Intelligent control household equipment terminal system

Abstract

The invention relates to an intelligent control household equipment terminal system which is characterized in that a connection module discovers and accesses intelligent equipment in a whole house and registers equipment information into the system; the collection module carries out preprocessing operation on the collected data; the analysis module carries out deep mining and analysis on user behavior data, equipment state data and the like, formulates an equipment linkage strategy and an energy-saving scheme, and recommends proper equipment and scene modes for the user according to a user portrait and a recommendation algorithm; the control module matches and generates a control instruction according to a preset rule and the real-time data, and issues the control instruction to the corresponding intelligent equipment through the system platform; and the monitoring module monitors the intelligent equipment in the whole house in real time, and dynamically adjusts an equipment linkage strategy and an energy-saving scheme according to real-time monitoring data and user feedback. Through user behavior analysis and an intelligent recommendation technology, personalized demands and preferences of the user can be more accurately understood, customized service and recommendation are provided, and the user experience is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to an intelligent control home device terminal system. Background Art

[0002] The intelligent control home device terminal system is a highly integrated smart home solution. Through advanced Internet of Things technology, artificial intelligence algorithms and cloud computing platforms, it seamlessly connects various electrical appliances in the home (such as lights, air conditioners, curtains, security systems, etc.) to a unified intelligent management platform. Users only need to interact with mobile phone apps, voice assistants or smart panels to easily achieve remote monitoring and automatic control of home devices. However, the intelligent control home device terminal system under the existing technology often controls the equipment based on preset rules or simple sensor data, ignoring the personalized needs and habits of users. This makes the system unable to provide accurate and personalized services, reduces the user experience, and lacks the ability to formulate optimization strategies based on big data analysis, so that system optimization often relies on manual experience and simple rules, and it is difficult to achieve global optimization and dynamic adjustment. Summary of the invention

[0003] The present invention is intended to provide an intelligent control home device terminal system to solve the deficiencies in the prior art. The technical problem to be solved by the present invention is achieved through the following technical solutions.

[0004] An intelligent control home device terminal system, characterized in that it comprises: a connection module, a collection module, an analysis module, a control module and a monitoring module; The connection module includes a self-checking unit and an access unit; the self-checking unit is used to perform a self-check on the system after the system is started, confirm that there are no serious errors or warning information, and initialize the system; the access unit is used to start the device discovery process, perform device identity authentication and compatibility checks, register device information, assign identifiers to registered devices, access devices and complete system synchronization; The acquisition module is used to collect data from smart devices throughout the house, pre-process the collected data and store it in the data warehouse; The analysis module includes: a user behavior analysis submodule, a device status assessment submodule, an environmental change trend prediction submodule and a strategy formulation and intelligent recommendation submodule; the user behavior analysis submodule uses clustering, association rule mining and sequence pattern analysis to identify the user's usage habits, preferences and patterns; the device status assessment submodule evaluates the operating status, performance and reliability of the device through statistical analysis and trend prediction methods; the environmental change trend prediction submodule uses time series analysis and machine learning algorithms to predict the environmental change trend; the strategy formulation and intelligent recommendation submodule builds a user portrait based on user behavior data, preference settings and other information, and recommends suitable devices and scene modes to users based on the user portrait and recommendation algorithm; The control module is used to match and generate control instructions according to preset rules and real-time data, and send the control instructions to the corresponding smart devices through the system platform to achieve remote control and linkage of the devices; The monitoring module is used to monitor the operating status, network connection and data security of smart devices throughout the house in real time, and dynamically adjust the device linkage strategy and energy-saving plan based on real-time monitoring data and user feedback.

[0005] A further improvement of the present invention is: Furthermore, the steps of the self-check unit are: turning on the system power, starting the self-check unit to check the hardware connection; loading and running the system firmware or operating system; verifying the accuracy of the system time and calibrating it if necessary; checking the system log to confirm that there are no serious errors or warning messages; loading and parsing the configuration file, and then configuring the system service; setting monitoring indicators and alarm thresholds; if the monitoring indicators and alarm thresholds are not exceeded, the system starts the device discovery process to detect whether there are any accessible smart devices around, and if so, connect them.

[0006] Furthermore, the steps of the access unit are: after the system starts the device discovery process, monitor the device response and collect the basic information of the device; authenticate each discovered device; check whether the device is compatible with the current system, register the device information that passes the identity authentication and compatibility check into the system database, and store the basic information, status, and configuration parameters of the device; assign a unique device identifier UUID to each registered device; establish a communication connection with the device based on the device type and configuration; synchronize the device status and system configuration to ensure that the device runs according to the system requirements; add the device to the corresponding device group or scene; mark the device access process as completed, and update the system interface and log to reflect the newly accessed device information; notify the system administrator or user that the device has been successfully accessed and is available for use.

[0007] Furthermore, in the acquisition module, the specific steps of collecting data from smart devices throughout the house, preprocessing the collected data and storing it in the data warehouse are as follows: Define data sources, identify sensors and devices in the whole house smart devices, list all sensors and devices involved in data collection; determine the user interfaces through which users interact with the system, and identify the types of data generated by these user interfaces; Set a fixed collection frequency, and set triggers for device status changes or user operations to collect relevant data immediately; regularly synchronize user behavior data or device configuration information from the user device or the cloud; periodically collect data from sensors, devices, and user interfaces at a set collection frequency through APIs, message queues, or direct connections; Store the collected data in a local or cloud database, and identify and process missing data points by interpolation, deletion, or marking as missing; use statistical methods to identify outliers and correct or remove them based on business logic; then check and delete duplicate data records; Convert data from different sources into a unified format; aggregate multiple data points into higher-level data units as needed; extract features useful for subsequent analysis from raw data; use lossless compression algorithms to reduce storage space in scenarios where the accuracy of raw data needs to be maintained; Use AES and RSA encryption algorithms to encrypt data containing user privacy or sensitive information; use data warehouse technology Hive and ETL tools to integrate data, integrate data from different data sources together to form a unified data view, and verify data integrity.

