Smart home control method and smart home control system

Abstract

The invention discloses a smart home control method and a smart home control system, relates to the technical field of smart home, and aims to solve the technical problem that a current smart home system still has deficiencies in the aspects of intelligent degree, safety and personalized service. A deep learning model is adopted to predict user behaviors, based on user feedback and real-time data, a deep reinforcement learning formula is introduced, a safety protection mechanism process is reinforced, an AI-driven intrusion detection system is added, anomaly detection is performed on a video stream, abnormal behaviors in a home environment are identified, and an intelligent health management process is performed. Intelligent and automatic control of the home equipment is realized, the intelligent level of the home environment is improved, the safety protection capability is enhanced, personalized service development is promoted, the use efficiency and convenience of the equipment are improved, and the living experience and satisfaction of a user are remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and more specifically, to a smart home control method and a smart home control system. Background Art

[0002] Smart home uses residence as a platform and connects various devices in the home through Internet of Things technology to achieve an intelligent living environment. Smart home uses integrated wiring technology, network communication technology, security technology, automatic control technology, audio and video technology to integrate facilities related to home life, build an efficient management system for residential facilities and family schedule affairs, and improve home safety, convenience, comfort, and artistry.

[0003] However, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, smart home systems have become an important part of modern families. However, existing smart home systems still have shortcomings in terms of intelligence, security, and personalized services. Therefore, a more advanced and comprehensive smart home control method and system is needed to meet users' pursuit of high-quality life.

[0004] In view of this, we propose a smart home control method and a smart home control system. Summary of the invention

[0005] The purpose of the present invention is to provide a smart home control method and a smart home control system to solve the technical problems that the current smart home system still has deficiencies in intelligence, security and personalized services.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a smart home control method, comprising the following steps:

[0007] S1, advanced intelligent prediction and adaptive control process;

[0008] Use deep learning models to predict user behavior, and introduce deep reinforcement learning formulas based on user feedback and real-time data;

[0009] S2. Strengthen the security protection mechanism process;

[0010] By adding an AI-driven intrusion detection system, anomaly detection is performed on video streams to identify abnormal behavior in the home environment;

[0011] S3, Intelligent health management process;

[0012] By analyzing the user's sleep quality and integrating data from different health monitoring devices, a comprehensive health profile is formed and improvement suggestions are provided;

[0013] S4, advanced intelligent voice assistant process;

[0014] It uses semantic analysis algorithms in natural language processing technology to understand user instructions in combination with contextual information and make personalized content recommendations for users.

[0015] S5, Smart home scene automation and programming process;

[0016] By supporting user-defined scenario logic and control flow, combined with the use of workflow engine technology, automatic triggering and execution sequence control between tasks can be achieved;

[0017] S6. Family member management and authority control process;

[0018] By establishing a family member database, different device usage permissions and control ranges can be assigned to different family members.

[0019] The present invention designs the control mode of smart furniture by combining the usage scenarios and introduces standard algorithm formulas for verification, thus realizing the intelligent and automatic control of home appliances, improving the intelligent level of the home environment, enhancing the safety protection capability, promoting the development of personalized services, and improving the efficiency and convenience of equipment use, thus significantly enhancing the user's living experience and satisfaction. Users will be able to enjoy a more comfortable, safe, convenient and personalized smart home life.

[0020] Preferably, the S1 advanced intelligent prediction and adaptive control process also includes the following steps:

[0021] S101, behavior prediction;

[0022] A deep learning model is used to predict user behavior to improve the accuracy of behavior prediction. The deep learning model can be expressed as:

[0023]

[0024] Among them, Q, K, and V are query, key, and value matrices respectively;

[0025] dk is the dimension of the key;

[0026] S102, adaptive learning mechanism;

[0027] Based on user feedback and real-time data, the deep reinforcement learning formula DQN is introduced to handle more complex environments and strategies. The update formula of DQN can be expressed as:

[0028]

[0029] Among them, θ t and θ - are the parameters of the current network and the target network respectively;

[0030] a is the learning rate and γ is the discount factor.

