A method for zero-shot interpretable chemical industry anomaly detection

The chemical industry anomaly detection method, which utilizes multimodal fusion and language guidance, addresses the issues of insufficient model transparency and positioning accuracy. It achieves high-precision detection and report generation in zero-sample scenarios, adapting to the needs of industrial processes.

CN122176354APending Publication Date: 2026-06-09SHANGHAI JINGYI IND CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI JINGYI IND CO LTD
Filing Date
2026-01-29
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing anomaly detection models in the chemical industry lack transparency and accuracy, and have poor generalization ability in zero-sample scenarios, making it difficult to adapt to diverse anomaly patterns. Furthermore, their output formats are inconsistent, making it difficult to integrate into downstream quality control workflows.

Method used

A multimodal anomaly detection method is constructed. Through cross-modal aligned representation, enhanced fine-tuning, and language guidance, a structured report is generated to achieve pixel-level localization and evidence tracing. An interpretable anomaly detection report is generated by employing a frozen multimodal encoder, slot attention mechanism, and bidirectional cross-attention fusion features.

Benefits of technology

It achieves high-precision detection and positioning in zero-sample scenarios, generates reports in a unified and parseable format, meets industrial auditing needs, reduces reliance on labeled data, and improves detection efficiency and robustness.

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Abstract

This invention discloses a method for zero-shot interpretable anomaly detection in the chemical industry, relating to the field of anomaly detection technology in the chemical industry. The method includes: constructing a training set and a structured reporting protocol; optimizing the model through 2D-3D comparative pre-training, supervised fine-tuning, and reinforcement fine-tuning with verifiable rewards; collecting and preprocessing multimodal data of the object to be detected; extracting features through a frozen encoder; fusing features through cross-source interaction, modality alignment, and bidirectional cross-attention; parsing evidence slot features and spatial support graphs to generate a structured report containing fields such as defect type and location; extracting anomaly-related statement features; and generating pixel-level anomaly masks and evidence tracing chains through language-guided two-hop grounding. This invention requires no target anomaly annotation data, achieves zero-shot generalization, combines detection and location accuracy with report interpretability, adapts to industrial quality inspection processes, and has a high degree of automation.
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