Movable intelligent household equipment automatic layout optimization system

Abstract

The invention discloses an automatic layout optimization system for movable intelligent household equipment, and the system comprises a sensor network, a data processing module, a multi-target optimization decision module, a central control screen interaction module, an automatic execution module, and a feedback and adaptive optimization module. The sensor network collects user behavior data, equipment state data and environment change data through a plurality of sensors, the system can adjust the layout of the home equipment in real time according to multi-source data (such as user behavior, environment change and equipment state), and the function and use convenience of the home equipment are optimized. By means of intelligent automatic layout, the requirement of a user for manually adjusting equipment is greatly reduced, and more convenient intelligent home experience is provided.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a mobile smart home equipment automatic layout optimization system. Background Art

[0002] With the popularity of smart home devices in households, the number and types of smart devices are increasing, such as smart lamps, smart speakers, air purifiers, and smart TVs. However, the layout and placement of these devices often directly affect their efficiency, space utilization, and user experience. Traditional home layout adjustments rely on manual equipment, which is not only time-consuming and labor-intensive, but also difficult to make dynamic adjustments with user behavior and environmental changes. Most existing technologies rely on manual intervention by users and lack an intelligent automatic layout optimization mechanism. Therefore, a system that can automatically optimize device layout and has adaptive optimization capabilities is needed. Summary of the invention

[0003] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a mobile smart home device automatic layout optimization system.

[0004] (II) Technical solution To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a mobile smart home device automatic layout optimization system, comprising a sensor network, a data processing module, a multi-objective optimization decision module, a central control screen interaction module, an automatic execution module and a feedback and adaptive optimization module, wherein the sensor network collects user behavior data, device status data and environmental change data through multiple sensors, and the sensor types include cameras, infrared sensors, inertial sensors, laser radars, ultrasonic sensors, temperature and humidity sensors, etc.; After the system receives the data transmitted by the sensor network, the data processing module performs real-time analysis through the edge computing unit. The data processing module uses machine learning and pattern recognition technology to conduct in-depth analysis of user behavior patterns, device status, and environmental changes; Based on the information output by the data processing module, the system uses a multi-objective optimization algorithm (such as genetic algorithm, ant colony algorithm, etc.) through the multi-objective optimization decision module, comprehensively considering user habits, equipment usage efficiency, environmental adaptability and energy-saving requirements, and generates multiple layout optimization plans; The central control screen is the main interface between the user and the system. The central control screen interaction module enables the central control screen to display the optimized layout plan, and allows the user to view the analysis results generated by the system, providing options for users to fine-tune or select the plan according to their needs; The automatic execution module can control devices with automatic movement capabilities (such as smart sweeping robots, air purifiers, etc.). The system automatically controls the devices to move to the specified location according to the optimization plan; The feedback and adaptive optimization module can collect user satisfaction and improvement suggestions on layout adjustments through the central control screen based on user feedback, and then adjust subsequent optimization plans through an adaptive optimization algorithm to form a continuous optimization closed loop.

[0005] A mobile smart home device automatic layout optimization system specifically includes the following steps: The first step is the data collection stage; The second step is data processing and analysis stage; The third step is the layout optimization decision-making stage; The fourth step is the execution and feedback stage; Step 5: Adaptive optimization and feedback loop.

[0006] Further, the improvements of the present invention include: 1) User behavior data collection: through cameras, infrared sensors, inertial sensors, etc., the system collects the user's activity trajectory, resident area and interaction behavior with the device in real time. For example, the frequency of users' activities in a specific area, the room they stay in for a long time and the habit of using a certain device will be analyzed by analyzing the frequency of use of the device, the distance from the user and other related information to deduce the optimal placement of the device; 2) Device status data collection: The system collects the operating status of each smart home device, including the current working mode (on / off, operating mode, etc.), power consumption, and the health of the device (such as whether maintenance is required). This data will be used to analyze the device's usage efficiency and optimization goals; 3) Environmental change data collection: Through lidar, ultrasonic sensors, etc., the system can detect in real time the layout changes of objects such as furniture and walls, as well as environmental data such as temperature and humidity in the room. These data are crucial for adjusting the position of the equipment, because different environmental conditions may require different equipment placement.

[0007] Further, the present invention has the following improvements: 1) User behavior pattern analysis: the system uses algorithms such as cluster analysis and behavior pattern recognition to analyze the activity patterns and habits of users, and identifies frequently used home areas and high-frequency usage scenarios of devices by mining the historical behavior data of users. For example, the system can identify which rooms users stay in for a long time and which devices are most frequently used in these areas, thereby suggesting that devices should be placed in these areas first; 2) Equipment efficiency analysis: The system combines the frequency of use and power consumption of the equipment to evaluate whether the equipment is in an efficient working state. For example, the system can determine whether a certain equipment is often idle or improperly used, and decide whether to adjust its position to improve its efficiency. 3) Analysis of environmental changes: Through real-time analysis of environmental data, the system can identify changes in room temperature and humidity, and the impact of furniture movement on equipment layout, and adjust the placement of equipment accordingly. For example, the position of an air purifier may need to be re-optimized due to changes in furniture placement to ensure maximum air circulation and purification effects.

