CN118963162B - A smart home control method and system - Google Patents

A smart home control method and system Download PDF

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CN118963162B
CN118963162B CN202411065876.4A CN202411065876A CN118963162B CN 118963162 B CN118963162 B CN 118963162B CN 202411065876 A CN202411065876 A CN 202411065876A CN 118963162 B CN118963162 B CN 118963162B
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CN118963162A (en
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林云
瞿诗
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Yancheng Yuanzhen Technology Service Co ltd
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Yancheng Xiaoai Big Model Development Co ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2642—Domotique, domestic, home control, automation, smart house
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Quality & Reliability (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

The invention discloses an intelligent home control method which specifically comprises the following steps of S1, data acquisition, S2, data analysis and modeling, S3, intelligent contextual model switching, S4, intelligent energy-saving algorithm application, S5, user feedback and system optimization. According to the invention, through real-time analysis of the user behavior data, the system can automatically detect the state of the user and switch to a corresponding mode according to the detection result. Through automatic control and dynamic environment adjustment, the system significantly improves the quality of life of the user. The user does not need to manually operate the intelligent equipment, and the system can automatically adapt to living habits and environmental changes of the user and provide a more comfortable and convenient living environment.

Description

Smart home control method and system
Technical Field
The invention relates to the technical field of intelligent home control, in particular to an intelligent home control method and system.
Background
With the rapid development of internet of things (IoT) technology, smart home systems are widely used in daily life. The intelligent home system interconnects and intercommunicates various devices and sensors in the home through the Internet, remote monitoring and control of the devices are realized, and convenience and comfort level of life are greatly improved. The smart home systems in the market at present are usually controlled remotely through terminal devices such as smart phones, tablet computers and the like. These systems may implement basic device management and control functions such as lighting control, temperature regulation, security monitoring, etc.
In daily life, the behavior pattern of a user has an important influence on the effective control of the smart home system. Especially the sleeping behavior of the user, the demands on the home environment can vary significantly. For example, after a user falls asleep, high-brightness illumination is not required, and the temperature of the air conditioner is also required to be adjusted in time so as to ensure a comfortable sleeping environment. However, the existing smart home systems are still relatively extensive in control in this regard, and cannot accurately detect and respond to the sleep behavior of the user.
With the development of sensor technology, intelligent home systems can acquire more and more real-time environmental data, such as temperature, humidity, illumination intensity, and the like. These data are of great importance for the optimal control of the device. Through the real-time monitoring and analysis of the environmental data, the intelligent home system can realize finer equipment control, for example, the indoor illumination brightness can be automatically adjusted according to the outdoor illumination intensity, or the running mode of the air conditioner can be dynamically adjusted according to the indoor temperature and humidity. However, the existing systems still have limited application in this regard and cannot fully utilize environmental data to achieve dynamic optimal control of the device.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
In order to solve the technical problems, the invention provides the following technical scheme:
In a first aspect, an embodiment of the present invention provides an intelligent home control method, which specifically includes the following steps:
S1, data acquisition, namely detecting environmental parameters and personnel activities in a room through sensors arranged in each room and intelligent equipment worn by a user;
S2, analyzing and modeling the data, namely analyzing the collected environmental data and behavior data by using a machine learning algorithm, establishing a user behavior model and an environmental model, and predicting the changes of the user behavior and the environment;
s3, intelligent contextual model switching, namely presetting a plurality of contextual models according to the analyzed behavioral models, and automatically switching to corresponding contextual models according to user behaviors and environmental data monitored in real time;
S4, the intelligent energy-saving algorithm is applied, namely continuously collecting the environmental sensor data, analyzing the current environmental condition through the intelligent energy-saving algorithm, and dynamically adjusting the working state of the intelligent household equipment according to the environmental data analysis result;
and S5, user feedback and system optimization, namely collecting the user feedback and suggestions through an application program, and continuously optimizing the control method of the intelligent home according to the suggestions.
