sequence of the expansion valve is determined according to the first exhaust gas temperature, and the opening of the expansion valve is controlled using the first opening sequence.

[0011] In one possible implementation, obtaining first data of the air-conditioning system includes:

[0012] Collect the ambient temperature, indoor temperature, outdoor temperature and compressor frequency data of the current environment of the air conditioning system;

[0013] Data preprocessing is performed on the ambient temperature, the indoor temperature, the outdoor temperature, and the compressor frequency data to obtain corresponding first data.

[0014] In one possible implementation, the adaptive model is established by the following steps:

[0015] Create a dynamic model of the air conditioning system based on the mapping relationship between the exhaust temperature and expansion valve opening of the air conditioning system, as well as the dynamic adjustment relationship between the data;

[0016] The historical exhaust temperature data acquired in advance is input into the dynamic model to perform self-adaptive adjustment pre-training of the data, thereby obtaining a trained self-adaptive model.

[0017] In one possible implementation, inputting the first data into the adaptive model for data adaptive processing, and then performing parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model includes:

[0018] Inputting the ambient temperature, the indoor temperature, the outdoor temperature and the compressor frequency data into the adaptive model for data adaptive dynamic processing to obtain an adaptive exhaust temperature and an adaptive opening of the expansion valve;

[0019] performing parameter estimation processing on the adaptive exhaust temperature to obtain a second exhaust temperature corresponding to the air-conditioning system, where the second exhaust temperature represents a set exhaust temperature of the air-conditioning system;

[0020] Parameter estimation processing is performed on the adaptive opening to obtain a second opening of the expansion valve of the air-conditioning system, where the second opening represents a set opening of the expansion valve in the air-conditioning system.

[0021] In one possible implementation, inputting the estimated parameter and the first data into the adaptive model for data prediction processing to obtain the first exhaust temperature of the adaptive model includes:

[0022] inputting the second exhaust temperature and the second opening degree into the adaptive model respectively to initialize the data of the adaptive model to obtain an initialized adaptive model, wherein the adaptive model is used for predicting dynamic changes of data;

[0023] The first data is input into the initialized adaptive model for data prediction to obtain a first exhaust temperature representing a specified moment.

[0024] In one possible implementation, determining a first opening sequence of the expansion valve according to the first exhaust gas temperature includes:

[0025] calculating a difference between each first exhaust gas temperature and each corresponding second exhaust gas temperature to obtain a difference temperature sequence of all the differences;

[0026] The differential temperature sequence is optimized, and the opening degree is calculated for the processed sequence to obtain a first opening degree sequence of the expansion valve.

[0027] In one possible implementation, controlling the opening of the expansion valve by using the first opening sequence of the expansion valve includes:

[0028] Selecting the first opening value in the first opening sequence as initialization data of the expansion valve opening;

[0029] Adaptively adjusting the model parameters of the adaptive model using the differential temperature sequence and the target control algorithm to obtain an adaptive opening of the expansion valve;

[0030] The expansion valve opening is adjusted according to the adaptive opening.

[0031] In one possible implementation, the method further includes:

[0032] Setting a control period for the expansion valve opening and a collection period for the first exhaust gas temperature;

[0033] The expansion valve opening of the air-conditioning system is adaptively controlled according to the control period and the sampling period.

[0034] In a second aspect, an embodiment of the present application provides a control device for an expansion valve, comprising:

[0035] an acquisition module, configured to acquire first data of the air-conditioning system, wherein the first data is used to represent the exhaust temperature of the air-conditioning system in a real-time environment;

[0036] a parameter estimation module, configured to input the first data into an adaptive model for data adaptive processing, and then perform parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model, wherein the adaptive model is used to characterize dynamic changes in the data and a mapping relationship between the exhaust temperature and the opening of the expansion valve of the air-conditioning system, and the estimated parameters carry the set exhaust temperature data of the air-conditioning system and the set opening of the expansion valve;

[0037] a prediction module, configured to input the estimated parameter and the first data into the adaptive model respectively for data prediction processing to obtain a first exhaust temperature of the adaptive model;

[0038] A control module is configured to determine a first opening sequence of the expansion valve according to the first exhaust gas temperature, and to control the opening of the expansion valve using the first opening sequence.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, wherein the processor is configured to execute a control program for an expansion valve stored in the memory to implement any of the expansion valve control methods described in the first aspect.

[0040] In a fourth aspect, an embodiment of the present application provides a storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the expansion valve control method described in any one of the first aspects.

