Smart home regulation and control system and method based on floor mopping machine

Abstract

The invention provides a smart home regulation and control system and method based on a floor mopping machine. The method comprises the following steps: collecting real-time obstacle distribution conditions and activity track data of family members in a family environment; calculating an obstacle avoidance and seamless coverage cleaning path of the floor mopping machine based on the real-time obstacle distribution condition and the movement track data; evaluating home environment requirements by using a fuzzy logic algorithm to generate suitable living conditions; based on the suitable living conditions, the living habit data of the family members and the historical cleaning data, a clustering analysis method and a decision tree algorithm are applied to formulate a preliminary cleaning plan, the preliminary cleaning plan is optimized, and a personalized cleaning plan is generated; implementing the personalized cleaning plan to obtain an air quality index of the home environment, and generating an intelligent home regulation and control scheme based on the air quality index; according to the technical scheme provided by the invention, the intelligent level and user experience of the smart home system are improved, and the smart home system is more efficient, comfortable and environment-friendly.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of smart home technology, and in particular to a smart home control system and method based on a floor cleaning machine. Background Art

[0002] With the development of smart home technology, users have an increasing demand for intelligent management of their home environment. In terms of daily cleaning, users hope that automated equipment can complete cleaning tasks efficiently, safely and in a personalized manner.

[0003] The current floor-cleaning robot solutions on the market already have basic obstacle avoidance functions and fixed cleaning path planning, but these solutions still have significant shortcomings. For example, traditional cleaning equipment usually relies on simple sensors and preset paths to perform cleaning tasks, which are prone to jamming or collision problems in complex and changeable home environments, affecting cleaning efficiency and equipment life. Summary of the invention

[0004] The embodiments of the present invention provide a smart home control system and method based on a floor scrubbing machine, which are used to solve the problems in the prior art that the machine cannot adapt to changes in the home environment and is prone to jamming or collision, thus affecting the cleaning efficiency and life of the equipment.

[0005] In a first aspect, an embodiment of the present invention provides a smart home control method based on a floor cleaning machine, comprising: Collect real-time obstacle distribution in the home environment and activity trajectory data of family members; Based on the real-time obstacle distribution and the activity trajectory data, calculating the obstacle avoidance and seamless coverage cleaning path of the floor scrubber; Based on the obstacle avoidance and seamless coverage cleaning path, a fuzzy logic algorithm is used to evaluate the home environment requirements to generate suitable living conditions; Based on the suitable living conditions, the living habits data of the family members and the historical cleaning data, a cluster analysis method and a decision tree algorithm are applied to formulate a preliminary cleaning plan, optimize the preliminary cleaning plan, and generate a personalized cleaning plan; The personalized cleaning plan is implemented to obtain the air quality index of the home environment, and based on the air quality index, the suction strength and water flow of the floor scrubber are dynamically adjusted to generate a smart home control solution.

[0006] Optionally, based on the obstacle avoidance and seamless coverage cleaning path, a fuzzy logic algorithm is used to evaluate the home environment requirements to generate suitable living conditions, including: Based on the obstacle avoidance and seamless coverage cleaning path, the working status of the mopping machine and the home environment parameters, a fuzzy logic algorithm is used to evaluate the demand status of the home environment to obtain a target home environment demand evaluation result; Based on the home environment demand assessment result, adjusting the working state of the target smart home device to generate a target working state; Based on the target working state, an adaptive control algorithm is introduced to dynamically adjust the operating parameters of each smart home device to generate suitable living conditions, wherein the operating parameters include a wind speed setting value and a humidity setting value.

[0007] Optionally, based on the obstacle avoidance and seamless coverage cleaning path, the working state of the mopping machine and the home environment parameters, a fuzzy logic algorithm is used to evaluate the demand status of the home environment to obtain a target home environment demand evaluation result, including: Acquire home environment data from a plurality of sensors, wherein the home environment data includes home environment parameters; Analyze the home layout and device distribution using a graph neural network to optimize the application scope and accuracy of the home environment data and generate a home environment status description. During the optimization process, use a reinforcement learning algorithm to train the graph neural network so that the graph neural network can adapt to changes in the home layout in real time. Based on the obstacle avoidance and seamless coverage cleaning path, the working status of the mopping machine and the home environment status description, a fuzzy logic algorithm is used to evaluate the demand status of the home environment to obtain a preliminary home environment demand evaluation result. In the process of using the fuzzy logic algorithm for evaluation, a convolutional neural network is used to analyze historical environmental data to extract potential environmental features to optimize the evaluation; A support vector machine model is introduced to identify the optimal equipment configuration under different environmental conditions, and combined with the preliminary home environment demand assessment results, a target home environment demand assessment result is generated.

[0008] Optionally, based on the suitable living conditions, the living habits data of family members and the historical cleaning data, a cluster analysis method and a decision tree algorithm are applied to formulate a preliminary cleaning plan, optimize the preliminary cleaning plan, and generate a personalized cleaning plan, including: Using a cluster analysis method to group the suitable living conditions, family members' living habits data, and historical cleaning data to obtain a cleaning task classification; Based on the cleaning task classification, a decision tree algorithm is applied to determine an optimal cleaning sequence and an optimal cleaning frequency to generate a preliminary cleaning plan; Acquire activity images of family members, apply a target detection algorithm to analyze the activity images of family members to calculate the residence time and activity frequency of family members in each room, and use a bidirectional long short-term memory network to predict the long-term activity trend of family members based on the residence time and activity frequency, so as to refine the preliminary cleaning plan and obtain a refined cleaning plan; A reinforcement learning algorithm is used to simulate the application effects of different cleaning strategies in the refined cleaning plan to search for a combination of an optimal cleaning time and an optimal cleaning frequency. Based on the combination of the optimal cleaning time and the optimal cleaning frequency, a Bayesian optimization algorithm is used to optimize the refined cleaning plan to obtain a personalized cleaning plan.

[0009] Optionally, obtaining family member activity images, applying a target detection algorithm to analyze the family member activity images to calculate the residence time and activity frequency of the family members in each room, and using a bidirectional long short-term memory network to predict the long-term activity trend of the family members based on the residence time and activity frequency, so as to refine the preliminary cleaning plan and obtain a refined cleaning plan, including: Acquire family member activity images, and use a target detection algorithm to identify target objects in the family member activity images to obtain family member location information; Based on the location information of the family members, the residence time and activity frequency of the family members in each room are calculated, the residence time and activity frequency are classified by using a spatiotemporal graph convolutional network to obtain classification results, and based on the classification results, the behavior patterns of the family members are identified; Analyze the residence time, the activity frequency, and the behavior pattern using a bidirectional long short-term memory network to obtain a behavior analysis result, and predict the long-term activity trend of the family members based on the behavior analysis result to generate a long-term activity trend prediction result; Based on the long-term activity trend prediction results, the cleaning frequency and cleaning time corresponding to each room are adjusted to obtain the adjusted cleaning frequency and cleaning time. The preliminary cleaning plan is refined using the adjusted cleaning frequency and cleaning time to obtain a refined cleaning plan.

