Field Equipment Data Hierarchies for Real-Time Quality Assessment
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Solution Overview
Problem
Current systems for managing and analyzing field equipment data in geologic environments lack efficient methods for real-time data quality assessment and customizable data hierarchy generation, leading to suboptimal operational decisions and resource management.
Innovation Solution
A method and system that receive field equipment data, detect data schemas, configure listeners, and assess data using an assessment engine to generate customizable and navigable hierarchies of data metric values, enabling real-time data quality monitoring and actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data management systems are used for field equipment data, then system simplicity is maintained, but real-time data quality assessment capability is insufficient
Solution Approach 1:
The system segments data quality assessment into multiple hierarchical levels (field level, equipment level, data metric level) that can be independently configured and assessed. Each level has its own assessment criteria and metrics, allowing precise data quality evaluation without requiring complete system redesign.
Solution Approach 2:
The system performs preliminary schema detection and listener configuration before actual data assessment begins. Data quality metrics are pre-defined and prepared, enabling real-time assessment without complex runtime processing.
2Adaptability or versatility
If fixed data structures are used, then data processing simplicity is maintained, but adaptability to different data schemas is reduced
Solution Approach 1:
The system dynamically adapts to different data schemas through automatic schema detection and adaptive listener configuration. The data hierarchy structure is flexible and can be customized based on the specific data source being assessed, allowing the system to handle diverse equipment data formats without manual reconfiguration.
Solution Approach 2:
The system performs self-configuration by automatically detecting data schemas and generating appropriate listener settings. This self-service capability enables the system to adapt to new data sources without requiring extensive manual programming or configuration.
3Loss of information
If comprehensive data assessment is performed, then data quality insight is improved, but processing time increases
Solution Approach 1:
The system pre-configures assessment metrics and hierarchy structures before data processing begins. Common data quality metrics are pre-defined at each hierarchical level, enabling rapid assessment without extensive runtime computation.
Solution Approach 2:
The assessment process is divided into hierarchical segments that can be evaluated independently. This segmentation allows the system to provide comprehensive data quality information at multiple levels without requiring complete re-assessment of all data, reducing overall processing time.
4Ease of operation
If customizable data hierarchies are generated, then user navigation flexibility is improved, but system configuration complexity increases
Solution Approach 1:
The data hierarchy is dynamically generated based on the detected data schema and assessment requirements. Users can customize the hierarchy structure through configurable parameters that control aggregation levels and metric groupings, allowing flexible navigation without hard-coded complex configurations.
Solution Approach 2:
The hierarchical data structure serves multiple functions simultaneously: it organizes data for assessment, enables navigation, and supports various query types. This multi-functionality reduces the need for separate configuration systems for each operation.
Data Source
AI summary
A method can include receiving field equipment data from a source; detecting a data schema for the source; configuring a listener for the source according to a corresponding detected data schema to receive additional field equipment data; and assessing at least a portion of the additional field equipment data using an assessment engine to generate a hierarchy of data metric values for the source, where the hierarchy is customizable and navigable responsive to receipt of instructions.