[0041] The expansion valve control scheme provided in an embodiment of the present application obtains first data from an air conditioning system, the first data representing the exhaust temperature of the air conditioning system in real-time; inputs the first data into an adaptive model for data adaptive processing; and then performs parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model. The adaptive model is used to characterize dynamic changes in the data and a mapping relationship between the exhaust temperature of the air conditioning system and the expansion valve opening. The estimated parameters carry the set exhaust temperature data of the air conditioning system and the set expansion valve opening; the estimated parameters and the first data are respectively input into the adaptive model for data prediction processing to obtain a first exhaust temperature of the adaptive model; a first opening sequence of the expansion valve is determined based on the first exhaust temperature, and the expansion valve opening is controlled using the first opening sequence. By obtaining real-time data from the current air conditioning environment and using a pre-established adaptive model, a target exhaust temperature and a target expansion valve opening can be predicted. The adaptive model then dynamically adjusts the real-time data and adaptively controls the exhaust temperature and expansion valve opening using the adaptive model, so that the air conditioning system stably maintains the indoor temperature within the user-set range. This solution can achieve the accuracy and energy efficiency issues in the control of electronic expansion valves of household air conditioners, and has the technical effects of improving control accuracy, optimizing energy efficiency performance, and optimizing user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings. These exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements. Unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0045] Figure 1 A flow chart of a method for controlling an expansion valve provided in an embodiment of the present application;

[0046] Figure 2 A flow chart of another expansion valve control method provided in an embodiment of the present application;

[0047] Figure 3 A flow chart of another expansion valve control method provided in an embodiment of the present application;

[0048] Figure 4 A control flow chart of the expansion valve opening provided in an embodiment of the present application;

[0049] Figure 5 A schematic structural diagram of a control device for an expansion valve provided in an embodiment of the present application;

[0050] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] In the embodiments of this application, the terms "including" and "having" are intended to convey an open-ended, inclusive meaning and indicate that additional elements / components / etc. may be present in addition to the listed elements / components / etc. The terms "first" and "second" are used merely as labels and do not limit the quantity of their objects. Furthermore, the various elements and regions in the drawings are shown for schematic purposes only, and thus this application is not limited to the sizes or distances shown in the drawings.

[0053] To facilitate understanding of the embodiments of the present application, further explanation will be given below with reference to specific embodiments in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present application.

[0054] Figure 1 A flow chart of a method for controlling an expansion valve provided in an embodiment of the present application. Applicable to the process of controlling the opening of an expansion valve of an air-conditioning device. Figure 1 The control method of the expansion valve specifically includes:

[0055] S101. Acquire first data of an air-conditioning system, where the first data is used to represent the exhaust temperature of the air-conditioning system in a real-time environment.

[0056] This application is applied to the expansion valve opening control process of air-conditioning equipment and other exhaust equipment. By establishing an adaptive model and collecting real-time data of indoor and outdoor ambient temperatures, the target temperature set by the user in the air-conditioning equipment and the target opening of the expansion valve are predicted. The real-time data collected in real time is used to obtain the real-time exhaust temperature and real-time opening of the air conditioner through the adaptive model. Based on the periodic collection of real-time data, the real-time data is adaptively adjusted through the adaptive model so that the exhaust temperature output after the real-time data passes through the adaptive model is closer to the target exhaust temperature, and the opening of the expansion valve is closer to the target opening set by the user, so as to achieve the effect of the ambient temperature change on the exhaust temperature set by the air conditioner in the current environment. By adopting an adaptive model predictive control algorithm, combining real-time sensor data and model prediction, it is possible to accurately predict the exhaust temperature at a future time and achieve precise control of the electronic expansion valve, thereby improving the temperature control accuracy of the air-conditioning system. At the same time, the adaptive algorithm can estimate and update the parameters of the system dynamic model in real time, adapt to system changes and uncertainties, improve the adaptive performance of the control, and make the system have better stability and robustness.

[0057] The air conditioning system mentioned here can be understood as the control system of a household air conditioning device, or can be understood as the control system corresponding to an exhaust temperature control device that has the same function as an air conditioner. The first data mentioned here can be understood as the real-time temperature data of the exhaust device such as the air conditioner under the current temperature environment.

[0058] Furthermore, in the working environment of the air-conditioning equipment, real-time temperature data around the air-conditioning system corresponding to the current temperature environment of the air-conditioning equipment is collected through a data collector or data sensor, and the temperature data is saved as the first data to prepare for the next step of predicting the target temperature value of the air-conditioning system.

[0059] S102. Input the first data into the adaptive model for data adaptive processing, and then perform parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model. The adaptive model is used to characterize the dynamic changes of the data and the mapping relationship between the exhaust temperature and the opening of the expansion valve of the air-conditioning system. The estimated parameters carry the set exhaust temperature data of the air-conditioning system and the set opening of the expansion valve.

[0060] The adaptive model mentioned here can be understood as a model based on physical equations or an empirical model used to describe the system's response characteristics and dynamic changes. Parameter estimation can be understood as the process of estimating and optimizing the parameters in the adaptive model using a parameter estimation algorithm. The estimated parameters here can be understood as the set target parameters that characterize the air conditioning system, namely the target exhaust temperature and the target expansion valve opening.

[0061] Furthermore, after collecting the first data representing the real-time temperature, the first data is input as input data into the established adaptive model for data adaptive processing, and then the adaptively processed data is subjected to parameter estimation processing. The first data is estimated and optimized using the parameter estimation method to obtain estimated parameter information that is infinitely close to or equivalent to the target exhaust temperature and target opening, providing reference data for the subsequent adaptive adjustment of the real-time data.

[0062] S103 , inputting the estimated parameter and the first data into the adaptive model respectively for data prediction processing to obtain a first exhaust temperature of the adaptive model.

[0063] The first exhaust temperature mentioned here can be understood as the real-time exhaust temperature in the temperature environment where the air-conditioning system is located.

