Ultra-low delay smart home collaboration system and method based on edge computing gateway

Abstract

The invention discloses an ultra-low delay smart home cooperation system and method based on an edge computing gateway, the system comprises smart home equipment, the edge computing gateway and a cloud server, and the edge computing gateway comprises a local storage module, a data processing module, a communication module and an equipment cooperation control module. Four core units including a local storage module, a data processing module, a communication module and an equipment cooperative control module are integrated through an edge computing gateway to form an acquisition-processing-control localized closed loop, and real-time data processing, equipment state monitoring and cooperative decision are sunk to an edge layer. The method breaks through the hundred millisecond delay bottleneck of the traditional cloud architecture, realizes millisecond response speed, and in addition, only uploads desensitized abstract data to the cloud by only performing necessary processing on the original data of the equipment at the edge gateway, thereby avoiding the risk of user privacy disclosure.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of smart home, and in particular to a super-low-delay smart home coordination system and method based on an edge computing gateway. BACKGROUND

[0002] With the development of the Internet of Things technology, smart home devices are increasing, such as smart lamps, smart home appliances, smart security devices and the like. These devices are connected through a network to realize intelligent control and collaborative work. However, a traditional smart home system usually adopts a cloud computing architecture, and device data needs to be uploaded to the cloud for processing and analysis, which leads to high data transmission delay, especially when the network is unstable or the data volume is large, the delay problem is more prominent, which affects the real-time performance of the smart home system and the user experience. In addition, uploading a large amount of data to the cloud also has the risk of data security and privacy protection. In view of the above, the application provides a super-low-delay smart home coordination system and method based on an edge computing gateway. SUMMARY

[0003] Based on the technical problems existing in the background technology, the application provides a super-low-delay smart home coordination system and method based on an edge computing gateway.

[0004] The super-low-delay smart home coordination system based on the edge computing gateway provided by the application comprises smart home devices, an edge computing gateway and a cloud server. The edge computing gateway comprises a local storage module, a data processing module, a communication module and a device coordination control module.

[0005] The smart home devices are connected with the edge computing gateway and the device coordination control module, and the communication module is connected with the cloud server.

[0006] Preferably, the local storage module is used for storing configuration information, historical data and coordination control rules of the smart home devices.

[0007] The stored configuration information comprises the model, communication parameters and function parameters of the devices, so that the edge computing gateway can correctly identify and manage the devices.

[0008] The stored historical data is used for subsequent data analysis and system optimization.

[0009] The stored coordination control rules define the collaborative work logic between different devices, which comprises automatically triggering the air conditioner to start and adjusting to a suitable temperature while closing the curtains to block direct sunlight when the temperature sensor detects that the indoor temperature is too high.

[0010] Preferably, the data processing module is used for real-time processing and analysis of the data sent by the smart home devices, and the specific logic steps are as follows:

[0011] (1) First, the collected data is pre-processed, including data cleaning, filtering, normalization operation, removing noise and abnormal data, improving the accuracy and reliability of the data;

[0012] (2) According to the preset algorithm and model, the processed data is analyzed to extract valuable information, including the change trend of the environmental state and the running state of the equipment; for example, through the analysis of temperature, humidity and light data, it can be judged whether the current indoor environment is suitable for human habitation, and provide basis for the cooperative control of the equipment.

[0013] Preferably, the communication module is responsible for communicating with smart home devices and cloud servers. When communicating with smart home devices, it supports multiple communication protocols, including Wi-Fi, Bluetooth, ZigBee, Z-Wave, to be compatible with different brands and types of devices, and to realize seamless access of devices. When communicating with the cloud server, through wired or wireless broadband network connection, the key data processed by the edge computing gateway is uploaded to the cloud server, and the update instructions and control strategies sent by the cloud server are received. For example, when the cloud server has new cooperative control rules or device firmware updates, the communication module transmits these information to the edge computing gateway.

[0014] Preferably, the device cooperative control module is used to generate corresponding control instructions according to the results analyzed by the data processing module and the preset cooperative control rules, and send them to the smart home devices to realize the cooperative work between the devices. The device cooperative control module is also used to monitor the state of the device and the change of the environment in real time, and dynamically adjust the control strategy to ensure the efficiency and real-time performance of the cooperative control of the device. For example, when detecting that the user is back home, a series of cooperative actions of the devices are triggered through the state change of the smart door lock, such as automatically turning on the indoor light, adjusting the air conditioner temperature, playing welcome music, etc.

