Air conditioner dynamic test environment adjusting method, working condition device and storage medium

Abstract

The application discloses a kind of air conditioner dynamic test environment's adjusting method, working condition equipment and storage medium, it is related to air conditioning technical field, the method includes: the target environment parameter and environment test parameter of target test environment are obtained;With the deviation value of the adjusting result of working condition equipment and the target environment parameter is less than preset deviation or reaches minimum deviation as target, the environment parameter given value of the working condition equipment is determined according to the target environment parameter;According to the environment parameter given value and the environment test parameter, the working condition equipment is controlled to operate.The application aims to improve the matching degree of actual working condition and test demand in air conditioner dynamic test environment, to improve air conditioner dynamic test accuracy.

Description

Technical Field

[0001] This application relates to the field of air conditioning technology, and in particular to methods for adjusting the dynamic testing environment of air conditioning, operating equipment, and storage media. Background Technology

[0002] Before leaving the factory, air conditioners usually need to undergo performance and energy efficiency tests under certain test conditions, and these test conditions are generally achieved by adjusting the test environment equipment.

[0003] In related technologies, during the dynamic testing of air conditioners, the target environmental value required for the test is generally used as the setpoint for the operating equipment. The operating equipment performs PID control on the test environment based on the setpoint and the environmental test value. However, the response speed of operating equipment such as heating, cooling, and humidification is relatively slow, and the adjustment has obvious lag. The above-mentioned PID control method is prone to overshoot or delay, which can easily cause the actual operating conditions in the dynamic test environment to deviate from the test requirements, affecting the accuracy of the air conditioner dynamic test. Summary of the Invention

[0004] The main objective of this application is to provide a method for adjusting the dynamic testing environment of an air conditioner, an operating condition device, and a storage medium, aiming to improve the matching degree between the actual operating conditions and testing requirements in the dynamic testing environment of an air conditioner, thereby improving the accuracy of dynamic testing of air conditioners.

[0005] To achieve the above objectives, this application proposes a method for adjusting the dynamic test environment of an air conditioner, applied to an operating device. The operating device is configured to adjust the target test environment in which the air conditioner is located. The method for adjusting the dynamic test environment of the air conditioner includes: Obtain the target environment parameters and environment test parameters of the target test environment; With the goal of the deviation between the adjustment result of the working equipment and the target environmental parameter being less than a preset deviation or reaching the minimum deviation, the environmental parameter setpoint of the working equipment is determined based on the target environmental parameter. The operation of the equipment under the specified conditions is controlled based on the given environmental parameters and the environmental test parameters.

[0006] In one embodiment, the step of determining the environmental parameter setpoint of the operating equipment based on the target environmental parameter, with the deviation between the adjustment result of the operating equipment and the target environmental parameter being less than a preset deviation or reaching a minimum deviation, includes: Based on the target correspondence, the given value of the environmental parameter is determined according to the target environmental parameters, the initial environmental parameters of the target test environment, and the target value of the environmental parameters of the target test environment before the current time. The target correspondence relationship is a relationship constructed with the goal of the deviation between the adjustment result of the working equipment and the target environmental parameter being less than a preset deviation or reaching the minimum deviation. The target correspondence relationship is the correspondence between the target environmental parameter, the initial environmental parameter, the target value of the environmental parameter, and the given value of the environmental parameter.

[0007] In one embodiment, the method for adjusting the dynamic testing environment of the air conditioner further includes: Obtain the system transfer function, which represents the relationship between the adjustment result of the operating condition equipment in the preset state and the given value of the operating condition equipment. The preset state includes the air outlet of the air conditioner being sent into the target test environment, and the target value of the target test environment being used as the given value, and the operating condition equipment being controlled to operate according to the given value and the test value of the target test environment. The feedforward compensation function is determined based on the system transfer function. The feedforward compensation function represents the relationship between the target value and the given value of the operating equipment when the deviation between the adjustment result of the operating equipment and the target value is minimized. The target correspondence is determined based on the feedforward compensation function.

[0008] In one embodiment, the step of obtaining the system transfer function includes: Obtain the function model of the system transfer function and the test data of the target test environment. The test data includes multiple continuously collected target values ​​and corresponding adjustment results in the preset state. Based on the experimental data, the model parameters in the function model are determined, and the function model with known model parameters is identified as the system transfer function.

[0009] In one embodiment, the step of obtaining the function model of the system transfer function includes: Obtain the first transfer function model, the second transfer function model, the third transfer function model, and the fourth transfer function model; The function model is determined based on the first transfer function model, the second transfer function model, the third transfer function model, and the fourth transfer function model; Wherein, the first transfer function model represents the dynamic response process of environmental parameters in the air receiving chamber to the air outlet parameters of the air conditioner; the second transfer function model represents the dynamic response process of local environmental parameters in the outlet area of ​​the downstream airflow measuring device in the air receiving chamber to the airflow measuring device's outlet parameters; the third transfer function model represents the dynamic response process of the adjustment result of the operating equipment to the PID control of the operating equipment based on the target value and the test value; and the fourth transfer function model represents the dynamic response process of the adjustment result of the operating equipment to local environmental parameters. In one embodiment, the step of determining the feedforward compensation function based on the system transfer function includes: When the system transfer function is a minimum phase system, the reciprocal of the system transfer function is determined to be the feedforward compensation function; When the system transfer function is a non-minimum phase system, the system transfer function is converted into a minimum phase system transfer function using an all-pass function, and the reciprocal of the minimum phase system transfer function is determined as the feedforward compensation function; And / or, the step of determining the target correspondence based on the feedforward compensation function includes: Based on a preset time interval, the feedforward compensation function is transformed from a continuous-time transfer function form into a discrete-time state-space form to obtain the target correspondence.

