Control method of air conditioner, air conditioner and storage medium

Abstract

The invention discloses a control method of an air conditioner, the air conditioner and a storage medium, and relates to the field of heat pump systems.The method comprises the steps that actual working condition data of the air conditioner are obtained; the variable temperature load and the temperature maintaining load of the air conditioner are determined according to the actual working condition data, the variable temperature load is the heat exchange amount needed when the temperature of the indoor space adjusted by the air conditioner changes in the target direction, and the temperature maintaining load is the heat exchange amount needed when the temperature of the indoor space is maintained; and the air conditioner is controlled to operate according to the variable-temperature load and the temperature maintaining load. The control comfort of the air conditioner is improved, and energy consumption is reduced.

Description

Technical Field

[0001] This application relates to the field of heat pump system technology, and more particularly to control methods for air conditioners, air conditioners, and storage media. Background Technology

[0002] During the operation of an air conditioner, the indoor heat exchanger is in a heat exchange state through the coordinated operation of components such as the compressor, fan, and electronic expansion valve. The heat exchange between the air and the indoor heat exchanger can regulate the indoor environment.

[0003] In related technologies, the various devices in an air conditioner are generally characterized by indoor load based on indoor temperature or humidity, and the operation of each device is regulated according to the indoor temperature or humidity. However, indoor temperature or humidity cannot accurately represent the actual load of the air conditioner, which can easily lead to problems of discomfort and energy consumption. Summary of the Invention

[0004] The main objective of this application is to provide a control method for an air conditioner, an air conditioner, and a storage medium, which aims to improve the comfort of air conditioner control and save energy.

[0005] To achieve the above objectives, this application proposes a control method for an air conditioner, the control method comprising: Obtain the actual operating condition data of the air conditioner; The variable temperature load and the temperature maintenance load of the air conditioner are determined based on the actual operating data. The variable temperature load is the heat exchange required when the indoor space is regulated by the air conditioner to change the temperature in the target direction. The temperature maintenance load is the heat exchange required to maintain the temperature of the indoor space. The air conditioner is operated according to the variable temperature load and the constant temperature load.

[0006] In one embodiment, the step of controlling the operation of the air conditioner according to the variable temperature load and the constant temperature load includes: The air conditioner is controlled to operate based on the sum of the variable temperature load and the constant temperature load.

[0007] In one embodiment, the step of determining the variable temperature load and constant temperature load of the air conditioner based on the actual operating condition data includes: The actual operating condition data is input into the load prediction model to obtain the output results of the load prediction model, which include the variable temperature load and the constant temperature load. The load prediction model is a model generated based on deep learning.

[0008] In one embodiment, the load prediction model includes an input layer, a feature extraction layer, a hidden state layer, and an output layer connected in sequence. The input layer is used to acquire input operating condition data. The feature extraction layer is used to extract spatiotemporal features from the input operating condition data. The hidden state layer includes a first latent variable representing the building parameters of the building where the air conditioner is located and a second latent variable representing the heat conduction parameters between the indoor space and the external environment. The output layer is used to calculate the variable temperature load based on the first latent variable and the input operating condition data. The output layer is also used to calculate the constant temperature load based on the first latent variable, the second latent variable, and the input operating condition data.

[0009] In one embodiment, the building parameters include the length, width, and height of the interior space.

[0010] In one embodiment, the heat conduction parameters include the thermal resistance of the enclosure structure of the indoor space, the heat transfer coefficient of the enclosure structure, and the temperature difference coefficient between the indoor space and the external space under different environmental conditions.

[0011] In one embodiment, the temperature difference coefficient includes a first temperature difference coefficient, a second temperature difference coefficient, and a third temperature difference coefficient. The first temperature difference coefficient is the temperature difference coefficient when the external space corresponding to the wall of the indoor space is outdoor air. The second temperature difference coefficient is the temperature difference coefficient when the external space corresponding to the wall of the indoor space is an indoor environment without air conditioning. The third temperature difference coefficient is the temperature difference coefficient when the external space corresponding to the wall of the indoor space is an indoor environment with air conditioning.

[0012] In one embodiment, before the step of obtaining the actual operating condition data of the air conditioner, the method further includes: Acquire historical operating condition data of the air conditioner and the corresponding output capacity of the air conditioner, wherein the parameter types of the historical operating condition data are set to correspond to the parameter types of the actual operating condition data; The model parameters in the preset deep learning model are trained based on the historical operating data and the output capability. The preset deep learning model with determined model parameters is used as the load prediction model.