[0015] Preferably, the cloud server serves as the remote management and data storage center of the whole system, mainly used for storing a large amount of historical data, user configuration information and system management data, and providing remote monitoring, system upgrade, data analysis and personalized service functions. Users can remotely access the cloud server through mobile APP or Web interface to view the real-time state and historical data of smart home devices, configure and manage devices, and obtain personalized services and suggestions. In addition, the cloud server can also deeply analyze and mine the data uploaded by the edge computing gateway to provide data support for the optimization and upgrade of the system. For example, by analyzing the usage data of a large number of users, the usage habits and preferences of users are found out, so as to optimize the cooperative control rules and service strategies of the devices.

[0016] The application also proposes an ultra-low delay smart home collaboration method based on an edge computing gateway, including the following steps:

[0017] S1: Device data acquisition: real-time acquisition of environmental data and device state data through various sensors in the smart home device;

[0018] S2: Data transmission to edge computing gateway: the collected data is transmitted to the edge computing gateway through the communication module built-in the smart home device, and in the data transmission process, the corresponding communication protocol and data format are adopted to ensure reliable data transmission and avoid data loss or errors;

[0019] S3: Edge computing gateway data preprocessing: after the edge computing gateway receives the data sent by the smart home device, data preprocessing is performed;

[0020] S4: Local data analysis and decision-making: the preprocessed data enters the data processing module of the edge computing gateway for local analysis and decision-making. The data processing module analyzes the data according to the preset algorithm and model, extracts key information, and judges whether the current state needs to trigger the collaborative control action of the device according to the collaborative control rules stored in the local storage module. For example, when the temperature sensor detects that the indoor temperature exceeds the preset threshold, the data processing module judges that the air conditioner needs to be turned on and adjusted to the appropriate temperature according to the collaborative control rules;

[0021] S5: Whether cloud support is needed: during the local data analysis and decision-making process, it is judged whether cloud server support is needed. If the current analysis and decision-making can be completed locally in the edge computing gateway, it is not necessary to upload to the cloud server. If more complex data analysis, global optimization or remote control is needed, the relevant data will be uploaded to the cloud server. For example, when long-term environmental data needs to be analyzed to optimize the energy consumption of the entire smart home system, the data needs to be uploaded to the cloud server for in-depth analysis;

[0022] S6: Data upload to cloud server: when cloud support is needed, the edge computing gateway uploads the relevant data to the cloud server through the communication module using a secure communication protocol. The uploaded data includes preprocessed and preliminary analyzed data, as well as device state information;

[0023] S7: Cloud server processing and feedback: After the cloud server receives the data uploaded by the edge computing gateway, it performs further processing and analysis. The cloud server can use powerful computing resources and storage capabilities to deeply mine and analyze a large amount of data, generate globally optimized control strategies, device firmware update packages, and personalized service recommendations. After processing is completed, the cloud server feeds back the results to the edge computing gateway. For example, the cloud server analyzes the smart home data of multiple families and finds a more energy-saving device coordination control strategy, which is then sent to the edge computing gateway.

[0024] S8: Edge computing gateway receives feedback: The edge computing gateway receives the results fed back by the cloud server, whether it is a new control strategy, an update instruction, or other information, and stores it in the local storage module for subsequent device coordination control. If cloud support is not required, the edge computing gateway directly enters the step of generating device coordination control instructions.

[0025] S9: Generate device coordination control instructions: The device coordination control module of the edge computing gateway generates specific device coordination control instructions based on the results of local analysis and decision or the information fed back by the cloud server, combined with the locally stored coordination control rules. The control instructions include control of individual devices and coordination control of multiple devices to ensure that the coordinated work of devices meets the user's needs and preset rules.

[0026] S10: Control instructions are sent to smart home devices: The generated control instructions are sent to the corresponding smart home devices through the communication module of the edge computing gateway. According to the communication protocol supported by the device, the appropriate transmission method is selected to ensure that the control instructions can be accurately and timely delivered to the device.

