Dynamic ID Generation for Connected Vehicle Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in efficiently extracting and querying vehicle events from vast amounts of data sent by connected vehicles, as existing systems struggle to parse through messages to identify events within specific time windows or parameters, such as a trip or ignition cycle, due to the lack of dynamic identifiers that link messages to human-defined concepts.
Innovation Solution
The implementation of an ontology or knowledge database on vehicles to dynamically define triggers, which generates dynamic identifiers for connected data messages based on predefined conditions, allowing for enhanced data grouping and querying by client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the cloud system receives and stores all raw connected data messages from millions of vehicles, then the complete vehicle data is available for analysis, but the data volume becomes extremely large and query efficiency deteriorates
Solution Approach 1:
The system performs preliminary action by generating dynamic identifiers and annotating data messages at the vehicle edge device before transmission to the cloud. This pre-processing groups messages by contextual concepts (trips, ignition cycles, charging events) in advance, so that when queries arrive at the cloud system, the data is already organized and ready for efficient retrieval without requiring full data parsing and filtering operations
Solution Approach 2:
The system segments the large volume of raw vehicle data into smaller, contextually-grouped units using dynamic identifiers. Each identifier represents a specific conceptual group (e.g., all messages within a trip, all messages during a charging cycle), dividing the monolithic data stream into manageable, query-ready segments that can be efficiently retrieved and processed
2Stability of the object's composition
If the system uses predefined static identifiers for data grouping, then the data organization structure is simple and stable, but the system cannot adapt to varying event definitions and concepts across different vehicles or use cases
Solution Approach 1:
The system implements dynamics by using configurable trigger conditions and contextual parameters that can be dynamically adjusted based on vehicle type, use case, and event definitions. The dynamic identifiers are generated based on real-time evaluation of trigger conditions (e.g., ignition on/off, door open/close, charging start/stop) rather than static pre-assigned IDs, allowing the data organization structure to adapt flexibly to different scenarios while maintaining consistent grouping logic
Solution Approach 2:
The trigger-based dynamic identifier system serves multiple functions: it can define trips, ignition cycles, charging events, or any other conceptual groupings by simply configuring different trigger conditions. This universal approach allows the same system architecture to handle diverse event definitions across different vehicle types and use cases without requiring separate organization schemes for each scenario
3Measurement precision
If the cloud system parses through all messages to identify events within specific time windows, then complete event detection is achieved, but the processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary action by evaluating trigger conditions and generating dynamic identifiers at the edge device before data transmission to the cloud. This pre-processing ensures that messages are already tagged with their contextual group identifiers, eliminating the need for the cloud system to parse through all messages to identify events within specific time windows, thereby significantly reducing processing time while maintaining accurate event detection
Data Source
AI summary
A knowledge database describes a plurality of triggers, each trigger including a first condition for activating the trigger and a second condition for deactivating the trigger. A controller of the vehicle processes the triggers defined by the knowledge database, including to compare data elements of connected data messages with the conditions of the triggers. The controller augments the connected data messages that occur while the respective trigger is activated with a generated dynamic ID corresponding to the activating of the respective trigger.