基于多链存储优化的水产品交易匹配模型研究

区块链技术应用到水产品线上交易架构中可以使交易双方隐私信息得到基本保障，然而，目前区块链水产品线上交易模型和系统存在海量数据存储负载大、维护成本高、数据查询效率低等问题。为进一步缓解以上问题，在梳理和分析水产品交易流程基础上，根据水产品交易业务技术需求，提出了基于多链存储优化的水产品交易匹配模型。该模型在智能合约中通过贪心算法实现了效率较高的多属性水产品线上交易匹配过程，通过区块链多通道技术构建了水产品交易多链架构，实现了用户交易信息分布式存储，提高了交易信息查询效率，同时，采用区块链与本地数据库双模式存储技术，缓解了区块链网络中各个节点海量数据存储的负载。基于Hyperledger Fabric平台实现了基于多链存储优化的水产品交易原型系统。该原型系统测试结果表明，临界值900s平均最多可以完成1296笔交易，说明系统在处理千条交易数据量时可以正常运行，满足水产品线上交易平台日常实际交易业务需求，同时在链上存储1600条合同信息时查询1条用户合同信息平均时间为4.018s，多链存储结构提高了链上数据查询速度。

Aquatic Product Trading Matching Model Based on Multi-chain Storage Optimization

The application of blockchain technology in the online trading architecture of aquatic products can provide basic protection for the privacy information of both parties involved in the transaction. However, currently, blockchain-based aquatic product online trading models and systems are suffering from problems such as large data storage loads, high maintenance costs, and low data query efficiency. To further alleviate the above problems, based on the sorting and analysis of the aquatic product trading process, and according to the technical requirements of aquatic product trading business, a trading matching model for aquatic products based on multi-chain storage optimization was proposed. This model achieved a highly efficient multi-attribute online trading matching process for aquatic products through greedy algorithms in smart contracts, and constructed a multi-chain architecture for aquatic product online trading through blockchain multi-channel technology, achieving distributed storage of user transaction information and thus improving the efficiency of transaction information query. Meanwhile, this trading matching model adopted a dual storage technology of blockchain and local database, which alleviated the load of massive data storage at various nodes in the blockchain network. Then, a prototype system for aquatic product online trading based on multi-chain storage optimization was implemented on the Hyperledger Fabric platform. The performance test results of the prototype system indicated that it took 900s to complete 1296 transaction matchings on average, indicating that the system can operate normally when processing a volume of thousands of transaction data, meeting the needs of the online trading platform for aquatic products. At the same time, when storing 1600 contract information on the chain, the average time to query a user’s contract information was 4.018s, which indicated that the multi-chain data storage structure improved the speed of on-chain data queries.