Indoor air quality adjusting method, device and equipment based on user preference

Abstract

The invention relates to an indoor air quality adjusting method, device and equipment based on user preferences. The method comprises the steps of obtaining current state data of a user and current air data of an environment where the user is located; the current state data are input into a preset air quality preference model, required air data corresponding to the user are obtained, whether the current air data reach the required air data or not is judged, and the preset air quality preference model is obtained by learning through a random forest algorithm and a deep reinforcement learning algorithm based on multiple pieces of historical data; and if any parameter in the current air data does not reach the required air data, target control equipment and control parameters thereof are determined according to the parameters which do not reach the required air data, and the target equipment is controlled according to the control parameters. Therefore, user preference air quality decision control is realized through integrated hardware and various algorithms, real intelligent and personalized management is realized, and multiple requirements of the user on comfort, health and energy conservation are met.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a method, device and equipment for adjusting indoor air quality based on user preference. Background Art

[0002] With the continuous improvement of electronic information technology and control technology and the gradual acceleration of social informatization, many smart home control systems have emerged in the home field. However, these traditional control systems have problems such as control methods relying on human triggering and scene settings, insufficient refinement in the setting of control conditions, and difficulty in adapting to the real environment of multiple factors and multiple devices, and poor control effects that cannot meet the dual needs of comfort, health and energy saving.

[0003] In the related art, the air conditioning function of the smart home control system is usually adjusted and optimized based on the user's settings and the environmental data collection of some scenes, so that the air conditioning function of the smart home control system is more in line with the user's requirements.

[0004] However, the above-mentioned technical means are too dependent on human triggering and system scenario settings, and cannot adapt to complex environments with multiple factors and multiple devices. They are also unable to interact and feedback with users in a timely manner, which greatly reduces the user experience and needs to be urgently resolved. Summary of the invention

[0005] The present invention provides a method, device and equipment for adjusting indoor air quality based on user preferences, so as to solve the problems in related technologies that the control method of smart home control system relies on personnel triggering and scene setting, and cannot adapt to the complex environment of multiple factors and multiple devices. By integrating hardware and multiple algorithms, the control effect of indoor air quality is improved, and truly intelligent and personalized management is realized, multiple epidemic prevention requirements are achieved, and users' multiple needs for comfort, health and energy saving are met.

[0006] To achieve the above object, a first embodiment of the present invention provides a method for adjusting indoor air quality based on user preferences, comprising the following steps:

[0007] Acquire the current state data of the user and the current air data of the environment where the user is located;

[0008] Inputting the current state data into a preset air quality preference model to obtain the required air data corresponding to the user, and determining whether the current air data reaches the required air data, wherein the preset air quality preference model is based on the historical state data of the user and the air data corresponding to the historical data, and is learned using a random forest algorithm and a deep reinforcement learning algorithm;

[0009] If any parameter in the current air data does not reach the required air data, a target control device and a control parameter of the target device are determined according to the parameter that does not reach the required air data, and the target device is controlled according to the control parameter of the target device.

[0010] According to one embodiment of the present invention, before inputting the current state data into the preset air quality preference model, the method further includes:

[0011] Acquire historical status data of the user and demand air data corresponding to the historical status data;

[0012] Based on a preset division ratio, the historical state data and the demand air data corresponding to the historical state data are divided into a training set and a test set, wherein the training set and the test set each include multiple groups of data, each group of data consists of a state data and the demand air data corresponding to the state data;

[0013] Based on the random forest algorithm and the deep reinforcement learning algorithm, the training set is learned to obtain an initial air quality preference model, and the test set is used to test the initial air quality preference model. If the test result meets the preset test conditions, the initial air quality preference model is used as the preset air quality preference model.

[0014] According to one embodiment of the present invention, after testing the initial air quality preference model using the test set, the method further includes:

[0015] If the test result does not meet the preset test condition, the preset division ratio is adjusted, and a new air quality preference model is relearned based on the adjusted training set until the test result of the new initial air quality preference model meets the preset test condition, and the new initial air quality preference model is used to obtain the preset air quality preference model.

[0016] According to an embodiment of the present invention, after controlling the target device according to the control parameter of the target device, the method further includes:

[0017] Determining whether a device adjustment instruction of the user is received;

[0018] The target control device is controlled based on the device adjustment instruction, and the control result and the current state data are stored in a target memory, and the preset air quality preference model is updated according to the control result and the current state data.

