The rapid development of digital technologies, such as big data and artificial intelligence, has led to the emergence of Internet-based platforms for government services and credit dissemination. These platforms have accumulated a vast amount of credit data. However, the current social credit system still faces issues such as opaque credit information, easily tampered data, and insufficient traceability. Our proposal uses blockchain technology to establish a digital government-based credit data-sharing framework (TCDSF). This framework provides a tamper-proof trust mechanism that enhances information transparency, reduces the cost of inter-organisational data sharing, and mitigates data security risks; To protect the credit subject's privacy during the trust collaboration process, we have designed a master-slave chain sharing and collaboration mechanism. This mechanism uses both on-chain and off-chain storage modes to reduce the storage pressure of distributed nodes and enhance flexibility and scalability; We ensure the security of shared data in the channel through role-based access control measures and an asymmetric encryption mechanism for credit data. Experiments have demonstrated that the framework is capable of providing continuous, efficient, and reliable management and traceability services, as well as efficient resource utilization and adaptable data processing and storage, compared to a single chain.
Lonicera japonica is a traditional Chinese herbal medicine, and root rot is a common systemic disease on Lonicera japonica, and the occurrence of root rot seriously affects the yield and quality of Lonicera japonica. In this paper, we study to realize the detection of root rot plants in a wide range of Lonicera japonica fields based on UAV remote sensing images to help accurately detect and treat root rot plants. A YOLOX target detection model was trained using UAV orthophotos to identify root rot disease plants and healthy plants in the field. After training and optimization, the average precision (Average Precision, AP) of the model reached 91.75% and 94.44%, respectively, which can efficiently and accurately detect root rot plants in the field for timely treatment.
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