Smart residence control method based on Internet of Things

Abstract

The invention discloses a smart residence control method based on the Internet of Things, and the method specifically comprises the steps: an intelligent recognition and configuration module which automatically recognizes and configures accessed Internet of Things equipment; the edge calculation and communication management module is used for executing protocol conversion, data standardization and communication management; edge calculation processing is carried out, and protocol conversion and data format standardization are completed at an equipment end; intelligent communication management is realized, a communication path is optimized, communication flow is adjusted, and stable data transmission is ensured; security and privacy protection: implementing data encryption and privacy protection strategies, and monitoring security threats; the unified control and management module is used for managing equipment instructions and feedback in a centralized manner and carrying out self-adaptive adjustment; and the user interaction and intelligent monitoring module provides equipment state display, intelligent reminding and data analysis. The method effectively solves the problem of equipment automatic identification and management in a smart residence environment with various equipment types and heterogeneous communication protocols, and improves the stability and safety of an equipment system.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things (IoT), and in particular to a smart house control method based on IoT. Background Art

[0002] With the rapid development of Internet of Things technology, more and more smart devices are being introduced into residential environments. These devices include but are not limited to smart appliances such as HVAC, sensors, cameras, security systems, etc., which interact with residential networks through multiple communication protocols (such as Wi-Fi, Zigbee, Bluetooth, etc.). However, due to the different communication protocols and data formats adopted by different devices, this has brought great challenges to the interoperability between devices, the management efficiency of the system, and the stability of data transmission.

[0003] Traditional smart home control methods often rely on manual configuration and fixed communication protocols, which not only increases the complexity of device access and management, but also limits the scalability and adaptability of the system. When a new device is connected to the network, it is often difficult for the system to automatically identify the device attributes and configure them appropriately, resulting in the device not being able to work properly or communication failures. In addition, as the number of devices increases, how to optimize the communication path and traffic management in the network while ensuring communication security to ensure the efficient operation of the system has also become a technical problem that needs to be solved urgently.

[0004] Therefore, developing a control method that can realize automatic device identification, intelligent configuration, and efficient management in a smart residential environment with diverse device types and heterogeneous communication protocols is of great significance for improving the intelligence level of the system and user experience. This method requires the comprehensive application of advanced technologies such as machine learning, edge computing, and topology optimization to ensure that each device can seamlessly access the network, maintain stable and efficient communication, and achieve effective protection of data security. Summary of the invention

[0005] The present invention provides a smart home control method based on the Internet of Things to solve the problem of how to realize automatic identification, configuration and efficient management of devices through the Internet of Things technology in a smart home environment with diverse device types and heterogeneous communication protocols, and ensure that each device can seamlessly access the network and maintain stable communication and security.

[0006] In order to solve the above problems, the present invention provides a smart house control method based on the Internet of Things, comprising:

[0007] A smart house control method based on the Internet of Things, comprising:

[0008] Intelligent identification and configuration module, automatically identifying and configuring connected IoT devices;

[0009] Edge computing and communication management module, which performs protocol conversion, data standardization and communication management;

[0010] Edge computing processing completes protocol conversion and data format standardization on the device side;

[0011] Intelligent communication management, optimizing communication paths, adjusting communication traffic, and ensuring stable data transmission;

[0012] Security and privacy protection, implement data encryption and privacy protection policies, and monitor security threats.

[0013] Unified control and management module, centrally manage device instructions and feedback and make adaptive adjustments;

[0014] User interaction and intelligent monitoring module, providing equipment status display, intelligent reminders and data analysis.

[0015] As a preferred solution, the smart house control method based on the Internet of Things also includes:

[0016] Automatic device identification, receiving and processing input data from IoT devices, using machine learning models to extract and classify device feature vectors;

[0017] Intelligent configuration and adaptation: Generate and allocate communication protocols and network parameters based on recognition results, and adapt data formats;

[0018] Dynamic learning and optimization, monitoring equipment usage, optimizing recognition models and configuration parameters.

[0019] As a preferred solution, the smart house control method based on the Internet of Things also includes:

[0020] Centralized control command processing, receiving and distributing control commands;

[0021] Adaptive system management, dynamically adjust system configuration, and automatically reconfigure device parameters;

[0022] Feedback analysis and optimization: collect equipment feedback data and optimize control logic and system configuration.

