Whole house intelligent scene adaptive control method and system based on artificial intelligence

Abstract

The invention relates to the technical field of smart home, in particular to a whole-house intelligent scene self-adaptive control method and system based on artificial intelligence. Environment data and user behavior data of a whole house are collected in real time; analyzing the collected environment data and user behavior data, and identifying an environment change trend and a user behavior mode; according to the environment change trend and the user behavior mode, a corresponding intelligent scene is generated, and operation parameters corresponding to the intelligent scene are executed; when the operation parameters corresponding to the intelligent scene are executed, user feedback data are received, and the operation parameters corresponding to the intelligent scene are dynamically adjusted according to the user feedback data to realize intelligent scene adaptive control, so that the operation state of the home equipment can be automatically adjusted according to environment changes and user behaviors; self-adaptive control of a whole house intelligent scene is realized, personalized scene setting is provided, and user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to an artificial intelligence-based whole-house smart scene adaptive control method and system. Background Art

[0002] With the rapid development of the Internet of Things (IoT) and artificial intelligence (AI) technologies, smart home systems are becoming increasingly popular. Existing smart home systems typically rely on preset scene modes, requiring users to manually select or adjust scene settings. These systems are unable to automatically adapt to environmental changes and user behavior. These systems lack adaptive capabilities, failing to meet personalized user needs. Furthermore, their operation is complex and results in a poor user experience. For example, these systems control device operation based on preset schedules or sensor triggers, automatically turning lights on and off based on time or adjusting air conditioning based on temperature. However, these systems lack a deep understanding of user behavior and environmental changes, preventing them from achieving truly adaptive control. Summary of the Invention

[0003] In view of this, the object of the present invention is to provide an artificial intelligence-based whole-house intelligent scene adaptive control method and system.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: An artificial intelligence-based whole-house intelligent scene adaptive control method includes the following steps: S1, real-time collection of whole-house environmental data and user behavior data; S2. Analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; S3. Generate a corresponding smart scene based on environmental change trends and user behavior patterns, and execute operating parameters corresponding to the smart scene; S4. When executing the operating parameters corresponding to the smart scene, receive user feedback data, and dynamically adjust the operating parameters corresponding to the smart scene according to the user feedback data to achieve adaptive control of the smart scene.

[0005] Furthermore, the smart scenes in step S3 include waking up scene, leaving home scene, returning home scene, sleeping scene, watching movie scene, reading scene, security scene, energy saving scene and air purification scene; When the time reaches 7:00 a.m. and the light intensity is less than 200 lux and human activity is detected, it is determined as a wake-up scene. The curtains are controlled to open 50%, the light brightness is adjusted to 30%, preset soothing music is played, and the air conditioning temperature is adjusted to 24°C. When the door and window magnetic sensors detect that the door is closed and the human infrared sensor detects no activity for more than 5 minutes, it is determined as a leaving scene, and all lights and air conditioners are turned off, and security mode is activated; When the door and window magnetic sensors detect that the door is open and the human infrared sensor detects human activity, it is determined to be a homecoming scene, and the light brightness is controlled to 50%, the air conditioning temperature is adjusted to 26°C, a preset welcome voice is played, and the curtains are closed; When the time reaches 10 pm and the light intensity is less than 50 lux and a person is detected to be stationary, it is determined to be a sleeping scene. The light brightness is controlled to 10%, the air conditioning temperature is adjusted to 26°C, the curtains are closed, and the preset sleep-inducing music is played; When a voice command is received and the content of the voice command is to turn on the viewing mode, or when the TV is detected to be on, it is determined to be a viewing scene, and the light brightness is controlled to 20%, the air conditioning temperature is adjusted to 24℃, the curtains are closed, and the background music is turned off; When a voice command is received and the content of the voice command is to turn on the reading mode, or when it is detected that the user has been sitting at the desk for more than 5 minutes, it is determined to be a reading scene, and the light brightness is controlled to 80%, the air conditioning temperature is adjusted to 26℃, the curtains are opened 50%, and preset light music is played; When the door or window magnetic sensor detects abnormal opening or the security camera detects movement, it is determined to be a security scene, and an alarm sound is played, the light flashes, the video is started, and an alarm message is sent to the user's mobile terminal; When it is detected that no one is in the room for more than 30 minutes and the air conditioner or lighting equipment is on, it is determined to be an energy-saving scenario, and the air conditioner, lighting equipment, curtains are closed, and the room enters low power consumption mode; When the air quality sensor detects PM2.5 > 35µg / m³ or CO2 > 1000ppm, it is determined to be an air purification scenario, and the air purifier and fresh air system are started, and a notification message is sent to the user's mobile terminal.

