Intelligent furniture linkage control system and method based on multi-modal perception

Abstract

This invention provides a smart furniture linkage control system and method based on multimodal perception, belonging to the field of smart home control technology. The system includes a multimodal perception module, a central control module, a device linkage module, a communication adaptation module, a data storage module, and a user interaction module. The multimodal perception module collects environmental parameters, user behavior data, and device status data in real time. The central control module performs fusion analysis on multi-source data using a weighted fusion algorithm, and generates and issues control commands based on user-personalized settings and preset linkage rules. The device linkage module enables coordinated actions of smart furniture from multiple brands. The user interaction module provides convenient multi-mode interaction and rule customization functions. This invention overcomes the shortcomings of existing technologies, such as single perception dimension, rigid linkage logic, and poor device compatibility, achieving multi-scenario, high-precision intelligent linkage and improving living comfort and intelligence levels.

Description

Technical Field

[0001] This invention relates to the field of smart home control technology, specifically to a smart furniture linkage control system and method based on multimodal perception. Background Technology

[0002] With the rapid development of smart home technology, smart furniture has gradually entered ordinary households, providing users with a more convenient and comfortable living experience. Currently, most existing smart furniture control systems adopt single-modal sensing methods, such as control only through voice commands, control only through infrared sensors, or manual control only through a mobile app. These systems suffer from problems such as limited sensing dimensions, rigid linkage logic, and poor user interaction experience.

[0003] Specifically, single voice control is susceptible to environmental noise interference, suffers from low command recognition accuracy, and cannot respond to non-voice-based user needs; single infrared sensor control can only achieve simple trigger-based actions and cannot adaptively adjust according to user habits and environmental changes; while manual control requires active user operation, failing to achieve true "seamless intelligence." Furthermore, existing systems often employ independent communication protocols for different brands and types of smart furniture devices, resulting in poor compatibility and difficulty in achieving cross-device collaborative linkage. Moreover, linkage strategies are mostly fixed presets, unable to dynamically adjust according to personalized user needs and real-time scenarios, leading to low system intelligence and failing to meet diverse and personalized user living needs.

[0004] Furthermore, existing intelligent furniture control systems lack an effective fusion mechanism for processing sensory data. Multimodal data (such as voice, images, environmental parameters, and device status) are independent of each other, making collaborative data analysis and efficient utilization impossible, thus affecting the accuracy and timeliness of linkage control. Therefore, developing an intelligent furniture control system and method that can integrate multimodal sensory data, achieve device compatibility and collaboration, and possess adaptive linkage capabilities has become an urgent technical problem to be solved. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a smart furniture linkage control system and method based on multimodal perception. By integrating multi-dimensional perception data, it achieves precise linkage and adaptive adjustment of smart furniture, improving the user's living experience and solving the defects of existing smart furniture control systems, such as single perception dimension, rigid linkage logic, poor device compatibility, and low level of intelligence.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: an intelligent furniture linkage control system based on multimodal perception, the system comprising a multimodal perception module, a central control module, an equipment linkage module, a communication adaptation module, a data storage module, and a user interaction module; Multimodal sensing module: used to collect three types of multimodal sensing data: indoor environmental parameters, user behavior data, and equipment status data, and to preprocess the collected data to obtain standardized sensing data; the multimodal sensing module includes an environmental sensing unit, a user behavior sensing unit, and an equipment status sensing unit; Environmental sensing unit: Composed of temperature sensor, humidity sensor, light sensor, air quality sensor and noise sensor, used to collect indoor temperature, humidity, light intensity, PM2.5 concentration, noise decibel and other environmental parameters in real time, and uses a moving average algorithm to filter data fluctuations to ensure the stability of environmental data; The user behavior perception unit consists of a high-definition camera, a human infrared sensor, and a voice collector. It is used to collect user behavior data such as body movements, location information, and voice commands. The camera uses image recognition algorithms to identify user actions (such as getting up, sitting down, waving, etc.), the infrared sensor is used to locate the user's position and movement status, and the voice collector is used to collect user voice commands and perform noise reduction processing. The device status perception unit establishes a connection with each smart furniture device to collect real-time operating status data of the smart furniture, including device on / off status, operating parameters (such as air conditioner temperature, light brightness, curtain opening degree), fault information, etc., to achieve real-time monitoring of device status.

