AI-based home smart interaction control system
By incorporating environmental perception, multimodal interaction, and AI decision-making modules, the system intelligently adjusts the brightness and color of lights, solving the problem of limited interaction methods in existing smart home systems and achieving personalized lighting and safety warnings.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- FUJIAN ZHILIAN ALL THINGS TECH CO LTD
- Filing Date
- 2025-10-22
- Publication Date
- 2026-04-24
AI Technical Summary
Existing smart home systems have relatively simple interaction methods and lack the ability to proactively perceive and adaptively adjust to changes in user behavior and environment. They are unable to comprehensively consider multiple factors to provide personalized services, especially in complex scenarios where they cannot accurately judge and adjust lighting parameters in a timely manner to ensure user safety and comfort.
An environmental perception module collects environmental and user information through sensors, a multimodal interaction module processes user commands, a brightness determination module analyzes sound and hazard factors, an AI decision-making module controls the brightness level of the lights based on the brightness factor, and a camera locates and activates spotlights as a warning in dangerous situations.
It enables real-time adjustment of light brightness and color based on user location and environment, providing a personalized lighting experience that is energy-efficient and safe. It can accurately locate and warn in potentially dangerous situations, avoiding energy waste.
Smart Images
Figure CN121143071B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of AI interaction technology, and in particular to an AI-based smart home interactive control system. Background Technology
[0002] With the development of IoT and AI technologies, smart home systems are becoming increasingly popular. However, existing smart home systems have many problems in terms of interaction.
[0003] On the one hand, the interaction methods are relatively simple, relying heavily on user-initiated operations, and lack the ability to proactively perceive and adaptively adjust to changes in user behavior and environment;
[0004] For example, traditional lighting control often requires users to manually turn it on and off or switch through fixed scene modes, and cannot make intelligent adjustments based on the user's real-time location in the room, ambient light, and potential hazards.
[0005] On the other hand, their understanding and ability to handle complex scenarios are limited, making it difficult to comprehensively consider multiple factors to provide personalized services.
[0006] For example, in scenarios with frequent user activity or potential dangers, it may be impossible to accurately judge and adjust parameters such as light brightness and color in a timely manner to ensure user safety and comfort.
[0007] Therefore, there is an urgent need for an AI-based smart home interactive control system that can comprehensively perceive environmental and user information to achieve intelligent interaction, energy saving, and security. Summary of the Invention
[0008] The purpose of this invention is to propose an AI-based smart home interactive control system to solve the above-mentioned problems.
[0009] To achieve the above objectives, the present invention adopts the following technical solution:
[0010] Includes the following parts:
[0011] Environmental sensing module: Collects environmental and user information from various parts of the room through various sensors;
[0012] Multimodal interaction module: Receives user interaction commands and processes them before executing corresponding operations;
[0013] No-command lighting interaction determination module: Determines which lighting fixtures to turn on based on the user's location information, ambient light information, and the lighting fixture's light coverage information in the room;
[0014] Brightness determination module: After analyzing the sound information and influencing object information in the room, a sound assessment coefficient and a danger coefficient are obtained; and after comprehensively analyzing the sound assessment coefficient and the danger coefficient, a brightness coefficient is obtained.
[0015] AI decision-making interaction module: Determines the brightness level of the lamps based on the brightness coefficient and turns on the lamps according to the brightness level.
[0016] Preferably, the environmental perception module specifically includes the following parts:
[0017] The room is evenly divided into a preset number of areas, and a preset number of brightness sensors, human presence sensors, and light color sensors are deployed in each area to monitor the ambient brightness, human position, and existing ambient light color in real time.
[0018] Preferably, the multimodal interaction module specifically includes the following parts:
[0019] The system collects users' voice commands through microphones in various areas of the room, converts the voice into text using a voice recognition algorithm, parses the text to understand the intent of the command, and executes the corresponding operation.
[0020] Using cameras in various areas of the room, the system identifies user gestures based on computer vision technology and performs corresponding operations based on the identified gestures.
[0021] Remotely control room lights via a smart home app.
[0022] Preferably, the step of determining which lights to turn on based on the user's location information, ambient light information, and the light coverage information of the lights specifically includes the following:
[0023] Using the user's two ears as endpoints, connect the two endpoints to form a straight line, and determine the center point of the line; draw a straight line perpendicular to the ground with the center of the line as the origin, and call it the baseline line;
[0024] Construct a cylindrical model with the base line as the height and the preset size as the radius, and determine the area covered by the cylindrical model;
[0025] Obtain the coverage area corresponding to the light from each lamp, match the coverage area corresponding to the light from each lamp with the coverage area covered by the cylindrical model, and obtain the lamp corresponding to the complete filling of the coverage area covered by the cylindrical model, and record it as the predicted lamp.
