Implementation method and device of smart home gateway

Abstract

The invention relates to the technical field of smart home, and discloses a smart home gateway implementation method and device, and an equipment access layer which is used for accessing multiple types of smart equipment in a smart home scene and collecting real-time operation data and environment sensing data of the smart equipment; the data processing layer is connected with the equipment access layer and is used for performing cleaning, format conversion, multi-source data fusion and storage on the acquired data to form a standardized smart home data set; and an intelligent analysis layer which is connected with the data processing layer and is configured with a user behavior prediction model, an equipment linkage strategy generation algorithm and an equipment fault early warning model. The system has the following beneficial effects: compatibility is strong: five mainstream protocols such as Zigbee and Wi-Fi are supported, more than 95% of intelligent household equipment can be accessed, and all of the equipment can be managed by one gateway; the real-time performance is high, edge processing and MQTT lightweight transmission are achieved, the data acquisition delay is small, and the control instruction response delay is low.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart home, and in particular to a smart home family gateway implementation method and device. BACKGROUND

[0002] With the development of Internet of Things technology, smart home devices (such as smart home appliances, environmental sensors, and security devices) are becoming increasingly diverse, but existing smart home systems still face the following core problems: Poor device compatibility: Different brands and types of smart devices use different communication protocols (such as Zigbee, Wi-Fi, and Bluetooth), and lack a unified access gateway, resulting in users needing to install multiple APPs for separate management and cumbersome operation; Low data processing efficiency: Most systems rely on cloud processing of data, and lack edge computing capabilities locally, resulting in high data transmission delays (usually over 1 second) and inability to meet real-time control requirements (such as abnormal responses from security devices); Insufficient intelligence: Existing systems rely mostly on manual user control or simple timing tasks, do not generate personalized strategies based on user behavior habits, and cannot automatically implement device coordination based on environmental changes (such as not automatically adjusting curtains and lights when light is insufficient); Weak fault control: Lack of real-time monitoring and risk assessment of device operating status, device faults (such as short circuits in home appliances and sensor malfunctions) can only be discovered after the fact, causing property damage (such as short circuit-induced fires); Lack of coordination: Each device operates independently and cannot form a "device-environment-user" collaborative closed loop (such as not being able to simultaneously turn off lights and appliances and turn on security when the user leaves home).

[0003] In existing technologies, some home gateways attempt to solve the protocol adaptation problem, but have weaknesses such as weak data processing capabilities, simple analysis models, and lack of fault early warning, and cannot meet the core needs of smart home systems for "unified management, intelligent coordination, and safety and reliability". SUMMARY

[0004] To overcome the deficiencies of existing technologies, the present application proposes a safe power cabinet, which solves the problems of device compatibility, real-time performance, intelligence, and fault control through a five-layer architecture of "device access-data processing-intelligent analysis-control execution-interaction".

[0005] To solve the above technical problems, the basic technical solution proposed by the present application is a smart home family gateway device, comprising: A device access layer for accessing multiple types of smart devices in a smart home scenario, collecting real-time operating data and environmental sensing data of each smart device; A data processing layer connected with the device access layer, configured to clean, format, fuse and store the collected data to form a standardized smart home dataset; An intelligent analysis layer connected with the data processing layer, configured with a user behavior prediction model, a device linkage strategy generation algorithm and a device fault warning model, configured to analyze the standardized smart home dataset to generate a user personalized control strategy, a device linkage scheme and a fault warning information; A control execution layer connected with the intelligent analysis layer, configured to receive and execute the user personalized control strategy and the device linkage scheme, and feed back the device execution result to the intelligent analysis layer; An interaction layer connected with the intelligent analysis layer and the control execution layer, configured to realize the interaction between the user and the home gateway, display the smart home system running state, the fault warning information and the device execution result, and receive the user instruction.