[0027] S11: Devices perform corresponding operations: After receiving the control instructions, the smart home devices perform corresponding operations, including adjusting the brightness and color of smart lamps according to the instructions, changing the operating mode and temperature settings of air conditioners according to the instructions, and opening or closing smart curtains according to the instructions. Through the execution of devices, the coordinated control of the smart home system is realized to meet the user's needs.

[0028] Preferably, in S1, the various sensors include temperature sensors, humidity sensors, light sensors, and human infrared sensors. Environmental data includes temperature, humidity, light intensity, and human presence. The state data of the device itself includes the on-off state, brightness, and color of the lamp, the operating mode and working state of the household appliance.

[0029] Preferably, in the S3, the data preprocessing includes data cleaning, data filtering and data normalization processing. In the data cleaning, the completeness and consistency of the data are checked to remove repeated, erroneous or invalid data. The specific steps of the data cleaning are as follows:

[0030] S3011: Set the effective range of the data, wherein the effective range of the temperature sensor is -40℃ to 120℃;

[0031] S3012: Traverse the collected data and check whether each data is within the effective range. If not, mark it as invalid data;

[0032] S3013: For the continuously collected data, if multiple consecutive invalid data appear, interpolation processing is performed according to the front and rear valid data to fill in the missing data;

[0033] Data filtering: the mean filtering algorithm is used to denoise the data. The average value in the data window is calculated to smooth the data and reduce the influence of noise. The specific steps are as follows:

[0034] S3021: Set a data window size, n consecutive data points;

[0035] S3022: For each data point, calculate the average value of the previous and the next data points as the filtered value of the data point;

[0036] S3023: For the beginning and end of the data sequence, due to the insufficient data points, the boundary extension method is used to copy the boundary data to supplement the data in the window;

[0037] Data normalization: the minimum-maximum normalization method is used to convert different types and ranges of data into a unified [0, 1] or [-1, 1] range, so as to facilitate subsequent data analysis and algorithm processing. The specific steps are as follows:

[0038] S3031: Calculate the minimum value min and the maximum value max of the data;

[0039] S3032: For each data point x, the normalized value is

[0040] Preferably, in the S9, the cooperative control adopts a cooperative control algorithm based on rules and event driving;

[0041] The rule-based cooperative control is to predefine a series of cooperative control rules, which describe the cooperative actions of different devices under different environmental conditions. The rule can be defined as "when the light sensor detects that the light intensity is lower than the preset threshold and the human infrared sensor detects that a person exists, automatically turn on the light in the living room and adjust to the comfortable brightness". The rule is represented in the form of condition-action pair (If-Then), wherein the condition part is composed of the logical combination of multiple sensor data, and the action part specifies the device control instructions to be executed;

[0042] The event-driven cooperative control is to trigger the corresponding cooperative control action when an event occurs. The event can be an external event or an internal event. The event-driven cooperative control can respond to changes in the system in real time, improving the flexibility and real-time performance of the system.

[0043] The specific steps of the device cooperative control algorithm are as follows:

[0044] S901: Real-time monitoring of data and event information collected by smart home devices;

[0045] S902: For each new data or event, determine whether the condition part of the rule is met according to the preset cooperative control rule;

[0046] S903: If the condition is met, generate the corresponding control instruction and send it to the related device to perform the cooperative action;

[0047] S904: Record the execution result of the cooperative control for subsequent audit and system optimization.

[0048] Compared with the existing technology, the beneficial effects of the present application are:

[0049] 1. By processing the core data in the edge computing gateway, the cloud server is not needed, the data transmission delay is reduced from the traditional cloud architecture of hundreds of milliseconds to milliseconds, especially in the network fluctuation or offline scene, the device real-time linkage can still be guaranteed, and the edge gateway can be directly triggered to generate control instructions according to the device state change, thereby saving the cloud round-trip time, realizing the "collection-processing-control" full-link localization, and significantly improving the response speed in emergency scenes;

[0050] 2. By processing the device raw data only in the edge gateway, only the desensitized summary data is uploaded to the cloud, thereby avoiding the risk of user privacy leakage;

[0051] 3. Based on the three-layer architecture of "data preprocessing + rule matching + event driving", support the automation linkage of complex scenes (such as "insufficient light + human existence → turn on the light and adjust the color temperature" "air conditioner running + door and window opening → automatically remind to close the window"), and the rules can be updated remotely through the cloud, adapt to the personalized needs of users, and the communication module supports 10+ mainstream protocols such as Wi-Fi, ZigBee, Bluetooth, breaks the "brand barrier" of traditional systems, realizes plug and play and collaborative control between devices of different manufacturers, and builds an open and compatible smart home ecology.