[0013] In one embodiment, the control method for the air conditioner further includes: Using the constraint that the sum of the variable temperature load and the constant temperature load output by the output layer equals the output capacity of the corresponding air conditioner, a first loss function corresponding to the output layer is constructed. The step of training the model parameters in the preset deep learning model based on the historical working condition data and the output capability includes: The model parameters in the preset deep learning model are iteratively trained based on the historical operating data, the output capability, and the first loss function.

[0014] In one embodiment, the control method for the air conditioner further includes: Using the consistency of the building parameters and / or the heat conduction parameters at different times as constraints, a second loss function corresponding to the hidden state layer is constructed; The step of iteratively training the model parameters in the preset deep learning model based on the historical operating data, the output capability, and the first loss function includes: The model parameters in the preset deep learning model are iteratively trained based on the historical operating data, the output capability, the first loss function, and the second loss function.

[0015] In one embodiment, the step of constructing the second loss function corresponding to the hidden state layer, using the consistency of the building parameters and / or the heat conduction parameters at different times as constraints, includes: The second loss function is constructed by taking the consistency of the building parameters, thermal resistance, and heat transfer coefficient at different times, as well as the consistency of the temperature difference coefficient within the same time period, as constraints.

[0016] In addition, to achieve the above objectives, this application also proposes an air conditioner, the air conditioner comprising: 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 control method for the air conditioner as described above.

[0017] 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 conditioner control method described above.

[0018] The one or more technical solutions proposed in this application have at least the following technical effects: the variable temperature load and constant temperature load of the air conditioner are determined based on actual operating data, and the operation of the air conditioner is controlled according to the variable temperature load and constant temperature load. Based on this, the operation of the air conditioner is no longer controlled based on indoor temperature or humidity. The variable temperature load and constant temperature load can accurately represent the actual output demand of the air conditioner, thereby achieving precise control of the operation of the air conditioner and effectively improving the comfort of air conditioner control and saving energy consumption. Attached Figure Description

[0019] 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.

[0020] 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.

[0021] Figure 1 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the control method of the air conditioner in this application embodiment; Figure 2 This is a flowchart illustrating an embodiment of the control method for an air conditioner according to this application. Figure 3 This is a schematic diagram of the load prediction model involved in the embodiment of the control method for the air conditioner of this application.

[0022] 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

[0023] 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.

[0024] 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.

[0025] The main solution of this application embodiment is: to obtain the actual operating condition data of the air conditioner; to determine the variable temperature load and the temperature maintenance load of the air conditioner based on the actual operating condition data, wherein the variable temperature load is the heat exchange required when the indoor space is adjusted by the air conditioner to change the temperature in a target direction, and the temperature maintenance load is the heat exchange required to maintain the temperature of the indoor space; and to control the operation of the air conditioner based on the variable temperature load and the temperature maintenance load.

[0026] In this embodiment, for ease of description, the following description uses an air conditioner as the subject of execution.

[0027] In related technologies, the various devices in air conditioners are generally characterized by indoor load based on indoor temperature or humidity, and the operation of each device is regulated according to the indoor temperature or humidity. However, indoor temperature or humidity cannot accurately represent the actual load of the air conditioner, which can easily lead to problems of discomfort and energy consumption.

[0028] This application provides the above-mentioned solution, which determines the variable temperature load and constant temperature load of the air conditioner based on actual operating data, and controls the operation of the air conditioner according to the variable temperature load and constant temperature load. Based on this, the operation of the air conditioner is no longer controlled based on indoor temperature or humidity. The variable temperature load and constant temperature load can accurately represent the actual output demand of the air conditioner, thereby achieving precise control of the operation of the air conditioner and effectively improving the comfort of air conditioner control and saving energy consumption.

[0029] This application provides an air conditioner. The air conditioner can be any type of air conditioner, such as a wall-mounted air conditioner, a floor-standing air conditioner, a window air conditioner, a ceiling-mounted air conditioner, or a multi-split air conditioner.

[0030] In this embodiment, refer to Figure 1 The air conditioner includes a compressor 1, a reversing assembly 2 (such as a four-way valve), and an indoor heat exchanger, a throttling device 3, and an outdoor heat exchanger connected in sequence.

[0031] Indoor heat exchangers are equipped with indoor fans 4, and outdoor heat exchangers are equipped with outdoor fans 5.