[0010] In one embodiment, the step of determining the model parameters in the function model based on the experimental data, and determining the function model with known model parameters as the system transfer function, includes: A data object is created using the iddata function, the data object including multiple target values ​​and corresponding adjustment results; Based on the function model, create MATLAB functions, and use the idgrey function to create the corresponding gray box model of the MATLAB functions; Based on the greyest function, the parameters of the greybox model are determined using the data object, and the system transfer function is obtained.

[0011] In one embodiment, the step of determining the environmental parameter setpoint of the operating equipment based on the target environmental parameter, with the deviation between the adjustment result of the operating equipment and the target environmental parameter being less than a preset deviation or reaching a minimum deviation, includes: Based on the target environmental parameters, determine multiple reference values ​​for environmental parameters; The reference values ​​of each environmental parameter are sequentially input into the preset model, and it is determined whether the deviation between the adjustment result output by the preset model and the target environmental parameter is less than the preset deviation. The environmental parameter reference value whose deviation value is less than the preset deviation is determined as the environmental parameter setpoint; The input parameters of the preset model are the given values ​​of the working equipment, and the output parameters of the preset model are the adjustment results of the working equipment. The preset model includes a neural network model.

[0012] In one embodiment, the target environmental parameter includes a target dry-bulb temperature, and the environmental test parameter includes a dry-bulb test temperature; and / or, the target environmental parameter includes a target wet-bulb temperature, and the environmental test parameter includes a wet-bulb test temperature.

[0013] In addition, to achieve the above objectives, this application also proposes an operating condition device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the adjustment method for the dynamic testing environment of an air conditioner as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the air conditioning dynamic test environment adjustment method described above.

[0015] The one or more technical solutions proposed in this application have at least the following technical effects: In the process of adjusting the air conditioning dynamic test environment by the operating equipment, the target environmental parameter of the test requirement is no longer directly used as the given value of the operating equipment. Instead, the objective is to determine the given value of the environmental parameter corresponding to the target environmental parameter so as to control the operation of the operating equipment by taking the deviation between the adjustment result of the operating equipment and the target environmental parameter as less than the preset deviation or the minimum deviation. Based on this, the matching degree between the actual operating conditions and the test requirements in the air conditioning dynamic test environment can be improved, thereby improving the accuracy of the air conditioning dynamic test. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the adjustment method of the dynamic testing environment of the air conditioner in the embodiments of this application; Figure 2This is a flowchart illustrating an embodiment of the method for adjusting the dynamic testing environment of an air conditioner according to this application. Figure 3 This is a schematic diagram illustrating the control flow of dry-bulb temperature and wet-bulb temperature in the target test environment involved in the embodiment of the air conditioning dynamic test environment adjustment method of this application; Figure 4 This is a schematic diagram illustrating the process of determining the target correspondence between target environmental parameters and given environmental parameter values, provided in Embodiment 2 of the method for adjusting the dynamic testing environment of air conditioning in this application. Figure 5 This is a schematic diagram illustrating the process of establishing the function model of the system transfer function involved in Embodiment 2 of the method for adjusting the dynamic test environment of the air conditioner in this application; Figure 6 This is a schematic diagram of the software architecture of the feedforward compensator involved in Embodiment 2 of the method for adjusting the dynamic test environment of the air conditioner in this application; Figure 7 This is a flowchart illustrating the third embodiment of the method for adjusting the dynamic testing environment of an air conditioner according to this application. Figure 8 This is a schematic diagram of the preset model establishment process involved in Embodiment 3 of the method for adjusting the dynamic testing environment of the air conditioner in this application; Figure 9 This is a flowchart illustrating an application example of the method for adjusting the dynamic testing environment of an air conditioner in this application, specifically in Embodiment 3.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application embodiment is: to obtain the target environmental parameters and environmental test parameters of the target test environment; to determine the environmental parameter setpoint of the working condition equipment based on the target environmental parameters, with the deviation between the adjustment result of the working condition equipment and the target environmental parameters being less than a preset deviation or reaching the minimum deviation; and to control the operation of the working condition equipment based on the environmental parameter setpoint and the environmental test parameters.

[0023] In this embodiment, for ease of description, the following description will focus on the working equipment as the subject of execution.

[0024] In related technologies, during the dynamic testing of air conditioners, the target environmental value required for the test is generally used as the setpoint for the operating equipment. The operating equipment performs PID control on the test environment based on the setpoint and the environmental test value. However, the response speed of operating equipment such as heating, cooling, and humidification is relatively slow, and the adjustment has obvious lag. The above-mentioned PID control method is prone to overshoot or delay, which can easily cause the actual operating conditions in the dynamic test environment to deviate from the test requirements, affecting the accuracy of the air conditioner dynamic test.

[0025] This application provides the above-mentioned solution, in which, during the process of adjusting the air conditioning dynamic test environment by the operating equipment, the target environmental parameter of the test requirement is no longer directly used as the given value of the operating equipment. Instead, the goal is to determine the environmental parameter given value corresponding to the target environmental parameter so as to control the operation of the operating equipment by taking the deviation between the adjustment result of the operating equipment and the target environmental parameter as less than the preset deviation or the minimum deviation. Based on this, the matching degree between the actual operating conditions and the test requirements in the air conditioning dynamic test environment can be improved, thereby improving the accuracy of air conditioning dynamic testing.