[0064] Furthermore, based on the estimated parameters representing the target temperature value and the first data representing the real-time temperature, a prediction process is performed through an adaptive model to obtain the real-time exhaust temperature that can be predicted at future moments, and the exhaust temperature data corresponding to each future moment is obtained, providing a data basis for adaptively adjusting the real-time exhaust temperature.

[0065] S104 : Determine a first opening sequence of the expansion valve according to the first exhaust gas temperature, and control the opening of the expansion valve using the first opening sequence.

[0066] The first opening sequence mentioned here can be understood as corresponding to one opening for each real-time exhaust temperature data, and all real-time exhaust temperatures are sorted into one opening sequence. The control mentioned here can be understood as adjusting the opening size of the expansion valve.

[0067] Furthermore, a real-time exhaust temperature data set is compared and optimized with the estimated target exhaust temperature. An adjusted opening is determined based on the mapping between exhaust temperature and expansion valve opening. All real-time exhaust temperature data is adjusted using the same method to obtain a first opening sequence. Based on the current operating environment of the air conditioning system, an opening from the first opening sequence is selected as the adjustment for the expansion valve opening, thereby achieving the purpose of controlling the expansion valve opening of the air conditioning system by detecting the real-time exhaust temperature.

[0068] The expansion valve control method provided in an embodiment of the present application obtains first data from an air conditioning system, the first data being used to represent the exhaust temperature of the air conditioning system in a real-time environment; inputs the first data into an adaptive model for data adaptive processing; and then performs parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model. The adaptive model is used to characterize dynamic changes in the data and a mapping relationship between the exhaust temperature of the air conditioning system and the expansion valve opening. The estimated parameters carry the set exhaust temperature data of the air conditioning system and the set opening of the expansion valve; the estimated parameters and the first data are respectively input into the adaptive model for data prediction processing to obtain a first exhaust temperature of the adaptive model; a first opening sequence of the expansion valve is determined based on the first exhaust temperature, and the expansion valve opening is controlled using the first opening sequence. By obtaining real-time data in the current air conditioning environment and using a pre-established adaptive model, a target exhaust temperature and a target opening of the expansion valve can be predicted. The adaptive model then dynamically adjusts the real-time data and adaptively controls the exhaust temperature and the expansion valve opening using the adaptive model, so that the air conditioning system stably maintains the indoor temperature within a user-set range. This solution can achieve the accuracy and energy efficiency issues in the control of electronic expansion valves of household air conditioners, and has the technical effects of improving control accuracy, optimizing energy efficiency performance, and optimizing user experience.

[0069] Figure 2 This is a flow chart of another expansion valve control method provided in an embodiment of the present application, which is applied to the expansion valve opening control process of air-conditioning equipment. Figure 2 This is introduced based on the previous embodiment. Figure 2 The control method of the expansion valve further includes:

[0070] S201. Acquire first data of an air-conditioning system, where the first data is used to represent the exhaust temperature of the air-conditioning system in a real-time environment.

[0071] This application is applied to the expansion valve opening control process of air-conditioning equipment and other exhaust equipment. By establishing an adaptive model and collecting real-time data of indoor and outdoor ambient temperatures, the target temperature set by the user in the air-conditioning equipment and the target opening of the expansion valve are predicted. The real-time data collected in real time is used to obtain the real-time exhaust temperature and real-time opening of the air conditioner through the adaptive model. Based on the periodic collection of real-time data, the real-time data is adaptively adjusted through the adaptive model so that the exhaust temperature output after the real-time data passes through the adaptive model is closer to the target exhaust temperature, and the opening of the expansion valve is closer to the target opening set by the user, so as to achieve the effect of the ambient temperature change on the exhaust temperature set by the air conditioner in the current environment. By adopting an adaptive model predictive control algorithm, combining real-time sensor data and model prediction, it is possible to accurately predict the exhaust temperature at a future time and achieve precise control of the electronic expansion valve, thereby improving the temperature control accuracy of the air-conditioning system. At the same time, the adaptive algorithm can estimate and update the parameters of the system dynamic model in real time, adapt to system changes and uncertainties, improve the adaptive performance of the control, and make the system have better stability and robustness.

[0072] The air conditioning system mentioned here can be understood as the control system of a household air conditioning device, or can be understood as the control system corresponding to an exhaust temperature control device that has the same function as an air conditioner. The first data mentioned here can be understood as the real-time temperature data of the exhaust device such as the air conditioner under the current temperature environment.

[0073] Furthermore, in the working environment of the air-conditioning equipment, real-time temperature data around the air-conditioning system corresponding to the current temperature environment of the air-conditioning equipment is collected through a data collector or data sensor, and the temperature data is saved as the first data to prepare for the next step of predicting the target temperature value of the air-conditioning system.

[0074] S202: Input the ambient temperature, indoor temperature, outdoor temperature and compressor frequency data into the adaptive model for data adaptive dynamic processing to obtain the adaptive exhaust temperature and the adaptive opening of the expansion valve.

[0075] S203 : Perform parameter estimation processing on the adaptive exhaust temperature to obtain a second exhaust temperature corresponding to the air-conditioning system, where the second exhaust temperature represents a set exhaust temperature of the air-conditioning system.

[0076] S204 : Perform parameter estimation processing on the adaptive opening to obtain a second opening of the expansion valve of the air-conditioning system, where the second opening represents a set opening of the expansion valve of the air-conditioning system.

