Network environment self-adaptive smart home control method

Abstract

The invention belongs to the technical field of smart home control, particularly relates to a network environment self-adaptive smart home control method, and aims to solve the problem that the user experience and the intelligent degree of smart home are reduced due to the fact that automatic control cannot be performed according to control operation habits in the existing smart home equipment control process. According to the technical scheme, the method comprises the following steps that S1, a control system is prepared, the control system comprises an intelligent device module, the intelligent device module is connected with a selection module and a device access module, the selection module is connected with a device adding module, the device adding module is connected with a login module, and the login module is connected with the intelligent device module; the equipment access module is connected with a data acquisition module, the data acquisition module is connected with a data analysis module, and the intelligent home control system can perform automatic control according to control operation habits in the use process, so that the user experience and the intelligent degree of the intelligent home can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home control, and in particular to a network environment adaptive smart home control method. Background Art

[0002] With the rapid development of science and technology, people have greater demands for comfortable, convenient and intelligent home life. With the emergence of smart homes, users can intelligently control smart products such as smart switches, TVs, air conditioners, door locks, etc. through the Internet. The current smart home control method is generally that users connect to the smart home cloud service center through the Internet, and then send control instructions to the cloud service center. After the cloud service center parses the control instructions, it sends the parsed control instructions to the specific smart home control center. The smart home control center forwards the control instructions to the smart device. After the smart device receives the control instructions, it completes the control operation.

[0003] In the prior art, during the control process of smart home devices, it is impossible to perform automatic control according to the control operation habits, which reduces the user experience and the intelligence level of the smart home. For this reason, we propose a network environment adaptive smart home control method to solve the above problem. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that in the prior art, the smart home equipment control process cannot be automatically controlled according to the control operation habits, thereby reducing the user experience and the intelligence level of the smart home, and proposes a smart home control method that is adaptive to the network environment.

[0005] The present application provides a network environment adaptive smart home control method using the following technical solutions:

[0006] A network environment adaptive smart home control method comprises the following steps:

[0007] S1: Prepare a control system, the control system includes an intelligent device module, the intelligent device module is connected to a selection module and a device access module, the selection module is connected to a device adding module, the device adding module is connected to a login module, the device access module is connected to a data acquisition module, the data acquisition module is connected to a data analysis module, the data analysis module is connected to a recording module and an interaction module, the interaction module is connected to a control module, the control module is connected to a setting module, a mobile phone APP module and an automatic matching module, the mobile phone APP module is connected to a remote control module, the recording module is connected to a learning module, the learning module is connected to a control model module, and the control model module is connected to an automatic matching module;

[0008] S2: First, log in to the system through the login module, search for smart devices through the device adding module, select the smart devices to be added through the selection module, connect to the added smart devices through the device access module, collect data from the smart devices through the data collection module, and analyze the collected data through the data analysis module;

[0009] S3: Then, the interaction module uploads the analysis results to the control module, controls the parameters of the smart device and remotely controls it through the mobile phone APP module and the remote control module, and the setting module sets the automation rules of the control module;

[0010] S4: Finally, the recording module records the analysis results of the data analysis module in real time, the learning module learns according to the recorded historical data, and builds the corresponding control model and stores it in the control model module, the recommendation module recommends the parameter settings of the intelligent device, and the automatic matching module matches the control model in the control model module according to the real-time data analysis results to realize the automatic control of the intelligent device.

[0011] Furthermore, the login module includes a registration unit, a setting unit and a login unit, the registration unit is connected to the setting unit, and the setting unit is connected to the login unit.

[0012] Furthermore, the device adding module includes a scanning unit, an input unit and a first matching unit, the scanning unit is connected to the input unit, and the input unit is connected to the first matching unit.

[0013] Furthermore, the control model module includes a preset unit, a generation unit and a storage unit, the preset unit is connected to the generation unit, and the generation unit is connected to the storage unit.

[0014] Furthermore, the automatic matching module includes an analyzing unit, a judging unit and a second matching unit, the analyzing unit is connected to the judging unit, and the judging unit is connected to the second matching unit.

[0015] Furthermore, the login module is used to register an account and log into the system, and the device adding module searches for smart devices by scanning a QR code, entering a serial number, and pairing via Bluetooth.

[0016] Furthermore, the device access module is used to connect the smart device to the control system, and realize information interconnection and intercommunication with the control module through the wireless communication network and the wired communication line.

[0017] Furthermore, the data acquisition module is used to collect real-time data of the smart device and transmit it to the data analysis module, and the data analysis module stores and analyzes the collected data.