[0031] Preferably, the S2 enhanced security protection mechanism process also includes the following steps:

[0032] S201. Multi-layer security protection: Add an AI-driven intrusion detection system and use the convolutional neural network (CNN) to detect anomalies in video streams. The convolutional layer operation of CNN can be expressed as:

[0033]

[0034] in, is the (i, j) position output of the kth feature map;

[0035] is the corresponding position value of the input feature map;

[0036] is the convolution kernel weight;

[0037] b k is the bias term;

[0038] f is the activation function;

[0039] S202, abnormal behavior detection: use the support vector machine algorithm to identify abnormal behaviors in the home environment. Let the feature vector be x and the abnormal behavior label be y. Then the decision function of the SVM classifier is:

[0040]

[0041] Among them, k is the kernel function;

[0042] a is the Lagrange multiplier;

[0043] b is the bias term.

[0044] Preferably, the S3 intelligent health management process also includes the following steps:

[0045] S301, sleep monitoring and improvement: Use sleep staging algorithm to analyze the user's sleep quality and provide improvement suggestions;

[0046] S302, Health data integration: Integrate data from different health monitoring devices through data fusion algorithms to form a comprehensive health record.

[0047] Preferably, the S4 advanced intelligent voice assistant process also includes the following steps:

[0048] S401, context understanding: using semantic analysis algorithms in natural language processing technology and combining context information to understand user instructions;

[0049] S402, Personalized recommendation: Use collaborative filtering algorithm to make personalized content recommendations based on the user's historical behavior and preferences.

[0050] Preferably, the S5 smart home scene automation and programming process also includes the following steps:

[0051] S501, scene programming: Provide graphical or coded programming tools to support user-defined scene logic and control flow;

[0052] S502, Automated Task Chain: Use workflow engine technology to achieve automatic triggering and execution sequence control between tasks.

[0053] Preferably, the S6 family member management and authority control process further includes the following steps:

[0054] S601, Family Member Management: Establish family member database, support adding, deleting, and modifying member information;

[0055] S602, fine-grained permission control: adopt a role-based access control model to assign different device usage permissions and control scopes to different family members.

[0056] Preferably, the sleep staging algorithm adopts an automatic staging algorithm based on brain waves, and the data fusion algorithm adopts Kalman filtering.

[0057] Preferably, the collaborative filtering algorithm adopts one of User-based CF and Item-based CF.

[0058] A smart home control system includes a central control unit for global data collection, processing, decision making and command distribution;

[0059] The central control unit is connected to an intelligent device layer for device control, a network communication layer for ensuring stable connection and efficient communication between devices, and a user interaction layer for providing multiple interaction methods, and the intelligent device layer, network communication layer, and user interaction layer are jointly connected to a cloud service platform for data storage, analysis, remote access, and software updates;

[0060] The central control unit integrates a high-performance processor, a big data processing module and an AI algorithm engine;

[0061] The smart device layer includes smart lighting systems, smart temperature control systems, smart security systems, and smart health monitoring devices;

[0062] The network communication layer adopts one or more of Zigbee, Wi-Fi, and Bluetooth communication protocols, and an integrated encryption module is integrated in the network communication layer;

[0063] The user interaction layer includes a smartphone APP module, a voice assistant module, and a touch screen panel module, and the touch screen panel is integrated with a user customization module, a scene mode selection module, and a device status viewing module;

[0064] The cloud service platform includes a data storage module, a data analysis module, a remote access module and a software update module.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The present invention designs the control mode of smart furniture by combining the usage scenarios and introduces standard algorithm formulas for verification, thus realizing the intelligent and automatic control of home appliances, improving the intelligent level of the home environment, enhancing the safety protection capability, promoting the development of personalized services, and improving the efficiency and convenience of equipment use, thus significantly enhancing the user's living experience and satisfaction. Users will be able to enjoy a more comfortable, safe, convenient and personalized smart home life. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a schematic block diagram of the smart home control system in the present invention;