[0023] As a preferred solution, the smart house control method based on the Internet of Things also includes:

[0024] Real-time monitoring and status display, providing a user interface to display device status information;

[0025] Intelligent reminders and suggestions, generating reminders and operation suggestions based on device status;

[0026] Historical data analysis and operation records: analyze equipment historical data and operation records to provide data support.

[0027] As a preferred solution, the communication path optimization in the intelligent communication management module is dynamically adjusted according to the network status and transmission requirements of the device.

[0028] As a preferred solution, the control instruction distribution in the unified control and management module is implemented through an intelligent management platform to ensure the functional consistency of each device in the system.

[0029] As a preferred solution, the system dynamically adjusts the communication protocol and parameters of the device by learning user behavior data and device operating status.

[0030] As a preferred solution, the system generates and adjusts new control logic and configuration strategy according to device feedback data.

[0031] As a preferred solution, the intelligent reminders and suggestions are generated based on abnormal status or maintenance requirements of the equipment.

[0032] As a preferred solution, the historical data analysis is implemented through a data analysis tool provided by the system to identify long-term performance changes of the equipment.

[0033] The key innovative features of the present invention include:

[0034] (1) Intelligent identification and configuration module: Uses pre-trained machine learning models to automatically identify and configure newly connected IoT devices, ensuring that the devices are seamlessly connected and operate efficiently in the network.

[0035] (2) Edge computing and communication management module: Edge computing technology is used to complete protocol conversion and data standardization processing near the device end, and advanced mathematical models such as topology and complex analysis are used to optimize communication paths and traffic management.

[0036] (3) Adaptive system management module: Dynamically adjust and optimize system configuration based on the device's operating status and user behavior data, and improve system intelligence through an adaptive learning mechanism.

[0037] (4) User interaction and intelligent monitoring module: It provides a user-friendly interface, supports real-time monitoring, intelligent reminders, historical data analysis, and interaction with the intelligent management system, ensuring that users can intuitively understand the status of the equipment and make corresponding adjustments.

[0038] The following are its main beneficial effects:

[0039] The present invention solves the problems of device identification, configuration and management in a complex environment with diverse device types and heterogeneous communication protocols in smart homes by combining Internet of Things technology, machine learning algorithms and edge computing technology. An automatic identification and configuration module is adopted, and through a pre-trained machine learning model, accurate identification and intelligent configuration of devices are achieved, ensuring that the devices can seamlessly access the network, and after the devices are connected, most of the protocol conversion and data format standardization processing are completed through edge computing technology, reducing the load of the central server, reducing data transmission delays, and ensuring the real-time response capability of the system. In addition, the present invention introduces an intelligent communication management and security protection module, which optimizes the communication path and traffic management through complex mathematical models and algorithms, ensuring efficient and stable data transmission between devices, and ensuring data security in the smart home environment by implementing data encryption and privacy protection strategies. Compared with traditional methods, the present invention effectively improves the adaptability, scalability and security of the system, and significantly improves the efficiency of device management and communication in the smart home environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a smart house control method based on the Internet of Things provided by an embodiment of the present invention.

[0041] Figure 2 It is a structural block diagram of a smart home control method based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The present invention discloses a smart residential control method based on the Internet of Things, which specifically includes: an intelligent identification and configuration module, which automatically identifies and configures the connected Internet of Things devices; an edge computing and communication management module, which performs protocol conversion, data standardization and communication management; edge computing processing, which completes protocol conversion and data format standardization on the device side; intelligent communication management, which optimizes the communication path, adjusts the communication flow, and ensures stable data transmission; security and privacy protection, which implements data encryption and privacy protection strategies, and monitors security threats; a unified control and management module, which centrally manages device instructions and feedback and performs adaptive adjustments; a user interaction and intelligent monitoring module, which provides device status display, intelligent reminders and data analysis. This method effectively solves the problem of automatic device identification and management in a smart residential environment with diverse device types and heterogeneous communication protocols, and improves the stability and security of the device system.

