Air conditioner and control method and device thereof, storage medium and computer program product

Abstract

The invention provides an air conditioner and a control method and device thereof, a storage medium and a computer program product, the method comprises the following steps: acquiring multiple groups of user physiological state parameters and environmental parameters in the environment collected in advance and corresponding air conditioner operation scene modes to obtain an original data set; data in the obtained original data set is preprocessed, N samples are obtained, and each sample comprises m features; performing model training by using the preprocessed data, removing one feature in sequence in each training, retaining the other m-1 features for model training, evaluating and determining at least one feature with the minimum correlation with a prediction result, and removing the at least one feature with the minimum correlation with the prediction result to obtain a prediction model; and collecting current user physiological state parameters and environment parameters in the environment, inputting the parameters into the prediction model, and predicting to obtain a current air conditioner operation scene mode. According to the scheme, the scene prediction accuracy of the air conditioner algorithm model can be optimized.

Description

Technical Field

[0001] The present invention relates to the field of control, and in particular to an air conditioner and a control method, device, storage medium and computer program product thereof. Background Art

[0002] With the widespread adoption of intelligent air-conditioning systems in modern buildings, the performance of system control models directly impacts energy efficiency and user comfort. However, intelligent air-conditioning control models in related technologies generally employ fixed parameter sets or strategies based on manual experience. This static parameter configuration approach struggles to adapt to dynamic changes in the actual operating environment. In practical applications, air-conditioning systems are affected by a variety of factors, including indoor and outdoor temperature fluctuations, user behavior patterns, and equipment aging, all of which can lead to dynamic changes in system parameters. Therefore, control models that lack dynamic parameter optimization capabilities often lead to reduced system performance and difficulty achieving precise control.

[0003] From a technical implementation perspective, the methods used in related technologies have obvious limitations in parameter optimization. Some studies have attempted to screen key parameters through correlation analysis using statistical methods, but this approach often ignores the time series characteristics and dynamic interactions between parameters. For example, in air-conditioning systems, there are complex time series correlations between multiple parameters such as temperature, humidity, and wind speed. Parameter analysis at a single time point is difficult to accurately reflect the system dynamics. This parameter screening method based on static correlation analysis is prone to the accumulation of model errors, which in turn affects the control accuracy of the system. Summary of the Invention

[0004] The main purpose of the present invention is to overcome the defects of the above-mentioned related technologies and provide an air conditioner and its control method, device, storage medium and computer program product to solve the problem that the air conditioner control model in the related technologies has obvious limitations in parameter optimization and dynamic adjustment, making it difficult to achieve precise control.

[0005] On the one hand, the present invention provides a method for controlling an air conditioner, comprising: obtaining a plurality of pre-collected sets of user physiological state parameters and environmental parameters in the environment and corresponding air conditioner operation scene modes to obtain an original data set; pre-processing the data in the obtained original data set to obtain N samples, each sample containing m features, wherein each set of user physiological state parameters and environmental parameters and corresponding air conditioner operation scene modes in the original data set is used as one sample, and the user physiological state parameters and environmental parameters in each sample are used as features; using the pre-processed data to perform model training to obtain a prediction model for predicting the air conditioner operation scene mode; wherein, each training step eliminates one feature of the m features in turn, retains the remaining m-1 features for model training, and evaluates and determines at least one feature with the smallest correlation with the prediction result, removes at least one feature with the smallest correlation with the prediction result, and obtains the prediction model; collecting the current user physiological state parameters and environmental parameters in the environment and inputting them into the prediction model to predict the current air conditioner operation scene mode, so as to control the operation of the air conditioner according to the predicted current air conditioner operation scene mode.

[0006] Optionally, each time training is performed, one of the m features is eliminated in turn, and the remaining m-1 features are retained for model training, and at least one feature with the smallest correlation with the prediction result is evaluated and determined, including: each time training is performed, one of the m features is eliminated in turn, and the remaining m-1 features are retained, and the data remaining after eliminating one feature is divided into a training set and a test set according to a preset ratio, and the training set is input into the prediction model for training, and the prediction model after training generates a prediction result through the test set, and calculates the evaluation index A according to the prediction result until all features have been eliminated; and the evaluation index A calculated according to the prediction result after each training is completed is compared, and at least one feature with the largest evaluation index after elimination is determined as the at least one feature with the smallest correlation with the prediction result.

[0007] Optionally, calculating the evaluation index according to the prediction result includes: calculating the evaluation index A by the following formula:

[0008] ;

[0009] Where N is the number of samples and Nr is the number of correctly predicted samples.