[0026] It obtains the ambient light intensity within the area covered by the cylindrical model and presets a light intensity threshold. Based on the comparison between the ambient light intensity within the area covered by the cylindrical model and the light intensity threshold, it controls whether the predicted lights are turned on or off.
[0027] Preferably, obtaining the sound evaluation coefficients includes the following parts:
[0028] Acquire the sound data emitted by the user while walking, and extract the maximum and minimum decibels from the sound data; calculate the difference between the maximum and minimum decibels to obtain the decibel range value;
[0029] The time interval between each step a user takes while walking is obtained, and the average of each time interval is calculated to obtain the average interval.
[0030] The distance between each step a user takes while walking is recorded as the behavioral step distance. The average of each behavioral step distance is calculated by accumulating the total steps.
[0031] Extract the maximum behavior step size from each behavior step size, and calculate the step size deviation value by comparing the maximum behavior step size with the mean step size.
[0032] The sound evaluation coefficient is obtained by comprehensively analyzing the decibel range, the mean interval, and the step deviation.
[0033] Preferably, obtaining the risk factor includes the following parts:
[0034] Obtain the area covered by the cylindrical model constructed based on the current user's location, as well as the objects within that area; and denote the area covered by the cylindrical model constructed based on the current user's location as the behavior area, and the objects within the behavior area as the influencing objects;
[0035] Obtain the volume of the object within the action range, and the volume of the action range; then divide the volume of the object within the action range by the volume of the action range to obtain the occupancy ratio;
[0036] The image information of the affected object is acquired. After preprocessing the image information, the outer contour of the affected object is obtained. The angles of each part of the outer contour are extracted from the outer contour of the affected object, and the number of contour parts with acute angles is accumulated to obtain the sharp angle quantity.
[0037] Using the user's two shoulders as endpoints, connect the two endpoints with a straight line and take the midpoint as the observation point. The straight line is called the baseline. Starting from the observation point, draw a straight line perpendicular to the baseline, with the direction of the straight line parallel to the ground, extending towards the user's front. This straight line is called the reference line.
[0038] Approximate the bottom of the affected object as a circle, and after taking its center, record the center of the affected object as the corresponding point;
[0039] Connect the observation point and the corresponding point with a straight line to obtain the line of intersection. Calculate the angle between the line of intersection and the reference line, and record this angle as the corresponding angle.
[0040] The risk factor is obtained by comprehensively processing the occupancy ratio, the amount of sharp corners, and the corresponding angles.
[0041] Preferably, the step of obtaining the luminance coefficient after comprehensively analyzing the sound evaluation coefficient and the danger coefficient specifically includes:
[0042] After normalizing the sound evaluation coefficient and the hazard coefficient, the sound evaluation coefficient and the hazard coefficient are used as the major semi-axis and minor semi-axis of the ellipse, respectively, to construct an elliptical model. The area of the elliptical model is then calculated and denoted as the luminance coefficient.
[0043] Preferably, the three preset threshold value ranges are used, each threshold value range corresponds to a brightness level, and the brightness coefficient is matched with the three preset threshold value ranges to obtain the brightness level corresponding to the brightness coefficient; wherein the brightness level includes level one brightness, level two brightness and warning brightness.
[0044] Preferably, when the brightness level corresponding to the brightness coefficient is Level 1 brightness: the predicted lamp is turned on at a preset power until the user's behavior range is outside the coverage area corresponding to the lamp's light;
[0045] When the brightness coefficient corresponds to a brightness level of level two: increase the power of the luminaire based on a brightness level of level one;
[0046] When the brightness level corresponding to the brightness coefficient is the warning brightness: based on the brightness level of level two, the predicted lights will start at the highest power; and after the location of the affected object is located by the camera, the spotlights will be turned on and the light from the spotlights will shine on the affected object.