[0026] This application provides an operating condition device for adjusting the target test environment where an air conditioner is located.

[0027] In this embodiment, refer to Figure 1 The working equipment includes a control device 100, an adjustment device connected to the control device 100, and an environmental monitoring module 400.

[0028] The regulating device may include a temperature regulating device 200 and / or a humidity regulating device 300, etc. The temperature regulating device 200 is configured to regulate the temperature of the target test environment, such as a refrigeration / heater, etc.; the humidity regulating device 300 is configured to regulate the humidity of the target test environment, such as a humidifier, etc.

[0029] The environmental monitoring module 400 can be set in the target test environment to detect the environmental parameters of the target test environment, which may include dry-bulb temperature and / or wet-bulb temperature and / or moisture content, etc.

[0030] In this embodiment, the target testing environment is an enthalpy difference laboratory.

[0031] The control device 100 includes: at least one processor 1001; and a memory 1002 communicatively connected to the at least one processor 1001, and a timer 1003, etc.; wherein the memory 1002 stores instructions that can be executed by the at least one processor 1001, the instructions being executed by the at least one processor 1001 to enable the at least one processor 1001 to perform the air conditioning dynamic test environment adjustment method in the following embodiment.

[0032] The following is for reference. Figure 1The diagram illustrates a structural schematic suitable for implementing the control device 100 in the embodiments of this application. The control device 100 in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 1 The control device 100 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0033] like Figure 1 As shown, the control device 100 may include a processor 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in memory 1002. The program in memory 1002 may be a program in read-only memory (ROM) or a program loaded from a storage device into random access memory (RAM). The RAM also stores various programs and data required for the operation of the control device 100. The processor 1001 and memory 1002 (ROM and RAM) are interconnected via a bus. An input / output (I / O) interface is also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tapes, hard disks, etc.; and communication devices. The communication device allows the control device 100 to communicate wirelessly or wiredly with other devices to exchange data. Although the control unit 100 with various systems is shown in the figure, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0034] Specifically, according to the embodiments disclosed in this application, the method flow described in the following embodiments can be implemented as a computer software program. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device. When the computer program is executed by the processor 1001, it performs the functions defined in the air conditioning dynamic test environment adjustment method of the embodiments disclosed in this application.

[0035] The operating condition equipment provided in this application, employing the air conditioning dynamic test environment adjustment method described in the following embodiments, can solve the technical problem of how to improve the matching degree between actual operating conditions and test requirements in the air conditioning dynamic test environment, thereby improving the accuracy of air conditioning dynamic testing. Compared with the prior art, the beneficial effects of the operating condition equipment provided in this application are the same as those of the air conditioning dynamic test environment adjustment method provided in the following embodiments, and other technical features in this operating condition equipment are the same as those disclosed in the method of the following embodiments, and will not be repeated here.

[0036] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or working device capable of performing the above functions. The following description uses a working device as an example to illustrate this embodiment and the subsequent embodiments.

[0037] Based on this, this application provides a method for adjusting the dynamic test environment of an air conditioner, applied to an operating equipment, wherein the operating equipment is configured to adjust the target test environment in which the air conditioner is located, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the method for adjusting the dynamic testing environment of an air conditioner according to this application.

[0038] In this embodiment, the method for adjusting the dynamic testing environment of the air conditioner includes steps S10 to S30: Step S10: Obtain the target environment parameters and environment test parameters of the target test environment; The target test environment is the operating environment in which the air conditioner is tested. In this embodiment, the target test environment is an enthalpy difference laboratory.

[0039] The target environmental parameters are the target values ​​that the environmental parameters of the target test environment required for the dynamic test of the air conditioner need to reach. When the environmental parameters of the target test environment reach the target environmental parameters, it can be considered that the actual operating conditions of the target test environment have reached the test conditions required for the dynamic test of the air conditioner.

[0040] The environmental test parameters are the environmental parameters of the target test environment currently being tested.

[0041] The target environmental parameters may include at least one of the following: target ambient temperature, target ambient humidity, target ambient temperature, etc. The target ambient temperature may include the target dry-bulb temperature and / or the target wet-bulb temperature, etc.

[0042] Environmental testing parameters may include at least one of the following: environmental testing temperature, environmental testing humidity, environmental testing humidity, etc. The environmental testing temperature may include dry-bulb testing temperature and / or wet-bulb testing temperature, etc.

[0043] The target environmental parameters and environmental test parameters are set accordingly. In this embodiment, the target environmental parameters include the target dry-bulb temperature and the target wet-bulb temperature, and the environmental test parameters include the dry-bulb test temperature and the wet-bulb test temperature.

[0044] In this embodiment, step S10 is executed when the air conditioner in the target test environment is in a dynamic test state.

[0045] Step S20: Taking the deviation between the adjustment result of the working equipment and the target environmental parameter as the target, the environmental parameter setpoint of the working equipment is determined according to the target environmental parameter. The adjustment result of the operating condition equipment is the actual value achieved by the environmental parameters of the target test environment under the adjustment of the operating condition equipment.

[0046] The preset deviation can be set according to the test accuracy required under the actual test conditions of the air conditioner. If the deviation value is less than the preset deviation, it means that the actual operating conditions of the target test environment under the adjustment of the operating equipment are consistent with the test conditions required for dynamic testing; if the deviation value is greater than or equal to the preset deviation, it means that the actual operating conditions of the target test environment under the adjustment of the operating equipment are not consistent with the test conditions required for dynamic testing.