[0010] Optionally, based on the real-time obstacle distribution and the activity trajectory data, calculating the obstacle avoidance and seamless coverage cleaning path of the floor scrubber includes: Based on the real-time obstacle distribution and the activity trajectory data, generating fused data; Applying a convolutional neural network to perform feature extraction on the fused data to identify location information of obstacles and activity patterns of family members, generating a preliminary home environment map based on the location information of obstacles and activity patterns of family members, and using a synchronous positioning and map building algorithm to perform real-time updating processing on the preliminary home environment map to obtain an updated home environment map; Based on the updated home environment map, the A* algorithm and the plowing path planning algorithm are used to calculate the obstacle avoidance and seamless coverage cleaning path of the floor scrubber.

[0011] Optionally, the personalized cleaning plan is implemented to obtain an air quality index of the home environment, and based on the air quality index, the suction strength and water flow of the floor scrubber are dynamically adjusted to generate a smart home control scheme, including: Implementing the personalized cleaning plan, and using a variety of sensors to monitor in real time the air quality indicators of the home environment during the implementation of the personalized cleaning plan; Based on the air quality index, a long short-term memory network is used to predict the air quality change trend, and according to the air quality change trend and the demand conditions of the home environment, a fuzzy logic control system is used to dynamically adjust the suction strength and water flow of the floor scrubber to obtain an adjusted cleaning operation result; Based on the adjusted cleaning operation results, an ant colony optimization algorithm is used to generate a smart home control solution.

[0012] In a second aspect, an embodiment of the present invention provides a smart home control system based on a floor cleaning machine, comprising: A collection module is used to collect real-time obstacle distribution in the home environment and activity trajectory data of family members; A calculation module, used to calculate the obstacle avoidance and seamless coverage cleaning path of the floor scrubber based on the real-time obstacle distribution and the activity trajectory data; An evaluation module for the obstacle avoidance and seamless coverage cleaning path, using a fuzzy logic algorithm to evaluate home environment requirements to generate suitable living conditions; An optimization module, for formulating a preliminary cleaning plan based on the suitable living conditions, the living habits data of family members and the historical cleaning data, applying a cluster analysis method and a decision tree algorithm, optimizing the preliminary cleaning plan, and generating a personalized cleaning plan; The generation module is used to implement the personalized cleaning plan, obtain the air quality index of the home environment, and dynamically adjust the suction strength and water flow of the floor scrubber based on the air quality index to generate a smart home control plan.

[0013] In a third aspect, an embodiment of the present invention provides a computing device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any smart home control method based on a floor cleaning machine as described in the first aspect.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the smart home control method based on a floor cleaning machine as described in any one of the first aspects.

[0015] In an embodiment of the present invention, real-time obstacle distribution conditions and activity trajectory data of family members in a home environment are collected; based on the real-time obstacle distribution conditions and the activity trajectory data, the obstacle avoidance and seamless coverage cleaning path of a floor scrubber is calculated; based on the obstacle avoidance and seamless coverage cleaning path, a fuzzy logic algorithm is used to evaluate home environment requirements to generate suitable living conditions; based on the suitable living conditions, the living habit data of family members and historical cleaning data, a cluster analysis method and a decision tree algorithm are applied to formulate a preliminary cleaning plan, the preliminary cleaning plan is optimized, and a personalized cleaning plan is generated; the personalized cleaning plan is implemented to obtain air quality indicators of the home environment, and based on the air quality indicators, the suction strength and water flow of the floor scrubber are dynamically adjusted to generate a smart home control plan. The technical solution provided by the present invention ensures that the floor scrubber completes the cleaning task efficiently and safely by avoiding obstacles and seamlessly covering the cleaning path. It uses fuzzy logic algorithms to evaluate the needs of the family environment to generate suitable living conditions, and formulates personalized cleaning plans based on the living habits data and historical cleaning data of family members, which can adapt to the specific needs of different families and improve the user experience; dynamically adjust the suction strength and water flow of the floor scrubber to improve the cleaning effect and reduce resource waste; the smart home control solution minimizes energy consumption and noise impact while ensuring the cleaning effect, thereby optimizing the operating efficiency and performance of the entire smart home system and achieving efficient, comfortable and environmentally friendly home environment management. Among them, based on the long-term activity trend prediction results, the system dynamically adjusts the cleaning frequency and cleaning time of each room, refines the preliminary cleaning plan, generates a personalized cleaning plan, and ensures the best cleaning effect and user experience; the system monitors and predicts the trend of air quality changes in real time, and dynamically adjusts the operating parameters of the floor scrubber to improve the cleaning effect and reduce resource waste.

[0016] These and other aspects of the present invention will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a smart home control method based on a floor cleaning machine provided in an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a smart home control system based on a floor cleaning machine provided in an embodiment of the present invention; Figure 3A schematic diagram of the structure of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0020] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0021] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] Figure 1 A flowchart of a smart home control method based on a floor cleaning machine is provided for an embodiment of the present invention, such as Figure 1 As shown, the method includes: With the continuous development of smart home technology, users have put forward higher requirements for the intelligent management of the home environment. In terms of daily cleaning, users expect that automated equipment can not only complete cleaning tasks efficiently and safely, but also provide personalized cleaning solutions based on the living habits of family members and real-time environmental changes. In addition, real-time monitoring and improvement of indoor air quality is also an important requirement for improving living comfort. However, the existing floor scrubber solutions still have many shortcomings in terms of intelligent obstacle avoidance and air quality control. Based on this, the present invention provides a smart home control method based on a floor scrubber, such as Figure 1 ,include: Step 101: Collect real-time obstacle distribution in the home environment and activity trajectory data of family members; In this step, the real-time obstacle distribution refers to the real-time position and shape information of all obstacles in the home environment obtained by sensors (such as lidar, ultrasonic sensors, etc.). The activity trajectory data of family members refers to the movement paths and residence time of family members in different rooms recorded by cameras or other sensors (such as infrared sensors).

[0023] This step collects real-time obstacle distribution and family members’ activity trajectory data through sensors installed in various rooms of the home (such as LiDAR, ultrasonic sensors, cameras, etc.). For example, in a typical home environment, there may be furniture such as sofas and coffee tables in the living room, and the positions and shapes of these obstacles will be accurately captured by the LiDAR sensor. At the same time, the camera will record the activity trajectories of family members, such as children playing in the living room and adults cooking in the kitchen. These data are transmitted to the central control system for further processing.

[0024] Step 102: Calculating the obstacle avoidance and seamless coverage cleaning path of the floor scrubbing robot based on the real-time obstacle distribution and the activity trajectory data; In this step, obstacle avoidance and seamless coverage of the cleaning path refers to the optimal cleaning path of the mopping machine calculated based on the real-time obstacle distribution and the activity trajectory of family members, ensuring efficient and complete coverage of the entire cleaning area.

