Biometric Interaction System Using Segmented Server Architecture
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Solution Overview
Problem
Current man-machine interaction methods in large gatherings are inefficient and lack intelligence, relying on manual guidance and primitive interaction mechanisms, such as electronic bulletin boards and passive information display, which fail to engage participants effectively.
Innovation Solution
A system and method that utilizes biological recognition technologies, including face and voice recognition, to collect and preprocess user data, assign recognition tasks, extract characteristics, and generate interaction responses, enhancing the intelligence of man-machine interaction through a networked interaction end, central server, and computing server setup.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual guidance and electronic bulletin boards are used for participant guidance, then the system is simple to implement, but the interaction intelligence and flexibility are insufficient
Solution Approach 1:
The system divides the interaction process into multiple segments: biological sample collection at interaction ends, data transmission to central servers, recognition task distribution to computing servers, and result feedback. This segmentation allows each component to perform specialized functions, improving overall intelligence while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a central server as an intermediary between interaction ends and computing servers. This intermediary coordinates the complex interactions, manages task distribution, and handles data flow, thereby enabling intelligent interaction without requiring direct complex connections between all system components.
2Adaptability or versatility
If biological recognition technology is applied to collect user data, then the interaction becomes intelligent and flexible, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by collecting and preprocessing biological sample information at interaction ends before transmitting to central servers. The central server also pre-processes data by filtering out samples that do not meet preset conditions, reducing the computational burden on computing servers and accelerating the overall processing time.
Solution Approach 2:
The patent extracts and filters out unnecessary or non-conforming biological sample information during the preprocessing stage. By removing invalid samples before they enter the recognition system, the computational resources are focused only on valid data, thereby reducing processing time while maintaining high interaction flexibility.
3Ease of operation
If multiple staff are deployed for guidance and interaction, then the service quality is high, but the operational cost increases
Solution Approach 1:
The system enables self-service interaction where participants automatically provide biological samples (facial images, voice recordings) and receive personalized guidance through the intelligent system. The interaction ends automatically collect data, transmit to servers, and execute pre-programmed interaction protocols, eliminating the need for staffed guidance while maintaining high service quality.
Solution Approach 2:
The patent replaces the mechanical system of human staff providing guidance and interaction with an automated intelligent system. The central server and computing servers perform recognition tasks, data processing, and interaction coordination automatically, substituting human labor with computational mechanisms that provide consistent, high-quality service without requiring additional staff.
Data Source
AI summary
The application discloses a main-machine interaction method and system, a computer device, and a storage medium. The method includes: an interaction end collects user information including biological sample information and preprocesses and filters the user information, and sends the preprocessed information to a central server; the central server assigns a biological recognition task and distributes the biological recognition task to a computing server; the computing server extracts biological characteristics from the biological sample information according to the biological recognition task, and returns obtained extraction results to the central server; the central server combines the extraction results to get a processing result; the processing result is returned to the interaction end; the interaction end responds to the processing result to complete a man-machine interaction with the user.