CN117031977A - Smart home control method and system - Google Patents
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- CN117031977A CN117031977A CN202311118988.7A CN202311118988A CN117031977A CN 117031977 A CN117031977 A CN 117031977A CN 202311118988 A CN202311118988 A CN 202311118988A CN 117031977 A CN117031977 A CN 117031977A
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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
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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
- 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]
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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
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2642—Domotique, domestic, home control, automation, smart house
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- 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
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- 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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Abstract
The invention relates to an intelligent home control method and system, which belong to the field of intelligent home, wherein the method comprises the steps of sensing home environment parameters such as temperature, humidity, light intensity and the like through a sensor; predicting the optimal working state of the household equipment according to the perceived parameters by using a prediction model; comparing the predicted result with the actual environment parameters, and dynamically adjusting the working state of the household equipment; and a control signal is sent to the household equipment through wireless communication, so that self-adaptive control is realized. The user can realize intelligent regulation house equipment's luminance, temperature, humidity etc. promotes quality of life and comfort level. The invention has the advantages of being capable of adjusting household equipment in real time according to the change of environmental parameters and providing automatic, intelligent, efficient and energy-saving household control experience. The intelligent home control method and the system can be widely applied to households, offices or other places.
Description
Technical Field
The invention belongs to the field of intelligent home, and particularly relates to an intelligent home control method and system.
Background
At present, an intelligent home system is widely applied in the market, and remote control and intelligent management of home equipment are realized by using sensors, data processing and communication technologies. However, existing smart home systems have some problems.
Firstly, the control strategy of the existing system is mainly based on fixed preset rules, and cannot adapt to the personalized requirements of different users. The living habits and preferences of users are different, and the traditional preset rules cannot completely meet the personalized requirements.
Second, existing systems lack the ability to adapt to environmental changes. Parameters such as temperature, humidity, illumination intensity and the like of the household environment can be changed continuously, but the existing system cannot adjust the control strategy in real time, so that the control effect of the intelligent household is limited.
In addition, the interaction mode of the existing system is relatively simple, and the operation can be carried out only through a mobile phone or a specific control panel, so that the system is not convenient and flexible.
Therefore, a new smart home control system is needed, which can realize adaptive control according to environmental changes and user requirements, and provide personalized and convenient user experience. The system should have intelligent learning and analysis capability, be able to generate accurate control strategies according to environmental parameters and user data, and support remote control and various interaction modes.
Disclosure of Invention
The invention can realize personalized intelligent control, self-adaptive environment control, remote control and data interaction, data analysis and optimization, and improve life convenience, comfort and the like through the predictive model.
In order to achieve the above effects, the invention is realized by adopting the following technical scheme: the method comprises the following steps:
step 1, sensing household environmental parameters including temperature, humidity and light intensity through a sensor;
step 2, predicting the optimal working state of the household equipment by using a predictable model according to the perceived environmental parameters;
step 3, comparing the predicted result with the actual environment parameters, and dynamically adjusting the working state of the household equipment;
and step 4, transmitting a control signal to the household equipment through wireless communication to realize self-adaptive control.
Further, the sensor comprises a temperature and humidity sensor, a light sensor and a gas sensor; the dynamic adjustment of the working state of the household equipment comprises the adjustment of the brightness, the temperature and the humidity of the household equipment.
Further, the predictive model is obtained through training by adopting a machine learning algorithm and can be optimized iteratively according to real-time environment parameters.
Further, the specific model formula of the predictable model is as follows:
y＝b+Σ(αi*K(xi,x))-b (1)
wherein, y: predicted optimal working state of home equipment, b: bias terms, i.e., constant terms of the model; αi: support Lagrangian multiplier coefficients in vector regression; xi: input vectors in the training data; x: an input vector with observed real-time environmental parameters; k (xi, x): a kernel function for calculating similarity between input vectors;
deducing the optimal working state of the household equipment through the predicted value y; the model predicts by computing the similarity between the input vector xi and the real-time environmental parameter x, in combination with the lagrangian multiplier coefficient αi.
