Vehicle Data Platform With Time-Series Characteristic Values
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Solution Overview
Problem
In a vehicle data collection and provision system, it is challenging to ensure an adequate observation period for vehicle data characteristics, especially when multiple applications with varying data generation periods and time responsiveness operate on different vehicle types and models.
Innovation Solution
A data provision platform that repeatedly acquires instantaneous vehicle data, stores it in a time-series format, and provides it to data use units along with characteristic values. These characteristic values are predetermined and generated based on the time-series data, ensuring accurate representation of data changes regardless of the data acquisition interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data acquisition interval is reduced to capture rapid changes, then measurement precision improves, but loss of time increases and productivity decreases
Solution Approach 1:
The system pre-calculates and stores characteristic values (maximum, minimum, average, standard deviation) from time-series data in advance. When a data acquisition request arrives, the pre-computed characteristic values are immediately provided to the application, eliminating the need for real-time calculation and reducing data acquisition time while maintaining measurement precision.
Solution Approach 2:
Instead of providing raw instantaneous data that requires post-processing, the system creates and provides copies of processed data in the form of characteristic values. These characteristic values represent the essential features of the time-series data, allowing applications to quickly understand data changes without processing raw data themselves.
2Productivity
If data acquisition interval is increased to improve productivity, then productivity improves, but measurement precision deteriorates
Solution Approach 1:
The system continuously collects and stores time-series data with high temporal resolution in the background, preparing the data in advance. When an application requests data, the system can immediately provide both instantaneous values and pre-computed characteristic values, achieving high productivity without sacrificing the ability to detect rapid changes.
Solution Approach 2:
The system acts as an intermediary between the data source and the application by maintaining a buffer of time-series data and pre-computing characteristic values. This intermediary layer allows the application to receive processed information quickly while the system maintains high-resolution data for accurate change detection.
3Loss of information
If characteristic values are pre-calculated and attached to data, then information completeness improves, but device complexity increases
Solution Approach 1:
The system implements a universal data provision interface that provides both instantaneous data and characteristic values through a single standardized mechanism. The characteristic value attachment function serves multiple applications simultaneously, reducing overall system complexity despite the added processing capability.
Solution Approach 2:
The system creates simplified copies of complex time-series data in the form of characteristic values (maximum, minimum, average, standard deviation). These copies capture the essential characteristics without requiring applications to process the full complexity of the original time-series data, thereby reducing information loss while managing complexity.
Data Source
AI summary
A data provision platform, a data provision system, a data provision method, or a program repeatedly acquires instantaneous data that is an instantaneous value of vehicle data transmitted from each unit of a vehicle, stores a time-series of the instantaneous data, extracts the instantaneous data of a request data, provide the instantaneous data to s data use unit, and attach a characteristic value to the instantaneous data provided to the data use unit, the characteristic value being: specified in advance according to request data and generated based on the time-series.