[0006] Preferably, the device access layer comprises: A multi-protocol adaptation module supporting Zigbee, Wi-Fi, Bluetooth, LoRa and NB-IoT protocols, configured to adapt the smart devices (such as smart home appliances, environmental sensors and human body detectors) of different communication protocols; A data collection module, configured to combine the "timed collection" and "event triggered collection" to obtain the running parameters (such as home appliance power, switch state and temperature setting value) and the environmental perception data (such as indoor temperature and humidity, light intensity, PM2.5 concentration and human body presence state) of the smart devices in real time; A data transmission module, configured to use the MQTT lightweight communication protocol to transmit the collected data to the data processing layer in real time, and the transmission delay is not more than 500 ms.

[0007] Preferably, the data processing layer comprises: An edge distributed storage unit, configured to use the distributed storage architecture of "local edge node + cloud cooperation", to store the real-time data by Redis cache and the historical data by HDFS locally, and to synchronize and backup the key data in the cloud, thereby supporting the storage of massive structured data (such as device running parameters) and unstructured data (such as indoor environment images); A data cleaning unit, configured to remove redundant data and correct error data (such as temperature and humidity values beyond the reasonable range and sudden change of home appliance power data) by using the outlier detection algorithm (such as 3σ principle), and to convert different format data (such as XML, JSON and binary) into the standard JSON format; The multi-source data fusion unit adopts a "CNN-LSTM hybrid deep learning algorithm" to correlate and integrate heterogeneous data (such as device state data and environmental perception data, user operation data), forming a unified smart home data view containing "device-environment-user" three-dimensional information.

[0008] Preferably, the intelligent analysis layer comprises: The user behavior prediction module, based on the LSTM neural network algorithm, combines the historical data of the user using the smart device in the past 1-3 months (such as the time of turning on the air conditioner every day, the frequency of adjusting the light brightness, the window curtain opening and closing period), the user's work and rest habits (such as getting up time, leaving home time, and going to bed time), and the seasonal factors (such as the air conditioner usage frequency in summer is higher than in winter), to predict the user's device usage preferences and needs in the next 7-14 days; The device linkage strategy module generates a device linkage scheme (such as "automatically closing the curtains and turning on the living room main light when the light intensity is insufficient" and "automatically turning on the air conditioner and setting it to 26°C when the temperature is too high") according to real-time environmental data (such as light intensity below 200 lux, indoor temperature above 28°C) and device state data (such as curtains open, air conditioner off) through a decision tree algorithm. The fault warning module quantitatively evaluates the device fault risk by using a risk matrix model based on the analysis of historical fault data of the smart device (such as historical short circuit records of household appliances, number of sensor failures) and real-time operating parameters (such as smart socket current exceeding 10A, water heater water temperature rising 10°C / min), and divides the risk level (high, medium, low).

[0009] Preferably, the control execution layer comprises: The device control unit interfaces with the control interface of each smart device (such as interfacing with a smart air conditioner through an API, interfacing with smart lights through Zigbee instructions, and interfacing with a smart door lock through Bluetooth instructions), for executing device on-off control, parameter adjustment (such as air conditioner temperature setting, light brightness adjustment, and curtain opening degree control) instructions. The state feedback unit collects the running state of the device after execution (such as whether the air conditioner is successfully turned on, whether the light brightness reaches the set value), and transmits the feedback data to the intelligent analysis layer through the MQTT protocol for subsequent strategy optimization. The collaborative control unit pushes linkage control instructions to associated devices in the smart home system, realizing multi-device collaborative operation (such as "when the user leaves home, automatically turn off all lights + cut off unnecessary household appliance power + lock the smart door lock + turn on the security camera").

[0010] Preferably, the interaction layer comprises a Web management platform, a mobile APP, and a voice interaction module; wherein: The web management platform supports smart home data visualization display (such as device status dashboard, environmental data trend curve, fault warning list) and system parameter configuration (such as device communication protocol adaptation, data acquisition frequency setting); The mobile APP (supporting iOS / Android system) supports device remote control, fault warning information push (such as pop-up window + push notification), and user-defined linkage rule (such as "automatically turn off the living room lights after 10 pm"). The voice interaction module integrates a speech recognition engine (such as the speech SDK of Kedou Xunfei), and supports user control of devices or information acquisition through voice commands (such as "turn on the air conditioner in the bedroom" and "query indoor humidity").

