Intelligent AI family climate control device

Abstract

The invention relates to the technical field of AI family climate control, in particular to an intelligent AI family climate control device which comprises a data acquisition module, a data processing module, an execution control module, a user interaction module and a cloud server. The data acquisition module is used for acquiring indoor environment parameters in real time, including temperature, humidity, air quality index, carbon dioxide concentration and outdoor weather information; the data processing module is internally provided with a climate regulation algorithm based on artificial intelligence, the algorithm compares and analyzes the collected environmental parameters and comfort preference parameters preset by a user, and predicts the environmental change trend in a period of time in the future through a machine learning model; the execution control module is connected with control interfaces of climate adjusting devices such as an air conditioner, a humidifier, an air purifier and a dehumidifier and automatically adjusts the working state and the operation mode of each device according to the adjusting instruction output by the data processing module.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of AI home climate control, in particular to an intelligent AI home climate control device. BACKGROUND

[0002] With the development of smart home technology, the home environment control system has gradually developed from the traditional single air conditioner or humidifier control to a multifunctional integrated system integrating temperature regulation, humidity management, air purification, and energy consumption management. The current market home climate control equipment mostly relies on temperature and humidity sensors to collect data, and runs through setting fixed working modes or time periods. However, such systems often lack comprehensive judgment ability for multi-dimensional environmental factors, such as insufficient comprehensive analysis of air quality index (PM2.5, PM10, formaldehyde, etc.) and outdoor climate conditions, resulting in problems such as reaction lag and insufficient adjustment accuracy when responding to complex and variable environmental changes. In addition, the existing system also has certain limitations in user experience, which can usually only be controlled through a simple remote control or basic App, lacks learning and adaptation function for user long-term use habits, and is difficult to realize differentiated comfort optimization for different users. With the increasing attention to life quality and health, the existing technology still has a lot of room for improvement in terms of comprehensive environmental perception, intelligent adjustment strategy, energy saving effect, and interactive friendliness with users.

[0003] Under the dual demands of energy conservation and environmental protection and comfortable life, it has become an important direction for the industry to develop a home climate control device that can monitor a variety of indoor and outdoor environmental parameters in real time and automatically optimize the adjustment strategy based on artificial intelligence algorithms. Traditional climate control systems mostly use static rule control, i.e. starting and stopping the device by fixed threshold, but this approach ignores the dynamic nature of climate change and the individual differences in user preferences, resulting in energy waste or decreased comfort. For example, when the outside temperature drops sharply and the indoor humidity is too low, the traditional system may only start the heater and ignore humidification, causing dry indoor discomfort. At the same time, in the multi-device collaborative operation scenario, the existing system lacks device priority and load balancing strategy, which may simultaneously enable high-energy consumption devices during peak power periods, increasing the burden on the power grid and energy costs. With the integration of AI technology, cloud computing, and the Internet of Things, it is possible to realize multi-dimensional integration of environmental perception, trend prediction, behavior learning, and remote control, but the current commercial systems still have deficiencies in algorithm depth, self-adaptation ability, and cross-device collaborative control. Therefore, an intelligent AI home climate control device is urgently needed, which can integrate multi-source environmental data, achieve predictive regulation through self-learning algorithms, and significantly reduce energy consumption while ensuring comfort.

[0004] In view of the above, in order to overcome the above technical problems, the present application designs an intelligent AI home climate control device, which solves the above technical problems. SUMMARY

[0005] The technical purpose to be achieved by the present application is to design an intelligent AI home climate control device, which improves the algorithm depth, self-adaptive ability, cross-device collaborative control, etc.

[0006] In order to achieve the above technical purpose, the present application provides the following technical scheme: An intelligent AI home climate control device, comprising a data acquisition module, a data processing module, an execution control module, a user interaction module and a cloud server; The data acquisition module is used for real-time acquisition of indoor environmental parameters, including temperature, humidity, air quality index, carbon dioxide concentration and outdoor weather information; the data processing module is internally provided with a climate regulation algorithm based on artificial intelligence, which compares and analyzes the collected environmental parameters with the user's preset comfort preference parameters, and predicts the environmental change trend in the future period of time through a machine learning model; the execution control module is connected with the control interfaces of air conditioners, humidifiers, air purifiers, dehumidifiers and other climate regulation devices, and automatically adjusts the working state and operation mode of each device according to the regulation instructions output by the data processing module; the user interaction module includes a mobile phone App, a smart voice assistant and a local touch screen, and the user can real-time view environmental data, adjust preference settings and obtain energy consumption reports through any interaction mode; the cloud server is used for storing historical environmental data, user preference records and algorithm model parameters, and performing bidirectional data exchange with the data processing module, so as to realize dynamic optimization and remote collaborative control according to user habits, so as to ensure the comfort, stability and energy saving of indoor climate.

