With the application of artificial intelligence technology in the judicial field， charge prediction based on case description has become an important research content. It aims at predicting the charges according to the case description. The terms of case contents are professional， and the description is concise and rigorous. However， the existing methods often rely on text features， but ignore the difference of relevant elements and lack effective utilization of elements of action words in diverse cases. To solve the above problems， a multi-task learning model of charge prediction based on action words was proposed. Firstly， the spans of action words were generated by boundary identifier， and then the core contents of the case were extracted. Secondly， the subordinate charge was predicted by constructing the structure features of action words. Finally， identification of action words and charge prediction were uniformly modeled， which enhanced the generalization of the model by sharing parameters. A multi-task dataset with action word identification and charge prediction was constructed for model verification. The experimental results show that the proposed model achieves the F value of 83.27% for action word identification task， and the F value of 84.29% for charge prediction task； compared with BERT-CNN， the F value respectively increases by 0.57% and 2.61%， which verifies the advantage of the proposed model in identification of action words and charge prediction.

The current service resources in Internet of Services （IoS） show a trend of refinement and specialization. Services with single function cannot meet the complex and changeable requirements of users. Service integrating and scheduling methods have become hot spots in the field of service computing. However， most existing service integrating and scheduling methods only consider the satisfaction of user requirements and do not consider the sustainability of the IoS ecosystem. In response to the above problems， a service integration method based on adaptive multi?objective reinforcement learning was proposed. In this method， a multi?objective optimization strategy was introduced into the framework of Asynchronous Advantage Actor?Critic （A3C） algorithm， so as to ensure the healthy development of the IoS ecosystem while satisfying user needs. The integrated weight of the multi?objective value was able to adjusted dynamically according to the regret value， which improved the imbalance of sub?objective values in multi?objective reinforcement learning. The service integration verification was carried out in a real large?scale service environment. Experimental results show that the proposed method is faster than traditional machine learning methods in large?scale service environment， and has a more balanced solution quality of each objective compared with Reinforcement Learning （RL） with fixed weights.

The role-based access control policy is mainly to take the role mapping to achieve inter-domain interoperability. The role mapping does not consider the extent of the same role of impact on different domains, and different level of mutual trust between one domain and the other domain. Role mapping properties of the threshold and domain properties of the threshold were proposed, to solve the different problems of the same role on different domains and inter-domain trust level, and the different domains were organized in a more fine-grained access control, thus further improving the inter-domain interoperability security issues.