[0011] An implementation method of a smart home gateway, characterized in that it comprises the following steps: S1: Access multiple types of smart devices through a device access layer, adopt a "timed acquisition + event-triggered acquisition" mechanism, and acquire the running data and environmental perception data of each smart device in real time; S2: The data processing layer cleans (removes redundant / error data), formats (unifies to JSON format), multi-source data fusion (associates device-environment-user data), and stores (local + cloud collaborative storage) the collected data, forming a standardized smart home data set; S3: The intelligent analysis layer calls user behavior prediction models, device linkage strategy generation algorithms, and device fault warning models to analyze the standardized smart home data set, generating user personalized control strategies, device linkage schemes, and fault warning information; S4: The control execution layer receives and executes the user personalized control strategies and device linkage schemes, and feeds back the device execution results to the intelligent analysis layer; S5: The interaction layer displays the smart home system running state, fault warning information, and device execution results, and receives and responds to user instructions (such as manual control of devices and modification of linkage rules).

[0012] Preferably, in step S1, the timed acquisition interval is dynamically adjusted according to the device type: the environmental sensor (such as a temperature and humidity sensor) has an acquisition interval of 10 seconds, the smart appliance (such as an air conditioner or a washing machine) has an acquisition interval of 30 seconds, and the security device (such as a camera) has an acquisition interval of 5 seconds; The event-triggered acquisition mechanism includes: when the human body detector detects human movement, it immediately acquires indoor light / wetness data; when the smart door lock detects an unlocking action, it immediately acquires the door lock state and the status of indoor security devices; The multi-protocol adaptation module automatically identifies the communication protocol of the intelligent device, and after protocol adaptation is completed through a protocol conversion chip (such as a Zigbee-Wi-Fi conversion chip), data is transmitted to the data processing layer.

[0013] Preferably, in step S3: The user behavior prediction model adopts an LSTM neural network algorithm, is built based on a TensorFlow framework, input parameters include user device use history data in the last 1-3 months, user work and rest records (obtained through mobile phone alarm data / smart bracelet sleep data) and seasonal indexes (such as summer index 1.2 and winter index 0.8), and output future 7-14 day user device use prediction results (such as “turn on the bedroom light automatically at 7 o'clock in the morning on weekdays” and “turn on the living room air conditioner automatically at 2 o'clock in the afternoon on weekends”), and the prediction accuracy is not less than 88%; The device linkage strategy generation algorithm is trained through a decision tree model, training samples include historical environment data, device state data and user manual linkage operation records, and the linkage scheme generated by the model supports secondary editing of the user (such as adjusting the linkage trigger threshold and adding associated devices).

[0014] Preferably, in step S3: when the fault early warning model identifies the risk of the intelligent device, early warning information is generated according to the risk level: High-risk (such as intelligent socket current exceeding 15A, gas sensor detecting gas leakage): within 5 seconds, through a mobile APP pop-up window + SMS push to the user, and automatically triggering an emergency response plan (such as cutting off the power supply of the faulty device, starting the indoor exhaust system, and linking the security camera to record video); Medium-risk (such as smart light brightness adjustment delay, air conditioner refrigeration efficiency decrease by 10%): within 30 seconds, the mobile APP pushes the early warning information to prompt the user to check the device state; Low-risk (such as occasional extension of sensor data collection interval, device response delay less than 1 second): recorded in the system log, and displayed on the Web management platform regularly.

[0015] The present application has the following advantages: Compared with the prior art, the application has the following beneficial effects: strong compatibility: supports five mainstream protocols such as Zigbee and Wi-Fi, can access more than 95% of smart home devices, and realizes "one gateway controls all"; high real-time performance: edge processing + MQTT lightweight transmission, small data acquisition delay and low control instruction response delay, meets the needs of security and emergency control; high degree of intelligence: personalized control strategy can be automatically generated; device linkage response time is less than or equal to 1 second, without manual intervention; safety and reliability: automatic triggering of emergency solutions reduces fault loss; user experience is optimized: multi-terminal interaction supports remote control, voice instructions and customized strategies, and adapts to different user habits. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0017] Figure 1 The system block diagram of the smart home gateway device of the present application is shown in the figure. Figure 2 The flowchart of the implementation method of the smart home gateway of the present application is shown in the figure. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the drawings in the specification and the specific embodiments.