[0010] Optionally, it also includes: obtaining the user physiological state parameters and environmental parameters of the environment collected within the preset time, and the air-conditioning operation scene mode predicted by the prediction model at every preset time; re-training the model based on the user physiological state parameters and environmental parameters of the environment collected within the preset time, and the air-conditioning operation scene mode predicted by the prediction model to update the input features of the model.

[0011] Optionally, preprocessing the data in the collected original data set includes:

[0012] Standardizing the data in the original data set; and / or removing outliers and / or filling missing values in the data in the original data set.

[0013] Optionally, the user's physiological state parameters include: at least one of body temperature, heart rate, blood pressure, respiratory rate, and exercise volume; and / or the environmental parameters include: at least one of temperature, humidity, air quality, wind speed, wind direction, noise, PM2.5 concentration, carbon dioxide concentration, carbon monoxide concentration, air conditioning operation mode, and air flow distribution.

[0014] Another aspect of the present invention provides an air conditioner control device, comprising: an acquisition unit, configured to acquire pre-collected multiple sets of user physiological state parameters and environmental parameters in an environment, and corresponding air conditioner operation scene modes, to obtain an original data set; a processing unit, configured to pre-process data in the original data set acquired by the acquisition unit to obtain N samples, each sample containing m features, wherein each set of user physiological state parameters and environmental parameters and corresponding air conditioner operation scene mode in the original data set serves as one sample, and the user physiological state parameters and environmental parameters in each sample serve as features; a training unit, configured to perform model training using the data pre-processed by the processing unit, to obtain a prediction model for predicting the air conditioner operation scene mode; wherein each training step sequentially removes one of the m features, retains the remaining m-1 features for model training, evaluates and determines at least one feature with the smallest correlation with the prediction result, removes the at least one feature with the smallest correlation with the prediction result, and obtains the prediction model; a prediction unit, configured to collect the current user physiological state parameters and environmental parameters in the environment, input them into the prediction model, predict the current air conditioner operation scene mode, and control the operation of the air conditioner according to the predicted current air conditioner operation scene mode.

[0015] Optionally, the training unit eliminates one feature of the m features in turn during each training, retains the remaining m-1 features for model training, and evaluates and determines at least one feature with the smallest correlation with the prediction result, including: eliminating one feature of the m features in turn during each training, retaining the remaining m-1 features, dividing the remaining data after eliminating one feature into a training set and a test set according to a preset ratio, inputting the training set into the prediction model for training, generating a prediction result through the test set for the trained prediction model, and calculating an evaluation index A based on the prediction result until all features have been eliminated; comparing the evaluation index A calculated based on the prediction result after each training is completed, and determining at least one feature with the largest evaluation index after elimination as the at least one feature with the smallest correlation with the prediction result.

[0016] Optionally, the training unit calculates the evaluation index according to the prediction result, including: calculating the evaluation index A by the following formula:

[0017] ;

[0018] Where N is the number of samples and Nr is the number of correctly predicted samples.

[0019] Optionally, the acquisition unit is further used to: acquire the user's physiological state parameters and environmental parameters in the environment collected within the preset time, and the air-conditioning operation scene mode predicted by the prediction model at every preset time; the training unit is further used to: re-train the model based on the user's physiological state parameters and environmental parameters in the environment collected within the preset time obtained by the acquisition unit, and the air-conditioning operation scene mode predicted by the prediction model, so as to update the input features of the model.

[0020] Optionally, the processing unit preprocesses the data in the collected original data set, including: standardizing the data in the original data set; and / or removing outliers and / or filling missing values in the data in the original data set.

[0021] Optionally, the user's physiological state parameters include: at least one of body temperature, heart rate, blood pressure, respiratory rate, and exercise volume; and / or the environmental parameters include: at least one of temperature, humidity, air quality, wind speed, wind direction, noise, PM2.5 concentration, carbon dioxide concentration, carbon monoxide concentration, air conditioning operation mode, and air flow distribution.

[0022] Another aspect of the present invention provides a storage medium having a computer program stored thereon, wherein the program implements the steps of any of the aforementioned methods when executed by a processor.

[0023] In another aspect, the present invention provides an air conditioner, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the aforementioned methods when executing the program.

[0024] In another aspect, the present invention provides an air conditioner comprising any of the aforementioned control devices.

[0025] In another aspect, the present invention provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any of the aforementioned methods are implemented.

