A smart home control method and system

Abstract

The present invention discloses a smart home control method, which specifically includes the following steps: S1, data collection; S2, data analysis and modeling; S3, intelligent scene mode switching; S4, intelligent energy-saving algorithm application; S5, user feedback and system optimization. The present invention can automatically detect the user's status through real-time analysis of user behavior data, and switch to the corresponding mode according to the detection result. Through automatic control and dynamic environmental adjustment, the system significantly improves the user's quality of life. The user does not need to manually operate the smart device, and the system can automatically adapt to the user's living habits and environmental changes to provide a more comfortable and convenient living environment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent home control, and in particular to an intelligent home control method and system. Background Art

[0002] With the rapid development of the Internet of Things (IoT), smart home systems have become widely used in daily life. These systems connect various devices and sensors in the home via the internet, enabling remote monitoring and control of these devices, significantly improving convenience and comfort. Currently, smart home systems on the market are typically remotely controlled via devices such as smartphones and tablets. These systems provide basic device management and control functions, such as lighting control, temperature regulation, and security monitoring.

[0003] In daily life, user behavior patterns have a significant impact on the effective control of smart home systems. This is particularly true of sleep patterns, which significantly alter the user's demands on the home environment. For example, after falling asleep, high-brightness lighting is no longer required, and the air conditioning temperature needs to be adjusted appropriately to ensure a comfortable sleeping environment. However, existing smart home systems still have relatively crude control in this area, failing to accurately detect and respond to users' sleep behaviors.

[0004] With the advancement of sensor technology, smart home systems are able to capture an increasing amount of real-time environmental data, such as temperature, humidity, and light intensity. This data is crucial for optimizing device control. By monitoring and analyzing this real-time environmental data, smart home systems can achieve more refined device control, such as automatically adjusting indoor lighting brightness based on outdoor light intensity or dynamically adjusting air conditioning operation based on indoor temperature and humidity. However, existing systems are still limited in this regard and are unable to fully utilize environmental data for dynamic, optimized device control. Summary of the Invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a smart home control method, which specifically includes the following steps:

[0008] S1. Data collection: Detecting environmental parameters and human activities in the room through sensors installed in each room and smart devices worn by users;

[0009] S2. Data Analysis and Modeling: Use machine learning algorithms to analyze collected environmental and behavioral data, establish user behavior models and environmental models, and predict changes in user behavior and the environment;

[0010] S3, Intelligent scene mode switching: Preset multiple scene modes based on analyzed behavior patterns, and automatically switch to the corresponding scene mode based on real-time monitored user behavior and environmental data;

[0011] S4. Application of intelligent energy-saving algorithms: Continuously collect environmental sensor data, analyze current environmental conditions through intelligent energy-saving algorithms, and dynamically adjust the working status of smart home devices based on the results of environmental data analysis;

[0012] S5. User feedback and system optimization: Collect user feedback and suggestions through the application, and continuously optimize the control method of smart home based on the suggestions.

[0013] As a preferred solution of the smart home control method of the present invention, the construction of the user behavior model in S2 specifically includes the following steps:

[0014] Data preprocessing: preprocess the collected user behavior data including motion data, heart rate data, and environmental data;

[0015] Sleep behavior judgment: Based on the pre-processed data, the comprehensive sleep state judgment value is obtained through comprehensive calculation to make judgments;

[0016] Mode switching: When the user is determined to be asleep, the system switches to sleep mode and adjusts associated devices.

[0017] As a preferred solution of the smart home control method of the present invention, the calculation formula of the comprehensive sleep state judgment value is as follows:

[0018]

[0019] Where A(t) is the user's motion data at time t, indicating the user's motion amplitude or frequency at time t; H(t) is the heart rate data at time t, indicating the user's heart rate at time t; E(t) is the environmental data at time t, indicating the temperature, humidity, and light intensity of the user's surrounding environment; α, β, and γ are weight coefficients, which are adjusted according to the specific application scenario; μ A , μ H , μ E Represent the mean of motion data, heart rate data, and environmental data respectively; σA , σ H , σ E are the standard deviations of motion data, heart rate data, and environmental data respectively; n, m, and p are the number of sampling points of motion data, heart rate data, and environmental data respectively.