[0006] Furthermore, step S3 is specifically as follows: S31. Preset a database, wherein the database stores a plurality of different preset scenarios and operating parameters corresponding to the preset scenarios; S32, receiving a preset scene selected by the user from a database; S33. Generate a corresponding smart scene based on the environmental change trend, user behavior pattern and the selected preset scene, and execute the operating parameters corresponding to the smart scene.

[0007] Furthermore, step S2 is specifically as follows: Artificial intelligence algorithms are used to analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns.

[0008] An artificial intelligence-based whole-house intelligent scene adaptive control system includes a processor and a memory, wherein the memory stores a program or instruction. When the program or instruction is executed by the processor, the following steps are implemented: S1, real-time collection of whole-house environmental data and user behavior data; S2. Analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; S3. Generate a corresponding smart scene based on environmental change trends and user behavior patterns, and execute operating parameters corresponding to the smart scene; S4. When executing the operating parameters corresponding to the smart scene, receive user feedback data, and dynamically adjust the operating parameters corresponding to the smart scene according to the user feedback data to achieve adaptive control of the smart scene.

[0009] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: the smart scenes in step S3 include waking up scene, leaving home scene, returning home scene, sleeping scene, watching movie scene, reading scene, security scene, energy saving scene and air purification scene; When the time reaches 7:00 a.m. and the light intensity is less than 200 lux and human activity is detected, it is determined as a wake-up scene. The curtains are controlled to open 50%, the light brightness is adjusted to 30%, preset soothing music is played, and the air conditioning temperature is adjusted to 24°C. When the door and window magnetic sensors detect that the door is closed and the human infrared sensor detects no activity for more than 5 minutes, it is determined as a leaving scene, and all lights and air conditioners are turned off, and security mode is activated; When the door and window magnetic sensors detect that the door is open and the human infrared sensor detects human activity, it is determined to be a homecoming scene, and the light brightness is controlled to 50%, the air conditioning temperature is adjusted to 26°C, a preset welcome voice is played, and the curtains are closed; When the time reaches 10 pm and the light intensity is less than 50 lux and a person is detected to be stationary, it is determined to be a sleeping scene. The light brightness is controlled to 10%, the air conditioning temperature is adjusted to 26°C, the curtains are closed, and the preset sleep-inducing music is played; When a voice command is received and the content of the voice command is to turn on the viewing mode, or when the TV is detected to be on, it is determined to be a viewing scene, and the light brightness is controlled to 20%, the air conditioning temperature is adjusted to 24℃, the curtains are closed, and the background music is turned off; When a voice command is received and the content of the voice command is to turn on the reading mode, or when it is detected that the user has been sitting at the desk for more than 5 minutes, it is determined to be a reading scene, and the light brightness is controlled to 80%, the air conditioning temperature is adjusted to 26℃, the curtains are opened 50%, and preset light music is played; When the door or window magnetic sensor detects abnormal opening or the security camera detects movement, it is determined to be a security scene, and an alarm sound is played, the light flashes, the video is started, and an alarm message is sent to the user's mobile terminal; When it is detected that no one is in the room for more than 30 minutes and the air conditioner or lighting equipment is on, it is determined to be an energy-saving scenario, and the air conditioner, lighting equipment, curtains are closed, and the room enters low power consumption mode; When the air quality sensor detects PM2.5 > 35µg / m³ or CO2 > 1000ppm, it is determined to be an air purification scenario, and the air purifier and fresh air system are started, and a notification message is sent to the user's mobile terminal.

[0010] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: S31. Preset a database, wherein the database stores a plurality of different preset scenarios and operating parameters corresponding to the preset scenarios; S32, receiving a preset scene selected by the user from a database; S33. Generate a corresponding smart scene based on the environmental change trend, user behavior pattern and the selected preset scene, and execute the operating parameters corresponding to the smart scene.

[0011] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: Artificial intelligence algorithms are used to analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns.

