Internet of Things intelligent application method and device based on large language model

Abstract

The invention discloses an Internet of Things intelligent application method and device based on a large language model, belongs to the technical field of Internet of Things equipment, aims at solving the problems of complexity and inconvenience of user interaction, insufficient compatibility between equipment and poor security and privacy, and comprises the following steps: S1, demand analysis and system design; s2, data collection and preprocessing; s3, language model integration and processing; s4, intelligent decision making and automatic management; by setting the natural language processing module and the large language model, the natural language instruction of the user can be understood and generated through the natural language processing technology, so that the user can carry out equipment control and information query by using simple statements, the complexity of operation is reduced, and meanwhile, by combining the voice recognition technology, the user experience is improved. The user can interact with the Internet of Things system through voice, the model can accurately understand voice input, real-time feedback and operation execution are provided, and user experience and operation convenience are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet of Things devices, and specifically relates to an Internet of Things intelligent application method and device based on a large language model. Background Art

[0002] The Internet of Things is a technical system that connects various physical devices, such as sensors, home appliances, vehicles, and machinery and equipment, through the Internet, enabling them to communicate with each other, exchange data, and perform intelligent control. The core concept of the Internet of Things is to enable objects in the physical world to have the ability to sense, identify, process, store, and transmit data, thereby achieving collaboration and automation between devices; the Internet of Things connects the physical world with the digital world, creating a smarter, more convenient, and more efficient way of living and working. From smart homes to industrial automation, from smart transportation to smart healthcare, the application scenarios of the Internet of Things are spread across all walks of life. With the development of technology, the potential of the Internet of Things will be further released, changing people's lives and production methods.

[0003] The existing IoT smart applications still have the following problems:

[0004] 1. Complex and inconvenient user interaction: User interaction in traditional IoT systems mostly relies on specific applications and interfaces, which may require users to master complex control panels, dashboard device management tools, etc. At the same time, voice recognition and natural language processing capabilities are not accurate enough, resulting in users often having to perform cumbersome steps or repeat instructions when operating;

[0005] 2. Insufficient compatibility between devices: Devices in the IoT environment usually come from different manufacturers and use different protocols, which leads to compatibility issues between devices, making interoperability between devices poor and making it difficult for users to seamlessly integrate multiple devices into an intelligent system. Existing systems require dedicated applications or hardware to control each device, and lack an intelligent unified control platform to automate device collaboration.

[0006] 3. Poor security and privacy: The security of IoT devices is generally poor. The devices are vulnerable to hacker attacks, and users' privacy and data may be leaked. Existing IoT security measures mostly rely on manual settings and monitoring and cannot respond to threats dynamically.

[0007] Therefore, there is a need for an IoT intelligent application method and device based on a large language model to solve the problems of complex and inconvenient user interaction, insufficient compatibility between devices, and poor security and privacy in the prior art. Summary of the invention

[0008] The purpose of the present invention is to provide an Internet of Things intelligent application method and device based on a large language model to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solution: an Internet of Things intelligent application method and device based on a large language model, comprising the following steps:

[0010] S1. Requirements analysis and system design: clarify the goals of IoT smart applications, select appropriate IoT devices, determine how the large language model interacts with the IoT system, and design the data flow;

[0011] S2, Data collection and preprocessing: Configure IoT devices to collect sensor data in real time and transmit it to the processing platform through the network, and perform preprocessing operations such as denoising, formatting, and cleaning on the collected data;

[0012] S3, Language model integration and processing: Users interact with the IoT system through voice or text. The large language model is used to parse the natural language instructions input by the user, understand their intentions, and convert them into corresponding device control or data query operations. The large language model handles complex semantic understanding, recognizes and processes multi-round dialogues, fuzzy queries or multi-task instructions;

[0013] S4, Intelligent decision-making and automated management: Based on the data collected by IoT devices, the big language model performs intelligent analysis and decision support, and converts user instructions into automated control actions through the language model;

[0014] S5. Fault diagnosis and anomaly detection: Use large language models combined with historical data of IoT devices to identify potential failure modes. Large language models combined with machine learning algorithms monitor anomalies in the IoT system in real time. When an anomaly is detected, the system automatically generates an alarm notification and communicates with users or administrators through natural language.

[0015] S6. Cross-device collaboration and multi-tasking: The language model handles the collaborative work of multiple devices. When users issue multiple commands at the same time, the language model parses and intelligently handles multiple tasks at the same time.

[0016] S7. User interaction and feedback mechanism: IoT systems based on large language models can provide natural language feedback to users. Based on the user's historical interaction data, the language model learns user preferences and provides personalized services.