[0008] Furthermore, the user behavior analysis submodule uses clustering, association rule mining, and sequence pattern analysis to identify user usage habits, preferences, and patterns, specifically: Extract user behavior-related data from the data warehouse and pre-process the data; Based on the K-means clustering algorithm, users are grouped according to their behavior characteristics; clustering parameters are set, clustering algorithms are executed, and user groups are obtained; Analyze the behavioral characteristics of each user group and identify the usage habits and preferences of different user groups; apply the association rule mining algorithm Apriori to analyze the association between user behaviors; set support and confidence thresholds to mine frequent item sets and strong association rules between user behaviors; Analyze association rules to understand users’ behavior patterns and habits in different scenarios; use the sequence pattern mining algorithm PrefixSpan to analyze the time series pattern of user behavior; Identify the time sequence and dependencies between user behaviors; analyze sequence patterns to understand the dynamic changes and trends of user behaviors.

[0009] Furthermore, the equipment status assessment submodule evaluates the operating status, performance and reliability of the equipment through statistical analysis and trend prediction methods, specifically: collecting equipment operation data from equipment sensors and log systems, including operating status, fault records, and maintenance history; preprocessing the data, including removing noise data, filling missing values, and converting data formats; obtaining statistical indicators such as equipment operating time, downtime, failure rate, and maintenance cycle; analyzing equipment performance indicators to identify common problems and abnormal behavior patterns of equipment; applying time series analysis technology ARIMA to predict the future operating status of the equipment; evaluating equipment reliability, predicting potential failure points and maintenance needs; and formulating preventive maintenance plans based on prediction results.

[0010] Furthermore, the environmental change trend prediction submodule uses time series analysis and machine learning algorithms to predict environmental change trends, specifically: collect environmental data from environmental sensors and extract characteristic variables related to environmental changes; normalize or standardize the characteristic variables; predict environmental change trends based on linear regression algorithms; use historical environmental data as training sets to train the algorithm and tune parameters; evaluate the prediction performance of the model; use the trained model to predict future environmental change trends; analyze the prediction results and identify key nodes and trends of environmental changes.

[0011] Furthermore, the strategy formulation and intelligent recommendation submodule builds a user portrait based on user behavior data, preference settings and other information, and recommends suitable devices and scenario modes to users based on the user portrait and recommendation algorithm. Specifically: combine the user behavior analysis results to obtain the user's actual needs and usage scenarios; based on device status data and environmental data, evaluate the performance and reliability of the current system, and identify potential problems and improvement points; formulate linkage strategies between devices based on user needs and environmental changes; design energy-saving plans based on user behavior and device status; formulate security warning and response strategies based on environmental data and device status; test the formulated strategies in a simulated environment to verify their effectiveness and reliability; build a user portrait based on user behavior data, preference settings and other information, including the user's age, gender, interests and hobbies, and living habits; select a collaborative filtering recommendation algorithm based on the application scenario and user portrait characteristics, recommend suitable devices and scenario modes to users, and transmit the recommendation results to the user end for display to the user.

[0012] Furthermore, in the control module, the specific steps are: presetting control rules in the system according to the living habits, safety requirements, and energy-saving requirements of family members; when the system is started or the rules are updated, loading the rules in the rule base into the memory for fast matching and execution; comparing the real-time data with the conditions in the rule base to find all the rules that meet the conditions; if multiple rules meet the conditions at the same time, sorting them according to the preset priority to ensure that the most important rules are executed first; generating corresponding control instructions according to the matched rules and priorities; encapsulating the control instructions in the format required by the system platform, and sending the encapsulated control instructions to the corresponding smart device; waiting for and receiving confirmation feedback from the target device on the execution of the instructions; if the instruction is executed successfully, recording and updating the device status; if the execution fails, retrying or alarming according to the reason for the failure; recording the generation time, issuance time, target device, and execution result information of each control instruction; and updating the status information of the home device in the system according to the execution result of the instruction.

[0013] Furthermore, in the monitoring module, the specific steps are: Start the system service and load all necessary configuration parameters; establish network connections with all smart devices to ensure smooth communication; initialize the data collection and storage system; loop through all smart devices and send query instructions to obtain the current status of the device; record and store device status data in the database or memory database; regularly check the network connection status with each smart device, and if any abnormal connection is found, try to re-establish the connection and record the abnormal log; monitor the encryption status during data transmission to ensure data security, and regularly check whether the stored data has abnormal access or tampering signs; Receive user feedback on system performance, equipment control effects, user interface friendliness, etc.; store feedback data in the feedback database; regularly aggregate monitoring data and user feedback data to form analysis reports; analyze equipment operation status data to evaluate equipment health status and failure rate; analyze user feedback data to evaluate user satisfaction and common problems; evaluate the efficiency and applicability of existing linkage strategies and energy-saving solutions based on current environmental conditions and user behavior patterns; Generate new device linkage strategies or optimize existing strategies based on data analysis results; formulate new energy-saving plans based on the balance between energy efficiency and user comfort; test the effectiveness of new strategies in a simulated environment; deploy tested new strategies or optimized plans to the actual system, send instructions to smart devices, and update their control logic and parameters; continue to monitor the system operation status and user feedback after the implementation of the new strategy, and continuously iterate the optimization strategy based on new monitoring data and user feedback to form a closed-loop feedback mechanism The embodiments of the present invention include the following advantages: The present invention realizes the control of smart devices by integrating connection modules, acquisition modules, analysis modules, control modules and monitoring modules, and through user behavior analysis and intelligent recommendation technology, it can more accurately understand the user's personalized needs and preferences, provide customized services and recommendations, and significantly improve the user experience. According to the results of big data analysis, it can formulate more scientific and reasonable optimization strategies, and continuously and dynamically adjust them as the data is updated to ensure that the system is always in the optimal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a structural schematic diagram of the intelligent control home device terminal system of the present invention. DETAILED DESCRIPTION