[0007] Central Control Module: As the core of the system, it establishes communication connections with the multimodal sensing module, device linkage module, communication adaptation module, data storage module, and user interaction module. It receives standardized sensing data output from the multimodal sensing module, performs data fusion analysis using a multimodal data fusion algorithm, and generates linkage control commands based on user-personalized settings and preset linkage rules. It is also responsible for the overall system scheduling, data processing, and anomaly handling. Employing an edge computing architecture, it achieves real-time local processing of sensing data, reducing cloud dependency and control latency. Device linkage module: Connected to the central control module, it receives linkage control commands from the central control module and controls each smart furniture device to perform corresponding actions, realizing coordinated linkage between devices; the smart furniture devices include smart lighting devices, smart air conditioners, smart curtains, smart sofas, smart beds, smart desks, etc. The device linkage module can adjust the operating parameters of the devices according to the control commands, or trigger the devices to start, stop, switch modes, etc., and supports the trigger-condition-action (TCA) model to achieve flexible automated response; Communication Adaptation Module: Used to achieve communication adaptation between various modules of the system and between the system and smart furniture devices. It supports multiple mainstream communication protocols such as Zigbee, Wi-Fi, and Matter. Through the protocol conversion unit, it connects devices with different protocols to the system, solving compatibility issues of different brands and types of devices, and realizing cross-platform and cross-device interconnection. At the same time, it adopts encrypted communication protocols to ensure the security and privacy of data transmission. Data storage module: Used to store raw sensing data collected by the multimodal sensing module, preprocessed standardized data, user-personalized setting data, preset linkage rule data, device operation log data, etc.; It adopts a combination of local storage and cloud backup. Local storage is used to save real-time data and core configuration to ensure the system can run normally when the network is offline, while cloud backup is used for data redundancy protection to prevent data loss, and at the same time provides data support for user behavior analysis and linkage rule optimization. User interaction module: Used to realize the interaction between users and the system, including functions such as command input, parameter setting, and status viewing; The user interaction module includes a local interaction unit (such as a touch control panel or voice interaction terminal) and a remote interaction unit (such as a mobile APP or mini program). Users can perform on-site operations through the local interaction unit, and can also remotely control smart furniture, set linkage rules, and view the operating status through the remote interaction unit. It supports "tap-to-connect" for quick network connection and device control, improving the convenience of interaction.

[0008] A method for intelligent furniture linkage control based on multimodal perception, the method being implemented based on the aforementioned intelligent furniture linkage control system based on multimodal perception, includes the following steps: S1. System Initialization: Start the smart furniture linkage control system, complete the self-test of each module, the communication adapter module automatically scans and identifies all smart furniture devices in the room, establishes communication connection between the system and each device, reads the user's personalized settings and preset linkage rules saved in the data storage module, and completes the system initialization configuration; at the same time, calibrate each sensing sensor to ensure the accuracy of the sensing data, load the Magma intelligent agent basic model, and complete the initialization of the device control logic; S2. Multimodal Data Acquisition and Preprocessing: The multimodal sensing module acquires indoor environmental parameters, user behavior data, and equipment status data in real time, and preprocesses the acquired raw data: The environmental parameter data is filtered and denoised to remove outliers and convert the data into a standardized format. The system analyzes user behavior data, identifies user actions using image recognition algorithms, converts voice commands into text commands using voice recognition algorithms, and determines user location using infrared positioning algorithms. Verify the equipment status data, confirm the validity of the data, extract key status parameters, form standardized sensing data, and send it to the central control module. S3. Multimodal Data Fusion Analysis: The central control module receives standardized sensing data and uses a multimodal data fusion algorithm to fuse and analyze environmental parameter data, user behavior data, and device status data. Combined with personalized user settings and preset linkage rules, it determines the current user needs and scenario status. If a user's voice command is detected, the system analyzes the user's actual needs by combining environmental parameters and device status. For example, if the user says "make the living room cooler", the system determines that the user's needs are to lower the air conditioner temperature or increase the fan speed, based on the current room temperature and the air conditioner's operating status. If user behavior is detected, such as the user getting up or sitting down, the user's intention is determined by combining the user's location and environmental parameters. For example, if the user gets up in the bedroom, the user may need to turn on the lights, based on the light intensity. If abnormal environmental parameters are detected, such as excessively high temperature or excessively low humidity, the system will determine whether to trigger device linkage based on the device status. For example, if the temperature is higher than the preset threshold and the air conditioner is off, the system will trigger the air conditioner to turn on. S4. Generation and Execution of Linkage Control Commands: Based on the fusion analysis results, the central control module generates corresponding linkage control commands and sends them to the device linkage module through the communication adapter module. After receiving the control commands, the device linkage module controls each smart furniture device to perform corresponding actions, realizing collaborative linkage between devices. For example, when the "Home Mode" is triggered, after the door lock is unlocked, the device linkage module controls the lights to turn on, the air conditioner to adjust to the preset temperature, the curtains to open, and the background music to play. All actions are executed in sequence to ensure the continuity of the user experience. S5. Data Feedback and Rule Optimization: The device linkage module feeds back the execution results of the smart furniture devices to the central control module. The central control module stores the execution results and real-time sensing data in the data storage module. At the same time, the central control module adaptively optimizes the preset linkage rules based on user behavior data and device operation data through machine learning algorithms, adjusting linkage parameters and triggering conditions to match user habits. For example, by analyzing long-term user data, it was found that users habitually set the air conditioner to sleep mode after 10 PM every night. The system automatically optimizes the linkage rules, automatically triggering the air conditioner's sleep mode at 10 PM every night without manual operation by the user. S6. Exception Handling: The central control module continuously monitors the operating status of each module in the system and the operating status of smart furniture devices. If module failures, device offline status, or abnormal data transmission are detected, an alarm prompt will be immediately sent to the user through the user interaction module, and emergency measures will be taken, such as disconnecting the connection of the faulty device and enabling the backup linkage plan, to ensure the stability of the overall system operation. For example, if it was originally planned to play music through a smart speaker and a speaker failure is detected, it will automatically switch to the TV audio system to complete the same function, avoiding the interruption of the linkage process.