[0032] The exhaust port of compressor 1, the return port of compressor 1, the indoor heat exchanger, and the outdoor heat exchanger are all connected to the reversing assembly 2. The reversing assembly 2 has a first operating state and a second operating state. When the reversing assembly 2 is running in the first operating state, the exhaust port of the compressor 1 is connected to the outdoor heat exchanger and the return port of the compressor 1 is connected to the indoor heat exchanger. When the compressor 1 is turned on, the refrigerant discharged by the compressor 1 flows through the outdoor heat exchanger, the throttling device 3 and the indoor heat exchanger in sequence and then flows back to the compressor 1. The indoor heat exchanger is in the evaporation state. When the reversing assembly 2 is in the second operating state, the exhaust port of the compressor 1 is connected to the indoor heat exchanger and the return port of the compressor 1 is connected to the outdoor heat exchanger. When the compressor 1 is turned on, the refrigerant discharged by the compressor 1 flows through the indoor heat exchanger, the throttling device 3 and the outdoor heat exchanger in sequence and then flows back to the compressor 1. The indoor heat exchanger is in a condensing state.

[0033] The air conditioner also includes an environmental detection module 6 to detect environmental status parameters of the environment in which the air conditioner is located. The environment may include indoor and outdoor environments, and the environmental status parameters may include at least one of the following: ambient temperature, ambient humidity, ambient enthalpy, and ambient moisture content.

[0034] Reference Figure 1 The air conditioner also includes a control device 100, and the compressor 1, throttling device 3, indoor fan 4, outdoor fan 5, commutation assembly 2, and environmental detection module 6 mentioned above are all communicatively connected to the control device 100.

[0035] 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 conditioner control method in the following embodiment.

[0036] The following is for reference. Figure 1 The 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.

[0037] like Figure 1As 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.

[0038] 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 control method of the air conditioner of the embodiments disclosed in this application.

[0039] The air conditioner provided in this application, employing the control method of the air conditioner in the following embodiments, can solve the technical problem of how to improve the comfort of air conditioner control and save energy consumption. Compared with the prior art, the beneficial effects of the air conditioner provided in this application are the same as the beneficial effects of the control method of the air conditioner provided in the following embodiments, and other technical features in this air conditioner are the same as the features disclosed in the method of the following embodiments, and will not be repeated here.

[0040] 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 air conditioner capable of performing the above functions. The following description uses an air conditioner as an example to illustrate this embodiment and the subsequent embodiments.

[0041] Based on this, the embodiments of this application provide a control method for an air conditioner, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the control method for the air conditioner of this application.

[0042] In this embodiment, the control method of the air conditioner includes steps S10 to S30: Step S10: Obtain the actual operating condition data of the air conditioner; Actual operating condition data refers to data related to the load during the current operation of the air conditioner. Actual operating condition data may include data from one or more different times.

[0043] Actual operating data may include the air conditioner's own status data and the environmental data of the environment in which the air conditioner is located.

[0044] In this embodiment, the actual operating data includes, but is not limited to, the following data: return air temperature (current indoor temperature), indoor heat exchanger temperature, outdoor temperature, indoor temperature when the air conditioner is not turned on, set temperature, humidity, fan speed, compressor operating frequency, initial temperature difference, evaporation temperature, condensation temperature, etc.

[0045] During the operation of the air conditioner in the preset mode, step S10 is executed. In the preset mode, the indoor heat exchanger is in a condensing state or an evaporating state. For example, the preset mode can be a heating mode, a cooling mode, or a dehumidification mode.

[0046] Step S20: Determine the variable temperature load and temperature maintenance load of the air conditioner based on the actual operating data. The variable temperature load is the heat exchange required when the indoor space is regulated by the air conditioner to change the temperature in the target direction. The temperature maintenance load is the heat exchange required to maintain the temperature of the indoor space. The target direction is the direction of the desired temperature change in the indoor environment based on the preset mode currently operating with the air conditioner. When the preset mode is cooling or dehumidifying, the target direction is downward; when the preset mode is heating, the target direction is upward.

[0047] In this embodiment, maintaining temperature means keeping the temperature constant. In other implementations, maintaining temperature may also mean keeping the temperature fluctuation value less than a preset value.

[0048] Specifically, a correspondence between actual operating data and variable temperature load and constant temperature load can be established in advance. The correspondence can be in the form of a relational formula or an algorithm model. Based on this correspondence, the variable temperature load and constant temperature load corresponding to the current actual operating data can be determined.

[0049] In one implementation, the variable-temperature load and the constant-temperature load can be respectively configured with corresponding relationships to the operating data: a first relationship between the operating data and the variable-temperature load, and a second relationship between the operating data and the constant-temperature load are pre-established. First data corresponding to the variable-temperature load in the actual operating data is determined, and second data corresponding to the constant-temperature load in the actual operating data is determined. Based on the first relationship, the variable-temperature load corresponding to the first data is determined, and based on the second relationship, the constant-temperature load corresponding to the second data is determined. The first relationship may include a functional expression or an algorithmic model, and the second relationship may also include a functional expression or an algorithmic model.