[0008] Further, the improvements of the present invention are: 1) Multi-objective optimization algorithm generates schemes: the system uses a multi-objective optimization algorithm to generate multiple layout schemes based on user behavior patterns, device status and environmental data. These schemes may be based on different optimization goals, such as improving device usage efficiency, reducing device idle time, improving energy saving effects, optimizing user experience, etc. The system scores each scheme and provides users with detailed scheme comparison and explanation on the central control screen; 2) Central control screen interaction and solution selection: The central control screen will list various layout optimization solutions and display the specific advantages and disadvantages of each solution. Users can view the simulated layout effect of each solution through the central control screen, including comparison charts before and after equipment movement, equipment usage prediction, energy efficiency analysis, etc. Users can manually fine-tune certain solutions through the central control screen, or directly select the recommended best solution.

[0009] Further, the present invention has the following improvements: 1) Automatically adjust the device position: for devices with automatic movement function (such as sweeping robots, smart speakers, etc.), the system automatically moves these devices to the best position according to the optimization plan, and the central control screen will display the real-time progress of the device movement, and the user can intervene or cancel the movement operation at any time; 2) Manual adjustment suggestions: For devices that do not have the ability to move automatically, the system provides users with manual adjustment suggestions through the central control screen, including information such as the best placement of the device, the direction of movement, and the required distance of movement. Users can manually move the device according to the suggestions. The central control screen will update the device position in real time and record the user's movement operations. 3) User feedback collection: After the system completes the layout adjustment, a feedback window will pop up on the central control screen to ask the user about their satisfaction with the new layout and further suggestions for improvement. The user's feedback data will be stored in the system for subsequent adaptive optimization processes.

[0010] Further, the present invention has the following improvements: 1) Feedback collection and analysis: the system analyzes the feedback information submitted by the user through the central control screen and adjusts the layout optimization scheme in combination with the user's actual usage experience. The system also automatically learns according to the changes in user habits to adjust future optimization strategies; 2) Adaptive optimization algorithm: An adaptive optimization algorithm is an algorithm that can automatically adjust and optimize in a dynamic environment. Its core idea is to achieve continuous optimization by self-adjusting according to changes in the environment or system through continuous feedback and learning. In the smart home device layout optimization system, the main task of the adaptive optimization algorithm is to optimize the device layout in real time according to changes in user behavior, environmental conditions and device status, so that the system always maintains the best operating state. The system adjusts the weights of the multi-objective optimization algorithm based on the positive or negative feedback from users to make it more adaptable to the user's personalized needs. For example, if a user repeatedly reports that he is dissatisfied with the movement of a certain device, the system will adjust the algorithm to reduce the frequent movement of the device.

[0011] (III) Beneficial effects Compared with the prior art, the present invention provides a mobile smart home device automatic layout optimization system, which has the following beneficial effects: 1. Intelligent and efficient layout optimization; The system can adjust the layout of home appliances in real time based on multi-source data (such as user behavior, environmental changes, and device status), optimizing the functionality and ease of use of home appliances. Intelligent automatic layout greatly reduces the need for users to manually adjust devices, providing a more convenient smart home experience.

[0012] 2. Dynamic adaptive capability; The system uses an adaptive optimization algorithm and has the ability to learn and adjust itself in real time. It can continuously adjust the device layout as user behavior and environment change to ensure that the best solution is always provided, which is especially suitable for smart home applications in complex and dynamic environments.

[0013] 3. Multi-objective optimization solutions; Through the multi-objective optimization algorithm, the system can simultaneously consider multiple different optimization goals, such as user convenience, space utilization, energy efficiency, maximization of equipment functions, etc. Users can choose the layout that best meets their needs from a variety of optimization schemes, making the entire home environment more personalized.

[0014] 4. Efficient edge computing and rapid response; The system architecture uses edge computing technology to quickly process sensor data on local devices (such as smart gateways and central control screens). This not only improves the system's response speed, but also reduces dependence on the cloud, ensuring efficient operation even when the network is unstable or latency is high.

[0015] 5. Accurate user behavior analysis and prediction; Combining machine learning and pattern recognition technology, the system can accurately analyze users' daily behavior habits and predict future behavior, thus providing a scientific basis for layout optimization. In long-term use, the system can also learn users' preferences through continuous iteration and provide smarter device layout adjustments.