[0025] After receiving the obstacle distribution and activity trajectory data, this step uses an advanced path planning algorithm to combine these data to calculate the obstacle avoidance and seamless coverage cleaning path of the floor scrubber. For example, in the above scenario, the system will plan a path that avoids the sofa and coffee table and ensures that the floor scrubber can cover the entire living room area efficiently and without omission. This path planning not only improves cleaning efficiency, but also avoids the floor scrubber from jamming or colliding.

[0026] Step 103: Based on the obstacle avoidance and seamless coverage cleaning path, a fuzzy logic algorithm is used to evaluate the home environment requirements to generate suitable living conditions; After calculating the obstacle avoidance and seamless coverage cleaning path, this step uses fuzzy logic algorithms to evaluate the home environment needs and generate suitable living conditions. For example, if the current home environment has high humidity and odor, the fuzzy logic algorithm may recommend increasing the operating time and intensity of the air purifier, while adjusting the water flow of the floor scrubber to improve the cleaning effect. This method can flexibly respond to different environmental conditions and provide more comfortable living conditions.

[0027] Step 104: Based on the suitable living conditions, the living habits data of family members and the historical cleaning data, a cluster analysis method and a decision tree algorithm are applied to formulate a preliminary cleaning plan, optimize the preliminary cleaning plan, and generate a personalized cleaning plan; This step combines suitable living conditions, family members' living habits data (such as children often play in the living room, adults usually clean before going to bed at night), and historical cleaning data to develop a preliminary cleaning plan using cluster analysis methods and decision tree algorithms. For example, the system may classify family members into two categories: those who are active during the day and those who are active at night, and develop corresponding cleaning plans based on their activity time. Then, the preliminary cleaning plan is further optimized to generate a personalized cleaning plan. This ensures that the cleaning tasks meet the specific needs of each family member and improve the overall cleaning efficiency.

[0028] Step 105: Implement the personalized cleaning plan to obtain the air quality index of the home environment, and dynamically adjust the suction strength and water flow of the floor scrubber based on the air quality index to generate a smart home control plan; In this step, the air quality index refers to the quality parameters of the air in the home environment that are monitored in real time by a variety of sensors (such as PM2.5 sensors, carbon dioxide sensors, humidity sensors, etc.), including particulate matter concentration, carbon dioxide concentration, humidity, and temperature.

[0029] In this step, multiple sensors are used to monitor the air quality indicators of the home environment in real time during the implementation of the personalized cleaning plan. For example, the PM2.5 sensor may detect a high concentration of particulate matter in the living room area. Based on these air quality indicators, a long short-term memory network is used to predict future air quality trends, and a fuzzy logic control system is used to dynamically adjust the suction strength and water flow of the floor scrubber. Assuming that the particle concentration is predicted to continue to rise, the system will increase the suction strength and increase the water flow to more effectively remove dust and pollutants. Finally, based on the adjusted cleaning operation results, the ant colony optimization algorithm is used to generate a smart home control plan to ensure the best overall home environment management effect.

[0030] By collecting obstacle distribution and family members' activity trajectory data in real time, the embodiment of the present invention can calculate an efficient obstacle avoidance and seamless coverage cleaning path to ensure the safety and coverage of the cleaning process; use fuzzy logic algorithm to evaluate family environment needs, generate suitable living conditions, and improve living comfort; combine cluster analysis methods and decision tree algorithms to formulate and optimize personalized cleaning plans, so that cleaning tasks are more in line with the specific needs of each family member; by real-time monitoring of air quality and dynamically adjusting the operating parameters of the floor scrubber, the cleaning effect is further improved, and a fully optimized smart home control plan is generated. This method not only improves cleaning efficiency and user experience, but also significantly improves the cleanliness, comfort and intelligent management level of the home environment. In addition, through intelligent management and optimization, the system can maximize energy conservation and achieve green environmental protection goals while ensuring cleaning effects.

[0031] Traditional cleaning equipment usually relies on fixed parameter settings to perform cleaning tasks and cannot dynamically adjust its operation mode according to the complex home environment. In order to overcome this limitation, the present invention provides a specific embodiment, step 103, based on the obstacle avoidance and seamless coverage cleaning path, using a fuzzy logic algorithm to evaluate the home environment requirements to generate suitable living conditions, specifically including the following steps: Step 301: Based on the obstacle avoidance and seamless coverage cleaning path, the working status of the mopping machine and the home environment parameters, a fuzzy logic algorithm is used to evaluate the demand status of the home environment to obtain a target home environment demand evaluation result; In this step, the working state of the floor scrubber refers to the current operating parameters and running state of the floor scrubber, such as suction strength, water flow, battery power, etc. Home environment parameters refer to various environmental factors including temperature, humidity, air quality (such as PM2.5 concentration, carbon dioxide concentration), light intensity, etc.

[0032] This step collects the obstacle avoidance and seamless coverage cleaning path of the floor scrubber, the current working status (such as medium suction strength and low water flow), and home environment parameters (such as living room temperature of 22°C, humidity of 50%, and PM2.5 concentration of 30µg / m³). Then, the fuzzy logic algorithm is used to comprehensively analyze these data to evaluate the demand status of the home environment. For example, in a family with children, if the humidity in the bedroom is low and the PM2.5 concentration is high, the fuzzy logic algorithm may recommend increasing the humidity and increasing the operating intensity of the air purifier. This can generate the target home environment demand assessment results to guide the subsequent smart home device adjustments.

[0033] Step 302: Based on the home environment demand assessment result, adjust the working state of the target smart home device to generate a target working state; This step adjusts the working state of the target smart home devices based on the results of the home environment demand assessment. For example, the system will adjust the humidity setting value of the humidifier from 40% to 55%, and adjust the wind speed setting value of the air purifier from low to high. In addition, the system will adjust the temperature setting value of the air conditioner from 24°C to 22°C to ensure that the indoor temperature is suitable. Through these adjustments, the target working state is generated, so that various smart home devices can work together to improve the quality of the home environment.

[0034] Step 303: Based on the target working state, an adaptive control algorithm is introduced to dynamically adjust the operating parameters of each smart home device to generate suitable living conditions, wherein the operating parameters include a wind speed setting value and a humidity setting value; After generating the target working state, this step introduces an adaptive control algorithm to dynamically adjust the operating parameters of each smart home device based on real-time feedback. For example, when the humidity sensor detects that the humidity in the bedroom has reached 55%, the system will automatically reduce the operating intensity of the humidifier; if the PM2.5 sensor detects that the concentration of particulate matter is still high, the system will continue to maintain the high wind speed of the air purifier until the air quality meets the standard. Through this dynamic adjustment mechanism, the system can not only respond quickly to environmental changes, but also ensure that each device operates in the best state, thereby generating more suitable living conditions. Specific operating parameters include wind speed setpoints and humidity setpoints, as well as other related parameters such as temperature setpoints and lighting intensity.