[0026] According to the technical solution of the present invention, by constructing an m×n parameter-time matrix as a benchmark input, based on the correlation quantification mechanism of the single variable elimination method, one characteristic variable is eliminated in each iteration to generate a new input matrix, and a neural network algorithm model is used to perform m parallel predictions. The prediction results of each prediction are compared, the variables with the lowest correlation are located, and the characteristic variables with the smallest correlation are eliminated, thereby optimizing the accuracy of the model prediction scene and reducing the error in real-time control of air-conditioning scene modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0028] Figure 1 1 is a schematic diagram of an embodiment of a method for controlling an air conditioner provided by the present invention;

[0029] Figure 2 is a method diagram of another embodiment of the air conditioner control method provided by the present invention;

[0030] Figure 3 A schematic diagram showing the operation logic of a specific embodiment of the air conditioning control method provided by the present invention is shown;

[0031] Figure 4 This is a flow chart of a specific embodiment of the air conditioner control method provided by the present invention;

[0032] Figure 5 It is a structural block diagram of an embodiment of the air conditioner control device provided by the present invention. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] From a technical implementation perspective, the methods used in related technologies have obvious limitations in parameter optimization. Some studies have attempted to screen key parameters through correlation analysis using statistical methods, but this approach often ignores the time series characteristics and dynamic interactions between parameters. For example, in air-conditioning systems, there are complex time series correlations between multiple parameters such as temperature, humidity, and wind speed. Parameter analysis at a single time point is difficult to accurately reflect the system dynamics. This parameter screening method based on static correlation analysis is prone to the accumulation of model errors, which in turn affects the control accuracy of the system.

[0036] In addition, the control models in related technologies show obvious computational inefficiency when faced with parameter redundancy problems. In practical applications, air-conditioning systems usually involve dozens or even hundreds of parameters, and there may be a high degree of correlation between these parameters. The models in related technologies often require all parameters to be analyzed and calculated one by one, resulting in a waste of computing resources and an increase in processing delays. At the same time, due to the lack of quantitative analysis of the real-time contribution of each parameter to the prediction results, it is difficult for the system to achieve accurate dynamic control, and it is prone to problems such as delayed control response and increased energy consumption. This lag effect not only affects the user's comfort experience, but also leads to energy waste, which is contrary to the original intention of the intelligent air-conditioning system to save energy and reduce consumption.

[0037] In summary, the intelligent air-conditioning control model in related technologies has obvious limitations in parameter optimization and dynamic adjustment. There is an urgent need for a new control method that can capture the dynamic changes of parameters in real time and quantify the contribution of each parameter, so as to improve the control accuracy and response speed of the system and realize truly intelligent and energy-saving operation.

[0038] The invention provides a method for controlling an air conditioner.

[0039] Figure 1 1 is a schematic diagram of an embodiment of the air conditioner control method provided by the present invention.

[0040] like Figure 1 As shown, according to one embodiment of the present invention, the air conditioner control method includes at least step S110, step S120, step S130 and step S140.

[0041] Step S110 , obtaining a plurality of pre-collected sets of user physiological state parameters and environmental parameters in the environment and corresponding air-conditioning operation scene modes to obtain an original data set.

[0042] The user physiological state parameters may specifically include: at least one of body temperature, heart rate, blood pressure, respiratory rate, and physical activity; the environmental parameters may specifically include: at least one of temperature, humidity, air quality, wind speed, wind direction, noise, PM2.5 concentration, carbon dioxide concentration, carbon monoxide concentration, air conditioner operating mode, and airflow distribution. The user physiological state parameters may be collected, for example, via a wearable device (such as a smart bracelet or smartwatch). The environmental parameters may be detected by sensors installed on the air conditioner, such as temperature sensors and humidity sensors. For each of the multiple sets of user physiological state parameters and environmental parameters, the user physiological state parameters and environmental parameters for each set are collected simultaneously.

[0043] The corresponding air conditioning operation scene mode is determined based on the user's physiological state parameters and used as a label y. For example, the air conditioning operation scene mode corresponding to the user's physiological state parameters can be manually labeled in advance. The air conditioning operation scene mode can specifically include at least one of: a sports scene, a pathological state, a sleep scene, and an allergy scene.

[0044] For example, by combining wearable devices with the built-in health module of the air conditioner, user-end data (body temperature, heart rate, blood pressure, respiratory rate, exercise volume, etc.) can be collected through wearable devices (such as smart bracelets, smart watches, etc.), and sensor modules can be installed on the air-conditioning unit to monitor environmental parameters (such as temperature, humidity, noise, wind speed, etc.). Based on the collected data, the user's health status (such as normal, exercise, rest, etc.) is preliminarily set and labeled.

