Information Providing System for Dynamic Field Maintenance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information providing systems struggle to efficiently provide optimal content to users in dynamic field environments, particularly in maintenance and repair scenarios where device configurations and parts may differ from manual descriptions, leading to difficulties in identifying anomalies and updating information effectively.
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, allowing for the output of relevant content based on degrees of content and scene association, with features like ID history storage and meta-information integration for efficient information retrieval and updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a learning model is generated every time dealing with a new situation, then the information can be updated to match actual field conditions, but it requires time and costs
Solution Approach 1:
The system pre-generates multiple learning models corresponding to different situations or scenarios before actual work begins. When a new situation arises, the system selects from pre-generated models rather than generating a new one from scratch, thereby reducing time consumption while maintaining information accuracy.
Solution Approach 2:
The system changes parameters such as situation type, device configuration, or environmental conditions to differentiate between various work scenarios. By parameterizing the learning models according to these changes, the system can efficiently select appropriate pre-generated models for different field situations without regenerating them.
2Reliability
If detailed information about device configurations and parts is provided, then the information accuracy is improved, but the device complexity increases
Solution Approach 1:
The system segments detailed device information into multiple categories such as device configuration, part specifications, installation conditions, and maintenance procedures. Each category is stored as a separate learning model, allowing the system to retrieve only the relevant segment needed for a specific task rather than processing all detailed information at once.
Solution Approach 2:
The system introduces an intermediary layer of classification and indexing between raw device information and the final output. This intermediary structure organizes detailed information into manageable units with associated metadata, enabling accurate information retrieval without exposing the underlying complexity of the data structure.
3Reliability
If manual procedures are followed strictly, then the information provided is accurate, but the adaptability to actual field conditions decreases
Solution Approach 1:
The system transitions from static manual procedures to dynamic learning models that can adapt to varying field conditions. The learning models are trained on diverse scenarios and can dynamically adjust their recommendations based on actual device configurations, part replacements, and environmental factors encountered in the field.
Solution Approach 2:
The system incorporates feedback mechanisms where actual work outcomes and field conditions are fed back into the learning models. This feedback loop allows the models to continuously improve their accuracy and adaptability, maintaining procedure accuracy while enhancing flexibility to handle real-world variations.
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 an output unit outputs the contents corresponding to the ID information. After the output from the output unit, the ID information, acquired by the first evaluation unit, is stored in an ID history unit.