[0043] An Internet of Things-based smart home control method provided in an embodiment of the present invention is mainly used to solve the technical problem solved by the present invention, which is "how to use Internet of Things technology to achieve automatic identification, configuration and efficient management of devices in a smart home environment with diverse device types and heterogeneous communication protocols, and ensure that each device can seamlessly access the network and maintain stable communication and security."

[0044] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit and scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0046] Embodiment 1:

[0047] Reference Figure 1 , is a flow chart of a smart home control method based on the Internet of Things provided by an embodiment of the present invention. The smart home control method based on the Internet of Things may at least include steps S100-S400:

[0048] S100, the system receives basic information of the device and extracts feature vectors, inputs them into the machine learning model for classification and recognition, and automatically generates and optimizes configuration parameters based on the recognition results.

[0049] S200, the system uses edge nodes to perform protocol conversion and data format standardization, and adjusts communication paths and traffic through intelligent communication management.

[0050] S300, the system uniformly receives control instructions and distributes them to devices, dynamically adjusts configuration parameters through adaptive system management, monitors device operation in real time, and adjusts system configuration based on feedback.

[0051] S400, the system displays the equipment status through the user interface, saves and analyzes historical data to support future decision-making.

[0052] S100, the system receives basic device information and extracts feature vectors, inputs them into the machine learning model for classification and recognition, and automatically generates and optimizes configuration parameters based on the recognition results. Step S100 includes steps S110-S130:

[0053] S110, receiving and processing basic information of newly connected IoT devices. Specifically:

[0054] The input terminal data includes but is not limited to device type D t , Brand and Model D m 、Communication Protocol P i And data format F iEtc. The input data is collected through the system's sensor network and device interfaces and transmitted to the central processing unit, where the pre-trained machine learning model analyzes and processes the data.

[0055] The machine learning model extracts the key feature vector V of the device by analyzing the input data i , the feature vector is used to describe the key attributes of the device. Specifically, the feature vector V i is defined as follows:

[0056] V i =[D t ,D m ,P i ,F i ]

[0057] Among them, V i The feature vector representing device i is a collection of device attributes; D t Indicates the type of device (such as sensor, camera, home appliance, etc.); D m Indicates the brand and model of the device, which is the manufacturer information and product model identification of the device; i Indicates the communication protocol type supported by the device, such as Wi-Fi, Zigbee, Bluetooth, etc.; F i Indicates the data format type of the device, such as XML, JSON, Protobuf, etc.

[0058] The feature vector V i The input is analyzed by the classifier in the machine learning model. The classifier can be a multi-layer neural network, random forest, support vector machine and other algorithm models, through which the device is accurately classified. The classification result generates the device identification result R i , including the classification label, brand, supported communication protocol type and data format of the device. Specifically, the recognition result R i is defined as follows:

[0059] R i =[L i ,B i ,P r ,F r ]

[0060] Among them, R i Indicates the recognition result of device i, L i Indicates the classification label of the device (such as sensor, camera, etc.), B i represents the brand recognition result of the device, P r Indicates the recommended communication protocol type after device identification. rIndicates the recommended data format after device recognition.

[0061] S120: Automatically generate preliminary configuration parameters of the device according to the recognition result. Specifically:

[0062] The system will i Automatically generate the initial configuration parameters of the device C i To ensure that the device can operate efficiently in the existing network. The initial configuration parameters C i Including communication protocol distribution P c , Network parameter settings N p And data format adaptation F c .

[0063] First, the system uses the recognition result R i Recommended communication protocol type P in r and recommended data format F r Assign the optimal communication protocol stack P to the device c The communication protocol allocation function f(P_i, N_p) is specifically defined as follows:

[0064] P c =f(P r ,N p )

[0065] Among them, P c represents the communication protocol configuration of device i, P r Indicates the recommended communication protocol type after device i is identified, N p Indicates the current status parameters of the network, including bandwidth utilization, network delay, packet loss rate, etc.

[0066] The system calculates the optimal communication protocol configuration of the device through the function to ensure that the device can be successfully connected and run in the network. r , perform preliminary adaptation of the data format and adjust the data format of the device to the format used by the system standard F c The data format adaptation function is defined as follows:

[0067] F c =g(F r ,N p )

[0068] Among them, F c Indicates the data format configuration of device i, F r Indicates the recommended data format after device i is identified, N p Represents the current state parameters of the network.