[0077] The ambient temperature, indoor temperature, outdoor temperature and compressor frequency data are input into the adaptive model for data adaptive dynamic processing to obtain the adaptive exhaust temperature and the adaptive opening of the expansion valve; the adaptive exhaust temperature and the adaptive opening are respectively subjected to parameter estimation processing, and the second exhaust temperature corresponding to the air-conditioning system and the second opening of the expansion valve of the air-conditioning system are output, the second exhaust temperature represents the set exhaust temperature of the air-conditioning system, and the second opening represents the set opening of the air-conditioning system.

[0078] The adaptive model is used to characterize the mapping relationship between the exhaust gas temperature and the expansion valve opening of the air conditioning system. The second exhaust gas temperature and the second opening are combined to form an estimated parameter.

[0079] The second exhaust temperature mentioned here can be understood as the target exhaust temperature representing the current working state of the air-conditioning system. The second opening degree mentioned here can be understood as the target opening degree representing the current temperature environment of the air-conditioning system.

[0080] Furthermore, the ambient temperature, indoor temperature, outdoor temperature and compressor frequency data under the current real-time temperature environment of the air-conditioning system are collected by sensors or collectors, and the above collected data are input into the established adaptive model. Through the parameter estimation method, the target exhaust temperature of the air-conditioning system under the current real-time temperature environment is predicted, and the target opening of the expansion valve is predicted. The target exhaust temperature is used as the second exhaust temperature, and the target opening is output as the second opening, providing a reference value for the next step of adjusting the exhaust temperature and opening.

[0081] S205 , inputting the second exhaust temperature and the second opening degree into the adaptive model respectively to initialize the data of the adaptive model to obtain an initialized adaptive model, which is used for predicting dynamic changes of data.

[0082] S206 : Input the first data into the initialized adaptive model to perform data prediction to obtain a first exhaust temperature representing a specified moment.

[0083] The first exhaust temperature mentioned here can be understood as the real-time exhaust temperature in the temperature environment where the air-conditioning system is located.

[0084] Furthermore, the target exhaust temperature and target opening are input as initialization data into the adaptive model to initialize the model. The collected real-time exhaust temperature is then input into the initialized adaptive model for data prediction. The target exhaust temperature is compared with the real-time exhaust temperature to obtain the predicted real-time exhaust temperature at each moment in the future. This is used as the first exhaust temperature output, providing a data basis for adaptively adjusting the real-time exhaust temperature.

[0085] Furthermore, exhaust temperature prediction is performed based on the dynamically adjusted adaptive model and real-time temperature data collected by sensors. Adaptive model predictive control methods, such as recursive least squares (RLS) or extended Kalman filtering (EKF), are used. The model predicts the first exhaust temperature at a future moment, combining variables such as the current outdoor and indoor ambient temperatures and refrigerant flow rate.

[0086] For example, P = A + B * Touter Loop + C * Tinner Loop, where P is the exhaust temperature, A is the basic constant parameter, and B / C are coefficients calculated based on Touter Loop and Tinner Loop. The program calculates the predicted real-time exhaust temperature for the current environment based on the outer and inner loop intervals. The mapping between the predicted exhaust temperatures and the corresponding target exhaust temperatures is organized into a table, and the final predicted first exhaust temperature is obtained by looking up the table.

[0087] S207 , calculating the difference between each first exhaust gas temperature and each corresponding second exhaust gas temperature to obtain a temperature sequence of all differences.

[0088] S208 , optimizing the difference temperature sequence, and performing opening calculation on the processed sequence to obtain a first opening sequence of the expansion valve.

[0089] The optimization processing mentioned here can be understood as the optimization algorithm of nonlinear optimization or model predictive control.

[0090] Furthermore, a difference calculation is performed between a first exhaust temperature and a corresponding second exhaust temperature to obtain a temperature difference, and all first exhaust temperatures are calculated using the same difference algorithm to obtain a corresponding difference temperature list.

[0091] Furthermore, based on the adaptive model of system dynamics and the estimated parameters, the exhaust gas temperature at a future time is predicted. Based on the error between the predicted temperature and the target temperature, the opening of the electronic expansion valve is calculated using an optimization algorithm (such as nonlinear optimization or model predictive control) based on the current system state and the predicted exhaust gas temperature, resulting in a first opening sequence of the optimal electronic expansion valve opening.

[0092] S209: Select the first opening value in the first opening sequence as initialization data for the expansion valve opening.

[0093] Furthermore, the first opening in the sequence is selected as the electronic expansion valve opening at the current moment, and its actual opening (initial value) is controlled to ensure that the compressor has sufficient suction superheat and the system can operate reliably.

[0094] S210 , adaptively adjusting the model parameters of the adaptive model using the temperature difference sequence and the target control algorithm to obtain an adaptive opening of the expansion valve.

[0095] S211. Adjust the expansion valve opening according to the adaptive opening.