[0047] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:
[0048] 1. This invention uses a brightness determination module to analyze sound information and information about influencing objects in a room to calculate a sound assessment coefficient, a danger coefficient, and a brightness coefficient. The AI decision-making interaction module determines whether there is a dangerous situation based on the brightness coefficient. When the brightness coefficient reaches the warning brightness level, the system will not only start the predictive lights at the highest power, but also use a camera to accurately locate the position of the influencing object using the principle of triangulation, and turn on spotlights to mark the influencing object with red flashing warning to remind the user to pay attention to potential dangers.
[0049] 2. This invention uses an environmental sensing module to monitor ambient brightness, human position, and light color in real time. Combined with a commandless lighting interaction determination module and an AI decision-making interaction module, it can intelligently determine the on / off state and brightness level of the lights. During the day, when the living room is bright enough, even if the user issues a light-on command, the system will make a comprehensive judgment to avoid unnecessary energy waste. At night, after the user falls asleep, if no human activity is detected for a period of time, the system will automatically dim or turn off the lights. At the same time, it can intelligently turn on the appropriate lights according to the user's location and ambient light. It can also automatically adjust the light color according to the user's behavior and environmental needs in different rooms. For example, if the ambient light in the study is too cool, the light will be automatically adjusted to a warm yellow. At night, the bedroom lights will be adjusted according to the human body's biological clock, providing users with a personalized lighting experience that is both energy-saving and comfortable. Attached Figure Description
[0050] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:
[0051] Figure 1 This is a flowchart of the present invention. Detailed Implementation
[0052] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.
[0053] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.
[0054] Please see Figure 1 As shown, the present invention provides a technical solution:
[0055] The AI-based smart home interactive control system includes the following components:
[0056] Environmental sensing module: Collects environmental and user information from various parts of the room through various sensors;
[0057] The environmental perception module specifically includes the following parts:
[0058] The room space is evenly divided into a preset number of areas, and a preset number of brightness sensors, human presence sensors, and light color sensors are deployed in each area to monitor the ambient brightness, human position, and existing ambient light color in real time.
[0059] Brightness sensor: Real-time monitoring of ambient brightness; for example, during the day, if the brightness of the living room is higher than the set threshold (such as 500 lux), even if the user issues a command to turn on the lights, the system will combine other factors to determine whether it is really necessary to turn on the lights, so as to prevent unnecessary energy waste; in the evening, as the brightness gradually decreases, the sensor feeds the data back to the system to provide a basis for automatic lighting.
[0060] Human presence sensor: Using infrared or millimeter-wave radar technology, it accurately detects whether there is a person in the room; in the bedroom, after a person falls asleep at night, if no human activity is detected for a period of time (such as 30 minutes), the system automatically dims or turns off the lights to avoid the lights affecting sleep and save energy; when someone is detected entering the room, the lighting control system is immediately activated to prepare to respond to interactive commands;
[0061] Light color sensor: It senses the existing light color in the room to achieve adaptive adjustment of the light color; for example, in a study, if the ambient light is cool-toned, when the user turns on the light, the system automatically adjusts the light to a warm yellow, creating a comfortable reading atmosphere; in the bedroom, at night, according to the human body's biological clock, the light can be gradually adjusted from bright white light to soft warm light, which helps users relax and fall asleep.
[0062] Multimodal interaction module: Receives user interaction commands and processes them before executing corresponding operations;
[0063] The multimodal interaction module specifically includes the following parts:
[0064] The system collects users' voice commands through microphones in various areas of the room, converts the voice into text using a voice recognition algorithm, parses the text to understand the intent of the command, and executes the corresponding operation.
[0065] For example, when a user is busy in the kitchen with their hands covered in grease, they can say "Turn on the kitchen light." The voice recognition algorithm will convert the voice into text, then analyze the command intent to turn on the kitchen light. Personalized wake words can be set for different rooms, such as "Baby, turn on the light" in the children's room, to enhance the fun and sense of personalization.
[0066] Using cameras in various areas of the room, the system identifies user gestures based on computer vision technology and performs corresponding operations based on the identified gestures.
[0067] For example, when watching TV in the living room, users don't need to look for the remote control. They can simply make a fist and then open it in front of the camera, and the camera will recognize the gesture and turn on the living room chandelier. They can also adjust the brightness of the lights by waving their hands. For elderly people or users who are not familiar with operating smart devices, gesture interaction is simple and intuitive, lowering the barrier to entry.
[0068] Remotely control room lights via a smart home app;
[0069] For example, you can turn on the bedroom lights on your way home from get off work to create a warm atmosphere; the app can also set scheduled tasks, such as automatically turning on the bedroom lights at 7 a.m. every morning to simulate natural waking up.