[0020] According to the drawings Figure 1 - Figure 2 The smart home gateway device comprises: A device access layer is used to access multiple types of smart devices in a smart home scene, and collect real-time running data and environment sensing data of each smart device. The device access layer comprises: A multi-protocol adaptation module supports Zigbee, Wi-Fi, Bluetooth, LoRa and NB-IoT protocols, and is used to adapt smart devices (such as smart home appliances, environmental sensors and human body detectors) of different communication protocols. The data acquisition module adopts a combination of "timed acquisition + event triggered acquisition" to obtain the running parameters (such as the power of household appliances, the on-off state, and the temperature setting value) of the intelligent device and the environmental perception data (such as the indoor temperature and humidity, the light intensity, the PM2.5 concentration, and the human presence state) in real time. The data transmission module adopts the MQTT lightweight communication protocol to transmit the collected data to the data processing layer in real time, and the transmission delay is not more than 500 ms.

[0021] The data processing layer is connected with the device access layer and is used for cleaning, format conversion, multi-source data fusion, and storage of the collected data to form a standardized smart home data set. The data processing layer includes: The edge distributed storage unit adopts a distributed storage architecture of "local edge node + cloud cooperation". The local edge node stores real-time data through Redis cache and stores historical data through HDFS. The cloud synchronously backs up key data and supports storage of massive structured data (such as device running parameters) and unstructured data (such as indoor environment images). The data cleaning unit removes redundant data and corrects error data (such as temperature and humidity values beyond the reasonable range and sudden changes in the power of household appliances) through an outlier detection algorithm (such as the 3σ principle), and uniformly converts different format data (such as XML, JSON, and binary) into a standard JSON format. The multi-source data fusion unit uses a "CNN-LSTM hybrid deep learning algorithm" to associate and integrate heterogeneous data (such as device state data and environmental perception data, and user operation data) to form a unified smart home data view containing "device-environment-user" three-dimensional information.

[0022] The intelligent analysis layer is connected with the data processing layer and is configured with a user behavior prediction model, a device linkage strategy generation algorithm, and a device fault warning model. It is used for analyzing the standardized smart home data set to generate user personalized control strategies, device linkage schemes, and fault warning information. The intelligent analysis layer includes: The user behavior prediction module is based on the LSTM neural network algorithm and combines the historical data of the user's use of the intelligent device in the past 1-3 months (such as the time of turning on the air conditioner every day, the frequency of adjusting the light brightness, and the window curtain opening and closing period), the user's work and rest habits (such as the time of getting up, leaving home, and going to bed), and the seasonal factors (such as the higher frequency of using the air conditioner in summer than in winter) to predict the user's device use preferences and needs in the next 7-14 days. The device linkage strategy module generates device linkage schemes (such as "automatically close curtains and turn on the main living room light when there is insufficient light" and "automatically turn on the air conditioner and set it to 26℃ when the temperature is too high") based on real-time environmental data (such as light intensity below 200 lux and indoor temperature above 28℃) and device status data (such as curtains open and air conditioner closed) through decision tree algorithm. The fault warning module analyzes historical fault data of smart devices (such as historical short circuit records of home appliances and number of sensor failures) and real-time operating parameters (such as smart socket current exceeding 10A and water heater water temperature rising by 10℃ / min), and uses a risk matrix model to quantitatively assess the risk of device faults and classify the risk levels (high, medium, and low).

[0023] A control execution layer, connected to the intelligent analysis layer, is used to receive and execute the user-personalized control strategies and device linkage schemes, and simultaneously feed back the device execution results to the intelligent analysis layer; the control execution layer includes: The device control unit interfaces with the control interfaces of various smart devices (such as connecting to smart air conditioners via API, smart lights via Zigbee commands, and smart door locks via Bluetooth commands) to execute device on / off control and parameter adjustment commands (such as setting air conditioner temperature, adjusting light brightness, and controlling the opening and closing of curtains). The status feedback unit collects the operating status of the device in real time (such as whether the air conditioner is successfully turned on or whether the brightness of the lights has reached the set value), and transmits the feedback data to the intelligent analysis layer via the MQTT protocol for subsequent strategy optimization. The collaborative control unit pushes linkage control commands to related devices in the smart home system to enable multi-device collaborative operation (such as "automatically turn off all lights + cut off power to non-essential home appliances + lock the smart door lock + turn on the security camera when the user leaves home").