[0012] The beneficial effects of the present invention are: The present invention provides an artificial intelligence-based adaptive control method and system for whole-house smart scenes, which collects environmental data and user behavior data of the whole house in real time; analyzes the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; generates corresponding smart scenes according to environmental change trends and user behavior patterns, and executes operating parameters corresponding to the smart scenes; when executing the operating parameters corresponding to the smart scenes, receives user feedback data, and dynamically adjusts the operating parameters corresponding to the smart scenes according to the user feedback data to achieve adaptive control of smart scenes, thereby automatically adjusting the operating status of home appliances according to environmental changes and user behavior, achieving adaptive control of whole-house smart scenes, and providing personalized scene settings to enhance user experience; automatically adjusts the operating status of equipment according to actual needs, reduces energy waste, and supports the access of multiple home appliances, which is easy to expand and upgrade. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1Shown is a flowchart of the steps of an artificial intelligence-based whole-house intelligent scene adaptive control method of the present invention; Figure 2 The figure shows a structural block diagram of an artificial intelligence-based whole-house intelligent scene adaptive control system of the present invention; Description of Figure Numbers: 1-Processor; 2-Memory. DETAILED DESCRIPTION

[0014] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: like Figure 1 As shown, the present invention provides an artificial intelligence-based whole-house intelligent scene adaptive control method, comprising the following steps: S1, real-time collection of whole-house environmental data and user behavior data; S2. Analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; S3. Generate a corresponding smart scene based on environmental change trends and user behavior patterns, and execute operating parameters corresponding to the smart scene; S4. When executing the operating parameters corresponding to the smart scene, receive user feedback data, and dynamically adjust the operating parameters corresponding to the smart scene according to the user feedback data to achieve adaptive control of the smart scene.

[0015] From the above description, it can be seen that the present invention has the following beneficial effects: The present invention provides an artificial intelligence-based adaptive control method for whole-house smart scenes, which collects environmental data and user behavior data of the whole house in real time; analyzes the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; generates corresponding smart scenes according to the environmental change trends and user behavior patterns, and executes operating parameters corresponding to the smart scenes; when executing the operating parameters corresponding to the smart scenes, receives user feedback data, and dynamically adjusts the operating parameters corresponding to the smart scenes according to the user feedback data to achieve adaptive control of smart scenes, thereby achieving automatic adjustment of the operating status of home appliances according to environmental changes and user behavior, achieving adaptive control of whole-house smart scenes, and providing personalized scene settings to enhance user experience; automatically adjusts the operating status of equipment according to actual needs, reduces energy waste, and supports the access of multiple home appliances, which is easy to expand and upgrade.

[0016] Furthermore, the smart scenes in step S3 include waking up scene, leaving home scene, returning home scene, sleeping scene, watching movie scene, reading scene, security scene, energy saving scene and air purification scene; When the time reaches 7:00 a.m. and the light intensity is less than 200 lux and human activity is detected, it is determined as a wake-up scene. The curtains are controlled to open 50%, the light brightness is adjusted to 30%, preset soothing music is played, and the air conditioning temperature is adjusted to 24°C. When the door and window magnetic sensors detect that the door is closed and the human infrared sensor detects no activity for more than 5 minutes, it is determined as a leaving scene, and all lights and air conditioners are turned off, and security mode is activated; When the door and window magnetic sensors detect that the door is open and the human infrared sensor detects human activity, it is determined to be a homecoming scene, and the light brightness is controlled to 50%, the air conditioning temperature is adjusted to 26°C, a preset welcome voice is played, and the curtains are closed; When the time reaches 10 pm and the light intensity is less than 50 lux and a person is detected to be stationary, it is determined to be a sleeping scene. The light brightness is controlled to 10%, the air conditioning temperature is adjusted to 26°C, the curtains are closed, and the preset sleep-inducing music is played; When a voice command is received and the content of the voice command is to turn on the viewing mode, or when the TV is detected to be on, it is determined to be a viewing scene, and the light brightness is controlled to 20%, the air conditioning temperature is adjusted to 24℃, the curtains are closed, and the background music is turned off; When a voice command is received and the content of the voice command is to turn on the reading mode, or when it is detected that the user has been sitting at the desk for more than 5 minutes, it is determined to be a reading scene, and the light brightness is controlled to 80%, the air conditioning temperature is adjusted to 26℃, the curtains are opened 50%, and preset light music is played; When the door or window magnetic sensor detects abnormal opening or the security camera detects movement, it is determined to be a security scene, and an alarm sound is played, the light flashes, the video is started, and an alarm message is sent to the user's mobile terminal; When it is detected that no one is in the room for more than 30 minutes and the air conditioner or lighting equipment is on, it is determined to be an energy-saving scenario, and the air conditioner, lighting equipment, curtains are closed, and the room enters low power consumption mode; When the air quality sensor detects PM2.5 > 35µg / m³ or CO2 > 1000ppm, it is determined to be an air purification scenario, and the air purifier and fresh air system are started, and a notification message is sent to the user's mobile terminal.

[0017] From the above description, it can be seen that through the above specific judgment method, a variety of scenes can be identified, including getting up scene, leaving home scene, returning home scene, sleeping scene, watching movie scene, reading scene, security scene, energy-saving scene and air purification scene, and the corresponding operating parameters can be automatically executed according to the identified scenes, that is, the operating status of home appliances can be automatically adjusted to improve the level of intelligence.