Further, the predicting step of the predictable model is as follows:
s201, data acquisition and preparation:
a. collecting and recording household environment parameter data such as temperature, humidity and light intensity;
b. dividing the collected data into an input vector xi and a corresponding optimal working state y as a training data set;
s202, feature selection and conversion:
a. characteristic selection is carried out on the collected data, and parameters related to the working state of household equipment are selected;
b. normalizing or standardizing the characteristic data to ensure that the data are in the same scale range; s203, training a model:
a. training a model by using a support vector regression algorithm;
b. training through the input vector xi and the corresponding optimal working state y, and searching for an optimal prediction function;
s204, model prediction:
a. after feature selection and conversion, an input vector x is obtained;
b. and predicting by using the trained support vector regression model through a formula (1).
c. Obtaining a prediction result y, which represents the optimal working state of household equipment;
s205 control action execution:
a. and comparing the difference between the predicted result y and the actual environment parameter.
b. According to the difference, the working state of the household equipment is adjusted
c. And sending a control signal to the household equipment through wireless communication so as to enable the household equipment to execute corresponding actions.
In yet another aspect, a smart home control system, the system adapted for use in the method, the system comprising
The sensor module is used for sensing the environmental parameters and generating sensor data;
the data processing module is used for receiving the sensor data and analyzing and processing the sensor data;
the control module is used for controlling the operation of the household equipment according to the analysis and processing result;
the learning module is used for learning the living habit and preference of the user and providing personalized intelligent control according to the learning result;
the communication module is used for carrying out data interaction and remote control with external equipment;
the user interface module is used for displaying the current intelligent control result and receiving the input of a user; the storage module is used for storing information such as sensor data, learning results, user data and the like; and the processing module is used for processing and managing data interaction and scheduling among the modules.
Further, the learning module generates a personalized intelligent control strategy through learning the behaviors and habits of the user, and works cooperatively with the control module to provide accurate control.
Further, the user interface module adopts a graphical interface to display the current intelligent control result and receives the input and feedback of the user.
Further, the control module is matched with a preset intelligent control strategy according to the numerical representation of the environment state, and controls the operation of the household equipment.
Furthermore, the communication module supports a wireless communication technology, and realizes data interaction and remote control with external equipment such as a mobile phone, a tablet personal computer and the like.
The invention has the beneficial effects that:
(1) Personalized intelligent control: through learning life habits and preferences of users, the intelligent home control system can provide personalized intelligent control strategies, meet requirements of different users, and improve user experience.
(2) Adaptive environmental control: the system can dynamically adjust the intelligent control strategy according to environmental parameters sensed in real time, such as temperature, humidity, illumination intensity and the like, so as to adapt to environmental changes and realize more accurate and effective household equipment control.
(3) Remote control and data interaction: the system can perform data interaction and remote control with external equipment in support of wireless communication technology. The user can remotely control the intelligent home through the mobile phone, the tablet personal computer and other equipment, so that convenient and flexible operation is realized.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a block diagram of a system of the present invention;
FIG. 3 is a flow chart of the present invention for predicting an optimal operating state of a home appliance using a predictive model;
FIG. 4 is a flow chart for dynamically adjusting the operating state of a home device;
fig. 5 is a flowchart for implementing adaptive control by transmitting a control signal to a home device through wireless communication.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Exemplary embodiments of the present invention are illustrated in the accompanying drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
As shown in fig. 1, the method comprises the following steps:
step 1, sensing household environmental parameters including temperature, humidity and light intensity through a sensor;
the sensor comprises a temperature and humidity sensor, a light sensor and a gas sensor; the dynamic adjustment of the working state of the household equipment comprises the adjustment of the brightness, the temperature and the humidity of the household equipment.
Step 2, predicting the optimal working state of the household equipment by using a predictable model according to the perceived environmental parameters;
the predictive model is obtained by training a machine learning algorithm and can be optimized iteratively according to real-time environment parameters.
