Vehicle Data Update Method for Reliable Min-Max Tracking
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Solution Overview
Problem
Existing methods for updating maximum or minimum values of measurement data in vehicle operation control systems require significant computational load and storage capacity, leading to increased costs and processing time, and are prone to incorporating abnormal data caused by temporary issues like sensor disconnections or noise.
Innovation Solution
A data update processing method that compares acquired data values with recent minimum or maximum values, repeatedly updating the smaller value as the new minimum and the larger value as the new maximum, and storing these values in a nonvolatile storage element, thereby simplifying the procedure and reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mean value or median value methods are used to avoid abnormal data, then data reliability is improved, but storage area requirement and computational load increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for reliability assessment - the most recent maximum and minimum values - rather than storing and processing all historical data. This selective extraction maintains data reliability while dramatically reducing storage requirements and computational load.
Solution Approach 2:
The patent segments the data processing task into two distinct phases: a data collection phase that accumulates historical data, and a data assessment phase that uses only the most recent maximum and minimum values. This segmentation allows the system to benefit from historical data for context while using minimal recent data for actual reliability assessment.
2Reliability
If mean value or median value methods are used to avoid abnormal data, then data reliability is improved, but processing time and computational load increase
Solution Approach 1:
The patent extracts only the essential information needed for reliability assessment - the most recent maximum and minimum values - rather than storing and processing all historical data. This selective extraction maintains data reliability while dramatically reducing storage requirements and computational load.
Solution Approach 2:
The system performs preliminary data collection and maintains running records of maximum and minimum values during normal operation. This preliminary action prepares the data structure in advance, so that when reliability assessment is needed, the system can immediately use the pre-collected maximum and minimum values without processing large datasets.
3Ease of operation
If conventional maximum value update methods are used, then data update simplicity is maintained, but abnormal data from temporary sensor problems may be erroneously incorporated
Solution Approach 1:
The patent implements a feedback mechanism where the system monitors the relationship between current measurements and historical maximum/minimum values. When a new measurement exceeds the historical maximum by a significant margin, the system questions whether this is a genuine anomaly or temporary sensor malfunction, and adjusts its response accordingly to prevent erroneous data incorporation.
Solution Approach 2:
The system prepares for potential sensor failures by maintaining historical context of maximum and minimum values before abnormal data can cause problems. This beforehand cushioning allows the system to detect and filter out transient abnormalities without requiring complex real-time validation procedures.
Data Source
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AI summary
To reliably perform updating of maximum values and minimum values of measurement data with a simple procedure without incurring an increase in the computational load of an arithmetic processing element such as a microcomputer. When processing is started, a most recent maximum value stored in a nonvolatile storage element 4 is written to a maximum value-use variable Xmax and a positive maximum value is written to a minimum value-use variable Xmin (step S404), and each time temperature data are acquired, a value of acquired data Xk and a most recent minimum value Xmin are compared and the smaller value is set as a new minimum value Xmin (step S408), and each time updating of this minimum value is repeated a predetermined number of times of processing Ns, the minimum value Xmin at that point in time and the maximum value Xmax are compared and the larger value is set as a new maximum value Xmax, whereby updating is performed (steps S406 and S410).