Content Model Database for Dynamic Field Information Delivery
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Solution Overview
Problem
Existing information providing systems struggle to efficiently identify and provide optimal content to users in dynamic field environments, particularly in maintenance and repair scenarios where device configurations and conditions change, leading to difficulties in matching manual procedures and requiring frequent updates of learning models.
Innovation Solution
An information providing system that utilizes a content model database and scene model database to associate video information from user terminals with reference IDs and scene IDs, generating ID lists and summaries to output relevant content based on degrees of association, allowing for efficient and flexible information delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a learning model is generated based on rules from a manual to support work, then work support information can be provided, but when the actual field or device configuration changes, the learning model becomes obsolete and requires frequent regeneration, consuming time and resources
Solution Approach 1:
The system pre-establishes multiple levels of content association (first, second, and third levels) between video information and reference IDs before actual work occurs. This preliminary structuring allows the system to quickly navigate and retrieve relevant content without regenerating learning models when field conditions change, thus resolving the contradiction between adaptability and time loss.
Solution Approach 2:
The content association is divided into multiple hierarchical levels: first-level association between video information and reference IDs, second-level association between reference IDs and contents, and third-level association between scene information and video information. This segmentation enables flexible navigation and quick adaptation to different field scenarios without requiring complete model regeneration, addressing both adaptability and time efficiency.
2Quantity of substance
If multiple contents are stored in a database to cover various field scenarios, then comprehensive work support can be provided, but it becomes difficult to efficiently narrow down and select the most relevant content without sequential filtering
Solution Approach 1:
The content database is organized through multi-level association structures where video information is linked to reference IDs at the first level, reference IDs are linked to actual contents at the second level, and scene information is linked to video information at the third level. This segmentation enables efficient narrowing down of contents by allowing the system to traverse only the necessary levels based on the current work scenario, maintaining comprehensive content coverage while improving selection efficiency.
Solution Approach 2:
The system adds a dimensional hierarchy to content storage by introducing multiple levels of association (first, second, and third levels) between different data elements. This dimensional structure allows for efficient content narrowing by enabling the system to jump between different association levels based on the current work context, thus improving productivity while maintaining comprehensive content coverage.
3Measurement precision
If detailed information about device configurations and anomaly locations is collected to provide accurate work support, then the accuracy of anomaly identification improves, but the complexity of data processing and information management increases
Solution Approach 1:
The system extracts and stores only the essential association relationships between video information, scene information, and reference IDs at different levels. By taking out and pre-organizing only the necessary association data rather than storing all possible detailed configurations, the system achieves accurate anomaly identification while reducing data processing complexity and storage requirements.
Solution Approach 2:
The system performs preliminary association between video information, scene information, and reference IDs at multiple levels before actual anomaly detection occurs. This pre-establishment of association relationships allows for accurate anomaly identification without requiring complex real-time data processing, thus resolving the contradiction between measurement precision and device complexity.
Data Source
AI summary
A content model data base stores past target information, which includes past first video information acquired in advance, reference IDs, which are linked with the past target information, and which correspond to contents, and three or more levels of degrees of content association between the past target information and the reference IDs. A first acquiring unit acquires the target information from a user terminal, a first evaluation unit looks up the content model database and acquires ID information, which includes the degrees of content association between the target information and the reference IDs, and a judging unit judges the ID information. Contents that correspond to the ID information are output to the user terminal based on the result of judgment by the judging unit.