AI large model-based friendly smart home system for disabled people

Abstract

The invention discloses a handicapped friendly smart home system based on an AI large model, and the system comprises the following modules: a voice control module which is used for receiving and analyzing a voice instruction of a user through the voice recognition and natural language processing technology, and generating a corresponding control signal; the visual auxiliary module is used for collecting environment information through a camera and a sensor, performing object recognition, text reading and environment perception in combination with an AI large model, and providing real-time assistance for visually impaired people; the automatic task module is used for automatically executing control tasks, including light adjustment, temperature control, music playing and the like, of the household equipment according to the behavior mode of the user and a preset condition; the health monitoring module is used for collecting health data of a user through a sensor, performing data analysis in combination with an AI large model, and giving an alarm under an abnormal condition; and the barrier-free navigation module is used for providing an intelligent navigation function for the physically handicapped people through an AI technology and helping the physically handicapped people move freely at home.

Description

Technical Field

[0001] The present invention is applied to the field of artificial intelligence, specifically a smart home system friendly to disabled people based on an AI large model. Background Art

[0002] With the rapid development of artificial intelligence and Internet of Things technologies, smart home systems have gradually become an important part of modern family life. By integrating various sensors, controllers, and network communication technologies, smart home systems can achieve automated control and remote management of home appliances, greatly improving the convenience and comfort of users' lives. However, most existing smart home systems are designed for ordinary users and do not fully consider the special needs of disabled people, resulting in many difficulties for disabled people when using these systems.

[0003] Disabled people face various challenges in their daily lives, mainly including the following aspects:

[0004] Visually impaired people: Visually impaired people have difficulty obtaining visual information, such as reading text, recognizing objects, and navigating. Traditional smart home systems usually rely on visual interfaces for operation, and visually impaired people cannot effectively use these systems.

[0005] Hearing impaired people: Hearing impaired people have difficulties in communication and information acquisition, especially in scenarios that require voice interaction, such as answering calls and watching videos. Most existing smart home systems rely on voice control, and hearing impaired people have difficulty operating these systems effectively.

[0006] People with physical disabilities: People with physical disabilities have difficulties in moving and operating traditional home appliances, such as turning on and off lights, controlling household appliances, and adjusting curtains. Existing smart home systems usually require users to operate through touchscreens or physical buttons, and people with physical disabilities have difficulty completing these operations.

[0007] The elderly or disabled people: The elderly and disabled people need the support of assistive devices in their daily lives, such as health monitoring, emergency calling, and automated life services. The functions of existing smart home systems in health monitoring and emergency response are relatively limited and cannot meet the needs of these users.

[0008] Defects of the prior art:

[0009] Currently, there are some assistive devices and technologies for disabled people on the market, but most of these technologies have single functions and have not been deeply integrated with smart home systems, having the following defects:

[0010] Single function: Existing assistive devices are usually designed for a certain type of disabled people. For example, voice assistants are only applicable to hearing impaired people, and visual assistive devices are only applicable to visually impaired people, lacking comprehensive solutions.

[0011] Complex operation: Existing assistive devices usually require users to perform complex settings and operations, and disabled people face high learning costs when using these devices.

[0012] Lack of personalization: Existing smart home systems fail to be customized according to the personalized needs of disabled people and cannot provide targeted services and support.

[0013] Insufficient health monitoring: The functions of existing smart home systems in health monitoring and emergency response are relatively limited and cannot provide comprehensive health management and safety guarantees for disabled people. Summary of the Invention

[0014] The technical problem to be solved by the present invention is to provide a smart home system friendly to disabled people based on an AI large model in view of the deficiencies of the prior art.