[0017] S8, System feedback and optimization: After the system is deployed, by continuously monitoring the operating status of IoT devices, data transmission, and the response quality of the language model, possible bottlenecks or problems in the system are identified, user feedback is collected, the actual usage of IoT devices is analyzed, and speech recognition accuracy, natural language processing capabilities, and decision models are optimized;

[0018] S9. Security and privacy protection: Implement strict identity authentication and access control mechanisms, and use encryption and anonymization technologies to protect user privacy.

[0019] It should be noted in the solution that in the requirements analysis and system design steps, the design of the data flow includes the steps of IoT devices collecting data, sending the data to the cloud platform, and the language model making decisions based on the data.

[0020] It is further worth noting that in the data collection and preprocessing steps, the sensor data includes environmental data, equipment status, and video monitoring data.

[0021] It should be further explained that in the intelligent decision-making and automated management steps, for complex systems, the language model can automatically adjust device settings based on real-time sensor data.

[0022] As a preferred implementation, the user interaction and feedback mechanism step supports multi-modal interaction of text, voice, and image.

[0023] As a preferred implementation, in the security and privacy protection step, data encryption and identity authentication are performed repeatedly.

[0024] As a preferred implementation, the method for implementing the above-mentioned IoT intelligent application method based on a large language model includes:

[0025] Natural language processing module: uses a large language model to perform semantic understanding, intent recognition, and natural language generation tasks to achieve interaction between voice or text and the system;

[0026] Device management and control module: responsible for communicating with IoT devices, receiving and executing control instructions from users or automation systems, and coordinating device behaviors;

[0027] Intelligent decision-making and reasoning module: Based on user needs, environmental data, equipment status and other information, it conducts intelligent analysis and decision support, and combines the reasoning ability of large language models to provide personalized and automated decision-making;

[0028] User behavior personalized service module: Analyze user behavior and preferences, provide personalized services, and continuously improve system responses through the combination of machine learning and large language models to adapt to the needs of different users;

[0029] Multi-device collaborative automatic control module: responsible for coordinating the operation and automation tasks of each device;

[0030] Data analysis and anomaly detection module: Analyze data in the IoT system, identify potential problems, report anomalies in a timely manner, and perform predictive maintenance;

[0031] Security and privacy protection module: ensure the security of the IoT system, prevent unauthorized access and data leakage, and protect user privacy;

[0032] Interface and integration module: provides interfaces with external systems, applications, and services, supports system scalability and compatibility, and facilitates the integration of different IoT devices and services;

[0033] Feedback and user interface module: provides an interactive interface between users and the system, and provides feedback to users in various ways;

[0034] System monitoring and performance optimization module: responsible for monitoring the overall operation status of the IoT system, performing resource management, performance evaluation and optimization.

[0035] As a preferred implementation, the multi-device collaborative automatic control module includes device linkage, scenario mode management and cross-device task scheduling.

[0036] Compared with the prior art, the method and device for intelligent application of the Internet of Things based on a large language model provided by the present invention have at least the following beneficial effects:

[0037] (1) By setting up a natural language processing module, the large language model can understand and generate the user's natural language instructions through natural language processing technology, so that users can use simple sentences to control devices and query information, reducing the complexity of operations. At the same time, combined with voice recognition technology, users can interact with the IoT system through voice. The model can accurately understand voice input, provide real-time feedback and operation execution, significantly improving user experience and operational convenience;

[0038] (2) By setting up a multi-device collaborative automatic control module, the large language model can be used as a unified natural language interface to control devices of different manufacturers, protocols, and platforms through standardized voice or text commands. Users can directly issue unified commands to control multiple devices at the same time without caring about the specific protocols and control methods of the devices. The device can automatically infer and schedule the collaboration between devices by combining sensor data and device status;

[0039] (3) By setting up a security and privacy protection module and combining it with encryption technology, it can help encrypt and anonymize user data, perform privacy protection operations, guide users to make security settings, and improve the overall security of the system. At the same time, it can analyze the interaction data between users and devices, detect abnormal behavior or potential security threats, and issue security alerts. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of the method of the present invention;

[0041] Figure 2It is a structural block diagram of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The present invention will be further described below in conjunction with the embodiments.