[0068] Figure 2 is a schematic block diagram of the cloud service platform in the present invention;

[0069] Figure 3 is a schematic block diagram of the intelligent device layer in the present invention;

[0070] Figure 4 is a schematic block diagram of the network communication layer in the present invention;

[0071] Figure 5 is a schematic block diagram of the user interaction layer in the present invention;

[0072] Figure 6 It is a schematic block diagram of the central control unit in the present invention. DETAILED DESCRIPTION

[0073] In order to facilitate those skilled in the art to understand the technical solution of the present invention, the technical solution of the present invention is further described in conjunction with the accompanying drawings of the specification.

[0074] Embodiment 1:

[0075] The present invention provides a smart home control method, comprising the following steps:

[0076] S1, advanced intelligent prediction and adaptive control process;

[0077] Use deep learning models to predict user behavior, and introduce deep reinforcement learning formulas based on user feedback and real-time data;

[0078] S2. Strengthen the security protection mechanism process;

[0079] By adding an AI-driven intrusion detection system, anomaly detection is performed on video streams to identify abnormal behavior in the home environment;

[0080] S3, Intelligent health management process;

[0081] By analyzing the user's sleep quality and integrating data from different health monitoring devices, a comprehensive health profile is formed and improvement suggestions are provided;

[0082] S4, advanced intelligent voice assistant process;

[0083] It uses semantic analysis algorithms in natural language processing technology to understand user instructions in combination with contextual information and make personalized content recommendations for users.

[0084] S5, Smart home scene automation and programming process;

[0085] By supporting user-defined scenario logic and control flow, combined with the use of workflow engine technology, automatic triggering and execution sequence control between tasks can be achieved;

[0086] S6. Family member management and authority control process;

[0087] By establishing a family member database, different device usage permissions and control ranges can be assigned to different family members.

[0088] The present invention designs the control mode of smart furniture by combining the usage scenarios and introduces standard algorithm formulas for verification, thus realizing the intelligent and automatic control of home appliances, improving the intelligent level of the home environment, enhancing the safety protection capability, promoting the development of personalized services, and improving the efficiency and convenience of equipment use, thus significantly enhancing the user's living experience and satisfaction. Users will be able to enjoy a more comfortable, safe, convenient and personalized smart home life.

[0089] In an embodiment of the present invention, the S1 advanced intelligent prediction and adaptive control process further includes the following steps:

[0090] S101, behavior prediction;

[0091] A deep learning model is used to predict user behavior to improve the accuracy of behavior prediction. The deep learning model can be expressed as:

[0092]

[0093] Among them, Q, K, and V are query, key, and value matrices respectively;

[0094] dk is the dimension of the key;

[0095] S102, adaptive learning mechanism;

[0096] Based on user feedback and real-time data, the deep reinforcement learning formula DQN is introduced to handle more complex environments and strategies. The update formula of DQN can be expressed as:

[0097]

[0098] Among them, θ t and θ - are the parameters of the current network and the target network respectively;

[0099] a is the learning rate and γ is the discount factor.

[0100] In an embodiment of the present invention, the S2 enhanced security protection mechanism process further includes the following steps:

[0101] S201. Multi-layer security protection: Add an AI-driven intrusion detection system and use the convolutional neural network (CNN) to detect anomalies in video streams. The convolutional layer operation of CNN can be expressed as:

[0102]

[0103] in, is the (i, j) position output of the kth feature map;

[0104] is the corresponding position value of the input feature map;

[0105] is the convolution kernel weight;

[0106] b k is the bias term;

[0107] f is the activation function;

[0108] S202, abnormal behavior detection: use the support vector machine algorithm to identify abnormal behaviors in the home environment. Let the feature vector be x and the abnormal behavior label be y. Then the decision function of the SVM classifier is:

[0109]

[0110] Among them, k is the kernel function;

[0111] a is the Lagrange multiplier;

[0112] b is the bias term.