[0009] Furthermore, the multi-modal data fusion algorithm uses a weighted fusion algorithm to assign different weights according to the reliability and importance of different perceptual data. For example, the weight of user voice commands is higher than that of environmental parameters to ensure the accuracy of control commands. At the same time, it combines the Set-of-Mark (SoM) and Trace-of-Mark (ToM) technologies of the Magma agent to achieve device operation area recognition and action trajectory prediction, enhancing the intelligence level of linkage control.

[0010] Furthermore, the user interaction module supports the customization of personalized linkage rules. Users can set linkage modes in different scenarios (such as waking up mode, sleeping mode, movie-watching mode, leaving-home mode) through the mobile APP or touch control panel, customize the linked devices, execution actions, and trigger conditions to meet the diverse needs of users. At the same time, it supports voice command learning, and users can record custom command phrases and map them to preset linkage action sequences.

[0011] Furthermore, the central control module has edge computing capabilities, which can locally process real-time perceptual data, reduce data transmission latency, and ensure the real-time nature of linkage control. At the same time, it supports cloud collaboration. Users can view historical data and remotely set linkage rules through the cloud platform to achieve local and cloud collaborative control. By adopting the core advantages of the Home Assistant local deployment architecture, the system's custom integration ability and cross-brand compatibility are enhanced.

[0012] Furthermore, the communication adaptation module supports the Matter protocol to achieve seamless docking of smart devices from different brands, solving the problem of poor compatibility of existing devices. At the same time, it uses encryption transmission technology to encrypt perceptual data and control commands to prevent data leakage and illegal control, protecting user privacy and system security. It supports rapid device network configuration and realizes automatic identification and activation of devices through JSON configuration fragments.

[0013] (III) Beneficial Effects The present invention provides a smart furniture linkage control system and method based on multi-modal perception, with the following beneficial effects: 1. Comprehensive perception dimensions and high control precision: This invention collects three types of data—environment, user behavior, and device status—through a multimodal perception module, achieving multi-dimensional and comprehensive perception. Combined with multimodal data fusion algorithms and Magma intelligent agent technology, it can accurately analyze user needs and scene status, avoiding the limitations of a single perception method, improving the accuracy and rationality of linkage control, and achieving true "seamless intelligence."

[0014] 2. Strong device compatibility and flexible linkage: The communication adapter module supports multiple mainstream communication protocols, especially the Matter protocol, which solves the compatibility problem of smart furniture devices of different brands and types, and realizes cross-device and cross-platform collaborative linkage; at the same time, it supports user-defined linkage rules. Combined with the TCA model, the linkage logic can be flexibly adjusted according to user needs and scenario changes to meet the personalized needs of users.

