Smart city AI assistant system

Abstract

The invention relates to the technical field of smart city man-machine interaction technologies, in particular to a smart city AI assistant system, which comprises a conversational user interface (CUI), the conversational user interface comprises a natural language interaction module, a voice interaction module, a text input interaction module and a context memory mechanism, through a conversational user interface (CUI), a user does not need to memorize complex icons and operation steps, the learning cost is reduced, the use efficiency is improved, various input modes such as texts and voices are provided, the user requirements in different scenes are met, the use convenience and flexibility are improved, intention recognition, entity extraction and sentiment analysis functions are built in, and the user experience is improved. Therefore, the system can more accurately understand user demands and make proper responses, and the professional knowledge of the smart city field is combined to ensure that the provided information is professional and accurate and help the user to solve specific problems.

Description

Technical Field

[0001] The present invention relates to the technical field of human-computer interaction technology in smart cities, and specifically to an AI assistant system for smart cities. Background Art

[0002] In the development process of smart cities, the traditional graphical user interface (GUI) has always been the main interaction method for digital products. The GUI enables users to interact with a computer by means of graphics, icons, buttons, menus and other elements, using a mouse, keyboard or touch screen. Although the GUI has obvious advantages in terms of intuitiveness and standardization, as the functions of digital products become increasingly complex, its user experience has gradually become a bottleneck.

[0003] Limitations of the Traditional Graphical User Interface (GUI)

[0004] High learning cost: As the system functions continue to expand, users need to invest more time and effort in memorizing the locations of function entrances and operation steps, which significantly increases the learning burden on users.

[0005] Increasing operational complexity: The increase in functions has made the operation process of the GUI more complex, not only increasing the cognitive burden on users, but also hindering the further improvement of the user experience.

[0006] Low efficiency: For tasks that need to be executed frequently, the GUI often involves multiple steps of operation, thus reducing work efficiency and user satisfaction.

[0007] Challenges in Data Processing and Analysis

[0008] Fixed query mode: Traditional methods limit the ability of users to flexibly access and analyze data according to their personal needs. Users can only rely on preset rules and query modes, making it difficult to conduct in-depth data exploration.

[0009] Lack of flexibility: Due to the complexity of data structures, traditional query methods are difficult to meet the diverse data analysis needs of users, which limits the depth and breadth of data analysis and affects the accuracy of decision-making.

[0010] Slow response speed: When dealing with a large amount of data, traditional methods often take a long time, which reduces the ability to support real-time decision-making, especially in smart city application scenarios that require quick responses.

[0011] Lack of Professional Answers and Knowledge Services

[0012] Lack of a professional knowledge base: Traditional smart city application platforms usually do not integrate professional knowledge bases in specific fields, so they cannot provide users with professional answers and in-depth support for business areas.

[0013] Limited Q&A function: Even if some platforms provide a Q&A function, it is often limited to a predefined set of questions and is difficult to handle complex and non-standard questions raised by users, restricting the practicality of the platform and user satisfaction.

[0014] Insufficient personalized service: Traditional platforms rarely consider the personalized needs of users when providing services, resulting in overly general service content that lacks pertinence and personalization and cannot meet the diverse needs of users. Summary of the Invention

[0015] (I) Technical problems to be solved

[0016] In view of the deficiencies of the prior art, the present invention provides a smart city AI assistant system.

[0017] (II) Technical solutions

[0018] To achieve the above object, the present invention provides the following technical solutions: A smart city AI assistant system of the present invention includes a conversational user interface (CUI), and the conversational user interface includes:

[0019] Natural language interaction module: Supports text input and voice input, and has functions of intention recognition, entity extraction, and sentiment analysis;

[0020] Voice interaction module: Integrates a screen reader and voice command functions to support barrier-free access;

[0021] Text input interaction module: Provides a text input method based on a keyboard or a touch screen;

[0022] Context memory mechanism: Maintains the coherence and consistency of the conversation during multiple interactions;

[0023] AI intelligent response mechanism: According to the specific needs and context of the user, combines the latest AI technologies to provide customized feedback and suggestions;

[0024] Security module: Includes functions of encrypted communication and sensitive information protection to ensure user privacy and data security;

[0025] Information update module: Regularly updates the language model and algorithms to synchronize the latest laws, regulations, policies, and technological developments, and ensures that the provided information is the latest and most accurate.