[0035] The embodiment of the present invention uses a fuzzy logic algorithm to generate a target home environment demand assessment result by comprehensively analyzing the obstacle avoidance and seamless coverage cleaning path, the working status of the mopping machine and the home environment parameters, thereby ensuring the comprehensiveness and accuracy of the assessment; the working status of the target smart home device is adjusted based on the assessment result to generate a target working status, so that each device can work together to improve the quality of the home environment; by introducing an adaptive control algorithm to dynamically adjust the operating parameters of each smart home device, the system can quickly respond to environmental changes and ensure that the device operates in the best state, thereby generating more suitable living conditions.

[0036] In order to formulate a more accurate and personalized cleaning strategy, it is necessary to more accurately evaluate the needs of the home environment. Based on this, the present invention provides a specific embodiment, step 301, based on the obstacle avoidance and seamless coverage cleaning path, the working state of the mopping machine and the home environment parameters, the fuzzy logic algorithm is used to evaluate the needs of the home environment, and the target home environment needs evaluation result is obtained, which specifically includes the following steps: Step 311: Acquire home environment data from multiple sensors, where the home environment data includes home environment parameters; In this step, home environment data refers to home environment parameters obtained from multiple sensors (such as temperature sensors, humidity sensors, PM2.5 sensors, etc.), including temperature, humidity, air quality, etc.

[0037] This step obtains home environment data from multiple sensors installed in various rooms, such as temperature sensors, humidity sensors, PM2.5 sensors, etc. For example, in a typical home environment, the living room may be equipped with temperature sensors, humidity sensors, and PM2.5 sensors, which monitor and transmit data to the central control system in real time. Assume that the current temperature in the living room is 22°C, the humidity is 50%, and the PM2.5 concentration is 30 µg / m³. These data form the basis of the home environment parameters for use in subsequent steps.

[0038] Step 312: Analyze the home layout and device distribution using a graph neural network to optimize the application scope and accuracy of the home environment data, generate a home environment state description, and use a reinforcement learning algorithm to train the graph neural network during the optimization process so that the graph neural network can adapt to changes in the home layout in real time; In this step, the home environment status description refers to the overall description of the home environment generated based on the results of graph neural network analysis, including room layout, device location and its operating status.

[0039] This step uses graph neural networks to analyze the home layout and device distribution. For example, the system generates a graph-structured data model based on the location of sensors and the distribution of devices, where nodes represent rooms or devices and edges represent the connections between them. The graph neural network is then trained using a reinforcement learning algorithm so that it can adapt to changes in the home layout in real time. For example, when family members move furniture or add new devices, the graph neural network automatically adjusts its model to ensure the accuracy of the description of the home environment status. The final description of the home environment status includes the layout of each room, the location of the device, and its operating status, providing a comprehensive view of the home environment.

[0040] Step 313: Based on the obstacle avoidance and seamless coverage cleaning path, the working state of the mopping machine, and the home environment state description, a fuzzy logic algorithm is used to evaluate the demand status of the home environment to obtain a preliminary home environment demand evaluation result. In the process of using the fuzzy logic algorithm to evaluate, a convolutional neural network is used to analyze historical environmental data to extract potential environmental features to optimize the evaluation; This step combines obstacle avoidance and seamless coverage of the cleaning path, the working status of the mopping machine (such as medium suction intensity and low water flow), and the description of the home environment status, and uses a fuzzy logic algorithm to evaluate the demand status of the home environment. For example, in the above scenario, if the humidity in the bedroom is detected to be low and the PM2.5 concentration is high, the fuzzy logic algorithm may recommend increasing the humidity and increasing the operating intensity of the air purifier. In order to optimize the evaluation results, the system also uses convolutional neural networks to analyze historical environmental data and extract potential environmental features. For example, a convolutional neural network can analyze the humidity change trend in the bedroom over the past week to help the system more accurately predict future humidity needs. Through this comprehensive evaluation, the system generates preliminary home environment demand assessment results.

[0041] To accurately assess the needs of the home environment and optimize the operating parameters of smart home devices accordingly. The embodiment of the present invention also provides a calculation formula for the preliminary home environment needs assessment results. The design of this formula takes into account the multiple influences of current home environment parameters, historical environment data, family member activity patterns, and smart home device status. It uses advanced algorithms such as graph neural networks, fuzzy logic algorithms, and long short-term memory networks to dynamically adjust the cleaning strategy to achieve more intelligent and accurate home environment management. The specific calculation formula is as follows: ; Indicates the results of the preliminary home environment needs assessment; Represents the weighted average of home environment parameters The weight coefficient of Represents the weighted average of historical environmental data The weight coefficient of represents the weighted influence of family members' activity patterns The weight coefficient of Indicates the description of the home environment status Function The weight coefficient of Indicates the working status of the floor scrubber Function The weight coefficient of .

[0042] Through the application of the above-mentioned formula for the preliminary home environment needs assessment results, the system can significantly improve the accuracy and intelligence level of home environment assessment. Specifically, the formula comprehensively considers the current environmental parameters, historical data, family members' activity patterns and equipment status, making the preliminary home environment needs assessment results more comprehensive and accurate, and better reflecting the actual needs of the home environment; through this formula, the system can dynamically adjust the cleaning strategy according to the real-time changing home environment to ensure the best cleaning effect and living experience; through reasonable weight setting and dynamic adjustment, the system can maximize energy conservation and achieve the goal of green environmental protection while ensuring the cleaning effect; through this formula, the system can intelligently arrange cleaning tasks according to the habits and activity patterns of family members, avoid disturbing the normal lives of family members, and improve the overall living comfort.

[0043] Step 314: introducing a support vector machine model to identify optimal device configurations under different environmental conditions, and combining the preliminary home environment demand assessment results to generate target home environment demand assessment results; After generating the preliminary home environment needs assessment results, this step introduces the support vector machine model to identify the optimal device configuration under different environmental conditions. For example, the support vector machine model can recommend the most appropriate settings for humidifiers, air conditioners, and air purifiers based on the current humidity, temperature, and air quality indicators. Combined with the preliminary home environment needs assessment results, the system generates the target home environment needs assessment results. For example, the system may adjust the humidity setting value of the humidifier from 40% to 55%, and adjust the wind speed setting value of the air purifier from low to high. Through this comprehensive analysis and optimization, the system can generate more accurate target home environment needs assessment results, ensuring that each smart home device can operate in the best condition and improve the overall home environment quality.