[0050] In another implementation, a correspondence between the variable-temperature load and the constant-temperature load and the operating data is established: a third relationship is pre-established between the operating data, the variable-temperature load, and the constant-temperature load. Based on this third relationship, the variable-temperature load and the constant-temperature load corresponding to the current actual operating data can be determined. The third relationship may take the form of a functional expression or an algorithm model.

[0051] In this embodiment, both the variable temperature load and the temperature maintenance load are determined based on actual operating data. In other implementations, the variable temperature load can be determined based on actual operating data during the variable temperature phase, and the temperature maintenance load can be determined based on actual operating data during the temperature maintenance phase.

[0052] Step S30: Control the operation of the air conditioner according to the variable temperature load and the constant temperature load.

[0053] The compressor in the air conditioner is controlled to adjust its operating frequency, the fan speed, and the opening of the electronic expansion valve according to the variable temperature load and the constant temperature load.

[0054] In this embodiment, the air conditioner's operation is controlled based on the sum of the variable temperature load and the temperature maintenance load. When the sum of the variable temperature load and the temperature maintenance load is greater than the air conditioner's current output capacity, the compressor is controlled to increase its operating frequency and the fan to increase its operating speed. When the sum of the variable temperature load and the temperature maintenance load is less than or equal to the air conditioner's current output capacity, the compressor and fan are controlled to maintain their current state, or the compressor is controlled to decrease its operating frequency and the fan to decrease its operating speed. This method of controlling the air conditioner's operation based on the sum of the variable temperature load and the temperature maintenance load allows for the control of the air conditioner's output capacity primarily based on the variable temperature load when the indoor space is in a temperature-changing phase, and primarily based on the temperature maintenance load when the indoor space is in a temperature-maintaining phase.

[0055] In other implementations, when controlling the air conditioner based on variable temperature load and constant temperature load, a first weighting value corresponding to the variable temperature load and a second weighting value corresponding to the constant temperature load can be determined based on the temperature difference between the indoor temperature regulated by the air conditioner and the set temperature. The target load is then calculated by weighted averaging of the variable temperature load and the constant temperature load based on the first and second weighting values. The air conditioner is then controlled to operate according to the target load. When the target load is greater than the air conditioner's current output capacity, the compressor is controlled to increase its operating frequency and the fan to increase its operating speed. When the target load is less than or equal to the air conditioner's current output capacity, the compressor and fan are controlled to maintain their current state, or the compressor is controlled to decrease its operating frequency and the fan to decrease its operating speed.

[0056] This embodiment provides a control method for an air conditioner. Based on actual operating data, the variable temperature load and the constant temperature load of the air conditioner are determined. The operation of the air conditioner is controlled according to the variable temperature load and the constant temperature load. Based on this, the operation of the air conditioner is no longer controlled based on indoor temperature or humidity. The variable temperature load and the constant temperature load can accurately represent the actual output demand of the air conditioner, thereby achieving precise control of the operation of the air conditioner and effectively improving the comfort of air conditioner control and saving energy consumption.

[0057] Based on any of the above embodiments, in the second embodiment of this application, content that is consistent with or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter. Based on this, step S20 includes: The actual operating condition data is input into the load prediction model to obtain the output results of the load prediction model, which include the variable temperature load and the constant temperature load; wherein, the load prediction model is a model generated based on deep learning.

[0058] The input parameters of the load prediction model are operating condition parameters, and the output parameters of the load prediction model are variable temperature load and constant temperature load.

[0059] The deep learning algorithms used in load forecasting models may include at least one of the following: recurrent neural network (RNN), temporal convolutional network (TCN), long short-term memory network (LSTM), transformer network (Transformer), graph neural network (GNN), etc.

[0060] Different load forecasting models can be used depending on the preset operating mode of the air conditioner.

[0061] In this embodiment, the simultaneous determination of variable temperature load and constant temperature load based on the load prediction model is beneficial for accurately detecting the actual load of the air conditioner, which can further improve the accuracy of air conditioner control, thereby further improving comfort and saving energy.

[0062] In one feasible implementation, combined with Figure 3 The load prediction model includes an input layer, a feature extraction layer, a hidden state layer, and an output layer connected in sequence. The input layer is used to acquire input operating condition data. The feature extraction layer is used to extract spatiotemporal features from the input operating condition data. The hidden state layer includes a first latent variable representing the building parameters of the building where the air conditioner is located and a second latent variable representing the heat conduction parameters between the indoor space and the external environment. The output layer is used to calculate the variable temperature load based on the first latent variable and the input operating condition data. The output layer is also used to calculate the constant temperature load based on the first latent variable, the second latent variable, and the input operating condition data.