[0096] Furthermore, based on real-time temperature data collected by sensors and the error between the real-time data and the target data predicted by the adaptive model, control parameters, including proportional gain and integral time, are adaptively adjusted to improve control accuracy and stability. Adaptive control methods, such as adaptive model predictive control (AMPC) and adaptive PID control, are employed to select an appropriate control algorithm based on the actual system conditions. By estimating and adjusting model parameters, the control algorithm can adapt to the dynamic changes and uncertainties of the system, improving the robustness and adaptability of the control. This reduces energy waste, improves the energy efficiency of the air conditioning system's refrigeration system, and reduces energy consumption and operating costs.

[0097] Another expansion valve control method provided in an embodiment of the present application collects the real-time exhaust temperature of the air-conditioning system and establishes an adaptive model. The real-time exhaust temperature is input into the adaptive model for parameter estimation processing to obtain the target exhaust temperature and the target opening, and the adaptive model is initialized. The mapping relationship between the real-time exhaust temperature and the target exhaust temperature is then optimized by the adaptive model to obtain the predicted real-time exhaust temperature at each moment in the future. The opening is calculated based on the predicted real-time exhaust temperature to obtain an opening sequence, and the first opening value in the opening sequence is selected as the initialization data for the opening of the expansion valve of the air-conditioning equipment. The real-time exhaust temperature data collected by the sensor is used to realize adaptive adjustment of the exhaust temperature, thereby realizing the accuracy and energy efficiency problems in the control of the electronic expansion valve of the household air conditioner, and having the technical effects of improving control accuracy, optimizing energy efficiency performance, and optimizing user experience.

[0098] Figure 3 A flow chart of another expansion valve control method provided in an embodiment of the present application is provided, which is applied to the expansion valve opening control process of an air-conditioning device. Figure 3 The first embodiment is introduced. Figure 3 The control method of the expansion valve further includes:

[0099] S301 , collecting ambient temperature, indoor temperature, outdoor temperature and compressor frequency data of the current environment of the air-conditioning system.

[0100] S302: Preprocess the ambient temperature, indoor temperature, outdoor temperature, and compressor frequency data to obtain corresponding first data.

[0101] Data sensors or data collectors are used to obtain parameter data such as ambient temperature, indoor temperature, outdoor temperature, and compressor frequency in the air conditioner's current operating environment. The collected data is preprocessed, including filtering and denoising, to improve data accuracy and stability, and obtain the first real-time temperature data.

[0102] S303: Create a dynamic model of the air-conditioning system according to the mapping relationship between the exhaust temperature and the expansion valve opening of the air-conditioning system, and the dynamic adjustment relationship between the data.

[0103] S304 , inputting the pre-acquired historical exhaust temperature data into the dynamic model to perform pre-training for adaptive adjustment of the data, thereby obtaining a trained adaptive model.

[0104] Furthermore, a dynamic mathematical model of the household air conditioning system is established based on experimental data or physical principles. This dynamic model is created using a formula that maps the opening of the electronic expansion valve to the exhaust temperature. The dynamic model can be based on physical equations or empirical models to describe the system's response characteristics and dynamic changes.

[0105] S305: Input the first data into the adaptive model to perform data adaptive processing, and then perform parameter estimation processing on the adaptively processed data to obtain estimated parameters of the adaptive model.

[0106] Among them, the adaptive model is used to characterize the dynamic changes of data and the mapping relationship between the exhaust temperature and the expansion valve opening of the air-conditioning system. The estimated parameters carry the set exhaust temperature data of the air-conditioning system and the set opening of the expansion valve.

[0107] Furthermore, at system startup, an initial estimate of the model parameters is performed, using default values. After collecting first data representing the real-time temperature, this first data is fed into the established adaptive model as input. Parameter estimation methods are used to estimate and optimize the first data, yielding estimated parameter information that is as close to or equal to the target exhaust temperature and target opening degree. Based on the actual real-time temperature data collected by the sensor and the system output, the adaptive model parameters are estimated and updated online to reduce estimation errors and improve model accuracy. This provides reference data for subsequent adaptive adjustments to the real-time data.

[0108] S306 , inputting the estimated parameters and the first data into the adaptive model respectively for data prediction processing to obtain the first exhaust temperature of the adaptive model.

[0109] The first exhaust temperature mentioned here can be understood as the real-time exhaust temperature in the temperature environment where the air-conditioning system is located.

[0110] Furthermore, based on the estimated parameters representing the target temperature value and the first data representing the real-time temperature, a prediction process is performed through an adaptive model to obtain the real-time exhaust temperature that can be predicted at future moments, and the exhaust temperature data corresponding to each future moment is obtained, providing a data basis for adaptively adjusting the real-time exhaust temperature.

[0111] Furthermore, exhaust temperature prediction is performed based on the dynamically adjusted adaptive model and real-time temperature data collected by sensors. Adaptive model predictive control methods, such as recursive least squares (RLS) or extended Kalman filtering (EKF), are used. The model predicts the first exhaust temperature at a future moment, combining variables such as the current outdoor and indoor ambient temperatures and refrigerant flow rate.

[0112] S307 : Determine a first opening sequence of the expansion valve according to the first exhaust gas temperature, and control the opening of the expansion valve using the first opening sequence.

[0113] The first opening sequence mentioned here can be understood as corresponding to one opening for each real-time exhaust temperature data, and all real-time exhaust temperatures are sorted into one opening sequence. The control mentioned here can be understood as adjusting the opening size of the expansion valve.