[0113] In an embodiment of the present invention, the S3 intelligent health management process further includes the following steps:

[0114] S301, sleep monitoring and improvement: Use sleep staging algorithm to analyze the user's sleep quality and provide improvement suggestions;

[0115] S302, Health data integration: Integrate data from different health monitoring devices through data fusion algorithms to form a comprehensive health record.

[0116] In an embodiment of the present invention, the S4 advanced intelligent voice assistant process also includes the following steps:

[0117] S401, context understanding: using semantic analysis algorithms in natural language processing technology and combining context information to understand user instructions;

[0118] S402, Personalized recommendation: Use collaborative filtering algorithm to make personalized content recommendations based on the user's historical behavior and preferences.

[0119] In an embodiment of the present invention, the S5 smart home scene automation and programming process further includes the following steps:

[0120] S501, scene programming: Provide graphical or coded programming tools to support user-defined scene logic and control flow;

[0121] S502, Automated Task Chain: Use workflow engine technology to achieve automatic triggering and execution sequence control between tasks.

[0122] In an embodiment of the present invention, the S6 family member management and authority control process further includes the following steps:

[0123] S601, Family Member Management: Establish family member database, support adding, deleting, and modifying member information;

[0124] S602, fine-grained permission control: adopt a role-based access control model to assign different device usage permissions and control scopes to different family members.

[0125] In an embodiment of the present invention, the sleep staging algorithm adopts an automatic staging algorithm based on brain waves, and the data fusion algorithm adopts Kalman filtering.

[0126] In an embodiment of the present invention, the collaborative filtering algorithm adopts one of User-based CF and Item-based CF.

[0127] Embodiment 2:

[0128] like Figures 1 to 6 As shown, the present invention provides a smart home control system, including a central control unit for global data collection, processing, decision making and command distribution;

[0129] The central control unit is connected to an intelligent device layer for device control, a network communication layer for ensuring stable connection and efficient communication between devices, and a user interaction layer for providing multiple interaction methods, and the intelligent device layer, network communication layer, and user interaction layer are jointly connected to a cloud service platform for data storage, analysis, remote access, and software updates;

[0130] The central control unit integrates a high-performance processor, a big data processing module and an AI algorithm engine;

[0131] The smart device layer includes smart lighting systems, smart temperature control systems, smart security systems, and smart health monitoring devices;

[0132] The network communication layer adopts one or more of Zigbee, Wi-Fi, and Bluetooth communication protocols, and an integrated encryption module is integrated in the network communication layer;

[0133] The user interaction layer includes a smartphone APP module, a voice assistant module, and a touch screen panel module, and the touch screen panel is integrated with a user customization module, a scene mode selection module, and a device status viewing module;

[0134] The cloud service platform includes a data storage module, a data analysis module, a remote access module and a software update module.

[0135] The embodiments of the present invention disclose preferred embodiments, but are not limited thereto. A person skilled in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not deviate from the spirit of the present invention, they are all within the protection scope of the present invention.

Claims

1. A smart home control method, characterized in that: The following steps are involved: S1, advanced intelligent prediction and adaptive control process; Use deep learning models to predict user behavior, and introduce deep reinforcement learning formulas based on user feedback and real-time data; S2. Strengthen the security protection mechanism process; By adding an AI-driven intrusion detection system, anomaly detection is performed on video streams to identify abnormal behavior in the home environment; S3, Intelligent health management process; By analyzing the user's sleep quality and integrating data from different health monitoring devices, a comprehensive health profile is formed and improvement suggestions are provided; S4, advanced intelligent voice assistant process; It uses semantic analysis algorithms in natural language processing technology to understand user instructions in combination with contextual information and make personalized content recommendations for users. S5, Smart home scene automation and programming process; By supporting user-defined scenario logic and control flow, combined with the use of workflow engine technology, automatic triggering and execution sequence control between tasks can be achieved; S6. Family member management and authority control process; By establishing a family member database, different device usage permissions and control ranges can be assigned to different family members.