[0070] No-command lighting interaction determination module: Determines which lighting fixtures to turn on based on the user's location information, ambient light information, and the lighting fixture's light coverage information in the room;
[0071] The system determines which lights to turn on based on the user's location information, ambient light information, and the light coverage information of the lights. Specifically, this includes the following:
[0072] Using the user's two ears as endpoints, connect the two endpoints to form a straight line, and determine the center point of the line; draw a straight line perpendicular to the ground with the center of the line as the origin, and call it the baseline line;
[0073] Construct a cylindrical model with the base line as the height and the preset size as the radius, and determine the area covered by the cylindrical model;
[0074] Obtain the coverage area corresponding to the light from each lamp, match the coverage area corresponding to the light from each lamp with the coverage area covered by the cylindrical model, and obtain the lamp corresponding to the complete filling of the coverage area covered by the cylindrical model, and record it as the predicted lamp.
[0075] It also obtains the ambient light intensity within the area covered by the cylindrical model, presets a light intensity threshold, and controls whether the predicted lights are turned on or off based on the comparison between the ambient light intensity within the area covered by the cylindrical model and the light intensity threshold.
[0076] If the ambient light intensity within the area covered by the cylindrical model is lower than the light intensity threshold, the predictive lights will be turned on; otherwise, the predictive lights will remain off.
[0077] Brightness determination module: After analyzing the sound information and influencing object information in the room, a sound assessment coefficient and a danger coefficient are obtained; and after comprehensively analyzing the sound assessment coefficient and the danger coefficient, a brightness coefficient is obtained.
[0078] The acquisition of sound evaluation coefficients includes the following parts:
[0079] Acquire the sound data emitted by the user while walking, and extract the maximum and minimum decibels from the sound data; calculate the difference between the maximum and minimum decibels to obtain the decibel range value;
[0080] The time interval between each step a user takes while walking is obtained, and the average of each time interval is calculated to obtain the average interval.
[0081] The distance between each step a user takes while walking is recorded as the behavioral step distance. The average of each behavioral step distance is calculated by accumulating the total steps.
[0082] Extract the maximum behavior step size from each behavior step size, and calculate the step size deviation value by comparing the maximum behavior step size with the mean step size.
[0083] The sound evaluation coefficient is obtained by comprehensively analyzing the decibel range, the mean interval, and the step deviation.
[0084] The weighting factors for the preset decibel range, interval mean, and step deviation are calculated by multiplying the decibel range, interval mean, and step deviation with their corresponding weighting factors and then summing them to obtain the sound evaluation coefficient.
[0085] The acquisition of the risk factor includes the following parts:
[0086] Obtain the area covered by the cylindrical model constructed based on the current user's location, as well as the objects within that area; and denote the area covered by the cylindrical model constructed based on the current user's location as the behavior range, and the objects within the behavior range as the influencing objects;
[0087] Obtain the volume of the object within the action range, and the volume of the action range; then divide the volume of the object within the action range by the volume of the action range to obtain the occupancy ratio;
[0088] The image information of the affected object is acquired. After preprocessing the image information, the outer contour of the affected object is obtained. The angles of each part of the outer contour are extracted from the outer contour of the affected object, and the number of contour parts with acute angles is accumulated to obtain the sharp angle quantity.
[0089] Using the user's two shoulders as endpoints, connect the two endpoints with a straight line and take the midpoint as the observation point. The straight line is called the baseline. Starting from the observation point, draw a straight line perpendicular to the baseline, with the direction of the straight line parallel to the ground, extending towards the user's front. This straight line is called the reference line.
[0090] Approximate the bottom of the affected object as a circle, and after taking its center, record the center of the affected object as the corresponding point;
[0091] Connect the observation point and the corresponding point with a straight line to obtain the line of intersection. Calculate the angle between the line of intersection and the reference line, and record this angle as the corresponding angle.
[0092] The risk factor is obtained by comprehensively processing the occupancy ratio, the amount of sharp corners, and the corresponding angles.
[0093] After normalizing the occupancy ratio, the amount of the cusp angle, and the corresponding angle, the product of the occupancy ratio and the amount of the cusp angle is used as one leg of the right triangle, and the corresponding angle is used as the other leg of the right triangle. The remaining leg is then connected to form a complete right triangle.