[0020] As a preferred solution of the smart home control method described in the present invention, when the S(t) value is small and reaches a preset sleep judgment threshold, it indicates that the user is in a relatively static state and can be judged as being in a sleep state; when the S(t) value is large and exceeds the preset sleep judgment threshold, it indicates that the user is in an active state and does not meet the sleep characteristics. The specific expression formula is as follows:

[0021]

[0022] Where S is the preset threshold, and D is the indicator of whether it is a sleep state.

[0023] As a preferred solution of the smart home control method of the present invention, the construction of the environment model in S2 specifically includes the following steps:

[0024] Data preprocessing: Preprocess the collected environmental data, including temperature, humidity, light intensity, and equipment power consumption;

[0025] Environmental data analysis: Analyze the current environmental status through complex functions based on environmental data;

[0026] Equipment work turntable adjustment: adjust the working status of the equipment according to the analysis results to achieve energy saving goals.

[0027] As a preferred solution of the smart home control method of the present invention, the environmental data analysis formula is as follows:

[0028]

[0029] Where E(t) is the total energy consumption of the environment at time t; T(t), H(t), L(t), and P(t) are the ambient temperature, humidity, light intensity, and equipment power consumption data, respectively; μ T 、μ H 、μ L 、μ P are the mean values ​​of temperature, humidity, light, and equipment power consumption data respectively; σ T , σ H , σ L , σ P are the standard deviations of temperature, humidity, light, and equipment power consumption data respectively; n, m, p, and q are the number of sampling points of temperature, humidity, light, and equipment power consumption data respectively; α, β, γ, and δ are weight coefficients.

[0030] As a preferred embodiment of the smart home control method described in the present invention, when the E(t) value is small and the difference from the preset threshold is within a preset range, it indicates that both the environmental data and the device power consumption meet the energy-saving characteristics, and the system determines that the current device operating state is an energy-saving state; when the E(t) value is large and the difference from the preset threshold exceeds the preset range, it indicates that the environmental data and the device power consumption deviate from the energy-saving characteristics, and the system needs to adjust the device operating state to achieve the energy-saving goal. The specific formula is as follows:

[0031]

[0032] in is the preset range; E is the preset threshold; Indicates whether it is an energy-saving state indicator.

[0033] In a second aspect, in order to further solve the problems in smart home control, the present invention provides a smart home control system, including:

[0034] Sensor module: responsible for collecting environmental data and user behavior data, including temperature, humidity, light, user heart rate, and smart device power consumption data;

[0035] Data processing module: pre-processes and stores data collected by sensors;

[0036] Intelligent control module: analyzes and makes decisions based on pre-processed data and performs corresponding equipment control;

[0037] User interface module: interacts with users, provides system status information and receives user instructions;

[0038] Communication module: responsible for data communication between modules and between the system and external devices

[0039] As a preferred solution of the smart home control system described in the present invention, the modules specifically include:

[0040] The data processing module includes: a data preprocessing unit for denoising and normalizing the data; a data storage unit for storing the preprocessed data;

[0041] The intelligent control module includes: a data analysis unit that analyzes environmental data and user behavior data through algorithms; a decision-making unit that determines the working state of the device based on the analysis results and executes the corresponding control strategy; and a control unit that sends control instructions to each intelligent device to adjust its working state.

[0042] The user interface module includes: a display unit that displays information such as the current system status, energy consumption data, and equipment working status; an input unit that receives user manual control instructions and preference settings;

[0043] The communication module includes: a wired communication unit, which realizes communication between modules through wired means; and a wireless communication unit, which realizes communication between modules and connection with external devices through wireless means.

[0044] In a third aspect, an embodiment of the present invention further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of a smart home control method as described in the first aspect of the present invention is implemented.

[0045] Beneficial effects of the present invention:

[0046] 1. By analyzing user behavior data in real time, the system automatically detects the user's status and switches to the appropriate mode based on the detection results. Through automated control and dynamic environmental adjustment, the system significantly improves the user's quality of life. Users no longer need to manually operate smart devices; the system automatically adapts to their living habits and environmental changes, providing a more comfortable and convenient living environment.

