Aiming at the problems of limited attack rounds and high attack complexity of Blow-CAST-Fish （Blow-C.Adams S.Tavares-Fish） algorithm， a key recovery attack of Blow-CAST-Fish algorithm based on differential table was proposed. Firstly， after analyzing the collision of S-box， based on the collision of two S-boxes and a single S-box respectively， the 6-round and 12-round differential characteristics were constructed. Secondly， the differential tables of f₃ were calculated， and three rounds were expanded based on the specific differential characteristic， thereby determining the relationship between ciphertext difference and the input and output differences of f₃. Finally， the plaintexts meeting the conditions were selected to encrypt， the input and output differences of f₃ were calculated according to the ciphertext difference， and the corresponding input and output pairs were found by querying the differential table， as a result， the subkeys were obtained. At the situation of two S-boxes collision， the proposed attack completed a differential attack of 9-round Blow-CAST-Fish algorithm， compared with the comparison attack， the number of attack rounds was increased by one， and the time complexity was reduced from 2^107.9 to 2⁷⁴. At the situation of single S-box collision， the proposed attack completed a differential attack of 15-round Blow-CAST-Fish algorithm， compared with the comparison attack， although the number of attack rounds was reduced by one， the proportion of weak keys was increased from {\mathrm{2}}^{-\mathrm{52.4}} to {\mathrm{2}}^{-\mathrm{42}} and the data complexity was reduced from 2⁵⁴ to 2⁴⁷. The test results show that the attack based on differential table can increase the efficiency of attack based on the same differential characteristics.

Concerning that the federated chain lacks visualization methods to show the resource usage， health status， mutual relationship and consensus transaction process of each node， a Fabric consensus transaction Tracking method based on custom Log （FTL） was proposed. Firstly， Hyperledger Fabric， a typical federation framework， was used as the infrastructure to build the bottom layer of FTL. Then， the custom consensus transaction logs of the Fabric were collected and parsed by using the ELK （Elasticsearch， Logstash， Kibana） tool chain， and Spring Boot was used as the business logic processing framework. Finally， Graphin which focuses on graph analysis was utilized to realize the visualization of consensus trade trajectory. Experimental results show that compared with native Fabric applications， FTL Fabric?based application framework only experienced an 8.8% average performance decline after the implementation of visual tracking basis without significant latency， which can provide a more intelligent blockchain supervision solution for regulators.