[0052] The application forms a "collection-processing-control" localization closed loop by collecting the four core units of the local storage module, the data processing module, the communication module and the device collaborative control module, breaking through the hundred-millisecond delay bottleneck of the traditional cloud architecture by sinking the real-time data processing, device state monitoring and collaborative decision to the edge layer, realizing the millisecond-level response speed, and avoiding the risk of user privacy leakage by only processing the original data of the device at the edge gateway and uploading the desensitized summary data to the cloud. BRIEF DESCRIPTION OF DRAWINGS

[0053] Fig. 1 The block diagram of the ultra-low delay smart home collaborative system based on the edge computing gateway proposed by the application is shown in the figure.

[0054] Fig. 2 The flow chart of the ultra-low delay smart home collaborative method based on the edge computing gateway proposed by the application is shown in the figure. DETAILED DESCRIPTION

[0055] The application will be further described below in combination with specific embodiments.

[0056] EMBODIMENT

[0057] REFERENCE Figs. 1-2 The embodiment proposes an ultra-low delay smart home collaborative system based on an edge computing gateway, which includes smart home devices, an edge computing gateway and a cloud server, the edge computing gateway includes a local storage module, a data processing module, a communication module and a device collaborative control module.

[0058] The smart home devices are connected with the edge computing gateway and the device collaborative control module, and the communication module is connected with the cloud server.

[0059] The local storage module is used to store the configuration information, historical data and collaborative control rules of the smart home devices.

[0060] The stored configuration information includes the model, communication parameters and function parameters of the device, so that the edge computing gateway can correctly identify and manage each device.

[0061] The stored historical data is used for subsequent data analysis and system optimization;

[0062] The stored cooperative control rules define the cooperative work logic between different devices, including automatically triggering the air conditioner to turn on and adjust to the appropriate temperature when the temperature sensor detects that the indoor temperature is too high, and closing the curtains to block direct sunlight;

[0063] The data processing module is used for real-time processing and analysis of data sent by smart home devices, and the specific logic steps are as follows:

[0064] (1) First, the collected data is preprocessed, including data cleaning, filtering, normalization, removing noise and abnormal data, and improving the accuracy and reliability of the data;

[0065] (2) According to the preset algorithm and model, the processed data is analyzed to extract valuable information, including the change trend of the environment state and the running state of the device; for example, through the analysis of temperature, humidity and light data, it can be judged whether the current indoor environment is suitable for human habitation, and provide basis for the cooperative control of the device;

[0066] The communication module is responsible for communicating with smart home devices and cloud servers. When communicating with smart home devices, it supports multiple communication protocols, including Wi-Fi, Bluetooth, ZigBee, Z-Wave, to be compatible with different brands and types of devices, and to realize seamless access of devices. When communicating with the cloud server, it connects through wired or wireless broadband network to upload the key data processed by the edge computing gateway to the cloud server, and receives the update instructions and control strategies sent by the cloud server. For example, when the cloud server has new cooperative control rules or device firmware updates, the communication module transmits these information to the edge computing gateway;

[0067] The device cooperative control module is used to generate corresponding control instructions according to the results analyzed by the data processing module and the preset cooperative control rules, and send them to the smart home devices to realize the cooperative work between devices. The device cooperative control module is also used to monitor the state of the device and the change of the environment in real time, and dynamically adjust the control strategy to ensure the efficiency and real-time performance of the cooperative control of the device. For example, when detecting that the user is back home, a series of cooperative actions of the device are triggered through the state change of the smart door lock, such as automatically turning on the indoor light, adjusting the air conditioner temperature, playing welcome music, etc.

