An artificial intelligence driven archive breakage identification repair and archiving management system and method

Abstract

The application provides an artificial intelligence driven archive damage identification repair and archiving management system and method, collects archive image data to extract explicit and implicit damage features, obtains archive damage attribute information through damage node association; detects target archive category attributes, extracts damage repair samples of sample archives of the same category, makes confidence prediction on the damage repair parameters of the target archive, and obtains repair parameter prediction values; and determines the repair behavior difference degree of the target archive and the sample archive in combination with the damage attribute information and the values. The artificial intelligence model completes intelligent damage identification, intelligent repair and standardized archiving management in turn according to the current management requirements of the archives and the difference degree. The scheme realizes the integration and intelligentization of archive damage processing and archiving management, and greatly improves the accuracy, standardization level and overall processing efficiency of archive management.

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