[0044] The present invention obtains home environment data from multiple sensors, uses graph neural networks to analyze home layout and device distribution, and generates a home environment status description, thereby ensuring the scope and accuracy of data application; by analyzing historical environmental data through fuzzy logic algorithms combined with convolutional neural networks, the system can more accurately evaluate the demand status of the home environment and generate preliminary home environment demand assessment results; introduces a support vector machine model to identify the optimal device configuration under different environmental conditions, and generates a target home environment demand assessment result in combination with the preliminary assessment results, so that each smart home device can operate in the best state, thereby improving the quality of the overall home environment.

[0045] Traditional cleaning equipment usually relies on fixed parameter settings to perform cleaning tasks and cannot dynamically adjust its operating mode according to the unique needs of each family. In order to better meet the specific needs of different families, based on this, the present invention provides a specific embodiment, step 104, based on the suitable living conditions, family members' living habits data and historical cleaning data, apply cluster analysis methods and decision tree algorithms to formulate a preliminary cleaning plan, optimize the preliminary cleaning plan, and generate a personalized cleaning plan, specifically including the following steps: Step 401: using a cluster analysis method to group the suitable living conditions, family members' living habits data and historical cleaning data to obtain a cleaning task classification; In this step, the family members' living habits data refers to the time and location information of the family members' daily activities, for example, a family member usually goes to the kitchen between 7 and 8 in the morning. The historical cleaning data refers to the record of cleaning tasks in the past period of time, including cleaning time, frequency, and effect.

[0046] This step uses cluster analysis to group suitable living conditions, family members' living habits data, and historical cleaning data. For example, in a family of four, the system may divide the day into several time periods based on the habits of family members (such as 7 to 8 in the morning is breakfast time, 6 to 8 in the evening is dinner time), combined with suitable living conditions (such as the bedroom needs to maintain high humidity and low PM2.5 concentration) and historical cleaning data (such as a comprehensive cleaning every Saturday at 9 am). Through cluster analysis, the system groups these data and generates different cleaning task classifications, such as morning cleaning, evening cleaning, and weekend cleaning.

[0047] Step 402: Based on the cleaning task classification, a decision tree algorithm is applied to determine an optimal cleaning sequence and an optimal cleaning frequency to generate a preliminary cleaning plan; After generating the cleaning task classification, this step applies the decision tree algorithm to determine the optimal cleaning order and optimal cleaning frequency under each classification. For example, for the task of morning cleaning, the system may recommend cleaning the kitchen first (because family members frequently move around here in the morning) and then cleaning the living room; while for the task of weekend cleaning, it is recommended to start from the bedroom and gradually expand to other rooms. In this way, the system generates a preliminary cleaning plan. Assume that the decision tree algorithm analysis shows that the best cleaning order is to clean the high-frequency activity areas first and then the low-frequency activity areas, and the best cleaning frequency is to clean the high-frequency activity areas once a morning and perform a comprehensive cleaning once a week. This ensures that the cleaning tasks are both efficient and meet the actual needs of family members.

[0048] Step 403: Obtain family member activity images, apply a target detection algorithm to analyze the family member activity images to calculate the residence time and activity frequency of the family members in each room, and use a bidirectional long short-term memory network to predict the long-term activity trend of the family members based on the residence time and activity frequency, so as to refine the preliminary cleaning plan and obtain a refined cleaning plan; This step uses cameras or other sensors to obtain images of family members' activities, and applies target detection algorithms to analyze these images to calculate the residence time and activity frequency of family members in each room. For example, the system finds that a family member often spends two hours in the study on weekday evenings, and spends more time in the living room on weekends. The system then uses a bidirectional long short-term memory network to analyze these residence times and activity frequencies to predict the long-term activity trends of family members. Suppose the bidirectional long short-term memory network predicts that the activity time of the family member in the study will gradually decrease in the next few weeks, while the activity time in the living room will increase. Based on these prediction results, the system refines the preliminary cleaning plan, adjusts the cleaning frequency and key areas, and generates a refined cleaning plan. For example, the system may recommend reducing the cleaning frequency of the study and increasing the cleaning frequency of the living room.

[0049] Step 404: using a reinforcement learning algorithm to simulate the application effects of different cleaning strategies in the refined cleaning plan to search for a combination of an optimal cleaning time and an optimal cleaning frequency, and based on the combination of the optimal cleaning time and the optimal cleaning frequency, using a Bayesian optimization algorithm to optimize the refined cleaning plan to obtain a personalized cleaning plan; After generating a refined cleaning plan, this step uses a reinforcement learning algorithm to simulate the effects of different cleaning strategies to search for the best cleaning time and frequency combination. For example, the system simulates the effects of cleaning at different time periods (such as 7 a.m., 12 p.m., and 8 p.m.), and records the changes in environmental quality and feedback from family members after each cleaning. Through this simulation, the system finds the best cleaning time and frequency combination. Suppose the reinforcement learning algorithm finds that cleaning at 7 a.m. can significantly improve daytime air quality, while cleaning at 8 p.m. is more conducive to nighttime rest. Finally, the system uses the Bayesian optimization algorithm to further optimize the refined cleaning plan to ensure the best effect of the overall cleaning plan. For example, the Bayesian optimization algorithm may suggest focusing on high-frequency activity areas in the morning and on bedrooms in the evening, ultimately generating a personalized cleaning plan.

[0050] The embodiment of the present invention utilizes a cluster analysis method to group data on suitable living conditions, living habits of family members, and historical cleaning data, and generates a cleaning task classification, thereby ensuring the validity and practicality of the data; a decision tree algorithm is applied to determine the optimal cleaning sequence and frequency, and a preliminary cleaning plan is generated, thereby making the cleaning task more efficient and in line with actual needs; by acquiring activity images of family members and applying a target detection algorithm and a bidirectional long short-term memory network, the system can accurately calculate the residence time and activity frequency of family members, and predict their long-term activity trends, thereby refining the cleaning plan and improving the accuracy of the plan; by simulating the application effects of different cleaning strategies through a reinforcement learning algorithm, and optimizing the cleaning plan using a Bayesian optimization algorithm, the system can generate a personalized cleaning plan to ensure the best cleaning effect and user experience.

[0051] Traditional cleaning equipment cannot dynamically adjust its operation mode according to the specific activity mode of each family member. In order to better adapt to the unique living habits of different family members, based on this, the present invention provides a specific embodiment, step 403, obtain family member activity images, apply the target detection algorithm to analyze the family member activity images to calculate the residence time and activity frequency of family members in each room, based on the residence time and activity frequency, use the bidirectional long short-term memory network to predict the long-term activity trend of family members, so as to refine the preliminary cleaning plan and obtain the refined cleaning plan, which specifically includes the following steps: Step 411: acquiring family member activity images, and applying a target detection algorithm to identify target objects in the family member activity images to obtain family member location information; In this step, the family members' activity images refer to the activity videos or images of the family members in different rooms acquired through cameras or other sensors.