[0015] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0016] It should be noted that the above detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs.

[0017] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0019] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0020] For ease of description, spatially relative terms, such as "above", "above", "on the upper surface of", "above", etc., may be used herein to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" may include both "above" and "below". The device may also be positioned in other different ways, such as rotated 90 degrees or in other orientations, and the spatially relative descriptions used herein are interpreted accordingly.

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings: See also Figure 1 , the present invention discloses an intelligent control home device terminal system, including: a connection module, a collection module, an analysis module, a control module and a monitoring module; The connection module includes a self-checking unit and an access unit; the self-checking unit is used to perform a self-check on the system after the system is started, confirm that there are no serious errors or warning information, and initialize the system; the access unit is used to start the device discovery process, perform device identity authentication and compatibility checks, register device information, assign identifiers to registered devices, access devices and complete system synchronization; The acquisition module is used to collect data from smart devices throughout the house, pre-process the collected data and store it in the data warehouse; The analysis module includes: a user behavior analysis submodule, a device status assessment submodule, an environmental change trend prediction submodule and a strategy formulation and intelligent recommendation submodule; the user behavior analysis submodule uses clustering, association rule mining and sequence pattern analysis to identify the user's usage habits, preferences and patterns; the device status assessment submodule evaluates the operating status, performance and reliability of the device through statistical analysis and trend prediction methods; the environmental change trend prediction submodule uses time series analysis and machine learning algorithms to predict the environmental change trend; the strategy formulation and intelligent recommendation submodule builds a user portrait based on user behavior data, preference settings and other information, and recommends suitable devices and scene modes to users based on the user portrait and recommendation algorithm; The control module is used to match and generate control instructions according to preset rules and real-time data, and send the control instructions to the corresponding smart devices through the system platform to achieve remote control and linkage of the devices; The monitoring module is used to monitor the operating status, network connection and data security of smart devices throughout the house in real time, and dynamically adjust the device linkage strategy and energy-saving plan based on real-time monitoring data and user feedback.

[0022] The steps of the self-check unit are: turn on the system power, start the self-check unit to check the hardware connection; load and run the system firmware or operating system; verify the accuracy of the system time and calibrate it when necessary; check the system log to confirm that there are no serious errors or warning messages; load and parse the configuration file, and then configure the system service; set monitoring indicators and alarm thresholds; if the monitoring indicators and alarm thresholds are not exceeded, the system starts the device discovery process to detect whether there are any accessible smart devices around, and if so, connect them.

[0023] The steps of the access unit are: after the system starts the device discovery process, monitor the device response and collect the basic information of the device; authenticate each discovered device; check whether the device is compatible with the current system, register the device information that passes the authentication and compatibility check into the system database, and store the basic information, status, and configuration parameters of the device; assign a unique device identifier UUID to each registered device; establish a communication connection with the device based on the device type and configuration; synchronize the device status and system configuration to ensure that the device runs according to the system requirements; add the device to the corresponding device group or scene; mark the device access process as completed, and update the system interface and log to reflect the newly accessed device information; notify the system administrator or user that the device has been successfully accessed and is available for use.

[0024] In the acquisition module, the specific steps of collecting data from smart devices throughout the house, preprocessing the collected data and storing it in the data warehouse are as follows: Define data sources, identify sensors and devices in the whole house smart devices, list all sensors and devices involved in data collection; determine the user interfaces through which users interact with the system, and identify the types of data generated by these user interfaces; Set a fixed collection frequency, and set triggers for device status changes or user operations to collect relevant data immediately; regularly synchronize user behavior data or device configuration information from the user device or the cloud; periodically collect data from sensors, devices, and user interfaces at a set collection frequency through APIs, message queues, or direct connections; Store the collected data in a local or cloud database, and identify and process missing data points by interpolation, deletion, or marking as missing; use statistical methods to identify outliers and correct or remove them based on business logic; then check and delete duplicate data records; Convert data from different sources into a unified format; aggregate multiple data points into higher-level data units as needed; extract features useful for subsequent analysis from raw data; use lossless compression algorithms to reduce storage space in scenarios where the accuracy of raw data needs to be maintained; Use AES and RSA encryption algorithms to encrypt data containing user privacy or sensitive information; use data warehouse technology Hive and ETL tools to integrate data, integrate data from different data sources together to form a unified data view, and verify data integrity.