[0047] Minimum deviation refers to the state in which the actual working conditions of the target test environment, under the adjustment of the working equipment, best match the test conditions required for dynamic testing.

[0048] The environmental parameter setpoints are used to control the target test environment for the operation of the equipment under operating conditions, and are the target values ​​that need to be achieved.

[0049] In one implementation, a conversion relationship between target environmental parameters and given environmental parameter values ​​can be pre-defined. Based on this conversion relationship, the target environmental parameters can be converted into given environmental parameter values. This conversion relationship may take the form of calculation formulas, mapping tables, neural network models, etc.

[0050] In another implementation, multiple environmental parameter reference values ​​can be determined based on the target environmental parameters. The deviation value between the adjustment result corresponding to each environmental parameter reference value and the target environmental parameter can be determined based on the relationship between the preset given value and the adjustment result. The environmental parameter reference value with a deviation value less than the preset deviation is determined as the environmental parameter given value, or the environmental parameter reference value with the smallest deviation value is determined as the environmental parameter given value.

[0051] In this embodiment, the target environmental parameters include the target dry-bulb temperature and the target wet-bulb temperature. The adjustment results include the dry-bulb temperature adjustment results and the wet-bulb temperature adjustment results. The environmental parameter setpoints include the dry-bulb temperature setpoint and the wet-bulb temperature setpoint. The target is that the deviation between the dry-bulb temperature adjustment result and the target dry-bulb temperature is less than a preset deviation or reaches the minimum deviation. The dry-bulb temperature setpoint is determined based on the target dry-bulb temperature. The target is that the deviation between the wet-bulb temperature adjustment result and the target wet-bulb temperature is less than a preset deviation or reaches the minimum deviation. The wet-bulb temperature setpoint is determined based on the target wet-bulb temperature.

[0052] Step S30: Control the operation of the working condition equipment according to the given environmental parameter values ​​and the environmental test parameters.

[0053] In this embodiment, based on PID control, the target control signal for the operating equipment is determined according to the given environmental parameters and environmental test parameters. The operating equipment is then controlled according to the target control signal to adjust the target test environment. PID control calculates the control quantity based on the system error using proportional, integral, and derivative terms.

[0054] When the air conditioner is in dynamic testing state, steps S10 to S30 are executed cyclically. The test results of the air conditioner are determined based on the operating status parameters of the air conditioner during this process. For example, the test results may include the energy efficiency value of the air conditioner, etc.

[0055] Based on the air conditioning dynamic test environment adjustment method mentioned in this embodiment, the control flow for controlling the dry-bulb temperature and wet-bulb temperature of the target test environment is as follows: Figure 3 As shown, Figure 3 (a) The target value for dry bulb temperature control is the target dry bulb temperature, the compensation value for dry bulb temperature control is the setpoint for dry bulb temperature, the measured value for dry bulb temperature is the dry bulb test temperature, the refrigeration / heater is the device in the working equipment, and the enthalpy difference laboratory is the target test environment. Figure 3 In (b), the target value for wet-bulb temperature control is the target wet-bulb temperature, the compensation value for wet-bulb temperature control is the wet-bulb temperature setpoint, the measured value for wet-bulb temperature is the wet-bulb test temperature, the humidifier is a device in the working equipment, and the enthalpy difference laboratory is the target test environment.

[0056] This embodiment provides a method for adjusting the dynamic test environment of an air conditioner. During the adjustment of the dynamic test environment of the air conditioner by the operating equipment, the target environmental parameter of the test requirement is no longer directly used as the given value of the operating equipment. Instead, the goal is to determine the environmental parameter given value corresponding to the target environmental parameter so as to control the operation of the operating equipment by taking the deviation between the adjustment result of the operating equipment and the target environmental parameter as less than the preset deviation or the minimum deviation. Based on this, the matching degree between the actual operating conditions and the test requirements in the dynamic test environment of the air conditioner can be improved, thereby improving the accuracy of the dynamic test of the air conditioner.

[0057] Based on any of the above embodiments, in the second embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter. Based on this, step S20 includes: determining the parameter difference between the target environmental parameter and the initial environmental parameter of the target test environment; determining the environmental parameter setpoint based on the target correspondence relationship, according to the target environmental parameter, the initial environmental parameter of the target test environment, and the target environmental parameter target value of the target test environment before the current time; wherein, the target correspondence relationship is a relationship constructed with the goal of the deviation between the adjustment result of the working condition equipment and the target environmental parameter being less than a preset deviation or reaching a minimum deviation, and the target correspondence relationship is the correspondence between the target environmental parameter, the initial environmental parameter, the target environmental parameter value, and the environmental parameter setpoint value.

[0058] The initial environmental parameters are the environmental parameters of the indoor environment detected at the initial moment when the air conditioner is tested in the target test environment, which may include the initial dry-bulb temperature and / or the initial wet-bulb temperature.

[0059] The target environmental parameter value is the target environmental parameter determined before the current time, and the determination process is the same as the current process of determining the target environmental parameter. The target environmental parameter can be used as the target environmental parameter value when determining new target environmental parameters in the future, and the subsequent process of determining new target environmental parameters is the same as the current process of determining the target environmental parameter.

[0060] The target correspondence can take the form of calculation formulas, mapping tables, machine learning models, etc. When the target environmental parameters include target dry-bulb temperature and target wet-bulb temperature, different target correspondences can be set for the target dry-bulb temperature and target wet-bulb temperature, respectively. Then, based on the different target correspondences, the target dry-bulb temperature and target wet-bulb temperature are determined according to the corresponding target environmental parameters, the target values ​​of the environmental parameters, and the initial environmental parameters.