As a preferable scheme of the intelligent home control method, the construction of the user behavior model in the S2 specifically comprises the following steps:
Preprocessing the received user behavior data, wherein the preprocessing comprises action data, heart rate data and environment data;
sleep behavior judgment, namely obtaining a comprehensive sleep state judgment value through comprehensive calculation based on the preprocessed data to judge;
and (3) mode switching, namely switching to a sleep mode and adjusting the associated equipment when the user is judged to be in the sleep state.
As a preferable scheme of the intelligent home control method, the comprehensive sleep state judgment value calculation formula is as follows:
wherein A (t) is user action data at time t and represents action amplitude or frequency of a user at time t, H (t) is heart rate data at time t and represents heart rate of the user at time t, E (t) is environment data at time t and represents temperature, humidity and illumination intensity of surrounding environment of the user, alpha, beta and gamma are weight coefficients and are adjusted according to specific application scenes, mu A,μH,μE respectively represents average values of the action data, the heart rate data and the environment data, sigma A,σH,σE respectively represents standard deviations of the action data, the heart rate data and the environment data, and n, m and p respectively represent sampling points of the action data, the heart rate data and the environment data.
The intelligent home control method is characterized in that when the S (t) value is smaller and reaches a preset sleep judgment threshold, the user is in a relatively static state and can be judged to be in a sleep state, when the S (t) value is larger and is larger than the preset sleep judgment threshold, the user is in an active state and does not accord with sleep characteristics, and the specific expression formula is as follows:
wherein S is a preset threshold, and D is a sleep state index.
As a preferable scheme of the intelligent home control method, the construction of the environment model in the S2 specifically comprises the following steps:
preprocessing the collected environmental data, including temperature, humidity, illumination intensity and equipment power consumption;
analyzing the current environment state through complex functions based on the environment data;
and adjusting the working state of the equipment according to the analysis result, thereby realizing the energy-saving aim.
As a preferable scheme of the intelligent home control method, the environment data analysis formula is specifically as follows:
Wherein E (T) is total environmental energy consumption at time T, T (T), H (T), L (T) and P (T) are respectively environmental temperature, humidity, illumination intensity and equipment power consumption data, mu T、μH、μL、μP is respectively average value of the temperature, humidity, illumination and equipment power consumption data, sigma T、σH、σL、σP is respectively standard deviation of the temperature, humidity, illumination and equipment power consumption data, n, m, P, q is respectively sampling point number of the temperature, humidity, illumination and equipment power consumption data, and alpha, beta, gamma and delta are weight coefficients.
The invention relates to an intelligent home control method, which is a preferable scheme, wherein the E (t) value is smaller and the difference value between the E (t) value and a preset threshold value is within a preset range, the E (t) value and the preset threshold value indicate that the environmental data and the equipment power consumption both accord with energy-saving characteristics, the system judges that the current equipment working state is an energy-saving state, when the E (t) value is larger and the difference value between the E (t) value and the preset threshold value exceeds the preset range, the E (t) value indicates that the environmental data and the equipment power consumption deviate from the energy-saving characteristics, and the system needs to adjust the equipment working state to achieve an energy-saving target, and the specific formula is as follows:
wherein the method comprises the steps of E is a preset threshold value; Whether the energy-saving state index is adopted.
In a second aspect, in order to further solve the problem in smart home control, an embodiment of the present invention provides a smart home control system, including:
The sensor module is responsible for collecting environmental data and user behavior data, including temperature, humidity, illumination, user heart rate and intelligent equipment power consumption data;
the data processing module is used for preprocessing and storing the data collected by the sensor;
the intelligent control module is used for analyzing and deciding the preprocessed data and executing corresponding equipment control;
The user interface module is used for interacting with a user, providing system state information and receiving user instructions;
communication module responsible for data communication between modules and between system and external equipment
As a preferable scheme of the intelligent home control system, the invention specifically comprises the following steps:
the data processing module comprises a data preprocessing unit, a data storage unit, a data processing unit and a data processing unit, wherein the data preprocessing unit is used for denoising and normalizing data;
The intelligent control module comprises a data analysis unit, a decision unit, a control unit and a control unit, wherein the data analysis unit analyzes environmental data and user behavior data through an algorithm, and the decision unit decides the working state of equipment according to analysis results and executes a corresponding control strategy;
The user interface module comprises a display unit, an input unit, a control unit and a control unit, wherein the display unit displays the current state of the system, the energy consumption data, the working state of equipment and other information;
The communication module comprises a wired communication unit and a wireless communication unit, wherein the wired communication unit is used for realizing communication among the modules in a wired mode, and the wireless communication unit is used for realizing communication among the modules in a wireless mode and connection with external equipment.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory and a processor, where the memory stores a computer program, where the computer program when executed by the processor implements any step of a smart home control method according to the first aspect of the present invention.