[0018] Furthermore, step S3 is specifically as follows: S31. Preset a database, wherein the database stores a plurality of different preset scenarios and operating parameters corresponding to the preset scenarios; S32, receiving a preset scene selected by the user from a database; S33. Generate a corresponding smart scene based on the environmental change trend, user behavior pattern and the selected preset scene, and execute the operating parameters corresponding to the smart scene.

[0019] From the above description, it can be seen that users can select the scenes that need to be configured from the database, and then optimize the selected scenes according to environmental change trends and user behavior patterns to generate corresponding smart scenes and realize customized configuration.

[0020] Furthermore, step S2 is specifically as follows: Artificial intelligence algorithms are used to analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns.

[0021] From the above description, we can see that using artificial intelligence algorithms to analyze data can have self-learning capabilities and improve the degree of customization.

[0022] See Figure 2 The present invention provides an artificial intelligence-based whole-house intelligent scene adaptive control system, including a processor and a memory, wherein the memory stores a program or instruction, and when the program or instruction is executed by the processor, the following steps are implemented: S1, real-time collection of whole-house environmental data and user behavior data; S2. Analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; S3. Generate a corresponding smart scene based on environmental change trends and user behavior patterns, and execute operating parameters corresponding to the smart scene; S4. When executing the operating parameters corresponding to the smart scene, receive user feedback data, and dynamically adjust the operating parameters corresponding to the smart scene according to the user feedback data to achieve adaptive control of the smart scene.

[0023] From the above description, it can be seen that the present invention has the following beneficial effects: The present invention provides an artificial intelligence-based whole-house smart scene adaptive control system, which collects environmental data and user behavior data of the whole house in real time; analyzes the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; generates corresponding smart scenes according to environmental change trends and user behavior patterns, and executes operating parameters corresponding to the smart scenes; when executing the operating parameters corresponding to the smart scenes, receives user feedback data, and dynamically adjusts the operating parameters corresponding to the smart scenes according to the user feedback data to achieve smart scene adaptive control, thereby automatically adjusting the operating status of home appliances according to environmental changes and user behavior, achieving adaptive control of the whole-house smart scene, and providing personalized scene settings to enhance user experience; automatically adjusts the operating status of equipment according to actual needs, reduces energy waste, and supports the access of multiple home appliances, which is easy to expand and upgrade.

[0024] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: the smart scenes in step S3 include waking up scene, leaving home scene, returning home scene, sleeping scene, watching movie scene, reading scene, security scene, energy saving scene and air purification scene; When the time reaches 7:00 a.m. and the light intensity is less than 200 lux and human activity is detected, it is determined as a wake-up scene. The curtains are controlled to open 50%, the light brightness is adjusted to 30%, preset soothing music is played, and the air conditioning temperature is adjusted to 24°C. When the door and window magnetic sensors detect that the door is closed and the human infrared sensor detects no activity for more than 5 minutes, it is determined as a leaving scene, and all lights and air conditioners are turned off, and security mode is activated; When the door and window magnetic sensors detect that the door is open and the human infrared sensor detects human activity, it is determined to be a homecoming scene, and the light brightness is controlled to 50%, the air conditioning temperature is adjusted to 26°C, a preset welcome voice is played, and the curtains are closed; When the time reaches 10 pm and the light intensity is less than 50 lux and a person is detected to be stationary, it is determined to be a sleeping scene. The light brightness is controlled to 10%, the air conditioning temperature is adjusted to 26°C, the curtains are closed, and the preset sleep-inducing music is played; When a voice command is received and the content of the voice command is to turn on the viewing mode, or when the TV is detected to be on, it is determined to be a viewing scene, and the light brightness is controlled to 20%, the air conditioning temperature is adjusted to 24℃, the curtains are closed, and the background music is turned off; When a voice command is received and the content of the voice command is to turn on the reading mode, or when it is detected that the user has been sitting at the desk for more than 5 minutes, it is determined to be a reading scene, and the light brightness is controlled to 80%, the air conditioning temperature is adjusted to 26℃, the curtains are opened 50%, and preset light music is played; When the door or window magnetic sensor detects abnormal opening or the security camera detects movement, it is determined to be a security scene, and an alarm sound is played, the light flashes, the video is started, and an alarm message is sent to the user's mobile terminal; When it is detected that no one is in the room for more than 30 minutes and the air conditioner or lighting equipment is on, it is determined to be an energy-saving scenario, and the air conditioner, lighting equipment, curtains are closed, and the room enters low power consumption mode; When the air quality sensor detects PM2.5 > 35µg / m³ or CO2 > 1000ppm, it is determined to be an air purification scenario, and the air purifier and fresh air system are started, and a notification message is sent to the user's mobile terminal.