[0016] 6. User-friendly interactive interface; Through the central control screen, users can directly view the optimized layout plan, provide feedback and make manual adjustments. This human-computer interaction method enhances the user's sense of control over the system while retaining the advantages of automation, making the user experience more personalized and intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The system of the present invention is shown in FIG. Figure 1 ; Figure 2 The system of the present invention is shown in FIG. Figure 2 . DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1-2 , a mobile smart home device automatic layout optimization system of the present invention includes a sensor network, a data processing module, a multi-objective optimization decision module, a central control screen interaction module, an automatic execution module and a feedback and adaptive optimization module, wherein the sensor network collects user behavior data, device status data and environmental change data through multiple sensors, and the sensor types include cameras, infrared sensors, inertial sensors, laser radars, ultrasonic sensors, temperature and humidity sensors, etc.; Camera: Working principle: The camera captures visible light images and video data through an optical lens, uses digital signal processing (DSP) or artificial intelligence algorithms to analyze video content, and extracts key information such as user behavior and object location. The system can use computer vision technology (such as target detection, facial recognition, posture estimation, etc.) to track the user's movement trajectory in the room and the use of the device; Purpose: Used to identify the user's behavior trajectory, the specific location of the equipment in the room and changes in its surrounding environment.

[0020] Infrared sensor: Working principle: Infrared sensors sense the presence, distance or movement of objects by emitting infrared rays and detecting reflected signals. Common working modes are active infrared and passive infrared (PIR). PIR sensors are particularly suitable for detecting heat radiation from the human body and are used to determine whether someone is moving in a specific area.

[0021] Purpose: Used to monitor user activities, determine whether there are people in the room, and the frequency and area of ​​human activities.

[0022] Inertial sensors (accelerometers and gyroscopes): Working principle: Inertial sensors sense the motion state of a device by measuring acceleration (accelerometer) and angular velocity (gyroscope). Accelerometers can measure the acceleration changes of a device in three-dimensional space, and gyroscopes can sense the angular changes of a device. The combination of the two can be used to measure the specific position and movement of a device; Purpose: Used to monitor the movement status of household devices, ensure that the device can be accurately recorded when it is moved, and to detect whether there is any unexpected device movement.

[0023] LiDAR: Working principle: LiDAR builds a 3D model of the surrounding environment by emitting laser light and measuring the time it takes for the laser pulse to return. LiDAR can accurately measure the distance between an object and the sensor, and generate a point cloud map of the space by scanning, providing depth information of the environment; Purpose: Used to detect the position and distance of objects in a room. It is particularly suitable for building a three-dimensional model of a room to help determine the optimal layout of home appliances.

[0024] Ultrasonic Sensors: Working principle: Ultrasonic sensors calculate the distance of an object by emitting high-frequency sound waves and measuring the time it takes for the sound waves to be reflected back from an object. Ultrasonic sensors have high measurement accuracy and are suitable for short-distance ranging tasks; Purpose: Used to detect the distance between static objects such as furniture and walls and home appliances to ensure that the location of the equipment does not conflict with other objects.

[0025] Temperature and humidity sensor: Working principle: Temperature and humidity sensors collect environmental parameters by detecting the temperature and humidity in the environment. Temperature sensors usually use thermistors or thermocouples, while humidity sensors rely on capacitive or resistive elements to measure the water vapor content in the air; Purpose: Used to monitor the environmental status of the room and determine whether the equipment needs to be adjusted according to the temperature and humidity changes. For example, the placement of air purifiers or air conditioning equipment can be optimized according to the temperature and humidity distribution of the room.

[0026] Light Sensor: Working principle: Light sensors sense the lighting conditions in a room by detecting the light intensity in the environment. Photodiodes or photoresistors are common light sensors that sense changes in light intensity and convert them into electrical signals for output; Purpose: Used to determine the light intensity in the room, and to assist the system in deciding the placement of lighting equipment or adjusting the brightness.

[0027] Smoke Sensor: Working principle: Smoke sensors usually use ionization or photoelectric principles to detect smoke particles in the air. Ionization smoke sensors sense changes in smoke particles by ionizing molecules in the air, while photoelectric sensors trigger alarms by detecting the degree to which a light beam is blocked by smoke; Purpose: Used to monitor air quality and safety in home environments, especially to remind users to adjust the location of equipment when there is a fire hazard.

[0028] Each sensor plays a specific role in the system, forming a basic layer of data collection through the sensor network, providing detailed data information for equipment layout optimization. The collaborative work of various sensors ensures that the system can monitor user behavior, equipment status and environmental changes in real time, thereby providing data support for multi-objective optimization decisions, and ultimately interacting with users through the central control screen to complete layout adjustment and optimization.

[0029] After the system receives the data transmitted by the sensor network, the data processing module performs real-time analysis through the edge computing unit. The data processing module uses machine learning and pattern recognition technology to conduct in-depth analysis of user behavior patterns, device status, and environmental changes; 1. Edge computing; Principle: Edge computing is a distributed computing architecture in which data processing tasks are not performed on remote cloud servers, but on "edge" devices close to the data source. This means that the data collected by sensors can be processed locally (such as in home devices, smart gateways, central control screens, etc.), reducing data transmission delays and network loads, and providing real-time response capabilities.

