ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

With the widespread adoption of mechanical cotton harvesting in Xinjiang, challenges such as data silos, delayed information sharing, and inadequate whole-process monitoring have hindered precise and transparent quality traceability from seed cotton to lint cotton. To address this, a blockchain-based quality traceability model for machine-harvested cotton was proposed by leveraging the decentralized and tamper-resistant features of blockchain technology and the real-time data acquisition capabilities of IoT devices. A logistic regression-based off-chain/on-chain collaborative data query optimization was introduced to achieve intelligent pre-caching of high-frequency data. Additionally, an access control model integrating reinforcement learning and elliptic curve cryptography was designed to enhance data security and privacy protection. The quality traceability system was developed on the ChainMaker open-source blockchain platform. Performance tests demonstrated that the system reduced query latency from 72.37ms to 60.14ms in regular scenarios and further decreased it to 32.75ms in high-frequency scenarios, with optimization efficiency improving as data volume increased, meeting real-time user query demands. In addition, through plaintext sensitivity and key sensitivity tests, confirming average ciphertext change rates of 87.78% and 82.68%, respectively. These results ensured the privacy and security of data during cross-institutional collaboration. The model established a closed-loop architecture of “IoT data collection-blockchain notarization-smart contract verification-multi-level access control” fulfilling enterprises’ requirements for privacy data permission management and secure sharing while enhancing information retrieval efficiency.