[0026] Preferably, the CUI further includes an adaptive interaction module, and the adaptive interaction module includes interaction mode analysis, dialogue style adjustment, and dialogue response speed adjustment.

[0027] More preferably, the CUI further includes a multi-round dialogue mechanism for allowing the user to ask multiple questions or instructions in a single session and being able to understand the logical relationship between the previous and subsequent questions.

[0028] More preferably, the CUI includes a multilingual communication database for adapting to the language habits of users in different regions and adjusting the language interaction mode.

[0029] Preferably, the CUI further includes:

[0030] Domain knowledge base: Integrating professional knowledge in the field of smart cities to provide users with professional answers and services;

[0031] Dynamic data analysis module: Automatically extracts data from the private database to achieve dynamic data analysis and improve the practicality and intelligence level of the system.

[0032] Further preferably, the CUI further includes:

[0033] Personalized recommendation engine: Based on the user's historical behavior and personal preferences, provides personalized service recommendations;

[0034] Intelligent query converter: Adopts graph-based retrieval augmented generation (GraphRAG) technology and artificial intelligence agent (AI Agent), and realizes efficient natural language to SQL query conversion through a reinforcement learning mechanism, which is especially suitable for processing complex data structures and changing query requirements in smart cities.

[0035] More preferably, the CUI further includes:

[0036] Seamless integration module: Can be seamlessly integrated with existing smart city infrastructure without changing the existing user work process, providing users with a continuous service experience;

[0037] Cross-platform compatibility module: Supports multiple device platforms such as desktop and mobile, ensuring that users can enjoy consistent service quality at any time and place.

[0038] Preferably, the CUI further includes:

[0039] Visual data analysis tool: Provides users with intuitive data display and analysis functions to help users better understand and utilize a large amount of data in smart cities;

[0040] User feedback loop: Collects users' opinions and suggestions on services, continuously improves and optimizes system performance, and forms a virtuous closed-loop of user interaction and service upgrade.

[0041] Further preferably, the CUI further includes:

[0042] Scenario awareness module: Provides services and suggestions that are more in line with the actual scenario according to the user's location, environmental conditions, and current activity factors;

[0043] Community Interaction Platform: Facilitate communication among citizens and between citizens and the government, and encourage public participation in urban construction and development;

[0044] More preferably, the CUI further includes:

[0045] Resource Scheduling Optimization Module: Provide optimization solutions for resource allocation problems in urban management;

[0046] Real-time Event Response Module: Can monitor and respond to urban emergencies in real time, promptly release information to relevant departments and the public, and assist in emergency management and decision-making support;

[0047] Continuous Learning and Self-evolution Module: Continuously optimize its own performance and service capabilities by accumulating user interaction data and feedback information and applying machine learning and deep learning algorithms.

[0048] (III) Beneficial Effects

[0049] Compared with the prior art, the present invention provides a smart city AI assistant system, which has the following beneficial effects:

[0050] Improve user experience

[0051] Simplify the operation process: Through the conversational user interface (CUI), users do not need to remember complex icons and operation steps, reducing the learning cost and improving the usage efficiency.

[0052] Multimodal input support: Provide multiple input methods such as text and voice, meet the user needs in different scenarios, and improve the convenience and flexibility of use.

[0053] Natural language processing: Built-in functions of intent recognition, entity extraction, and sentiment analysis enable the system to more accurately understand user needs and make appropriate responses.

[0054] Enhance the professionalism and accuracy of services

[0055] Domain knowledge base integration: Combine professional knowledge in the field of smart cities to ensure that the provided information is both professional and accurate, and help users solve specific problems.

[0056] Dynamic data analysis: Automatically extract data from the private database to achieve dynamic data analysis and provide real-time and accurate data support.

