Sensor Data Frame Mapping for Automated Reasonability Analysis
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Solution Overview
Problem
Large-scale data processing from various sources, particularly in aircraft systems, faces challenges in handling unstructured data sets due to format inequalities, leading to inefficiencies in data analysis and accuracy, as existing systems are not designed for search, storage, and retrieval of vital information, necessitating manual inspection and resource-intensive processing.
Innovation Solution
A method for data parameter reasonability analysis that receives a source data file with unknown data frames, compares sensor data to compatible parameter values, generates a difference map, assigns a data frame format based on minimal differences, and determines reasonableness using parameter value definitions and rules, providing a report on data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to determine data reasonableness, then data accuracy can be verified, but processing time and resource consumption increase significantly
Solution Approach 1:
The system performs self-validation by automatically comparing sensor data against predefined parameter definitions, constraints, and cause-and-effect relationships. The data processing system itself verifies the reasonableness of incoming data without requiring external manual inspection, thereby maintaining accuracy while eliminating time loss.
Solution Approach 2:
The patent replaces manual mechanical inspection with an automated computational system that uses parameter definitions, constraints, and logical rules to verify data reasonableness. This substitution of human effort with algorithmic processing maintains verification accuracy while dramatically reducing processing time.
2Adaptability or versatility
If data from multiple sources with different formats is processed, then comprehensive data analysis is enabled, but format inequalities require manual inspection and increase complexity
Solution Approach 1:
The system employs a universal parameter definition framework that can accommodate data from multiple sensor types and formats. By defining standardized parameters with associated constraints and relationships, the system achieves multi-functionality in handling diverse data sources without proportionally increasing processing complexity.
Solution Approach 2:
The patent transforms heterogeneous data from different formats into a standardized parameter structure. By changing the representation of incoming data to fit predefined parameter definitions and constraints, the system enables comprehensive multi-source analysis while managing complexity through consistent parameter transformation rules.
3Ease of manufacture
If standard database management techniques are used for Big Data processing, then existing infrastructure can be utilized, but effectiveness decreases for large volumes of unstructured data
Solution Approach 1:
The system segments the data processing task into distinct components: data reception, parameter definition application, constraint verification, and reasonableness determination. This segmentation allows the use of existing database infrastructure for data storage while applying specialized processing logic only where needed, thereby maintaining ease of implementation while improving processing effectiveness for unstructured data.
Data Source
AI summary
An example method for data parameter reasonability analysis includes receiving a source data file including a plurality of data frames having formats that are unknown, and a data frame includes a data word layout having data words concatenated together and the data words are representative of outputs from a plurality of sensors, comparing the sensor data to compatible parameter values for a type of sensor mapped to the location of the sensor data in the data word layout as for available data frame formats, generating a difference map illustrating one or more data words having sensor data incompatible with the type of sensor mapped to the location of the sensor data in the data word layout, assigning a data frame format to the plurality of data frames, determining reasonableness of the sensor data in the source data file, and providing a report as to reasonability of the sensor data.