[0063] The feature extraction layer captures the spatiotemporal features of the input data through temporal networks (such as RNNs, TCNs, LSTMs, Transformers, etc.) or graph networks (GNNs). Combined with... Figure 3 The functions of each module in the feature extraction layer are as follows: Dilated Causal Convolution: This method mainly uses convolutional kernels to extract spatiotemporal features from the input data in the input layer. Specifically, it captures the dependencies between features through fully connected layers, strengthens the short-term temporal relationships of features through causal layers, and captures long-term temporal relationships through extended layers to obtain feature data. Weight Normalization: Normalizes the weights of the convolution kernels, enabling the use of normalized kernels for spatiotemporal feature extraction during extended causal convolution. This reduces gradient inflation or vanishing during model training and improves training efficiency. Rectified Linear Activation (ReLU): A commonly used activation function that performs non-negative processing on the above feature data. Values ​​greater than 0 are retained, and values ​​less than or equal to 0 are set to 0. This activation function can be used to extract the non-linear relationship between input and output data. Dropout: Randomly resets the feature values ​​in the data after rectified linear activation to zero, reducing the interdependence between model nodes, thereby reducing the risk of overfitting and structural risks in model design.

[0064] The data obtained from the random deactivation process can be reprocessed in the same way as described above. After a certain number of repetitions, the data obtained from the random deactivation process can be input into the hidden state layer for further processing.

[0065] Building parameters may include the dimensions of the interior space, such as length, width, and height. Correspondingly, the first implicit variable may include a first sub-variable corresponding to length, a second sub-variable corresponding to width, and a third sub-variable corresponding to height. In some implementations, the height of the interior space may be a given value (e.g., 2.8 meters; different values ​​may be set for different types of buildings). In this case, the building parameters represented by the first implicit variable may include the length and width of the interior space.

[0066] Thermal conduction parameters may include the thermal resistance of the building envelope of the indoor space and / or the heat transfer coefficient of the building envelope and / or the temperature difference coefficient between the indoor space and the external space under different environmental conditions, etc.

[0067] An indoor space can have multiple walls, and the environmental conditions of the external spaces corresponding to different walls can be different, resulting in different indoor-outdoor temperature differences between the external and indoor spaces. Based on this, a temperature difference coefficient can be used as a weighted average of the indoor-outdoor temperature differences for the corresponding external spaces. By calculating the indoor-outdoor temperature differences for different environmental conditions and the corresponding temperature difference coefficients, a weighted average can be obtained to represent the overall temperature difference between the indoor space and all external spaces.

[0068] The temperature difference coefficient may include a first temperature difference coefficient, a second temperature difference coefficient, and a third temperature difference coefficient, or the temperature difference coefficient may include a first temperature difference coefficient and a second temperature difference coefficient. When the external space corresponding to the wall is outdoor air, the corresponding temperature difference coefficient may be the first temperature difference coefficient; when the external space corresponding to the wall is an indoor environment without air conditioning, the corresponding temperature difference coefficient may be the second temperature difference coefficient; when the external space corresponding to the wall is an indoor environment with air conditioning, the corresponding temperature difference coefficient may be the third temperature difference coefficient. Wherein, the sum of the first temperature difference coefficient, the second temperature difference coefficient, and the third temperature difference coefficient is 1, then the third temperature difference coefficient can be determined based on the first and second temperature difference coefficients.

[0069] In this embodiment, the heat conduction parameters include the ratio of the heat transfer coefficient to the thermal resistance of the maintenance structure, the first temperature difference coefficient, and the second temperature difference coefficient. The ratio can be defined as the heat conduction coefficient. Correspondingly, the second implicit variable may include the fourth sub-variable corresponding to the heat conduction coefficient, the fifth sub-variable corresponding to the first temperature difference coefficient, and the sixth sub-variable corresponding to the second temperature difference coefficient.

[0070] The spatiotemporal features extracted by the feature extraction layer are input into the hidden state layer. No feature extraction is performed in the hidden state layer; each latent variable is simply aligned and concatenated with the corresponding feature in the input spatiotemporal features. The first and second latent variables in the hidden state layer can be continuously corrected during the model's iterative training process, thereby continuously approaching the actual state value of the indoor space.

[0071] The output layer can use the current value of the first hidden variable and the corresponding first sub-data in the input operating condition data to calculate the variable temperature load according to the following formula (1): (1); in, For variable temperature load, For specific heat capacity, air density, The length of the indoor space. The width of the interior space. Where m is the height of the indoor space, and m is the air quality of the indoor space. Here, represents the temperature change value of the indoor space, where , All are known constants. , , All of these are the current values ​​of the first hidden variable. These are parameter values ​​determined based on the input operating condition data.

