Natural language driven bluetooth device interaction method and system based on ai large model mobile app

Abstract

The application relates to a natural language driven Bluetooth device interaction method and system based on an AI large model mobile APP in the field of Bluetooth device interaction, and the interaction method comprises the following steps: Bluetooth pairing and connection, natural language interaction and analysis, mapping of analysis results into preset codes, Bluetooth instruction issuing, terminal analysis and execution, data storage and optimization, the application extends the interaction capability of a mobile phone AI large model to a low-power-consumption and low-cost intelligent terminal through Bluetooth direct connection, and gives the AI emotional and personified physical feedback 'body'; the user does not need to remember instructions, and can trigger head nodding, light and other responses through natural conversation; the interaction has zero threshold, voice and text data are completely processed locally, are not transmitted to the cloud, and are safe and worry-free in terms of privacy; interaction history is stored locally, the AI understands the user better and better, and model manufacturers can only use a simple interface to expand the hardware ecology of the App, realize a soft and hard integrated closed loop, and achieve the effects of controllable cost, low power consumption and rapid reaction of the intelligent terminal.

Description

Technical Field

[0001] This invention relates to the field of Bluetooth device interaction, specifically to a natural language-driven Bluetooth device interaction method and system based on an AI large-scale model mobile APP. Background Technology

[0002] With the nation prioritizing artificial intelligence (AI) development, AI technology has permeated daily life. Currently, major domestic and international internet companies and technology firms have launched their own large-scale AI models and accompanying mobile apps, including:

[0003] The group of major internet companies includes: ByteDance's "Doubao," Alibaba's "Qwen," Tencent's "Hunyuan," Baidu's "Wenxin Yiyan," and iFlytek's "Xunfei Xinghuo." The group of emerging technology companies includes: DeepSeek (known for its open-source nature and high cost-effectiveness), Kimi (focusing on ultra-long contextual understanding), Zhipu Qingyan (GLM series) from Zhipu Huazhang, Baichuan Intelligence's "Baichuan," MiniMax, Lingyi Wanwu, and Jieyue Xingchen. These AI large-scale model mobile apps primarily interact with users through virtual voice or text interactions on smartphones, tablets, and other mobile devices.

[0004] However, existing technologies have the following significant shortcomings:

[0005] First, the interaction is simplistic and lacks physical feedback: all current mainstream AI large-scale model mobile apps exist as pure software, with interaction limited to screen text, voice output, or static emoticons. Users cannot obtain concrete, anthropomorphic physical action feedback from the AI ​​(such as nodding or shaking its head). The powerful AI brain lacks a physically interactive "body" to coordinate with it.

[0006] Secondly, existing smart hardware has many shortcomings: although there are smart speakers, smart ornaments and other products on the market, they are essentially independent smart devices. They must have built-in WiFi modules and complex smart processing chips (such as SoC, high-performance MCU, etc.) to achieve independent networking and operation. This results in many problems such as high hardware costs, high power consumption, high communication latency (requiring cloud relay), data privacy risks, and built-in AI capabilities that are far weaker than those of large models on mobile devices.

[0007] Secondly, there is a lack of natural language-based hardware interaction methods: In existing technologies, to trigger actions on external devices, users must use a separate control interface, click buttons, input specific command words, or navigate to a dedicated page, which cannot achieve a seamless interaction that allows devices to respond naturally, just like chatting with a person.

[0008] Finally, existing Bluetooth actuators are complex in structure and expensive: most Bluetooth actuators on the market use a dual-chip solution of "Bluetooth module + independent MCU" or a complex general-purpose Bluetooth SoC responsible for protocol parsing, command decoding, action scheduling, and other calculations. This results in high device cost, high power consumption, and long response links, making it difficult to meet the demands of consumer products for low cost, low power consumption, and fast response. Therefore, how to achieve lightweight Bluetooth device interaction driven by natural language has become an urgent technical problem to be solved. Summary of the Invention

[0009] The purpose of this invention is to address the above-mentioned shortcomings and provide a natural language-driven Bluetooth device interaction method and system based on a large AI model mobile APP, so as to solve the technical problem in the background art of how to realize the physical linkage of various AI models through Bluetooth interaction and reduce device cost and power consumption.