[0069] S130, by continuously monitoring the use of the device, dynamically adjust and optimize the device's identification model and configuration strategy.

[0070] After the initial configuration of the device is completed, the system enters the dynamic learning and optimization stage. The dynamic learning and optimization step is to monitor the device usage U i Continuous monitoring, dynamic adjustment and optimization of equipment identification models and configuration strategies. i Including parameters such as device response time, communication success rate, and data transmission delay.

[0071] The system converts the feedback data U i The weight parameter set W used to update the device identification model i The updated weight parameter set W_i^{\prime} is calculated by the following adjustment function:

[0072] W′ i =h(W i ,U i )

[0073] Among them, W′ i represents the weight parameter set of the optimized device identification model, W i Represents the weight parameter set of the original device identification model, U i Represents a set of usage feedback data of device i.

[0074] The updated weight parameter set W′ i It will be used for retraining and optimization of the model to ensure that the model can better adapt to the identification and configuration requirements of new devices and unknown protocols. At the same time, the system will be based on the optimized weight parameter W′ i Regenerate optimized configuration parameters C′ i , to ensure the continued efficient operation of the device in the network.

[0075] The optimized configuration parameter C′ i It will be passed to the subsequent edge computing and communication management module, in which the optimized parameter C′ i Used to accurately calculate the protocol conversion time T between devices c and communication path selection P p , ensuring the system's adaptability and efficient operation.

[0076] S200, the system uses edge nodes to perform protocol conversion and data format standardization, and adjusts communication paths and traffic through intelligent communication management. Step S200 at least includes steps S210-S230:

[0077] S210: The system uses edge computing nodes to perform protocol conversion and data format standardization on the received device data. Specifically:

[0078] ① Protocol conversion: In order to ensure that the device can be seamlessly connected and compatible with the system standard communication protocol, the system performs protocol conversion through a multivariate regression model. Assume that the original communication protocol of the device is The converted standard protocol is Then we have:

[0079]

[0080] in, Indicates the original communication protocol type of the input. It represents the standard communication protocol type. α1, α2, and α3 are the coefficients of the regression model, which are determined by the previous training data. represents the rate of change of the protocol over time, Represents the gradient change of the protocol space, which is used to consider the multi-dimensional properties of the protocol. i It is the optimized equipment configuration parameter, which is calculated by the previous module S130.

[0081] ② Data format standardization: After completing the protocol conversion, the system uses a combination of complex analysis and functional analysis to standardize the data format. Suppose the input data format is The standardized format is Then we have:

[0082]

[0083] in, Indicates the input data format, Indicates the standardized data format. It is the Fourier transform in complex analysis, which is used to convert the data format into the frequency domain. It is the integral term in functional analysis, which indicates the weighted adjustment of data format in different dimensions. i Calibration parameters during data format standardization.

[0084] S220, the system manages the communication paths and traffic between devices through an innovative model. Specifically:

[0085] ③ Communication path optimization: The system is based on the device configuration parameter C′ i and network status parameter N s Select the best communication path Communication path optimization via the Riemann curvature tensor R in topology ij Perform the calculation:

[0086]

[0087] in, represents the optimal communication path, R ij (C′ i ,N s ) is the Riemann curvature tensor in topology, which is used to measure the curvature change of the communication path between devices under different configurations and network states. exp(-λ k ·dist(x i ,x j )) represents the attenuation function of the distance between nodes on the path, λ k is the attenuation coefficient.

[0088] ④ Traffic management: After selecting the optimal path, the system dynamically manages the traffic through the Bellman-Ford algorithm. Assuming the traffic management parameter in the network is L_m, then:

[0089]

[0090] Among them, L m Indicates traffic management parameters, used to determine the distribution of communication traffic. s Traffic parameters representing the initial network state. ij represents the capacity constraint of each link on the communication path. ij Indicates the data traffic between devices.

[0091] S230. Combining abstract algebra and number theory, the system performs data encryption and privacy protection strategies.