[0114] Furthermore, a real-time exhaust temperature data set is compared and optimized with the estimated target exhaust temperature. An adjusted opening is determined based on the mapping between exhaust temperature and expansion valve opening. All real-time exhaust temperature data is adjusted using the same method to obtain a first opening sequence. Based on the current operating environment of the air conditioning system, an opening from the first opening sequence is selected as the adjustment for the expansion valve opening, thereby achieving the purpose of controlling the expansion valve opening of the air conditioning system by detecting the real-time exhaust temperature.

[0115] Optionally, an adaptive model predictive controller is designed to adjust the opening of the electronic expansion valve to achieve stable control of the target exhaust temperature. The controller includes a parameter estimation module, a model prediction module, and a feedback control module. The parameter estimation module estimates and updates model parameters based on real-time sensor data and system output. The model prediction module calculates the opening of the electronic expansion valve based on the current system state and the predicted exhaust temperature. The feedback control module adjusts the controller output by comparing it with the actual exhaust temperature to achieve stable control of the exhaust temperature.

[0116] Optionally, in a possible example scenario, the designed controller algorithm is implemented on the control unit or controller hardware in the air-conditioning system; a data sensor is used to collect ambient temperature data in real time to perform parameter estimation, exhaust temperature prediction, and electronic expansion valve opening calculation; finally, the controller output is used to adjust the opening of the electronic expansion valve to stably control the exhaust temperature.

[0117] Optionally, optimize and debug the controller, improving and adjusting the parameter estimation algorithm and control strategy through actual system testing and performance evaluation. Adjust controller parameters, such as the convergence rate of the adaptive model parameter estimation algorithm and controller gain, to achieve better control performance and stability.

[0118] Optionally, a controller with multiple functions can be set to control and adjust the real-time exhaust temperature and expansion valve opening:

[0119] Static model predictive control: In the absence of a dynamic model, the static model predictive control method can be used to predict the exhaust temperature and realize the control of the electronic expansion valve by establishing an empirical relationship between the system input and output.

[0120] Proportional-Integral-Differential (PID) controller: In terms of control algorithm, the classic PID controller can be used. Based on real-time sensor data and control error, the opening of the electronic expansion valve is calculated through proportional, integral, and differential control algorithms to achieve temperature control.

[0121] Rule-based control: A rule-based control method can be used to adjust the opening of the electronic expansion valve according to pre-set rules and conditions through logical judgment and control rules to achieve control of the exhaust temperature.

[0122] Fuzzy control: By adopting the fuzzy control method and establishing a fuzzy rule base and fuzzy reasoning system, the opening of the electronic expansion valve is adjusted according to the real-time sensor data and control error to achieve the control of the exhaust temperature.

[0123] S308: Setting a control period for the expansion valve opening and a collection period for the first exhaust gas temperature.

[0124] S309: Adaptively control the opening of the expansion valve of the air-conditioning system according to the control period and the sampling period.

[0125] Furthermore, appropriate control and sampling periods are set to meet real-time control requirements and system responsiveness. The control period determines the control frequency, while the sampling period determines the frequency of data collection. Both need to be appropriately selected based on the actual application scenario to achieve real-time dynamic control of the air conditioning system.

[0126] In one possible example scenario, Figure 4A control flow chart of the expansion valve opening is provided for the embodiment of the present application. A system dynamic model is established based on the actual verification situation. The dynamic model includes startup control---stability control---overshoot control. After the initial opening is given at startup, the model parameters are updated, and then the target exhaust is determined based on the outer loop and the inner loop to achieve exhaust temperature prediction control; the opening adjustment amount δP is determined by the difference between the target exhaust temperature and the actual exhaust temperature, and the opening calculation is performed to achieve the purpose of stabilizing the exhaust temperature to the target. If the adjustment amount is too large, the current opening can be maintained by overshoot control. After the maintenance time is up, the difference between the target exhaust temperature and the actual exhaust temperature is judged. Only after a real opening or closing is performed can the stage of judging whether it is overshooting be re-entered, until the actual exhaust temperature is infinitely close to the target exhaust temperature, and the real-time dynamic parameter adjustment of the expansion valve opening is achieved. And by setting the control cycle and the data acquisition cycle, the control cycle optimization is completed, thereby achieving the purpose of dynamic control of the opening.

[0127] An embodiment of the present application provides another method for controlling an expansion valve, which collects ambient temperature data of an air-conditioning system through a sensor to obtain real-time exhaust temperature, establishes an adaptive model, and uses the ambient temperature data to input into the adaptive model for estimation processing to obtain a target exhaust temperature and a target opening; then, the exhaust temperature at the predicted moment is predicted by collecting real-time data, an opening sequence is obtained based on the mapping relationship between the exhaust temperature and the expansion valve opening, and the opening of the air-conditioning system is initialized; periodic control and periodic acquisition are designed to achieve real-time and simultaneous adjustment of the exhaust temperature and expansion valve opening of the air-conditioning system, thereby achieving accuracy and energy efficiency issues in the control of electronic expansion valves for household air conditioners, and having the technical effects of improving control accuracy, optimizing energy efficiency performance, and optimizing user experience.