[0015] 3. High level of intelligence and excellent user experience: The central control module adopts an edge computing and cloud-based collaborative architecture to achieve real-time processing of sensing data and rapid issuance of linkage commands, reducing control latency; at the same time, it adaptively optimizes linkage rules through machine learning algorithms to fit user habits, eliminating the need for frequent manual operation and improving user convenience and comfort; it supports multiple interaction methods, including voice, touch, remote APP, and tap-to-interact, further enhancing the interactive experience.

[0016] 4. High system stability and good security: This invention has a comprehensive anomaly handling mechanism that can monitor the operating status of the system and equipment in real time, detect and handle faults in a timely manner, and ensure stable system operation; it adopts a combination of local storage and cloud backup to prevent data loss; at the same time, it uses encrypted communication technology to ensure the security of data transmission and device control and protect user privacy; and it introduces a data retransmission mechanism to reduce the communication packet loss rate and improve system stability.

[0017] 5. Energy-efficient and reduces operation and maintenance costs: Through environmental sensing and equipment status monitoring, the system can automatically shut down unnecessary equipment when no one is around, and dynamically adjust equipment operating parameters according to seasonal and environmental changes to reduce energy consumption; at the same time, the system has equipment fault alarm and status monitoring functions, which facilitates users to maintain equipment in a timely manner and reduce operation and maintenance costs. Attached Figure Description

[0018] Figure 1 This is a structural block diagram of the intelligent furniture linkage control system based on multimodal perception of the present invention; Figure 2 This is a flowchart illustrating the intelligent furniture linkage control method based on multimodal perception according to the present invention.

[0019] The corresponding numbers in the attached diagram are as follows: 1. Multimodal sensing module; 11. Environmental sensing unit; 12. User behavior sensing unit; 13. Equipment status sensing unit; 2. Central control module; 3. Equipment linkage module; 4. Communication adaptation module; 5. Data storage module; 6. User interaction module; 61. Local interaction unit; 62. Remote interaction unit. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: like Figure 1 As shown, an intelligent furniture linkage control system based on multimodal perception includes a multimodal perception module 1, a central control module 2, an equipment linkage module 3, a communication adaptation module 4, a data storage module 5, and a user interaction module 6.

[0022] The multimodal sensing module 1 includes an environmental sensing unit 11, a user behavior sensing unit 12, and a device status sensing unit 13. The environmental sensing unit 11 uses a DHT11 temperature and humidity sensor, a BH1750 light sensor, a PM2.5 sensor, and a noise sensor to collect indoor temperature, humidity, light intensity, PM2.5 concentration, and noise decibels, respectively. The sampling frequency is 1 time / minute, and a moving average algorithm is used to filter data fluctuations. The user behavior sensing unit 12 uses a high-definition camera, a human infrared sensor, and a voice collector. The camera is a 1080P high-definition camera that uses the YOLOv5 image recognition algorithm to identify user actions. The infrared sensor is an HC-SR501 human infrared sensor used to locate the user's position. The voice collector uses a microphone module to transmit voice data after noise reduction processing. The device status sensing unit 13 connects to devices such as smart lighting, air conditioners, curtains, sofas, and beds through a communication interface to collect data such as device on / off status and operating parameters.

[0023] The central control module 2 uses an STM32F407 microcontroller as the core controller, integrates an edge computing module, realizes local real-time processing of sensing data, uses a weighted fusion algorithm to perform fusion analysis of multimodal data, and combines the Magma intelligent agent model to realize motion trajectory prediction and device operation recognition. It is also responsible for system scheduling and anomaly handling. The communication adaptation module 4 supports three communication protocols: Zigbee, Wi-Fi, and Matter. It uses a CC2530 chip as a Zigbee gateway and an ESP8266 module to realize Wi-Fi communication. It realizes the conversion of different protocols through a protocol conversion unit and uses the AES encryption algorithm to ensure data transmission security. The data storage module 5 uses an SD card for local storage and also realizes cloud backup through the Alibaba Cloud platform, with a storage capacity of 32GB. The user interaction module 6 includes a touch control panel (using a TFTLCD touch screen) and a mobile APP (supporting Android and iOS systems), which supports the "tap-to-connect" quick network function to realize command input, parameter setting, and status viewing.

[0024] Example 2: like Figure 2 As shown, a smart furniture linkage control method based on multimodal perception, implemented based on the system in Example 1, includes the following steps: S1. System Initialization: Start the system, each module completes self-test, the communication adaptation module 4 automatically scans indoor smart furniture devices, establishes communication connections, reads user personalized settings (such as air conditioner preset temperature 26℃, light brightness 80%) and preset linkage rules (such as wake-up mode: curtains open → lights turn on → air conditioner starts) from the data storage module 5, and completes system initialization; at the same time, each sensor is calibrated, the Magma intelligent agent basic model is loaded, and the device control logic initialization is completed.