[0025] The user behavior analysis submodule uses clustering, association rule mining, and sequence pattern analysis to identify user usage habits, preferences, and patterns. Specifically: Extract user behavior-related data from the data warehouse and pre-process the data; Based on the K-means clustering algorithm, users are grouped according to their behavior characteristics; clustering parameters are set, clustering algorithms are executed, and user groups are obtained; Analyze the behavioral characteristics of each user group and identify the usage habits and preferences of different user groups; apply the association rule mining algorithm Apriori to analyze the association between user behaviors; set support and confidence thresholds to mine frequent item sets and strong association rules between user behaviors; Analyze association rules to understand users’ behavior patterns and habits in different scenarios; use the sequence pattern mining algorithm PrefixSpan to analyze the time series pattern of user behavior; Identify the time sequence and dependencies between user behaviors; analyze sequence patterns to understand the dynamic changes and trends of user behaviors.

[0026] The equipment status assessment submodule evaluates the equipment's operating status, performance, and reliability through statistical analysis and trend prediction methods. Specifically, it collects equipment operating data from equipment sensors and log systems, including operating status, fault records, and maintenance history; preprocesses data, including removing noise data, filling missing values, and converting data formats; obtains statistical indicators such as equipment operating time, downtime, failure rate, and maintenance cycle; analyzes equipment performance indicators and identifies common equipment problems and abnormal behavior patterns; uses time series analysis technology ARIMA to predict the future operating status of equipment; evaluates equipment reliability and predicts potential failure points and maintenance needs; and formulates preventive maintenance plans based on prediction results.

[0027] The environmental change trend prediction submodule uses time series analysis and machine learning algorithms to predict environmental change trends. Specifically, it collects environmental data from environmental sensors and extracts characteristic variables related to environmental changes; normalizes or standardizes the characteristic variables; predicts environmental change trends based on a linear regression algorithm; uses historical environmental data as a training set to train the algorithm and tune its parameters; evaluates the model's prediction performance; uses the trained model to predict future environmental change trends; and analyzes the prediction results to identify key nodes and trends in environmental changes.

[0028] The strategy formulation and intelligent recommendation submodule builds a user portrait based on user behavior data, preference settings and other information, and recommends suitable devices and scenario modes to users based on the user portrait and recommendation algorithm. Specifically: Combine the results of user behavior analysis to obtain the user's actual needs and usage scenarios; Based on device status data and environmental data, evaluate the performance and reliability of the current system, and identify potential problems and improvement points; Develop linkage strategies between devices based on user needs and environmental changes; Design energy-saving plans based on user behavior and device status; Develop security warning and response strategies based on environmental data and device status; Test the formulated strategies in a simulated environment to verify their effectiveness and reliability; Build a user portrait based on user behavior data, preference settings and other information, including the user's age, gender, interests and hobbies, and living habits; Select a collaborative filtering recommendation algorithm based on application scenarios and user portrait characteristics to recommend suitable devices and scenario modes to users, and transmit the recommendation results to the user end for display to the user.

[0029] In the control module, the specific steps are as follows: preset control rules in the system according to the living habits, safety needs, and energy-saving requirements of family members; when the system starts or the rules are updated, load the rules in the rule base into the memory for fast matching and execution; compare the real-time data with the conditions in the rule base to find all the rules that meet the conditions; if multiple rules meet the conditions at the same time, sort them according to the preset priority to ensure that the most important rules are executed first; generate corresponding control instructions according to the matched rules and priorities; package the control instructions in the format required by the system platform, and send the packaged control instructions to the corresponding smart device; wait for and receive confirmation feedback from the target device on the execution of the instruction; if the instruction is executed successfully, record and update the device status; if the execution fails, retry or alarm processing is performed according to the reason for the failure; record the generation time, issuance time, target device, and execution result information of each control instruction; according to the execution result of the instruction, update the status information of the home device in the system.

[0030] In the monitoring module, the specific steps are: Start the system service and load all necessary configuration parameters; establish network connections with all smart devices to ensure smooth communication; initialize the data collection and storage system; loop through all smart devices and send query instructions to obtain the current status of the device; record and store device status data in the database or memory database; regularly check the network connection status with each smart device, and if any abnormal connection is found, try to re-establish the connection and record the abnormal log; monitor the encryption status during data transmission to ensure data security, and regularly check whether the stored data has abnormal access or tampering signs; Receive user feedback on system performance, equipment control effects, user interface friendliness, etc.; store feedback data in the feedback database; regularly aggregate monitoring data and user feedback data to form analysis reports; analyze equipment operation status data to evaluate equipment health status and failure rate; analyze user feedback data to evaluate user satisfaction and common problems; evaluate the efficiency and applicability of existing linkage strategies and energy-saving solutions based on current environmental conditions and user behavior patterns; Generate new device linkage strategies or optimize existing strategies based on data analysis results; formulate new energy-saving plans based on the balance between energy efficiency and user comfort; test the effectiveness of new strategies in a simulated environment; deploy tested new strategies or optimized plans to the actual system, send instructions to smart devices, and update their control logic and parameters; continue to monitor the system operation status and user feedback after the implementation of the new strategy, and continuously iterate the optimization strategy based on new monitoring data and user feedback to form a closed-loop feedback mechanism Example: The present invention discloses an intelligent control home device terminal system, comprising: The connection module is used to discover and access smart devices throughout the house, verify device identity and compatibility, register device information into the system, and assign a unique device ID; The acquisition module is used to collect data from various sensors, devices, user interaction interfaces and other channels, and perform pre-processing operations such as cleaning, conversion, compression and encryption on the collected data; The analysis module is used to use big data analysis technology to conduct in-depth mining and analysis of user behavior data, device status data, etc. Based on the analysis results, it formulates optimization strategies such as device linkage strategies and energy-saving plans, and recommends suitable devices and scenario modes to users based on user portraits and recommendation algorithms; The control module is used to match and generate control instructions according to preset rules and real-time data, and send the control instructions to the corresponding smart devices through the system platform to achieve remote control and linkage of the devices; The monitoring module is used to monitor the operating status, network connection, data security, etc. of smart devices throughout the house in real time, and dynamically adjust optimization strategies such as device linkage strategies and energy-saving plans based on real-time monitoring data and user feedback.