[0007] Preferably, the artificial intelligence algorithm of the data processing module includes a multi-dimensional parameter correlation model based on deep neural network, which uses historical data to perform nonlinear fitting and multivariate optimization on temperature, humidity, air quality and other parameters, so as to automatically predict the future environmental change trend under the conditions of external weather change, indoor personnel quantity change, etc., and adjust the operation mode of air conditioner, humidifier or air purifier in advance, so as to avoid the influence of environmental fluctuation on user comfort.

[0008] Preferably, the execution control module is provided with a device priority management mechanism, when it is detected that multiple devices need to be started at the same time, the start-stop sequence and operation power are automatically allocated according to the current energy consumption level, device operation efficiency and user preference, so as to maximize the reduction of energy consumption under the premise of meeting the comfort, and automatically switch to energy saving mode during the peak period of power load.

[0009] Preferably, the user interaction module supports voice-controlled multi-round dialogue mode, can actively feedback the current environmental state and predicted adjustment result after recognizing the user password, and recommend energy-saving operation scheme or comfort improvement suggestion to the user when necessary, and the voice recognition process supports switching between local offline mode and online cloud mode to ensure that the system can still operate normally when the network is unstable.

[0010] Preferably, the air quality sensor in the data acquisition module can detect the concentrations of PM2.5, PM10, formaldehyde, TVOC and other pollutants, and judge the best time for indoor and outdoor air exchange in combination with meteorological information, and start the air purifier or close the ventilation equipment in advance when the air quality deteriorates, to prevent outdoor pollutants from entering the indoor environment.

[0011] Preferably, the cloud server has a model self-adaptive updating function, can perform online training and parameter optimization on the climate adjustment algorithm based on different seasons, climate zones, population structures and other factors, and push the updated algorithm model to the local data processing module to realize intelligent adaptive adjustment across seasons and regions.

[0012] Preferably, the execution control module can be linked with intelligent curtains, intelligent windows, electric ventilation devices and other external environment adjustment devices, and when it is predicted that the sunlight is too strong, the external temperature abnormally rises or falls, it automatically executes the operation of opening and closing the curtains, opening or closing the windows, etc., and forms a collaborative adjustment mechanism with the air conditioner and other devices, thereby reducing the load of a single device.

[0013] Preferably, the data processing module supports self-learning function based on user feedback, and when the user manually adjusts the device under certain environmental conditions, the system records the current temperature, humidity, air quality and other states, and adds the adjustment behavior as a learning sample to the algorithm training data set, so as to automatically execute the user's desired adjustment strategy under similar conditions in the future.

[0014] Preferably, the mobile phone App of the user interaction module has an environmental data visualization interface, which can display the temperature, humidity and air quality change trend in different time periods in the form of curve graph, column graph and heat map, and generate energy-saving statistical report and comfort score to help the user evaluate and adjust the climate control strategy.

[0015] Preferably, the system has an emergency response logic, which will immediately send a push alarm to the user and automatically execute a safety mode when detecting that the indoor temperature, humidity or air quality index exceeds the safety threshold, for example, closing the external ventilation, starting the maximum wind speed purification or humidification mode, until the environmental parameters return to the safety range.

[0016] The beneficial effects of the present application are as follows: (1) The present application realizes accurate prediction and dynamic adjustment of various environmental parameters such as indoor temperature, humidity, air quality, etc. by combining deep neural networks, multi-dimensional parameter correlation modeling and user self-learning mechanism. Compared with traditional climate control systems that rely on real-time feedback from a single sensor for passive adjustment, the present application can generate optimal operation strategies in advance and automatically execute them before external climate conditions, indoor personnel quantity or activity changes, thereby significantly reducing the decline in comfort caused by environmental fluctuations. This predictive adjustment method not only improves response speed and control accuracy, but also reduces energy waste caused by frequent start-stop of equipment, prolonging equipment life. In addition, through linkage control with intelligent curtains, windows and ventilation systems, the present application can make full use of natural ventilation and shading means to reduce the running time of high-energy-consuming equipment such as air conditioners, achieving the dual goals of energy saving and comfort.