[0045] Step S120 , preprocessing the data in the obtained original data set to obtain N samples, each sample containing m features.

[0046] Specifically, each group of user physiological state parameters and environmental parameters and the corresponding air-conditioning operation scene mode in the original data set is taken as a sample, and the user physiological state parameters and environmental parameters in each sample are taken as features, that is, as the input of the model, and the corresponding air-conditioning operation scene mode is taken as the model output. The original data set D is preprocessed. For example, the data set D contains N samples, and each sample contains m features. The features include: user physiological state parameter features and environmental parameter features. The user physiological state parameter features can specifically include: at least one of user body temperature x1, heart rate x2, blood pressure x3, respiratory rate x4, and exercise amount x5; the environmental parameter features can specifically include: temperature x6, humidity x7, air quality x8, wind speed x9, wind direction x10, etc. 10 , noise x 11 、PM2.5 concentration x 12 , carbon dioxide concentration x 13 , carbon monoxide concentration x 14 , Air conditioning operation mode x 15 , air flow distribution x 16 At least one of the air-conditioning operation scene modes (for example, at least one of the sports scene y1, pathological state y2, sleep scene y3, and allergy scene y4) is used as the classification label, represented by label y, which is the model output.

[0047] In order to make the feature x i The data in the original dataset are normalized on the same scale. Assume that the eigenvalue x i The mean value is μ i , with a standard deviation of σ i , the standardized feature x i ′ can be expressed as:

[0048] ;

[0049] Optionally, before normalization, the data in the original dataset may be subjected to outlier removal and / or missing value filling. For example, outliers that significantly deviate from the normal range may be deleted, or outliers may be replaced using statistics such as the mean or median. For example, missing values may be filled using the mean, median, or mode to maintain data distribution characteristics, or missing values may be filled using linear or polynomial interpolation.

[0050] Step S130 , performing model training using the preprocessed data to obtain a prediction model for predicting the air conditioner operation scene mode.

[0051] Specifically, the preprocessed data includes N samples, each sample including m features. During each training session, one of the m features is removed, and the remaining m-1 features are retained for model training. At least one feature with the lowest correlation with the prediction result is evaluated and determined. The at least one feature with the lowest correlation with the prediction result is removed, and the remaining features are retained as input features of the prediction model to obtain the prediction model.

[0052] Preferably, the preprocessed data is divided into a training set C and a test set T according to a preset ratio, for example, the training set C and the test set T are divided according to a ratio of 7:3. The training set C is used to input into the prediction model for training, and the trained model is used to generate prediction results in the test set T. The evaluation index A is used to calculate and judge the model results:

[0053] ;

[0054] Among them, N is the total number of samples, and Nr is the number of correctly predicted samples.

[0055] Specifically, each training session sequentially removes one of the m features, retaining the remaining m-1 features. The remaining data after removing one feature is divided into a training set and a test set according to a preset ratio. The training set is input into the prediction model for training. After training, the prediction model generates a prediction result using the test set, and the evaluation index A is calculated based on the prediction result, until all features have been removed and m training sessions are completed. The evaluation index A calculated based on the prediction result after each training session is compared, and at least one feature with the largest evaluation index after removal is determined as the at least one feature with the smallest correlation with the prediction result. Since the evaluation index represents the accuracy of the prediction model, if the accuracy of the prediction model is higher after removing a certain feature, it means that the correlation between the feature and the prediction result is smaller. Therefore, at least one feature with the smallest correlation with the prediction result among the model input features can be removed, that is, at least one feature with the largest evaluation index after removal.

[0056] Step S140 , collecting the current user physiological state parameters and environmental parameters in the environment and inputting them into the prediction model, predicting the current air-conditioning operation scene mode, and controlling the operation of the air-conditioning according to the predicted current air-conditioning operation scene mode.

[0057] Specifically, based on the remaining features after removing at least one feature with the smallest correlation with the prediction result, the current user physiological state parameters and environmental parameters in the environment are collected, and the current air-conditioning operation scene mode is predicted by inputting the prediction model to control the operation of the air conditioner according to the current air-conditioning operation scene mode.