[0019] According to an embodiment of the present invention, the indoor air quality adjustment method based on user preference further includes:

[0020] Determine whether there is an external access request to the target memory;

[0021] If the target memory has the external access request, verifying the external access request;

[0022] If the verification fails, an access denial reminder is issued and a reminder message is sent to the preset mobile terminal.

[0023] According to an embodiment of the present invention, the indoor air quality adjustment method based on user preference further includes:

[0024] Determine whether a data display request is received;

[0025] If the data display request is received, the current state data and / or the current air data are displayed.

[0026] According to the indoor air quality adjustment method based on user preference proposed in the embodiment of the present invention, the air quality preference model is trained through the user's historical data and the corresponding demand air data, the user's current state data and current air data are input into the trained air quality preference model, a personalized device control strategy is generated, and the terminal device is controlled according to the generated control strategy to adjust the air quality of the environment in which the user is located. Thus, by integrating hardware and multiple algorithms, the control effect of indoor air quality is improved, and truly intelligent and personalized management is realized, multiple epidemic prevention requirements are met, and the user's multiple needs for comfort, health, and energy saving are met.

[0027] To achieve the above object, a second embodiment of the present invention provides an indoor air quality adjustment device based on user preference, comprising:

[0028] An acquisition module, used to acquire the current state data of the user and the current air data of the environment where the user is located;

[0029] A prediction decision module, used for inputting the current state data into a preset air quality preference model, obtaining the required air data corresponding to the user, and judging whether the current air data reaches the required air data, wherein the preset air quality preference model is based on the historical state data of the user and the air data corresponding to the historical data, and is learned by using a random forest algorithm and a deep reinforcement learning algorithm;

[0030] A control module is used to determine a target control device and control parameters of the target device according to the parameters that do not reach the required air data when any parameter in the current air data does not reach the required air data, and control the target device according to the control parameters of the target device.

[0031] According to one embodiment of the present invention, before inputting the current state data into the preset air quality preference model, the prediction decision module is further used to:

[0032] Acquire historical status data of the user and demand air data corresponding to the historical status data;

[0033] Based on a preset division ratio, the historical state data and the demand air data corresponding to the historical state data are divided into a training set and a test set, wherein the training set and the test set each include multiple groups of data, each group of data consists of a state data and the demand air data corresponding to the state data;

[0034] Based on the random forest algorithm and the deep reinforcement learning algorithm, the training set is learned to obtain an initial air quality preference model, and the test set is used to test the initial air quality preference model. If the test result meets the preset test conditions, the initial air quality preference model is used as the preset air quality preference model.

[0035] According to one embodiment of the present invention, after testing the initial air quality preference model using the test set, the prediction decision module is further used to:

[0036] If the test result does not meet the preset test condition, the preset division ratio is adjusted, and a new air quality preference model is relearned based on the adjusted training set until the test result of the new initial air quality preference model meets the preset test condition, and the new initial air quality preference model is used to obtain the preset air quality preference model.

[0037] According to an embodiment of the present invention, after controlling the target device according to the control parameters of the target device, the control module is further used to:

[0038] Determining whether a device adjustment instruction of the user is received;

[0039] The target control device is controlled based on the device adjustment instruction, and the control result and the current state data are stored in a target memory, and the preset air quality preference model is updated according to the control result and the current state data.

[0040] According to one embodiment of the present invention, the indoor air quality adjustment device based on user preference is further used for:

[0041] Determine whether there is an external access request to the target memory;

[0042] If the target memory has the external access request, verifying the external access request;

[0043] If the verification fails, an access denial reminder is issued and a reminder message is sent to the preset mobile terminal.

[0044] According to one embodiment of the present invention, the indoor air quality adjustment device based on user preference is further used for:

[0045] Determine whether a data display request is received;

[0046] If the data display request is received, the current state data and / or the current air data are displayed.

[0047] According to the indoor air quality adjustment device based on user preference proposed in the embodiment of the present invention, the air quality preference model is trained through the user's historical data and the corresponding required air data, the user's current state data and current air data are input into the trained air quality preference model, a personalized device control strategy is generated, and the terminal device is controlled according to the generated control strategy to adjust the air quality of the environment in which the user is located. Thus, by integrating hardware and multiple algorithms, the control effect of indoor air quality is improved, and truly intelligent and personalized management is realized, multiple epidemic prevention requirements are met, and the user's multiple needs for comfort, health, and energy saving are met.