The invention has the beneficial effects that:
1. through real-time analysis of the user behavior data, the system can automatically detect the state of the user and switch to a corresponding mode according to the detection result. Through automatic control and dynamic environment adjustment, the system significantly improves the quality of life of the user. The user does not need to manually operate the intelligent equipment, and the system can automatically adapt to living habits and environmental changes of the user and provide a more comfortable and convenient living environment.
2. Through real-time monitoring environmental data (such as temperature, humidity, illumination intensity and the like), the working state of various intelligent devices is dynamically adjusted, so that the household energy utilization is more efficient. The system is innovative in intelligent control and dynamic adjustment, so that the household energy utilization is more efficient.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flow chart of a method for controlling smart home according to the present invention;
fig. 2 is a frame diagram of an intelligent home control system according to the present invention;
FIG. 3 is a comparison line graph of the present invention with the prior art.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1-2, for a first embodiment of the present invention, the present invention provides an intelligent home control method, which specifically includes the following steps:
S1, data acquisition, namely detecting environmental parameters and personnel activities in a room through sensors arranged in each room and intelligent equipment worn by a user;
S2, analyzing and modeling the data, namely analyzing the collected environmental data and behavior data by using a machine learning algorithm, establishing a user behavior model and an environmental model, and predicting the changes of the user behavior and the environment;
s3, intelligent contextual model switching, namely presetting a plurality of contextual models according to the analyzed behavioral models, and automatically switching to corresponding contextual models according to user behaviors and environmental data monitored in real time;
S4, the intelligent energy-saving algorithm is applied, namely continuously collecting the environmental sensor data, analyzing the current environmental condition through the intelligent energy-saving algorithm, and dynamically adjusting the working state of the intelligent household equipment according to the environmental data analysis result;
and S5, user feedback and system optimization, namely collecting the user feedback and suggestions through an application program, and continuously optimizing the control method of the intelligent home according to the suggestions.
The construction of the user behavior model in S2 specifically includes the following steps:
Preprocessing the received user behavior data, wherein the preprocessing comprises action data, heart rate data and environment data;
sleep behavior judgment, namely obtaining a comprehensive sleep state judgment value through comprehensive calculation based on the preprocessed data to judge;
and (3) mode switching, namely switching to a sleep mode and adjusting the associated equipment when the user is in the sleep state, and intelligently judging the behavior of the user and adjusting the equipment by processing and analyzing the real-time data.
Further, the comprehensive sleep state judgment value has the following calculation formula:
Wherein A (t) is user action data at time t and represents action amplitude or frequency of a user at time t, H (t) is heart rate data at time t and represents heart rate of the user at time t, E (t) is environment data at time t and represents temperature, humidity and illumination intensity of surrounding environment of the user, alpha, beta and gamma are weight coefficients and are adjusted according to specific application scenes, mu A,μH,μE respectively represents average values of the action data, the heart rate data and the environment data, sigma A,σH,σE respectively represents standard deviations of the action data, the heart rate data and the environment data, and n, m and p respectively represent sampling points of the action data, the heart rate data and the environment data.
Further, when the S (t) value is smaller and reaches a preset sleep judgment threshold, the user is in a relatively static state and can be judged to be in a sleep state, and when the S (t) value is larger and is larger than the preset sleep judgment threshold, the user is in an active state and does not accord with sleep characteristics, and the specific expression is as follows:
And S is a preset threshold value, D is a sleep state index, and the behavior of the user is judged by comparing the judgment of the S (t) value with the preset threshold value.