[0025] From the above description, it can be seen that through the above specific judgment method, a variety of scenes can be identified, including getting up scene, leaving home scene, returning home scene, sleeping scene, watching movie scene, reading scene, security scene, energy-saving scene and air purification scene, and the corresponding operating parameters can be automatically executed according to the identified scenes, that is, the operating status of home appliances can be automatically adjusted to improve the level of intelligence.

[0026] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: S31. Preset a database, wherein the database stores a plurality of different preset scenarios and operating parameters corresponding to the preset scenarios; S32, receiving a preset scene selected by the user from a database; S33. Generate a corresponding smart scene based on the environmental change trend, user behavior pattern and the selected preset scene, and execute the operating parameters corresponding to the smart scene.

[0027] From the above description, it can be seen that users can select the scenes that need to be configured from the database, and then optimize the selected scenes according to environmental change trends and user behavior patterns to generate corresponding smart scenes and realize customized configuration.

[0028] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: Artificial intelligence algorithms are used to analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns.

[0029] From the above description, we can see that using artificial intelligence algorithms to analyze data can have self-learning capabilities and improve the degree of customization.

[0030] Several preferred embodiments or application examples are listed below to help those skilled in the art better understand the technical content of the present invention and the technical contribution made by the present invention relative to the prior art: Preferred embodiment one: like Figure 1 As shown, the present invention provides an artificial intelligence-based whole-house intelligent scene adaptive control method, comprising the following steps: S1, real-time collection of whole-house environmental data and user behavior data; S2. Analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; Step S2 is specifically as follows: Artificial intelligence algorithms are used to analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns.

[0031] S3. Generate a corresponding smart scene based on environmental change trends and user behavior patterns, and execute operating parameters corresponding to the smart scene; The smart scenes in step S3 include waking up scene, leaving home scene, returning home scene, sleeping scene, watching movies scene, reading scene, security scene, energy saving scene and air purification scene; When the time reaches 7:00 a.m. and the light intensity is less than 200 lux and human activity is detected, it is determined as a wake-up scene. The curtains are controlled to open 50%, the light brightness is adjusted to 30%, preset soothing music is played, and the air conditioning temperature is adjusted to 24°C. When the door and window magnetic sensors detect that the door is closed and the human infrared sensor detects no activity for more than 5 minutes, it is determined as a leaving scene, and all lights and air conditioners are turned off, and security mode is activated; When the door and window magnetic sensors detect that the door is open and the human infrared sensor detects human activity, it is determined to be a homecoming scene, and the light brightness is controlled to 50%, the air conditioning temperature is adjusted to 26°C, a preset welcome voice is played, and the curtains are closed; When the time reaches 10 pm and the light intensity is less than 50 lux and a person is detected to be stationary, it is determined to be a sleeping scene. The light brightness is controlled to 10%, the air conditioning temperature is adjusted to 26°C, the curtains are closed, and the preset sleep-inducing music is played; When a voice command is received and the content of the voice command is to turn on the viewing mode, or when the TV is detected to be on, it is determined to be a viewing scene, and the light brightness is controlled to 20%, the air conditioning temperature is adjusted to 24℃, the curtains are closed, and the background music is turned off; When a voice command is received and the content of the voice command is to turn on the reading mode, or when it is detected that the user has been sitting at the desk for more than 5 minutes, it is determined to be a reading scene, and the light brightness is controlled to 80%, the air conditioning temperature is adjusted to 26℃, the curtains are opened 50%, and preset light music is played; When the door or window magnetic sensor detects abnormal opening or the security camera detects movement, it is determined to be a security scene, and an alarm sound is played, the light flashes, the video is started, and an alarm message is sent to the user's mobile terminal; When it is detected that no one is in the room for more than 30 minutes and the air conditioner or lighting equipment is on, it is determined to be an energy-saving scenario, and the air conditioner, lighting equipment, curtains are closed, and the room enters low power consumption mode; When the air quality sensor detects PM2.5 > 35µg / m³ or CO2 > 1000ppm, it is determined to be an air purification scenario, and the air purifier and fresh air system are started, and a notification message is sent to the user's mobile terminal.