[0048] To achieve the above-mentioned objectives, the third aspect of the present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the indoor air quality adjustment method based on user preferences as described in the above-mentioned embodiment.

[0049] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the indoor air quality adjustment method based on user preferences as described in the above embodiments.

[0050] To achieve the above objectives, a fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the indoor air quality adjustment method based on user preferences as described in the above embodiments.

[0051] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0053] Figure 1 A flowchart of a method for adjusting indoor air quality based on user preferences provided by an embodiment of the present invention;

[0054] Figure 2 A schematic diagram of the structure of a system for indoor air quality adjustment method based on user preference according to a specific embodiment of the present invention;

[0055] Figure 3 A schematic diagram of a flow chart of an indoor air quality adjustment method based on user preference according to a specific embodiment of the present invention;

[0056] Figure 4 A flowchart of specific implementation steps of intelligent control of an indoor air quality adjustment method based on user preferences according to a specific embodiment of the present invention;

[0057] Figure 5 A block diagram of an indoor air quality adjustment device based on user preference according to an embodiment of the present invention;

[0058] Figure 6 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention.

[0059] Reference numerals:

[0060] 10-indoor air quality adjustment device based on user preference, 100-acquisition module, 200-prediction and decision module, 300-control module, 601-memory, 602-processor, 603-communication interface. DETAILED DESCRIPTION

[0061] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0062] The following describes a method, device and apparatus for adjusting indoor air quality based on user preference according to an embodiment of the present invention with reference to the accompanying drawings. First, the method for adjusting indoor air quality based on user preference according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0063] Figure 1 The present invention is a flowchart of a method for adjusting indoor air quality based on user preference provided by an embodiment of the present invention.

[0064] like Figure 1 As shown, the indoor air quality adjustment method based on user preference includes the following steps:

[0065] In step S101, the current state data of the user and the current air data of the environment where the user is located are obtained.

[0066] Among them, the user's current status data includes the user's current physical health status, and the current air data of the user's environment includes the ultrafine particle data, fine particle data, total suspended particulate matter data, formaldehyde concentration data, carbon dioxide concentration data, temperature and humidity data, and energy consumption data of the current environment.

[0067] Specifically, the embodiment of the present invention can obtain the current state data of the user and the current air data of the user's environment by implanting a perception module in the wearable device. The perception module includes a sensor module for sensing various parameters and data in the environment (such as ultrafine particle concentration, fine particle concentration, total suspended particle concentration, formaldehyde concentration, carbon dioxide concentration, temperature and humidity, energy consumption, etc.) and a wearable device module for sensing the user's physical health status (such as the user's breathing rate, heart rate, allergy information, etc.), which can realize real-time monitoring of the real environment and user status. Among them, the wearable device includes an environmental monitoring sensor (ultrafine particle monitoring sensor, fine particle monitoring sensor, total suspended particle monitoring sensor, formaldehyde monitoring sensor, carbon dioxide monitoring sensor, temperature and humidity monitoring sensor, energy consumption monitoring sensor), a physiological monitoring sensor (such as a heart rate sensor, a breathing rate sensor, a blood oxygen sensor, a skin galvanic response sensor), a data processing unit, a wireless communication module, and a user feedback module.

[0068] For example, when a user wears a wearable device (such as a wristband), the built-in sensor of the device monitors the air pollution and health data around the user. Ultrafine particle sensors are used to monitor the concentration of viral aerosols, fine particle sensors are used to monitor pollution caused by human activities such as cooking and smoking, total suspended particle sensors are used to monitor pollen pollution, formaldehyde sensors are used to monitor decoration pollution, and carbon dioxide sensors are used to monitor the overall ventilation situation. The heart rate sensor uses photoelectric volume pulse wave (PPG) technology, and the smart health bracelet measures the user's heart rate fluctuations in real time, especially when the user is in an allergic period or in an unstable health state, to detect abnormal increases in heart rate. The respiratory rate sensor is used to monitor the user's respiratory rate, and analyzes the tiny vibrations of the chest or wrist to identify changes in breathing patterns. This method can be used to determine whether the user's breathing is restricted or accelerated when the air pollution or allergen concentration is high. The blood oxygen sensor measures the blood oxygen saturation level in the blood through an optical sensor, and monitors the decrease in blood oxygen when the user's respiratory function is affected, such as when the concentration of particulate matter in the air is high. The galvanic skin response sensor is used to detect the conductivity of the skin, reflecting the user's stress level or physical load. This data helps assess the potential impact of air pollution on users' bodies.

