Vehicle Device Diagnosis Using Nearby Vehicle Learning Values
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Solution Overview
Problem
Existing diagnosis technologies for vehicle-mounted devices struggle to accurately diagnose abnormalities due to environmental changes such as road surface shape, gradient, and weather, which affect the reliability of cumulative data from devices of the same type.
Innovation Solution
A diagnosis device and method that acquire and calculate learning values from nearby vehicles within a predetermined range, using inter-vehicle communication to compare detection values with calculated learning values, and adjust for reliability and environmental consistency, enabling accurate abnormality sign diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cumulative data from devices of the same type is used for diagnosis, then the quantity of data increases, but the reliability of diagnosis deteriorates due to environmental variations
Solution Approach 1:
The patent applies local quality by selecting learning vehicles based on similar travel environments rather than using all available data uniformly. The diagnosis device calculates similarity between travel environments and selectively acquires learning values only from vehicles with comparable conditions, ensuring that the data source matches the specific local context of the target vehicle.
Solution Approach 2:
The patent changes the parameter of data selection from simple quantity accumulation to quality-based filtering using environment similarity. By introducing environment similarity as a selection criterion, the system transforms the approach from collecting all cumulative data to selectively collecting data that meets specific environmental parameter matches.
2Quantity of substance
If learning values from all vehicles are used, then the quantity of learning data increases, but the accuracy of abnormality detection deteriorates due to environmental differences
Solution Approach 1:
The system ensures that learning data is locally relevant by filtering based on travel environment similarity. Only vehicles with comparable environmental conditions contribute their learning values, ensuring that the learning data accurately reflects the specific operational context of the target vehicle.
Solution Approach 2:
The patent introduces travel environment similarity as an intermediary criterion between the target vehicle and potential learning vehicles. This intermediary filter ensures that only appropriately matched vehicles contribute their data, bridging the gap between data quantity and diagnostic accuracy.
3Adaptability or versatility
If data from vehicles in different environments is used for learning, then the availability of learning data improves, but the consistency of diagnosis results deteriorates
Solution Approach 1:
The patent maintains diagnosis consistency by ensuring that learning data comes from vehicles with similar travel environments. The environment similarity calculation acts as a quality filter that preserves the compositional consistency of the learning dataset, ensuring all contributing vehicles operate under comparable conditions.
Data Source
AI summary
A diagnosis device includes a learning information acquisition unit configured to acquire a learning value of a first device mounted in at least one first vehicle present in a predetermined range from a second vehicle, a learning unit configured to calculate a learning value of a second device mounted in the second vehicle using the learning value of the first device, and a diagnosis unit configured to diagnose an operation state of the second device by comparing a detection value of the second device with the learning value of the second device.