[0031] Preferably, the connection module includes a self-checking unit and an access unit; the self-checking unit is used to perform a self-check on the system after the system is started, confirm that there are no serious errors or warning information, and initialize the system; the access unit is used to start the device discovery process, perform device identity authentication and compatibility check, register device information, assign identifiers to registered devices, access devices and complete system synchronization; when the connection module is working, it includes the following steps: S101: The system power is turned on and the self-test unit is started; S102: The self-check unit checks the hardware connection, including whether the processor, memory, storage device, network interface, etc. are connected normally; S103: Load and run the system firmware or operating system to ensure that the basic operating environment is ready; S104: Verify the accuracy of the system time and calibrate if necessary; check the system log to confirm that there are no serious errors or warning messages; S105: Load and parse configuration files, including network settings, security policies, and user permissions; S106: configuring system services, such as database services, message queue services, and remote access services; S107: Setting monitoring indicators and alarm thresholds; S108: Start the device discovery process, use the SSDP protocol to send a broadcast signal or scan the devices in the network; S109: monitor the device response and collect basic information of the device, such as device type, model, IP address, and MAC address; S110: Authenticate each discovered device to confirm its legitimacy and security, using pre-shared keys, digital certificates, and OAuth mechanisms for verification; S111: Check whether the device is compatible with the current system, including communication protocol and function support; S112: registering the device information that has passed the identity authentication and compatibility check into the system database, storing the basic information, status, and configuration parameters of the device; S113: Allocate a unique device identifier UUID to each registered device; S114: Establishing a communication connection with the device, such as a TCP / IP connection or a WebSocket connection, according to the device type and configuration; S115: Synchronize device status and system configuration to ensure that the device operates according to system requirements; add the device to the corresponding device group or scene; S116: Mark the device access process as completed, and update the system interface and log to reflect the newly accessed device information; S117: Notify the system administrator or user that the device has been successfully connected and is available for use.

[0032] Preferably, the acquisition module collects data from various sensors, devices, user interaction interfaces and other channels, including environmental data, device status data, and user behavior data, receives data from sensors, devices and user interfaces through APIs, message queues, and direct connections, stores the received data in a local database or a cloud database to ensure data persistence and accessibility, and performs pre-processing operations such as cleaning, conversion, compression and encryption on the collected data to ensure data accuracy, integrity and security; when the acquisition module is working, it includes the following steps: S201: Define data sources, identify sensors and devices, and list all sensors and devices involved in data collection; determine the interfaces through which users interact with the system and identify the types of data generated by these interfaces; S202: Setting a fixed acquisition frequency; S203: For events such as device status changes or user operations, triggers are set to instantly collect relevant data; S204: Regularly synchronize user behavior data or device configuration information from the user device or the cloud; S205: receiving data from sensors, devices, and user interfaces through APIs, message queues, and direct connections; S206: Storing the received data in a local database or a cloud database to ensure data persistence and accessibility; S207: Identify and process missing data points by interpolation, deletion, or marking as missing; S208: using statistical methods to identify outliers, and correcting or eliminating them according to business logic; S209: Check and delete duplicate data records; S210: Convert data from different sources into a unified format; aggregate multiple data points into higher-level data units as needed, such as daily average temperature and weekly electricity consumption; S211: Extract features useful for subsequent analysis from raw data, such as user behavior patterns and equipment usage efficiency; S212: For scenarios where the original data accuracy needs to be maintained, a lossless compression algorithm is used to reduce storage space; S213: Use AES and RSA encryption algorithms to encrypt data containing user privacy or sensitive information to ensure the security of data transmission and storage; S214: Use data warehouse technology Hive and ETL tools to integrate data from different data sources to form a unified data view; S215: Verify data integrity.

[0033] Preferably, the analysis module includes: a user behavior analysis submodule, a device status assessment submodule, an environmental change trend prediction submodule and a strategy formulation and intelligent recommendation submodule; the user behavior analysis submodule uses clustering, association rule mining and sequence pattern analysis to identify user usage habits, preferences and patterns; the device status assessment submodule evaluates the operating status, performance and reliability of the device through statistical analysis and trend prediction methods; the environmental change trend prediction submodule predicts the changing trend of the environment using time series analysis and machine learning algorithms; the strategy formulation and intelligent recommendation submodule builds a user portrait based on user behavior data, preference settings and other information, and recommends suitable devices and scenario modes to users based on the user portrait and recommendation algorithm, providing a personalized service experience.