[0015] To solve the above technical problem, a smart home system friendly to disabled people based on an AI large model of the present invention includes the following modules:

[0016] Voice control module: It is used to receive and parse the voice commands of users through voice recognition and natural language processing technologies and generate corresponding control signals;

[0017] Visual assistance module: It is used to collect environmental information through cameras and sensors, combine with an AI large model for object recognition, text reading and environmental perception, and provide real-time assistance for visually impaired people;

[0018] Automation task module: It is used to automatically execute the control tasks of home appliances according to the user's behavior patterns and preset conditions, including light adjustment, temperature control, music playback, etc.;

[0019] Health monitoring module: It is used to collect the health data of users through sensors, combine with an AI large model for data analysis, and issue an alarm in case of abnormalities;

[0020] Barrier-free navigation module: It is used to provide intelligent navigation functions for physically disabled people through AI technology to help them move freely at home;

[0021] Emotional support module: It is used to provide emotional support and mental health services for disabled people through an AI chatbot and emotional analysis technology.

[0022] As a possible implementation, further, the voice control module includes:

[0023] Voice collection unit: It is used to collect the voice commands of users;

[0024] A voice processing unit for noise reduction, echo cancellation, speech recognition, and natural language processing of voice commands;

[0025] A control logic unit for generating control signals based on the output of the voice processing unit;

[0026] A device interface unit for sending control signals to smart home devices;

[0027] A feedback unit for providing voice feedback to the user on the execution results of the device.

[0028] As a possible implementation, further, the visual assistance module includes:

[0029] A camera unit for collecting environmental images;

[0030] A sensor unit for collecting environmental data;

[0031] An object recognition unit for identifying objects in the environment through an AI large model;

[0032] A text reading unit for reading text information in the environment through an AI large model;

[0033] An environmental perception unit for generating an environmental description through an AI large model to assist visually impaired people in navigation and daily tasks.

[0034] As a possible implementation, further, the automated task module includes:

[0035] A requirement analysis unit for communicating with the user or caregiver to determine automated task requirements;

[0036] A task classification unit for classifying requirements into timed tasks, condition-triggered tasks, and behavior-based tasks;

[0037] A task configuration unit for configuring triggers and actuators to generate automated tasks;

[0038] A task execution unit for executing corresponding automated tasks according to trigger conditions.

[0039] As a possible implementation, further, the health monitoring module includes:

[0040] A data collection unit for collecting the user's health data through sensors, including heart rate, blood pressure, body temperature, and blood oxygen;

[0041] A data processing unit for preprocessing and analyzing the collected data;

[0042] A data transmission unit for transmitting the processed data to the cloud or a local server;

[0043] A user interface unit for displaying health data through a mobile phone APP or a web page and providing health advice and alerts.

[0044] As a possible implementation, further, the barrier-free navigation module includes:

[0045] An intelligent wheelchair control unit for achieving autonomous navigation of an intelligent wheelchair through AI technology;

[0046] A voice prompt unit for helping visually impaired people use household devices through voice prompts;

[0047] A visual assistance unit for providing visual assistance through a camera and sensors to help visually impaired people avoid obstacles.

[0048] As a possible implementation, further, the emotional support module includes:

[0049] An emotion analysis unit for analyzing the emotional state of a user through AI technology;

[0050] A chatbot unit for interacting with the user through an AI chatbot to provide emotional support and mental health services;

[0051] A feedback unit for generating corresponding feedback and suggestions according to the emotional state of the user.

[0052] As a possible implementation, further, the system further includes:

[0053] A user interaction optimization module: for optimizing speech recognition, control logic, and the user interface according to user feedback to improve the user experience;

[0054] A data security module: for encrypting and storing user data, strictly controlling data access permissions, and ensuring user privacy and security.

[0055] As a possible implementation, further, the system communicates with smart home devices through the Internet of Things protocol to achieve remote control and status monitoring of the devices.

[0056] As a possible implementation, further, the system supports multimodal interaction, combining speech, vision, and gesture recognition technologies to provide a more natural interaction method for disabled people.

[0057] The present invention adopts the above technical solutions and has the following beneficial effects:

[0058] Improve life convenience: Through voice control, multimodal interaction, and automated tasks, disabled people can easily operate household devices and reduce the burden of daily operations.