[0069] In step S102, the current state data is input into a preset air quality preference model to obtain the user's required air data, and it is determined whether the current air data reaches the required air data, wherein the preset air quality preference model is based on the user's historical state data and the air data corresponding to the historical data, and is learned using a random forest algorithm and a deep reinforcement learning algorithm.

[0070] It should be noted that, before inputting the current state data into the preset air quality preference model, the embodiment of the present invention may also perform a preliminary analysis on the current state data and filter the collected data to filter out noise interference.

[0071] Specifically, the preset air quality preference model can analyze the user's preference for indoor air quality in different scenarios and the user's air quality requirements in different health conditions (for example, during allergy season or colds) based on the user's historical status data and the air data corresponding to the historical data. Through deep reinforcement learning, the user's operating behavior is feature extracted to build the user's air quality preference model. After inputting the current status data into the preset air quality preference model, the user's required air data in the future time period is predicted according to the random forest algorithm and the deep reinforcement learning algorithm, and it is determined whether the current air data meets the required air data.

[0072] For example, an embodiment of the present invention continuously collects user preference data for indoor air quality in different scenarios. These data may include but are not limited to user preference settings for air humidity, temperature, air freshness, etc. in different seasons and time periods, as well as user air quality requirements in different health states (such as allergy season or during a cold). The user's operating behavior is feature extracted through a machine learning algorithm to construct an air quality preference model. The user's behavior pattern is analyzed through a deep learning algorithm to accurately capture the user's air quality preference trend. The user's required air data in the future time period is predicted using a random forest algorithm and a deep reinforcement learning algorithm. For example, when the allergy season is about to begin, the system can predict in advance that the user may need more efficient air purification, and automatically adjust the equipment operation mode to reduce allergens in the air, and compare the required air data with the current air data.

[0073] In step S103, if any parameter in the current air data does not reach the required air data, the target control device and the control parameter of the target device are determined according to the parameter that does not reach the required air data, and the target device is controlled according to the control parameter of the target device.

[0074] Specifically, if any parameter in the current air data does not meet the required air data, it means that the current ambient air quality does not meet the ambient air quality required by the user. According to the parameters of the required air data, the target device to be controlled and the control parameters of the target device can be determined. After obtaining the control parameters of the target device, the target device is controlled according to the control parameters.

[0075] For example, based on the control parameters of the target device, the wind speed and filtration level of the air purifier are adjusted, the opening and closing of the window opener are controlled, and the temperature and humidity settings of the air conditioning system are automatically adjusted. For example, when the system detects that the user is in a period of high allergy incidence or abnormal health status, it will issue an early warning based on this information and execute the corresponding adjustment strategy. For example, it will automatically improve the filtration efficiency of the air purifier, adjust the temperature and humidity settings of the air conditioning system to reduce allergens in the air, and close the windows in advance to prevent harmful substances in the outdoor air from entering the room.

[0076] Optionally, in some embodiments, after controlling the target device according to the control parameters of the target device, it also includes: determining whether a device adjustment instruction from the user is received; controlling the target control device based on the device adjustment instruction, and storing the control results and current status data to the target memory, and updating the preset air quality preference model according to the control results and current status data.

[0077] Specifically, when the system controls the target device according to the control parameters of the target device, if the user is not satisfied with the air quality adjustment result after adjusting the air quality, the user initiates the device adjustment instruction. After receiving the user's device adjustment instruction, the system controls the target device based on the device adjustment instruction, and stores the control result and current state data in the target memory, and continuously updates the preset air quality preference model. By recording the environmental changes and user satisfaction after each user intervention, the control strategy is continuously revised through reinforcement learning, making the control process more accurate and efficient.

[0078] For example, when a user manually adjusts the wind speed of an air purifier in similar situations multiple times, the system will automatically learn the user's operational preference and automatically apply the preference setting under similar environmental conditions, reducing the user's operational burden.

[0079] In addition, through deep reinforcement learning algorithms, the effect of each control parameter is evaluated, and multi-dimensional evaluation criteria (such as air quality improvement effect, energy consumption, changes in user health status, etc.) are set to optimize the strategy.