[0030] Application in the system: 1) Edge computing allows large amounts of data generated by sensors (such as images, temperature, humidity, and light) to be quickly analyzed locally, reducing the time delay for data transmission to the cloud; The system can quickly respond to changes in the room. For example, when a user moves a device, edge computing can instantly adjust the device layout. 2) Local data processing reduces the reliance on network connections, ensuring that the device can still operate even when the network is unstable; 2. Machine Learning: Principle: Machine learning is a technology that uses algorithms to automatically learn from data and infer patterns. Through training with historical data, machine learning models can automatically identify patterns, predict future behaviors, and optimize themselves when new data arrives. In smart home systems, commonly used machine learning techniques include supervised learning, unsupervised learning, and reinforcement learning. Application in the system: 1) User behavior prediction: Through machine learning algorithms (such as decision trees, support vector machines, or neural networks), the system can analyze users’ behavior habits (such as the time of day when they use their devices most often and the areas they frequently move around in), predict their future behavior, and provide suggestions for adjusting device layout; 2) Environmental data analysis: Based on environmental data provided by sensors, machine learning models can identify patterns related to equipment layout; for example, temperature or light changes may affect the optimal location of air purifiers or lamps; 3) Adaptive optimization: The machine learning model can be continuously updated over time, and the optimization strategy can be adjusted through user feedback to achieve continuous optimization of device layout; 3. Pattern Recognition: Principle: Pattern recognition is a technology that allows computers to automatically detect and classify certain structures or regularities from input data. It can extract features from data (such as objects in images, user behavior patterns) and classify, identify or predict them. Pattern recognition is usually based on statistics and machine learning algorithms. Application in the system: 1) Image and video analysis: The system uses pattern recognition technology to analyze room layout, user behavior, and object locations through image and video data captured by the camera. For example, it uses object detection technology to identify the specific location of equipment in the room and determine whether the equipment needs to be moved. 2) User behavior pattern recognition: By analyzing the user's behavior pattern, the system can determine the user's daily activity area and identify specific actions (such as sitting, standing, moving, etc.), thereby providing a basis for adjusting the equipment layout; 3) Environmental pattern detection: Through data collected by sensors (such as temperature, humidity, and light sensors), the system can identify recurring patterns in the environment; for example, when the light is strong in the morning, the system may automatically adjust the position of curtains or lamps; Relationship and Collaboration: 1. Edge computing provides basic computing support: Edge computing in the data processing module provides the ability to quickly process large-scale sensor data. Through local processing, sensor data does not need to be transmitted to the cloud, which greatly improves the response speed, especially in scenarios where user behavior and environmental changes are monitored in real time. Edge computing also provides a basic computing environment for the operation of machine learning and pattern recognition, ensuring that they can be quickly analyzed and predicted locally. 2. Machine learning is responsible for decision optimization: The data collected and initially processed by edge computing will be further analyzed and decided by machine learning models; machine learning models can learn user behavior and environmental patterns from historical data and generate intelligent layout suggestions; as data continues to accumulate, machine learning models will automatically adapt to new data and achieve adaptive optimization, ensuring that the intelligence of the system continues to improve over time; Pattern recognition enables precise perception: Pattern recognition technology plays a "perception" role in the entire process. It helps the system understand user behavior and environmental changes by analyzing data from cameras and various sensors. This provides precise input data for machine learning models, thereby optimizing the accuracy and rationality of decisions.

[0031] Based on the information output by the data processing module, the system uses a multi-objective optimization algorithm (such as genetic algorithm, ant colony algorithm, etc.) through the multi-objective optimization decision module, comprehensively considering user habits, equipment usage efficiency, environmental adaptability and energy-saving requirements, and generates multiple layout optimization plans; 1. The principle of multi-objective optimization algorithm; Multi-objective optimization is an optimization method that handles multiple conflicting objectives. Unlike single-objective optimization (where there is only one objective to be minimized or maximized), multi-objective optimization requires considering multiple competing objective functions simultaneously when solving the problem; Objective function: Multiple objective functions in the system may include the ease of use of the equipment, energy efficiency, space utilization, user comfort, etc. Each objective is expressed as a function in mathematical form, and the optimization algorithm finds an optimal solution by weighing the weights of each objective.

[0032] Pareto Optimal Solution: Since multi-objective optimization usually cannot optimize all objectives at the same time, the system will select the solution through the Pareto Optimal Solution. The Pareto Optimal Solution is a set of solutions, in which no solution can improve one objective without degrading other objectives.

[0033] 2. Application in the system: In the smart home device layout optimization system, the multi-objective optimization algorithm optimizes the device layout based on the following multiple objectives: Maximize equipment functional efficiency: For example, air purifiers should be placed in areas with poor air quality; smart lamps should be arranged according to the light distribution in the room; User behavior matching: For example, commonly used devices should be placed as close as possible to the areas where users frequently move, reducing the distance users move and improving user convenience; Optimize space utilization: While optimizing the location of equipment, maximize the utilization of space and avoid furniture or equipment blocking each other.