[0128] Figure 5 This is a schematic diagram of the structure of an expansion valve control device provided in an embodiment of the present application. It is used in the expansion valve opening control process of air-conditioning equipment. Figure 4 The control device of the expansion valve specifically includes:

[0129] An acquisition module 51 is used to acquire first data of the air-conditioning system, where the first data is used to represent the exhaust temperature of the air-conditioning system in a real-time environment;

[0130] a parameter estimation module 52 for inputting the first data into the adaptive model for data adaptive processing, and then performing parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model. The adaptive model is used to characterize dynamic changes in the data and a mapping relationship between the exhaust temperature and the expansion valve opening of the air conditioning system. The estimated parameters carry the set exhaust temperature data of the air conditioning system and the set opening of the expansion valve;

[0131] A prediction module 53 is configured to input the estimated parameters and the first data into the adaptive model for data prediction processing to obtain a first exhaust temperature of the adaptive model;

[0132] The control module 54 is configured to determine a first opening sequence of the expansion valve according to the first exhaust gas temperature, and control the opening of the expansion valve using the first opening sequence.

[0133] The control device of the expansion valve provided in this embodiment may be as follows Figure 5 The control device of the expansion valve shown in FIG. 1 can perform the following steps: Figure 1-4 All steps of the expansion valve control method are achieved Figure 1-4 For details on the technical effects of the expansion valve control method shown, please refer to Figure 1-4 For the sake of brevity, the relevant description will not be repeated here.

[0134] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 The electronic device 600 shown includes: at least one processor 601, a memory 602, at least one network interface 604 and another user interface 603. The various components in the electronic device 600 are coupled together via a bus system 605. It is understood that the bus system 605 is used to achieve connection and communication between these components. In addition to including a data bus, the bus system 605 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 605 is not shown in FIG. Figure 6 Various buses are labeled as bus system 605.

[0135] The user interface 603 may include a display, a keyboard, or a pointing device (eg, a mouse, a trackball, a touchpad, or a touch screen).

[0136] It is understood that the memory 602 in the embodiment of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 602 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0137] In some embodiments, the memory 602 stores the following elements, executable units, or data structures, or a subset thereof, or an extended set thereof: an operating system 6021 and application programs 6022 .

[0138] The operating system 6021 includes various system programs, such as a framework layer, a core library layer, and a driver layer, for implementing various basic services and processing hardware-based tasks. The application 6022 includes various application programs, such as a media player and a browser, for implementing various application services. The program implementing the method of the embodiment of the present application can be included in the application 6022.

[0139] In the embodiment of the present application, by calling the program or instructions stored in the memory 602, specifically, the program or instructions stored in the application 6022, the processor 601 is configured to execute the method steps provided in each method embodiment, for example, including:

[0140] Acquire first data of the air-conditioning system, the first data being used to represent the exhaust temperature under the real-time environment of the air-conditioning system; input the first data into an adaptive model for data adaptive processing, and then perform parameter estimation processing on the adaptively processed data to obtain estimated parameters of the adaptive model, the adaptive model being used to characterize dynamic changes in the data, and characterize a mapping relationship between the exhaust temperature of the air-conditioning system and the opening of the expansion valve, the estimated parameters carrying the set exhaust temperature data of the air-conditioning system and the set opening of the expansion valve; input the estimated parameters and the first data into the adaptive model respectively for data prediction processing to obtain the first exhaust temperature of the adaptive model; determine a first opening sequence of the expansion valve according to the first exhaust temperature, and control the opening of the expansion valve using the first opening sequence.

[0141] The methods disclosed in the above embodiments of the present application can be applied to or implemented by processor 601. Processor 601 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in processor 601 or by software instructions. The above processor 601 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software units in the decoding processor. The software units can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory 602 , and the processor 601 reads the information in the memory 602 and completes the steps of the above method in combination with its hardware.

[0142] It is understood that the embodiments described herein may be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or a combination thereof.

[0143] For software implementation, the technology described herein can be implemented by a unit that performs the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0144] The electronic device provided in this embodiment may be Figure 6 The electronic device shown in FIG. 1 can perform the following operations: Figure 1-4 All steps of the expansion valve control method are achieved Figure 1-4 For details on the technical effects of the expansion valve control method shown, please refer to Figure 1-4 For the sake of brevity, the relevant description will not be repeated here.

[0145] The present application also provides a storage medium (computer-readable storage medium). The storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; and the memory may also include a combination of the aforementioned types of memory.

[0146] When one or more programs in the storage medium can be executed by one or more processors, the expansion valve control method executed on the control device side of the expansion valve can be implemented.

[0147] The processor is configured to execute a control program for the expansion valve stored in the memory to implement the following steps of a control method for the expansion valve executed on a control device side of the expansion valve:

[0148] Acquire first data of the air-conditioning system, the first data being used to represent the exhaust temperature under the real-time environment of the air-conditioning system; input the first data into an adaptive model for data adaptive processing, and then perform parameter estimation processing on the adaptively processed data to obtain estimated parameters of the adaptive model, the adaptive model being used to characterize dynamic changes in the data, and characterize a mapping relationship between the exhaust temperature of the air-conditioning system and the opening of the expansion valve, the estimated parameters carrying the set exhaust temperature data of the air-conditioning system and the set opening of the expansion valve; input the estimated parameters and the first data into the adaptive model respectively for data prediction processing to obtain the first exhaust temperature of the adaptive model; determine a first opening sequence of the expansion valve according to the first exhaust temperature, and control the opening of the expansion valve using the first opening sequence.