[0024] The interaction layer, connected to both the intelligent analysis layer and the control execution layer, enables user interaction with the home gateway, displays the smart home system's operating status, fault warning information, and device execution results, and receives user commands. The interaction layer includes a web management platform, a mobile app, and a voice interaction module; wherein: The web management platform supports the visualization of smart home data (such as device status dashboards, environmental data trend curves, and fault warning lists) and the configuration of system parameters (such as device communication protocol adaptation and data acquisition frequency settings). The mobile app (supports iOS / Android systems) supports remote device control, fault warning information push (such as pop-up windows + push notifications), and user-defined linkage rules (such as "automatically turn off the living room lights after 10 pm"). Voice interaction module integrates speech recognition engine (such as the speech SDK of Kedou Xunfei), supports users to control devices or obtain information through voice instructions (such as "turn on the air conditioner in the bedroom" and "query indoor humidity").

[0025] An implementation method of a smart home gateway, comprising the following steps: S1: Access multiple types of smart devices through a device access layer, adopt a "timely collection + event-triggered collection" mechanism, and collect running data and environmental sensing data of each smart device in real time; in step S1: the timely collection interval is dynamically adjusted according to the device type: the collection interval of an environmental sensor (such as a temperature and humidity sensor) is 10 seconds, the running parameter collection interval of a smart home appliance (such as an air conditioner or a washing machine) is 30 seconds, and the key state collection interval of a security device (such as a camera) is 5 seconds; The event-triggered collection mechanism includes: collecting indoor light / temperature and humidity data immediately when a human body detector detects human body movement, and collecting door lock state and indoor security device state immediately when a smart door lock detects an unlocking action; The multi-protocol adaptation module automatically identifies the communication protocol of the smart device, completes protocol adaptation through a protocol conversion chip (such as a Zigbee-Wi-Fi conversion chip), and then transmits data to the data processing layer.

[0026] S2: The data processing layer cleans (removes redundant / error data), formats (unifies to JSON format), multi-source data fuses (associates device-environment-user data), and stores (local + cloud collaborative storage) the collected data, and forms a standardized smart home data set; S3: The intelligent analysis layer calls a user behavior prediction model, a device linkage strategy generation algorithm, and a device fault early warning model, analyzes the standardized smart home data set, and generates user personalized control strategies, device linkage schemes, and fault early warning information; in step S3: The user behavior prediction model adopts an LSTM neural network algorithm, is built based on a TensorFlow framework, input parameters include user device use history data in the past 1-3 months, user work and rest records (obtained through mobile phone alarm data / smart bracelet sleep data), and seasonal indexes (such as summer index 1.2 and winter index 0.8), and output future 7-14 day user device use prediction results (such as "turn on the bedroom light at 7 o'clock in the morning on weekdays" and "turn on the living room air conditioner at 2 o'clock in the afternoon on weekends"), and the prediction accuracy is not less than 88%; The device linkage strategy generation algorithm is trained by a decision tree model, training samples include historical environment data, device state data and user manual linkage operation records, and the linkage scheme generated by the model supports secondary editing by the user (such as adjusting the linkage trigger threshold, adding associated devices). In step S3: when the fault early warning model identifies the risk of intelligent device failure, generate early warning information according to the risk level: High-risk (such as intelligent socket current exceeding 15A, gas sensor detecting gas leakage): within 5 seconds, pop-up window + SMS push to user through mobile APP, and automatically trigger emergency response plan (such as cutting off power supply of faulty device, starting indoor exhaust system, and recording video by linkage security camera); Medium risk (such as smart lighting brightness adjustment delay, air conditioner refrigeration efficiency decreased by 10%): within 30 seconds, push early warning information through mobile APP, prompting the user to check the device status; Low risk (such as occasional extension of sensor data collection interval, device response delay less than 1 second): record to system log, and display on Web management platform regularly.