[0058] Depending on the air conditioner's operating scenario, corresponding control strategies can be used to control the air conditioner's operation. For example, when the air conditioner's operating scenario is sports mode, rapid cooling can be initiated (such as increasing the compressor operating frequency and / or increasing the internal fan speed), and the fresh air system can be activated to exchange fresh air. When the air conditioner's operating scenario is pathological mode, the cabinet's ultraviolet sterilization function can be activated, and the humidity can be adjusted to a preset humidity range (for example, through the air conditioner's humidification or dehumidification function). When the air conditioner's operating scenario is night mode, the air conditioner can be adjusted to silent mode to prevent interruptions to the user's sleep.

[0059] Figure 2 2 is a method diagram of another embodiment of the air conditioner control method provided by the present invention.

[0060] like Figure 2 As shown, according to another embodiment of the present invention, the air conditioner control method further includes step S150 and step S160.

[0061] Step S150 , obtaining the user's physiological state parameters and environmental parameters collected in the preset time and the air-conditioning operation scene mode predicted by the prediction model at preset intervals.

[0062] Step S160 re-trains the model based on the user's physiological state parameters and environmental parameters acquired within the preset time and the air-conditioning operation scene mode predicted by the prediction model.

[0063] Specifically, feature filtering is triggered every preset time interval to obtain the user's physiological state parameters and environmental parameters collected during this period, as well as the air conditioner operating scenario patterns predicted by the prediction model. The model is then retrained, and at least one feature with the least correlation with the prediction result is removed to update the input features of the prediction model. For example, feature filtering may be triggered every 6 hours to obtain the user's physiological state parameters and environmental parameters collected within the previous 6 hours, as well as the air conditioner operating scenario patterns predicted by the prediction model, and the model is retrained.

[0064] To clearly illustrate the technical solution of the present invention, the execution process of the air conditioner control method provided by the present invention is described below with reference to a specific embodiment.

[0065] Figure 3 FIG. 1 shows a schematic diagram of the operation logic of a specific embodiment of the air conditioning control method provided by the present invention. Figure 3As shown, the data collection component uses wearable devices to collect user physiological parameters, including body temperature, heart rate, blood pressure, respiratory rate, and physical activity. The unit's sensors collect relevant indoor parameters and determine the corresponding air conditioning operation scenario. The personalized mode establishment component uses the collected data to train a predictive model, dividing the preprocessed data into training and test sets for model training. The intelligent response component collects user physiological parameters and environmental parameters in real time, uses the predictive model to determine the current air conditioning operation scenario, and controls the air conditioning operation based on the predicted air conditioning operation scenario.

[0066] Figure 4 FIG. 1 is a flow chart of a specific embodiment of the air conditioning control method provided by the present invention. Figure 4 As shown, while the air conditioner is running, user physiological state parameters and indoor related parameters (environmental parameters) are collected. The collected data is preprocessed to extract eigenvalues Xi. Data preprocessing includes identifying and removing abnormal data values, filling in missing values, and normalizing the data. Each eigenvalue Xi is removed in turn. Training and testing are performed, and evaluation index A is output. Each evaluation index A is compared in turn, and the correlation between each eigenvalue is output. The feature with the smallest correlation is removed, and the input features of the prediction model are dynamically updated every 6 hours. User physiological state parameters and indoor related parameters (environmental parameters) are collected in real time. The prediction model determines the current air conditioner operation scenario mode, and the air conditioner operation is controlled based on the predicted current air conditioner operation scenario mode.

[0067] The invention also provides a control device for an air conditioner.

[0068] Figure 5 FIG. 1 is a structural block diagram of an embodiment of the air conditioner control device provided by the present invention. Figure 5 As shown, the air conditioner control device 100 includes: an acquisition unit 110 , a processing unit 120 , a training unit 130 and a prediction unit 140 .

[0069] The acquisition unit 110 is used to acquire a plurality of pre-collected sets of user physiological state parameters and environmental parameters in the environment and corresponding air-conditioning operation scene modes to obtain an original data set.

[0070] The user physiological state parameters may specifically include: at least one of body temperature, heart rate, blood pressure, respiratory rate, and exercise volume; the environmental parameters may specifically include: at least one of temperature, humidity, air quality, wind speed, wind direction, noise, PM2.5 concentration, carbon dioxide concentration, carbon monoxide concentration, air conditioner operating mode, and airflow distribution. The user physiological state parameters may be collected, for example, via a wearable device (such as a smart bracelet or smart watch). The environmental parameters may be detected by sensors installed on the air conditioner, such as temperature sensors and humidity sensors. Among the multiple sets of user physiological state parameters and environmental parameters for the environment, the user physiological state parameters and environmental parameters for each set are collected at the same time. Among the multiple sets of user physiological state parameters and environmental parameters for the environment, the user physiological state parameters and environmental parameters for each set are collected at the same time.