[0025] S2. Multimodal Data Acquisition and Preprocessing: Environmental sensing unit 11 collects real-time data on indoor temperature (28℃), humidity (50%), light intensity (300 lux), and PM2.5 concentration (35 μg / m³). 3 The noise level is 45 dB. After filtering and noise reduction, it is converted into standardized data. The user behavior perception unit 12 recognizes the user's getting up through the camera, the infrared sensor detects that the user is in the bedroom, and the voice collector does not collect any voice commands. The device status perception unit 13 collects that the curtains are closed, the lights are off, and the air conditioner is off. After verification, it is sent to the central control module 2.

[0026] S3. Multimodal data fusion analysis: The central control module 2 receives standardized sensing data, analyzes it through a weighted fusion algorithm, and combines it with the user's preset wake-up mode rules to determine that the user's current intention is to wake up, and the wake-up mode linkage needs to be triggered.

[0027] S4. Generation and execution of linkage control instructions: The central control module 2 generates a wake-up mode linkage control instruction and sends it to the device linkage module 3 through the communication adapter module 4. After receiving the instruction, the device linkage module 3 controls the curtains to open slowly (the opening degree is adjusted from 0% to 100%, which takes 30 seconds), controls the bedroom lights to turn on (the brightness is adjusted to 80%), and controls the air conditioner to start and adjust to the preset temperature of 26℃, so as to realize the coordinated linkage of devices.

[0028] S5. Data Feedback and Rule Optimization: The device linkage module 3 feeds back the execution results of the curtains, lights, and air conditioner to the central control module 2. The central control module 2 stores the execution results and real-time sensing data to the data storage module 5. At the same time, the system analyzes the user's wake-up time (such as a long-term wake-up time of 7:00) through machine learning algorithms, automatically optimizes the linkage rules, and automatically triggers the wake-up mode at 7:00 every day without the need for the user to manually trigger it.

[0029] S6. Anomaly Handling: If the system detects that the air conditioner is offline, it will immediately send an alarm notification to the user via the mobile APP, and at the same time stop the air conditioner linkage command, while maintaining the normal operation of the curtains and lights to ensure that the user experience is not seriously affected; after the air conditioner is reconnected to the network, the system will automatically send control commands to restore the air conditioner linkage operation.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart furniture linkage control system based on multimodal perception, characterized in that, include: The multimodal sensing module is used to collect three types of multimodal sensing data: indoor environmental parameters, user behavior data, and equipment status data. The collected data is preprocessed to obtain standardized sensing data. The multimodal sensing module includes an environmental sensing unit, a user behavior sensing unit, and an equipment status sensing unit. The central control module is communicatively connected to the multimodal sensing module, device linkage module, communication adaptation module, data storage module, and user interaction module. It is used to receive the standardized sensing data, perform fusion analysis through multimodal data fusion algorithm, generate linkage control commands in combination with user personalized settings and preset linkage rules, and is also responsible for overall system scheduling, data processing, and anomaly handling. The device linkage module is connected to the central control module and is used to receive the linkage control command and control each smart furniture device to perform corresponding actions to achieve coordinated linkage. The communication adaptation module is used to realize communication adaptation between various modules of the system and between the system and smart furniture devices. It supports multiple mainstream communication protocols such as Zigbee, WiFi, and Matter. Through the protocol conversion unit, it connects devices with different protocols to the system and ensures data transmission security. The data storage module is used to store the raw sensing data, standardized data, user personalized setting data, preset linkage rule data and device operation log data, and adopts a combination of local storage and cloud backup. The user interaction module is used to enable interaction between users and the system. It includes a local interaction unit and a remote interaction unit, and supports command input, parameter setting, status viewing, and "tap-to-connect" quick network connection and device control.

2. The intelligent furniture linkage control system based on multimodal perception according to claim 1, characterized in that: The environmental sensing unit consists of a temperature sensor, a humidity sensor, a light sensor, an air quality sensor, and a noise sensor. It collects indoor temperature, humidity, light intensity, PM2.5 concentration, and noise levels in real time, and uses a moving average algorithm to filter out data fluctuations. The user behavior sensing unit consists of a high-definition camera, a human infrared sensor, and a voice collector. It identifies user actions through image recognition algorithms, locates the user's position and movement status through infrared sensors, and collects and reduces noise from the user's voice commands. The device status sensing unit is connected to each smart furniture device and collects real-time operating status data of the smart furniture, including device on / off status, operating parameters, and fault information.