[0034] Energy efficiency optimization: Reduce energy consumption by optimizing the layout of equipment, such as the layout of smart curtains should be adjusted according to the position of natural light to maximize the use of natural light.

[0035] 3. Technical principles of multi-objective optimization: Multi-objective optimization algorithms usually use the following methods: Genetic algorithm: Genetic algorithm is a common multi-objective optimization method. It imitates the biological evolution process and generates a set of solutions (i.e. layout plans) through operations such as selection, crossover, and mutation. The system gradually optimizes the quality of the solution in each iteration until a set of Pareto optimal solutions is found; Particle swarm optimization algorithm: Particle swarm optimization finds the optimal solution by simulating the movement of particles in the solution space. Each particle represents a possible solution, and the particle is constantly adjusted according to its position and speed, and finally converges to the optimal solution. This algorithm is suitable for quickly solving equipment layout problems.

[0036] Ant Colony Algorithm: The ant colony algorithm finds the optimal solution by imitating the foraging path of ants. Ants choose the shortest path based on the strength of the "pheromone" on the path. It is suitable for multi-objective path optimization and equipment layout optimization.

[0037] 4. Relationship of multi-objective optimization algorithms: In the system architecture, the multi-objective optimization algorithm is closely related to other modules, especially the data processing module and the central control screen interaction module: Data processing module: The basis of multi-objective optimization is high-quality data input. The data processing module processes the user behavior and environmental data collected by sensors and provides them to the optimization algorithm as input. Edge computing and pattern recognition technology can process data quickly and accurately, ensuring that the optimization algorithm obtains accurate user and environmental information in real time.

[0038] Central control screen interactive module: The Pareto optimal solution generated by the optimization algorithm is displayed to the user through the central control screen. The user can select the solution that best meets the needs from multiple optimization solutions, or provide additional manual adjustment suggestions. This feedback is input into the optimization algorithm again for further optimization.

[0039] Adaptive feedback mechanism: The multi-objective optimization algorithm can be adjusted through user feedback and feedback on actual execution results. The system will continuously update weights and optimization strategies based on long-term user feedback, thereby generating solutions that are more in line with user preferences in the future.

[0040] 5. Steps of optimization algorithm: 1. Initialization: The system first collects all the initial data, including user behavior, device status, environmental conditions, etc. Then it builds the objective function based on these data (such as functional efficiency of the device, user convenience, energy consumption, etc.).

[0041] 2. Generate an initial solution set: Generate a set of initial solution sets using genetic algorithms, particle swarm optimization or other algorithms. These solution sets represent different equipment layout solutions.

[0042] 3. Evaluate the solution set: By evaluating the objective function value, the performance of each solution on different objectives is judged. The system calculates the objective value of each solution and uses the concept of Pareto optimal solution to screen the solution set.

[0043] 4. Select Pareto optimal solution: Generate a set of Pareto front solutions, that is, a set of solutions that perform relatively well in multiple objectives. The system will recommend these solutions to users.

[0044] 5. User feedback and adjustment: Users view the optimization plan through the central control screen and make modification suggestions. The system further optimizes the layout plan based on user feedback.

[0045] 6. Execute and monitor: Once the user selects a solution, the system executes the layout adjustment of the device and monitors the layout effect, continuing to collect data through the sensor network to support future optimization iterations.

[0046] The multi-objective optimization algorithm plays a vital role in the smart home device layout optimization system. By weighing multiple objectives, it is able to generate a reasonable layout solution to ensure the optimal function of the device, user convenience and energy efficiency. The data processing module in the system architecture provides the basic data support required by the optimization algorithm, while the central control screen interaction module continuously improves the optimization process through user feedback to form a dynamically adjusted adaptive layout system.

[0047] The central control screen is the main interface between the user and the system. The central control screen interaction module enables the central control screen to display the optimized layout plan, and allows the user to view the analysis results generated by the system, providing options for users to fine-tune or select the plan according to their needs; The automatic execution module can control devices with automatic movement capabilities (such as smart sweeping robots, air purifiers, etc.). The system automatically controls the devices to move to the specified location according to the optimization plan; The feedback and adaptive optimization module can collect user satisfaction and improvement suggestions on layout adjustments through the central control screen based on user feedback, and then adjust subsequent optimization plans through an adaptive optimization algorithm to form a continuous optimization closed loop.

[0048] A mobile smart home device automatic layout optimization system specifically includes the following steps: The first step is the data collection stage; The second step is data processing and analysis stage; The third step is the layout optimization decision-making stage; The fourth step is the execution and feedback stage; Step 5: Adaptive optimization and feedback loop.