[0010] The objective of this invention is achieved through the following means:

[0011] A natural language-driven Bluetooth device interaction method based on an AI large-scale model mobile app, the interaction method including:

[0012] Step S101, Bluetooth pairing and connection: The mobile terminal launches the AI ​​big model mobile APP and turns on the Bluetooth function, scans for nearby smart control terminals, filters target devices according to device identifiers, automatically initiates pairing authentication and establishes a BLE low power Bluetooth direct link. After the first pairing, the device capability information is stored locally in the mobile APP, and can be automatically reconnected in the future.

[0013] Step S102, Natural Language Interaction and Parsing: Users engage in natural dialogue with the large model within the APP via voice or text. The APP calls the large model's interface or SDK to perform full-dimensional parsing of the input content.

[0014] Step S103: The parsing result is mapped to a preset code. According to the parsing result, the APP converts the user's semantics, emotions and intentions into a preset code of ultra-short bytes according to the preset mapping rules. The preset code corresponds to the terminal hardware action, and the mapping rules support user-defined configuration.

[0015] Step S104: Bluetooth command is sent. The APP sends the preset code to the smart control terminal in the form of a command frame through the BLE Bluetooth direct connection communication link.

[0016] Step S105, Terminal parsing and execution: The intelligent control terminal receives the instruction frame and parses the preset code using the internal highly integrated Bluetooth SoC or the simplified architecture of Bluetooth module + low power MCU, directly driving the motor and LED module to perform the corresponding actions. The terminal does not store any user information.

[0017] Step S106, Data retention and optimization: All interactive text, voice and execution records are retained locally in the mobile APP for large model behavior analysis and continuous optimization.

[0018] Specifically, the drive motor is a micro DC motor or a coreless motor, which, together with a reduction gear set, achieves a nodding / shaking oscillation angle of ±15°~±45°.

[0019] Furthermore, after the initial pairing is completed in step S101, the APP records the device's action code and / or action type hardware capability information and stores it in the local storage of the mobile APP's terminal. In subsequent interactions, the APP can automatically identify and reconnect without requiring the user to repeat the operation.

[0020] Specifically, the intelligent control terminal adopts a highly integrated Bluetooth SoC single-chip solution, which integrates Bluetooth radio frequency and MCU core; or adopts a dual-chip minimalist architecture of BLE module + independent low-power MCU; after the MCU parses the preset code, it directly outputs PWM / level signal to drive H-bridge motor drive circuit and RGB LED drive circuit without performing semantic or emotion calculation.

[0021] Furthermore, step S102 includes:

[0022] Semantic dimension parsing: Based on the natural language understanding capabilities of the large model, the intent classification and semantic tendency analysis of user statements are performed to identify affirmative / acceptance, negative / questioning or neutral semantics, and the parsing output is semantic labels and their confidence scores;

[0023] Emotion Dimension Analysis: Based on the sentiment analysis capabilities of the large model, the emotional features in the user's statements are identified and quantified. The emotion categories include at least one of joy, anger, sadness, calmness, anxiety, depression, and excitement. The analysis outputs the corresponding emotion labels and their intensity values.

[0024] Intent dimension analysis: Based on the dialogue context and user statement characteristics, identify the user's deep intent for this interaction. The intent categories include at least one of the following: consultation, companionship, entertainment, stress relief, seeking help, confirmation, and venting.

[0025] Furthermore, in step S103, the mapping rules allow users to customize and save the correspondence between preset codes and actions / emotions through the mobile APP interface.

[0026] Furthermore, step S104 includes: the Bluetooth communication module of the mobile APP is responsible for the intermediary transmission of data, without encapsulating or performing complex data processing; semantic parsing and / or instruction generation and / or instruction issuance are completed locally through the mobile APP terminal to achieve synchronous response between chat semantics and motor actions.

[0027] Furthermore, step S105 includes: after the intelligent control terminal receives the simplified instruction frame through the built-in Bluetooth communication module, its internal Bluetooth SoC or Bluetooth module and low-power MCU perform simple parsing of the preset code, and directly drive the corresponding motor drive circuit and / or LED drive circuit to complete the nodding, shaking and lighting effects. The intelligent control terminal adopts a simplified hardware architecture, only performs instruction parsing and hardware driving, does not store any user information, has a fast response speed and low power consumption.

[0028] Furthermore, the intelligent control terminal adopts a highly integrated Bluetooth SoC single-chip solution, which integrates Bluetooth radio frequency and MCU core on a single chip, or adopts a dual-chip minimalist architecture of Bluetooth module and independent low-power MCU.