[0080] Optionally, in some embodiments, the indoor air quality adjustment method based on user preferences further includes: determining whether there is an external access request to the target memory; if there is an external access request to the target memory, verifying the external access request; if the verification fails, issuing an access denial reminder, and sending a reminder message to a preset mobile terminal.

[0081] Specifically, when a user issues an access request to a target storage device because the user needs to change the historical data and preference data stored in the target storage device, the embodiment of the present invention needs to verify the access request, for example, by setting a face recognition device or a voiceprint fingerprint authentication device to verify the identity of the person who issued the access request. If the verification fails, the access request is rejected, a denied access reminder is issued, and the reminder information is sent to a preset mobile terminal through a communication connection to remind the user that there may be potential security threats.

[0082] Optionally, in some embodiments, the indoor air quality adjustment method based on user preference further includes: determining whether a data display request is received; if a data display request is received, displaying current status data and / or current air data.

[0083] Specifically, the embodiment of the present invention is equipped with a visualization interface module. When the user needs to obtain the current air data, he can send a data display request through a preset mobile terminal; when the system receives the data display request, it displays the current status data and current air data on the preset mobile terminal or display terminal according to the data display request.

[0084] For example, users can view current environmental data in a visual interface, and can also use a health bracelet to get real-time feedback on the current air quality improvement effect. Smart health bracelets can work seamlessly with residential air pollution intelligent control systems to ensure that the system can not only sense environmental data, but also capture the user's health status in real time. Through these data, the system can predict and manage air quality, adjust control strategies, prevent allergic reactions, reduce indoor air pollution, and prevent the occurrence of airborne viruses, further optimizing indoor air quality management.

[0085] Therefore, the air quality preference model is trained through the user's historical data and the corresponding demand air data, and the user's current state data and current air data are input into the trained air quality preference model to generate a personalized equipment control strategy. The terminal equipment is controlled according to the generated control strategy, and the preference model is continuously adjusted and optimized according to the user's feedback and satisfaction, so that the air quality preference model is more in line with the user's actual needs.

[0086] Optionally, in some embodiments, before inputting the current status data into a preset air quality preference model, it also includes: obtaining the user's historical status data and the demand air data corresponding to the historical status data; based on a preset division ratio, dividing the historical status data and the demand air data corresponding to the historical status data into a training set and a test set, wherein the training set and the test set each include multiple groups of data, and each group of data consists of a piece of status data and the demand air data corresponding to the piece of status data; based on a random forest algorithm and a deep reinforcement learning algorithm, learning the training set to obtain an initial air quality preference model, and using the test set to test the initial air quality preference model, if the test result meets the preset test conditions, the initial air quality preference model is used as the preset air quality preference model.

[0087] Specifically, the training process of the air quality preference model includes: obtaining the user's historical status data and the demand air data corresponding to the historical status data. For example, when the user is in an allergic period, the corresponding demand air data is the total suspended particulate matter data of the indoor air is less than 0.3 mg / m3; after obtaining the user's historical status data and the demand air data corresponding to the historical status data, the historical data and the corresponding demand air data are grouped, each group of data is composed of a state data and the demand air data corresponding to the state data, and then according to the preset division ratio, the multiple data groups are divided into training sets and test sets, and the training sets and test sets both include multiple groups of data; and according to the random forest algorithm and the deep reinforcement learning algorithm, the training set is learned, the user's preferences are extracted to construct the initial air quality preference model, and the initial air quality preference model is tested according to the test set. After the test, if the test result meets the preset test conditions, the initial air quality preference model is used as the preset air quality preference model.

[0088] Optionally, in some embodiments, after testing the initial air quality preference model using the test set, it also includes: if the test result does not meet the preset test condition, adjusting the preset division ratio, and re-learning based on the adjusted training set to obtain a new air quality preference model, until the test result of the new initial air quality preference model meets the preset test condition, and the new initial air quality preference model is obtained as the preset air quality preference model.

[0089] Specifically, the initial air quality preference model is tested according to the test set. When the test results do not meet the preset test conditions, the division ratio of the training set and the test set is adjusted, and the data group is redivided to obtain new training sets and test sets. The initial air quality preference model is reconstructed based on the adjusted training set, and the initial air quality preference model is tested according to the test set. The above operations are repeated until the test results meet the preset test conditions, and the initial air quality preference model is used as the preset air quality preference model.

