IoT Smart City Chatbot With Semantic Search for Personalized Responses
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Solution Overview
Problem
Traditional smart city service platforms struggle to provide personalized information services for diverse user groups due to slow response speed and poor accuracy in meeting varying user demands.
Innovation Solution
A smart city service system utilizing an Internet of Things large model, incorporating a chatbot with a user service model and service information management model, enables personalized responses by preprocessing user queries, determining target service models, and generating customized information through semantic search and emotional analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional service platforms are used, then system complexity is low, but response speed and personalization accuracy are poor
Solution Approach 1:
The service platform is segmented into multiple independent modules: user profile module, service information database, chatbot with semantic search, target service model selection, and response generation system. Each module operates independently with defined interfaces, enabling parallel processing and faster response while maintaining manageable complexity through modular architecture.
Solution Approach 2:
User profiles and service preferences are pre-established in the database before actual service requests. The system performs preliminary semantic analysis and model selection based on pre-stored user characteristics, eliminating the need for real-time complex computations and thereby improving response speed without proportionally increasing system complexity.
2Measurement precision
If customized training for different user types is implemented, then personalization accuracy improves, but device complexity and computational requirements increase
Solution Approach 1:
Instead of using a single complex model for all users, the system employs multiple specialized service models (target service models) each optimized for specific user types or service domains. The chatbot selects the appropriate model based on user profile characteristics, providing high personalization accuracy for each user segment while keeping individual model complexities manageable.
Solution Approach 2:
The system changes parameters such as model selection, search depth, and response generation settings based on user profile attributes (industry, occupation, age, etc.). This dynamic parameter adjustment enables customized training effects for different user types without requiring a completely different system architecture for each user category.
3Reliability
If semantic search and model selection are performed for each query, then service accuracy improves, but processing time increases
Solution Approach 1:
Service information and user profiles are pre-processed and stored in optimized formats in the database before queries are received. The system performs preliminary indexing and categorization, enabling fast semantic search and model selection when actual service requests come in, thereby maintaining high accuracy while reducing processing time.
Solution Approach 2:
The system creates and maintains simplified copies or representations of service information (semantic indices, vector embeddings) that can be quickly searched and matched against user queries. These pre-computed representations enable rapid model selection and accurate service delivery without requiring complex real-time analysis of all raw service data.
Data Source
AI summary
Provided is a smart city service system based on an Internet of Things large model. The system includes a smart city user platform and a smart city service platform, the smart city service platform includes a chatbot, and the chatbot is configured to: obtain a query sent by a user through a user interface; determine, based on the query, initial service information of the query from a service information database through semantic search; determine, based on the query and modal information of the initial service information, a target service model from a user service model library; generate a response to the query based on the query, the initial service information, and an expression evaluation value through the target service model; and send the response to the smart city user platform, and output the response through the user interface.