[0094] Obtain the side length of the triangle corresponding to the corresponding angle, take the midpoint as the center of the circle, and use half the value of the corresponding angle as the radius to draw a circle around the center to cut the triangle; calculate the area of the remaining triangle as the risk factor.
[0095] The luminance coefficient is obtained by comprehensively analyzing the sound evaluation coefficient and the hazard coefficient, specifically including:
[0096] After normalizing the sound evaluation coefficient and the hazard coefficient, the sound evaluation coefficient and the hazard coefficient are used as the major semi-axis and minor semi-axis of the ellipse, respectively, to construct an ellipse model. The area of the ellipse model is then calculated and recorded as the luminance coefficient.
[0097] The formula for calculating the area of the elliptical model is as follows: ;
[0098] in and These are the major and minor axes of the ellipse, respectively.
[0099] AI decision-making interaction module: Determines the brightness level of the lamps based on the brightness coefficient and turns on the lamps according to the brightness level; if the brightness coefficient is greater than the value range corresponding to the maximum brightness level, an alert operation is initiated.
[0100] Three sets of threshold value ranges are preset, and each set of threshold value ranges corresponds to a brightness level. The brightness coefficient is matched with the three sets of preset threshold value ranges to obtain the brightness level corresponding to the brightness coefficient; the brightness level includes level 1 brightness, level 2 brightness, and warning brightness.
[0101] When the brightness level corresponding to the brightness coefficient is Level 1: the predicted light fixture will turn on at a preset power until the user's activity range moves out of the coverage area corresponding to the light fixture's light.
[0102] When the brightness coefficient corresponds to a brightness level of level two: increase the power of the luminaire based on a brightness level of level one;
[0103] When the brightness level corresponding to the brightness coefficient is the warning brightness: based on the brightness level of level 2, the predicted lights will start at the highest power; and after locating the position of the affected object through the camera, the spotlights will be turned on, and the light from the spotlights will shine on the affected object to mark the affected object as a warning; the spotlights will be turned on and flash red at a frequency of 5Hz.
[0104] The steps for locating the affected object using a camera include the following:
[0105] By using the principle of triangulation and combining images taken by multiple cameras from different angles, the three-dimensional coordinates of the object can be calculated. For example, using a binocular stereo vision system, the three-dimensional position of the object can be determined by calculating the parallax of corresponding points in the images of the left and right cameras and combining the camera parameters.
[0106] The calculated coordinates of the affected object are converted into the coordinates required in practical applications, and the location information is output. For example, the image coordinates are converted into world coordinates to obtain the world coordinates of the affected object. Based on the world coordinates of the affected object, the illumination location of the spotlight is determined.
[0107] The above formulas are derived from software simulations using a large amount of data and are selected to be close to the actual values. The influence weighting factors and specific coefficient values in the formulas are set by those skilled in the art based on the actual situation and can be adjusted and modified in the future.
[0108] The above description of the embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. An AI-based smart home interactive control system, characterized in that, Includes the following parts: Environmental sensing module: Collects environmental and user information from various parts of the room through various sensors; Multimodal interaction module: Receives user interaction commands and processes them before executing corresponding operations; No-command lighting interaction determination module: Determines which lighting fixtures to turn on based on the user's location information, ambient light information, and the lighting fixture's light coverage information in the room; Brightness determination module: After analyzing the sound information and influencing object information in the room, the sound assessment coefficient and the danger coefficient are obtained; The luminance coefficient is obtained by comprehensively analyzing the sound evaluation coefficient and the hazard coefficient. The acquisition of sound evaluation coefficients includes the following parts: Acquire the sound data emitted by the user while walking, and extract the maximum and minimum decibels from the sound data; calculate the difference between the maximum and minimum decibels to obtain the decibel range value; The time interval between each step a user takes while walking is obtained, and the average of each time interval is calculated to obtain the average interval. The distance between each step a user takes while walking is recorded as the behavioral step distance. The average of each behavioral step distance is calculated by accumulating the total steps. Extract the maximum behavior step size from each behavior step size, and calculate the step size deviation value by comparing the maximum behavior step size with the mean step size. The sound evaluation coefficient is obtained by comprehensively analyzing the decibel range, the mean interval, and the step deviation. The acquisition of the risk factor includes the following parts: Obtain the area covered by the cylindrical model constructed based on the current user's location, as well as the objects within that area; and denote the area covered by the cylindrical model constructed based on the current user's location as the behavior area, and the objects within the behavior area as the influencing objects; Obtain the volume of the object within the action's range, and the volume of the action's range itself; The volume of the affected object within the behavioral range is divided by the volume of the behavioral range to obtain the occupancy ratio; The image information of the affected object is acquired. After preprocessing the image information, the outer contour of the affected object is obtained. The angles of each part of the outer contour are extracted from the outer contour of the affected object, and the number of contour parts with acute angles is accumulated to obtain the sharp angle quantity. Using the user's two shoulders as endpoints, connect the two endpoints with a straight line and take the midpoint as the observation point. The straight line is called the baseline. Starting from the observation point, draw a straight line perpendicular to the baseline, with the direction of the straight line parallel to the ground, extending towards the user's front. This straight line is called the reference line. Approximate the bottom of the affected object as a circle, and after taking its center, record the center of the affected object as the corresponding point; Connect the observation point and the corresponding point with a straight line to obtain the line of intersection. Calculate the angle between the line of intersection and the reference line, and record this angle as the corresponding angle. The risk factor is obtained by comprehensively processing the occupancy ratio, the amount of sharp corners, and the corresponding angles. AI decision-making interaction module: Determines the brightness level of the lamps based on the brightness coefficient and turns on the lamps according to the brightness level.