[0061] In this embodiment, a first parameter difference between the target environmental parameter and the initial environmental parameter can be determined, and a second parameter difference between the target environmental parameter value before the current time and the initial environmental parameter can be determined. The target correspondence is the correspondence between the first parameter difference, the second parameter difference, the initial environmental parameter, and the given environmental parameter value. Based on this target correspondence, the given environmental parameter value is determined according to the first parameter difference, the second parameter difference, and the initial environmental parameter. The target environmental parameter value before the current time is the target environmental parameter value of the time preceding the current time.

[0062] In one application example, the target correspondence is in the state-space form of the transfer function, and the target correspondence is as follows: (1) dRAT'(n) = c11 x1(n) + c12 x2(n) + d (RAT(n)-init_FFC) (2) x1(n) = a11 x1(n-1) + a12 x2(n-1) + b11 (RAT(n-1)-init_FFC) (3) x2(n) = a21 x1(n-1) + a22 x2(n-1) + b21 (RAT(n-1)-init_FFC) (4)RAT'(n) = init_FFC + dRAT'(n); In this formula, dRAT', x1, and x2 are intermediate variables, (n) represents the current time, (n-1) represents the previous time, RAT(n) is the target environmental parameter, init_FFC is the initial environmental parameter, (RAT(n)-init_FFC) is the first parameter difference mentioned above, (RAT(n-1)-init_FFC) is the second parameter difference mentioned above, and c11, c12, d, a11, a12, b11, a21, a22, and b21 are coefficients pre-determined based on the deviation value being less than a preset deviation or reaching the minimum deviation. Based on this, by substituting the target environmental parameter value RAT(n-1), the target environmental parameter RAT(n), and the initial environmental parameter init_FFC before the current time into the above formula, the current environmental parameter given value RAT'(n) can be calculated. In this formula, x1(n-1) and x2(n-1) are the calculation results calculated and recorded at the previous time, and x1(0) and x2(0) can be set to the corresponding initial values ​​(e.g., 0).

[0063] In this embodiment, by using the above method, the target environmental parameter value before the current moment, the initial environmental parameter, and the current target environmental parameter are used to determine the current environmental parameter setpoint. This ensures that the determination of the environmental parameter setpoint can adapt to the dynamic changes of the target environmental parameter value in the target test environment during the air conditioning dynamic test. This is beneficial to further improve the accuracy of the environmental parameter setpoint, thereby further ensuring that the deviation between the adjustment result of the operating equipment and the target environmental parameter is reduced, so as to further improve the matching degree between the actual operating conditions and test requirements in the air conditioning dynamic test environment, and improve the accuracy of the air conditioning dynamic test.

[0064] In one feasible implementation, refer to Figure 4 Before step S20, the following are also included: Step S01: Obtain the system transfer function, which represents the relationship between the adjustment result of the working condition equipment in the preset state and the given value of the working condition equipment. The preset state includes the air outlet of the air conditioner being sent into the target test environment, and the target value of the target test environment being used as the given value, and the working condition equipment being controlled to operate according to the given value and the test value of the target test environment. The target value is the desired value that the environmental parameters of the target test environment need to achieve.

[0065] The given value is the control target of the environmental parameters of the target test environment used to control the operation of the equipment under control conditions.

[0066] The test values ​​are environmental parameters of the target test environment in the actual test.

[0067] In the preset state, the target value is used as the given value of the working equipment, and the current test value is combined to perform PID control on the working equipment.

[0068] In this embodiment, the physical response process in the preset state represented by the system transfer function and its corresponding control model are as follows: Figure 5 As shown in the figure, X represents the target value, Y represents the adjustment result, and Xs represents the air outlet parameters during the air conditioner test. During the process of adjusting the air conditioner to the target test environment, the air outlet of the air conditioner in the target test environment flows through the air receiving chamber and then through the air volume measuring device. In a local area near its air outlet, it mixes with the air in the target test environment. This local air further diffuses and mixes with other indoor air. The environmental state achieved by the mixed air can be considered as the adjustment result of the air conditioner.

[0069] In this embodiment, the function model of the system transfer function and the experimental data of the target test environment are obtained. The experimental data includes multiple continuously collected target values ​​and corresponding adjustment results in the preset state. Model parameters in the function model are determined based on the experimental data, and the function model with known model parameters is identified as the system transfer function. The experimental data consists of data detected during the air conditioner's testing in the target test environment up to the current moment. Target values ​​and corresponding adjustment results are collected sequentially in the preset state according to time sequence, obtaining multiple target values ​​and their corresponding test values ​​as experimental data. The adjustment results are obtained by detecting the actual environmental parameters of the target test environment under the control condition of the equipment according to the target values.

[0070] The target value in the experimental data is used as the known input parameter of the function model, and the adjustment result is used as the known output parameter of the function model, thereby determining the model parameters in the function model.

[0071] To improve the efficiency of model parameter identification, in this embodiment, the `iddata` function is used to create a data object, which includes multiple target values ​​and corresponding adjustment results. A MATLAB function is created based on the function model, and the `idgrey` function is used to create a gray-box model corresponding to the MATLAB function. Based on the `greyest` function, the parameters of the gray-box model are determined using the data object to obtain the system transfer function. The `iddata` function is a key function in the system identification toolbox, mainly used to create and manage input / output data objects for system modeling, simulation, and verification. MATLAB functions are the core module of MATLAB system programming, used to encapsulate independent functions and achieve "communication" through input / output parameters. The `idgrey` function is a core function in the MATLAB system identification toolbox, used for parameter identification of linear gray-box models. The `greyest` function is a core function in the MATLAB system identification toolbox, used for parameter estimation of gray-box models.