[0057] Intelligent query converter: Adopt GraphRAG technology and AI Agent to achieve efficient natural language to SQL query conversion through a reinforcement learning mechanism, especially suitable for dealing with complex data structures and changing query requirements.

[0058] Improve work efficiency

[0059] Multi-round dialogue mechanism: allows users to ask multiple questions or instructions in the same conversation, and understand the logical connection between previous and subsequent questions, so as to complete complex tasks in one go.

[0060] Adaptive interaction module: automatically adjusts the conversation style and response speed according to the user's interaction mode, optimizes the user experience and reduces unnecessary communication costs.

[0061] Contextual awareness module: Provides services and suggestions that are more in line with actual scenarios based on the user's location, environmental conditions, and current activity factors, enhancing the relevance and practicality of the services.

[0062] Ensure information security

[0063] Encrypted communication and protection of sensitive information: SSL / TLS protocol and other security measures are used to ensure user privacy and data security and prevent unauthorized data leakage.

[0064] Access control: Implement strict permission management and authentication measures to ensure that only authorized personnel can access sensitive information.

[0065] Promoting community interaction and social participation

[0066] Community interaction platform: promote communication between citizens and between citizens and the government, encourage public participation in urban construction and development, build a harmonious community environment, and enhance social cohesion.

[0067] Real-time incident response module: monitors and responds to emergencies in the city in real time, releases information to relevant departments and the public in a timely manner, assists in emergency management and decision support, and improves the city's ability to respond to emergencies.

[0068] Continuous improvement and service upgrade

[0069] User feedback loop: Collect users' opinions and suggestions on services, continuously improve and optimize system performance, and form a virtuous closed loop of user interaction and service upgrades.

[0070] Continuous learning and self-evolution module: By continuously accumulating user interaction data and feedback information, using machine learning and deep learning algorithms to continuously optimize its own performance and service capabilities, ensuring that the system always remains advanced and competitive.

[0071] Expand service scope and service quality

[0072] Multilingual communication database: supports multiple languages, adapts to the language habits of users in different regions, adjusts the language interaction mode, expands the service scope, and enhances international service capabilities.

[0073] Cross-platform Compatibility Module: It supports various device platforms such as desktop and mobile, ensuring that users can enjoy consistent service quality at any time and place, improving the availability and flexibility of the system.

[0074] Resource Scheduling Optimization

[0075] Resource Scheduling Optimization Module: For the resource allocation problem in urban management, it provides optimization solutions, improves resource utilization rate, reduces waste, and helps to enhance urban management efficiency. Description of the Drawings

[0076] Figure 1 It is a schematic diagram of the system module of the present invention;

[0077] Figure 2 It is a schematic diagram of the further optimized function of the system module of the present invention;

[0078] Figure 3 It is a schematic diagram of the technical implementation of the system module of the present invention;

[0079] Figure 4 It is a schematic diagram of the technical implementation of the optimized function of the system module of the present invention;

[0080] Figure 5 It is a schematic diagram of the system process of the present invention. Detailed Implementation Manner

[0081] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0082] Please refer to Figures 1-5 , a smart city AI assistant system of the present invention includes a conversational user interface (CUI), and the conversational user interface includes:

[0083] Natural Language Interaction Module: It supports text input and voice input, and has functions of intention recognition, entity extraction, and sentiment analysis;

[0084] Voice Interaction Module: It integrates a screen reader and voice command functions to support barrier-free access;

[0085] Text Input Interaction Module: It provides text input methods based on keyboard or touch screen;

[0086] Context Memory Mechanism: It maintains the coherence and consistency of the conversation during multiple interactions;

[0087] AI Intelligent Response Mechanism: Provide customized feedback and suggestions according to the specific needs and context of users, combined with the latest AI technologies;

[0088] Security Module: Include encryption communication and sensitive information protection functions to ensure the security of user privacy and data;

[0089] Information Update Module: Regularly update language models and algorithms to synchronize the latest laws, regulations, policies, and technological developments, ensuring that the information provided is up-to-date and accurate.