[0052] This step obtains images of family members' activities through cameras installed in each room. For example, in a family of four, the system will capture the movement of a family member between the living room, bedroom, and kitchen. Then, the target detection algorithm is applied to analyze these images to identify the specific location of the family members. Suppose the system detects that a family member appears in the kitchen at 7 am and then enters the living room at 8 am, so the system can obtain the location information of the family member in each room.

[0053] Step 412: Based on the family member location information, calculate the residence time and activity frequency of the family members in each room, classify the residence time and activity frequency using a spatiotemporal graph convolutional network to obtain a classification result, and identify the behavior pattern of the family members based on the classification result; In this step, family member location information refers to the specific location of family members in each room identified by the target detection algorithm. Dwelling time and activity frequency refer to the length of time and frequency of family members staying in each room. Behavior patterns refer to behavioral characteristics identified based on the dwelling time and activity frequency of family members, such as being active in the kitchen in the morning and resting in the living room at night.

[0054] After obtaining the location information of family members, this step calculates their residence time and activity frequency in each room. For example, the system finds that a family member is active in the kitchen from 7 to 8 am every day and in the living room from 5 to 7 pm. Then, the system uses the spatiotemporal graph convolutional network to classify these residence times and activity frequencies and generate classification results. Assume that the spatiotemporal graph convolutional network classifies the above behaviors as activities in the kitchen in the morning and activities in the living room in the afternoon. Based on these classification results, the system further identifies the behavior patterns of family members, such as preparing breakfast in the kitchen in the morning and relaxing in the living room in the afternoon.

[0055] Step 413: using a bidirectional long short-term memory network to analyze the residence time, the activity frequency, and the behavior pattern to obtain a behavior analysis result, and based on the behavior analysis result, predicting the long-term activity trend of the family members to generate a long-term activity trend prediction result; This step uses the bidirectional long short-term memory network to comprehensively analyze the residence time, activity frequency, and behavior patterns to generate behavior analysis results. For example, the bidirectional long short-term memory network may find that a family member's activity frequency on weekends is significantly higher than on weekdays, and more time is spent in the living room and study. Based on these behavior analysis results, the system further predicts the long-term activity trends of family members. Suppose the bidirectional long short-term memory network predicts that in the next few weeks, the family member will be more active in the living room and study on weekends, and mainly in the kitchen and bedroom on weekdays. In this way, the system generates long-term activity trend prediction results and provides a prediction of future family members' activities.

[0056] Step 414: Based on the long-term activity trend prediction result, the cleaning frequency and cleaning time corresponding to each room are adjusted to obtain adjusted cleaning frequency and cleaning time, and the preliminary cleaning plan is refined using the adjusted cleaning frequency and cleaning time to obtain a refined cleaning plan; After this step generates the long-term activity trend forecast results, the system dynamically adjusts the cleaning frequency and cleaning time of each room based on these forecast results. For example, if the forecast results show that a family member will be more active in the living room and study on weekends in the next few weeks, the system will increase the cleaning frequency of these two rooms accordingly, and may adjust the cleaning time to a time period before the weekend. Suppose the system decides to increase the cleaning frequency of the living room from once a week to once every three days, and the cleaning frequency of the study from twice a week to once every two days. In this way, the system refines the preliminary cleaning plan and generates a more accurate refined cleaning plan to ensure that the cleaning tasks of each room are both efficient and meet actual needs.

[0057] By acquiring images of family members' activities and applying a target detection algorithm, the system of the embodiment of the present invention can accurately identify the specific locations of family members, thereby ensuring the accuracy of the data; based on the location information of family members, the system calculates their residence time and activity frequency in each room, and uses a spatiotemporal graph convolutional network for classification to identify the behavior patterns of family members, thereby improving the depth and breadth of data analysis; by analyzing residence time, activity frequency and behavior patterns through a bidirectional long short-term memory network, the system can predict the long-term activity trends of family members and provide the ability to predict future activities; based on the long-term activity trend prediction results, the system dynamically adjusts the cleaning frequency and cleaning time of each room, refines the preliminary cleaning plan, and generates a personalized cleaning plan to ensure the best cleaning effect and user experience.

[0058] Traditional cleaning equipment usually relies on simple sensors and preset paths to perform cleaning tasks, which makes them incapable of handling dynamically changing home environments. In order to overcome this limitation, the present invention provides a specific embodiment, step 102, based on the real-time obstacle distribution and the activity trajectory data, calculating the obstacle avoidance and seamless coverage cleaning path of the floor scrubber, specifically comprising the following steps: Step 201: generating fused data based on the real-time obstacle distribution and the activity trajectory data; In this step, the system fuses the real-time obstacle distribution and activity trajectory data to generate fused data as the basis for subsequent analysis.

[0059] Step 202: applying a convolutional neural network to perform feature extraction on the fused data to identify the location information of obstacles and the activity patterns of family members, generating a preliminary home environment map based on the location information of obstacles and the activity patterns of family members, and using a synchronous positioning and map building algorithm to perform real-time updating processing on the preliminary home environment map to obtain an updated home environment map; After generating the fused data, this step applies a convolutional neural network to extract features from the data and identify the location information of obstacles and the activity patterns of family members. For example, a convolutional neural network can identify the specific locations of obstacles such as sofas and coffee tables in the living room, and determine the time periods and frequencies of family members' activities in the living room. Based on these features, the system generates a preliminary home environment map. To ensure the accuracy and real-time nature of the map, the system uses a synchronous positioning and mapping algorithm to update the preliminary home environment map in real time. Assuming that a family member moves a piece of furniture or changes their activity pattern, the synchronous positioning and mapping algorithm will automatically adjust the map to ensure that it is always up to date, and finally generate an updated home environment map.

[0060] Step 203: Based on the updated home environment map, the obstacle avoidance and seamless coverage cleaning path of the floor scrubber is calculated using the A* algorithm and the plowing path planning algorithm; After generating the updated home environment map in this step, the system uses the A* algorithm and the plowing path planning algorithm to calculate the obstacle avoidance and seamless coverage cleaning path of the floor scrubber. For example, the system uses the A* algorithm to calculate the optimal path from the starting point to the end point based on the distribution of obstacles in the updated home environment map to ensure that all obstacles are avoided. At the same time, the system uses the plowing path planning algorithm to ensure seamless coverage of the cleaning area to avoid missing or repeated cleaning. Suppose the system calculates a cleaning path that starts from the entrance of the living room, bypasses the sofa and coffee table, and finally covers the entire living room. This path planning not only improves cleaning efficiency, but also ensures cleaning quality.

[0061] By fusing real-time obstacle distribution and activity trajectory data, the system generates fused data, providing a comprehensive basis for subsequent analysis; a convolutional neural network is used to extract features from the fused data, identify the location information of obstacles and the activity patterns of family members, generate a preliminary home environment map, and update the map in real time through a synchronous positioning and map construction algorithm, thereby ensuring the accuracy and real-time nature of the map; based on the updated home environment map, the system uses the A* algorithm and the plowing path planning algorithm to calculate the obstacle avoidance and seamless coverage cleaning path of the mopping machine, thereby ensuring that the cleaning task is both efficient and without omissions.