[0044] See also Figure 1-2 The present invention provides an Internet of Things intelligent application method and device based on a large language model, comprising the following steps:

[0045] S1. Requirements analysis and system design: clarify the goals of IoT smart applications, select appropriate IoT devices, determine how the large language model interacts with the IoT system, and design the data flow;

[0046] S2, Data collection and preprocessing: Configure IoT devices to collect sensor data in real time and transmit it to the processing platform through the network, and perform preprocessing operations such as denoising, formatting, and cleaning on the collected data;

[0047] S3, Language model integration and processing: Users interact with the IoT system through voice or text. The large language model is used to parse the natural language instructions input by the user, understand their intentions, and convert them into corresponding device control or data query operations. The large language model handles complex semantic understanding, recognizes and processes multi-round dialogues, fuzzy queries or multi-task instructions;

[0048] S4, Intelligent decision-making and automated management: Based on the data collected by IoT devices, the big language model performs intelligent analysis and decision support, and converts user instructions into automated control actions through the language model;

[0049] S5. Fault diagnosis and anomaly detection: Use large language models combined with historical data of IoT devices to identify potential failure modes. Large language models combined with machine learning algorithms monitor anomalies in the IoT system in real time. When an anomaly is detected, the system automatically generates an alarm notification and communicates with users or administrators through natural language.

[0050] S6. Cross-device collaboration and multi-tasking: The language model handles the collaborative work of multiple devices. When users issue multiple commands at the same time, the language model parses and intelligently handles multiple tasks at the same time.

[0051] S7. User interaction and feedback mechanism: IoT systems based on large language models can provide natural language feedback to users. Based on the user's historical interaction data, the language model learns user preferences and provides personalized services.

[0052] S8, System feedback and optimization: After the system is deployed, by continuously monitoring the operating status of IoT devices, data transmission, and the response quality of the language model, possible bottlenecks or problems in the system are identified, user feedback is collected, the actual usage of IoT devices is analyzed, and speech recognition accuracy, natural language processing capabilities, and decision models are optimized;

[0053] S9. Security and privacy protection: Implement strict identity authentication and access control mechanisms, and use encryption and anonymization technologies to protect user privacy.

[0054] Further as Figure 1 As shown, it is worth specifying that in the demand analysis and system design steps, the design of the data flow includes steps such as IoT devices collecting data, sending the data to the cloud platform, and the language model making decisions based on the data. The data collected in real time by IoT devices, such as temperature, humidity, light and other environmental data, are sent to the cloud platform for centralized processing. The language model can analyze the data in real time and automatically make decisions based on predefined rules or learned patterns. This automated decision can reduce human intervention and improve response speed and accuracy. The large language model can analyze the data of each device and provide personalized control strategies based on user behavior and environmental conditions.

[0055] Further as Figure 1 As shown, it is worth specifying that in the data collection and preprocessing steps, the sensor data includes environmental data, device status, and video surveillance data. By combining environmental data, device status, and video surveillance data, the system can perceive and understand the environment in multiple dimensions. The system can infer the needs or problems in the current scenario based on these data and respond more intelligently and dynamically. The combination of environmental data, device status, and video surveillance data enables the system to perform more comprehensive and in-depth data analysis, thereby improving security and user experience.

[0056] Further as Figure 1 As shown, it is worth mentioning that in the intelligent decision-making and automated management steps, for complex systems, the language model can automatically adjust the equipment settings based on real-time sensor data. By automatically adjusting the equipment settings, the need for manual operation is reduced, which not only improves work efficiency, but also reduces the risk of human error and ensures the stable operation of the system. Based on real-time sensor data, the language model can intelligently adjust the workload of the equipment, optimize energy efficiency, reduce unnecessary energy waste, and thus reduce overall energy costs.

[0057] Further as Figure 1 As shown, it is worth specifying that in the user interaction and feedback mechanism steps, multimodal interaction of text, voice, and image is supported. Different users may have different preferences and needs. Supporting multiple interaction methods such as text, voice, and image can provide users with flexible choices to ensure that they can interact with the IoT system in the most comfortable and convenient way. The combination of text, voice, and image interaction makes the communication between people and IoT devices more in line with the usage habits of natural language.

[0058] Further as Figure 1 As shown, it is worth specifying that in the security and privacy protection steps, data encryption and identity authentication are performed repeatedly. By repeatedly encrypting data at different levels or stages, even if an attacker successfully breaks through a certain layer of security protection, it is difficult to decrypt all encrypted information. Multiple encryption strategies increase the complexity of encryption and decryption, which greatly increases the time and computing resources required for cracking, thereby effectively reducing the risk of data leakage. Through multiple identity authentications, it can effectively prevent forged devices or malicious nodes from accessing the system.