[0032] Through the above specific judgment method, a variety of scenes can be identified, including getting up scene, leaving home scene, returning home scene, sleeping scene, watching movie scene, reading scene, security scene, energy-saving scene and air purification scene, and the corresponding operating parameters can be automatically executed according to the identified scenes, that is, the operating status of home appliances can be automatically adjusted to improve the level of intelligence.

[0033] Step S3 is specifically as follows: S31. Preset a database, wherein the database stores a plurality of different preset scenarios and operating parameters corresponding to the preset scenarios; S32, receiving a preset scene selected by the user from a database; S33. Generate a corresponding smart scene based on the environmental change trend, user behavior pattern and the selected preset scene, and execute the operating parameters corresponding to the smart scene.

[0034] S4. When executing the operating parameters corresponding to the smart scene, receive user feedback data, and dynamically adjust the operating parameters corresponding to the smart scene according to the user feedback data to achieve adaptive control of the smart scene.

[0035] In this embodiment, the specific scheme is as follows: 1. Data collection: Real-time collection of environmental data and user behavior data through a variety of sensors (such as temperature, humidity, light, human infrared, etc.).

[0036] The equipment and settings used are shown in Table 1: Table 1 2. Data analysis: Use artificial intelligence algorithms (such as machine learning and deep learning) to analyze collected data and identify user behavior patterns and environmental change trends.

[0037] For example, if a user's motion sensor detects human presence at 7:00 a.m. every day and the light intensity is less than 200 lux, indicating motion, the curtain motor will then report a 50% opening, the light brightness will be reported between 25% and 40%, and soothing music will play. Learning will then be automatically recorded based on this habit.

[0038] 3. Scenario prediction: Based on the analysis results, predict the scenario modes that the user may need (such as home mode, sleep mode, entertainment mode, etc.), automatically generate and execute the corresponding scenarios.

[0039] The scenarios that can be generated, scenario trigger conditions, and scenario execution actions are shown in Table 2: Table 2 4. Adaptive control: Automatically adjust the operating status of home appliances such as lights, air conditioners, curtains, audio equipment, etc. based on the prediction results.

[0040] 5. User Feedback: The user interaction module collects user feedback, continuously optimizes AI algorithms, and improves the accuracy of scenario prediction and control. For example, if a user repeatedly adjusts a specific value within 5 minutes of executing an action in a scenario, the system will automatically record the learning. If the user adjusts the action within 5 minutes more than 5 times within 7 days, the system can automatically modify the scenario action to the intermediate option between the multiple user-modified values ​​or the state after multiple adjustments.

[0041] Preferred embodiment 2: See Figure 2 The present invention provides an artificial intelligence-based whole-house intelligent scene adaptive control system, including a processor 1 and a memory 2, wherein the memory 2 stores a program or instruction, and when the program or instruction is executed by the processor 1, the following steps are implemented: S1, real-time collection of whole-house environmental data and user behavior data; S2. Analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; S3. Generate a corresponding smart scene based on environmental change trends and user behavior patterns, and execute operating parameters corresponding to the smart scene; S4. When executing the operating parameters corresponding to the smart scene, receive user feedback data, and dynamically adjust the operating parameters corresponding to the smart scene according to the user feedback data to achieve adaptive control of the smart scene.

[0042] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: the smart scenes in step S3 include waking up scene, leaving home scene, returning home scene, sleeping scene, watching movie scene, reading scene, security scene, energy saving scene and air purification scene; When the time reaches 7:00 a.m. and the light intensity is less than 200 lux and human activity is detected, it is determined as a wake-up scene. The curtains are controlled to open 50%, the light brightness is adjusted to 30%, preset soothing music is played, and the air conditioning temperature is adjusted to 24°C. When the door and window magnetic sensors detect that the door is closed and the human infrared sensor detects no activity for more than 5 minutes, it is determined as a leaving scene, and all lights and air conditioners are turned off, and security mode is activated; When the door and window magnetic sensors detect that the door is open and the human infrared sensor detects human activity, it is determined to be a homecoming scene, and the light brightness is controlled to 50%, the air conditioning temperature is adjusted to 26°C, a preset welcome voice is played, and the curtains are closed; When the time reaches 10 pm and the light intensity is less than 50 lux and a person is detected to be stationary, it is determined to be a sleeping scene. The light brightness is controlled to 10%, the air conditioning temperature is adjusted to 26°C, the curtains are closed, and the preset sleep-inducing music is played; When a voice command is received and the content of the voice command is to turn on the viewing mode, or when the TV is detected to be on, it is determined to be a viewing scene, and the light brightness is controlled to 20%, the air conditioning temperature is adjusted to 24℃, the curtains are closed, and the background music is turned off; When a voice command is received and the content of the voice command is to turn on the reading mode, or when it is detected that the user has been sitting at the desk for more than 5 minutes, it is determined to be a reading scene, and the light brightness is controlled to 80%, the air conditioning temperature is adjusted to 26℃, the curtains are opened 50%, and preset light music is played; When the door or window magnetic sensor detects abnormal opening or the security camera detects movement, it is determined to be a security scene, and an alarm sound is played, the light flashes, the video is started, and an alarm message is sent to the user's mobile terminal; When it is detected that no one is in the room for more than 30 minutes and the air conditioner or lighting equipment is on, it is determined to be an energy-saving scenario, and the air conditioner, lighting equipment, curtains are closed, and the room enters low power consumption mode; When the air quality sensor detects PM2.5 > 35µg / m³ or CO2 > 1000ppm, it is determined to be an air purification scenario, and the air purifier and fresh air system are started, and a notification message is sent to the user's mobile terminal.