[0071] The corresponding air conditioning operation scene mode is determined based on the user's physiological state parameters and used as a label y. For example, the air conditioning operation scene mode corresponding to the user's physiological state parameters can be manually labeled in advance. The air conditioning operation scene mode can specifically include at least one of: a sports scene, a pathological state, a sleep scene, and an allergy scene.

[0072] For example, by combining wearable devices with the built-in health module of the air conditioner, user-end data (body temperature, heart rate, blood pressure, respiratory rate, exercise volume, etc.) can be collected through wearable devices (such as smart bracelets, smart watches, etc.), and sensor modules can be installed on the air-conditioning unit to monitor environmental parameters (such as temperature, humidity, noise, wind speed, etc.). Based on the collected data, the user's health status (such as normal, exercise, rest, etc.) is preliminarily set and labeled.

[0073] The processing unit 120 is configured to preprocess the data in the original data set acquired by the acquisition unit 110 to obtain N samples, each sample containing m features.

[0074] Specifically, each group of user physiological state parameters and environmental parameters and the corresponding air-conditioning operation scene mode in the original data set is taken as a sample, and the user physiological state parameters and environmental parameters in each sample are taken as features, that is, as the input of the model, and the corresponding air-conditioning operation scene mode is taken as the model output. The original data set D is preprocessed. For example, the data set D contains N samples, and each sample contains m features. The features include: user physiological state parameter features and environmental parameter features. The user physiological state parameter features can specifically include: at least one of user body temperature x1, heart rate x2, blood pressure x3, respiratory rate x4, and exercise amount x5; the environmental parameter features can specifically include: temperature x6, humidity x7, air quality x8, wind speed x9, wind direction x10, etc. 10 , noise x 11 、PM2.5 concentration x12 , carbon dioxide concentration x 13 , carbon monoxide concentration x 14 , Air conditioning operation mode x 15 , air flow distribution x 16 At least one of the air-conditioning operation scene modes (for example, at least one of the sports scene y1, pathological state y2, sleep scene y3, and allergy scene y4) is used as the classification label, represented by label y, which is the model output.

[0075] In order to make the feature x i The data in the original dataset are normalized on the same scale. Assume that the eigenvalue x i The mean value is μ i , with a standard deviation of σ i , the standardized feature x i ′ can be expressed as:

[0076] ;

[0077] Optionally, before normalization, the data in the original dataset may be subjected to outlier removal and / or missing value filling. For example, outliers that significantly deviate from the normal range may be deleted, or outliers may be replaced using statistics such as the mean or median. For example, missing values may be filled using the mean, median, or mode to maintain data distribution characteristics, or missing values may be filled using linear or polynomial interpolation.

[0078] The training unit 130 is used to perform model training using the data pre-processed by the processing unit 120 to obtain a prediction model for predicting the air conditioner operation scene mode.

[0079] Specifically, the preprocessed data includes N samples, each sample including m features. During each training session, one of the m features is removed, and the remaining m-1 features are retained for model training. At least one feature with the lowest correlation with the prediction result is evaluated and determined. The at least one feature with the lowest correlation with the prediction result is removed, and the remaining features are retained as input features of the prediction model to obtain the prediction model.

[0080] Preferably, the preprocessed data is divided into a training set C and a test set T according to a preset ratio, for example, the training set C and the test set T are divided according to a ratio of 7:3. The training set C is used to input into the prediction model for training, and the trained model is used to generate prediction results in the test set T. The evaluation index A is used to calculate and judge the model results:

[0081] ;

[0082] Among them, N is the total number of samples, and Nr is the number of correctly predicted samples.

[0083] Specifically, each training session sequentially removes one of the m features, retaining the remaining m-1 features. The remaining data after removing one feature is divided into a training set and a test set according to a preset ratio. The training set is input into the prediction model for training. After training, the prediction model generates a prediction result using the test set, and the evaluation index A is calculated based on the prediction result, until all features have been removed and m training sessions are completed. The evaluation index A calculated based on the prediction result after each training session is compared, and at least one feature with the largest evaluation index after removal is determined as the at least one feature with the smallest correlation with the prediction result. Since the evaluation index represents the accuracy of the prediction model, if the accuracy of the prediction model is higher after removing a certain feature, it means that the correlation between the feature and the prediction result is smaller. Therefore, at least one feature with the smallest correlation with the prediction result among the model input features can be removed, that is, at least one feature with the largest evaluation index after removal.