[0090] This smart city AI assistant system provides a highly intelligent, personalized, and secure interaction platform through a conversational user interface (CUI). The system utilizes advanced artificial intelligence technologies such as natural language processing (NLP), speech recognition, and machine learning, combined with professional knowledge bases and services in the field of smart cities, to provide users with convenient functions such as information query, data analysis, and transaction processing. It not only simplifies the user operation process, reduces the usage threshold, but also improves the professionalism and accuracy of services.

[0091] Working principles of various preferred technical solutions

[0092] Natural Language Interaction Module

[0093] Working principle: Support text and voice input, with built-in intent recognition, entity extraction, and sentiment analysis functions.

[0094] Technical implementation:

[0095] Intent recognition: Use natural language processing (NLP) technology to classify user input and determine its intent. For example, recognize "Query the weather in Beijing tomorrow" as the "weather query" intent.

[0096] Entity extraction: Extract key information such as time, location, and person from sentences through methods such as named entity recognition (NER).

[0097] Sentiment analysis: Evaluate the sentiment tendency (positive, negative, or neutral) of user input to more accurately understand and respond to the user's emotional state.

[0098] Voice Interaction Module

[0099] Working principle: Integrate a screen reader and voice command functions to ensure that users with visual impairments or other special needs can access the system without barriers.

[0100] Technical implementation:

[0101] Speech recognition: Use automatic speech recognition (ASR) technology to convert the user's speech into text, and then parse and execute the corresponding instructions.

[0102] Screen reader: Utilizes text-to-speech (TTS) technology to provide audio feedback for users, suitable for visually impaired users.

[0103] Text input interaction module

[0104] Working principle: Provides text input methods based on keyboard or touch screen, suitable for the user needs in different scenarios.

[0105] Technical implementation:

[0106] Multimodal input: Supports multiple input methods (such as handwriting, speech-to-text), simplifies the information input process, and improves efficiency.

[0107] Context memory mechanism

[0108] Working principle: Maintains the coherence and consistency of the conversation in multiple interactions, enabling the system to better understand the current request based on the previous communication content.

[0109] Technical implementation:

[0110] Conversation history management: Records and stores the information of each round of conversation, including the user's input, the system's response, and the intermediate state.

[0111] Context capture and state tracking: Analyzes the relationship between the new message and the previous conversation, tracks the changes in the conversation state, and ensures the consistency of the continuous conversation.

[0112] AI intelligent response mechanism

[0113] Working principle: Provides customized feedback and suggestions according to the user's specific needs and context, combined with the latest AI technology.

[0114] Technical implementation:

[0115] Deep learning models: Applies models such as deep neural network (DNN), recurrent neural network (RNN), and long short-term memory network (LSTM) to analyze user questions and generate the answers that best meet the user's expectations.

[0116] Conversation management: Coordinates the work of multiple modules, plans appropriate response paths, and ensures the smooth and natural flow of the entire communication process.

[0117] Security module

[0118] Working principle: Includes functions of encrypted communication and sensitive information protection to ensure the security of user privacy and data.

[0119] Technical implementation:

[0120] Data encryption: Adopts SSL / TLS protocol to ensure communication security and prevent data from being stolen or tampered with during transmission.

[0121] Access control: Implement strict permission management and authentication measures to ensure that only authorized personnel can access sensitive information.

[0122] Information update module

[0123] Working principle: Regularly update the language model and algorithms to synchronize with the latest laws, regulations, policies, and technological developments, ensuring that the provided information is up-to-date and accurate.

[0124] Technical implementation:

[0125] Continuous Integration / Continuous Deployment (CI / CD): Establish an automated process to ensure that the language model and algorithms of the system can be updated in a timely manner.

[0126] Version control system: Manage different versions of the language model and algorithms for easy rollback and maintenance.

[0127] Adaptive interaction module

[0128] Working principle: Analyze the user's interaction patterns, automatically adjust the dialogue style and response speed, and optimize the user experience.

[0129] Technical implementation:

[0130] User behavior analysis: Collect and analyze the user's historical interaction data to understand their preferences and habits.