[0043] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: S31. Preset a database, wherein the database stores a plurality of different preset scenarios and operating parameters corresponding to the preset scenarios; S32, receiving a preset scene selected by the user from a database; S33. Generate a corresponding smart scene based on the environmental change trend, user behavior pattern and the selected preset scene, and execute the operating parameters corresponding to the smart scene.

[0044] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: Artificial intelligence algorithms are used to analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns.

[0045] The present invention has been described with reference to the above embodiments and accompanying drawings. However, the above embodiments are merely exemplary embodiments of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the present invention. On the contrary, modifications and equivalents falling within the spirit and scope of the claims are intended to be within the scope of the present invention.

Claims

1. A whole-house intelligent scene adaptive control method based on artificial intelligence, characterized in that: The following steps are involved: S1, real-time collection of whole-house environmental data and user behavior data; S2. Analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; S3. Generate a corresponding smart scene based on environmental change trends and user behavior patterns, and execute operating parameters corresponding to the smart scene; S4. When executing the operating parameters corresponding to the smart scene, receive user feedback data, and dynamically adjust the operating parameters corresponding to the smart scene according to the user feedback data to achieve adaptive control of the smart scene.

2. The whole-house intelligent scene adaptive control method based on artificial intelligence according to claim 1 is characterized in that: The smart scenes in step S3 include waking up scene, leaving home scene, returning home scene, sleeping scene, watching movies scene, reading scene, security scene, energy saving scene and air purification scene; When the time reaches 7:00 a.m. and the light intensity is less than 200 lux and human activity is detected, it is determined as a wake-up scene. The curtains are controlled to open 50%, the light brightness is adjusted to 30%, preset soothing music is played, and the air conditioning temperature is adjusted to 24°C. When the door and window magnetic sensors detect that the door is closed and the human infrared sensor detects no activity for more than 5 minutes, it is determined as a leaving scene, and all lights and air conditioners are turned off, and security mode is activated; When the door and window magnetic sensors detect that the door is open and the human infrared sensor detects human activity, it is determined to be a homecoming scene, and the light brightness is controlled to 50%, the air conditioning temperature is adjusted to 26°C, a preset welcome voice is played, and the curtains are closed; When the time reaches 10 pm and the light intensity is less than 50 lux and a person is detected to be stationary, it is determined to be a sleeping scene. The light brightness is controlled to 10%, the air conditioning temperature is adjusted to 26°C, the curtains are closed, and the preset sleep-inducing music is played; When a voice command is received and the content of the voice command is to turn on the viewing mode, or when the TV is detected to be on, it is determined to be a viewing scene, and the light brightness is controlled to 20%, the air conditioning temperature is adjusted to 24℃, the curtains are closed, and the background music is turned off; When a voice command is received and the content of the voice command is to turn on the reading mode, or when it is detected that the user has been sitting at the desk for more than 5 minutes, it is determined to be a reading scene, and the light brightness is controlled to 80%, the air conditioning temperature is adjusted to 26℃, the curtains are opened 50%, and preset light music is played; When the door or window magnetic sensor detects abnormal opening or the security camera detects movement, it is determined to be a security scene, and an alarm sound is played, the light flashes, the video is started, and an alarm message is sent to the user's mobile terminal; When it is detected that no one is in the room for more than 30 minutes and the air conditioner or lighting equipment is on, it is determined to be an energy-saving scenario, and the air conditioner, lighting equipment, curtains are closed, and the room enters low power consumption mode; When the air quality sensor detects PM2.5 > 35µg / m³ or CO2 > 1000ppm, it is determined to be an air purification scenario, and the air purifier and fresh air system are started, and a notification message is sent to the user's mobile terminal.