[0072] The output layer can use the current value of the second hidden variable and the corresponding second sub-data in the input operating condition data to calculate the temperature load according to the following formulas (2) and (3). : (2); (3); Both the heat transfer coefficient K and the thermal resistance R are related to heat conduction; therefore, the heat conduction coefficient Kr is defined as K / R. The length of the indoor space. The width of the interior space. The height of the interior space, Indoor and outdoor temperature difference is used to represent the overall temperature difference between the indoor space and all outdoor spaces. The outdoor air temperature, For the temperature of the indoor space, The temperature of the indoor environment without air conditioning. The temperature of the indoor environment where the air conditioner is on. The external space refers to the temperature difference between the outdoor and indoor spaces when the outdoor air is outside. The first temperature difference coefficient, This represents the temperature difference between the outdoor and indoor spaces when the outdoor space is an indoor environment without air conditioning. The second temperature difference coefficient, The temperature difference between the outdoor and indoor spaces when the outdoor space is an indoor environment with the air conditioning on. The third temperature difference coefficient, This represents the surface area of ​​an interior space. , , All are the current values ​​of the first hidden variable, K / R, , All of these are the current values ​​of the second hidden variable. , , , All data are from the current input operating conditions.

[0073] In this embodiment, the load prediction model is set up in the manner described above. The physical parameters such as building parameters and heat conduction parameters required to determine the variable temperature load and the constant temperature load are no longer fixed parameters set in advance. Instead, the air conditioner can autonomously sense and learn through the load prediction model, which helps to improve the accuracy of the determined variable temperature load and constant temperature load, so as to effectively control the comfort of air conditioning and save energy consumption.

[0074] In one feasible implementation, before the step of obtaining the actual operating condition data of the air conditioner, the method further includes: obtaining historical operating condition data of the air conditioner and the corresponding output capacity of the air conditioner, wherein the parameter type of the historical operating condition data is set to correspond to the parameter type of the actual operating condition data; training the model parameters in a preset deep learning model based on the historical operating condition data and the output capacity; and using the preset deep learning model with determined model parameters as the load prediction model.

[0075] Before the current moment, during the operation of the air conditioner in the preset mode, the air conditioner's operating condition data and output capacity are collected at set intervals to obtain operating condition data at multiple different times. After anomaly processing, data smoothing, and aggregation processing according to the preset intervals, the collected operating condition data is used as historical operating condition data. The collected operating condition data includes, but is not limited to, the following data: return air temperature, indoor heat exchanger temperature, outdoor temperature, indoor temperature when the air conditioner is not turned on, initial temperature difference, evaporation temperature, condensation temperature, set temperature, humidity, fan speed, compressor operating frequency, indoor temperature change value, temperature difference value between indoor space and different outdoor spaces, etc.

[0076] Output capacity can include the actual cooling capacity or actual heating capacity of the air conditioner, etc.

[0077] The preset deep learning model consists of an input layer, a feature extraction layer, a hidden state layer, and an output layer, connected sequentially as described above. Model parameters include neural network weights and trainable constants. The trainable constants include the first and second hidden variables in the hidden state layer. All model parameters of the preset deep learning model are initial, untrained values. When the model parameters of the preset deep learning model are iteratively trained until the training termination condition is met (e.g., the deviation between the predicted load and the actual load is less than a preset deviation), the model parameters can be considered determined, and the preset deep learning model at this point can be used as a load prediction model.

[0078] In this embodiment, combined with Figure 3 The control method for the air conditioner further includes: constructing a first loss function corresponding to the output layer, with the sum of the variable-temperature load and the constant-temperature load output by the output layer equal to the corresponding output capacity of the air conditioner as a constraint; the step of training the model parameters in the preset deep learning model based on the historical operating data and the output capacity includes: iteratively training the model parameters in the preset deep learning model based on the historical operating data, the output capacity, and the first loss function. Here, the trained model parameters are neural network weights. The corresponding output capacity refers to the output capacity corresponding to the operating data input to the current input layer. Based on this, the accuracy of the variable-temperature load and constant-temperature load output by the load prediction model can be guaranteed through the capacity conservation constraint.

[0079] In this embodiment, combined with Figure 3 The control method for the air conditioner further includes: constructing a second loss function corresponding to the hidden state layer with the consistency of the building parameters and / or the heat conduction parameters corresponding to different times as a constraint; the step of iteratively training the model parameters in the preset deep learning model according to the historical operating data, the output capacity, and the first loss function includes: iteratively training the model parameters in the preset deep learning model according to the historical operating data, the output capacity, the first loss function, and the second loss function.