2. The AI-based smart home interactive control system according to claim 1, characterized in that, The environmental perception module specifically includes the following components: The room is evenly divided into a preset number of areas, and a preset number of brightness sensors, human presence sensors, and light color sensors are deployed in each area to monitor the ambient brightness, human position, and existing ambient light color in real time.
3. The AI-based smart home interactive control system according to claim 2, characterized in that, The multimodal interaction module specifically includes the following parts: The system collects users' voice commands through microphones in various areas of the room, converts the voice into text using a voice recognition algorithm, parses the text to understand the intent of the command, and executes the corresponding operation. Using cameras in various areas of the room, the system identifies user gestures based on computer vision technology and performs corresponding operations based on the identified gestures. Remotely control room lights via a smart home app.
4. The AI-based smart home interactive control system according to claim 1, characterized in that, The process of determining which lights to turn on based on user location information, ambient light information, and light coverage information of the lights specifically includes the following: Using the user's two ears as endpoints, connect the two endpoints to form a straight line, and determine the center point of the line; draw a straight line perpendicular to the ground with the center of the line as the origin, and call it the baseline line; Construct a cylindrical model with the base line as the height and the preset size as the radius, and determine the area covered by the cylindrical model; Obtain the coverage area corresponding to the light from each lamp, match the coverage area corresponding to the light from each lamp with the coverage area covered by the cylindrical model, and obtain the lamp corresponding to the complete filling of the coverage area covered by the cylindrical model, and record it as the predicted lamp. It obtains the ambient light intensity within the area covered by the cylindrical model and presets a light intensity threshold. Based on the comparison between the ambient light intensity within the area covered by the cylindrical model and the light intensity threshold, it controls whether the predicted lights are turned on or off.
5. The AI-based smart home interactive control system according to claim 1, characterized in that, The brightness coefficient is obtained by comprehensively analyzing the sound evaluation coefficient and the danger coefficient, specifically including: After normalizing the sound evaluation coefficient and the hazard coefficient, the sound evaluation coefficient and the hazard coefficient are used as the major semi-axis and minor semi-axis of the ellipse, respectively, to construct an elliptical model. The area of the elliptical model is then calculated and denoted as the luminance coefficient.
6. The AI-based smart home interactive control system according to claim 5, characterized in that, Three sets of threshold value ranges are preset, and each set of threshold value ranges corresponds to a brightness level. The brightness coefficient is matched with the three sets of preset threshold value ranges to obtain the brightness level corresponding to the brightness coefficient. The brightness levels include Level 1 brightness, Level 2 brightness, and warning brightness.
7. The AI-based smart home interactive control system according to claim 6, characterized in that, When the brightness level corresponding to the brightness coefficient is Level 1: the predicted light fixture will turn on at a preset power until the user's activity range moves out of the coverage area corresponding to the light fixture's light. When the brightness coefficient corresponds to a brightness level of level 2: based on a brightness level of level 1, increase the power of the lamp to the preset power; When the brightness level corresponding to the brightness coefficient is the warning brightness: based on the brightness level of level two, the predicted lights will start at the highest power; and after the location of the affected object is located by the camera, the spotlights will be turned on and the light from the spotlights will shine on the affected object.
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