Further, the construction of the environmental model in S2 specifically includes the following steps:
preprocessing the collected environmental data, including temperature, humidity, illumination intensity and equipment power consumption;
analyzing the current environment state through complex functions based on the environment data;
And adjusting the working state of the equipment according to the analysis result, so as to realize the energy-saving target, analyze the current environmental data in real time, and adjust the equipment, so that the comfort level of a user can be improved while the energy-saving target is realized.
Further, the environmental data analysis formula is specifically as follows:
Wherein E (T) is total environmental energy consumption at time T, T (T), H (T), L (T) and P (T) are respectively environmental temperature, humidity, illumination intensity and equipment power consumption data, mu T、μH、μL、μP is respectively average value of the temperature, humidity, illumination and equipment power consumption data, sigma T、σH、σL、σP is respectively standard deviation of the temperature, humidity, illumination and equipment power consumption data, n, m, P, q is respectively sampling point number of the temperature, humidity, illumination and equipment power consumption data, and alpha, beta, gamma and delta are weight coefficients.
When the E (t) value is larger and the difference value between the E (t) value and the preset threshold exceeds the preset range, the environment data and the equipment power consumption deviate from the energy-saving characteristic, and the system needs to adjust the equipment working state to achieve the energy-saving target, wherein the specific formula is as follows:
wherein the method comprises the steps of E is a preset threshold value; in order to determine whether the energy-saving state index is the energy-saving state index, the equipment is adjusted in real time through environmental factors, so that the equipment is in an energy-saving state.
The embodiment also provides an intelligent home control system, which comprises:
The sensor module is responsible for collecting environmental data and user behavior data, including temperature, humidity, illumination, user heart rate and intelligent equipment power consumption data;
the data processing module is used for preprocessing and storing the data collected by the sensor;
the intelligent control module is used for analyzing and deciding the preprocessed data and executing corresponding equipment control;
The user interface module is used for interacting with a user, providing system state information and receiving user instructions;
And the communication module is responsible for data communication between the modules and between the system and external equipment, and the steps of the method are realized through cooperation among the modules.
Further, each module specifically includes:
the data processing module comprises a data preprocessing unit, a data storage unit, a data processing unit and a data processing unit, wherein the data preprocessing unit is used for denoising and normalizing data;
The intelligent control module comprises a data analysis unit, a decision unit, a control unit and a control unit, wherein the data analysis unit analyzes environmental data and user behavior data through an algorithm, and the decision unit decides the working state of equipment according to analysis results and executes a corresponding control strategy;
The user interface module comprises a display unit, an input unit, a control unit and a control unit, wherein the display unit displays the current state of the system, the energy consumption data, the working state of equipment and other information;
The communication module comprises a wired communication unit and a wireless communication unit, wherein the wired communication unit is used for realizing communication among the modules in a wired mode, the wireless communication unit is used for realizing communication among the modules and connection with external equipment in a wireless mode, the wired mode can be an Ethernet mode or the like, and the wireless mode can be a Wi-Fi, zigbee, bluetooth mode or the like.
The embodiment also provides computer equipment, which is suitable for the condition of the intelligent home control method and comprises a memory and a processor, wherein the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the intelligent home control method as provided in the embodiment.
Example 2
Referring to fig. 3, a second embodiment of the present invention, which is different from the first embodiment, is provided with experimental comparison data of the present invention with the prior art in order to verify the advantageous effects thereof.
In the embodiment, two sets of intelligent home systems are adopted, wherein the system A is a traditional intelligent home system, and the system B is an intelligent home system adopting my invention.
Experimental environment:
the experimental place is a common house;
The experimental period is 7 days;
user behavior simulation, namely simulating activities of a user at home, including sleeping, watching television, cooking, bathing and the like;
and environmental data acquisition, namely detecting environmental data and user behavior data in real time by using a temperature sensor, a humidity sensor, an illumination sensor, a behavior monitor and the like.