[0059] Enhance the ability to live independently: Visual assistance and environmental perception functions help visually impaired people complete daily tasks independently, and the intelligent navigation function helps people with physical disabilities move freely.

[0060] Improve life safety: Health monitoring and emergency response functions monitor the user's health status in real time, and automatically issue an alarm when abnormal to ensure the user's safety.

[0061] Improve mental health: The emotional support module provides emotion monitoring and psychological support through AI chatbots and emotion analysis technology, improving the user's mental health.

[0062] Reduce nursing costs: Automated tasks and remote monitoring functions reduce the workload of nursing staff and lower nursing costs.

[0063] Intelligence and personalization: The system analyzes user behavior through deep learning, provides personalized scenarios and automated services, and improves the user experience.

[0064] Scalability and compatibility: Support multiple IoT protocols, be compatible with existing smart home devices, and the modular design facilitates system expansion and upgrade. Detailed implementation manners

[0065] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below.

[0066] Embodiment 1

[0067] A smart home system friendly to disabled people based on an AI large model includes the following modules:

[0068] Voice control module: Used to receive and parse the user's voice commands through voice recognition and natural language processing technologies, and generate corresponding control signals;

[0069] Visual assistance module: Used to collect environmental information through cameras and sensors, combine with the AI large model for object recognition, text reading and environmental perception, and provide real-time assistance for visually impaired people;

[0070] Automated task module: Used to automatically execute control tasks of home appliances according to the user's behavior patterns and preset conditions, including light adjustment, temperature control, music playing, etc.;

[0071] Health monitoring module: Used to collect the user's health data through sensors, combine with the AI large model for data analysis, and issue an alarm in case of abnormality;

[0072] Barrier-free navigation module: Used to provide intelligent navigation functions for people with physical disabilities through AI technology to help them move freely at home;

[0073] Emotional Support Module: Used to provide emotional support and mental health services for disabled people through AI chatbots and emotional analysis technology.

[0074] The voice control module includes:

[0075] A voice acquisition unit for acquiring the user's voice commands;

[0076] A voice processing unit for noise reduction, echo cancellation, speech recognition, and natural language processing of the voice commands;

[0077] A control logic unit for generating control signals based on the output of the voice processing unit;

[0078] A device interface unit for sending the control signals to the smart home devices;

[0079] A feedback unit for providing voice feedback of the execution results of the devices to the user.

[0080] The visual assistance module includes:

[0081] A camera unit for acquiring environmental images;

[0082] A sensor unit for acquiring environmental data;

[0083] An object recognition unit for recognizing objects in the environment through an AI large model;

[0084] A text reading unit for reading text information in the environment through an AI large model;

[0085] An environmental perception unit for generating an environmental description through an AI large model to assist visually impaired people in navigation and daily tasks.

[0086] The automated task module includes:

[0087] A requirement analysis unit for communicating with the user or caregiver to determine the automated task requirements;

[0088] A task classification unit for classifying the requirements into timed tasks, condition-triggered tasks, and behavior-based tasks;

[0089] A task configuration unit for configuring the triggers and actuators to generate automated tasks;

[0090] A task execution unit for executing the corresponding automated tasks according to the trigger conditions.

[0091] The health monitoring module includes:

[0092] A data acquisition unit for acquiring the user's health data through sensors, including heart rate, blood pressure, body temperature, and blood oxygen;

[0093] A data processing unit for preprocessing and analyzing the collected data;

[0094] A data transmission unit for transmitting the processed data to the cloud or a local server;

[0095] A user interface unit for displaying health data through a mobile APP or a web page and providing health advice and alerts.

[0096] The barrier-free navigation module includes:

[0097] An intelligent wheelchair control unit for achieving autonomous navigation of an intelligent wheelchair through AI technology;

[0098] A voice prompt unit for helping visually impaired people use household devices through voice prompts;

[0099] A visual assistance unit for providing visual assistance through a camera and sensors to help visually impaired people avoid obstacles.