[0084] The prediction unit 140 is used to collect the current user physiological state parameters and environmental parameters in the environment and input them into the prediction model to predict the current air-conditioning operation scene mode, so as to control the operation of the air-conditioning according to the predicted current air-conditioning operation scene mode.

[0085] Specifically, based on the remaining features after removing at least one feature with the smallest correlation with the prediction result, the current user physiological state parameters and environmental parameters in the environment are collected, and the current air-conditioning operation scene mode is predicted by inputting the prediction model to control the operation of the air conditioner according to the current air-conditioning operation scene mode.

[0086] Depending on the air conditioner's operating scenario, corresponding control strategies can be used to control the air conditioner's operation. For example, when the air conditioner's operating scenario is sports mode, rapid cooling can be initiated (such as increasing the compressor operating frequency and / or increasing the internal fan speed), and the fresh air system can be activated to exchange fresh air. When the air conditioner's operating scenario is pathological mode, the cabinet's ultraviolet sterilization function can be activated, and the humidity can be adjusted to a preset humidity range (for example, through the air conditioner's humidification or dehumidification function). When the air conditioner's operating scenario is night mode, the air conditioner can be adjusted to silent mode to prevent interruptions to the user's sleep.

[0087] Optionally, the acquisition unit 110 is further used to: acquire the user physiological state parameters and environmental parameters of the environment collected within the preset time, and the air-conditioning operation scene mode predicted by the prediction model at every preset time; the training unit 130 is further used to: re-train the model based on the user physiological state parameters and environmental parameters of the environment collected within the preset time obtained by the acquisition unit, and the air-conditioning operation scene mode predicted by the prediction model, so as to update the input features of the model.

[0088] Specifically, feature filtering is triggered every preset time interval to obtain the user's physiological state parameters and environmental parameters collected during this period, as well as the air conditioner operating scenario patterns predicted by the prediction model. The model is then retrained, and at least one feature with the least correlation with the prediction result is removed to update the input features of the prediction model. For example, feature filtering may be triggered every 6 hours to obtain the user's physiological state parameters and environmental parameters collected within the previous 6 hours, as well as the air conditioner operating scenario patterns predicted by the prediction model, and the model is retrained.

[0089] The present invention also provides a storage medium corresponding to the air conditioner control method, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0090] The present invention also provides an air conditioner corresponding to the air conditioner control method, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the aforementioned methods when executing the computer program.

[0091] The present invention also provides an air conditioner corresponding to the control device of the air conditioner, comprising any of the aforementioned control devices of the air conditioner.

[0092] The present invention also provides a computer program product corresponding to the air conditioner control method, comprising a computer program, which implements the steps of any of the aforementioned methods when executed by a processor.

[0093] Based on this, the solution provided by the present invention constructs an m×n parameter-time matrix as a benchmark input, and based on the correlation quantification mechanism of the single variable elimination method, eliminates one characteristic variable in each iteration to generate a new input matrix, uses a neural network algorithm model to perform m parallel predictions, compares the prediction results of each time, locates the variable with the lowest correlation, and eliminates the characteristic variable with the smallest correlation, thereby optimizing the accuracy of the model prediction scene and reducing the error in real-time control of air-conditioning scene mode.

[0094] Air conditioning systems in related technologies are unable to perceive the correlation between the user's physiological state and the environment. Furthermore, the algorithm models commonly used in fixed operating modes have low accuracy in predicting scenarios, making them prone to control errors, often leading to delayed environmental control responses and physical discomfort. This invention integrates environmental data with user physiological state parameters to achieve an adaptive learning air conditioning system. Based on variable correlation analysis, the accuracy of the air conditioning algorithm model's scenario predictions is optimized, reducing control errors and effectively improving the user experience and the level of air conditioning intelligence.

[0095] This solution can be expanded to air conditioning systems in smart homes and commercial buildings, improving environmental control precision and energy efficiency. Combined with the Internet of Things (IoT) platform, it can achieve precise temperature and humidity control in specialized environments like hospitals and laboratories, ensuring environmental safety. For inverter air conditioner production, this solution can be deployed via over-the-air (OTA) upgrades, enhancing product competitiveness without hardware replacement.

