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The high integration of cyber and physics is the development trend of intelligent distribution network in the future, but the cyber system not only supports the stable operation of the physical system, also brings some security risks to the cyber physical system of distribution network. Aiming at the requirements of real-time, accuracy, efficiency and other characteristics of distribution network monitoring, this paper proposes an early warning method of distribution network cyber physical system based on Hidden Markov model. Firstly, the online monitoring and early warning system architecture of distribution network information physical system is proposed, and then the early warning method of distribution network cyber physical system based on Hidden Markov model is established. Finally, an example is given to verify that the proposed strategy can accurately and efficiently early warn the fault.

Agricultural knowledge representation, organization and integrated application in distribute environment is a complex and difficult problem, mainly due to the distributed, polymorphic and heterogeneous characteristics of the agricultural knowledge resources. In order to overcome the problem, an application framework which supports the agricultural production knowledge integration based on semantic web technology was designed. The framework was divided into four layers including data layer, operation application layer, knowledge integration layer and knowledge service layer. The structure and function of each layer was introduced. The key techniques for knowledge representation and description, construction of knowledge chain, knowledge transformation method and knowledge retrieval and integration were studied. Finally, an agricultural knowledge semanteme retrieval prototype system was proposed. The new system was validated with more than 2000 documents describing agricultural production. The results show that the method established in the new system can improve the precision ratio effectively than the traditional methods.

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