[0100] The emotional support module includes:

[0101] An emotion analysis unit for analyzing the emotional state of a user through AI technology;

[0102] A chatbot unit for interacting with the user through an AI chatbot to provide emotional support and mental health services;

[0103] A feedback unit for generating corresponding feedback and suggestions according to the emotional state of the user.

[0104] The system further includes:

[0105] A user interaction optimization module: for optimizing speech recognition, control logic, and the user interface according to user feedback to improve the user experience;

[0106] A data security module: for encrypting and storing user data, strictly controlling data access permissions, and ensuring user privacy and security.

[0107] The system communicates with smart home devices through an Internet of Things protocol to achieve remote control and status monitoring of the devices.

[0108] The system supports multi-modal interaction, combining speech, vision, and gesture recognition technologies to provide a more natural interaction method for disabled people.

[0109] Embodiment 2

[0110] The present invention proposes a variety of innovative application methods for disabled persons by combining the DeepSeek local AI large model with smart home technology. From voice control and visual assistance to personalized scenarios and health monitoring, these technologies not only enhance the convenience and independence of disabled persons' lives, but also provide them with emotional support and mental health protection. The specific technical solutions are as follows:

[0111] Implementation steps of AI large model smart home voice control

[0112] 1. System architecture design

[0113] The smart home voice control system usually includes the following modules:

[0114] Voice acquisition module: Responsible for collecting users' voice commands.

[0115] Voice processing module: Includes voice preprocessing (noise reduction, echo cancellation), speech recognition (converting speech to text), and natural language processing (understanding instructions).

[0116] Control logic module: Generates control signals according to the recognition results.

[0117] Device interface module: Sends control signals to smart home devices.

[0118] Feedback module: Feeds back the execution results or status of the device to the user through voice.

[0119] 2. Hardware selection and setup

[0120] Voice acquisition device: Select a suitable microphone module to ensure the clarity of voice acquisition.

[0121] Processing core: Use a development board supporting AI functions (such as ESP32, STM32, or Raspberry Pi).

[0122] Smart home devices: Configure home appliances supporting voice control (such as lights, air conditioners, curtains, etc.).

[0123] 3. Software development and configuration

[0124] Speech recognition engine: Select an open-source or commercial speech recognition engine (such as Mycroft AI, Fun ASR, etc.). Customize and optimize the speech recognition engine to adapt to the smart home scenario.

[0125] Natural language processing (NLP):

[0126] Use the DeepSeek AI large model to process the text after speech recognition and extract the user's intention.

[0127] Configure the NLP model to support control instructions for smart home devices.

[0128] Control logic development:

[0129] Based on the output of the NLP module, develop control logic to convert user instructions into device control signals.

[0130] Send the control signals to the smart home devices via Internet of Things protocols (such as MQTT, CoAP).

[0131] 4. System integration and testing

[0132] Module connection:

[0133] Connect the voice acquisition module, processing core, and smart home devices to the same network.

[0134] Ensure that the communication protocols between all modules are consistent.

[0135] System integration:

[0136] Initialize all modules in the main program and sequentially call the voice acquisition, processing, and device control functions in the main loop.

[0137] Configure voice assistants (such as Xiaoyi, Xiaoai Tongxue) as control entrances.

[0138] Function testing:

[0139] Test the accuracy and response speed of voice recognition.

[0140] Verify the reliability of device control and the feedback mechanism.

[0141] 5. User interaction and optimization

[0142] Voice feedback:

[0143] Use TTS (Text-to-Speech) technology to feedback the device status or operation results to the user. Optimize the naturalness and clarity of voice feedback.

[0144] User experience optimization:

[0145] Adjust voice recognition and control logic according to user feedback.

[0146] Provide a user interface (such as a mobile phone APP) for users to manually control or adjust settings.

[0147] Specific implementation cases

[0148] Take the smart home voice control system based on the ESP32 development board as an example:

[0149] Hardware setup:

[0150] Use the ESP32 development board as the core processing unit. Connect the microphone module for voice collection and the speaker module for voice feedback.