[0068] The cloud server as the remote management and data storage center of the whole system is mainly used for storing a large amount of historical data, user configuration information and system management data, and simultaneously used for providing remote monitoring, system upgrading, data analysis and personalized service functions. The user can remotely access the cloud server through a mobile phone APP or a Web interface, view the real-time state and historical data of the smart home device, configure and manage the device, and obtain personalized services and suggestions. In addition, the cloud server can also perform deep analysis and mining on the data uploaded by the edge computing gateway, and provide data support for the optimization and upgrading of the system. For example, by analyzing the use data of a large number of users, the use habits and preferences of the users are found, so that the cooperative control rules and service strategies of the device are optimized.

[0069] The embodiment also proposes an ultra-low delay smart home cooperation method based on the edge computing gateway, including the following steps:

[0070] S1: Device data acquisition: real-time acquisition of environmental data and device state data through various sensors in the smart home device, wherein the various sensors include temperature sensors, humidity sensors, light sensors and human infrared sensors, the environmental data includes temperature, humidity, light intensity and human presence, and the device state data includes the switch state, brightness and color of the lamp, the operation mode and working state of the household appliance. These data are the basis for the system to perform cooperative control. The device performs data acquisition according to the preset acquisition frequency and mode to ensure the real-time and accuracy of the data;

[0071] S2: Data transmission to the edge computing gateway: the collected data is transmitted to the edge computing gateway through the communication module built-in the smart home device, and in the data transmission process, the corresponding communication protocol and data format are adopted to ensure the reliable transmission of the data and avoid data loss or error;

[0072] S3: Data preprocessing of the edge computing gateway: after the edge computing gateway receives the data sent by the smart home device, data preprocessing is performed;

[0073] The data preprocessing includes data cleaning, data filtering and data normalization processing. In the data cleaning, the integrity and consistency of the data are checked to remove repeated, erroneous or invalid data. For example, for the data collected by the temperature sensor, if there is an obviously unreasonable value (such as temperature exceeding the physical range), it is regarded as invalid data and is removed. The specific steps of data cleaning are as follows:

[0074] S3011: Set the effective range of the data, wherein the effective range of the temperature sensor is -40℃ to 120℃;

[0075] S3012: Traverse the collected data, check if each data is within the valid range, if not, mark it as invalid data;

[0076] S3013: For the continuously collected data, if multiple consecutive invalid data appear, interpolation processing is performed according to the front and rear valid data to fill in the missing data;

[0077] Data filtering, mean filtering algorithm is used to denoise the data, the average value in the data window is calculated to smooth the data and reduce the influence of noise; The specific steps are as follows:

[0078] S3021: Set a data window size, n consecutive data points;

[0079] S3022: For each data point, calculate the average value of the previous and the next data point as the filtered value of the data point;

[0080] S3023: For the beginning and end of the data sequence, due to the lack of data points, the boundary extension method is used to copy the boundary data to supplement the data in the window;

[0081] Data normalization, the minimum-maximum normalization method is used to convert different types and ranges of data into a unified [0, 1] or [-1, 1] range, so as to facilitate subsequent data analysis and algorithm processing; The specific steps are as follows:

[0082] S3031: Calculate the minimum value min and the maximum value max of the data;

[0083] S3032: For each data point x, the normalized value is

[0084] S4: Data local analysis and decision: The preprocessed data enters the data processing module of the edge computing gateway for local analysis and decision, the data processing module analyzes the data according to the preset algorithm and model (such as rule-based reasoning, machine learning algorithm, etc.), extracts key information (such as the change trend of environmental state, the abnormal state of equipment, etc.); At the same time, combined with the cooperative control rules stored in the local storage module, it is judged whether the current state needs to trigger the cooperative control action of the equipment; For example, when the temperature sensor detects that the indoor temperature exceeds the preset threshold, the data processing module judges that the air conditioner needs to be started and adjusted to the appropriate temperature according to the cooperative control rules;

[0085] S5: Whether cloud support is needed: In the process of data local analysis and decision-making, it is judged whether cloud server support is needed. If the current analysis and decision-making can be completed locally in the edge computing gateway (such as simple device control rules, local decision-making with high real-time requirements, etc.), it is not necessary to upload to the cloud server. If more complex data analysis, global optimization or remote control are needed, the relevant data will be uploaded to the cloud server. For example, when trend analysis of long-term environmental data is needed to optimize the energy consumption of the entire smart home system, the data needs to be uploaded to the cloud server for in-depth analysis;