[0017] (2) The present application has significant advantages in energy saving and user experience. The built-in device priority management mechanism and emergency response logic in the system can automatically allocate power and switch modes when multiple devices are running or environmental parameters exceed safety thresholds, maximizing energy consumption and peak power load while ensuring user comfort and safety. At the same time, the cloud server has a model self-adaptive updating function, which can continuously optimize the algorithm according to different seasons, regions and living habits, so that the system always maintains the best control effect; the user interaction module uses voice multi-turn dialogue and visual data analysis to enable users to intuitively understand environmental changes and energy consumption, and can adjust personalized preference settings at any time. This comprehensive climate management approach that combines predictive control, energy optimization and intelligent interaction significantly improves the comfort, economy and intelligence level of home life. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0019] The above and other aspects of the present application will now be described by way of example only, with reference to the accompanying drawings in which: Figure 1 is a schematic diagram of the overall control of the present application. DETAILED DESCRIPTION

[0020] In order to better understand the above technical solutions, the following will describe the above technical solutions in detail in combination with the drawings in the specification and specific embodiments.

[0021] As Figure 1As shown, an intelligent AI home climate control device includes a data acquisition module, a data processing module, an execution control module, a user interaction module, and a cloud server. The data acquisition module is provided with various high-precision sensors for real-time acquisition of indoor environmental parameters, including but not limited to temperature, humidity, air quality index (AQI), carbon dioxide concentration, formaldehyde and other harmful gas concentration. At the same time, it can be connected with external weather platforms or local weather sensors to obtain outdoor weather information such as temperature, humidity, wind speed, wind direction, air pollution level and weather change trend. The data processing module is built-in with an artificial intelligence-based climate regulation algorithm. The algorithm combines multi-source environmental data and user-preferred comfort parameters for comparison and analysis, predicts future environmental change trends through a deep learning model, including temperature change curve, humidity change trend and possible air quality fluctuations, and issues regulation instructions in advance before predicting adverse environmental conditions to achieve pre-control.

[0022] The execution control module is connected with the control interface of climate regulation devices such as air conditioners, humidifiers, air purifiers, dehumidifiers, intelligent curtains, ventilation equipment, etc. through wired or wireless connection, and can accurately control the start-stop state, running mode, running power and air volume of each device. When the data processing module outputs regulation instructions, the execution control module will schedule the devices according to the set priority strategy and energy-saving optimization logic to realize the coordinated operation of multiple devices, so as to reduce the overall energy consumption while ensuring indoor comfort. The user interaction module includes a mobile phone App, a smart voice assistant and a local touch screen. Users can view environmental data, adjust preference settings, select running modes (such as comfort mode, energy-saving mode, sleep mode, etc.) and obtain energy consumption reports in real time through any interaction mode. This module also supports voice dialogue interaction, and users can quickly complete temperature and humidity adjustment, air purifier start-stop and other operations through natural language instructions.

[0023] The cloud server is used for centralized storage of historical environmental data, user preference records and algorithm model parameters, and bidirectional data exchange with the data processing module through high-speed network to realize online upgrade and parameter optimization of the algorithm. The cloud server can train and optimize the climate regulation algorithm based on different seasons, climate regions and user living habits, and distribute the optimized model to the local data processing module, so that it has self-adaptation ability and cross-scene applicability. Through the coordinated cooperation of data acquisition, intelligent analysis, precise execution and humanized interaction, the device can realize automatic, personalized and energy-saving control of indoor climate, thereby providing a comfortable, healthy and low-energy consumption home environment management solution for users.

[0024] The artificial intelligence algorithm of the data processing module includes a multi-dimensional parameter correlation model based on a deep neural network, which can comprehensively process indoor and outdoor multi-source environmental information, including temperature, humidity, air quality, carbon dioxide concentration, and external weather data. By calling historical environmental data for feature extraction and correlation analysis, nonlinear fitting between multiple parameters and multivariate optimization calculation are realized. During operation, the model dynamically compares real-time collected data and user comfort preferences, predicts future environmental change trends such as temperature and humidity fluctuations, air quality deterioration or improvement, and generates adjustment strategies in advance. For example, when predicting a sudden drop in external temperature, the system can start the heating mode of the air conditioner in advance before the room temperature drops, and start the humidifier in advance before the humidity decreases, thereby reducing the discomfort caused by environmental changes. The algorithm can also adapt to different family structures and device configurations, improve prediction accuracy through continuous learning and parameter optimization, and avoid environmental control delays or comfort decreases caused by external climate changes or indoor personnel fluctuations.