The experimental steps are as follows:
The first day, two sets of systems are installed and configured, preliminary tests are carried out to ensure the normal operation of the systems, and the second to seventh days, formal experiments are carried out. And recording the working conditions of two sets of systems each day, including user behavior data, environment data and the working states of various intelligent devices. And simultaneously, recording the energy consumption condition.
The specific experimental data are as follows:
from the analysis of the experimental data described above, we can conclude that:
And the accuracy of user behavior analysis is that the system B can accurately judge the user state through real-time user behavior analysis and switch modes according to the state. For example, when the user falls asleep, system B can automatically turn off unnecessary electrical devices, entering sleep mode. This function shows high accuracy and reliability in experiments.
The system B dynamically adjusts the working state of the equipment by monitoring the environmental data in real time, so that the household energy utilization is more efficient. For example, when the illumination intensity is reduced, the system B automatically adjusts the light brightness, improving the comfort of the user, while saving energy.
Energy consumption comparison it can be seen from the experimental data that the total electrical energy consumption of system B is significantly lower than that of system a. In the experiment of 7 days, the average electric energy consumption of the system A is 5.13kWh, and the average electric energy consumption of the system B is 4.06kWh, so that the energy-saving effect is remarkable.
Through the comparative analysis, the intelligent home control method disclosed by the invention has obvious advantages in the aspects of user behavior analysis and environment data dynamic adjustment. These advantages not only improve the automation degree and user experience of the system, but also greatly reduce the energy consumption and realize the optimization of the household energy utilization.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (7)

1.一种智能家居控制方法，其特征在于：具体包括如下步骤：1. A smart home control method, characterized in that it specifically includes the following steps: S1、数据采集：通过安装在各个房间的传感器以及用户佩戴的智能设备检测房间内的环境参数以及人员活动的情况；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、数据的分析与建模：使用机器学习算法分析采集到的环境数据和行为数据，建立用户行为模型和环境模型，预测用户行为和环境的变化；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、智能情景模式切换：根据分析的行为模式预设多个情景模式，根据实时监测到的用户行为和环境数据自动切换到相应的情景模式；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、智能节能算法应用：持续收集环境传感器数据，通过智能节能算法分析当前的环境条件，根据环境数据分析结果，动态调整智能家居设备的工作状态；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、用户反馈与系统优化：通过应用程序对用户的使用反馈和建议进行收集，并根据建议持续优化智能家居的控制方法；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; 所述S2中用户行为模型的构建具体包括以下步骤：The construction of the user behavior model in S2 specifically includes the following steps: 数据预处理：对收集的用户行为数据进行预处理包括动作数据、心率数据、环境数据；Data preprocessing: preprocess the collected user behavior data including motion data, heart rate data, and environmental data; 睡眠行为判断：基于预处理后的数据，通过综合计算得到综合睡眠状态判断值进行判断；Sleep behavior judgment: Based on the pre-processed data, the comprehensive sleep state judgment value is obtained through comprehensive calculation for judgment; 模式切换：判断为用户处于睡眠状态时，切换至睡眠模式并对关联设备进行调节；Mode switching: When it is determined that the user is in sleep mode, switch to sleep mode and adjust the associated devices; 所述综合睡眠状态判断值计算公式如下：The calculation formula of the comprehensive sleep state judgment value is as follows: 其中为时间时刻的用户动作数据，表示用户在时间的动作幅度或频率；心率数据，表示用户在时间的心率；环境数据，表示用户周围环境的温度、湿度、光照强度；为权重系数，根据具体应用场景进行调整；，，分别表示动作数据、心率数据、环境数据的均值；分别为动作数据、心率数据、环境数据的标准差；，，分别为动作数据、心率数据、环境数据的采样点数；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; , , These are the sampling points for motion data, heart rate data, and environmental data respectively; 所述值达到预设的睡眠判断阈值时，表示用户处于相对静止状态，可判断为用户处于睡眠状态；当值大于预设的睡眠判断阈值时，表示用户处于活动状态，不符合睡眠特征，具体表现公式如下：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: 其中为预设阈值，为是否为睡眠状态指标。