Intelligent evaluation system and method for aircraft defects based on multi-modal fusion

The intelligent aircraft defect assessment system based on multimodal fusion has achieved the construction and dynamic optimization of a unified spatiotemporal benchmark for multimodal data, solving the problems of difficult multimodal fusion and poor environmental adaptability in aircraft defect detection, improving identification accuracy and the effectiveness of maintenance decisions, and ensuring the long-term reliability of the system.

CN120597038BActive Publication Date: 2026-06-02SICHUAN TIANFU NENGGU TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SICHUAN TIANFU NENGGU TECHNOLOGY CO LTD
Filing Date
2025-06-04
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing aircraft defect detection technologies suffer from difficulties in multimodal data fusion, poor adaptability to dynamic environments, and insufficient multi-constraint decision optimization capabilities, leading to misjudgments and missed detections. In long-term operation, these technologies are prone to accumulating errors due to sensor drift and environmental disturbances, making it difficult to meet the requirements of high-reliability operation and maintenance.

Method used

The intelligent aircraft defect assessment system employs multimodal fusion, which simultaneously collects data through visible light, infrared, ultrasonic, and X-ray sensors. After spatiotemporal alignment, a unified spatiotemporal benchmark is constructed. Low-rank feature components are extracted using tensor construction and hypergraph modeling. The optimal maintenance decision is generated by combining meta-prototype relationship networks and multi-objective game optimization, and the system parameters are dynamically adjusted through a closed-loop feedback mechanism.

Benefits of technology

It significantly improves the accuracy of complex defect identification, enhances the adaptability to new defect patterns, optimizes the balance between the economy and safety of maintenance decisions, reduces operation and maintenance costs, and ensures the long-term robustness and reliability of the system.

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Abstract

The application relates to the technical field of aircraft intelligent detection and maintenance systems, and discloses an aircraft defect intelligent evaluation system and method based on multi-modal fusion, which comprises a multi-modal data acquisition module, a tensor construction and decomposition module, an element prototype relationship network module, a multi-target game optimization module and a closed-loop feedback module. The method comprises the following steps: constructing a five-order feature tensor through multi-modal data synchronous acquisition and space-time alignment, extracting low-rank features through supergraph block item decomposition, dynamically generating a defect prototype set in combination with element learning, generating a maintenance decision by adopting a Nash equilibrium strategy to fuse multiple constraint conditions, and optimizing system parameters through closed-loop feedback to realize the full-process intelligentization of aircraft defect detection and maintenance. According to the application, high-precision defect detection is realized through multi-modal data fusion and supergraph modeling, intelligent decisions are generated in combination with dynamic prototype learning and multi-target game optimization, and the aircraft operation and maintenance efficiency and safety are continuously self-optimized with the aid of a closed-loop feedback mechanism, so that the full-process automation is improved.
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