POI State Extraction via Pre-trained Model Alignment
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Solution Overview
Problem
Current methods for acquiring POI state information, such as manual collection and user reporting, are resource-intensive, costly, and lack timeliness and accuracy due to the dynamic nature of POI data in city environments.
Innovation Solution
A method and apparatus utilizing a pre-trained POI-state identifying model to extract and align POI name and state information from Internet texts, employing a parallel processing approach and enhanced representation models like ERNIE to improve prediction accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual collection or user reporting is used to acquire POI state information, then the method is simple to implement, but it wastes human resources, has higher costs, and lacks timeliness and accuracy
Solution Approach 1:
The patent replaces manual collection and user reporting mechanisms with an automated computer vision system. The system uses image processing algorithms to automatically detect POI state changes (such as construction, renovation, or closure) by analyzing images captured from streets and areas, eliminating the need for human workers to physically inspect locations or rely on user reports.
Solution Approach 2:
The system enables self-service by automatically monitoring and updating POI information without requiring human intervention. The automated detection system continuously captures images and processes them to identify POI state changes, allowing the database to update itself based on visual evidence rather than requiring manual verification or user-initiated reports.
2Measurement precision
If manual collection methods are used, then the system complexity is low, but the timeliness and accuracy of POI state information are difficult to guarantee
Solution Approach 1:
The patent replaces manual inspection methods with automated computer vision technology. The system uses image processing algorithms to accurately detect POI state changes such as construction activities, renovations, or closures by analyzing visual data, providing precise and objective measurements of POI status without human subjectivity.
Solution Approach 2:
The system implements continuous monitoring by automatically capturing images at regular intervals and continuously processing them to detect POI state changes. This continuous action ensures that the system always has up-to-date information about POI statuses, eliminating the gaps in timeliness that occur with manual collection methods.
3Productivity
If user reporting is used to acquire POI state information, then the system remains simple, but it depends on human initiative seriously and lacks timeliness
Solution Approach 1:
The patent replaces user-reporting mechanisms with automated computer vision systems that continuously monitor POIs without requiring human initiation. The system automatically detects and reports POI state changes as they occur, eliminating the delays inherent in waiting for users to notice and report changes themselves.
Solution Approach 2:
The system performs preliminary action by continuously capturing and processing images before POI state changes are reported by users. The automated detection system is always ready to identify and report changes immediately when they occur, rather than waiting for users to initiate the reporting process after they become aware of the changes.
Data Source
AI summary
The present application discloses a method and apparatus for acquiring point-of-interest (POI) state information, a device and a computer storage medium, and relates to the field of big data. An implementation includes acquiring a text containing POI information in a preset time period from the Internet; and identifying the text using a pre-trained POI-state identifying model to obtain a binary group in the text, the binary group including a POI name and the POI state information corresponding to the POI name; wherein the POI-state identifying model performs label prediction of the POI name and a POI state on a word sequence corresponding to the text, and label prediction results of the POI name and the POI state are aligned to obtain the binary group. With the present application, a human cost may be saved, and timeliness and accuracy may be improved.