[0018] Furthermore, the interaction module is used to transmit the data analysis results to the control module, the control module is used to send control instructions to the smart device, and the remote control module is used to remotely control the smart device.

[0019] Furthermore, the recording module is used to record the collected real-time analysis data, the learning module performs learning according to the analysis results, and transmits the learning content to the control model module, and the control model module is used to generate a control model according to the learning results.

[0020] In summary, the present application includes at least one of the following beneficial technical effects:

[0021] 1. This solution logs into the system through the login module, searches for smart devices through the device adding module, selects the smart devices to be added through the selection module, connects the added smart devices through the device access module, collects data from the smart devices through the data collection module, and analyzes the collected data through the data analysis module;

[0022] 2. This solution uploads the analysis results to the control module through the interactive module, controls the parameters of the smart device and remotely controls it through the mobile phone APP module and the remote control module, and the setting module sets the automation rules of the control module;

[0023] 3. This solution uses the recording module to record the analysis results of the data analysis module in real time. The learning module learns according to the recorded historical data and builds the corresponding control model and stores it in the control model module. The recommendation module recommends parameter settings for smart devices. The automatic matching module matches the control model in the control model module according to the real-time data analysis results to achieve automatic control of smart devices.

[0024] The present invention can perform automatic control according to control operation habits during use, thereby effectively improving the user experience and the intelligence level of the smart home. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A structural block diagram of a control system of a network environment adaptive smart home control method proposed by the present invention;

[0026] Figure 2 A structural block diagram of a login module of a network environment adaptive smart home control method proposed by the present invention;

[0027] Figure 3 A structural block diagram of a device adding module for a network environment adaptive smart home control method proposed by the present invention;

[0028] Figure 4A structural block diagram of a control model module of a network environment adaptive smart home control method proposed by the present invention;

[0029] Figure 5 This is a structural block diagram of an automatic matching module of a network environment adaptive smart home control method proposed by the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0031] Embodiment 1

[0032] Reference Figure 1-Figure 5 , a network environment adaptive smart home control method, comprising:

[0033] S1: Prepare a control system, the control system includes an intelligent device module, the intelligent device module is connected to a selection module and a device access module, the selection module is connected to a device adding module, the device adding module is connected to a login module, the device access module is connected to a data acquisition module, the data acquisition module is connected to a data analysis module, the data analysis module is connected to a recording module and an interaction module, the interaction module is connected to a control module, the control module is connected to a setting module, a mobile phone APP module and an automatic matching module, the mobile phone APP module is connected to a remote control module, the recording module is connected to a learning module, the learning module is connected to a control model module, and the control model module is connected to the automatic matching module;

[0034] S2: First, log in to the system through the login module, search for smart devices through the device adding module, select the smart devices to be added through the selection module, connect to the added smart devices through the device access module, collect data from the smart devices through the data collection module, and analyze the collected data through the data analysis module;

[0035] S3: Then, the interaction module uploads the analysis results to the control module, controls the parameters of the smart device and remotely controls it through the mobile phone APP module and the remote control module, and the setting module sets the automation rules of the control module;

[0036] S4: Finally, the recording module records the analysis results of the data analysis module in real time, the learning module learns according to the recorded historical data, and builds the corresponding control model and stores it in the control model module, the recommendation module recommends the parameter settings of the smart device, the automatic matching module matches the control model in the control model module according to the real-time data analysis results, and is used to realize the automatic control of the smart device, the login module is used to register an account and log in to the system, the device adding module searches for smart devices by scanning QR codes, entering serial numbers and Bluetooth pairing, and the device access module is used to connect the smart device to the control system, and realize information interconnection with the control module through the wireless communication network and wired communication lines. Intercommunication: the data acquisition module is used to collect real-time data of smart devices and transmit it to the data analysis module. The data analysis module stores and analyzes the collected data. The interaction module is used to transmit the data analysis results to the control module. The control module is used to send control instructions to the smart devices. The remote control module is used to remotely control the smart devices. The recording module is used to record the collected real-time analysis data. The learning module learns according to the analysis results and transmits the learning content to the control model module. The control model module is used to generate a control model according to the learning results. The learning module adopts a control model learning algorithm. The control model learning algorithm includes: linear regression model, gradient descent algorithm and Q learning algorithm;

[0037] Linear Regression Model:

[0038] formula:

[0039] (y=\beta_0+\beta_1x_1+\beta_2x_2+\ldots+\beta_nx_n+\epsilon);