[0047] 2. By monitoring environmental data (such as temperature, humidity, and light intensity) in real time, the system dynamically adjusts the working status of various smart devices, making home energy use more efficient. The system's innovations in intelligent control and dynamic adjustment make home energy use more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort. Among them:

[0049] Figure 1 A flow chart of a smart home control method proposed by the present invention;

[0050] Figure 2 This is a framework diagram of a smart home control system proposed by the present invention;

[0051] Figure 3 A line chart comparing the present invention and the prior art. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0055] Example 1

[0056] Reference Figure 1-2 , which is the first embodiment of the present invention, provides a smart home control method, which specifically includes the following steps:

[0057] S1. Data collection: Detecting environmental parameters and human activities in the room through sensors installed in each room and smart devices worn by users;

[0058] S2. Data Analysis and Modeling: Use machine learning algorithms to analyze collected environmental and behavioral data, establish user behavior models and environmental models, and predict changes in user behavior and the environment;

[0059] S3, Intelligent scene mode switching: Preset multiple scene modes based on analyzed behavior patterns, and automatically switch to the corresponding scene mode based on real-time monitored user behavior and environmental data;

[0060] S4. Application of intelligent energy-saving algorithms: Continuously collect environmental sensor data, analyze current environmental conditions through intelligent energy-saving algorithms, and dynamically adjust the working status of smart home devices based on the results of environmental data analysis;

[0061] S5. User feedback and system optimization: Collect user feedback and suggestions through the application, and continuously optimize the control method of smart home based on the suggestions.

[0062] The construction of the user behavior model in S2 specifically includes the following steps:

[0063] Data preprocessing: preprocess the collected user behavior data including motion data, heart rate data, and environmental data;

[0064] Sleep behavior judgment: Based on the pre-processed data, the comprehensive sleep state judgment value is obtained through comprehensive calculation to make judgments;

[0065] Mode switching: When it is determined that the user is in sleep state, it switches to sleep mode and adjusts the associated devices. By processing and analyzing real-time data, it makes intelligent judgments on user behavior and adjusts the devices.

[0066] Furthermore, the calculation formula for the comprehensive sleep state judgment value is as follows:

[0067]

[0068] Where A(t) is the user's motion data at time t, indicating the user's motion amplitude or frequency at time t; H(t) is the heart rate data at time t, indicating the user's heart rate at time t; E(t) is the environmental data at time t, indicating the temperature, humidity, and light intensity of the user's surrounding environment; α, β, and γ are weight coefficients, which are adjusted according to the specific application scenario; μ A , μ H , μ E Represent the mean of motion data, heart rate data, and environmental data respectively; σ A , σ H , σ E are the standard deviations of motion data, heart rate data, and environmental data respectively; n, m, and p are the number of sampling points of motion data, heart rate data, and environmental data respectively.

[0069] Furthermore, when the S(t) value is small and reaches the preset sleep judgment threshold, it indicates that the user is in a relatively static state and can be judged as being in a sleep state. When the S(t) value is large and greater than the preset sleep judgment threshold, it indicates that the user is in an active state and does not meet the sleep characteristics. The specific performance formula is as follows:

[0070]

[0071] Where S is a preset threshold, and D is an indicator of whether it is a sleep state. The user's behavior is judged by comparing the S(t) value with the preset threshold.

[0072] Furthermore, the construction of the environment model in S2 specifically includes the following steps:

[0073] Data preprocessing: Preprocess the collected environmental data, including temperature, humidity, light intensity, and equipment power consumption;

[0074] Environmental data analysis: Analyze the current environmental status through complex functions based on environmental data;

[0075] Equipment turntable adjustment: Adjust the equipment's working status based on the analysis results to achieve energy-saving goals. Analyze current environmental data in real time and adjust the equipment to achieve energy-saving goals while also improving user comfort.

[0076] Furthermore, the environmental data analysis formula is as follows:

[0077]

[0078] Where E(t) is the total energy consumption of the environment at time t; T(t), H(t), L(t), and P(t) are the ambient temperature, humidity, light intensity, and equipment power consumption data, respectively; μ T 、μ H 、μ L 、μ P are the mean values ​​of temperature, humidity, light, and equipment power consumption data respectively; σ T , σ H , σ L , σ P are the standard deviations of temperature, humidity, light, and equipment power consumption data respectively; n, m, p, and q are the number of sampling points of temperature, humidity, light, and equipment power consumption data respectively; α, β, γ, and δ are weight coefficients.

[0079] Furthermore, if the E(t) value is small and the difference from the preset threshold is within the preset range, it means that both the environmental data and the device power consumption meet the energy-saving characteristics, and the system determines that the current device operating state is an energy-saving state; when the E(t) value is large and the difference from the preset threshold exceeds the preset range, it means that the environmental data and the device power consumption deviate from the energy-saving characteristics, and the system needs to adjust the device operating state to achieve the energy-saving goal. The specific formula is as follows:

[0080]

[0081] in is the preset range; E is the preset threshold; To determine whether it is an energy-saving state indicator, the device is adjusted in real time based on environmental factors to keep the device in an energy-saving state.