[0080] The consistency of building parameters and / or heat conduction parameters at different times here means that the building parameters and / or heat conduction parameters do not change over time. The constructed second loss function includes the following loss function relationship (4): (4); Where N is the number of samples during the training process. For the first i The variable in the first... j The values ​​in each sample For the first i The mean of the i-th variable in all samples, the th variable iThe variable refers to one of the following: length, width, height, first temperature difference coefficient, second temperature difference coefficient, and thermal conductivity coefficient. Here, 6 represents the total number of variables.

[0081] The loss value corresponding to the current training sample set can be calculated using the second loss function mentioned above. Based on the loss value, the values ​​of the hidden variables in the hidden state layer can be adjusted by backpropagation.

[0082] Since the area of ​​the same room remains unchanged during operation, and its length, width, and height are consistent; once the building envelope is completed, the building materials basically do not change, and the thermal resistance and heat transfer coefficient are consistent; the algebraic relationship between variables such as indoor and outdoor temperature difference and external space temperature is relatively stable, based on this, consistency constraints are added to the hidden variables in the hidden state layer in the above manner, thereby effectively improving the accuracy of the model's load prediction.

[0083] Furthermore, a second loss function is constructed using the consistency of the building parameters, thermal resistance, and heat transfer coefficient at different times as constraints, and the consistency of the temperature difference coefficient within the same time period as constraints. During this process, the temperature difference coefficients at different times may be different. Based on the constraints that the temperature difference coefficients within the same time period are consistent and that the temperature difference coefficients at different times may be different, the following loss function relationship (5) is constructed: (5); in, The training batch of samples is represented by , where i represents the samples in the current batch and h represents the time period. This represents the current sample and all samples before the current time in the h-th time period. Seed distribution, This represents the current sample and all samples before the current time in the h-th time period. Seed distribution, This represents the Jensen-Shannon divergence (used to assess distribution consistency). This indicates that the current sample is in the h-th time period. distributed, This indicates that the current sample is in the h-th time period. distributed.

[0084] The second loss function includes the loss function relationship (4) mentioned above and the loss function relationship (5) here. The loss function relationship (4) mentioned above can be used to calculate the first loss value corresponding to the building parameters (e.g., length, width, height, etc.) and thermal conductivity coefficient in the current training sample set. At this time, the total number of variables in the loss function relationship (4) can be 4. Based on the first loss value, the values ​​of the hidden variables corresponding to the building parameters (e.g., length, width, height, etc.) and thermal conductivity coefficient in the hidden state layer can be adjusted by backpropagation. The loss function relationship (5) mentioned above can be used to calculate the second loss value corresponding to the first temperature difference coefficient and the second temperature difference coefficient in the current training sample set. Based on the second loss value, the values ​​of the hidden variables corresponding to the first temperature difference coefficient and the second temperature difference coefficient in the hidden state layer can be adjusted by backpropagation.

[0085] In this embodiment, the first temperature difference coefficient and the second temperature difference coefficient may differ under different usage scenarios at different times (for example, if there is a neighbor on the other side of the wall, but the air conditioner is not turned on during the day and is turned on at night, then the first temperature difference coefficient and the second temperature difference coefficient are different during the day and at night), but they are consistent under the same scenario (for example, if there is a neighbor on the other side of the wall, and the air conditioner is turned on at 10 pm, then it is highly likely that the air conditioner will be turned on at 10 pm every day in summer). Based on this, the above loss function relationship (5) is constructed to ensure that the load prediction matches the actual scenario, so as to improve the accuracy of load prediction based on the model.

[0086] In this embodiment, the load prediction model trained in the above manner is beneficial for accurately predicting variable temperature loads and constant temperature loads, so as to improve the comfort of air conditioner control while saving energy.

[0087] In other embodiments, when the historical temperature-changing load and historical temperature-maintaining load corresponding to the historical operating condition data are known, a preset deep learning model can also be trained based on the historical operating condition data, historical temperature-changing load, and historical temperature-maintaining load.

[0088] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the control method 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 conditioner control method of 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 air conditioner; or it may exist independently and not be installed in the air conditioner.

[0092] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by a processor, cause the processor to execute the flow in the aforementioned air conditioner control method embodiment.

[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 control method of the air conditioner described above, which can solve the technical problem of how to improve the comfort of air conditioner control and save energy. 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 control method of the 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 control method for an air conditioner, characterized in that, The control method for the air conditioner includes: Obtain the actual operating condition data of the air conditioner; The variable temperature load and the temperature maintenance load of the air conditioner are determined based on the actual operating data. The variable temperature load is the heat exchange required when the indoor space is regulated by the air conditioner to change the temperature in the target direction. The temperature maintenance load is the heat exchange required to maintain the temperature of the indoor space. The air conditioner is operated according to the variable temperature load and the constant temperature load.