The specific model formula of the predictable model is as follows:
y＝b+Σ(αi*K(xi,x))-b (1)
wherein, y: predicted optimal working state of home equipment, b: bias terms, i.e., constant terms of the model; αi: support Lagrangian multiplier coefficients in vector regression; xi: input vectors in the training data; x: an input vector with observed real-time environmental parameters; k (xi, x): a kernel function for calculating similarity between input vectors;
deducing the optimal working state of the household equipment through the predicted value y; the model predicts by computing the similarity between the input vector xi and the real-time environmental parameter x, in combination with the lagrangian multiplier coefficient αi.
As shown in fig. 3, the prediction steps of the predictive model are as follows:
s201, data acquisition and preparation:
a. collecting and recording household environment parameter data such as temperature, humidity and light intensity;
b. dividing the collected data into an input vector xi and a corresponding optimal working state y as a training data set;
s202, feature selection and conversion:
a. characteristic selection is carried out on the collected data, and parameters related to the working state of household equipment are selected;
data including various parameters related to the operational status of the home device is collected. These parameters may include temperature, humidity, light intensity, current, power, etc.
Features are extracted from the collected data. And processing the data by a statistical method to obtain values or indexes describing different characteristics. The mean, standard deviation, minimum, maximum, etc. of each feature may be calculated.
b. Normalizing or standardizing the characteristic data to ensure that the data are in the same scale range;
and selecting a proper normalization method according to the distribution condition and the demand of the data. The common normalization method adopts a mean normalization method. The feature data is converted into the range of [0,1 ]. The formula is:
wherein, wherein:
y is normalized value
X is the original data value
Mu is the average value of the original data
Max is the maximum value of the original data
Min is the minimum of the raw data.
The normalization of the feature data can improve the convergence speed of the model, reduce the influence of the features on model training, and ensure that different features have the same weight.
S203, training a model:
training a model by using a support vector regression algorithm SVR;
SVR is selected as a model and an appropriate kernel function is selected based on the problem and the data. Parameter tuning is to adjust the super parameters of the SVR model to obtain better performance. Cross-validation, grid searching may be used to select the best super-parameters.
a. Training through the input vector xi and the corresponding optimal working state y, and searching for an optimal prediction function;
s204, model prediction:
a. after feature selection and conversion, an input vector x is obtained;
b. and predicting by using the trained support vector regression model through a formula (1).
c. Obtaining a prediction result y, which represents the optimal working state of household equipment;
s205 control action execution:
a. and comparing the difference between the predicted result y and the actual environment parameter.
b. According to the difference, the working state of the household equipment is adjusted
c. And sending a control signal to the household equipment through wireless communication so as to enable the household equipment to execute corresponding actions.
Step 3, comparing the predicted result with the actual environment parameters and dynamically adjusting the working state of the household equipment as shown in fig. 4;
s301, according to the difference between the predicted result and the actual environment parameter, a corresponding adjustment strategy is formulated. For example, if the predicted temperature is higher and the actual temperature is lower, the refrigeration appliance may be turned on or the set temperature value of the temperature controller may be adjusted.
S302, adjusting the working state of household equipment: and sending the adjustment information to the corresponding household equipment according to the formulated adjustment strategy so as to adjust the working state of the household equipment. Communication and control with the equipment can be realized through a home automation system or an intelligent home control platform.
S303, monitoring the adjusted environmental parameters: after the working state of the household equipment is adjusted, the change of the environmental parameters is monitored in real time, and the adjusting effect is ensured to accord with expectations.
S304 continuous optimization and improvement: and optimizing and improving the machine learning model according to the monitoring result and the actual use condition, and improving the prediction accuracy. Meanwhile, according to feedback and requirements of users, an adjustment strategy is gradually improved, so that the working state of the household equipment is more intelligent and meets the expectations of the users.
Step 4, as shown in fig. 5, the control signal is sent to the home equipment through wireless communication, so as to realize adaptive control.
