Protocol conversion is usually used to solve the problem of data interaction between different protocols， and its nature is to find mapping relationship between different protocol fields. In the traditional methods of protocol conversion， several drawbacks are identified： traditional conversions are mainly designed on the basis of specific protocols， so that they are static and lack flexibility， and are not suitable for environments with multi-protocol conversion； whenever a protocol changes， a reanalysis of the protocol’s structure and semantic fields is required to reconstruct the mapping relationship between fields， leading to an exponential increase in workload and a decrease in protocol conversion efficiency. Therefore， a general method of protocol conversion based on semantic similarity was proposed to enhance protocol conversion efficiency by exploring the relationship between fields intelligently. Firstly， the BERT （Bidirectional Encoder Representations from Transformers） model was employed to classify the protocol fields， and eliminate the fields that “should not” have mapping relationship. Secondly， the semantic similarities between fields were computed to reason the mapping relationship between fields， resulting in the formation of a field mapping table. Finally， a general framework for protocol conversion based on semantic similarity was introduced， and related protocols were defined for validation. Simulation results show that the precision of field classification of the proposed method reaches 94.44%； and the precision of mapping relationship identification of the proposed method reaches 90.70%， which is 13.93% higher than that of the method based on knowledge extraction. The above results verify that the proposed method is feasible， can identify the mapping relationships between different protocol fields quickly， and is suitable for scenarios with multi-protocol conversion in unmanned collaboration.

The traceability data of single-chain storage charity system have huge storage pressure， and the charity data need to be shared， may leading to the problem of privacy leakage. Therefore， a charity system traceability storage model oriented to master-slave chain was proposed. Firstly， a master chain and several slave chains were designed in the model. The master chain was mainly responsible for the query of charity traceability data and the supervision of slave chains， and the slave chains were responsible for the storage of a large number of charity traceability data. Then， an intelligent contract for the classification of charity traceability data was designed to classify charity data into public data and private data according to privacy requirements. The public data were stored in the master chain directly， while the private data were encrypted with Ciphertext-Policy Attribute-Based Encryption （CP-ABE） and stored in the slave chains， which ensured data privacy， thus achieving storage scalability and intelligence. Finally， the storage structure of Merkle tree was improved. By designing a smart contract to mark duplicate data， the same block detection and duplicate data deletion of blockchain system were completed， which avoided data redundancy and reduced storage consumption. Experimental results show that compared to the single-chain model， with the increase of total number of data， the proposed model has the response time of the master-slave chain stabilized at 0.53 s and the throughput stabilized at 149 B. It can be seen that the master-slave chain model improves search efficiency， optimizes storage space， and realizes data privacy protection.

In order to reduce the node localization error of DV-Hop algorithm in Wireless Sensor Network (WSN), a calculation method of average distance per hop was adjusted by using the shuffled frog leaping algorithm. The improved DV-Hop algorithm makes the average distance per hop closer to the actual value, thereby reducing the localization error. The simulation results indicate that the improved DV-Hop algorithm reduces localization error effectively and has good stability without additional devices; therefore, it is a practical localization solution for WSN.