Vehicle-Based Distributed Data Center for Fleet Event Analysis
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Solution Overview
Problem
Existing vehicle data systems lack an efficient mechanism to aggregate and analyze data from multiple vehicles as a distributed data center, limiting the ability to perform comprehensive fleet-wide analysis and response to vehicle events.
Innovation Solution
A system utilizing on-board resources of individual vehicles as a distributed data center, where vehicles share data processing and storage capabilities, enabling aggregation and presentation of data through a remote computing server with user interface capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If vehicle data is stored and processed locally in each vehicle, then data processing speed and response time are improved, but the ability to perform comprehensive fleet-wide analysis is limited
Solution Approach 1:
The system segments data processing into two levels: local processing within each vehicle for immediate response, and centralized processing through the remote server for fleet-wide analysis. Each vehicle maintains local storage and processing capabilities while also connecting to a remote server that aggregates data from multiple vehicles, enabling both fast local response and comprehensive fleet analysis.
Solution Approach 2:
The system adds a spatial dimension by deploying data processing capabilities across multiple physical locations (individual vehicles and a remote server). This distributed architecture allows data to be processed both locally at each vehicle and centrally at the remote server, simultaneously enabling fast local response and comprehensive fleet-wide analysis through aggregation of data from multiple sources.
2Adaptability or versatility
If a centralized data center is used for fleet data aggregation, then comprehensive fleet-wide analysis is enabled, but system complexity and dependency on central infrastructure increase
Solution Approach 1:
The system divides the data processing function across multiple independent units (individual vehicles and remote server). Each vehicle has its own processing capabilities that can operate independently, reducing the complexity burden on any single component while still enabling comprehensive fleet analysis through aggregation at the remote server.
Solution Approach 2:
Individual vehicles are equipped with their own data processing and storage capabilities, allowing them to autonomously process local data and respond to events independently. This self-service capability at the vehicle level reduces dependency on continuous central infrastructure and simplifies the overall system architecture.
3Loss of time
If data is stored locally in vehicles, then response time to vehicle events is improved, but the volume of data requiring aggregation and transmission increases
Solution Approach 1:
The system extracts and processes only the necessary data at the local vehicle level before transmitting to the remote server. By performing data processing and filtering locally, the system reduces the volume of raw data that needs to be aggregated and transmitted, while still maintaining fast response capability through local processing of critical events.
Data Source
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AI summary
This disclosure relates to a distributed data center that includes resources carried by a fleet of vehicles. The system includes sensors configured to generate output signals conveying information related to the vehicles. The system may detect vehicle events based on the information conveyed by the output signals. The system includes a remote computing server configured to present a user interface to a user. Through the user interface, the user may query information from one or more vehicles in the fleet. The distributed query is transmitted to individual vehicles, and results are locally processed in accordance with response constraints and subsequently transmitted back to the remote computing server for presentation to the user.