S401 connects the home device with the wireless communication network so that it can receive and transmit the control signal. The home devices may be paired with the communication nodes either by introducing intelligent gateway devices or directly.
S402 control signal generation and transmission: and generating corresponding control signals, such as an opening/closing command, a brightness/temperature adjusting command and the like, according to the prediction result and the adjustment strategy, and sending the control signals to corresponding household equipment through wireless communication.
S403, the household equipment receives and analyzes the control signal: the household equipment receives the control signal of wireless communication, analyzes and processes the control signal, and changes the working state of the household equipment according to the control signal.
S404 monitors control effects and feedback: and monitoring the working state of the household equipment and the change of the environmental parameters, and transmitting relevant feedback information back to the control system. This may be accomplished by sensor data of the home device or by reporting back of the device status.
S405 continuously monitors and manages: the working state and environmental parameters of the household equipment are continuously monitored, abnormal conditions are timely processed, equipment management and maintenance are carried out, and continuous and stable operation of the system is ensured.
Through the above process, the wireless communication can realize remote control and self-adaptive control on household equipment, and the intelligentization and user experience of a household system are improved.
Example two
As shown in fig. 2, an intelligent home control system, which is suitable for the method, comprises
The sensor module is used for sensing the environmental parameters and generating sensor data;
the data processing module is used for receiving the sensor data and analyzing and processing the sensor data; the data processing module is connected with the sensor module through the communication interface and receives the sensor data in real time. The sensor data may include environmental parameters such as indoor temperature, humidity, illumination intensity, etc., as well as device status data, etc. And preprocessing the received data, including data format conversion, denoising, outlier detection, correction and other operations. This helps to improve data quality and reliability.
Data analysis: analyzing and mining the preprocessed data, and extracting useful information and modes in the data by using methods such as machine learning algorithm, statistical analysis and the like.
And the storage module is used for storing information such as sensor data, learning results, user data and the like, and the data processing module is used for storing the received sensor data and can use a database.
The control module is used for controlling the operation of the household equipment according to the analysis and processing result; the control module is matched with a preset intelligent control strategy according to the numerical representation of the environment state, and controls the operation of the household equipment. And converting the generated control command into a corresponding control signal according to the control strategy and the communication protocol of the equipment, and preparing to send to the household equipment.
The learning module is used for learning the living habit and preference of the user and providing personalized intelligent control according to the learning result; the learning module generates a personalized intelligent control strategy through learning the behaviors and habits of the user, and works cooperatively with the control module to provide accurate control.
The communication module is used for carrying out data interaction and remote control with external equipment; selecting a communication protocol: depending on the system requirements and device characteristics, an appropriate communication protocol is selected, common communication protocols include Wi-Fi, zigbee, bluetooth, and the like. Communication connection establishment: and the communication module is used for establishing connection with external equipment, such as a smart phone, an intelligent gateway, a cloud server and the like. And (3) data transmission: the bidirectional data transmission is realized, and the bidirectional data transmission comprises the steps of receiving control instructions of external equipment, sending sensor data, control signals and the like. Data format conversion: according to the requirements of the communication protocol, the data is subjected to format conversion and coding processing so as to ensure the correctness and reliability of communication. Remote control function: and receiving a remote control instruction sent by the external equipment, analyzing the instruction content, and correspondingly adjusting the working state of the household equipment. Data interaction and sharing: and allowing data interaction and sharing with external equipment, for example, sending real-time state data and sensor data of the household equipment to the external equipment or a cloud server, or receiving data analysis results and control instructions sent by the external equipment. And (3) ensuring safety: and adopting security measures to ensure the security of communication, protecting the confidentiality of data by using an encryption algorithm, verifying the identity of external equipment and avoiding unauthorized access and data leakage. Exception handling and logging: abnormal conditions in the communication process, such as connection interruption, data loss and the like, are processed, and logs are recorded for fault investigation and monitoring.