[0082] This embodiment also provides a smart home control system, including:

[0083] Sensor module: responsible for collecting environmental data and user behavior data, including temperature, humidity, light, user heart rate, and smart device power consumption data;

[0084] Data processing module: pre-processes and stores data collected by sensors;

[0085] Intelligent control module: analyzes and makes decisions based on pre-processed data and performs corresponding equipment control;

[0086] User interface module: interacts with users, provides system status information and receives user instructions;

[0087] Communication module: responsible for data communication between modules and between the system and external devices, and implements the above method steps through collaboration between modules.

[0088] Furthermore, each module specifically includes:

[0089] The data processing module includes: a data preprocessing unit for denoising and normalizing the data; a data storage unit for storing the preprocessed data;

[0090] The intelligent control module includes: a data analysis unit that analyzes environmental data and user behavior data through algorithms; a decision-making unit that determines the working state of the device based on the analysis results and executes the corresponding control strategy; and a control unit that sends control instructions to each intelligent device to adjust its working state.

[0091] The user interface module includes: a display unit that displays information such as the current system status, energy consumption data, and equipment working status; an input unit that receives user manual control instructions and preference settings;

[0092] The communication module includes: a wired communication unit, which realizes communication between modules through wired means; a wireless communication unit, which realizes communication between modules and connection with external devices through wireless means. The wired means can adopt Ethernet and other means, and the wireless means can adopt Wi-Fi, Zigbee, Bluetooth and other means.

[0093] This embodiment also provides a computer device suitable for a smart home control method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a smart home control method proposed in the above embodiment.

[0094] Example 2

[0095] Reference Figure 3 , which is the second embodiment of the present invention. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, experimental comparison data between the present invention and the prior art are provided.

[0096] This embodiment adopts two smart home systems: system A is a traditional smart home system, and system B is a smart home system invented by us.

[0097] Experimental environment:

[0098] Experimental location: an ordinary residence;

[0099] Experimental duration: 7 days;

[0100] User behavior simulation: simulates user activities at home, including sleeping, watching TV, cooking, bathing, etc.

[0101] Environmental data collection: Use temperature sensors, humidity sensors, light sensors, behavior monitors, etc. to detect environmental data and user behavior data in real time.

[0102] Experimental steps:

[0103] Day 1: Install and configure both systems and conduct preliminary testing to ensure proper function. Days 2 to 7: Conduct formal experiments. Daily records will be kept of the systems' performance, including user behavior data, environmental data, and the operating status of various smart devices. Energy consumption will also be recorded.

[0104] The specific experimental data are as follows:

[0105]

[0106] Through the analysis of the above experimental data, we can draw the following conclusions:

[0107] Accuracy of User Behavior Analysis: System B uses real-time user behavior analysis to accurately determine the user's status and switch modes accordingly. For example, if the user falls asleep, System B can automatically shut down unnecessary electrical devices and enter sleep mode. This feature demonstrated high accuracy and reliability in experiments.

[0108] Dynamic adjustment of environmental data: System B monitors environmental data in real time and dynamically adjusts the operating status of devices, making home energy use more efficient. For example, when light intensity decreases, System B automatically adjusts the brightness of the lights, improving user comfort while saving energy.

[0109] Energy consumption comparison: From the experimental data, it can be seen that the total energy consumption of system B is significantly lower than that of system A. During the 7-day experiment, the average energy consumption of system A was 5.13kWh, while the average energy consumption of system B was 4.06kWh, showing significant energy saving effects.