[0025] The execution control module is provided with a device priority management mechanism for reasonable scheduling when multiple climate adjustment devices need to be started simultaneously. When the system detects concurrent operation requirements of air conditioners, humidifiers, air purifiers, and other devices, it first calculates the current overall energy consumption level, the running efficiency of each device, and the user's current comfort level requirement, and then dynamically allocates the start-stop sequence and running power of each device. For example, when the power supply pressure is high or during the peak power consumption period, the system will preferentially start devices with small power and significant impact on comfort, such as air purifiers or humidifiers, and limit the running power of the air conditioner to the energy-saving range. When in a non-peak period and the energy consumption budget is sufficient, multiple devices can be run simultaneously to achieve higher comfort. In addition, this mechanism can also be combined with the user's preset energy-saving mode, economic mode, and other running strategies to maximize energy consumption under the premise of ensuring that the indoor environment meets the basic comfort standard, thereby achieving a dynamic balance between economy and comfort.

[0026] The user interaction module supports voice-controlled multi-round dialogue mode, enabling continuous semantic interaction with the user after recognizing the user password. After the system receives a voice instruction, it will first feedback the current indoor and outdoor environmental status, including temperature, humidity, air quality, and energy consumption, and provide a preview of the upcoming adjustment strategy based on historical data and AI prediction results, such as "will reduce room temperature by 2℃ in 15 minutes and start air purification mode". When the user raises new demands, the system can confirm the details through multi-round dialogue, such as adjustment amplitude, duration, or priority. In addition, the voice recognition function supports local offline mode and cloud online mode switching. In the case of unstable network connection or network interruption, the local mode can guarantee the availability of basic voice control functions, while in the case of good network, the cloud mode can provide higher recognition accuracy and richer semantic analysis capabilities, thereby improving the flexibility and intelligence level of user interaction.

[0027] The air quality sensor in the data acquisition module can detect the concentration of various air pollutants, including PM2.5, PM10, formaldehyde, TVOC, etc., and comprehensively judge the best time for indoor and outdoor air exchange in combination with outdoor weather information. The system can monitor the air quality trend in real time and use AI prediction model to judge the air pollution possibility in the future. When detecting the outdoor air quality deterioration trend, such as rapid rise of PM2.5 concentration, the system will close the ventilation equipment or automatically close the smart window in advance to prevent polluted air from entering the indoor; when the indoor air quality decreases and the outdoor air quality is better, the system will open the ventilation or air purifier in time to quickly improve the indoor environment. This function not only guarantees the user's respiratory health, but also reduces the energy waste caused by long-time operation of the air purifier, achieving efficient and energy-saving air quality management through accurate judgment and dynamic scheduling.

[0028] The cloud server has a model self-adaptive updating function, which can continuously optimize the local climate regulation algorithm based on different seasons, climate zones, population structure, and family usage habits, etc. The server can periodically or when detecting specific environmental changes, perform deep analysis and modeling training on the stored historical data, generate control strategies that better meet the current environment and user needs, and push the updated model parameters to the local data processing module, ensuring that the system can still maintain high-precision prediction and high-efficiency control under changing environmental conditions. In addition, this updating mechanism also supports cross-regional adaptation, and users can obtain the best climate control effect without reconfiguration when moving to different residences or seasons. Through the bidirectional data interaction between the cloud and the local, global data resource sharing and personalized control strategy are organically combined, thereby significantly improving the intelligence level and application range of the system.

[0029] The execution control module can be linked with external environment regulation devices such as smart curtains, smart windows, electric ventilation devices, etc., to realize comprehensive utilization of climate control and natural conditions. When it is predicted that the room temperature may rise due to high sunlight intensity, the system can automatically close the curtains or adjust the angle in advance to reduce solar radiation heat; when it is detected that the external temperature is suitable and the air quality is good, the system can automatically open the windows or ventilation devices, use natural ventilation to replace air conditioning operation, and reduce energy consumption. In extreme weather conditions, such as sudden drop in external temperature or strong wind, the system will immediately close the windows, adjust the curtain position and cooperate with the air conditioning operation to prevent the indoor temperature and humidity from fluctuating too much. Through the coordinated control of external environment regulation devices, the module can effectively reduce the operating load of a single device, prolong the service life of the device, and further optimize the energy utilization efficiency.