[0029] Furthermore, the mobile app sends out extremely simple instruction frames via the BLE link that do not contain the user's original voice / text content, only include preset action codes and necessary check bits, and the frame length does not exceed four bytes.

[0030] Furthermore, the original voice, text, and preset code execution records generated during the interaction process are all stored locally on the mobile terminal and used to train a user-specific personalized large model. The intelligent control terminal does not retain any historical data.

[0031] A natural language-driven Bluetooth device interaction system is provided for executing a natural language-driven Bluetooth device interaction method based on an AI large model mobile APP, including a mobile APP terminal and a smart control terminal.

[0032] The mobile APP terminal is used to run the built-in AI big model on smartphones or tablets. The mobile APP terminal consists of a human-computer interaction interface module, a semantic emotion analysis module, a preset code mapping module, a Bluetooth communication management module, and a data retention and learning module.

[0033] The intelligent control terminal adopts a minimalist hardware architecture and does not store user information. The intelligent control terminal consists of a BLE Bluetooth communication module, an integrated MCU core, a motor drive module, an LED drive module, and a power supply module.

[0034] Furthermore, the human-computer interaction interface module is used to receive user voice or text input;

[0035] The semantic sentiment parsing module is used to call the large model API / SDK to perform semantic, sentiment, and intent parsing;

[0036] The preset code mapping module converts the parsing results into preset codes according to the processing rules;

[0037] The Bluetooth communication management module is used for device scanning, pairing, connection, and command issuance.

[0038] The data retention and learning module stores interactive data locally on the mobile terminal for model optimization.

[0039] Furthermore, the BLE Bluetooth communication module is used to receive command frames;

[0040] The integrated MCU core is used to parse preset code and output control signals;

[0041] The motor drive module is used to drive the nodding / shaking motor to perform actions;

[0042] The LED driver module is used to drive RGB LEDs to achieve lighting effects;

[0043] The power module supplies power to each module.

[0044] The beneficial effects of this invention are: extending the virtual interaction capabilities of mainstream AI large-scale model mobile apps to intelligent control terminals in the physical world via Bluetooth direct connection technology, giving AI a "body" capable of providing emotional and anthropomorphic physical feedback. Users do not need to memorize any command words or click control buttons; they only need to speak naturally, as if chatting with a friend, and the device can autonomously respond with nodding, shaking its head, and light feedback based on the attitude and emotions in the conversation, resulting in zero learning cost for the interaction.

[0045] The intelligent control terminal adopts a highly integrated Bluetooth SoC solution, or a simplified architecture of Bluetooth module + low-power MCU. Compared with traditional independent intelligent devices (which require WiFi module + high-performance SoC), the overall power consumption is lower by adopting low-power Bluetooth technology and a simplified hardware architecture, which significantly reduces the hardware manufacturing cost and overall power consumption of the terminal, and facilitates the large-scale popularization of products and long battery life.

[0046] All user voice and text interaction data is processed locally on the mobile app. The smart control terminal does not store any user information and does not need to be uploaded to any third-party servers, fundamentally eliminating the risk of data leakage during transmission and storage, and ensuring user privacy and security. It directly utilizes the powerful computing capabilities and continuously iterating AI models on the mobile device, and its capabilities far exceed those of any independent smart hardware.

[0047] Meanwhile, the interaction history is stored locally on the mobile app, enabling AI to continuously learn user habits and achieve personalized growth that "gets smarter with use." AI model companies do not need to modify the core architecture of their apps; they only need to add a minimalist pre-defined code output interface to provide physical interactive peripheral products for their massive user base, achieving a closed-loop ecosystem that combines software and hardware. Attached Figure Description

[0048] Figure 1 This is a flowchart of the natural language-driven Bluetooth device interaction method based on an AI large model mobile APP in this embodiment; Detailed Implementation

[0049] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0050] To make the technical problem to be solved, the technical solution and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0051] It should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this scheme and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0052] In this embodiment, refer to Figure 1 The core design concept of this invention, specifically implemented in this way, is as follows: the AI ​​large-scale model mobile APP completes all complex analysis and decision-making, while the intelligent control terminal is extremely simplified. The intelligent control terminal adopts a highly integrated Bluetooth SoC (a single chip integrating Bluetooth radio frequency and MCU core) or a simplified architecture of "Bluetooth module + low-power MCU". By receiving simplified preset codes (such as 001, 002, etc.) from the mobile APP, and after simple analysis by the internal MCU core, it directly drives the motor and lights, thereby achieving the technical effects of controllable cost, low power consumption, and rapid response. The intelligent control terminal does not store any form of user information; all user data is only stored locally on the mobile APP. The AI ​​large-scale model mobile APP can be based on the powerful and continuously iterating AI large-scale model on mobile phones or tablets, and its capabilities far exceed any smart hardware set independently on the intelligent control terminal.