[0092] Specifically:

[0093] ⑤Data encryption: To ensure the security of device data, the system introduces an abstract algebraic model based on elliptic curve cryptography (ECC). Suppose the input data of the device is The encrypted data is E i , then:

[0094]

[0095] Among them, E i represents the encrypted data, κ is the encryption key, which is generated by the modular inverse operation in number theory. G and H are the base points on the elliptic curve, which are used for data encryption.

[0096] ⑥ Privacy protection: The system also uses the Mobius inversion formula in number theory to achieve data anonymization. Suppose the privacy protection result of device data is A i , then:

[0097]

[0098] Among them, A i represents the anonymized data, μ(d) is the Möbius function, which is used for the inversion of private data. The data is processed on a logarithmic scale to further protect data privacy.

[0099] Description of the connection between modules

[0100] The submodules are closely related to each other. In the edge computing processing stage S210, the standard communication protocol generated and standard data formats It is used for path optimization and traffic management in the intelligent communication management stage. Further, in S220 and S230, the communication path and flow parameter L m It is used for data encryption and privacy protection in the security and privacy protection stages to ensure the security and stability of data during the entire communication process.

[0101] S300, the system uniformly receives control instructions and distributes them to the devices, dynamically adjusts configuration parameters through adaptive system management, monitors device operation in real time, and adjusts system configuration based on feedback. Step S300 at least includes steps S310-S330:

[0102] S310, receiving control instructions from users and automation systems, and centrally processing the control instructions. Specifically:

[0103] In the centralized control instruction processing step, the system first receives control instructions from the user and the automation system. The input end data includes the user's operation instruction U c , the current status of the device S e And environmental data E v The input data is collected in real time through the sensor network and device interface, and transmitted to the intelligent management platform for processing.

[0104] ① Command analysis: The system analyzes the input user command U c and device status S e Analyze the specific operation requirements of each device. This process is done through the instruction analysis function I p accomplish:

[0105] I p =f(U c ,S e ,E v )

[0106] Among them, I p Represents the parsed device operation instruction set, U c Indicates the user's operation instruction, Se Indicates the status data of the current device, E v Represents environmental data.

[0107] ②Instruction distribution: parsed instruction I p The system uses the communication protocol of the device to and standardized data formats To ensure that the instructions are executed synchronously between devices. This synchronous execution is performed through the instruction distribution function D_c:

[0108]

[0109] Among them, D c Indicates the control instructions for distribution, and They represent the standardized communication protocol and data format, which are derived from the processing results of S210. In this way, the system ensures that the control instructions of all devices can be accurately communicated and synchronized in the entire system.

[0110] S320: The system dynamically adjusts and optimizes the system configuration based on the device's operating status and user behavior data. Specifically:

[0111] This module uses the output data I in S310 p and D c Make adaptive adjustments.

[0112] ③ Dynamic configuration adjustment: The system monitors the operating status of the device in real time e and user behavior data U h , calculate and adjust the configuration parameters C″ of the device i This adjustment process is done through the dynamic configuration function C a accomplish:

[0113] C″ i =C a (S e ,U h ,D c )

[0114] Among them, C i Indicates the adjusted device configuration parameters, S e Indicates the current operating status of the device. h Represents user behavior data, including user operation habits and device usage history, D c This is the output result of the aforementioned centralized control instruction processing, ensuring that the adjusted configuration is consistent with the current instruction.

[0115] ④ Adaptive learning: The system continuously optimizes the device configuration parameters C″ through the adaptive learning mechanismi This process relies on the adaptive learning function L a , improve the intelligence of the system by analyzing device performance and user behavior data:

[0116] L a =h(U h ,S e )

[0117] Among them, L a Represents the adaptive learning function, which is used to optimize the configuration and operation strategy of the device. The result of adaptive learning L a Will further affect the dynamic configuration adjustment function C in S320 a , ensuring that the system can automatically reconfigure system parameters when equipment fails and the environment changes to maintain the system's optimal operating state.