[0049] Furthermore, 1) User behavior data collection: Through cameras, infrared sensors, and inertial sensors, the system collects users’ activity trajectories, resident areas, and interactions with devices in real time. For example, the frequency of users’ activities in a specific area, the rooms they stay in for a long time, and the habits of using a certain device will be analyzed to deduce the optimal placement of the device by analyzing the frequency of use of the device, the distance from the user, and other related information; 2) Device status data collection: The system collects the operating status of each smart home device, including the current working mode (on / off, operating mode, etc.), power consumption, and the health of the device (such as whether maintenance is required). This data will be used to analyze the efficiency of device use and optimize goals; 3) Environmental change data collection: Through lidar, ultrasonic sensors, etc., the system can detect layout changes of furniture, walls and other objects, as well as environmental data such as temperature and humidity in the room in real time. These data are crucial for adjusting the position of the equipment, because different environmental conditions may require different equipment placement.

[0050] Furthermore, 1) User behavior pattern analysis: The system uses algorithms such as cluster analysis and behavior pattern recognition to analyze the user's activity patterns and habits. By mining the user's historical behavior data, the system can identify the frequently used home areas and equipment usage scenarios. For example, the system can identify which rooms the user stays in for a long time and which equipment is used most frequently in these areas, and thus recommend that the equipment should be placed in these areas first.

[0051] 2) Equipment efficiency analysis: The system combines the frequency of use and power consumption of the equipment to evaluate whether the equipment is in an efficient working state. For example, the system can determine whether a certain equipment is often idle or used improperly, and decide whether to adjust its position to improve its efficiency.

[0052] 3) Analysis of environmental changes: Through real-time analysis of environmental data, the system can identify changes in room temperature and humidity, and the impact of furniture movement on equipment layout, and adjust the placement of equipment accordingly. For example, the position of an air purifier may need to be re-optimized due to changes in furniture placement to ensure maximum air circulation and purification effects.

[0053] Furthermore, 1) Multi-objective optimization algorithm generates solutions: The system uses a multi-objective optimization algorithm to generate multiple layout solutions based on user behavior patterns, device status, and environmental data. These solutions may be based on different optimization goals, such as improving device efficiency, reducing device idle time, improving energy saving, and optimizing user experience. The system scores each solution and provides users with detailed solution comparison and explanation on the central control screen; 2) Central control screen interaction and solution selection: The central control screen will list various layout optimization solutions and show the specific advantages and disadvantages of each solution. Users can view the simulated layout effect of each solution through the central control screen, including comparison pictures before and after the equipment is moved, equipment usage prediction, energy efficiency analysis, etc. Users can manually fine-tune certain solutions through the central control screen, or directly select the recommended best solution.

[0054] Furthermore, 1) Automatically adjust the device position: For devices with automatic movement functions (such as sweeping robots, smart speakers, etc.), the system automatically moves these devices to the best position according to the optimization plan. The central control screen will display the real-time progress of the device movement, and the user can intervene or cancel the movement operation at any time; 2) Manual adjustment suggestions: For devices that do not have the ability to move automatically, the system provides users with manual adjustment suggestions through the central control screen, including information such as the best placement of the device, the direction of movement, and the required distance of movement. Users can manually move the device according to the suggestions, and the central control screen will update the device position in real time and record the user's movement operations.

[0055] 3) User feedback collection: After the system completes the layout adjustment, a feedback window pops up on the central control screen to ask the user about their satisfaction with the new layout and further suggestions for improvement. The user's feedback data will be stored in the system for subsequent adaptive optimization processes.

[0056] Furthermore, 1) Feedback collection and analysis: The system analyzes the feedback information submitted by users through the central control screen and adjusts the layout optimization plan based on the user's actual usage experience. The system will also automatically learn according to changes in user habits to adjust future optimization strategies.

[0057] Adaptive optimization algorithm: Adaptive optimization algorithm is an algorithm that can automatically adjust and optimize in a dynamic environment. Its core idea is to achieve continuous optimization by self-adjusting according to changes in the environment or system through continuous feedback and learning. In the smart home device layout optimization system, the main task of the adaptive optimization algorithm is to optimize the device layout in real time according to changes in user behavior, environmental conditions and device status, so that the system always maintains the best operating state. Based on the positive or negative feedback from users, the system adjusts the weights of the multi-objective optimization algorithm to make it more adaptable to the personalized needs of users. For example, if a user repeatedly reports that he is dissatisfied with the movement of a certain device, the system will adjust the algorithm to reduce the frequent movement of the device.

[0058] 1. System architecture and module integration: The core of the patent protection lies in its unique system architecture, including the overall integration and collaborative work of the sensor network, data processing module, multi-objective optimization decision module, central control screen interaction module, automatic execution module, and feedback and adaptive optimization module. The combination of these modules and the data flow and control flow between them constitute the technical core of the patent.

[0059] 2. Multi-source data collection and real-time analysis: The patent integrates multiple sensors (such as cameras, infrared sensors, lidar, etc.) to achieve comprehensive data collection of user behavior, device status and environmental changes, and uses edge computing units for real-time analysis. This efficient collection and real-time analysis of multi-source data is one of the key points of patent protection.

