AI Robot Deployment Using Context Keywords and Density Data
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Solution Overview
Problem
Existing methods for deploying artificial intelligence robots in airports and multiplexes lack efficiency in determining optimal deployment areas based on factors influencing density in control areas, leading to suboptimal service provision.
Innovation Solution
An artificial intelligence server that obtains density data, identifies relevant context keywords, generates keyword combinations, determines confidence levels, and calculates average density values to select the most suitable deployment area for robots, ensuring efficient service delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to deploy robots in airports and multiplexes, then implementation is simple, but service efficiency and effectiveness deteriorate due to inability to determine optimal deployment areas based on density factors
Solution Approach 1:
The system performs preliminary analysis by obtaining density data for control areas and pre-processing this data to identify context keywords that influence density. This preliminary action enables the robot deployment system to have advance knowledge of high-density areas, allowing for optimized deployment decisions rather than random or manual placement.
Solution Approach 2:
The patent introduces an intermediary processing layer that takes raw density data and transforms it into meaningful context keywords and deployment area recommendations. This intermediary system includes modules for obtaining density data, processing the data to extract context keywords, and generating deployment area information, which mediates between the complex raw data and the simple deployment decision.
2Reliability
If robot deployment is based on comprehensive density analysis with multiple context keywords, then service quality improves, but system complexity increases
Solution Approach 1:
The patent segments the complex density analysis system into distinct functional modules: a density data obtaining module, a data processing module that extracts context keywords, and a deployment area determination module. Each module handles a specific aspect of the analysis, making the overall complex system more manageable and maintainable while still achieving high service quality through comprehensive analysis.
Solution Approach 2:
The system changes parameters by identifying and focusing on specific context keywords that have the most significant influence on density, rather than analyzing all possible parameters. This selective parameter approach maintains high reliability in deployment decisions while reducing the effective complexity of the analysis by concentrating on the most relevant factors.
3Measurement precision
If the system processes multiple context keywords and generates keyword combinations, then deployment accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by generating and evaluating keyword combinations selectively rather than exhaustively. The system processes multiple context keywords and generates relevant combinations to improve deployment accuracy, but appears to use thresholds or filtering mechanisms to avoid unnecessary processing of all possible combinations, thus balancing accuracy with processing time efficiency.
4Productivity
If robots are deployed in high-density areas determined by context analysis, then service effectiveness improves, but the requirement for data processing infrastructure increases
Solution Approach 1:
The patent creates a universal data processing framework that can handle multiple types of density data and context keywords through a single integrated system. The density data obtaining module and processing module are designed to be multi-functional, capable of processing various data sources and keyword types, which reduces the need for separate specialized systems and infrastructure.
Data Source
AI summary
An artificial intelligence server for determining a deployment area of a robot includes a memory and a processor. The memory is configured to store density data for a control area. The processor is configured to obtain a plurality of current context keywords corresponding to a current time point, determine at least one related keyword among the obtained plurality of current context keywords using the density data, and determine the deployment area of the robot based on density data corresponding to the determined related keyword.