[0118] S330: The system further optimizes the control logic and system configuration by analyzing the feedback data after the device executes the instruction. Specifically:

[0119] ⑤Feedback data collection: The system collects feedback data from the device in real time. d , including the device response time T r , Execution success rate S r and possible fault information F e The feedback data is collected by the feedback collection function F c To process:

[0120] F c =j(T r ,S r ,F e )

[0121] Among them, F c represents the collected feedback data set, T r Indicates the response time of the device, S r Indicates the success rate of the device executing instructions, F e Indicates device fault information.

[0122] ⑥Control logic optimization: According to the feedback data F in S330 c , the system optimizes the control logic L_c, and the adjusted control logic L_c^{\prime} is used for the next round of instruction processing and system configuration. The optimization process is implemented through the feedback optimization function Of:

[0123] L′ c =O f (F c ,C″ i )

[0124] Among them, L′ c Represents the optimized control logic, O f Represents the feedback optimization function, which is used to optimize the feedback data and the current configuration parameters C″ i Adjust the control logic.

[0125] Description of the connection between modules

[0126] The submodules are closely related to each other. The control instruction D generated by the centralized control instruction processing (S310) c Dynamic configuration adjustment C″ used in adaptive system management (S320) i At the same time, the feedback analysis and optimization (S330) module analyzes the feedback data F after the equipment is executed. c , further optimize the control logic L′ c This feedback loop ensures that the system can dynamically adjust and optimize its operating status and maintain optimal performance in the face of changing environments and user needs.

[0127] S400, the system displays the device status through the user interface, saves and analyzes historical data to support future decision-making. Step S400 at least includes steps S410-S430:

[0128] S410, the system collects real-time operation data of each device through the sensor network and device interface. Specifically:

[0129] In the real-time monitoring and status display step, the system first collects the real-time operation data of each device through the sensor network and the device interface. The input data includes the online status S of the device. o , operating parameters P r And abnormal alarm E a The input data is monitored by real-time monitoring function M t Processed and displayed on the user interface.

[0130] ①Data collection and processing: The system uses real-time monitoring function M t Set the device's online status S o , operating parameters P r and abnormal alarm E a Converted into display data D required by the user interface u :

[0131] D u =M t (S o ,P r ,E a )

[0132] Among them, D uIndicates the converted user interface display data, S o Indicates the online status of the device, P r Indicates the current operating parameters of the device, such as temperature, brightness, etc. a Indicates abnormal alarm data of the equipment, such as fault warning, over-temperature reminder, etc.

[0133] ②Interface display: Display data after conversion D u The information is updated in real time on the user interface. Users can intuitively monitor the operation of each device through the graphical interface and make corresponding adjustments and operations based on the information provided by the system.

[0134] The display data D generated by the step u It will serve as the input basis for the intelligent reminder and suggestion (S420) and historical data analysis and operation record (S430) modules.

[0135] S420: The system automatically generates intelligent reminders and operation suggestions based on the real-time status of the device, operating parameters, and user behavior data. Specifically:

[0136] In the smart reminder and suggestion step, the system is based on the real-time status S of the device. o , operating parameters P r And the user's behavior data U h , automatically generates intelligent reminders and operation suggestions. This process is done through the intelligent reminder function R m accomplish.

[0137] ③ Intelligent reminder generation: The system generates intelligent reminder function R m For the current device status S o , operating parameters P r and user behavior h Perform analysis and generate intelligent reminders t And suggestions S :

[0138] R t ,S S =R m (S o ,P r ,U h )

[0139] Among them, R t Indicates the generated intelligent reminder information, such as equipment failure reminders and maintenance suggestions. S Indicates generated action recommendations, such as device settings optimization and energy saving recommendations. h Represents user behavior data, including user operation history and usage habits.

[0140] The intelligent reminder and suggestiont and S S It will be displayed to the user through the user interface and serve as guidance information for user operations.

[0141] S430: The system saves and analyzes the historical operation data of the device and the user's operation records. Specifically:

[0142] In the historical data analysis and operation recording step, the system analyzes the historical operation data H of the equipment. d And the user's operation record O r The input data includes the display data D generated from the above steps. u , Intelligent reminder t and operation suggestions s The data is analyzed by the historical analysis function H a to be processed.

