In-Vehicle Data Processing Offloading to Cloud Servers
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Solution Overview
Problem
In-vehicle devices upload raw data to servers, which are not interpretable, leading to increased server processing loads and requiring additional processing to understand the data, thus increasing server load and development complexity.
Innovation Solution
A data processing method that includes reducing data amount, converting data into a preset format for the in-vehicle device, and normalizing it for cloud servers, utilizing detachable expansion units to offload processing from the server and enhance in-vehicle device functionality with minimal development effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw data is uploaded directly to the server, then the data amount is large, but the server processing load increases and data interpretability is poor
Solution Approach 1:
The in-vehicle device performs data processing operations (reducing data amount, format conversion, normalization) before uploading to the server. This preliminary processing ensures data is interpretable and reduces server workload, resolving the contradiction between data interpretability and data amount.
Solution Approach 2:
The in-vehicle device acts as an intermediary between the data source and server, performing transformations on the data before transmission. This intermediary processing step enables data to be both interpretable by the server and reduced in amount, resolving the technical contradiction.
2Loss of information
If the server performs all data processing, then data interpretability is achieved, but server processing load and development complexity increase
Solution Approach 1:
The data processing functionality is segmented between the in-vehicle device and the server. The in-vehicle device handles preliminary processing (reducing data amount, format conversion), while the server focuses on higher-level processing (normalization, analysis). This segmentation reduces server complexity while maintaining data interpretability.
Solution Approach 2:
Data processing operations are performed in advance at the in-vehicle device before data reaches the server. This preliminary action prepares the data in a format that reduces server processing complexity while ensuring interpretability, resolving the contradiction between data quality and server complexity.
3Adaptability or versatility
If new functions are added to the in-vehicle device, then functionality is enhanced, but development effort increases
Solution Approach 1:
The in-vehicle device employs dynamic processing capabilities that can be configured or activated as needed. The device can adaptively perform different processing operations (reducing data amount, format conversion, normalization) based on requirements, enabling functionality enhancement without proportionally increasing development effort through modular, configurable processing.
Data Source
AI summary
By a data processing method or a communication system, input data is acquired, first processing that is processing of reducing a data amount of the input data is performed, second processing of converting data obtained by the first processing into data having a preset format configured to be handled by an in-vehicle device unit is performed, third processing of converting data obtained by the second processing into normalization data configured to be handled by a cloud server is performed, and vehicle data is provided to the cloud server.