[0053] A natural language-driven Bluetooth device interaction method based on an AI large-scale model mobile app, the interaction method including:

[0054] Step S101: Bluetooth pairing and connection. The mobile terminal launches the AI ​​large-scale model mobile APP (e.g., ByteDance's "Doubao," Alibaba's "Tongyi Qianwen," Tencent's "Hunyuan," Baidu's "Wenxin Yiyan," or emerging technology companies like DeepSeek) and enables Bluetooth. It scans for nearby smart control terminals, filters target devices based on device identifiers, automatically initiates pairing authentication, and establishes a BLE low-power Bluetooth direct connection link. After the initial pairing, the device capability information is stored locally in the mobile APP, allowing for automatic reconnection in subsequent interactions. Specifically, after the initial pairing in step S101, the device capability information includes at least the device identifier, the set of supported action types, and the corresponding instruction code mapping relationship. The APP records the device's action code and / or action type hardware capability information and stores it in the local storage of the mobile APP's terminal. In subsequent interactions, the APP can automatically identify and reconnect, eliminating the need for repeated user operations.

[0055] Step S102, Natural Language Interaction and Parsing: Users engage in natural dialogue with the large model within the APP via voice or text. The APP calls the large model's interface or SDK to perform full-dimensional parsing of the input content.

[0056] Semantic dimension parsing: Based on the natural language understanding capabilities of the large model, the intent classification and semantic tendency analysis of user statements are performed to identify affirmative / agreement, negative / questioning or neutral semantics, and the parsing output is semantic labels and their confidence scores; specifically, affirmative / agreement can be (e.g., "right", "yes", "correct", "I think it's okay", "good idea", etc.) and negative / questioning can be (e.g., "incorrect", "no", "I disagree", "this is not good", "I have doubts", etc.), as well as neutral or irrelevant semantics.

[0057] Emotion Dimension Analysis: Based on the sentiment analysis capabilities of the large model, the emotional features in the user's statements are identified and quantified. The emotion categories include at least one of joy, anger, sadness, calmness, anxiety, depression, and excitement. The analysis outputs the corresponding emotion label and its intensity value.

[0058] Intent dimension analysis: Based on the dialogue context and user statement characteristics, identify the user's deep intent for this interaction. The intent categories include at least one of the following: consultation, companionship, entertainment, stress relief, seeking help, confirmation, and venting.

[0059] The above parsing process takes place entirely in natural dialogue, without requiring any explicit control commands from the user, achieving a natural interactive experience with "zero learning cost".

[0060] It should be noted that the aforementioned semantic sentiment analysis function is implemented by large AI model vendors (such as ByteDance's "Doubao" and DeepSeek) on their apps. This invention obtains the analysis results by calling their standard interfaces or integrating their SDKs, and maps the results into hardware control commands.

[0061] The mobile app sends a minimal command frame via the BLE link that does not contain the user's original voice / text content. It only contains the action preset code and necessary check bits, and the frame length does not exceed four bytes.

[0062] The original voice, text, and preset code execution records generated during the interaction process are stored locally on the mobile terminal and used to train a user-specific personalized large model. The intelligent control terminal does not retain any historical data.

[0063] Step S103: The parsing result is mapped to a preset code. According to the parsing result, the APP converts the user's semantics, emotions and intentions into a preset code of ultra-short bytes according to the preset mapping rules. The preset code corresponds to the terminal hardware action. The mapping rules support user-defined configuration. The mapping rules allow users to customize and save the correspondence between the preset code and the action / emotion through the mobile APP interface.

[0064] Specifically, the preset code is an ultra-short byte instruction (such as 1 byte or 2 bytes), which directly corresponds to the hardware action of the intelligent control terminal. The intelligent control terminal only needs to parse it to execute it.

