Double-model decision arbitration and multi-modal database association-based sporopollen identification method

By employing a dual-model decision arbitration method and linking it with a multimodal database, the problems of model decision transparency and multimodal knowledge base association in pollen identification were solved, achieving efficient and reliable pollen identification results and morphological interpretation, thus improving identification efficiency and accuracy.

CN122336744APending Publication Date: 2026-07-03NANJING INST OF GEOLOGY & PALAEONTOLOGY CAS
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Patent Information

Application Number
CN202610382264.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-26
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing pollen identification technologies suffer from opaque model decision features, isolated results, lack of multimodal knowledge base association, and inability to resolve decision conflicts between models, resulting in low identification efficiency and results that are difficult for researchers to accept.

Method used

Parallel inference is performed using a global modeling model based on visual Transformer and a convolutional neural network model based on YOLO. An arbitration engine is used for dynamic weighting to output the final classification. The morphological feature text description is obtained by association with a multimodal database.

Benefits of technology

It improves the scientific credibility and practical effectiveness of pollen identification, enhances the robustness of identification of complex samples through a dynamic arbitration mechanism, provides morphological interpretation and professional knowledge support, and simulates an expert-level identification process.

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Abstract

The invention discloses a sporopollen identification method based on dual-model decision arbitration and multi-modal database association, and belongs to the technical field of intelligent identification. The method comprises the following steps: acquiring a sporopollen image, respectively inputting the sporopollen image into a first model based on visual Transform and a second model based on YOLO for parallel inference, and outputting a candidate category and a prediction probability; inputting the result into an arbitration engine, directly judging if the highest confidence category is consistent, and performing dynamic weighted arbitration to obtain a final judgment category if the highest confidence category is not consistent; and then retrieving a corresponding morphological feature text description in real time in a multi-modal database, and synchronously pushing a judgment category, the text description, a feature thermodynamic diagram and an alternative reference on an interactive interface. According to the method, the black box defect of the model is overcome, macro and micro feature complementation and intelligent arbitration are realized, auxiliary recognition of'recognition and release 'is constructed, and the sporopollen recognition accuracy and interpretability are improved.
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