[0110] The above comparative analysis demonstrates that our invented smart home control method offers significant advantages in user behavior analysis and dynamic adjustment of environmental data. These advantages not only enhance system automation and user experience, but also significantly reduce energy consumption, achieving optimal home energy utilization.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A smart home control method, characterized in that: The specific steps include: S1. Data collection: Detect environmental parameters and human activities in the room through sensors installed in each room and smart devices worn by users; S2. Data analysis and modeling: Use machine learning algorithms to analyze the collected environmental data and behavioral data, establish user behavior models and environmental models, and predict changes in user behavior and environment; S3, intelligent scene mode switching: preset multiple scene modes according to the analyzed behavior patterns, and automatically switch to the corresponding scene mode according to the real-time monitored user behavior and environmental data; S4. Application of intelligent energy-saving algorithms: Continuously collect environmental sensor data, analyze current environmental conditions through intelligent energy-saving algorithms, and dynamically adjust the working status of smart home devices based on the results of environmental data analysis; S5. User feedback and system optimization: Collect user feedback and suggestions through the application, and continuously optimize the control method of smart home based on the suggestions; The construction of the user behavior model in S2 specifically includes the following steps: Data preprocessing: preprocess the collected user behavior data including motion data, heart rate data, and environmental data; Sleep behavior judgment: Based on the pre-processed data, the comprehensive sleep state judgment value is obtained through comprehensive calculation for judgment; Mode switching: When the user is determined to be in sleep mode, the system switches to sleep mode and adjusts the associated devices; The calculation formula of the comprehensive sleep state judgment value is as follows: in For time User action data at the moment, indicating the user's The amplitude or frequency of movement; Heart rate data, indicating the user's heart rate; Environmental data, indicating the temperature, humidity, and light intensity of the user's surroundings; is the weight coefficient, which is adjusted according to the specific application scenario; , , Respectively represent the mean of motion data, heart rate data, and environmental data; They are the standard deviations of motion data, heart rate data, and environmental data respectively; , , They are the number of sampling points for motion data, heart rate data, and environmental data respectively; Said When the value reaches the preset sleep judgment threshold, it means that the user is in a relatively static state and can be judged as being in a sleep state; When the value is greater than the preset sleep judgment threshold, it means that the user is in an active state and does not meet the sleep characteristics. The specific performance formula is as follows: in is the preset threshold, Indicates whether it is a sleep state indicator.

2. A smart home control method according to claim 1, characterized in that: The construction of the environment model in S2 specifically includes the following steps: Data preprocessing: preprocess the collected environmental data, including temperature, humidity, light intensity, and equipment power consumption; Environmental data analysis: Analyze the current environmental status through complex functions based on environmental data; Equipment working turntable adjustment: adjust the working status of the equipment according to the analysis results to achieve energy-saving goals.

3. A smart home control method according to claim 2, characterized in that: The environmental data analysis formula is as follows: in For time Total environmental energy consumption at all times; , , , They are ambient temperature, humidity, light intensity, and equipment power consumption data; , , , They are the mean values ​​of temperature, humidity, light, and equipment power consumption data; , , , They are the standard deviations of temperature, humidity, light, and equipment power consumption data; , , , These are the number of sampling points for temperature, humidity, light, and equipment power consumption data; is the weight coefficient.

4. A smart home control method according to claim 3, characterized in that: Said The difference between the value and the preset threshold is within the preset range, indicating that the environmental data and the equipment power consumption meet the energy-saving characteristics, and the system determines that the current equipment working state is energy-saving state; when When the difference between the value and the preset threshold exceeds the preset range, it means that the environmental data and the power consumption of the equipment deviate from the energy-saving characteristics. The system needs to adjust the working state of the equipment to achieve the energy-saving goal. The specific formula is as follows: in is the preset range; is the preset threshold; It is an indicator of whether it is energy-saving state.

5. A smart home control system, based on a smart home control method according to any one of claims 1 to 4, characterized in that: include: Sensor module: responsible for collecting environmental data and user behavior data, including temperature, humidity, light, user heart rate, and smart device power consumption data; Data processing module: pre-process and store the data collected by the sensor; Intelligent control module: analyzes and makes decisions on pre-processed data and executes corresponding equipment control; User interface module: interacts with users, provides system status information and receives user instructions; Communication module: responsible for data communication between modules and between the system and external devices.

6. The smart home control system according to claim 5, characterized in that: The modules specifically include: The data processing module includes: a data preprocessing unit for denoising and normalizing the data; a data storage unit for storing the preprocessed data; The intelligent control module includes: a data analysis unit, which analyzes environmental data and user behavior data through algorithms; a decision unit, which determines the working state of the device based on the analysis results and executes the corresponding control strategy; a control unit, which sends control instructions to each intelligent device to adjust its working state; The user interface module includes: a display unit that displays the current status of the system, energy consumption data and equipment working status information; an input unit that receives the user's manual control instructions and preference settings; The communication module includes: a wired communication unit, which realizes communication between modules in a wired manner; and a wireless communication unit, which realizes communication between modules and connection with external devices in a wireless manner.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a smart home control method described in any one of claims 1-4 are implemented.

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