in is the preset threshold, Indicates whether it is a sleep state indicator. 2.根据权利要求1所述的一种智能家居控制方法，其特征在于：所述S2中环境模型的构建具体包括以下步骤：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: 数据的预处理：对收集到的环境数据进行预处理，包括温度、湿度、光照强度、设备电力消耗；Data preprocessing: preprocess the collected environmental data, including temperature, humidity, light intensity, and equipment power consumption; 环境数据分析：基于环境数据通过复杂函数分析当前环境状态；Environmental data analysis: Analyze the current environmental status through complex functions based on environmental data; 设备工作转台调整：根据分析结果调整设备的工作状态，实现节能目标。Equipment working turntable adjustment: adjust the working status of the equipment according to the analysis results to achieve energy-saving goals. 3.根据权利要求2所述的一种智能家居控制方法，其特征在于：所述环境数据分析公式具体如下：3. A smart home control method according to claim 2, characterized in that: the environmental data analysis formula is as follows: 其中为时间时刻的环境总能耗；、、、分别为环境温度、湿度、光照强度、设备电力消耗数据；、、、分别为温度、湿度、光照、设备电力消耗数据的均值；、、、分别为温度、湿度、光照、设备电力消耗数据的标准差；、、、分别为温度、湿度、光照、设备电力消耗数据的采样点数；为权重系数。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.根据权利要求3所述的一种智能家居控制方法，其特征在于：所述值与预设阈值的差值在预设范围内，表示环境数据和设备电力消耗均符合节能特征，系统判断为当前设备工作状态为节能状态；当值与预设阈值的差值超过预设范围时，表示环境数据和设备电力消耗偏离节能特征，系统需要调整设备工作状态，以实现节能目标，具体公式表现如下：4. A smart home control method according to claim 3, characterized in that: 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: 其中为预设范围；为预设阈值；为是否为节能状态指标。in is the preset range; is the preset threshold; It is an indicator of whether it is energy-saving state. 5.一种智能家居控制系统，基于上述权利要求1-4任意一项所述的一种智能家居控制方法，其特征在于：包括：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 it comprises: 传感器模块：负责采集环境数据和用户行为数据，包括温度、湿度、光照、用户心率、智能设备电力消耗数据；Sensor module: responsible for collecting environmental data and user behavior data, including temperature, humidity, light, user heart rate, and smart device power consumption data; 数据处理模块：对传感器收集的数据进行预处理和存储；Data processing module: pre-processes and stores the data collected by the sensor; 智能控制模块：对预处理后的数据进行分析和决策，并执行相应的设备控制；Intelligent control module: analyzes and makes decisions on pre-processed data and executes corresponding equipment control; 用户接口模块：与用户进行交互，提供系统状态信息和接收用户指令；User interface module: interacts with users, provides system status information and receives user instructions; 通信模块：负责个模块之间及系统与外部设备之间的数据通信。Communication module: responsible for data communication between modules and between the system and external devices. 6.根据权利要求5所述的一种智能家居控制系统，其特征在于：所述各模块具体包括：6. A smart home control system according to claim 5, characterized in that: each module specifically comprises: 数据处理模块包括：数据预处理单元，对数据进行去噪、归一化处理；数据存储单元，对预处理后的数据进行存储；The data processing module includes: a data preprocessing unit for denoising and normalizing the data; a data storage unit for storing the preprocessed data; 智能控制模块包括：数据分析单元，通过算法对环境数据和用户行为数据进行分析；决策单元，根据分析结果决定设备的工作状态，执行相应的控制策略；控制单元，向各个智能设备发送控制指令，调整其工作状态；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; 用户接口模块包括：显示单元，显示系统当前状态、能耗数据和设备工作状态信息；输入单元，接收用户的手动控制指令和偏好设置；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; 通信模块包括：有线通信单元，通过有线方式实现各模块间的通信；无线通信单元，通过无线方式实现各模块间的通信及与外部设备的连接。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. 7.一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于：所述处理器执行所述计算机程序时实现权利要求1-4任一所述的一种智能家居控制方法的步骤。7. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of a smart home control method according to any one of claims 1 to 4 when executing the computer program.
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