[0034] Preferably, the user behavior analysis submodule uses clustering, association rule mining, and sequence pattern analysis to identify the user's usage habits, preferences, and patterns, including the following steps: S301: extracting user behavior related data from the data warehouse, such as device usage records, user setting changes, and interaction logs; S302: preprocessing the data, including data cleaning, data conversion, and data aggregation; S303: Selecting a K-means clustering algorithm to group users according to user behavior characteristics, such as device usage frequency, time period preference, and scene mode selection; S304: setting clustering parameters, such as the number of clusters and distance measurement method, executing the clustering algorithm, and obtaining user groups; S305: Analyze the behavioral characteristics of each user group and identify the usage habits and preferences of different user groups; S306: Applying the association rule mining algorithm Apriori to analyze the association relationship between user behaviors; S307: Setting support and confidence thresholds to mine frequent item sets and strong association rules between user behaviors; S308: Analyze association rules to understand user behavior patterns and habits in different scenarios; S309: Analyze the time series pattern of user behavior using the sequence pattern mining algorithm PrefixSpan; S310: Identify the time sequence and dependency relationship between user behaviors; S311: Analyze sequence patterns to understand the dynamic changes and trends of user behavior; The equipment status assessment submodule evaluates the equipment's operating status, performance, and reliability through statistical analysis and trend prediction methods, including the following steps: S401: Collect equipment operation data from equipment sensors and log systems, including operation status, fault records, and maintenance history; S402: preprocessing the data, such as removing noise data, filling missing values, and converting data formats; S403: Calculate statistical indicators such as equipment operation time, downtime, failure rate, and maintenance cycle; S404: Analyze the performance indicators of the device and identify common problems and abnormal behavior patterns of the device; S405: Use time series analysis technology ARIMA to predict the future operating status of the equipment; S406: Evaluate equipment reliability and predict potential failure points and maintenance needs; S407: Develop a preventive maintenance plan based on the forecast results; The environmental change trend prediction submodule uses time series analysis and machine learning algorithms to predict environmental change trends, including the following steps: S501: Collecting environmental data from environmental sensors; S502: extracting characteristic variables related to environmental changes; normalizing or standardizing the characteristic variables; S503: Selecting a linear regression algorithm to predict environmental change trends; S504: Use historical environmental data as a training set to train the algorithm and optimize parameters; S505: Evaluate the prediction performance of the model, such as accuracy, recall, and F1 score; S506: Use the trained model to predict future environmental change trends; analyze the prediction results to identify key nodes and trends of environmental changes, such as the time point of temperature increase, the cycle of humidity change, etc.

[0035] The strategy formulation and intelligent recommendation submodule builds a user profile based on user behavior data, preference settings and other information. Based on the user profile and recommendation algorithm, it recommends suitable devices and scenario modes for users, including the following steps: S601: Combine the user behavior analysis results to deeply understand the user's actual needs and usage scenarios; S602: Based on the equipment status data and environmental data, evaluate the performance and reliability of the current system and identify potential problems and improvement points; S603: Develop linkage strategies between devices based on user needs and environmental changes; design energy-saving solutions based on user behavior and device status; develop security warning and response strategies based on environmental data and device status; S604: Test the formulated strategy in a simulation environment to verify its effectiveness and reliability; S605: Building a user profile based on user behavior data, preference settings and other information, including the user's age, gender, interests, hobbies, and living habits; S606: Select a collaborative filtering recommendation algorithm based on the application scenario and user portrait characteristics; S607: Using collaborative filtering recommendation algorithm to recommend suitable devices and scene modes for the user; S608: Transmit the recommendation results to the user terminal and display them to the user.

[0036] Preferably, the control module pre-sets a series of control rules in the system. These rules are customized according to the living habits, safety requirements, energy-saving requirements, etc. of family members, and the real-time data is compared with the conditions in the rule base to find out all the rules that meet the conditions. According to the matched rules and priorities, the corresponding control instructions are generated. The control instructions are packaged in the format required by the system platform, and the packaged control instructions are sent to the corresponding smart devices through the system platform.

[0037] Preferably, the control module comprises the following steps when operating: S701: pre-set a series of control rules in the system, which are customized according to the living habits, safety requirements, energy-saving requirements, etc. of family members; S702: When the system is started or the rules are updated, the rules in the rule base are loaded into the memory for fast matching and execution; S703: Compare the real-time data with the conditions in the rule base to find all rules that meet the conditions; S704: If multiple rules meet the conditions at the same time, sort them according to the preset priority to ensure that the most important rule is executed first; S705: Generate corresponding control instructions according to the matched rules and priorities; S706: Encapsulate the control instruction in the format required by the system platform, including instruction type, target device ID, and parameter value information; S707: Send the packaged control instruction to the corresponding smart device; S708: Waiting for and receiving confirmation feedback from the target device on the execution of the instruction; if the instruction is successfully executed, recording and updating the device status; if the execution fails, retrying or alarming according to the failure reason; S709: Record the generation time, issuance time, target device, and execution result information of each control instruction; S710: Update the status information of the home device in the system according to the execution result of the instruction.

[0038] As a preferred option, the monitoring module conducts real-time monitoring of the operating status, network connection, data security, etc. of the entire house's smart devices to ensure stable system operation, collects user feedback on system performance, equipment control effects, etc. through the user interaction interface, and dynamically adjusts equipment linkage strategies, energy-saving solutions and other optimization strategies based on real-time monitoring data and user feedback to adapt to changing environments and user needs.