[0143] ④Data preservation and analysis: The system uses the historical analysis function H a For historical operation data H d and operation records r Analyze and generate long-term trend T l and performance changes P c :

[0144] T l ,P c =H a (H d ,O r )

[0145] Among them, T l Indicates identified long-term trends, such as a gradual decline in equipment performance and frequent use of a certain operating mode. c Indicates changes in the performance of a device, such as changes in the performance of the device under different environmental conditions. d Indicates the historical operation data of the device. r Indicates the user's operation record.

[0146] ⑤Interface display and feedback: analysis results T l and P c This information will be displayed to users through the user interface, and users can use it to understand the long-term performance of the equipment and provide data support for future decision-making.

[0147] Description of the connection between modules

[0148] The submodules are closely related to each other. Real-time monitoring and status display (S410) generates display data D uDirectly affects the generation process of intelligent reminders and suggestions (S420), and the output result R of the intelligent reminder and suggestion (S420) module t and S s The historical data analysis and operation record module (S430) generates a long-term trend T by analyzing the historical performance of the device and the user operation record. l and performance changes P c Feedback is fed back into the system to further optimize the user interface display and system suggestions. This feedback mechanism ensures that the system can continuously adapt to user needs and provide users with intelligent management support.

[0149] Embodiment 2:

[0150] Figure 2 1 is a block diagram of a smart home control method based on the Internet of Things provided by an embodiment of the present invention. Figure 2 As shown, the system may include:

[0151] The device access and identification module 10 is used to automatically identify newly connected IoT devices, including obtaining basic information about the device such as type, brand, communication protocol, and data format, analyzing and classifying it through a pre-trained machine learning model, and generating device identification results to ensure that the device can be seamlessly connected to the system.

[0152] The intelligent configuration and adaptation module 20 automatically configures the communication protocol and network parameters of the device based on the output of the device access and identification module to ensure that the device can operate efficiently in the existing network; it performs preliminary adaptation of the data format to ensure that the device data is compatible with other components of the system.

[0153] The edge computing processing module 30 is used to perform protocol conversion and data format standardization processing tasks near the device end, quickly process the data sent by the device through the edge computing node, reduce the load on the central server, reduce data transmission delays, and ensure the real-time response capability of the system.

[0154] The intelligent communication management module 40 manages the communication process of IoT devices, optimizes the communication path between devices, dynamically adjusts the communication traffic, and ensures efficient and stable data transmission; manages the priority of data transmission, avoids network congestion, and ensures that important data is transmitted first.

[0155] The security and privacy protection module 50 implements data encryption and privacy protection strategies at the edge computing node to ensure the secure transmission and storage of device data; monitors security threats during the communication process, automatically identifies and responds to potential network attacks or data leakage risks, and ensures the overall security of the system.

[0156] The centralized control and management module 60 is used to uniformly receive and process control instructions from users or automation systems, distribute them to corresponding devices and ensure synchronous execution; through the intelligent management platform, the operation and feedback of all devices are centrally managed, and the system configuration is adjusted in real time to ensure the optimal operating state.

[0157] The adaptive system management module 70 dynamically adjusts and optimizes the system configuration according to the device operating status and user behavior data; when the device fails or the environment changes, the system parameters are automatically reconfigured to maintain the optimal operating state of the system and gradually improve the intelligence level of the system.

[0158] The feedback analysis and optimization module 80 collects and analyzes feedback data after the device executes instructions, monitors the operation of the device in real time, identifies potential problems by analyzing the feedback information, and further optimizes the control logic and system configuration based on the feedback results to ensure efficient operation of the system.

[0159] The user interaction and intelligent monitoring module 90 provides a user-friendly interface, displays the status information of the access device in real time, supports users to monitor the system operation through a graphical interface, and helps users manage the system through intelligent reminders and suggestion functions; saves and analyzes the historical operation data of the equipment and user operation records, and provides data analysis tools to identify long-term trends and support future decisions.

[0160] The key innovative features of the present invention include:

[0161] (1) Intelligent identification and configuration module: Uses pre-trained machine learning models to automatically identify and configure newly connected IoT devices, ensuring that the devices are seamlessly connected and operate efficiently in the network.

[0162] (2) Edge computing and communication management module: Edge computing technology is used to complete protocol conversion and data standardization processing near the device end, and advanced mathematical models such as topology and complex analysis are used to optimize communication paths and traffic management.