[0086] S6: Data upload to the cloud server: When cloud support is needed, the edge computing gateway uploads relevant data to the cloud server through the communication module using a secure communication protocol. The uploaded data includes pre-processed and preliminary analyzed data, as well as device status information;

[0087] S7: Cloud server processing and feedback: After receiving the data uploaded by the edge computing gateway, the cloud server performs further processing and analysis. The cloud server can use powerful computing resources and storage capabilities to perform deep mining and analysis on large amounts of data, generating globally optimized control strategies, device firmware update packages, and personalized service recommendations. After processing is completed, the cloud server will feedback the results to the edge computing gateway. For example, the cloud server analyzes the smart home data of multiple families and finds a more energy-saving device coordination control strategy, which is then sent to the edge computing gateway;

[0088] S8: Edge computing gateway receives feedback: The edge computing gateway receives the feedback results from the cloud server, whether it is a new control strategy, update instruction or other information, which will be stored in the local storage module and used for subsequent device coordination control. If cloud support is not needed, the edge computing gateway will directly enter the step of generating device coordination control instructions;

[0089] S9: Generate device coordination control instructions: The device coordination control module of the edge computing gateway generates specific device coordination control instructions based on the results of local analysis and decision-making or the feedback information from the cloud server, combined with the locally stored coordination control rules. The control instructions include control of individual devices (such as turning on lights, adjusting air conditioner temperature, etc.) and coordination control of multiple devices (such as turning on lights and air conditioner at the same time, closing curtains, etc.), ensuring that the coordination work between devices meets the user's needs and preset rules;

[0090] The coordination control uses a rule-based and event-driven coordination control algorithm;

[0091] Rule-based collaborative control is to predefine a series of collaborative control rules, which describe the collaborative actions of different devices under different environmental conditions. The rules can be defined as "when the light sensor detects that the light intensity is lower than the preset threshold and the human infrared sensor detects that someone exists, automatically turn on the lights in the living room and adjust to a comfortable brightness". The rule representation adopts the form of condition-action pair (If-Then), where the condition part is composed of the logical combination of multiple sensor data, and the action part specifies the device control instructions to be executed;

[0092] Event-driven collaborative control is triggered when a certain event occurs (such as the user sending control instructions through the mobile phone APP, the device state changing, etc.), corresponding collaborative control actions are triggered. The event can be an external event (such as user operation) or an internal event (such as sensor data reaching a preset threshold). Event-driven collaborative control can respond to changes in the system in real time, improving the flexibility and real-time performance of the system.

[0093] The specific steps of the device collaborative control algorithm are as follows:

[0094] S901: Real-time monitoring of data and event information collected by smart home devices;

[0095] S902: For each new data or event, determine whether the condition part of the rule is met according to the preset collaborative control rule;

[0096] S903: If the condition is met, generate the corresponding control instruction and send it to the relevant device to perform the collaborative action;

[0097] S904: Record the execution results of the collaborative control for subsequent audit and system optimization;

[0098] S10: Control instruction sending to smart home devices: the generated control instruction is sent to the corresponding smart home device through the communication module of the edge computing gateway. According to the communication protocol supported by the device, the appropriate transmission method is selected to ensure that the control instruction can be accurately and timely delivered to the device;

[0099] S11: Device executes corresponding operation: after receiving the control instruction, the smart home device executes the corresponding operation, including adjusting the brightness and color of the smart lamp according to the instruction, changing the operation mode and temperature setting of the air conditioner according to the instruction, opening or closing the smart curtain according to the instruction, etc. Through the execution of the device, the collaborative control of the smart home system is realized, meeting the needs of the user.

[0100] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. Ultra-low latency smart home collaborative system based on edge computing gateway, characterized by: It includes smart home devices, edge computing gateways and cloud servers. The edge computing gateways include local storage modules, data processing modules, communication modules and device collaborative control modules. The smart home device is connected to the edge computing gateway and the device collaborative control module, and the communication module is connected to the cloud server.