[0096] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and implementations are within the scope and spirit of the present invention and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwiring, or a combination of any of these. Furthermore, each functional unit may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0098] The units described as separate components may or may not be physically separate, and the components of the control device may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the relevant technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0100] The foregoing description is merely an embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of the claims.

Claims

1. A method for controlling an air conditioner, characterized in that: include: Acquire multiple sets of pre-collected user physiological state parameters and environmental parameters in the environment and corresponding air-conditioning operation scene modes to obtain an original data set; Preprocessing the data in the obtained original data set to obtain N samples, each sample containing m features, wherein each set of user physiological state parameters and environmental parameters and the corresponding air conditioning operation scene mode in the original data set is used as one sample, and the user physiological state parameters and environmental parameters in each sample are used as features; Performing model training using the preprocessed data to obtain a prediction model for predicting the air conditioner operation scene mode; wherein, during each training, one of the m features is sequentially removed, and the remaining m-1 features are retained for model training, and at least one feature having the smallest correlation with the prediction result is evaluated and determined, and the at least one feature having the smallest correlation with the prediction result is removed to obtain the prediction model; The current user physiological state parameters and environmental parameters in the environment are collected and input into the prediction model to predict the current air-conditioning operation scene mode, so as to control the operation of the air-conditioning according to the predicted current air-conditioning operation scene mode.

2. The method according to claim 1, characterized in that Each time the training is performed, one of the m features is removed in turn, and the remaining m-1 features are retained for model training. At least one feature with the least correlation with the prediction result is evaluated and determined, including: Each time training, one of the m features is removed in turn, and the remaining m-1 features are retained. The remaining data after removing one feature is divided into a training set and a test set according to a preset ratio. The training set is input into the prediction model for training. After the training, the prediction model generates a prediction result through the test set, and the evaluation index A is calculated based on the prediction result, until all features have been removed; The evaluation index A calculated according to the prediction result after each training is completed is compared, and at least one feature with the largest evaluation index after elimination is determined as the at least one feature with the smallest correlation with the prediction result.

3. The method according to claim 2, characterized in that Calculate the evaluation index based on the prediction results, including: calculating the evaluation index A by the following formula: ； Where N is the number of samples and Nr is the number of correctly predicted samples.

4. The method according to any one of claims 1 to 3, characterized in that Also includes: Acquire, at preset intervals, user physiological state parameters and environmental parameters collected within the preset time period, and the air conditioning operation scene mode predicted by the prediction model; The model is retrained based on the user's physiological state parameters and environmental parameters collected in the preset time and the air-conditioning operation scene mode predicted by the prediction model.

5. The method according to any one of claims 1 to 3, characterized in that Preprocessing the data in the collected original data set includes: Standardizing the data in the original data set; and / or, Outliers are removed and / or missing values are filled in for the data in the original data set.

6. The method according to any one of claims 1 to 3, characterized in that The user's physiological state parameter includes at least one of body temperature, heart rate, blood pressure, respiratory rate, and exercise volume; and / or, The environmental parameters include: at least one of temperature, humidity, air quality, wind speed, wind direction, noise, PM2.5 concentration, carbon dioxide concentration, carbon monoxide concentration, air conditioning operation mode, and air flow distribution.

7. A control device for an air conditioner, characterized in that: include: An acquisition unit is used to acquire a plurality of pre-collected sets of user physiological state parameters and environmental parameters in the environment and corresponding air-conditioning operation scene modes to obtain an original data set; a processing unit, configured to preprocess the data in the original data set acquired by the acquisition unit to obtain N samples, each sample comprising m features, wherein each set of user physiological state parameters and environmental parameters and the corresponding air conditioning operation scene mode in the original data set is regarded as one sample, and the user physiological state parameters and environmental parameters in each sample are regarded as features; a training unit, configured to perform model training using the data preprocessed by the processing unit to obtain a prediction model for predicting the air conditioner operation scene mode; wherein, each training step sequentially removes one of the m features, retains the remaining m-1 features for model training, evaluates and determines at least one feature having the least correlation with the prediction result, and removes the at least one feature having the least correlation with the prediction result to obtain the prediction model; The prediction unit is used to collect the current user physiological state parameters and environmental parameters in the environment and input them into the prediction model to predict the current air-conditioning operation scene mode, so as to control the operation of the air-conditioning according to the predicted current air-conditioning operation scene mode.

8. A storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An air conditioner, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1 to 6 are implemented, and the method comprises the control device according to claim 7.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 1 to 6 when the computer program is executed by a processor.

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