[0040] Where (y) is the output variable, (\beta_0,\beta_1,\ldots,\beta_n) are model parameters, (x_1,x_2,\ldots,x_n) are input variables, and (\epsilon) is the error term;

[0041] Application: Used to establish a linear relationship between input variables and output variables;

[0042] Gradient Descent Algorithm:

[0043] formula:

[0044] (\theta_j=\theta_j-\alpha\frac{\partial}{\partial\theta_j}J(\theta));

[0045] Among them, (\theta_j) is the model parameter, (\alpha) is the learning rate, and (J(\theta)) is the loss function;

[0046] Application: Used to optimize model parameters to minimize the loss function;

[0047] Q learning algorithm (a type of reinforcement learning):

[0048] formula:

[0049] (Q(s,a)=Q(s,a)+\alpha[r+\gamma\max_{a'}Q(s',a')-Q(s,a)]);

[0050] Where (Q(s,a)) is the state-action value function, (\alpha) is the learning rate, (r) is the reward, (\gamma) is the discount factor, (s') is the next state, and (a') is the next action;

[0051] Application: Used to learn the best action to take in a given state;

[0052] The control module adopts control algorithms, including: PID control, fuzzy control, and adaptive control;

[0053] PID Control

[0054] PID control (proportional-integral-differential control) is a classic control algorithm widely used in various industrial control systems. The PID controller consists of three parts: proportional (P), integral (I) and differential (D), each of which has a different effect on the control output:

[0055] Proportional part: According to the current error size, directly output a control amount proportional to the control error to quickly reduce the error;

[0056] Integral part: Consider the cumulative effect of error, eliminate the static error of the system by integrating the error, and improve the control accuracy;

[0057] Differential part: According to the error change rate, the future error trend of the system is predicted in advance, and the corresponding control quantity is output to improve the dynamic performance of the system;

[0058] The advantages of PID control are simple structure, easy implementation and debugging, but it may not achieve the best control effect when facing nonlinear, time-varying and complex systems;

[0059] Fuzzy Control

[0060] Fuzzy control is a control algorithm based on fuzzy logic, which is particularly suitable for handling control systems with uncertainty and approximation. Fuzzy controllers convert precise control inputs into fuzzy control outputs by fuzzifying input quantities, formulating fuzzy rules, and performing fuzzy reasoning;

[0061] The advantage of fuzzy control is that it can handle uncertainty and fuzzy information and has good adaptability to the nonlinearity and time-varying nature of the system. However, the design of fuzzy controllers depends on the experience and knowledge of experts, and its control rules may be difficult to accurately describe and verify;

[0062] Adaptive Control

[0063] Adaptive control is a control algorithm that can automatically adjust control parameters according to system operating status and environmental changes. The adaptive controller monitors the system output and status in real time, compares them with the expected output, and adjusts the control parameters according to the error signal to achieve the best control effect;

[0064] The advantage of adaptive control is that it can automatically adapt to changes in the system and improve the robustness and accuracy of control. However, the design and implementation of adaptive controllers are relatively complex, requiring high computing power and storage resources, and may have convergence and stability problems.

[0065] Reference Figure 2 and Figure 3 The login module includes a registration unit, a setting unit and a login unit, the registration unit is connected to the setting unit, the setting unit is connected to the login unit, the device adding module includes a scanning unit, an input unit and a first matching unit, the scanning unit is connected to the input unit, and the input unit is connected to the first matching unit.

[0066] Reference Figure 4 The control model module includes a preset unit, a generation unit and a storage unit. The preset unit is connected to the generation unit, and the generation unit is connected to the storage unit.

[0067] Reference Figure 5 The automatic matching module includes an analyzing unit, a judging unit and a second matching unit. The analyzing unit is connected to the judging unit, and the judging unit is connected to the second matching unit.

[0068] The implementation principle in this embodiment is: first, log in to the system through the login module, search for smart devices through the device adding module, select the smart devices to be added through the selection module, connect the added smart devices through the device access module, the data acquisition module collects the data of the smart devices, and the data analysis module analyzes the collected data. Then, the interaction module uploads the analysis results to the control module, controls and remotely controls the parameters of the smart devices through the mobile phone APP module and the remote control module, and the setting module sets the automation rules of the control module. Finally, the recording module records the analysis results of the data analysis module in real time, the learning module learns according to the recorded historical data, and constructs the corresponding control model and stores it in the control model module. The recommendation module recommends the parameter settings of the smart devices, and the automatic matching module matches the control model in the control model module according to the real-time data analysis results to realize automatic control of the smart devices.