[0131] Personalized adjustment: Dynamically adjust the dialogue strategy based on the analysis results, such as changing the dialogue style and adjusting the response speed.

[0132] Multi-turn dialogue mechanism

[0133] Working principle: Allow the user to ask multiple questions or give multiple instructions in the same conversation and be able to understand the logical relationship between the previous and subsequent questions.

[0134] Technical implementation:

[0135] Dialogue graph construction: Create a dialogue tree or graph to represent different branches and paths of the conversation, helping the system understand complex dialogue structures.

[0136] Context-dependent parsing: Identify and process referential relationships and other context-dependent items in the conversation to ensure the relevance and accuracy of the response.

[0137] Multi-language communication database

[0138] Working principle: Support multiple languages, adapt to the language habits of users in different regions, and be able to adjust the language interaction mode according to the user's preferences.

[0139] Technical implementation:

[0140] Machine Translation: Achieve multilingual support using advanced machine translation technology.

[0141] Localization Service: Provide a personalized service experience according to the cultural characteristics and language habits of different regions.

[0142] Domain Knowledge Base

[0143] Working Principle: Integrate professional knowledge in the field of smart cities to provide users with professional answers and services.

[0144] Technical Implementation:

[0145] Knowledge Graph: Construct a detailed domain knowledge graph to enhance the reasoning ability and answer accuracy of the system.

[0146] Expert System: Integrate knowledge from industry experts to ensure that the information provided is both professional and accurate.

[0147] Dynamic Data Analysis Module

[0148] Working Principle: Automatically extract data from a private database to achieve dynamic data analysis and improve the practicality and intelligence level of the system.

[0149] Technical Implementation:

[0150] Data Mining and Analysis: Use data mining techniques and statistical analysis methods to extract valuable information from massive data.

[0151] Real-time Processing: Support real-time data stream processing to ensure that users obtain the latest and accurate data support.

[0152] Personalized Recommendation Engine

[0153] Working Principle: Provide personalized service recommendations based on users' historical behaviors and personal preferences.

[0154] Technical Implementation:

[0155] Collaborative Filtering: Use the similarity of user behaviors for recommendations.

[0156] Content Recommendation: Match relevant content according to users' interest points to improve the accuracy of recommendations.

[0157] Intelligent Query Transformer

[0158] Working Principle: Adopt GraphRAG technology and AI Agent to achieve efficient natural language to SQL query conversion through a reinforcement learning mechanism.

[0159] Technical Implementation:

[0160] Graph Retrieval-Augmented Generation (GraphRAG): Combines graph-structured data and text information to enhance the accuracy of query transformation.

[0161] Reinforcement learning: Trains intelligent agents to continuously optimize the query transformation process, improving efficiency and accuracy.

[0162] Seamless integration module

[0163] Working principle: Can be seamlessly integrated with existing smart city infrastructure without changing the existing user workflow.

[0164] Technical implementation:

[0165] API interface: Develops standard API interfaces for easy docking with other systems.

[0166] Plugin architecture: Designs a flexible plugin architecture to support rapid expansion and integration.

[0167] Cross-platform compatibility module

[0168] Working principle: Supports multiple device platforms such as desktop and mobile, ensuring that users can enjoy consistent service quality at any time and place.

[0169] Technical implementation:

[0170] Responsive design: Ensures good performance of the application on various screen sizes.

[0171] Multi-platform development framework: Builds a unified application using cross-platform development tools (such as React Native, Flutter).

[0172] Visual data analysis tool

[0173] Working principle: Provides users with intuitive data display and analysis functions to help users better understand and utilize the large amount of data in the smart city.

[0174] Technical implementation:

[0175] Data visualization library: Integrates powerful chart libraries (such as D3.js, ECharts) to provide rich visualization options.

[0176] Interactive dashboard: Creates an interactive dashboard interface for users to easily explore and analyze data.

[0177] User feedback loop

[0178] Working principle: Collects users' opinions and suggestions on the service and continuously improves and optimizes the system performance.

[0179] Technical implementation:

[0180] Feedback channels: Establish diverse feedback channels (such as online forms, social media comments).