[0151] Software development:

[0152] Configure the ESP32 development environment using the Arduino IDE

[0153] Integrate open-source speech recognition libraries (such as Fun ASR) and AI large models (such as DeepSeek). Develop control logic to convert voice commands into device control signals.

[0154] System integration:

[0155] Connect the ESP32 development board to smart home devices (such as lights, curtains). Control the devices through voice commands and provide feedback on the operation results through the speaker.

[0156] Specific implementation steps for AI large model smart home automation tasks

[0157] 1. Determine the automation task requirements

[0158] Requirement analysis: Communicate with disabled persons or their caregivers to understand their daily needs and pain points, such as reminding to take medicine, scheduling, environmental control, etc.

[0159] Task classification: Classify the requirements into timed tasks (such as reminding to take medicine at a fixed time every day), condition-triggered tasks (such as automatically turning on the light when the environmental light dims), and behavior-based tasks (such as automatically adjusting the temperature when the user gets up).

[0160] 2. Select suitable AI platforms and tools

[0161] AI platform: Select the AI platform DeepSeek that supports smart home control

[0162] etc. These platforms provide powerful natural language processing and decision-making capabilities.

[0163] Automation tool: Select a tool that supports building automation processes, such as Zapier

[0164] 、Make or Bika.ai. These tools can connect smart home devices and AI platforms.

[0165] 3. Configure the automation tasks

[0166] Create automation nodes:

[0167] Create automation task nodes in the selected automation tool.

[0168] Select triggers (such as time, events, or conditions) and actuators (such as sending notifications, controlling devices).

[0169] Configure triggers:

[0170] Time trigger: Set up a scheduled task, such as "Remind to take medicine at 8 am every day".

[0171] Event trigger: Set up event-based tasks, such as "When the environmental sensor detects insufficient light, trigger the light-on operation".

[0172] Condition trigger: Set up condition-based tasks, such as "When the indoor temperature is below 20°C, automatically turn on the heating".

[0173] Configure actuators:

[0174] Device control: Send the task results to the device through the smart home API or Internet of Things protocols (such as MQTT).

[0175] Notification reminder: Send reminders through voice assistants (such as Xiaoyi, Xiaoai Tongxue) or mobile apps.

[0176] 4. Integrate AI models

[0177] Call the AI model:

[0178] Connect the automation tasks to the AI model using the API interface and call the DeepSeek API.

[0179] Configure the request parameters, such as API Key, request method, and request body.

[0180] Process the AI response:

[0181] Parse the data returned by the AI model and extract the key information.

[0182] Execute the corresponding automation actions based on the decision results of the AI model.

[0183] 5. Test and optimize

[0184] Function testing:

[0185] Test whether the trigger conditions of the automation tasks are accurate.

[0186] Verify whether the device control and notification reminder functions are normal.

[0187] Performance optimization:

[0188] Use caching to reduce repeated calls.

[0189] Configure an error retry mechanism to ensure the stability of the tasks.

[0190] User Experience Optimization:

[0191] Adjust the task logic according to user feedback.

[0192] Provide a manual review step to ensure the accuracy of critical tasks.

[0193] 6. Practical Example: Medication Reminder in Smart Home

[0194] Requirement: The user needs to be reminded to take medicine regularly every day.

[0195] Implementation:

[0196] Trigger: Set a time trigger at 8 am every day. AI Processing: Call an AI model to generate voice reminder content.

[0197] Executor: Send a reminder through the voice assistant: "Good morning, it's time to take your medicine!"

[0198] Feedback: After the user confirms, the system records the task completion status.

[0199] Health Monitoring System Design

[0200] The core of the health monitoring system is to transmit the health data collected by sensors to the cloud or local server through Internet of Things technology, and use AI technology for data analysis and processing. The system architecture usually includes the following modules:

[0201] Data Acquisition Layer: Collect health data such as heart rate, blood pressure, body temperature, and blood oxygen through sensors.

[0202] Data Processing Layer: Preprocess, filter, and analyze the collected data.