[0065] As an example: the preset code mapping rules are as follows (taking a 1-byte instruction as an example):

[0066] Preset code Corresponding actions / effects Triggering conditions 001 Nod once The semantic meaning is "affirmation / acceptance". 002 Nod twice The meaning is "strongly affirm / strongly agree". 003 Shake head once The semantic meaning is "negation / questioning". 004 Shake head twice The meaning is "strongly negative / resolutely opposed". 011 Warm-colored breathing light The emotion is "joy / excitement". 012 Cool Gradient Light The mood is "sad / depressed". 013 Red flashing light The emotion is "anger / anxiety". 014 White constant light The emotion is "calm". 021 Nodding + warm light Affirmation + Positive Emotions (combined) 022 Oscillating light + cool-colored light Negation + negative emotions (compound) 000 Standby / No action Neutral semantics or low confidence

[0067] The code above is just an example; more code can be added depending on the device's capabilities. All mapping rules can be pre-configured in the mobile app or customized by the user on the app side.

[0068] Step S104: Bluetooth command is sent. The APP sends the preset code (such as 001) to the smart control terminal in the form of a command frame through the BLE Bluetooth direct communication link. The Bluetooth communication module of the mobile APP is only responsible for the intermediary transmission of data and does not perform data encapsulation or complex processing. The semantic parsing and / or command generation and / or command sending are completed locally by the mobile APP terminal to realize the synchronous response of chat semantics and motor action.

[0069] It should be noted that the entire process of semantic parsing, instruction generation, and instruction issuance is completed locally on the mobile APP terminal without going through any third-party cloud servers. The instruction transmission latency is extremely low (as low as less than 10ms), enabling synchronous response between chat semantics and motor actions.

[0070] Step S105: Terminal parsing and execution. The intelligent control terminal receives the instruction frame and parses the preset code using its internal highly integrated Bluetooth SoC or a simplified architecture of Bluetooth module + low-power MCU. This directly drives the motor and LED module to perform the corresponding actions. The terminal does not store any user information. After receiving the simplified instruction frame through its built-in Bluetooth communication module, the intelligent control terminal performs simple parsing of the preset code using its internal Bluetooth SoC or Bluetooth module and low-power MCU. It then directly drives the corresponding motor drive circuit and / or LED drive circuit to complete the nodding, shaking, and lighting effects. The intelligent control terminal adopts a simplified hardware architecture, only performing instruction parsing and hardware driving, without storing any user information. It has a fast response speed and low power consumption.

[0071] Specifically, the drive motor is a micro DC motor or a coreless motor, which, together with a reduction gear set, achieves a nodding / shaking oscillation angle of ±15°~±45°.

[0072] Specifically, the intelligent control terminal adopts a highly integrated Bluetooth SoC single-chip solution, which integrates Bluetooth radio frequency and MCU core; or adopts a dual-chip minimalist architecture of BLE module + independent low-power MCU; after the MCU parses the preset code, it directly outputs PWM / level signal to drive H-bridge motor drive circuit and RGB LED drive circuit without performing semantic or emotion calculation.

[0073] Step S106, Data retention and optimization: All interactive text, voice and execution records are retained locally in the mobile APP for large model behavior analysis and continuous optimization.

[0074] It's important to note that all text, voice, and corresponding pre-defined code execution records generated during the interaction process are continuously stored locally on the mobile device where the AI ​​big model mobile app resides (the smart control terminal does not store any user information). The mobile app uses this stored data to conduct deeper behavioral analysis and personalized learning on users, continuously optimizing and "cultivating" a smarter, more user-savvy big model.

[0075] A natural language-driven Bluetooth device interaction system is provided, which is used to execute a natural language-driven Bluetooth device interaction method based on an AI large model mobile APP, including a mobile APP terminal and a smart control terminal.

[0076] Specifically, the mobile APP terminal is installed and run on mobile terminals such as smartphones and tablets (mobile APP terminals include but are not limited to ByteDance's "Doubao", Alibaba's "Qwen", DeepSeek, Tencent's "Hunyuan", Baidu's "Wenxin Yiyan", iFlytek's "Xunfei Xinghuo", "Kimi", "Zhipu Qingyan", and "Baichuan").

[0077] The human-computer interaction interface module is used to receive user voice or text input.