2. The control method for an air conditioner as described in claim 1, characterized in that, The step of controlling the operation of the air conditioner according to the variable temperature load and the constant temperature load includes: The air conditioner is controlled to operate based on the sum of the variable temperature load and the constant temperature load.

3. The control method for an air conditioner as described in claim 1 or 2, characterized in that, The step of determining the variable temperature load and constant temperature load of the air conditioner based on the actual operating condition data includes: The actual operating condition data is input into the load prediction model to obtain the output results of the load prediction model, which include the variable temperature load and the constant temperature load. The load prediction model is a model generated based on deep learning.

4. The control method for an air conditioner as described in claim 3, characterized in that, The load prediction model includes an input layer, a feature extraction layer, a hidden state layer, and an output layer connected in sequence. The input layer is used to acquire input operating data. The feature extraction layer is used to extract spatiotemporal features from the input operating data. The hidden state layer includes a first latent variable representing the building parameters of the building where the air conditioner is located and a second latent variable representing the heat conduction parameters between the indoor space and the external environment. The output layer is used to calculate the variable temperature load based on the first latent variable and the input operating data. The output layer is also used to calculate the constant temperature load based on the first latent variable, the second latent variable, and the input operating data.

5. The control method for an air conditioner as described in claim 4, characterized in that, The architectural parameters include the length, width, and height of the interior space; And / or, the heat conduction parameters include the thermal resistance of the interior space's enclosure structure, the heat transfer coefficient of the enclosure structure, and the temperature difference coefficient between the interior space and the exterior space under different environmental conditions.

6. The control method for an air conditioner as described in claim 5, characterized in that, The temperature difference coefficient includes a first temperature difference coefficient, a second temperature difference coefficient, and a third temperature difference coefficient. The first temperature difference coefficient is the temperature difference coefficient when the external space corresponding to the wall of the indoor space is outdoor air. The second temperature difference coefficient is the temperature difference coefficient when the external space corresponding to the wall of the indoor space is an indoor environment without air conditioning. The third temperature difference coefficient is the temperature difference coefficient when the external space corresponding to the wall of the indoor space is an indoor environment with air conditioning.

7. The control method for an air conditioner as described in claim 4, characterized in that, Before the step of obtaining the actual operating condition data of the air conditioner, the method further includes: Acquire historical operating condition data of the air conditioner and the corresponding output capacity of the air conditioner, wherein the parameter types of the historical operating condition data are set to correspond to the parameter types of the actual operating condition data; The model parameters in the preset deep learning model are trained based on the historical operating data and the output capability. The preset deep learning model with determined model parameters is used as the load prediction model.

8. The control method for an air conditioner as described in claim 7, characterized in that, The control method for the air conditioner also includes: Using the constraint that the sum of the variable temperature load and the constant temperature load output by the output layer equals the output capacity of the corresponding air conditioner, a first loss function corresponding to the output layer is constructed. The step of training the model parameters in the preset deep learning model based on the historical working condition data and the output capability includes: The model parameters in the preset deep learning model are iteratively trained based on the historical operating data, the output capability, and the first loss function.

9. The control method for an air conditioner as described in claim 8, characterized in that, The control method for the air conditioner also includes: Using the consistency of the building parameters and / or the heat conduction parameters at different times as constraints, a second loss function corresponding to the hidden state layer is constructed; The step of iteratively training the model parameters in the preset deep learning model based on the historical operating data, the output capability, and the first loss function includes: The model parameters in the preset deep learning model are iteratively trained based on the historical operating data, the output capability, the first loss function, and the second loss function.

10. The control method for an air conditioner as described in claim 9, characterized in that, The heat conduction parameters include the thermal resistance of the building envelope of the indoor space, the heat transfer coefficient of the building envelope, and the temperature difference coefficient between the indoor space and the external space under different environmental conditions. The step of constructing the second loss function corresponding to the hidden state layer, using the consistency of the building parameters and / or the heat conduction parameters at different times as constraints, includes: The second loss function is constructed by taking the consistency of the building parameters, thermal resistance, and heat transfer coefficient at different times as constraints, and the consistency of the temperature difference coefficient within the same time period as constraints.

11. An air conditioner, characterized in that, The air conditioner 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 control method for the air conditioner as claimed in any one of claims 1 to 10.

12. 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 control method for the air conditioner as described in any one of claims 1 to 10.

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