[0203] Communication Layer: Transmit data to the cloud or local device using Bluetooth, Wi-Fi, or mobile network.

[0204] User Interface Layer: Display health data through a mobile APP or web page, and provide health advice and alerts.

[0205] System Management Layer: Responsible for system initialization, task scheduling, error handling, and resource management.

[0206] 2. Hardware Selection

[0207] Hardware selection is the basis for implementing the health monitoring system, and appropriate sensors and main control chips need to be selected:

[0208] Main Control Chip: Select a high-performance and low-power microcontroller, such as the STM32F103 or STM32F4 series.

[0209] Sensor Module:

[0210] Heart rate sensor: such as AD8232 electrocardiogram module or MAX30102 blood oxygen sensor.

[0211] Blood pressure sensor: A photoplethysmogram (PPG) sensor can be used.

[0212] Temperature sensor: such as DS18B20 digital temperature sensor.

[0213] Fall detection sensor: Combines an acceleration sensor and a gyroscope to detect whether the user has fallen.

[0214] Communication module: Select a Bluetooth module (such as HC-05) or a Wi-Fi module (such as ESP8266).

[0215] Display module: An OLED screen can be selected to display health data.

[0216] 3. Function implementation

[0217] (1) Data acquisition

[0218] Use sensors to collect the user's health data and perform preliminary processing through the main control chip.

[0219] For example, the heart rate sensor collects heart rate data through a photoelectric sensor and transmits it to the main control chip through the I2C interface.

[0220] (2) Data transmission

[0221] Transmit the collected data to the mobile APP or cloud server through the Bluetooth or Wi-Fi module.

[0222] For example, use the HC-05 Bluetooth module to transmit data to the mobile APP.

[0223] (3) Data analysis

[0224] Use DeepSeek AI technology to analyze the collected data and extract trends and anomalies in health indicators.

[0225] For example, analyze heart rate data through machine learning algorithms to determine whether there are anomalies.

[0226] (4) User interface

[0227] Display health data on the mobile APP or web page, providing visual charts and health advice. For example, use the MPAndroidChart library to generate line charts of heart rate and blood pressure.

[0228] (5) Abnormal alarm

[0229] When abnormal health data is detected, the system automatically sends an alarm to the user or medical staff.

[0230] For example, when the heart rate exceeds the threshold, the user is pushed a notification or a text message reminder through the APP.

[0231] 4. Functional Testing

[0232] Data accuracy testing: Verify whether the data collected by the sensor is accurate.

[0233] Communication stability testing: Ensure the stability and reliability of data transmission.

[0234] Alarm function testing: Test the alarm function of the system in case of anomalies.

[0235] 5. Performance Optimization

[0236] Data processing optimization: Use efficient algorithms to reduce data processing time.

[0237] Communication optimization: Reduce unnecessary network requests through a caching mechanism.

[0238] User experience optimization: Design a simple and beautiful user interface to improve the response speed of the APP.

[0239] 6. User Experience Optimization

[0240] Personalized recommendations: Provide personalized health recommendations based on user data.

[0241] Data security: Encrypt and store user data and strictly control data access permissions.

[0242] Interaction design: Provide voice feedback or text prompts to help users better understand health data.

[0243] 7. Case Analysis

[0244] Taking a certain health monitoring APP as an example, this APP connects to a smart bracelet via Bluetooth, collects real-time data such as heart rate and blood pressure, and displays the health trends through charts. When an anomaly is detected, the APP automatically sends an alarm to the user and provides health recommendations. In addition, this system also supports the remote medical function, and users can conduct online consultations through the APP.

[0245] The above are the embodiments of the present invention. For those of ordinary skill in the art, according to the teachings of the present invention, any equal changes, modifications, substitutions, and variations made within the scope of the patent application of the present invention without departing from the principles and spirit of the present invention shall fall within the scope covered by the present invention.