[0060] 3. Multi-objective optimization algorithm and solution generation: The patent uses a multi-objective optimization algorithm (such as genetic algorithm, ant colony algorithm, etc.) to generate multiple layout optimization solutions based on user behavior patterns, device status and environmental data. The application of this algorithm and its combination with data processing constitute the core technology of the patent in layout optimization.

[0061] 4. Central control screen interaction and user participation: The patent interacts with the user through the central control screen, allowing the user to view optimization plans, select plans or make fine adjustments, and provide real-time feedback on satisfaction with layout adjustments and improvement suggestions. This highly interactive user experience design is one of the important innovations of the patent and is also the focus of its protection.

[0062] 5. Automatic execution and feedback loop: The patent can realize the automatic movement and adjustment of the equipment, and form a continuous optimization closed loop through user feedback collection and adaptive optimization algorithm. This automatic execution and feedback loop mechanism enables the system to continuously learn and improve, so as to better adapt to user needs and environmental changes. It is another key protection point of the patent, the innovation and practicality of the overall technical solution: The technical solution proposed by the patent has significant innovation and practicality in the field of automatic layout optimization of smart home devices, and can significantly improve the efficiency of equipment use, energy saving effect and user experience. The innovation and practicality of this overall technical solution are also important aspects of patent protection.

[0063] This embodiment has the following advantages: 1. Intelligent and efficient layout optimization; The system can adjust the layout of home appliances in real time based on multi-source data (such as user behavior, environmental changes, and device status), optimizing the functionality and ease of use of home appliances. Intelligent automatic layout greatly reduces the need for users to manually adjust devices, providing a more convenient smart home experience.

[0064] 2. Dynamic adaptive capability; The system uses an adaptive optimization algorithm and has the ability to learn and adjust itself in real time. It can continuously adjust the device layout as user behavior and environment change to ensure that the best solution is always provided, which is especially suitable for smart home applications in complex and dynamic environments.

[0065] 3. Multi-objective optimization solutions; Through the multi-objective optimization algorithm, the system can simultaneously consider multiple different optimization goals, such as user convenience, space utilization, energy efficiency, maximization of equipment functions, etc. Users can choose the layout that best meets their needs from a variety of optimization schemes, making the entire home environment more personalized.

[0066] 4. Efficient edge computing and rapid response; The system architecture uses edge computing technology to quickly process sensor data on local devices (such as smart gateways and central control screens). This not only improves the system's response speed, but also reduces dependence on the cloud, ensuring efficient operation even when the network is unstable or latency is high.

[0067] 5. Accurate user behavior analysis and prediction; Combining machine learning and pattern recognition technology, the system can accurately analyze users' daily behavior habits and predict future behavior, thus providing a scientific basis for layout optimization. In long-term use, the system can also learn users' preferences through continuous iteration and provide smarter device layout adjustments.

[0068] 6. User-friendly interactive interface; Through the central control screen, users can directly view the optimized layout plan, provide feedback and make manual adjustments. This human-computer interaction method enhances the user's sense of control over the system while retaining the advantages of automation, making the user experience more personalized and intuitive.

[0069] In the description of this article, it should be noted that relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0070] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A mobile smart home device automatic layout optimization system, characterized by: It includes a sensor network, a data processing module, a multi-objective optimization decision module, a central control screen interaction module, an automatic execution module, and a feedback and adaptive optimization module. The sensor network collects user behavior data, device status data, and environmental change data through multiple sensors. The sensor types include cameras, infrared sensors, inertial sensors, lidars, ultrasonic sensors, temperature and humidity sensors, etc. After the system receives the data transmitted by the sensor network, the data processing module performs real-time analysis through the edge computing unit. The data processing module uses machine learning and pattern recognition technology to conduct in-depth analysis of user behavior patterns, device status, and environmental changes; Based on the information output by the data processing module, the system uses a multi-objective optimization algorithm (such as genetic algorithm, ant colony algorithm, etc.) through the multi-objective optimization decision module, comprehensively considering user habits, equipment usage efficiency, environmental adaptability and energy-saving requirements, and generates multiple layout optimization plans; The central control screen is the main interface between the user and the system. The central control screen interaction module enables the central control screen to display the optimized layout plan, and allows the user to view the analysis results generated by the system, providing options for users to fine-tune or select the plan according to their needs; The automatic execution module can control devices with automatic movement capabilities (such as smart sweeping robots, air purifiers, etc.). The system automatically controls the devices to move to the specified location according to the optimization plan; The feedback and adaptive optimization module can collect user satisfaction and improvement suggestions on layout adjustments through the central control screen based on user feedback, and then adjust subsequent optimization plans through an adaptive optimization algorithm to form a continuous optimization closed loop.