[0062] Traditional cleaning equipment usually relies on fixed suction strength and water flow settings to perform cleaning tasks, and cannot be dynamically adjusted according to changes in real-time air quality. In order to overcome this limitation, based on this, the present invention provides a specific embodiment, step 105, implement the personalized cleaning plan, obtain the air quality index of the home environment, and dynamically adjust the suction strength and water flow of the floor scrubber based on the air quality index to generate a smart home control plan, specifically including the following steps: Step 501: Implement the personalized cleaning plan, and use multiple sensors to monitor the air quality indicators of the home environment in real time during the implementation of the personalized cleaning plan.

[0063] When the personalized cleaning plan is started, the mopping machine will start the cleaning task according to the pre-set path and operating parameters. At the same time, the system will use a variety of sensors installed in the room (such as PM2.5 sensors, carbon dioxide sensors, humidity sensors, etc.) to monitor air quality indicators in real time. For example, in a home with children and pets, the sensor may detect a higher PM2.5 concentration in the living room area and a higher humidity in the bedroom area. These real-time data will be transmitted to the central control system for further analysis and processing.

[0064] Step 502: Based on the air quality index, a long short-term memory network is used to predict the air quality change trend, and according to the air quality change trend and the demand conditions of the home environment, a fuzzy logic control system is used to dynamically adjust the suction strength and water flow of the floor scrubber to obtain an adjusted cleaning operation result; After receiving the air quality indicators, this step uses the long short-term memory network to analyze these data and predict future air quality trends. For example, if the long short-term memory network predicts that the PM2.5 concentration in the living room area will increase significantly in the next hour, the system will use the fuzzy logic control system to dynamically adjust the suction strength and water flow of the floor scrubber based on this trend and the current family environment needs (such as children playing in the living room). Assuming the current PM2.5 concentration is 80µg / m³, the system may increase the suction strength from the default value to the maximum value and increase the water flow to enhance the cleaning effect. This dynamic adjustment mechanism ensures that the floor scrubber can more effectively remove dust and pollutants when the air quality deteriorates.

[0065] Step 503: Based on the adjusted cleaning operation result, a smart home control scheme is generated using an ant colony optimization algorithm; For example, after each cleaning task is completed, the system records the actual operating parameters of the floor scrubber (such as suction strength, water flow, cleaning time and path, etc.), and compares them with the predicted air quality change trend to evaluate the cleaning effect. For example, if it is found that the adjustment of suction strength and water flow effectively reduces the PM2.5 concentration, the system will use these data as the basis for optimization. Then, using the ant colony optimization algorithm, the system generates an optimized smart home control plan. This plan not only takes into account the cleaning effect, but also minimizes energy consumption and noise impact. For example, the system may recommend reducing unnecessary equipment operation during a specific time period, or adjusting the working mode of air conditioners and air purifiers to achieve the best overall home environment management effect.

[0066] The embodiment of the present invention ensures the pertinence and effectiveness of cleaning tasks by formulating personalized cleaning plans; monitors and predicts air quality change trends in real time, and dynamically adjusts the operating parameters of the floor scrubber to improve cleaning effects and reduce resource waste; and uses the smart home control scheme generated by the ant colony optimization algorithm to further optimize the operating efficiency and performance of the entire system. This method not only improves the cleanliness and comfort of the home environment, but also significantly improves the user's living experience and quality of life.

[0067] Figure 2 The present invention provides a schematic diagram of the structure of a smart home control system based on a floor cleaning machine, such as Figure 2 As shown, the system includes: A collection module 21 is used to collect real-time obstacle distribution in the home environment and activity trajectory data of family members; A calculation module 22, used to calculate the obstacle avoidance and seamless coverage cleaning path of the floor scrubber based on the real-time obstacle distribution and the activity trajectory data; An evaluation module 23, for the obstacle avoidance and seamless coverage cleaning path, uses a fuzzy logic algorithm to evaluate the home environment requirements to generate suitable living conditions; An optimization module 24 is used to formulate a preliminary cleaning plan based on the suitable living conditions, the living habits data of family members and the historical cleaning data by applying a cluster analysis method and a decision tree algorithm, optimize the preliminary cleaning plan, and generate a personalized cleaning plan; The generation module 25 is used to implement the personalized cleaning plan, obtain the air quality index of the home environment, and dynamically adjust the suction strength and water flow of the floor scrubber based on the air quality index to generate a smart home control plan.

[0068] Figure 2 The smart home control system based on the floor cleaning machine can perform Figure 1 The implementation principle and technical effect of the smart home control method based on the mopping machine described in the illustrated embodiment will not be described in detail. The specific manner in which each module and unit performs operations in the smart home control system based on the mopping machine in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0069] In one possible design, Figure 2 The smart home control system based on the floor cleaning machine of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0070] The processing component 32 is used to: collect real-time obstacle distribution and activity trajectory data of family members in the home environment; calculate the obstacle avoidance and seamless coverage cleaning path of the floor scrubber based on the real-time obstacle distribution and the activity trajectory data; evaluate the home environment requirements based on the obstacle avoidance and seamless coverage cleaning path using a fuzzy logic algorithm to generate suitable living conditions; based on the suitable living conditions, the living habits data of family members and historical cleaning data, apply cluster analysis methods and decision tree algorithms to formulate a preliminary cleaning plan, optimize the preliminary cleaning plan, and generate a personalized cleaning plan; implement the personalized cleaning plan to obtain the air quality index of the home environment, and based on the air quality index, dynamically adjust the suction strength and water flow of the floor scrubber to generate a smart home control plan.

[0071] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0072] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0073] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0074] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0075] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0076] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0077] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 The illustrated embodiment is a smart home control method based on a floor cleaning machine.

[0078] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0079] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0080] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart home control method based on a floor cleaning machine, characterized in that: include: Collect real-time obstacle distribution in the home environment and activity trajectory data of family members; Based on the real-time obstacle distribution and the activity trajectory data, calculating the obstacle avoidance and seamless coverage cleaning path of the floor scrubber; Based on the obstacle avoidance and seamless coverage cleaning path, a fuzzy logic algorithm is used to evaluate the home environment requirements to generate suitable living conditions; Based on the suitable living conditions, the living habits data of the family members and the historical cleaning data, a cluster analysis method and a decision tree algorithm are applied to formulate a preliminary cleaning plan, optimize the preliminary cleaning plan, and generate a personalized cleaning plan; The personalized cleaning plan is implemented to obtain the air quality index of the home environment, and based on the air quality index, the suction strength and water flow of the floor scrubber are dynamically adjusted to generate a smart home control solution.