[0090] In order to enable those skilled in the art to further understand the indoor air quality adjustment method based on user preference according to an embodiment of the present invention, it is described in detail below in conjunction with specific embodiments.

[0091] Specifically, Figure 2 As shown, Figure 2 A schematic diagram of the structure of a system for an indoor air quality adjustment method based on user preferences according to a specific embodiment of the present invention, wherein the system applicable to the indoor air quality adjustment method based on user preferences includes:

[0092] 1. Perception Module

[0093] It includes sensor modules for sensing various parameters and data in the environment (such as ultrafine particle concentration, fine particle concentration, total suspended particle concentration, formaldehyde concentration, carbon dioxide concentration, temperature and humidity, energy consumption, etc.) and wearable device modules for sensing the user's physical health status (such as the user's breathing rate, heart rate, allergy information, etc.). It can realize real-time monitoring of the real environment and user status.

[0094] 2. Storage Module

[0095] It includes a database for storing parameters and data obtained by the perception module from the real environment for the control module to read, storing the decisions taken by the control module, storing the prediction data of the personnel preference prediction model, and storing the user's operation behavior in the interaction module.

[0096] 3. Control module

[0097] It includes a deep reinforcement learning neural network module for formulating action strategies and a reward function module that measures the pros and cons of action strategies through multiple aspects such as particulate matter concentration control effect, energy consumption, consumables, and human health benefits. It can achieve a trade-off between indoor air quality and energy consumption.

[0098] 4. Communication module

[0099] It includes a transmission protocol module for clearly defining the transmission protocol used by the communication module, a local area network module for connecting the control module with various smart home devices in the real environment, and a communication interface module for connecting the perception module and other external devices.

[0100] 5. Prediction Module

[0101] It includes a random forest algorithm module for analyzing user health data, integrating user features, and predicting user preferences. The predictive model established by the algorithm module can predict the changing trend of air quality in advance and take measures in advance when necessary. It can also continuously optimize the control module and achieve steady learning of the system, creating a more intelligent and personalized indoor environment experience for users.

[0102] 6. Privacy protection module

[0103] It includes an access feature parameter module for identifying the access end and extracting features from its historical access records, and a risk control module for analyzing and controlling access segment risks, so that the control system can identify abnormal access trajectories, discover potential security threats in a timely manner, and take corresponding measures to protect user privacy.

[0104] 7. Interaction Module

[0105] It includes an interface design module for designing the appearance and interaction mode of the user interface, a visual interface module for displaying various parameters and energy consumption of the indoor air environment, a user input module for obtaining user data information, and a user feedback module for providing feedback to users so that users can understand whether their operations are executed.

[0106] Furthermore, if Figure 3 As shown, Figure 3 A flowchart of a method for adjusting indoor air quality based on user preferences according to a specific embodiment of the present invention includes the following steps:

[0107] S301, Data Collection and Transmission

[0108] The perception module collects environmental and user health data and uploads it to the storage module through the communication module.

[0109] S302, Data storage and processing

[0110] The storage module stores data and shares the data with the control module and the prediction module.

[0111] S303, Forecasting and Decision Making

[0112] The prediction module uses historical data and current data to predict user preferences and air quality changes, and provides optimization suggestions to the control module.

[0113] S304, Equipment Control

[0114] The control module generates control instructions based on the prediction results and real-time data, and directs the smart home devices to perform corresponding operations through the communication module.

[0115] S305, User Interaction and Feedback

[0116] The interactive module provides users with data visualization and control options, and ensures the security of user data through the privacy protection module.

[0117] S306, Data Learning and Optimization

[0118] The system continuously collects user operation and environmental change data, constantly updates the experience pool, and optimizes prediction models and control strategies.

[0119] Furthermore, if Figure 4 As shown, Figure 4 A flowchart of specific implementation steps of intelligent control of an indoor air quality adjustment method based on user preferences according to a specific embodiment of the present invention includes the following steps:

[0120] S401, Collection and Modeling of User Preference Data

[0121] Through the perception module and interaction module, users’ preference data on indoor air quality in different situations are continuously collected.