[0078] The semantic sentiment analysis module performs a full-dimensional analysis of the interactive content, extracting semantic tags, sentiment tags and their intensity, and intent tags. This module can be implemented by calling the standard API interfaces provided by AI large model vendors or integrating their SDKs. It can also be used to call large model APIs / SDKs for semantic, sentiment, and intent analysis.

[0079] The preset code mapping module converts the parsing results into preset codes according to processing rules (such as 001=nodding, 003=shaking head, 011=warm light, etc.), and also supports user-defined configurations.

[0080] The Bluetooth communication management module is used for device scanning, pairing, connection and command issuance. It only acts as an intermediary for data transmission and does not encapsulate data.

[0081] The data retention and learning module stores historical interaction data locally on the mobile terminal (mobile app on a phone or tablet) for continuous optimization and personalized development of the large model. The intelligent control terminal does not participate in any data storage.

[0082] The intelligent control terminal, serving as the physical feedback terminal for the AI ​​large-scale model mobile app, adopts a minimalist hardware architecture and does not store any user information. The BLE Bluetooth communication module is used to receive command frames. It supports BLE 4.0 and above protocols and has broadcasting, pairing, data pass-through, and command receiving capabilities.

[0083] The integrated MCU core is used to parse preset codes and output control signals. This module can adopt a Bluetooth SoC single-chip solution with an integrated MCU core (such as Nordic RF52 series, TICC26 series, etc.), or a dual-chip solution of Bluetooth module + independent low-power MCU. Its internal MCU core is responsible for performing simple parsing of the received preset codes and outputting corresponding control signals.

[0084] The motor drive module is used to drive the nodding / shaking motors to perform actions. It includes a motor drive chip (such as an H-bridge driver), controlled by control signals output by the Bluetooth SoC or MCU, to drive the nodding and / or shaking motors to perform corresponding actions. A miniature DC motor or a coreless motor is used in conjunction with a reduction mechanism to achieve the nodding and shaking actions.

[0085] Standby current can reach the microamp level, and a single charge can provide a long battery life. All complex analysis is completed on the mobile APP, and the intelligent control terminal only needs to analyze the preset code to drive the hardware. The response link is short and the latency can be as low as less than 10ms, realizing a real-time feedback experience of "speaking is acting".

[0086] The LED driver module includes an RGB LED and its driving circuit, which is controlled by the control signal output by the Bluetooth SoC or MCU to execute the corresponding lighting effect.

[0087] The power module includes a rechargeable lithium battery or a dry cell battery to provide power to the above modules.

[0088] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and modifications made to the above embodiments based on the present invention without departing from the scope of the present invention are within the scope of the present invention.

Claims

1. A method for natural language driven Bluetooth device interaction based on an AI large model mobile APP, characterized in that, The interaction method includes: Step S101, Bluetooth pairing and connection: The mobile terminal launches the AI ​​big model mobile APP and turns on the Bluetooth function, scans for nearby smart control terminals, filters target devices according to device identifiers, automatically initiates pairing authentication and establishes a BLE low power Bluetooth direct link. After the first pairing, the device capability information is stored locally in the mobile APP, and can be automatically reconnected in the future. Step S102, Natural Language Interaction and Parsing: Users engage in natural dialogue with the large model within the APP via voice or text. The APP calls the large model's interface or SDK to perform full-dimensional parsing of the input content. Step S103: The parsing result is mapped to a preset code. According to the parsing result, the APP converts the user's semantics, emotions and intentions into a preset code of ultra-short bytes according to the preset mapping rules. The preset code corresponds to the terminal hardware action, and the mapping rules support user-defined configuration. Step S104: Bluetooth command is sent. The APP sends the preset code to the smart control terminal in the form of a command frame through the BLE Bluetooth direct connection communication link. Step S105, Terminal parsing and execution: The intelligent control terminal receives the instruction frame and parses the preset code using the internal highly integrated Bluetooth SoC or the simplified architecture of Bluetooth module + low power MCU, directly driving the motor and LED module to perform the corresponding actions. The terminal does not store any user information. Step S106, Data retention and optimization: All interactive text, voice and execution records are retained locally in the mobile APP for large model behavior analysis and continuous optimization.