2. The method according to claim 1, characterized in that Based on the obstacle avoidance and seamless coverage cleaning path, a fuzzy logic algorithm is used to evaluate the home environment requirements to generate suitable living conditions, including: Based on the obstacle avoidance and seamless coverage cleaning path, the working status of the mopping machine and the home environment parameters, a fuzzy logic algorithm is used to evaluate the demand status of the home environment to obtain a target home environment demand evaluation result; Based on the home environment demand assessment result, adjusting the working state of the target smart home device to generate a target working state; Based on the target working state, an adaptive control algorithm is introduced to dynamically adjust the operating parameters of each smart home device to generate suitable living conditions, wherein the operating parameters include a wind speed setting value and a humidity setting value.

3. The method according to claim 2, characterized in that Based on the obstacle avoidance and seamless coverage cleaning path, the working status of the mopping machine and the home environment parameters, the fuzzy logic algorithm is used to evaluate the demand status of the home environment, and the target home environment demand evaluation results are obtained, including: Acquire home environment data from a plurality of sensors, wherein the home environment data includes home environment parameters; Analyze the home layout and device distribution using a graph neural network to optimize the application scope and accuracy of the home environment data and generate a home environment status description. During the optimization process, use a reinforcement learning algorithm to train the graph neural network so that the graph neural network can adapt to changes in the home layout in real time. Based on the obstacle avoidance and seamless coverage cleaning path, the working status of the mopping machine and the home environment status description, a fuzzy logic algorithm is used to evaluate the demand status of the home environment to obtain a preliminary home environment demand evaluation result. In the process of using the fuzzy logic algorithm for evaluation, a convolutional neural network is used to analyze historical environmental data to extract potential environmental features to optimize the evaluation; A support vector machine model is introduced to identify the optimal equipment configuration under different environmental conditions, and combined with the preliminary home environment demand assessment results, a target home environment demand assessment result is generated.

4. The method according to claim 1, characterized in that: Based on the suitable living conditions, the living habits data of the family members and the historical cleaning data, a cluster analysis method and a decision tree algorithm are applied to formulate a preliminary cleaning plan, optimize the preliminary cleaning plan, and generate a personalized cleaning plan, including: Using a cluster analysis method to group the suitable living conditions, family members' living habits data, and historical cleaning data to obtain a cleaning task classification; Based on the cleaning task classification, a decision tree algorithm is applied to determine an optimal cleaning sequence and an optimal cleaning frequency to generate a preliminary cleaning plan; Acquire activity images of family members, apply a target detection algorithm to analyze the activity images of family members to calculate the residence time and activity frequency of family members in each room, and use a bidirectional long short-term memory network to predict the long-term activity trend of family members based on the residence time and activity frequency, so as to refine the preliminary cleaning plan and obtain a refined cleaning plan; A reinforcement learning algorithm is used to simulate the application effects of different cleaning strategies in the refined cleaning plan to search for a combination of an optimal cleaning time and an optimal cleaning frequency. Based on the combination of the optimal cleaning time and the optimal cleaning frequency, a Bayesian optimization algorithm is used to optimize the refined cleaning plan to obtain a personalized cleaning plan.

5. The method according to claim 4, characterized in that Acquire family member activity images, apply target detection algorithm to analyze the family member activity images to calculate the residence time and activity frequency of family members in each room, and use bidirectional long short-term memory network to predict the long-term activity trend of family members based on the residence time and activity frequency, so as to refine the preliminary cleaning plan and obtain a refined cleaning plan, including: Acquire family member activity images, and use a target detection algorithm to identify target objects in the family member activity images to obtain family member location information; Based on the location information of the family members, the residence time and activity frequency of the family members in each room are calculated, the residence time and activity frequency are classified by using a spatiotemporal graph convolutional network to obtain classification results, and based on the classification results, the behavior patterns of the family members are identified; Analyze the residence time, the activity frequency, and the behavior pattern using a bidirectional long short-term memory network to obtain a behavior analysis result, and predict the long-term activity trend of the family members based on the behavior analysis result to generate a long-term activity trend prediction result; Based on the long-term activity trend prediction results, the cleaning frequency and cleaning time corresponding to each room are adjusted to obtain the adjusted cleaning frequency and cleaning time. The preliminary cleaning plan is refined using the adjusted cleaning frequency and cleaning time to obtain a refined cleaning plan.

6. The method according to claim 1, characterized in that Based on the real-time obstacle distribution and the activity trajectory data, the obstacle avoidance and seamless coverage cleaning path of the floor scrubber are calculated, including: Based on the real-time obstacle distribution and the activity trajectory data, generating fused data; Applying a convolutional neural network to perform feature extraction on the fused data to identify location information of obstacles and activity patterns of family members, generating a preliminary home environment map based on the location information of obstacles and activity patterns of family members, and using a synchronous positioning and map building algorithm to perform real-time updating processing on the preliminary home environment map to obtain an updated home environment map; Based on the updated home environment map, the A* algorithm and the plowing path planning algorithm are used to calculate the obstacle avoidance and seamless coverage cleaning path of the floor scrubber.

7. The method according to claim 1, characterized in that Implement the personalized cleaning plan to obtain the air quality index of the home environment, and dynamically adjust the suction strength and water flow of the floor scrubber based on the air quality index to generate a smart home control solution, including: Implementing the personalized cleaning plan, and using a variety of sensors to monitor in real time the air quality indicators of the home environment during the implementation of the personalized cleaning plan; Based on the air quality index, a long short-term memory network is used to predict the air quality change trend, and according to the air quality change trend and the demand conditions of the home environment, a fuzzy logic control system is used to dynamically adjust the suction strength and water flow of the floor scrubber to obtain an adjusted cleaning operation result; Based on the adjusted cleaning operation results, an ant colony optimization algorithm is used to generate a smart home control solution.

8. The intelligent home control system based on the floor cleaning machine is characterized by: include: A collection module is used to collect real-time obstacle distribution in the home environment and activity trajectory data of family members; A calculation module, used to calculate the obstacle avoidance and seamless coverage cleaning path of the floor scrubber based on the real-time obstacle distribution and the activity trajectory data; An evaluation module for the obstacle avoidance and seamless coverage cleaning path, using a fuzzy logic algorithm to evaluate home environment requirements to generate suitable living conditions; An optimization module, for formulating a preliminary cleaning plan based on the suitable living conditions, the living habits data of family members and the historical cleaning data, applying a cluster analysis method and a decision tree algorithm, optimizing the preliminary cleaning plan, and generating a personalized cleaning plan; The generation module is used to implement the personalized cleaning plan, obtain the air quality index of the home environment, and dynamically adjust the suction strength and water flow of the floor scrubber based on the air quality index to generate a smart home control plan.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the smart home control method based on the floor cleaning machine as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, the smart home control method based on the floor cleaning machine as described in any one of claims 1 to 7 is implemented.

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