2. A smart home control method according to claim 1, characterized in that: The S1 advanced intelligent prediction and adaptive control process also includes the following steps: S101, behavior prediction; A deep learning model is used to predict user behavior to improve the accuracy of behavior prediction. The deep learning model can be expressed as: Among them, Q, K, and V are query, key, and value matrices respectively; dk is the dimension of the key; S102, adaptive learning mechanism; Based on user feedback and real-time data, the deep reinforcement learning formula DQN is introduced to handle more complex environments and strategies. The update formula of DQN can be expressed as: Among them, θ t and θ - are the parameters of the current network and the target network respectively; a is the learning rate and γ is the discount factor.

3. A smart home control method according to claim 1, characterized in that: The S2 enhanced security protection mechanism process also includes the following steps: S201. Multi-layer security protection: Add an AI-driven intrusion detection system and use the convolutional neural network (CNN) to detect anomalies in video streams. The convolutional layer operation of CNN can be expressed as: in, is the (i, j) position output of the kth feature map; is the corresponding position value of the input feature map; is the convolution kernel weight; b k is the bias term; f is the activation function; S202, abnormal behavior detection: use the support vector machine algorithm to identify abnormal behaviors in the home environment. Let the feature vector be x and the abnormal behavior label be y. Then the decision function of the SVM classifier is: Among them, k is the kernel function; a is the Lagrange multiplier; b is the bias term.

4. The smart home control method according to claim 1, characterized in that: The S3 intelligent health management process also includes the following steps: S301, sleep monitoring and improvement: Use sleep staging algorithm to analyze the user's sleep quality and provide improvement suggestions; S302, Health data integration: Integrate data from different health monitoring devices through data fusion algorithms to form a comprehensive health record.

5. The smart home control method according to claim 1, characterized in that: The S4 advanced intelligent voice assistant process also includes the following steps: S401, context understanding: using semantic analysis algorithms in natural language processing technology and combining context information to understand user instructions; S402, Personalized recommendation: Use collaborative filtering algorithm to make personalized content recommendations based on the user's historical behavior and preferences.

6. A smart home control method according to claim 1, characterized in that: The S5 smart home scene automation and programming process also includes the following steps: S501, scene programming: Provide graphical or coded programming tools to support user-defined scene logic and control flow; S502, Automated Task Chain: Use workflow engine technology to achieve automatic triggering and execution sequence control between tasks.

7. The smart home control method according to claim 1, characterized in that: The S6 family member management and authority control process also includes the following steps: S601, Family Member Management: Establish family member database, support adding, deleting, and modifying member information; S602, fine-grained permission control: adopt a role-based access control model to assign different device usage permissions and control scopes to different family members.

8. A smart home control method according to claim 4, characterized in that: The sleep staging algorithm adopts an automatic staging algorithm based on brain waves, and the data fusion algorithm adopts Kalman filtering.

9. A smart home control method according to claim 5, characterized in that: The collaborative filtering algorithm adopts one of User-based CF and Item-based CF.

10. A smart home control system, used in a smart home control method according to claims 1 to 9, characterized in that: Includes a central control unit for global data collection, processing, decision making, and command distribution; The central control unit is connected to an intelligent device layer for device control, a network communication layer for ensuring stable connection and efficient communication between devices, and a user interaction layer for providing multiple interaction methods, and the intelligent device layer, network communication layer, and user interaction layer are jointly connected to a cloud service platform for data storage, analysis, remote access, and software updates; The central control unit integrates a high-performance processor, a big data processing module and an AI algorithm engine; The smart device layer includes smart lighting systems, smart temperature control systems, smart security systems, and smart health monitoring devices; The network communication layer adopts one or more of Zigbee, Wi-Fi, and Bluetooth communication protocols, and an integrated encryption module is integrated in the network communication layer; The user interaction layer includes a smartphone APP module, a voice assistant module, and a touch screen panel module, and the touch screen panel is integrated with a user customization module, a scene mode selection module, and a device status viewing module; The cloud service platform includes a data storage module, a data analysis module, a remote access module and a software update module.

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