[0059] Further as Figure 2 As shown, it is worth specifically stating that the IoT intelligent application method based on a large language model includes:

[0060] Natural language processing module: uses a large language model to perform semantic understanding, intent recognition, and natural language generation tasks to achieve interaction between voice or text and the system;

[0061] Device management and control module: responsible for communicating with IoT devices, receiving and executing control instructions from users or automation systems, and coordinating device behaviors;

[0062] Intelligent decision-making and reasoning module: Based on user needs, environmental data, equipment status and other information, it conducts intelligent analysis and decision support, and combines the reasoning ability of large language models to provide personalized and automated decision-making;

[0063] User behavior personalized service module: Analyze user behavior and preferences, provide personalized services, and continuously improve system responses through the combination of machine learning and large language models to adapt to the needs of different users;

[0064] Multi-device collaborative automatic control module: responsible for coordinating the operation and automation tasks of each device;

[0065] Data analysis and anomaly detection module: Analyze data in the IoT system, identify potential problems, report anomalies in a timely manner, and perform predictive maintenance;

[0066] Security and privacy protection module: ensure the security of the IoT system, prevent unauthorized access and data leakage, and protect user privacy;

[0067] Interface and integration module: provides interfaces with external systems, applications, and services, supports system scalability and compatibility, and facilitates the integration of different IoT devices and services;

[0068] Feedback and user interface module: provides an interactive interface between users and the system, and provides feedback to users in various ways;

[0069] System monitoring and performance optimization module: responsible for monitoring the overall operation status of the IoT system, performing resource management, performance evaluation and optimization.

[0070] Further as Figure 2 As shown, it is worth explaining in detail that the multi-device collaborative automatic control module includes device linkage, scenario mode management and cross-device task scheduling, which can provide users with a more efficient, intelligent, energy-saving and personalized smart life experience. Through these functions, devices can work together more flexibly and complete tasks automatically, thereby reducing the burden on users and improving the quality of life. It is also highly adaptable and sustainable.

[0071] This program has the following working process:

[0072] Step 1: Clarify the goals of IoT smart applications, select appropriate IoT devices, determine how the large language model interacts with the IoT system, and design the data flow;

[0073] Step 2: Configure IoT devices to collect sensor data in real time and transmit it to the processing platform through the network, and perform pre-processing operations such as denoising, formatting, and cleaning on the collected data;

[0074] Step 3: Users interact with the IoT system through voice or text. The large language model is used to parse the natural language instructions input by the user, understand their intentions, and convert them into corresponding device control or data query operations. The large language model handles complex semantic understanding, recognizes and processes multi-round dialogues, fuzzy queries or multi-task instructions;

[0075] Step 4: Based on the data collected by IoT devices, the big language model performs intelligent analysis and decision support, and converts user instructions into automated control actions through the language model;

[0076] Step 5: Use the big language model combined with the historical data of IoT devices to identify potential failure modes. The big language model combined with machine learning algorithms monitors abnormal conditions in the IoT system in real time. When an abnormality is detected, the system automatically generates an alarm notification and communicates with the user or administrator through natural language.

[0077] Step 6: The language model handles the collaborative work of multiple devices. The user issues multiple commands at the same time, and the language model parses and intelligently handles multiple tasks at the same time.

[0078] Step 7: The IoT system based on the large language model can provide natural language feedback to users. Based on the user's historical interaction data, the language model learns user preferences and provides personalized services;

[0079] Step 8: After the system is deployed, continuously monitor the operating status of IoT devices, data transmission, and the response quality of the language model to identify possible bottlenecks or problems in the system, collect user feedback, analyze the actual use of IoT devices, and optimize speech recognition accuracy, natural language processing capabilities, and decision models;

[0080] Step 9: Implement strict identity authentication and access control mechanisms, and use encryption and anonymization technologies to protect user privacy.

[0081] In summary: By setting up a natural language processing module, the large language model can understand and generate natural language instructions of users through natural language processing technology, so that users can use simple sentences to control devices and query information, reducing the complexity of operations. At the same time, combined with voice recognition technology, users can interact with the Internet of Things system through voice. The model can accurately understand voice input, provide real-time feedback and operation execution, significantly improving user experience and operational convenience; by setting up a multi-device collaborative automatic control module, the large language model can be used as a unified natural language interface to control devices of different manufacturers, protocols and platforms through standardized voice or text instructions. Users do not need to care about the specific protocols and control methods of the devices, and can directly issue unified instructions to control multiple devices at the same time. The device combines sensor data and device status to automatically infer and schedule collaboration between devices; by setting up a security and privacy protection module and combining encryption technology, it can help encrypt and anonymize user data, perform privacy protection operations, guide users to make security settings, and improve the overall security of the system. At the same time, it can analyze the interaction data between users and devices, detect abnormal behavior or potential security threats, and issue security alerts.

