AGENTS

AVM Video Fault Intelligent Analysis Agent

Log + Video + Screenshot multi-modal joint analysis to locate root causes of AVM visual faults.

Core Pain Points

1. Fault evidence is scattered across multiple media: AVM issues require simultaneously viewing logs, videos, screenshots, and problem descriptions; manually switching between files slows down localization.
2. Difficult multimodal correlation: Log timestamps, visual phenomena, and reproduction descriptions need to corroborate each other; reading a single piece of evidence in isolation can lead to misjudgment.
3. Root cause and fix suggestions depend on experience: Defect classification, root-cause judgment, and fix plans usually rely on senior personnel experience.

Solution & Technical Implementation

1. Multimodal input: Consumes logs, videos or screenshots, and problem descriptions, building a unified fault-analysis context.
2. RAG knowledge enhancement: Retrieves historical issues, diagnostic experience, and relevant technical materials.
3. Structured report generation: Outputs fault classification, root-cause location, evidence chain, and fix suggestions — forming an end-to-end diagnostic report.

Highlights

• Log + visual joint analysis: Places the timeline and visual phenomena into the same analysis flow.
• Evidence-chain presentation: Diagnostic conclusions are linked back to the input evidence for easy human review.
• Closed loop from root cause to fix suggestions: Describes not only the phenomenon but also the localization result and fix direction.

Business Value

• Reduces the time spent manually consolidating logs, videos, and screenshots, accelerating initial triage and localization of AVM issues.
• Multimodal evidence and knowledge-base retrieval lower the risk of missing evidence, repeat investigation, and experience bias.
• Report structure, fault classification, and analysis flow can be extended across vehicle models, versions, and other visual-class issues.

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