[0030] The data processing module supports self-learning function based on user feedback. When the user manually adjusts a device under certain environmental conditions, the system will automatically record the temperature, humidity, air quality and time at that time, and mark this adjustment behavior as the user's preference instruction. The system will include these records in the machine learning training data set to continuously optimize the climate regulation algorithm, so that it can automatically execute the user's desired adjustment strategy when similar environmental conditions occur in the future. This learning mechanism not only applies to the operation of a single device, but also can be used for optimization of multi-device collaborative control, so that the system gradually forms a personalized operation mode that highly meets the user's living habits in the long-term use, thereby continuously providing a comfortable and energy-saving home climate control experience without frequent manual intervention.

[0031] The mobile phone App of the user interaction module has an environmental data visualization function, which can visually display the indoor temperature, humidity and air quality change trend in different time periods through various forms such as curve graph, column graph and heat map, and can be compared and analyzed with outdoor environmental data to help users understand the reasons for environmental changes. The App can also generate periodic energy saving statistical reports to show the running time, energy consumption ratio and energy saving effect evaluation of each device, and provide comfort score to help users evaluate the system control effect. Users can adjust personal preference settings, optimize operation mode, or choose more efficient energy saving strategies according to these analysis results. In addition, the visualization function is deeply integrated with remote control, voice command and other interaction methods, so that users can conveniently control the home climate state at any time and in any place.

[0032] The system has emergency response logic that will trigger an alarm mechanism immediately when the indoor temperature, humidity or air quality index exceeds the safety threshold, sending real-time alarm information to the user through the mobile phone App, voice assistant or local touch screen, and simultaneously starting the corresponding safety mode. For example, when the air quality deteriorates sharply, the system will immediately close the external ventilation port and switch the air purifier to maximum wind speed operation; when the room temperature rises to a dangerous level, the air conditioning cooling mode will be started and the ventilation will be turned on to quickly cool down; when the humidity is too low or too high, the humidifier or dehumidifier will be started for rapid adjustment. This response mechanism can continue to operate until the environmental parameters return to the safe range, and relevant data is continuously monitored and recorded during the process for post-analysis and strategy optimization.

[0033] In the working process of the present application, for example, in the hot summer afternoon, the external temperature continues to rise, and the meteorological monitoring data predicts that there will be a short period of high temperature accompanied by mild air pollution from 2 pm to 5 pm. The intelligent AI home climate control device first acquires indoor parameters such as indoor temperature (27.5℃), humidity (62%), air quality index (AQI 58), carbon dioxide concentration (650 ppm) in real time through the data acquisition module, and simultaneously obtains outdoor temperature (33℃), humidity (55%), wind speed (2 m / s) and air pollution level prediction information from the cloud weather interface.

[0034] The data processing module calls a multi-dimensional parameter correlation model based on deep neural network to compare the current environmental data with the user's comfort preference (temperature 24℃, humidity 50%, air quality excellent), and combines the weather change trend to predict that the indoor temperature will rise to 29℃, the humidity will drop to 45%, and the AQI will rise to 85 in the next two hours. The system determines that this change will cause the user's comfort to decrease and may bring the risk of air pollution invasion, so it generates a control strategy in advance: moderately increase the air conditioning cooling power to medium-high grade; delay the start of the humidifier before the humidity drops; close the fresh air system before the AQI rises, start the air purifier and adjust to high efficiency mode.

[0035] After receiving the adjustment instructions, the execution control module adjusts the equipment operation in turn: the air conditioner enters the energy-saving cooling mode and maintains uniform air supply, the air purifier automatically switches to high wind volume 30 minutes before the AQI rises; the fresh air system is closed simultaneously to prevent outdoor pollutants from entering. At the same time, the smart curtain automatically closes half according to the external light intensity, reducing the additional heat load caused by direct sunlight.

[0036] Throughout the process, the user interaction module pushes real-time environmental status and adjustment schemes to the user via the mobile app, and provides an estimated energy saving percentage (about 15%). The user can enjoy automatic, advanced response, and energy-saving optimized indoor climate control without manual intervention. When the external temperature drops in the evening and the AQI returns to a good level, the system will automatically reduce the air conditioning power, turn off the air purifier high-efficiency mode, and restart the fresh air system when conditions are suitable, achieving a balance between comfort and energy efficiency.

[0037] Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not to be limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein. Although one or more example embodiments of the present disclosure have been described with reference to the accompanying drawings, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.