[0027] S4: The control execution layer receives and executes the user personalized control strategy and device linkage scheme, and feeds back the device execution result to the intelligent analysis layer; S5: The interaction layer displays the running state of the smart home system, fault early warning information and device execution result, and receives and responds to user instructions (such as manual control of devices and modification of linkage rules).

[0028] The implementation example is as follows: In this embodiment, the implementation method of the smart home gateway is as follows: S1: Data collection: Timely collection: temperature and humidity sensor collects data every 10 seconds (25℃, 50%RH), and air conditioner uploads power every 30 seconds (800W); Event-triggered collection: human body detector detects that the user has returned home, and immediately collects light data (150 lux) and door lock state (unlocked); Data transmission: transmit data to the data processing layer through MQTT.

[0029] S2: Data processing: Data cleaning: filter out abnormal values such as "temperature 60℃", and convert air conditioner XML data to JSON format; Data fusion: associate "user returns home (door lock unlocked) + light 150 lux + air conditioner off" to form a unified data view; Data storage: real-time data is stored in Redis, historical data is stored in HDFS, and key data is synchronized to Aliyun.

[0030] S3: Intelligent analysis: Behavior prediction: LSTM model predicts "the user will turn on the air conditioner 10 minutes after coming home" based on user historical data; Linkage strategy: Decision tree model generates "close the curtains + turn on the lights" solution based on "light 150 lux + curtains open"; Fault warning: Detecting that the intelligent socket current is 16A (over threshold 15A), determining high risk, generating warning information.

[0031] S4: Control execution: Execute linkage solution: Close the curtains, turn on the lights; Execute fault emergency: Cut off the power supply of the intelligent socket; State feedback: The curtain feedbacks "closed", and the socket feedbacks "powered off".

[0032] S5: Interactive display: APP displays "curtains closed, lights on" state, and pushes "intelligent socket current exceeds standard, powered off" warning; The user confirms the warning information through the APP and manually adjusts the light brightness to 60%.

[0033] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A smart home home gateway device, characterized by, Comprise: Device access layer for accessing multiple types of intelligent devices in smart home scenarios, collecting real-time operation data and environmental perception data of each intelligent device; Data processing layer connected with the device access layer, for cleaning, format conversion, multi-source data fusion and storage of the collected data, forming a standardized smart home data set; Intelligent analysis layer connected with the data processing layer, configured with user behavior prediction model, device linkage strategy generation algorithm and device fault warning model, for analyzing the standardized smart home data set, generating user personalized control strategy, device linkage scheme and fault warning information; Control execution layer connected with the intelligent analysis layer, for receiving and executing the user personalized control strategy and device linkage scheme, and feeding back the device execution result to the intelligent analysis layer; Interaction layer connected with the intelligent analysis layer and control execution layer respectively, for realizing the interaction between the user and the home gateway, displaying the smart home system operation state, fault warning information and device execution result, and receiving user instructions. 2.The smart home gateway device of claim 1, wherein: The device access layer comprises: Multi-protocol adaptation module supporting Zigbee, Wi-Fi, Bluetooth, LoRa and NB-IoT protocols for adapting intelligent devices with different communication protocols; Data acquisition module, which adopts the combination of "timed acquisition + event triggered acquisition", to obtain real-time operation parameters and environmental perception data of intelligent devices; Data transmission module, which adopts MQTT lightweight communication protocol to transmit the collected data to the data processing layer in real time, with transmission delay not exceeding 500ms. 3.The smart home gateway device of claim 1, wherein: The data processing layer comprises: Edge distributed storage unit, which adopts a distributed storage architecture of "local edge node + cloud cooperation", stores real-time data locally through Redis cache and historical data through HDFS, synchronously backs up key data in the cloud, and supports storage of massive structured and unstructured data; Data cleaning unit, which removes redundant data and corrects error data through an outlier detection algorithm, and uniformly converts different format data into standard JSON format; Multi-source data fusion unit, which uses a "CNN-LSTM hybrid deep learning algorithm" to associate and integrate heterogeneous data to form a unified smart home data view containing "device-environment-user" three-dimensional information. 4.The smart home gateway device of claim 1, wherein: The intelligent analysis layer comprises: User behavior prediction module based on LSTM neural network algorithm, which combines the historical data of user using intelligent devices in the past 1-3 months, user's work and rest habits and seasonal factors to predict the user's device usage preferences and needs in the next 7-14 days; Device linkage strategy module, which generates device linkage scheme through decision tree algorithm according to real-time environmental data and device state data; Fault warning module, which quantitatively evaluates the device fault risk by using a risk matrix model to analyze the historical fault data and real-time operation parameters of intelligent devices, and divides the risk level. 5.The smart home gateway device of claim 1, wherein: The control execution layer comprises: Device control unit, which is connected with the control interface of each intelligent device, for executing device switch control and parameter adjustment instructions; A state feedback unit collects the running state of the device after execution in real time, and transmits feedback data to the intelligent analysis layer through an MQTT protocol for subsequent strategy optimization. A cooperative control unit pushes linkage control instructions to associated devices in the smart home system to realize cooperative operation of multiple devices. 6.The smart home gateway device of claim 1, wherein, The interaction layer includes a Web management platform, a mobile APP, and a voice interaction module, wherein: The Web management platform supports visual display of smart home data and configuration of system parameters. The mobile APP supports remote control of devices, push of fault warning information, and user-defined linkage rules. The voice interaction module integrates a voice recognition engine and supports user control of devices or acquisition of information through voice instructions.