[0082] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An IoT intelligent application method based on a large language model, characterized in that: The following steps are involved: S1. Requirements analysis and system design: clarify the goals of IoT smart applications, select appropriate IoT devices, determine how the large language model interacts with the IoT system, and design the data flow; S2, Data collection and preprocessing: Configure IoT devices to collect sensor data in real time and transmit it to the processing platform through the network, and perform preprocessing operations such as denoising, formatting, and cleaning on the collected data; S3, Language model integration and processing: Users interact with the IoT system through voice or text. The large language model is used to parse the natural language instructions input by the user, understand their intentions, and convert them into corresponding device control or data query operations. The large language model handles complex semantic understanding, recognizes and processes multi-round dialogues, fuzzy queries or multi-task instructions; S4, Intelligent decision-making and automated management: Based on the data collected by IoT devices, the big language model performs intelligent analysis and decision support, and converts user instructions into automated control actions through the language model; S5. Fault diagnosis and anomaly detection: Use large language models combined with historical data of IoT devices to identify potential failure modes. Large language models combined with machine learning algorithms monitor anomalies in the IoT system in real time. When an anomaly is detected, the system automatically generates an alarm notification and communicates with users or administrators through natural language. S6. Cross-device collaboration and multi-tasking: The language model handles the collaborative work of multiple devices. When users issue multiple commands at the same time, the language model parses and intelligently handles multiple tasks at the same time. S7. User interaction and feedback mechanism: IoT systems based on large language models can provide natural language feedback to users. Based on the user's historical interaction data, the language model learns user preferences and provides personalized services. S8, System feedback and optimization: After the system is deployed, by continuously monitoring the operating status of IoT devices, data transmission, and the response quality of the language model, possible bottlenecks or problems in the system are identified, user feedback is collected, the actual usage of IoT devices is analyzed, and speech recognition accuracy, natural language processing capabilities, and decision models are optimized; S9. Security and privacy protection: Implement strict identity authentication and access control mechanisms, and use encryption and anonymization technologies to protect user privacy.

2. The method for intelligent application of the Internet of Things based on a large language model according to claim 1, characterized in that: In the demand analysis and system design steps, designing the data flow includes the steps of IoT devices collecting data, sending the data to the cloud platform, and the language model making decisions based on the data.

3. The method for intelligent application of the Internet of Things based on a large language model according to claim 1, characterized in that: In the data collection and preprocessing steps, the sensor data includes environmental data, equipment status, and video monitoring data.

4. The method for intelligent application of the Internet of Things based on a large language model according to claim 1, characterized in that: In the intelligent decision-making and automated management steps, for complex systems, the language model can automatically adjust device settings based on real-time sensor data.

5. The method for intelligent application of the Internet of Things based on a large language model according to claim 1, characterized in that: The user interaction and feedback mechanism steps support multi-modal interaction of text, voice and image.

6. The method for intelligent application of the Internet of Things based on a large language model according to claim 1, characterized in that: In the security and privacy protection steps, data encryption and identity authentication are performed repeatedly.

7. The IoT intelligent application device based on a large language model according to claim 1, characterized in that: A method for implementing an IoT intelligent application based on a large language model as claimed in any one of claims 1 to 6, comprising: Natural language processing module: uses a large language model to perform semantic understanding, intent recognition, and natural language generation tasks to achieve interaction between voice or text and the system; Device management and control module: responsible for communicating with IoT devices, receiving and executing control instructions from users or automation systems, and coordinating device behaviors; Intelligent decision-making and reasoning module: Based on user needs, environmental data, equipment status and other information, it conducts intelligent analysis and decision support, and combines the reasoning ability of large language models to provide personalized and automated decision-making; User behavior personalized service module: Analyze user behavior and preferences, provide personalized services, and continuously improve system responses through the combination of machine learning and large language models to adapt to the needs of different users; Multi-device collaborative automatic control module: responsible for coordinating the operation and automation tasks of each device; Data analysis and anomaly detection module: Analyze data in the IoT system, identify potential problems, report anomalies in a timely manner, and perform predictive maintenance; Security and privacy protection module: ensure the security of the IoT system, prevent unauthorized access and data leakage, and protect user privacy; Interface and integration module: provides interfaces with external systems, applications, and services, supports system scalability and compatibility, and facilitates the integration of different IoT devices and services; Feedback and user interface module: provides an interactive interface between users and the system, and provides feedback to users in various ways; System monitoring and performance optimization module: responsible for monitoring the overall operation status of the IoT system, performing resource management, performance evaluation and optimization.

8. The IoT intelligent application device based on a large language model according to claim 7, characterized in that: The multi-device collaborative automatic control module includes device linkage, scenario mode management and cross-device task scheduling.

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