[0072] In this embodiment, combined with Figure 5 The process involves obtaining a first transfer function model, a second transfer function model, a third transfer function model, and a fourth function model; determining the function model based on these models; wherein the first transfer function model represents the dynamic response process of environmental parameters in the air receiving chamber to the air outlet parameters of the air conditioner; the second transfer function model represents the dynamic response process of local environmental parameters in the downstream airflow measurement device outlet area of ​​the air receiving chamber to the airflow measurement device outlet parameters; the third transfer function model represents the dynamic response process of the adjustment result of the operating equipment to the PID control of the operating equipment based on the target value and the measured value; and the fourth transfer function model represents the dynamic response process of the adjustment result of the operating equipment to local environmental parameters. Furthermore, the process involves combining... Figure 5 Each process in the preset state can be simplified using the transfer function of a first-order response system: ; ; ; ; Based on this, the first transfer function model is: The second transfer function model is The third transfer function model is The fourth transfer function is The system transfer function model obtained after synthesis. for = a1, a2, and a3 are model parameters determined based on experimental data. The Laplace transform represents the adjustment result of the operating equipment. s represents the Laplace transform of the target value (i.e., the given value of the equipment under the preset state) in the target test environment, and s represents the Laplace transform domain.

[0073] Step S02: Determine the feedforward compensation function based on the system transfer function. The feedforward compensation function represents the relationship between the target value and the given value of the operating equipment when the deviation between the adjustment result of the operating equipment and the target value is minimized. In this embodiment, the feedforward compensation function is determined based on the reciprocal of the system transfer function.

[0074] In this embodiment, it is determined whether the current system transfer function is a minimum-phase system. When the system transfer function is a minimum-phase system, its reciprocal is determined as the feedforward compensation function. When the system transfer function is not a minimum-phase system, an all-pass function is used to convert the system transfer function into a minimum-phase system transfer function, and its reciprocal is determined as the feedforward compensation function. Specifically, the reciprocal of the system transfer function is obtained by swapping the numerator and denominator. Based on this, the deviation between the adjustment result and the target value can be minimized.

[0075] Step S03: Determine the target correspondence based on the feedforward compensation function.

[0076] In this context, the target value in the feedforward compensation function can be considered as the target environmental parameter in the target correspondence, and the given value in the feedforward compensation function can be considered as the given value of the environmental parameter in the target correspondence.

[0077] In one implementation, the feedforward compensation function can be directly used as the target correspondence. Then, the target environmental parameters can be transformed by Laplace and substituted into the feedforward compensation function. The output of the feedforward compensation function can be transformed by inverse Laplace to obtain the given value of the environmental parameters.

[0078] In another implementation, a preset time interval can be obtained. Based on this preset time interval, the feedforward compensation function is transformed from a continuous-time transfer function form into a discrete-time state-space form to obtain the target correspondence. The target correspondence includes matrices A, B, C, and D. The obtained target correspondence is as shown in formulas (1), (2), (3), and (4) above. Matrix A includes a11, a12, a21, and a22; matrix B includes b11 and b21; matrix C includes c11 and c12; and matrix D includes d. Based on this, combined with... Figure 6 The target correspondence can include the first compensation function Drybulb_FFC_processing() corresponding to dry-bulb temperature and the second compensation function Wetbulb_FFC_processing() corresponding to wet-bulb temperature. The first compensation function is used to calculate the given value of dry-bulb temperature, and the second compensation function is used to calculate the given value of wet-bulb temperature. Both the first and second compensation functions are built into the feedforward compensator. RAT0 is the initial value of dry-bulb temperature, RAB0 is the initial value of wet-bulb temperature, RAT(n) is the target dry-bulb temperature, RAB(n) is the target wet-bulb temperature, and the output RAT_FFC(n) is the given value of dry-bulb temperature, and the output RAB_FFC(n) is the given value of wet-bulb temperature.

[0079] In this embodiment, determining the target correspondence in the above manner helps to effectively reduce the overshoot or delay of environmental parameters in the target test environment, and further ensures the matching degree between the actual working conditions and the working conditions required for dynamic testing of air conditioning.

[0080] Based on any of the above embodiments, in the third embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter. Based on this, refer to... Figure 7 Step S20 includes: Step S21: Determine multiple environmental parameter reference values ​​based on the target environmental parameters; Multiple environmental parameter reference values ​​are determined based on a preset deviation, a preset step size, and target environmental parameters. Specifically, a candidate parameter range for the environmental parameter reference values ​​can be determined based on the preset deviation and target environmental parameters. The lower limit of the candidate parameter range is the difference between the target environmental parameter and the preset deviation, and the upper limit is the sum of the target environmental parameter and the preset deviation. Equal-interval sampling is performed within the candidate parameter range at a preset step size to obtain multiple environmental parameter reference values.

[0081] Step S22: Input the reference values ​​of each of the environmental parameters into the preset model in sequence and determine whether the deviation between the adjustment result output by the preset model and the target environmental parameter is less than the preset deviation. The input parameters of the preset model are the given values ​​of the working equipment, and the output parameters of the preset model are the adjustment results of the working equipment. Step S23: Determine the environmental parameter reference value whose deviation value is less than the preset deviation as the environmental parameter given value.