[0181] Data analysis: Regularly analyze user feedback to identify common problems and improvement suggestions.

[0182] Situational awareness module

[0183] Working principle: Provide services and suggestions that are more in line with the actual scenario based on factors such as the user's location, environmental conditions, and current activities.

[0184] Technical implementation:

[0185] Geolocation services: Utilize GPS and other positioning technologies to obtain user location information.

[0186] Environmental monitoring: Integrate sensor networks to monitor environmental changes in real time and provide relevant service prompts.

[0187] Community interaction platform

[0188] Working principle: Facilitate communication between citizens and between citizens and the government, and encourage public participation in urban construction and development.

[0189] Technical implementation:

[0190] Social network integration: Connect mainstream social platforms to expand the influence of the community.

[0191] Discussion forum: Establish a dedicated discussion area for citizens to express their opinions and suggestions.

[0192] Resource scheduling optimization module

[0193] Working principle: Provide optimization solutions for resource allocation problems in urban management.

[0194] Technical implementation:

[0195] Linear programming and simulation: Apply mathematical modeling and simulation technologies to optimize resource allocation strategies.

[0196] Intelligent scheduling algorithm: Develop efficient scheduling algorithms to maximize resource utilization.

[0197] Real-time event response module

[0198] Working principle: Monitor and respond to urban emergencies in real time, promptly release information to relevant departments and the public, and assist in emergency management and decision-making support.

[0199] Technical implementation:

[0200] Internet of Things (IoT) integration: Connect various sensors and monitoring devices in the city to achieve real-time data collection.

[0201] Early warning system: Establish an early warning mechanism to immediately trigger an alarm and activate the emergency response plan once abnormal situations are detected.

[0202] Continuous learning and self-evolution module

[0203] Working principle: By continuously accumulating user interaction data and feedback information, use machine learning and deep learning algorithms to continuously optimize its own performance and service capabilities.

[0204] Technical implementation:

[0205] Incremental learning: Based on the existing model, gradually introduce new data for fine-tuning to keep the model continuously updated.

[0206] Transfer learning: Transfer the knowledge of one domain to another domain to accelerate the learning speed of new tasks. Detailed work process

[0207] User initiates a request

[0208] The user sends a request to the CUI through the natural language interaction module, voice interaction module or text input interaction module, such as asking questions, giving commands or making inquiries.

[0209] Request parsing and intent recognition

[0210] After receiving the request, the CUI first parses the user's natural language expression by the natural language interaction module to identify the user's intent and key entities.

[0211] Context analysis and memory

[0212] If it is part of a continuous conversation, the context memory mechanism will review the previous conversation records to ensure that the current response is consistent with the previous content.

[0213] Intelligent response generation

[0214] The AI intelligent response mechanism generates customized feedback or suggestions according to the user's specific needs and context, combined with the latest AI technology.

[0215] For complex queries, the intelligent query converter converts natural language into SQL query statements to extract relevant data from the private database.

[0216] Data processing and analysis

[0217] The dynamic data analysis module processes the acquired data to generate intuitive charts or reports to assist users in understanding and decision-making.

[0218] If necessary, the personalized recommendation engine will also intervene at this stage to provide recommendations on services or products that the user may be interested in.

[0219] Security Check and Information Transmission

[0220] The security module encrypts all transmitted data to ensure the security of communication; meanwhile, sensitive information protection measures prevent unnecessary data exposure.

[0221] Response Output

[0222] The final reply is returned to the user in the form of voice or text, and the whole process is smooth and natural, as if talking to a real person.

[0223] User Feedback Collection

[0224] The user feedback loop collects users' opinions and suggestions on the service for the subsequent improvement and optimization of the system.

[0225] System Self-Optimization

[0226] The continuous learning and self-evolution module continuously accumulates user interaction data and feedback information, and uses machine learning and deep learning algorithms to adjust and optimize its own performance to adapt to new changes and development trends.

[0227] In summary, the various modules of the smart city AI assistant system cooperate with each other to jointly build an efficient, intelligent, secure and user-friendly interaction platform. Each module has its unique technical implementation method, aiming to provide the best service experience for users and promote the construction and development of the smart city.