Claims

1. A smart home system friendly to disabled people based on an AI large model, characterized in that, It includes the following modules: Voice control module: Used to receive and parse users' voice commands through speech recognition and natural language processing technologies, and generate corresponding control signals; Visual assistance module: Used to collect environmental information through cameras and sensors, combine with large AI models for object recognition, text reading, and environmental perception, and provide real-time assistance for visually impaired people; Automated task module: Used to automatically execute control tasks of home appliances according to users' behavior patterns and preset conditions, including light adjustment, temperature control, music playback, etc.; Health monitoring module: Used to collect users' health data through sensors, combine with large AI models for data analysis, and issue alarms in case of abnormalities; Barrier-free navigation module: Used to provide intelligent navigation functions for people with physical disabilities through AI technology to help them move freely at home; Emotional support module: Used to provide emotional support and mental health services for disabled people through AI chatbots and emotional analysis technologies.

2. The intelligent home system friendly to disabled persons based on the AI large model according to claim 1, wherein The voice control module includes: Voice collection unit: Used to collect users' voice commands; Voice processing unit: Used to perform noise reduction, echo cancellation, speech recognition, and natural language processing on voice commands; Control logic unit: Used to generate control signals according to the output of the voice processing unit; Device interface unit: Used to send control signals to smart home devices; Feedback unit: Used to feedback the execution results of the device to the user through voice.

3. The intelligent home system friendly to disabled persons based on the AI large model according to claim 1, wherein, The visual assistance module includes: Camera unit: Used to collect environmental images; Sensor unit: Used to collect environmental data; Object recognition unit: Used to recognize objects in the environment through large AI models; Text reading unit: Used to read text information in the environment through large AI models; Environmental perception unit: Used to generate environmental descriptions through large AI models to help visually impaired people navigate and perform daily tasks.

4. The intelligent home system friendly to disabled persons based on the AI large model according to claim 1, wherein, The automated task module includes: Requirement analysis unit: Used to communicate with users or caregivers to determine automated task requirements; Task classification unit: Used to classify requirements into timed tasks, condition-triggered tasks, and behavior-based tasks; Task configuration unit: Used to configure triggers and actuators to generate automated tasks; Task execution unit: Used to execute corresponding automated tasks according to trigger conditions.

5. The intelligent home system friendly to disabled persons based on the AI large model according to claim 1, wherein The health monitoring module includes: Data collection unit: Used to collect users' health data through sensors, including heart rate, blood pressure, body temperature, and blood oxygen; Data processing unit: Used to preprocess and analyze the collected data; Data transmission unit: Used to transmit the processed data to the cloud or local server; User interface unit: Used to display health data through mobile phone APPs or web pages and provide health advice and alarms.

6. The disabled-friendly smart home system based on the AI large model according to claim 1, wherein The barrier-free navigation module includes: Intelligent wheelchair control unit: Used to achieve autonomous navigation of intelligent wheelchairs through AI technology; Voice prompt unit: Used to help visually impaired people use home appliances through voice prompts; Visual assistance unit: Used to provide visual assistance through cameras and sensors to help visually impaired people avoid obstacles.

7. The intelligent home system friendly to disabled persons based on an AI large model according to claim 1, wherein The emotional support module includes: Emotional analysis unit: Used to analyze users' emotional states through AI technology; A chatbot unit for interacting with users through an AI chatbot to provide emotional support and mental health services; A feedback unit for generating corresponding feedback and suggestions based on the user's emotional state.

8. The intelligent home system friendly to disabled persons based on the AI large model according to claim 1, wherein The system further includes: A user interaction optimization module for optimizing speech recognition, control logic, and the user interface according to user feedback to enhance the user experience; A data security module for encrypting and storing user data and strictly controlling data access permissions to ensure user privacy and security.

9. The intelligent home system friendly to disabled persons based on the AI large model according to claim 1, wherein The system communicates with smart home devices through an Internet of Things protocol to achieve remote control and status monitoring of the devices.

10. The intelligent home system friendly to disabled persons based on the AI large model according to claim 1, characterized in that, The system supports multimodal interaction, combining speech, vision, and gesture recognition technologies to provide a more natural interaction method for disabled persons.