[0039] Preferably, the monitoring module comprises the following steps when working: S801: Start the system service and load all necessary configuration parameters, including the device list, initial linkage strategy, and energy saving plan; S802: Establish network connection with all smart devices to ensure smooth communication; S803: Initialize the data collection and storage system, including logs, monitoring data, and user feedback; S804: loop through all smart devices and send query instructions to obtain the current status of the devices; S805: Record and store device status data in a database or a memory database; S806: Regularly check the network connection status with each smart device. If an abnormal connection is found, try to re-establish the connection and record the abnormal log; S807: Monitor the encryption status during data transmission to ensure data security, and regularly check whether the stored data has any signs of abnormal access or tampering; S808: Receive user feedback on system performance, device control effects, user interface friendliness, etc.; S809: storing the feedback data in a feedback database, including user ID, feedback content, and feedback time; S810: Regularly aggregate monitoring data and user feedback data to form an analysis report; S811: Analyze equipment operation status data to evaluate equipment health status and failure rate; analyze user feedback data to evaluate user satisfaction and common problems; S812: Evaluate the efficiency and applicability of existing linkage strategies and energy-saving solutions based on current environmental conditions and user behavior patterns; S813: Generate new equipment linkage strategies or optimize existing strategies based on data analysis results; formulate new energy-saving plans based on the balance between energy efficiency and user comfort; S814: Test the effectiveness of new strategies in a simulated environment; S815: deploy the tested new strategy or optimization solution to the actual system, send instructions to the smart device, and update its control logic and parameters; S816: Continue to monitor the system operation status and user feedback after the implementation of the new strategy, and continuously iterate and optimize the strategy based on the new monitoring data and user feedback to form a closed-loop feedback mechanism.

[0040] In the above detailed description, reference is made to the accompanying drawings, which form a part of this document. In the accompanying drawings, similar symbols typically identify similar components unless the context indicates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein.

[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent control home device terminal system, characterized in that: include: Connection module, acquisition module, analysis module, control module and monitoring module; The connection module includes a self-checking unit and an access unit; the self-checking unit is used to perform a self-check on the system after the system is started, confirm that there are no serious errors or warning information, and initialize the system; the access unit is used to start the device discovery process, perform device identity authentication and compatibility checks, register device information, assign identifiers to registered devices, access devices and complete system synchronization; The acquisition module is used to collect data from smart devices throughout the house, pre-process the collected data and store it in the data warehouse; The analysis module includes: a user behavior analysis submodule, a device status assessment submodule, an environmental change trend prediction submodule and a strategy formulation and intelligent recommendation submodule; the user behavior analysis submodule uses clustering, association rule mining and sequence pattern analysis to identify the user's usage habits, preferences and patterns; the device status assessment submodule evaluates the operating status, performance and reliability of the device through statistical analysis and trend prediction methods; the environmental change trend prediction submodule uses time series analysis and machine learning algorithms to predict the environmental change trend; the strategy formulation and intelligent recommendation submodule builds a user portrait based on user behavior data, preference settings and other information, and recommends suitable devices and scene modes to users based on the user portrait and recommendation algorithm; The control module is used to match and generate control instructions according to preset rules and real-time data, and send the control instructions to the corresponding smart devices through the system platform to achieve remote control and linkage of the devices; The monitoring module is used to monitor the operating status, network connection and data security of smart devices throughout the house in real time, and dynamically adjust the device linkage strategy and energy-saving plan based on real-time monitoring data and user feedback.

2. The intelligent control home device terminal system according to claim 1, characterized in that: The steps of the self-check unit are: turn on the system power, start the self-check unit to check the hardware connection; load and run the system firmware or operating system; verify the accuracy of the system time and calibrate it when necessary; check the system log to confirm that there are no serious errors or warning messages; load and parse the configuration file, and then configure the system service; set monitoring indicators and alarm thresholds; if the monitoring indicators and alarm thresholds are not exceeded, the system starts the device discovery process to detect whether there are any accessible smart devices around, and if so, connect them.

3. The intelligent control home device terminal system according to claim 2, characterized in that: The steps of the access unit are: after the system starts the device discovery process, monitor the device response and collect the basic information of the device; authenticate each discovered device; check whether the device is compatible with the current system, register the device information that passes the authentication and compatibility check into the system database, and store the basic information, status, and configuration parameters of the device; assign a unique device identifier UUID to each registered device; establish a communication connection with the device based on the device type and configuration; synchronize the device status and system configuration to ensure that the device runs according to the system requirements; add the device to the corresponding device group or scene; mark the device access process as completed, and update the system interface and log to reflect the newly accessed device information; notify the system administrator or user that the device has been successfully accessed and is available for use.

4. The intelligent control home device terminal system according to claim 1 is characterized in that ,In the acquisition module, the specific steps of collecting data from ,all smart devices in the house, pre-processing the collected data and ,storing them in the data warehouse are as follows: Define data sources, identify sensors and devices in the whole house smart devices, list all sensors and devices involved in data collection; determine the user interfaces through which users interact with the system, and identify the types of data generated by these user interfaces; Set a fixed collection frequency, and set triggers for device status changes or user operations to instantly collect relevant data; synchronize user behavior data or device configuration information from the user device or the cloud regularly; Periodically collect data from sensors, devices, and user interfaces at a set collection frequency through APIs, message queues, or direct connections; Store the collected data in a local or cloud database, and identify and process missing data points by interpolation, deletion, or marking as missing; use statistical methods to identify outliers and correct or remove them based on business logic; then check and delete duplicate data records; Convert data from different sources into a unified format; aggregate multiple data points into higher-level data units as needed; extract features useful for subsequent analysis from raw data; use lossless compression algorithms to reduce storage space in scenarios where the accuracy of raw data needs to be maintained; Use AES and RSA encryption algorithms to encrypt data containing user privacy or sensitive information; use data warehouse technology Hive and ETL tools to integrate data, integrate data from different data sources together to form a unified data view, and verify data integrity.