The user interface module is used for displaying the current intelligent control result and receiving the input of a user; the user interface module adopts a graphical interface to display the current intelligent control result and receives the input and feedback of a user.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ReadOnlyMemory, ROM) or a random access memory (RandomABBessMemory, RAM).
It should be understood that the detailed description of the technical solution of the present invention, given by way of preferred embodiments, is illustrative and not restrictive. Modifications of the technical solutions described in the embodiments or equivalent substitutions of some technical features thereof may be performed by those skilled in the art on the basis of the present description; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The intelligent home control method is characterized by comprising the following steps of: the method comprises the following steps:
step 1, sensing household environmental parameters including temperature, humidity and light intensity through a sensor;
step 2, predicting the optimal working state of the household equipment by using a predictable model according to the perceived environmental parameters;
step 3, comparing the predicted result with the actual environment parameters, and dynamically adjusting the working state of the household equipment;
and step 4, transmitting a control signal to the household equipment through wireless communication to realize self-adaptive control.
2. The smart home control method as claimed in claim 1, wherein: the sensor comprises a temperature and humidity sensor, a light sensor and a gas sensor; the dynamic adjustment of the working state of the household equipment comprises the adjustment of the brightness, the temperature and the humidity of the household equipment.
3. The smart home control method as claimed in claim 1, wherein: the predictive model is obtained by training a machine learning algorithm and can be optimized iteratively according to real-time environment parameters.
4. A smart home control method as claimed in claim 3, wherein: the specific model formula of the predictable model is as follows:
y＝b+Σ(αi*K(xi,x))-b (1)
wherein, y: predicted optimal working state of home equipment, b: bias terms, i.e., constant terms of the model; αi: support Lagrangian multiplier coefficients in vector regression; xi: input vectors in the training data; x: an input vector with observed real-time environmental parameters; k (xi, x): a kernel function for calculating similarity between input vectors;
deducing the optimal working state of the household equipment through the predicted value y; the model predicts by computing the similarity between the input vector xi and the real-time environmental parameter x, in combination with the lagrangian multiplier coefficient αi.
5. A smart home control method as claimed in claim 3, wherein:
the prediction steps of the predictable model are as follows:
s201, data acquisition and preparation:
s202, feature selection and conversion:
s203, training a model:
s204, model prediction:
s205 control action execution.
6. A smart home control system, said system being adapted for use in a method as claimed in claims 1-5, characterized by: the system comprises
The sensor module is used for sensing the environmental parameters and generating sensor data;
the data processing module is used for receiving the sensor data and analyzing and processing the sensor data;
the control module is used for controlling the operation of the household equipment according to the analysis and processing result;
the learning module is used for learning the living habit and preference of the user and providing personalized intelligent control according to the learning result;
the communication module is used for carrying out data interaction and remote control with external equipment;
the user interface module is used for displaying the current intelligent control result and receiving the input of a user;
and the storage module is used for storing information such as sensor data, learning results, user data and the like.
7. The smart home control system of claim 6, wherein: the learning module generates a personalized intelligent control strategy through learning the behaviors and habits of the user, and works cooperatively with the control module to provide accurate control.
8. The smart home control system of claim 6, wherein: the user interface module adopts a graphical interface to display the current intelligent control result and receives the input and feedback of a user.
9. The smart home control system of claim 6, wherein: the control module is matched with a preset intelligent control strategy according to the numerical representation of the environment state, and controls the operation of the household equipment.
10. The smart home control system of claim 6, wherein: the communication module supports a wireless communication technology, and realizes data interaction and remote control with external equipment such as a mobile phone, a tablet personal computer and the like.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117369302A (en) * | 2023-11-24 | 2024-01-09 | 深圳市助尔达电子科技有限公司 | Intelligent home system based on model predictive control |
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| CN117369302A (en) * | 2023-11-24 | 2024-01-09 | 深圳市助尔达电子科技有限公司 | Intelligent home system based on model predictive control |
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