2. The natural language-driven Bluetooth device interaction method based on an AI large-scale model mobile APP according to claim 1, characterized in that: After the initial pairing is completed in step S101, the APP records the device's action code and / or action type hardware capability information and stores it in the local storage of the mobile APP's terminal. In subsequent interactions, the APP can automatically identify and reconnect without requiring the user to repeat the operation.

3. The natural language-driven Bluetooth device interaction method based on an AI large-scale model mobile APP according to claim 1, characterized in that: Step S102 includes: Semantic dimension parsing: Based on the natural language understanding capabilities of the large model, the intent classification and semantic tendency analysis of user statements are performed to identify affirmative / acceptance, negative / questioning or neutral semantics, and the parsing output is semantic labels and their confidence scores; Emotion Dimension Analysis: Based on the sentiment analysis capabilities of the large model, the emotional features in the user's statements are identified and quantified. The emotion categories include at least one of joy, anger, sadness, calmness, anxiety, depression, and excitement. The analysis outputs the corresponding emotion labels and their intensity values. Intent dimension analysis: Based on the dialogue context and user statement characteristics, identify the user's deep intent for this interaction. The intent categories include at least one of the following: consultation, companionship, entertainment, stress relief, seeking help, confirmation, and venting.

4. The natural language-driven Bluetooth device interaction method based on an AI large-scale model mobile APP according to claim 1, characterized in that: In step S103, the mapping rules allow users to customize and save the correspondence between preset codes and actions / emotions through the mobile APP interface.

5. The natural language-driven Bluetooth device interaction method based on an AI large-scale model mobile APP according to claim 1, characterized in that: Step S104 includes: the Bluetooth communication module of the mobile APP is responsible for the intermediary transmission of data, without encapsulating or performing complex data processing; the mobile APP terminal completes semantic parsing and / or instruction generation and / or instruction issuance locally to achieve synchronous response between chat semantics and motor actions.

6. The natural language-driven Bluetooth device interaction method based on an AI large-scale model mobile APP according to claim 1, characterized in that: Step S105 includes: After the intelligent control terminal receives the simplified instruction frame through the built-in Bluetooth communication module, its internal Bluetooth SoC or Bluetooth module and low-power MCU perform simple parsing of the preset code and directly drive the corresponding motor drive circuit and / or LED drive circuit to complete the nodding, shaking and lighting effects. The intelligent control terminal adopts a simplified hardware architecture, only performs instruction parsing and hardware driving, does not store any user information, has a fast response speed and low power consumption.

7. The natural language-driven Bluetooth device interaction method based on an AI large-scale model mobile APP according to claim 1, characterized in that: The original voice, text, and preset code execution records generated during the interaction process are stored locally on the mobile terminal and used to train a user-specific personalized large model. The intelligent control terminal does not retain any historical data.

8. A natural language-driven Bluetooth device interaction system based on an AI large-scale model mobile APP, characterized in that: The interactive system is used to execute the natural language driven Bluetooth device interaction method based on AI large model mobile APP as described in any one of claims 1-7, including a mobile APP terminal and a smart control terminal; The mobile APP terminal is used to run the built-in AI big model on smartphones or tablets. The mobile APP terminal consists of a human-computer interaction interface module, a semantic emotion analysis module, a preset code mapping module, a Bluetooth communication management module, and a data retention and learning module. The intelligent control terminal adopts a minimalist hardware architecture and does not store user information. The intelligent control terminal consists of a BLE Bluetooth communication module, an integrated MCU core, a motor drive module, an LED drive module, and a power supply module.

9. The natural language-driven Bluetooth device interaction system based on an AI large-scale model mobile APP according to claim 8, characterized in that: The human-computer interaction interface module is used to receive user voice or text input; The semantic sentiment parsing module is used to call the large model API / SDK to perform semantic, sentiment, and intent parsing; The preset code mapping module converts the parsing results into preset codes according to the processing rules; The Bluetooth communication management module is used for device scanning, pairing, connection, and command issuance. The data retention and learning module stores interactive data locally on the mobile terminal for model optimization.

10. The natural language-driven Bluetooth device interaction system based on an AI large-scale model mobile APP according to claim 8, characterized in that: The BLE Bluetooth communication module is used to receive command frames; The integrated MCU core is used to parse preset code and output control signals; The motor drive module is used to drive the nodding / shaking motor to perform actions; The LED driver module is used to drive RGB LEDs to achieve lighting effects; The power module supplies power to each module.

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