[0082] The preset model represents the relationship between a given value of the equipment under operating conditions and the corresponding adjustment result. In this embodiment, the preset model includes artificial intelligence models such as neural network models. Specifically, the aforementioned experimental data can be used as training samples to train the neural network model, thereby determining the model parameters within the neural network model and obtaining the preset model.

[0083] Based on a preset order of multiple environmental parameter reference values ​​(from smallest to largest or largest to smallest), one environmental parameter reference value is input as the setpoint for the operating equipment into a preset model. The preset model can output the corresponding adjustment result. It can determine whether the deviation between the adjustment result and the target environmental parameter is less than a preset deviation. If it is less than the preset deviation, it can be used as the environmental parameter setpoint. If it is greater than or equal to the preset deviation, the next environmental parameter reference value is input as the new setpoint for the operating equipment into the preset model, and the above process is repeated until an environmental parameter setpoint with a deviation value less than the preset deviation is obtained. Alternatively, all environmental parameter reference values ​​can be input into the preset model separately to obtain multiple deviation values ​​between the adjustment results and the target environmental parameter. Among these deviation values, the environmental parameter reference value with a deviation value less than the preset deviation is determined as the environmental parameter setpoint.

[0084] The preset model may include a first model corresponding to dry-bulb temperature and a second model corresponding to wet-bulb temperature. Multiple environmental parameter reference values ​​may include multiple dry-bulb temperature reference values ​​and multiple wet-bulb temperature reference values. Then, each dry-bulb temperature reference value is sequentially input into the first model, and it is determined whether the deviation between the dry-bulb temperature adjustment result output by the first model and the target dry-bulb temperature is less than a preset deviation. The dry-bulb temperature reference value with a deviation value less than the preset deviation is determined as the dry-bulb temperature setpoint. Similarly, each wet-bulb temperature reference value is sequentially input into the second model, and it is determined whether the deviation between the wet-bulb temperature adjustment result output by the second model and the target wet-bulb temperature is less than a preset deviation. The wet-bulb temperature reference value with a deviation value less than the preset deviation is determined as the wet-bulb temperature setpoint.

[0085] Combination Figure 8 and Figure 9 , Figure 8 The data-driven system model is a preset model. Figure 9 In the text, [Tlower, Tupper] represents the parameter range containing the reference values ​​of multiple environmental parameters. This is one of the reference values ​​for environmental parameters. The next environmental parameter reference value is used, the data-driven system model is the preset model, the predictive control result is the result output by the preset model, the target dry / wet bulb is the target environmental parameter, 0.1℃ is the preset deviation, and the output compensation value is the environmental parameter given value.

[0086] In this embodiment, the given values ​​of environmental parameters corresponding to the target environmental parameters are determined based on a neural network model. When building the model, it is not necessary to consider the physical process of the system; training can be performed directly based on experimental data, avoiding the model mismatch problem that may occur when modeling based on physical processes. At the same time, since the neural network model has an extremely fast computation speed, the process of finding the given values ​​of environmental parameters can also be output in real time, effectively meeting the requirements of operating conditions with rapid changes in environmental parameters such as dry / wet bulb, thereby further improving the accuracy of dynamic testing of air conditioning.

[0087] In some implementations, when the deviation values ​​corresponding to all environmental parameter reference values ​​in this embodiment are greater than or equal to the preset deviation, the environmental parameter setpoint with the minimum deviation value can be determined based on the method mentioned in the second embodiment above. This is beneficial for balancing adjustment efficiency and the matching degree between actual operating conditions and the operating conditions required for dynamic testing of air conditioning.

[0088] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the adjustment method of the dynamic test environment of the air conditioner in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0089] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the air conditioning dynamic test environment adjustment method in the above embodiments.

[0090] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0091] The aforementioned computer-readable storage medium may be included in the operating equipment; or it may exist independently and not assembled into the operating equipment.

[0092] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by a processor, cause the processor to execute the process described in the embodiment of the method for adjusting the dynamic testing environment of the air conditioner.

[0093] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0094] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described adjustment method for the dynamic testing environment of an air conditioner. This solves the technical problem of how to improve the matching degree between actual operating conditions and testing requirements in the dynamic testing environment of an air conditioner, thereby improving the accuracy of dynamic testing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the adjustment method for the dynamic testing environment of an air conditioner provided in the above embodiments, and will not be repeated here.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0096] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. Modules described in the embodiments of this application can be implemented in software or hardware. The names of modules do not necessarily limit the specific unit itself. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0097] The above descriptions are merely some embodiments of this application and do not limit the patent scope of this application. Any equivalent structural transformations made based on the technical concept of this application and the content of this specification and drawings, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application. Therefore, the protection scope of this application should be determined by the scope of the claims.

