A Blockchain- and Zero-Knowledge–Proof-Based framework for manufacturing Data-Asset qualification verification

In cross-enterprise manufacturing data spaces involving device-generated batches under dynamic authorization, trusted data sharing requires consistent verification of device identity, batch state, and current access qualification. Existing approaches either verify batch evidence and qualification states separately, which may weaken cross-context consistency, or combine them into a monolithic proof that requires unnecessary recomputation when authorization, revocation, or rule states change. To address this problem, this study proposes a state-aware dual-proof framework that separates a relatively stable data sub-proof from an epoch-specific qualification sub-proof while binding both through a shared device–asset–batch–qualification context. The framework further incorporates finalized-state synchronization, a context-bound nullifier for replay prevention, and auxiliary risk screening applied only after deterministic cryptographic qualification. Experiments on two public manufacturing datasets, with authorization, revocation, epoch, and attack states constructed under a unified protocol, show that the proposed method achieves Mean CARR values of 98.2% on CONTEXT and 97.1% on IoT-Enriched, with valid-request acceptance rates of 98.3% and 98.0%, respectively. Separated updating reduces qualification-update latency from 83.1 ms to 20.3 ms, corresponding to a 75.6% update redundancy reduction. Auxiliary risk admission achieves a Macro-F1 of 90.7% with a 4.9% false rejection rate. Scalability evaluation further shows that the main bottleneck shifts toward blockchain queuing and confirmation under high concurrency. These results indicate that context binding, separated state updates, and layered admission provide an effective verification strategy for cross-enterprise manufacturing data sharing with frequently changing qualifications.