Hyperledger Fabric is an alliance chain framework widely adopted both domestically and internationally. It exhibits characteristics such as numerous participating organizations, frequent transaction operations, and increased transaction conflicts in certain businesses based on Fabric technology. The multi-version concurrency control technology used in Fabric can partially resolve transaction conflicts as well as enhance system concurrency. However, this mechanism is imperfect and certain transaction data cannot be properly stored on the chain. To achieve complete, efficient, and trustworthy up-chain storage of massive transaction data, a data preprocessing mechanism based on the Fabric oracle machine is proposed. The Massive Conflict Preprocessing(MCPP) method is designed to ensure the integrity of transaction data with primary key conflicts through techniques including detection, monitoring, delayed submission, transaction locking, and reordering caching. Data transmission protection measures are introduced to utilize asymmetric encryption technology during transmission, preventing malicious nodes from forging authentication information and ensuring consistency before and after off-chain processing of transaction data. Theoretical analysis and experimental results demonstrate that this mechanism can effectively address concurrent conflict issues regarding up-chain massive transaction data in alliance chain platforms. When the transaction data scales reach 1 000 and 10 000, the MCPP method achieves time efficiency improvements of 38% and 21.4%, respectively, compared with the LMLS algorithm, with a success rate close to 100%. Thus, the proposed method exhibits efficiency and security, and does not impact Fabric system performance when concurrent conflicts do not occur