[0122] S402, Prediction and decision-making based on personnel preferences

[0123] The prediction module regularly calls the user preference data and real-time environmental data in the storage module, and uses the random forest algorithm and deep reinforcement learning algorithm to predict the user's air quality needs in the future time period.

[0124] S403, Generation of Personalized Control Strategy

[0125] The control module generates personalized equipment control strategies based on the prediction results and user preference model.

[0126] S404, Dynamic Learning and Adaptive Adjustment

[0127] The system continuously updates the preference model based on changes in user behavior.

[0128] According to the indoor air quality adjustment method based on user preference proposed in the embodiment of the present invention, the air quality preference model is trained through the user's historical data and the corresponding demand air data, the user's current state data and current air data are input into the trained air quality preference model, a personalized device control strategy is generated, and the terminal device is controlled according to the generated control strategy to adjust the air quality of the environment in which the user is located. Thus, by integrating hardware and multiple algorithms, the control effect of indoor air quality is improved, and truly intelligent and personalized management is realized, multiple epidemic prevention requirements are met, and the user's multiple needs for comfort, health, and energy saving are met.

[0129] Next, an indoor air quality adjustment device based on user preference according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0130] Figure 5 4 is a block diagram of an indoor air quality adjustment device based on user preference according to an embodiment of the present invention.

[0131] like Figure 5 As shown, the indoor air quality adjustment device 10 based on user preference includes: an acquisition module 100 , a prediction and decision module 200 and a control module 300 .

[0132] The acquisition module 100 is used to acquire the current state data of the user and the current air data of the environment where the user is located;

[0133] The prediction decision module 200 is used to input the current state data into a preset air quality preference model, obtain the required air data corresponding to the user, and determine whether the current air data reaches the required air data, wherein the preset air quality preference model is based on the user's historical state data and the air data corresponding to the historical data, and is learned using a random forest algorithm and a deep reinforcement learning algorithm;

[0134] The control module 300 is used to determine the target control device and the control parameter of the target device according to the parameter that does not reach the required air data when any parameter in the current air data does not reach the required air data, and control the target device according to the control parameter of the target device.

[0135] According to one embodiment of the present invention, before inputting the current state data into a preset air quality preference model, the prediction decision module 200 is further used to: obtain the user's historical state data and the demand air data corresponding to the historical state data; based on a preset division ratio, divide the historical state data and the demand air data corresponding to the historical state data into a training set and a test set, wherein the training set and the test set both include multiple groups of data, and each group of data consists of a state data and the demand air data corresponding to the state data; based on a random forest algorithm and a deep reinforcement learning algorithm, learn the training set to obtain an initial air quality preference model, and use the test set to test the initial air quality preference model, and if the test result meets the preset test conditions, use the initial air quality preference model as the preset air quality preference model.

[0136] According to one embodiment of the present invention, after testing the initial air quality preference model using a test set, the prediction decision module 200 is also used to: if the test result does not meet the preset test condition, adjust the preset division ratio, and re-learn based on the adjusted training set to obtain a new air quality preference model, until the test result of the new initial air quality preference model meets the preset test condition, and obtain the preset air quality preference model with the new initial air quality preference model.

[0137] According to one embodiment of the present invention, after controlling the target device according to the control parameters of the target device, the control module 300 is also used to: determine whether the user's device adjustment instruction is received; control the target control device based on the device adjustment instruction, and store the control result and current status data to the target memory, and update the preset air quality preference model according to the control result and current status data.

[0138] According to one embodiment of the present invention, the indoor air quality adjustment device 10 based on user preference is also used to: determine whether there is an external access request to the target memory; if there is an external access request to the target memory, verify the external access request; if the verification fails, issue an access denial reminder and send a reminder message to a preset mobile terminal.

[0139] According to an embodiment of the present invention, the indoor air quality adjustment device 10 based on user preference is further used to: determine whether a data display request is received; if a data display request is received, display current status data and / or current air data.

[0140] According to the indoor air quality adjustment device based on user preference proposed in the embodiment of the present invention, the air quality preference model is trained through the user's historical data and the corresponding required air data, the user's current state data and current air data are input into the trained air quality preference model, a personalized device control strategy is generated, and the terminal device is controlled according to the generated control strategy to adjust the air quality of the environment in which the user is located. Thus, by integrating hardware and multiple algorithms, the control effect of indoor air quality is improved, and truly intelligent and personalized management is realized, multiple epidemic prevention requirements are met, and the user's multiple needs for comfort, health, and energy saving are met.