3. The intelligent furniture linkage control system based on multimodal perception according to claim 1, characterized in that: The central control module adopts an edge computing architecture to realize local real-time processing of sensing data, reducing cloud dependence and control latency; the multimodal data fusion algorithm adopts a weighted fusion algorithm, which allocates weights according to the reliability and importance of different sensing data, and combines the Set-of-Mark and Trace-of-Mark technologies of Magma agents to realize device operation area recognition and motion trajectory prediction.

4. The intelligent furniture linkage control system based on multimodal perception according to claim 1, characterized in that: The device linkage module supports a trigger-condition-action model, adjusting device operating parameters or triggering device start / stop and mode switching actions according to control commands; the smart furniture devices include smart lighting devices, smart air conditioners, smart curtains, smart sofas, smart beds, and smart desks.

5. The intelligent furniture linkage control system based on multimodal perception according to claim 1, characterized in that: The communication adaptation module adopts an encrypted communication protocol, uses the AES encryption algorithm to ensure the security and privacy of data transmission, and uses JSON configuration fragments to enable devices to quickly configure the network and automatically identify and activate.

6. The intelligent furniture linkage control system based on multimodal perception according to claim 1, characterized in that: The user interaction module supports personalized linkage rules customization. Users can set up linkage for scenarios such as wake-up mode, sleep mode, movie-watching mode, and away-from-home mode, and customize the linkage devices, execution actions, and trigger conditions. It also supports voice command learning, allowing users to record custom command phrases and map them to linkage action sequences.

7. A method for intelligent furniture linkage control based on multimodal perception, applied to the system described in any one of claims 1-6, characterized in that, Includes the following steps: S1. System Initialization: Start the system, complete the self-test of each module, the communication adaptation module automatically scans and identifies indoor smart furniture devices, establishes communication connections, reads the user's personalized settings and preset linkage rules in the data storage module, completes the system initialization configuration, calibrates each sensing sensor, and loads the Magma agent basic model; S2. Multimodal data acquisition and preprocessing: The multimodal sensing module acquires indoor environmental parameters, user behavior data, and equipment status data in real time. It performs filtering and noise reduction, outlier removal, and format conversion on the raw data, parses the user behavior data, verifies the equipment status data, and forms standardized sensing data to be sent to the central control module. S3. Multimodal data fusion analysis: The central control module receives standardized sensing data, performs fusion analysis through a weighted fusion algorithm, and combines user-personalized settings and preset linkage rules to determine user needs and scene status; S4. Generation and execution of linkage control commands: The central control module generates linkage control commands based on the fusion analysis results and sends them to the device linkage module through the communication adapter module. The device linkage module controls each smart furniture device to perform corresponding actions according to the coordinated timing sequence. S5. Data Feedback and Rule Optimization: The device linkage module feeds back the device execution results to the central control module. The central control module stores the execution results and real-time sensing data to the data storage module. The preset linkage rules are adaptively optimized through machine learning algorithms to fit user habits. S6. Anomaly Handling: The central control module monitors the operating status of each module of the system and smart furniture devices in real time. If a module failure, device offline or abnormal data transmission is detected, an alarm prompt is sent to the user through the user interaction module, and emergency measures such as disconnecting the faulty device and activating the backup linkage scheme are taken to ensure the overall stable operation of the system.

8. The intelligent furniture linkage control system and method based on multimodal perception according to claim 7, characterized in that: In S3, if a user's voice command is detected, the user's actual needs are analyzed by combining environmental parameters and device status; if a user's action is detected, the user's intention is determined by combining the user's location and environmental parameters; if an abnormal environmental parameter is detected, the device status is used to determine whether to trigger device linkage.

9. The method according to claim 7, characterized in that: In S4, the linkage control commands are executed in a coordinated manner according to a preset timing sequence to achieve seamless linkage of scenarios such as "home mode" and "wake-up mode"; in S6, if the originally planned controlled equipment fails, the system automatically switches to a backup device with equivalent function to complete the same action, avoiding interruption of the linkage process.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent furniture linkage control method based on multimodal perception as described in any one of claims 7-9.

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