This invention relates to the field of archival
data management technology, specifically disclosing a
data processing optimization method for archival management systems. First,
proprietary format files are collected and converted to a standard format in a runnable native environment to form paired training data. Next, a semantic understanding network composed of
feature extraction units and
structure generation units is constructed and trained, enabling it to learn to identify semantic units from binary streams and reconstruct structured data. Then, this network is encapsulated as a
parsing program independent of the native
software environment, capable of directly reading
proprietary format files and outputting standard files containing complete geometry, topology, and attributes. The confidence level is calculated by comparing the output with the
verification file, and low-confidence samples are fed back to the
training set for incremental network training, continuously optimizing
parsing capabilities. This invention liberates data from technically locked archives and forms a self-evolving closed-
loop optimization system, ensuring the long-term
reusability of industrial digital assets.