[0149] Professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0150] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0151] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of this application. It should be understood that the above description is only the specific implementation methods of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. A method for controlling an expansion valve, characterized in that: include: Acquire first data of the air conditioning system, where the first data is used to represent the exhaust temperature of the air conditioning system in a real-time environment; Inputting the first data into an adaptive model for data adaptive processing, and then performing parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model, wherein the adaptive model is used to characterize dynamic changes in the data and a mapping relationship between the exhaust temperature and the opening of the expansion valve of the air-conditioning system, and the estimated parameters carry the set exhaust temperature data of the air-conditioning system and the set opening of the expansion valve; inputting the estimated parameter and the first data into the adaptive model respectively for data prediction processing to obtain a first exhaust temperature of the adaptive model; A first opening sequence of the expansion valve is determined according to the first exhaust gas temperature, and the opening of the expansion valve is controlled using the first opening sequence.

2. The method according to claim 1, characterized in that The obtaining of first data of the air-conditioning system includes: Collect the ambient temperature, indoor temperature, outdoor temperature and compressor frequency data of the current environment of the air conditioning system; Data preprocessing is performed on the ambient temperature, the indoor temperature, the outdoor temperature, and the compressor frequency data to obtain corresponding first data.

3. The method according to claim 1, characterized in that The adaptive model is established by the following steps: Create a dynamic model of the air conditioning system based on the mapping relationship between the exhaust temperature and expansion valve opening of the air conditioning system, as well as the dynamic adjustment relationship between the data; The historical exhaust temperature data acquired in advance is input into the dynamic model to perform self-adaptive adjustment pre-training of the data, thereby obtaining a trained self-adaptive model.

4. The method according to claim 2, characterized in that The step of inputting the first data into the adaptive model for data adaptive processing, and then performing parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model includes: Inputting the ambient temperature, the indoor temperature, the outdoor temperature and the compressor frequency data into the adaptive model for data adaptive dynamic processing to obtain an adaptive exhaust temperature and an adaptive opening of the expansion valve; performing parameter estimation processing on the adaptive exhaust temperature to obtain a second exhaust temperature corresponding to the air-conditioning system, where the second exhaust temperature represents a set exhaust temperature of the air-conditioning system; Parameter estimation processing is performed on the adaptive opening to obtain a second opening of the expansion valve of the air-conditioning system, where the second opening represents a set opening of the expansion valve in the air-conditioning system.

5. The method according to claim 4, characterized in that The step of inputting the estimated parameter and the first data into the adaptive model for data prediction processing to obtain the first exhaust temperature of the adaptive model includes: inputting the second exhaust temperature and the second opening degree into the adaptive model respectively to initialize the data of the adaptive model to obtain an initialized adaptive model, wherein the adaptive model is used for predicting dynamic changes of data; The first data is input into the initialized adaptive model for data prediction to obtain a first exhaust temperature representing a specified moment.

6. The method according to claim 5, characterized in that The step of determining a first opening sequence of the expansion valve according to the first exhaust gas temperature includes: calculating a difference between each first exhaust gas temperature and each corresponding second exhaust gas temperature to obtain a difference temperature sequence of all the differences; The differential temperature sequence is optimized, and the opening degree is calculated for the processed sequence to obtain a first opening degree sequence of the expansion valve.

7. The method according to claim 6, characterized in that The controlling the opening of the expansion valve by using the first opening sequence of the expansion valve includes: Selecting the first opening value in the first opening sequence as initialization data of the expansion valve opening; Adaptively adjusting the model parameters of the adaptive model using the differential temperature sequence and the target control algorithm to obtain an adaptive opening of the expansion valve; The expansion valve opening is adjusted according to the adaptive opening.

8. The method according to claim 1, characterized in that The method further comprises: Setting a control period for the expansion valve opening and a collection period for the first exhaust gas temperature; The expansion valve opening of the air-conditioning system is adaptively controlled according to the control cycle and the collection cycle.

9. A control device for an expansion valve, characterized in that: include: an acquisition module, configured to acquire first data of the air-conditioning system, wherein the first data is used to represent the exhaust temperature of the air-conditioning system in a real-time environment; a parameter estimation module, configured to input the first data into an adaptive model for data adaptive processing, and then perform parameter estimation on the adaptively processed data to obtain estimated parameters of the adaptive model, wherein the adaptive model is used to characterize dynamic changes in the data and a mapping relationship between the exhaust temperature and the opening of the expansion valve of the air-conditioning system, and the estimated parameters carry the set exhaust temperature data of the air-conditioning system and the set opening of the expansion valve; a prediction module, configured to input the estimated parameter and the first data into the adaptive model respectively for data prediction processing to obtain a first exhaust temperature of the adaptive model; A control module is configured to determine a first opening sequence of the expansion valve according to the first exhaust gas temperature, and to control the opening of the expansion valve using the first opening sequence.

10. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is configured to execute a control program for an expansion valve stored in the memory to implement the control method for the expansion valve according to any one of claims 1 to 8.

11. A storage medium, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the expansion valve control method according to any one of claims 1 to 8.

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