Claims

1. A method for adjusting the dynamic testing environment of an air conditioner, characterized in that, Applied to operating equipment, the operating equipment is configured to adjust the target test environment where the air conditioner is located, and the adjustment method of the air conditioner dynamic test environment includes: Obtain the target environment parameters and environment test parameters of the target test environment. The target environment parameters are the target values ​​that the target test environment parameters need to achieve for the dynamic test of the air conditioner. With the goal of the deviation between the adjustment result of the working equipment and the target environmental parameter being less than a preset deviation or reaching the minimum deviation, the environmental parameter setpoint of the working equipment is determined according to the target environmental parameter. The environmental parameter setpoint is the target value that the environmental parameter of the target test environment used to control the operation of the working equipment needs to achieve. The operation of the equipment under the specified conditions is controlled according to the given values ​​of the environmental parameters and the environmental test parameters. The step of determining the environmental parameter setpoint of the operating equipment based on the target environmental parameter, with the deviation between the adjustment result of the operating equipment and the target environmental parameter being less than a preset deviation or reaching a minimum deviation, includes: Based on the target correspondence, the given value of the environment parameter is determined according to the target environment parameter, the initial environment parameter of the target test environment, and the target value of the environment parameter of the target test environment before the current time. The target value of the environment parameter before the current time is the target value of the environment parameter of the previous time. The target correspondence relationship is a relationship constructed with the goal of the deviation between the adjustment result of the working equipment and the target environmental parameter being less than a preset deviation or reaching the minimum deviation. The target correspondence relationship is the correspondence between the target environmental parameter, the initial environmental parameter, the target value of the environmental parameter, and the given value of the environmental parameter. Alternatively, the step of determining the environmental parameter setpoint of the operating equipment based on the target environmental parameter, with the deviation between the adjustment result of the operating equipment and the target environmental parameter being less than a preset deviation or reaching a minimum deviation, includes: Based on the target environmental parameters, determine multiple reference values ​​for environmental parameters; The reference values ​​of each environmental parameter are sequentially input into the preset model, and it is determined whether the deviation between the adjustment result output by the preset model and the target environmental parameter is less than the preset deviation. The environmental parameter reference value whose deviation value is less than the preset deviation is determined as the environmental parameter setpoint; The input parameters of the preset model are the given values ​​of the working equipment, and the output parameters of the preset model are the adjustment results of the working equipment. The preset model includes a neural network model.

2. The method for adjusting the dynamic testing environment of an air conditioner as described in claim 1, characterized in that, The method for adjusting the dynamic test environment of the air conditioner also includes: Obtain the system transfer function, which represents the relationship between the adjustment result of the operating condition equipment in the preset state and the given value of the operating condition equipment. The preset state includes the air outlet of the air conditioner being sent into the target test environment, and the target value of the target test environment being used as the given value, and the operating condition equipment being controlled to operate according to the given value and the test value of the target test environment. The feedforward compensation function is determined based on the system transfer function. The feedforward compensation function represents the relationship between the target value and the given value of the operating equipment when the deviation between the adjustment result of the operating equipment and the target value is minimized. The target correspondence is determined based on the feedforward compensation function.

3. The method for adjusting the dynamic testing environment of an air conditioner as described in claim 2, characterized in that, The steps for obtaining the system transfer function include: Obtain the function model of the system transfer function and the test data of the target test environment. The test data includes multiple continuously collected target values ​​and corresponding adjustment results in the preset state. Based on the experimental data, the model parameters in the function model are determined, and the function model with known model parameters is identified as the system transfer function.

4. The method for adjusting the dynamic testing environment of an air conditioner as described in claim 3, characterized in that, The steps for obtaining the function model of the system transfer function include: Obtain the first transfer function model, the second transfer function model, the third transfer function model, and the fourth transfer function model; The function model is determined based on the first transfer function model, the second transfer function model, the third transfer function model, and the fourth transfer function model; Wherein, the first transfer function model represents the dynamic response process of environmental parameters in the air receiving chamber to the air outlet parameters of the air conditioner; the second transfer function model represents the dynamic response process of local environmental parameters in the outlet area of ​​the downstream air volume measuring device in the air receiving chamber to the air outlet parameters of the air volume measuring device; the third transfer function model represents the dynamic response process of the adjustment result of the operating equipment to the PID control of the operating equipment based on the target value and the test value; and the fourth transfer function model represents the dynamic response process of the adjustment result of the operating equipment to local environmental parameters.

5. The method for adjusting the dynamic testing environment of an air conditioner as described in claim 3, characterized in that, The step of determining the feedforward compensation function based on the system transfer function includes: When the system transfer function is a minimum phase system, the reciprocal of the system transfer function is determined to be the feedforward compensation function; When the system transfer function is a non-minimum phase system, the system transfer function is converted into a minimum phase system transfer function using an all-pass function, and the reciprocal of the minimum phase system transfer function is determined as the feedforward compensation function; And / or, the step of determining the target correspondence based on the feedforward compensation function includes: Based on a preset time interval, the feedforward compensation function is transformed from a continuous-time transfer function form into a discrete-time state-space form to obtain the target correspondence.

6. The method for adjusting the dynamic testing environment of an air conditioner as described in claim 3, characterized in that, The steps of determining the model parameters in the function model based on the experimental data, and determining the function model with known model parameters as the system transfer function, include: A data object is created using the iddata function, the data object including multiple target values ​​and corresponding adjustment results; Based on the function model, create MATLAB functions, and use the idgrey function to create the corresponding gray box model of the MATLAB functions; Based on the greyest function, the parameters of the greybox model are determined using the data object, and the system transfer function is obtained. The idgrey function is a core function in the MATLAB System Identification Toolbox, used for parameter identification of linear gray-box models, and the greyest function is a core function in the MATLAB System Identification Toolbox, used for parameter estimation of gray-box models.

7. The method for adjusting the dynamic testing environment of an air conditioner as described in any one of claims 1 to 6, characterized in that, The target environmental parameters include the target dry-bulb temperature, and the environmental test parameters include the dry-bulb test temperature; and / or, the target environmental parameters include the target wet-bulb temperature, and the environmental test parameters include the wet-bulb test temperature.

8. A working device, characterized in that, The operating equipment includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for adjusting the dynamic test environment of an air conditioner as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for adjusting the dynamic test environment of an air conditioner as described in any one of claims 1 to 7.

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