[0141] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0142] A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0143] When the processor 602 executes the program, the indoor air quality adjustment method based on user preference provided in the above embodiment is implemented.

[0144] Furthermore, the electronic device further comprises:

[0145] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0146] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0147] The memory 601 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0148] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0149] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0150] The processor 602 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0151] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned indoor air quality adjustment method based on user preference.

[0152] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned indoor air quality adjustment method based on user preference.

[0153] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0154] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0155] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for adjusting indoor air quality based on user preference, characterized in that: The following steps are involved: Acquire the current state data of the user and the current air data of the environment where the user is located; Inputting the current state data into a preset air quality preference model to obtain the required air data corresponding to the user, and determining whether the current air data reaches the required air data, wherein the preset air quality preference model is based on the historical state data of the user and the air data corresponding to the historical data, and is learned using a random forest algorithm and a deep reinforcement learning algorithm; If any parameter in the current air data does not reach the required air data, a target control device and a control parameter of the target device are determined according to the parameter that does not reach the required air data, and the target device is controlled according to the control parameter of the target device.

2. The method according to claim 1, characterized in that: Before inputting the current state data into the preset air quality preference model, the method further includes: Acquire historical status data of the user and demand air data corresponding to the historical status data; Based on a preset division ratio, the historical state data and the demand air data corresponding to the historical state data are divided into a training set and a test set, wherein the training set and the test set each include multiple groups of data, each group of data consists of a state data and the demand air data corresponding to the state data; Based on the random forest algorithm and the deep reinforcement learning algorithm, the training set is learned to obtain an initial air quality preference model, and the test set is used to test the initial air quality preference model. If the test result meets the preset test conditions, the initial air quality preference model is used as the preset air quality preference model.

3. The method according to claim 1, characterized in that After testing the initial air quality preference model using the test set, it also includes: If the test result does not meet the preset test condition, the preset division ratio is adjusted, and a new air quality preference model is relearned based on the adjusted training set until the test result of the new initial air quality preference model meets the preset test condition, and the new initial air quality preference model is used to obtain the preset air quality preference model.

4. The method according to claim 1, characterized in that: After controlling the target device according to the control parameter of the target device, the method further includes: Determining whether a device adjustment instruction of the user is received; The target control device is controlled based on the device adjustment instruction, and the control result and the current state data are stored in a target memory, and the preset air quality preference model is updated according to the control result and the current state data.

5. The method according to claim 1, characterized in that: Also includes: Determine whether there is an external access request to the target memory; If the target memory has the external access request, verifying the external access request; If the verification fails, an access denial reminder is issued and a reminder message is sent to the preset mobile terminal.

6. The method according to claim 1, characterized in that Also includes: Determine whether a data display request is received; If the data display request is received, the current state data and / or the current air data are displayed.

7. An indoor air quality control device based on user preference, characterized in that: include: An acquisition module, used to acquire the current state data of the user and the current air data of the environment where the user is located; A prediction decision module, used for inputting the current state data into a preset air quality preference model, obtaining the required air data corresponding to the user, and judging whether the current air data reaches the required air data, wherein the preset air quality preference model is based on the historical state data of the user and the air data corresponding to the historical data, and is learned by using a random forest algorithm and a deep reinforcement learning algorithm; A control module is used to determine a target control device and control parameters of the target device according to the parameters that do not reach the required air data when any parameter in the current air data does not reach the required air data, and control the target device according to the control parameters of the target device.

8. The device according to claim 7, characterized in that Before inputting the current state data into the preset air quality preference model, the prediction decision module is further used to: Acquire historical status data of the user and demand air data corresponding to the historical status data; Based on a preset division ratio, the historical state data and the demand air data corresponding to the historical state data are divided into a training set and a test set, wherein the training set and the test set each include multiple groups of data, each group of data consists of a state data and the demand air data corresponding to the state data; Based on the random forest algorithm and the deep reinforcement learning algorithm, the training set is learned to obtain an initial air quality preference model, and the test set is used to test the initial air quality preference model. If the test result meets the preset test conditions, the initial air quality preference model is used as the preset air quality preference model.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the indoor air quality adjustment method based on user preference as described in any one of claims 1 to 6.

10. A computer storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the indoor air quality adjustment method based on user preference as described in any one of claims 1 to 6.

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