3. The whole-house intelligent scene adaptive control method based on artificial intelligence according to claim 1 is characterized in that: Step S3 is specifically as follows: S31. Preset a database, wherein the database stores a plurality of different preset scenarios and operating parameters corresponding to the preset scenarios; S32, receiving a preset scene selected by the user from a database; S33. Generate a corresponding smart scene based on the environmental change trend, user behavior pattern and the selected preset scene, and execute the operating parameters corresponding to the smart scene.

4. The method for adaptive control of whole-house intelligent scenes based on artificial intelligence according to claim 1, characterized in that: Step S2 is specifically as follows: Artificial intelligence algorithms are used to analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns.

5. An artificial intelligence-based whole-house intelligent scene adaptive control system, characterized in that: The system comprises a processor and a memory, wherein the memory stores a program or instruction, and when the program or instruction is executed by the processor, the following steps are implemented: S1, real-time collection of whole-house environmental data and user behavior data; S2. Analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns; S3. Generate a corresponding smart scene based on environmental change trends and user behavior patterns, and execute operating parameters corresponding to the smart scene; S4. When executing the operating parameters corresponding to the smart scene, receive user feedback data, and dynamically adjust the operating parameters corresponding to the smart scene according to the user feedback data to achieve adaptive control of the smart scene.

6. The whole-house intelligent scene adaptive control system based on artificial intelligence according to claim 5 is characterized in that: When the program or instruction is executed by the processor, the following steps are implemented: the smart scenes in step S3 include waking up scene, leaving home scene, returning home scene, sleeping scene, watching a movie scene, reading scene, security scene, energy saving scene and air purification scene; When the time reaches 7:00 a.m. and the light intensity is less than 200 lux and human activity is detected, it is determined as a wake-up scene. The curtains are controlled to open 50%, the light brightness is adjusted to 30%, preset soothing music is played, and the air conditioning temperature is adjusted to 24°C. When the door and window magnetic sensors detect that the door is closed and the human infrared sensor detects no activity for more than 5 minutes, it is determined as a leaving scene, and all lights and air conditioners are turned off, and security mode is activated; When the door and window magnetic sensors detect that the door is open and the human infrared sensor detects human activity, it is determined to be a homecoming scene, and the light brightness is controlled to 50%, the air conditioning temperature is adjusted to 26°C, a preset welcome voice is played, and the curtains are closed; When the time reaches 10 pm and the light intensity is less than 50 lux and a person is detected to be stationary, it is determined to be a sleeping scene. The light brightness is controlled to 10%, the air conditioning temperature is adjusted to 26°C, the curtains are closed, and the preset sleep-inducing music is played; When a voice command is received and the content of the voice command is to turn on the viewing mode, or when the TV is detected to be on, it is determined to be a viewing scene, and the light brightness is controlled to 20%, the air conditioning temperature is adjusted to 24℃, the curtains are closed, and the background music is turned off; When a voice command is received and the content of the voice command is to turn on the reading mode, or when it is detected that the user has been sitting at the desk for more than 5 minutes, it is determined to be a reading scene, and the light brightness is controlled to 80%, the air conditioning temperature is adjusted to 26℃, the curtains are opened 50%, and preset light music is played; When the door or window magnetic sensor detects abnormal opening or the security camera detects movement, it is determined to be a security scene, and an alarm sound is played, the light flashes, the video is started, and an alarm message is sent to the user's mobile terminal; When it is detected that no one is in the room for more than 30 minutes and the air conditioner or lighting equipment is on, it is determined to be an energy-saving scenario, and the air conditioner, lighting equipment, curtains are closed, and the room enters low power consumption mode; When the air quality sensor detects PM2.5 > 35µg / m³ or CO2 > 1000ppm, it is determined to be an air purification scenario, and the air purifier and fresh air system are started, and a notification message is sent to the user's mobile terminal.

7. The whole-house intelligent scene adaptive control system based on artificial intelligence according to claim 5 is characterized in that: When the program or instruction is executed by the processor, the following steps are implemented: S31. Preset a database, wherein the database stores a plurality of different preset scenarios and operating parameters corresponding to the preset scenarios; S32, receiving a preset scene selected by the user from a database; S33. Generate a corresponding smart scene based on the environmental change trend, user behavior pattern and the selected preset scene, and execute the operating parameters corresponding to the smart scene.

8. The whole-house intelligent scene adaptive control system based on artificial intelligence according to claim 5 is characterized in that: When the program or instruction is executed by the processor, the following steps are implemented: Artificial intelligence algorithms are used to analyze the collected environmental data and user behavior data to identify environmental change trends and user behavior patterns.

Images