Blockchain Traceability and Visual Warning Based on Merkle Tree in Enterprise Data Assetization Transformation

Abstract

Large enterprise groups face issues such as low efficiency in data traceability and a disconnect between security and visualization in their data assetization transformation. Therefore, this paper raises a security threat perception and visualization warning model based on data visualization and blockchain traceability algorithm. This model combines Merkle Tree (MT), double hash chain, attribute encryption, and zero knowledge proof to achieve lightweight on chain auditing and privacy protection. It also collaborates with ForceTars2 and layered edge binding layout to generate dynamic risk topology, supporting full lifecycle trusted auditing and real-time threat perception. The experiment on the self-made enterprise supply chain threat perception dataset shows that the threat detection rate of the model is 98.11%, the false alarm rate is only 0.82%, the visual cognitive efficiency is 96.83%, the trusted data asset utilization rate is 96.82%, and the delay is controlled within 100 ms. Superior to existing mainstream solutions such as lightweight tracking algorithms based on MT and attribute based encryption privacy protection algorithms. The experimental results demonstrate that the model has good accuracy and applicability in enterprise level data security governance, providing an integrated governance solution with high concurrency, low latency, and high trustworthiness for data assetization transformation.