[0228] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made therein without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart city AI assistant system, characterized in that, including a Conversational User Interface (CUI), the Conversational User Interface includes: Natural Language Interaction Module: Supports text input and voice input, and has functions of intent recognition, entity extraction, and sentiment analysis; Voice Interaction Module: Integrates a screen reader and voice command functions to support barrier-free access; Text Input Interaction Module: Provides a text input method based on a keyboard or touch screen; Context Memory Mechanism: Maintains the coherence and consistency of the conversation during multiple interactions; AI Intelligent Response Mechanism: Provides customized feedback and suggestions according to the specific needs and context of the user, combined with the latest AI technologies; Security Module: Includes functions of encrypted communication and sensitive information protection to ensure user privacy and data security; Information Update Module: Regularly updates the language model and algorithms to synchronize the latest laws, regulations, policies, and technological developments, ensuring that the provided information is the latest and most accurate.

2. The smart city AI assistant system according to claim 1, characterized in that, The CUI further includes an Adaptive Interaction Module, and the Adaptive Interaction Module includes interaction mode analysis, dialogue style adjustment, and dialogue response speed adjustment.

3. The smart city AI assistant system according to claim 2, wherein, The CUI further includes a multi-turn dialogue mechanism for allowing the user to ask multiple questions or instructions in one session and being able to understand the logical relationship between the previous and subsequent questions.

4. The smart city AI assistant system according to claim 3, characterized in that, The CUI includes a multilingual communication database for adapting to the language habits of users in different regions and adjusting the language interaction mode.

5. The smart city AI assistant system according to claim 4, characterized in that, The CUI further includes: Domain Knowledge Base: Integrates professional knowledge in the field of smart cities to provide professional answers and services for users; Dynamic Data Analysis Module: Automatically extracts data from a private database to achieve dynamic data analysis and improve the practicality and intelligence level of the system.

6. The smart city AI assistant system according to claim 5, wherein The CUI further includes: Personalized Recommendation Engine: Provides personalized service recommendations based on the user's historical behavior and personal preferences; Intelligent Query Converter: Adopts Graph-based Retrieval-Augmented Generation (GraphRAG) technology and Artificial Intelligence Agent (AIAgent), and realizes efficient natural language to SQL query conversion through a reinforcement learning mechanism, which is especially suitable for processing complex data structures and changing query requirements in smart cities.

7. The smart city AI assistant system according to claim 6, wherein The CUI further includes: Seamless Integration Module: Can be seamlessly integrated with existing smart city infrastructure without changing the existing user work process, providing users with a continuous service experience; Cross-platform Compatibility Module: Supports multiple device platforms such as desktop and mobile, ensuring that users can enjoy consistent service quality at any time and place.

8. An AI assistant system for a smart city according to claim 7, characterized in that, The CUI further includes: Visual Data Analysis Tool: Provides users with intuitive data display and analysis functions to help users better understand and utilize a large amount of data in smart cities; User Feedback Loop: Collects users' opinions and suggestions on the service, continuously improves and optimizes the system performance, and forms a virtuous closed-loop of user interaction and service upgrade.

9. The smart city AI assistant system according to claim 8, wherein, The CUI further includes: Situation Awareness Module: Provides services and suggestions that are more in line with the actual scenario according to the user's location, environmental conditions, and current activity factors; Community Interaction Platform: Facilitates communication between citizens and between citizens and the government, and encourages public participation in urban construction and development.

10. The smart city AI assistant system according to claim 9, characterized in that, The CUI further includes: Resource Scheduling Optimization Module: Provide optimization solutions for resource allocation problems in urban management; Real-time Event Response Module: Capable of monitoring and responding to urban emergencies in real time, promptly releasing information to relevant departments and the public, and assisting in emergency management and decision-making support; Continuous Learning and Self-evolution Module: Continuously optimize its own performance and service capabilities by accumulating user interaction data and feedback information and applying machine learning and deep learning algorithms.

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