5. The intelligent control home device terminal system according to claim 1 is characterized in that ,The user behavior analysis submodule uses clustering, association rule mining, and ,sequence pattern analysis to identify user usage habits, preferences and ,patterns, specifically: Extract user behavior-related data from the data warehouse and pre-process the data; Based on the K-means clustering algorithm, users are grouped according to their behavior characteristics; Set clustering parameters, execute clustering algorithm, and obtain user groups; Analyze the behavioral characteristics of each user group and identify the usage habits and preferences of different user groups; Apply the association rule mining algorithm Apriori to analyze the association relationship between user behaviors; set support and confidence thresholds to mine frequent item sets and strong association rules between user behaviors; Analyze association rules to understand users’ behavior patterns and habits in different scenarios; use the sequence pattern mining algorithm PrefixSpan to analyze the time series pattern of user behavior; Identify the temporal order and dependencies between user behaviors; Analyze sequence patterns to understand the dynamic changes and trends of user behavior.

6. The intelligent control home device terminal system according to claim 1 is characterized in that ,The equipment status evaluation submodule evaluates the equipment operation status, ,performance and reliability through statistical analysis and trend prediction methods, specifically as follows: ,collecting equipment operation data from equipment sensors and log systems, including ,operation status, fault records, and maintenance history; preprocessing the data, including removing noise data, filling ,missing values, and converting data formats; obtaining statistical indicators such as ,equipment operation time, downtime, failure rate, maintenance cycle, etc.; analyzing the performance indicators of the equipment, ,and identifying common problems and abnormal behavior patterns of the equipment; Use time series analysis technology ARIMA to predict the future operating status of equipment; evaluate equipment reliability, predict potential failure points and maintenance needs; and formulate preventive maintenance plans based on prediction results.

7. The intelligent control home device terminal system according to claim 1 is characterized in that ,The environmental change trend prediction submodule uses time series analysis and ,machine learning algorithms to predict the changing trend of the environment, specifically by : ,collecting environmental data from environmental sensors and extracting ,characteristic variables related to environmental changes; Normalize or standardize the characteristic variables; predict the trend of environmental changes based on the linear regression algorithm; Use historical environmental data as a training set to train the algorithm and tune its parameters; evaluate the model's predictive performance; and use the trained model to predict future environmental change trends; Analyze the forecast results and identify the key nodes and trends of environmental changes.

8. The intelligent control home device terminal system according to claim 1 is characterized in that The strategy formulation and intelligent recommendation submodule builds a user portrait based on user behavior data, preference settings and other information, and recommends suitable devices and scenario modes to users based on the user portrait and recommendation algorithm. Specifically: Combine the user behavior analysis results to obtain the user's actual needs and usage scenarios; Based on device status data and environmental data, evaluate the performance and reliability of the current system, identify potential problems and improvement points; Develop linkage strategies between devices based on user needs and environmental changes; Design energy-saving plans based on user behavior and device status; Develop security warning and response strategies based on environmental data and device status; Test the formulated strategies in a simulated environment to verify their effectiveness and reliability; Build a user portrait based on user behavior data, preference settings and other information, including the user's age, gender, interests, hobbies, and living habits; Select a collaborative filtering recommendation algorithm based on application scenarios and user portrait characteristics to recommend suitable devices and scenario modes to users, and transmit the recommendation results to the user end for display to the user.

9. The intelligent control home device terminal system according to claim 1, characterized in that In the control module, the specific steps are as follows: preset control rules in the system according to the living habits, safety requirements, and energy-saving requirements of family members; when the system is started or the rules are updated, load the rules in the rule base into the memory for fast matching and execution; compare the real-time data with the conditions in the rule base to find all the rules that meet the conditions; if multiple rules meet the conditions at the same time, sort them according to the preset priority to ensure that the most important rules are executed first; generate corresponding control instructions according to the matched rules and priorities; encapsulate the control instructions in the format required by the system platform, and send the encapsulated control instructions to the corresponding smart device; wait for and receive confirmation feedback from the target device on the execution of the instruction; if the instruction is executed successfully, record and update the device status; if the execution fails, retry or alarm processing is performed according to the reason for the failure; record the generation time, issuance time, target device, and execution result information of each control instruction; according to the execution result of the instruction, update the status information of the home device in the system.

10. The intelligent control home device terminal system according to claim 1, characterized in that ,In the monitoring module, the specific steps are: Start the system service and load all necessary configuration parameters; establish network connections with all smart devices to ensure smooth communication; initialize the data collection and storage system; loop through all smart devices and send query instructions to obtain the current status of the device; record and store device status data in the database or memory database; regularly check the network connection status with each smart device, and if any abnormal connection is found, try to re-establish the connection and record the abnormal log; monitor the encryption status during data transmission to ensure data security, and regularly check whether the stored data has abnormal access or tampering signs; Receive user feedback on system performance, device control effects, user interface friendliness, etc.; storing the feedback data in a feedback database; Regularly aggregate monitoring data and user feedback data to form analysis reports; analyze equipment operation status data to evaluate equipment health status and failure rate; Analyze user feedback data to evaluate user satisfaction and common problems; evaluate the efficiency and applicability of existing linkage strategies and energy-saving solutions based on current environmental conditions and user behavior patterns; Generate new device linkage strategies or optimize existing strategies based on data analysis results; formulate new energy-saving plans based on the balance between energy efficiency and user comfort; test the effectiveness of new strategies in a simulated environment; deploy tested new strategies or optimized plans to actual systems, send instructions to smart devices, and update their control logic and parameters; Continue to monitor the system operation status and user feedback after the implementation of the new strategy, and continuously iterate and optimize the strategy based on new monitoring data and user feedback to form a closed-loop feedback mechanism.

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