2. The ultra-low latency smart home collaborative system based on edge computing gateway according to claim 1 is characterized in that: The local storage module is used to store configuration information, historical data, and collaborative control rules of smart home devices; The stored configuration information includes the device model, communication parameters, and functional parameters, so that the edge computing gateway can correctly identify and manage each device; The stored historical data is used for subsequent data analysis and system optimization; The stored collaborative control rules define the collaborative working logic between different devices, including automatically triggering the air conditioner to turn on and adjust to the appropriate temperature when the temperature sensor detects that the indoor temperature is too high, and closing the curtains to block direct sunlight.

3. The ultra-low latency smart home collaborative system based on edge computing gateway according to claim 1, characterized in that: The data processing module is used to process and analyze the data sent by smart home devices in real time. The specific logical steps are as follows: (1) First, preprocess the collected data, including data cleaning, filtering, and normalization operations to remove noise and abnormal data and improve the accuracy and reliability of the data; (2) Analyze the processed data according to the preset algorithms and models to extract valuable information, including the changing trends of environmental conditions and the operating status of equipment.

4. The ultra-low latency smart home collaborative system based on edge computing gateway according to claim 1, characterized in that: The communication module is responsible for communicating with smart home devices and cloud servers. When communicating with smart home devices, it supports multiple communication protocols, including Wi-Fi, Bluetooth, ZigBee, and Z-Wave, to be compatible with devices of different brands and types and achieve seamless access to devices. When communicating with cloud servers, it uploads key data processed by the edge computing gateway to the cloud server through a wired or wireless broadband network connection, and at the same time receives update instructions and control strategies sent by the cloud server.

5. The ultra-low latency smart home collaborative system based on edge computing gateway according to claim 1, characterized in that: The device collaborative control module is used to generate corresponding control instructions based on the results of the data processing module analysis and the preset collaborative control rules, and send them to smart home devices to achieve collaborative work between devices. The device collaborative control module is also used to monitor the status of the device and environmental changes in real time, dynamically adjust the control strategy, and ensure that the collaborative control of the devices is efficient and real-time.

6. The ultra-low latency smart home collaborative system based on edge computing gateway according to claim 1, characterized in that: The cloud server serves as the remote management and data storage center of the entire system. It is mainly used to store large amounts of historical data, user configuration information and system management data, and is also used to provide remote monitoring, system upgrades, data analysis and personalized service functions. Users can remotely access the cloud server through a mobile phone APP or Web interface to view the real-time status and historical data of smart home devices, configure and manage devices, and obtain personalized services and suggestions. In addition, the cloud server can also conduct in-depth analysis and mining of data uploaded by the edge computing gateway to provide data support for system optimization and upgrades.

7. Ultra-low latency smart home collaboration method based on edge computing gateway, characterized in that: The following steps are involved: S1: Device data collection: Real-time collection of environmental data and device status data through various sensors in smart home devices; S2: Data is sent to the edge computing gateway: The collected data is sent to the edge computing gateway through the built-in communication module of the smart home device. During the data transmission process, the corresponding communication protocol and data format are adopted to ensure reliable data transmission and avoid data loss or errors; S3: Edge computing gateway data preprocessing: After receiving the data sent by the smart home device, the edge computing gateway performs data preprocessing; S4: Local data analysis and decision-making: The pre-processed data enters the data processing module of the edge computing gateway for local analysis and decision-making. The data processing module analyzes the data based on preset algorithms and models to extract key information. At the same time, it combines the collaborative control rules stored in the local storage module to determine whether the current status requires triggering a collaborative control action for the device. S5: Whether cloud support is needed: During local data analysis and decision-making, determine whether cloud server support is needed. If the current analysis and decision can be completed locally on the edge computing gateway, there is no need to upload the data to the cloud server. If more complex data analysis, global optimization, or remote control is required, the relevant data will be uploaded to the cloud server. S6: Data upload to cloud server: When cloud support is required, the edge computing gateway uploads relevant data to the cloud server through the communication module using a secure communication protocol. The uploaded data includes pre-processed and preliminarily analyzed data, as well as device status information. S7: Cloud server processing and feedback: After receiving the data uploaded by the edge computing gateway, the cloud server performs further processing and analysis. The cloud server can use its powerful computing resources and storage capabilities to conduct in-depth mining and analysis of large amounts of data, generating globally optimized control strategies, device firmware update packages, and personalized service recommendations. After processing is complete, the cloud server feeds the results back to the edge computing gateway. S8: The edge computing gateway receives feedback: The edge computing gateway receives feedback from the cloud server, whether it is a new control strategy, update instructions or other information, and stores it in the local storage module for subsequent device collaborative control. If cloud support is not required, the edge computing gateway directly enters the step of generating device collaborative control instructions; S9: Generate device collaborative control instructions: The device collaborative control module of the edge computing gateway generates specific device collaborative control instructions based on the results of local analysis and decision-making or information fed back by the cloud server, combined with locally stored collaborative control rules. The control instructions include control of a single device and collaborative control of multiple devices, ensuring that the collaborative work between devices meets user needs and preset rules. S10: Control instructions are sent to smart home devices: The generated control instructions are sent to the corresponding smart home devices through the communication module of the edge computing gateway. According to the communication protocol supported by the device, the appropriate transmission method is selected to ensure that the control instructions can reach the device accurately and promptly. S11: The device performs corresponding operations: After receiving the control command, the smart home device performs the corresponding operation, including smart lamps adjusting brightness and color according to the command, air conditioners changing operating modes and temperature settings according to the command, and smart curtains opening or closing according to the command. Through the execution of the device, the coordinated control of the smart home system is realized to meet the needs of users.