2. The automatic layout optimization system for mobile smart home devices according to claim 1, characterized in that: The specific steps include: The first step is the data collection stage; The second step is data processing and analysis stage; The third step is the layout optimization decision-making stage; The fourth step is the execution and feedback stage; Step 5: Adaptive optimization and feedback loop.

3. The mobile smart home device automatic layout optimization system according to claim 2, characterized in that: 1) User behavior data collection: Through cameras, infrared sensors, and inertial sensors, the system collects users' activity trajectories, resident areas, and interactions with devices in real time. For example, the frequency of users' activities in specific areas, the rooms they stay in for a long time, and the habits of using a certain device. By analyzing the frequency of device use, the distance from the user, and other related information, the system can deduce the optimal placement of the device. 2) Device status data collection: The system collects the operating status of each smart home device, including the current working mode (on / off, operating mode, etc.), power consumption, and the health of the device (such as whether maintenance is required). This data will be used to analyze the device's usage efficiency and optimization goals; 3) Environmental change data collection: Through lidar, ultrasonic sensors, etc., the system can detect in real time the layout changes of objects such as furniture and walls, as well as environmental data such as temperature and humidity in the room. These data are crucial for adjusting the position of the equipment, because different environmental conditions may require different equipment placement.

4. The automatic layout optimization system for mobile smart home devices according to claim 3 is characterized in that: 1) User behavior pattern analysis: The system uses algorithms such as cluster analysis and behavior pattern recognition to analyze users’ activity patterns and habits. By mining users’ historical behavior data, the system identifies frequently used home areas and devices’ high-frequency usage scenarios. For example, the system can identify which rooms users stay in for a long time and which devices are used most frequently in these areas, and thus recommends that devices should be placed in these areas first. 2) Equipment efficiency analysis: The system combines the frequency of use and power consumption of the equipment to evaluate whether the equipment is in an efficient working state. For example, the system can determine whether a certain equipment is often idle or used improperly, and decide whether to adjust its position to improve its efficiency. 3) Analysis of environmental changes: Through real-time analysis of environmental data, the system can identify changes in room temperature and humidity, and the impact of furniture movement on equipment layout, and adjust the placement of equipment accordingly. For example, the position of an air purifier may need to be re-optimized due to changes in furniture placement to ensure maximum air circulation and purification effects.

5. The mobile smart home device automatic layout optimization system according to claim 4, characterized in that: 1) Multi-objective optimization algorithm to generate solutions: The system uses a multi-objective optimization algorithm to generate multiple layout solutions based on user behavior patterns, device status and environmental data. These solutions may be based on different optimization goals, such as improving device efficiency, reducing device idle time, improving energy saving effects, optimizing user experience, etc. The system scores each solution and provides users with detailed solution comparison and explanation on the central control screen; 2) Central control screen interaction and solution selection: The central control screen will list various layout optimization solutions and display the specific advantages and disadvantages of each solution. Users can view the simulated layout effect of each solution through the central control screen, including comparison charts before and after equipment movement, equipment usage prediction, energy efficiency analysis, etc. Users can manually fine-tune certain solutions through the central control screen, or directly select the recommended best solution.

6. The mobile smart home device automatic layout optimization system according to claim 5, characterized in that: 1) Automatically adjust the device position: For devices with automatic movement functions (such as sweeping robots, smart speakers, etc.), the system automatically moves these devices to the best position according to the optimization plan. The central control screen will display the real-time progress of the device movement, and users can intervene or cancel the movement operation at any time; 2) Manual adjustment suggestions: For devices that do not have the ability to move automatically, the system provides users with manual adjustment suggestions through the central control screen, including information such as the best placement of the device, the direction of movement, and the required distance of movement. Users can manually move the device according to the suggestions. The central control screen will update the device position in real time and record the user's movement operations. 3) User feedback collection: After the system completes the layout adjustment, a feedback window will pop up on the central control screen to ask the user about their satisfaction with the new layout and further suggestions for improvement. The user's feedback data will be stored in the system for subsequent adaptive optimization processes.

7. The mobile smart home device automatic layout optimization system according to claim 6, characterized in that: 1) Feedback collection and analysis: The system analyzes the feedback information submitted by users through the central control screen and adjusts the layout optimization plan based on the user's actual usage experience. The system will also automatically learn according to changes in user habits to adjust future optimization strategies. 2) Adaptive optimization algorithm: An adaptive optimization algorithm is an algorithm that can automatically adjust and optimize in a dynamic environment. Its core idea is to achieve continuous optimization by self-adjusting according to changes in the environment or system through continuous feedback and learning. In the smart home device layout optimization system, the main task of the adaptive optimization algorithm is to optimize the device layout in real time according to changes in user behavior, environmental conditions and device status, so that the system always maintains the best operating state. The system adjusts the weights of the multi-objective optimization algorithm based on the positive or negative feedback from users to make it more adaptable to the user's personalized needs. For example, if a user repeatedly reports that he is dissatisfied with the movement of a certain device, the system will adjust the algorithm to reduce the frequent movement of the device.

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