Event-Driven Data Extraction for Time-Based Factory Configuration
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Solution Overview
Problem
The existing systems for analyzing data in IoT-enabled factories face a significant workload challenge in associating sensor data with configuration data, as both change dynamically over time, making manual data extraction inefficient and labor-intensive, especially when detecting abnormalities that require information from multiple machines and workers.
Innovation Solution
A data extracting apparatus that accesses configuration information and uses data extraction rules defined for specific events, such as changes or abnormalities, to automatically detect and extract related data from sensor data, reducing the manual workload by utilizing a processor and storage device to execute detection, extraction, and output processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data association is performed, then data extraction accuracy is maintained, but workload and time consumption increase significantly
Solution Approach 1:
The system performs self-service by automatically extracting related data using predefined extraction rules and configuration information, without requiring manual intervention. The data extracting apparatus autonomously detects events, identifies relevant configuration data, and extracts associated information, thereby resolving the contradiction between productivity improvement and operational ease.
Solution Approach 2:
The invention introduces an intermediary mechanism in the form of configuration information and data extraction rules that mediate between sensor data and the required related data. This intermediary layer enables automated extraction by translating event detections into specific data retrieval operations, eliminating the need for manual data association while maintaining accuracy.
2Adaptability or versatility
If static configuration information is used, then data extraction is simplified, but adaptability to dynamic factory environments is reduced
Solution Approach 1:
The system implements dynamics by using time-stamped configuration information that reflects the actual state of the factory environment at the time of data acquisition. Instead of relying on static configuration data, the system dynamically retrieves configuration information corresponding to the acquisition time of sensor data, enabling adaptation to changing production line configurations, machine locations, and worker assignments.
Solution Approach 2:
The invention applies preliminary action by pre-defining data extraction rules for various event types before runtime. These rules specify how to extract related data for different sensor events, allowing the system to quickly adapt to dynamic environments without requiring complex real-time decision-making logic.
3Measurement precision
If comprehensive related data is extracted, then analysis accuracy improves, but data processing time increases
Solution Approach 1:
The system applies the extraction principle by selectively retrieving only the specific related data required for analyzing detected events, rather than extracting all available data. The data extraction rules define precise criteria for identifying relevant configuration information, ensuring that only necessary data elements are processed, thereby maintaining analysis accuracy while minimizing processing time.
Solution Approach 2:
The invention uses segmentation by dividing the data extraction process into distinct stages: event detection, rule matching, configuration information retrieval, and related data extraction. This segmented approach allows the system to process data efficiently by focusing on specific subsets of information relevant to each event type, reducing overall processing time while maintaining comprehensive analysis capability.
Data Source
AI summary
A data extracting apparatus comprises: a processor configured to execute a program; and a storage device configured to store the program, the data extracting apparatus being configured to access: configuration information that defines a work environment including a group of sensors; and a data extraction rule, which is used for extraction from the configuration information, and is defined for each event that indicates one of a change and abnormality of the work environment, and the processor being configured to execute: detection processing of detecting a specific event based on sensor data from the group of sensors; extraction processing of extracting related data related to the specific event from specific configuration information corresponding to an acquisition time of the sensor data out of the configuration information, based on a specific data extraction rule corresponding to the specific event; and output processing of outputting the related data.