8. The ultra-low-latency smart home collaboration method based on edge computing gateway according to claim 7 is characterized in that: In S1, various sensors include temperature sensors, humidity sensors, light sensors and human infrared sensors. Environmental data include temperature, humidity, light intensity and human presence. The status data of the device itself includes the switch status, brightness, color of the lamp, and the operating mode and working status of the home appliance.

9. The ultra-low-latency smart home collaboration method based on edge computing gateway according to claim 7, characterized in that: In S3, data preprocessing includes data cleaning, data filtering, and data normalization. During data cleaning, data integrity and consistency are checked to remove duplicate, erroneous, or invalid data. The specific steps of data cleaning are as follows: S3011: Set the valid range of the data, where the valid range of the temperature sensor is -40℃ to 120℃; S3012: Traverse the collected data and check whether each data is within the valid range. If not, mark it as invalid data; S3013: For continuously collected data, if multiple consecutive invalid data appear, interpolation processing is performed based on the previous and next valid data to fill the missing data; Data filtering uses a mean filter algorithm to denoise the data. This smoothes the data by calculating the average value within the data window to reduce the impact of noise. The specific steps are as follows: S3021: Set a data window size, n consecutive data points; S3022: For each data point, calculate its previous After The average value of the data points is used as the filtered value of the data point; S3023: For the beginning and end of the data sequence, due to insufficient data points, the boundary extension method is used to copy the boundary data to supplement the data in the window; Data normalization uses the minimum-maximum normalization method to convert data of different types and ranges into a unified range of [0,1] or [-1,1] to facilitate subsequent data analysis and algorithm processing. The specific steps are as follows: S3031: Calculate the minimum value min and the maximum value max of the data; S3032: For each data point x, the normalized value is 10. The ultra-low-latency smart home collaboration method based on edge computing gateway according to claim 7, characterized in that: In said S9, the collaborative control adopts a rule-based and event-driven collaborative control algorithm; Rule-based collaborative control predefines a series of collaborative control rules that describe the coordinated actions of different devices under different environmental conditions. For example, a rule might be defined as, "When the light sensor detects that the light intensity is below a preset threshold and the human infrared sensor detects the presence of a person, automatically turn on the living room lights and adjust them to a comfortable brightness." Rules are expressed as condition-action pairs, where the condition consists of a logical combination of multiple sensor data points, and the action specifies the device control instructions to be executed. Event-driven collaborative control triggers corresponding collaborative control actions when an event occurs. The event can be an external event or an internal event. Event-driven collaborative control can respond to changes in the system in real time, improving the flexibility and real-time performance of the system. The specific steps of the device collaborative control algorithm are as follows: S901: Real-time monitoring of data and event information collected by smart home devices; S902: For each new data or event, determine whether the condition part of the rule is met according to the preset collaborative control rule; S903: If the conditions are met, a corresponding control instruction is generated and sent to the relevant device to perform the coordinated action; S904: Record the execution results of collaborative control for subsequent auditing and system optimization.

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