Claims

1. A smart AI home climate control device, characterized in that: It includes a data acquisition module, a data processing module, an execution control module, a user interaction module, and a cloud server; The data acquisition module is used to collect indoor environmental parameters in real time, including temperature, humidity, air quality index, carbon dioxide concentration, and outdoor weather information; the data processing module has a built-in artificial intelligence-based climate regulation algorithm, which compares and analyzes the collected environmental parameters with the user's preset comfort preference parameters, and uses a machine learning model to predict the environmental change trend in the future. The execution control module is connected to the control interfaces of climate control devices such as air conditioners, humidifiers, air purifiers, and dehumidifiers, and automatically adjusts the working status and operating mode of each device according to the adjustment instructions output by the data processing module. The user interaction module includes a mobile app, a smart voice assistant, and a local touch screen, allowing users to view environmental data, adjust preference settings, and obtain energy consumption reports in real time through any interactive method. The cloud server is used to store historical environmental data, user preference records, and algorithm model parameters, and performs bidirectional data exchange with the data processing module, thereby realizing dynamic optimization and remote collaborative control based on user habits to ensure the comfort, stability, and energy efficiency of the indoor climate.

2. The intelligent AI home climate control device according to claim 1, characterized in that: The artificial intelligence algorithm of the data processing module includes a multi-dimensional parameter correlation model based on deep neural networks. The model uses historical data to perform nonlinear fitting and multivariate optimization on parameters such as temperature, humidity, and air quality, so as to automatically predict future environmental change trends under conditions such as changes in external weather and changes in the number of people indoors, and adjust the operating mode of air conditioners, humidifiers or air purifiers in advance, thereby avoiding the impact of environmental fluctuations on user comfort.

3. The intelligent AI home climate control device according to claim 1, characterized in that: The execution control module is equipped with a device priority management mechanism. When multiple devices need to be started at the same time, it automatically allocates the start-stop sequence and operating power according to the current energy consumption level, device operating efficiency and user preferences, so as to minimize energy consumption while meeting comfort requirements, and automatically switch to energy-saving mode during peak power load periods.

4. The intelligent AI home climate control device according to claim 1, characterized in that: The user interaction module supports multi-turn dialogue mode controlled by voice. After recognizing the user's command, it can proactively provide feedback on the current environmental status and predict adjustment results, and recommend energy-saving operation schemes or comfort improvement suggestions to the user when necessary. The voice recognition process supports switching between local offline mode and cloud online mode to ensure that the system can still operate normally when the network is unstable.

5. The intelligent AI home climate control device according to claim 1, characterized in that: The air quality sensor in the data acquisition module can detect the concentration of various pollutants such as PM2.5, PM10, formaldehyde, and TVOC. Combined with meteorological information, it can determine the best time for indoor and outdoor air exchange and start the air purifier or turn off the ventilation equipment in advance when the air quality deteriorates, so as to prevent outdoor pollutants from entering the indoor environment.

6. The intelligent AI home climate control device according to claim 1, characterized in that: The cloud server has a model adaptive update function, which can train and optimize the climate regulation algorithm online based on factors such as different seasons, climate zones, and population structure, and push the updated algorithm model to the local data processing module to achieve intelligent adaptive regulation across seasons and regions.

7. The intelligent AI home climate control device according to claim 1, characterized in that: The execution control module can be linked with external environment regulation devices such as smart curtains, smart windows, and electric ventilation devices. When it is predicted that the sunlight is too strong or the external temperature rises or falls abnormally, it will automatically perform operations such as opening and closing curtains and opening or closing windows, forming a coordinated regulation mechanism with equipment such as air conditioners, thereby reducing the load on individual devices.

8. The intelligent AI home climate control device according to claim 1, characterized in that: The data processing module supports a self-learning function based on user feedback. When a user manually adjusts the device under certain environmental conditions, the system records the current temperature, humidity, air quality, and other conditions, and adds the adjustment behavior as a learning sample to the algorithm training dataset so that the system can automatically execute the user's desired adjustment strategy under similar conditions in the future.

9. The intelligent AI home climate control device according to claim 1, characterized in that: The user interaction module's mobile app has an environmental data visualization interface that can display the trends of temperature, humidity, and air quality changes over different time periods in various ways, such as curves, bar charts, and heat maps. It also generates energy-saving statistical reports and comfort scores to help users evaluate and adjust climate control strategies.

10. The intelligent AI home climate control device according to claim 1, characterized in that: The control device has emergency response logic. When it detects that the indoor temperature, humidity or air quality index exceeds the safety threshold, it will immediately send a push alarm to the user and automatically execute a safety mode, such as turning off external ventilation, turning on the maximum fan speed purification or humidification mode, until the environmental parameters return to a safe range.

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