7. An implementation method of a smart home home gateway, characterized in that, The method comprises the following steps: S1: Access multiple types of smart devices through the device access layer, and adopt a "timed collection + event-triggered collection" mechanism to collect running data and environmental perception data of each smart device in real time; S2: The data processing layer cleans, formats, fuses, and stores the collected data to form a standardized smart home data set; S3: The intelligent analysis layer calls a user behavior prediction model, a device linkage strategy generation algorithm, and a device fault warning model to analyze the standardized smart home data set and generate user personalized control strategies, device linkage schemes, and fault warning information; S4: The control execution layer receives and executes the user personalized control strategies and device linkage schemes, and feeds back device execution results to the intelligent analysis layer; S5: The interaction layer displays the running state of the smart home system, fault warning information, and device execution results, and receives and responds to user instructions.

8. The implementation method of an intelligent home gateway according to claim 7, characterized in that, In step S1, the timed collection interval is dynamically adjusted according to the device type: the environmental sensor collection interval is 10 seconds, the smart appliance running parameter collection interval is 30 seconds, and the security device key state collection interval is 5 seconds; The event-triggered collection mechanism includes: collecting indoor light / temperature and humidity data immediately when a human body detector detects human movement, and collecting door lock state and indoor security device state immediately when a smart door lock detects an unlocking action; The multi-protocol adaptation module automatically identifies the communication protocol of the smart device, completes protocol adaptation through a protocol conversion chip, and then transmits data to the data processing layer.

9. The implementation method of an intelligent home gateway according to claim 7, characterized in that, In step S3: The user behavior prediction model adopts an LSTM neural network algorithm, is built based on a TensorFlow framework, input parameters include user device usage history data in the past 1-3 months, user work and rest records, and season indexes, and output future 7-14 day user device usage prediction results, and the prediction accuracy is not less than 88%; The device linkage strategy generation algorithm is trained through a decision tree model, training samples include historical environmental data, device state data, and user manual linkage operation records, and the linkage scheme generated by the model supports secondary editing by the user.

10. The implementation method of the smart home gateway according to claim 7, characterized in that, In step S3, when the fault warning model identifies a smart device fault risk, warning information is generated according to the risk level: High risk: 5 seconds through mobile app pop-up + SMS push to users, while automatically triggering emergency response plan; Medium risk: 30 seconds through mobile app push warning information, prompting users to check equipment status; Low risk: Record to system log, regularly displayed on the Web management platform.

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