Eriocheir sinensis should cultivate in high water quality ponds, which is affected by many combined factors such as physics, chemistry, biology etc. Using the real-time water quality monitoring historical data to test one of the water quality indexes and predict this index in the next time has great significance. The dissolved oxygen is one of the most important indexes in aquaculture, such as in the Eriocheir sinensis pond. This paper established a dissolved oxygen prediction model of water quality monitoring system based on BP neural network. The forecast data which is predicted by the established model could fit the actual monitoring data very well.
Founded on the requirements of ocean fishing vessel and its operating system, this paper discusses and structures the architecture model of open service components based on a new-type IOT (Internet of Things). Then, this paper disserts the dynamic representation method of service components relationship in the system based on the Petri net. Under the effect of this method, the users overall acquisition capability of the service component base has been enhanced, which contributes to discover the conflict and redundancy of solving among the service components and provide an intuitive illustration mechanism for the combination configuration of service components. The intelligence of the system and the advantage of SOA are evaluated as well. Meanwhile, the reliability of the method is proved by means of the experimental simulation in this paper.
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