[0069] Embodiment 2

[0070] The difference between this embodiment and the first embodiment is that the furniture device module is connected to a device integration module, and the device integration module is used to integrate smart devices of different brands, thereby avoiding the need for users to switch between multiple applications and simplifying the operation process.

[0071] Embodiment 3

[0072] The difference between this embodiment and the first embodiment is that the control module is connected to a voice command module, and the voice command module is used to send voice commands, so that the user can operate and control the smart device through simple voice commands, thereby improving the convenience of use.

[0073] Embodiment 4

[0074] The difference between this embodiment and the first embodiment is that the device access module is connected to a network security module, and the network security module is used to set a firewall to avoid the risk of intrusion after the smart device is connected to the Internet and to ensure user privacy.

[0075] Embodiment 5

[0076] The difference between this embodiment and the first embodiment is that the setting module is connected to a personalized setting module, and the personalized setting module is used to perform personalized settings according to user needs to improve the interactive experience.

[0077] Experimental example

[0078] By comparing the smart home control solutions of the entity archives proposed in Examples 1 to 5 with the conventional smart home control solutions, the experimental data are as follows:

[0079]

[0080] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A network environment adaptive smart home control method, characterized by: The following steps are involved: S1: Prepare a control system, the control system includes an intelligent device module, the intelligent device module is connected to a selection module and a device access module, the selection module is connected to a device adding module, the device adding module is connected to a login module, the device access module is connected to a data acquisition module, the data acquisition module is connected to a data analysis module, the data analysis module is connected to a recording module and an interaction module, the interaction module is connected to a control module, the control module is connected to a setting module, a mobile phone APP module and an automatic matching module, the mobile phone APP module is connected to a remote control module, the recording module is connected to a learning module, the learning module is connected to a control model module, and the control model module is connected to an automatic matching module; S2: First, log in to the system through the login module, search for smart devices through the device adding module, select the smart devices to be added through the selection module, connect to the added smart devices through the device access module, collect data from the smart devices through the data collection module, and analyze the collected data through the data analysis module; S3: Then, the interaction module uploads the analysis results to the control module, controls the parameters of the smart device and remotely controls it through the mobile phone APP module and the remote control module, and the setting module sets the automation rules of the control module; S4: Finally, the recording module records the analysis results of the data analysis module in real time, the learning module learns according to the recorded historical data, and builds the corresponding control model and stores it in the control model module, the recommendation module recommends the parameter settings of the intelligent device, and the automatic matching module matches the control model in the control model module according to the real-time data analysis results to realize the automatic control of the intelligent device.

2. A network environment adaptive smart home control method according to claim 1, characterized in that: The login module includes a registration unit, a setting unit and a login unit. The registration unit is connected to the setting unit, and the setting unit is connected to the login unit.

3. A network environment adaptive smart home control method according to claim 2, characterized in that: The device adding module comprises a scanning unit, an input unit and a first matching unit, wherein the scanning unit is connected to the input unit, and the input unit is connected to the first matching unit.

4. The network environment adaptive smart home control method according to claim 3, characterized in that: The control model module includes a preset unit, a generation unit and a storage unit. The preset unit is connected to the generation unit, and the generation unit is connected to the storage unit.

5. A network environment adaptive smart home control method according to claim 4, characterized in that: The automatic matching module comprises an analyzing unit, a judging unit and a second matching unit, wherein the analyzing unit is connected to the judging unit, and the judging unit is connected to the second matching unit.

6. A network environment adaptive smart home control method according to claim 5, characterized in that: The login module is used to register an account and log in to the system, and the device adding module searches for smart devices by scanning a QR code, entering a serial number, and pairing with Bluetooth.

7. A network environment adaptive smart home control method according to claim 6, characterized in that: The device access module is used to connect the intelligent device to the control system, and realize information interconnection and intercommunication with the control module through the wireless communication network and the wired communication line.

8. The network environment adaptive smart home control method according to claim 7, characterized in that: The data acquisition module is used to collect real-time data from smart devices and transmit it to the data analysis module, which stores and analyzes the collected data.

9. A network environment adaptive smart home control method according to claim 8, characterized in that: The interaction module is used to transmit the data analysis results to the control module, the control module is used to send control instructions to the smart device, and the remote control module is used to remotely control the smart device.

10. The network environment adaptive smart home control method according to claim 9, characterized in that: The recording module is used to record the collected real-time analysis data, the learning module learns according to the analysis results and transmits the learning content to the control model module, and the control model module is used to generate a control model according to the learning results.

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