[0163] (3) Adaptive system management module: Dynamically adjust and optimize system configuration based on the device's operating status and user behavior data, and improve system intelligence through an adaptive learning mechanism.

[0164] (4) User interaction and intelligent monitoring module: It provides a user-friendly interface, supports real-time monitoring, intelligent reminders, historical data analysis, and interaction with the intelligent management system, ensuring that users can intuitively understand the status of the equipment and make corresponding adjustments.

[0165] The following are its main beneficial effects:

[0166] The present invention solves the problems of device identification, configuration and management in a complex environment with diverse device types and heterogeneous communication protocols in smart homes by combining Internet of Things technology, machine learning algorithms and edge computing technology. An automatic identification and configuration module is adopted, and through a pre-trained machine learning model, accurate identification and intelligent configuration of devices are achieved, ensuring that the devices can seamlessly access the network, and after the devices are connected, most of the protocol conversion and data format standardization processing are completed through edge computing technology, reducing the load of the central server, reducing data transmission delays, and ensuring the real-time response capability of the system. In addition, the present invention introduces an intelligent communication management and security protection module, which optimizes the communication path and traffic management through complex mathematical models and algorithms, ensuring efficient and stable data transmission between devices, and ensuring data security in the smart home environment by implementing data encryption and privacy protection strategies. Compared with traditional methods, the present invention effectively improves the adaptability, scalability and security of the system, and significantly improves the efficiency of device management and communication in the smart home environment.

[0167] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes and substitutions within the technical scope disclosed in the present application, and the above should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A smart house control method based on the Internet of Things, characterized in that: The following steps are involved: S100, intelligent identification and configuration module, automatically identifies and configures connected IoT devices; S200, edge computing and communication management module, performs protocol conversion, data standardization and communication management; Edge computing processing completes protocol conversion and data format standardization on the device side; Intelligent communication management, optimizing communication paths, adjusting communication traffic, and ensuring stable data transmission; Security and privacy protection, implement data encryption and privacy protection policies, and monitor security threats. S300, unified control and management module, centrally manages device instructions and feedback and performs adaptive adjustments; S400, user interaction and intelligent monitoring module, provides equipment status display, intelligent reminders and data analysis.

2. The method according to claim 1, characterized in that Step S100 includes: S110, automatic device identification, receiving and processing input data of IoT devices, extracting and classifying device feature vectors using machine learning models; S120, intelligent configuration and adaptation, generating and allocating communication protocols and network parameters based on the recognition results, and performing data format adaptation; S130, dynamic learning and optimization, monitoring equipment usage, optimizing recognition models and configuration parameters.

3. The method according to claim 1, characterized in that Step S300 includes: S310, centralized control instruction processing, receiving and distributing control instructions; S320, adaptive system management, dynamic adjustment of system configuration, automatic reconfiguration of device parameters; S330, feedback analysis and optimization, collect equipment feedback data, optimize control logic and system configuration.

4. The method according to claim 1, characterized in that: Step S400 includes: S410, real-time monitoring and status display, providing a user interface to display device status information; S420, intelligent reminders and suggestions, generating reminders and operation suggestions based on device status; S430: Historical data analysis and operation records: analyze equipment historical data and operation records to provide data support.

5. The method according to claim 1, characterized in that The communication path optimization in the intelligent communication management module is dynamically adjusted according to the network status and transmission requirements of the device.

6. The method according to claim 1, characterized in that The control instruction distribution in the unified control and management module is implemented through the intelligent management platform to ensure the functional consistency of each device in the system.

7. The method according to claim 3, characterized in that In the step S320, the system dynamically adjusts the communication protocol and parameters of the device by learning user behavior data and device operation status.

8. The method according to claim 3, characterized in that In the step S330, the system generates and adjusts new control logic and configuration strategy according to the device feedback data.

9. The method according to claim 4, characterized in that In step S420, intelligent reminders and suggestions are generated based on abnormal status or maintenance requirements of the equipment.

10. The method according to claim 4, characterized in that In the step S430, historical data analysis is implemented through a data analysis tool provided by the system to identify long-term performance changes of the equipment.

Images