Key Technology Study On Grid For Ocean Date Visualization

Ocean is a huge and complex dynamic time-varying system, including various factors interaction with each other. It is very important to detect the special shape, structure and range of the ocean space data, and perform quantitative analysis, visualization and virtual simulation to show certain ocean phenomenon and its dynamics, find the main feature structure of the ocean environment elements that influence the phenomenon.China is an Ocean country, the social and economic development are depended on the ocean more and more. As a strategy of science and technology development, the "Digital Ocean " has been put into the "11th Five-year Plan" and the state ocean science and technology development project, by China government for the 21st Ocean Century. The "Digital Ocean" is proposed with the "Digital Earth". It is an information system composed of large ocean data with multi-resolutions and various types, and also many analysis algorithms and models. The "Digital Ocean" employs many technologies, such as the grid, virtual reality, visualization and etc, to simulate the various states of real-ocean with dynamic, visual and digital display. It is very important to improve the information level of the ocean management and service, to realize the open architecture integration and ocean information sharing service, and to support the application of ocean information better.The work in this paper is an important part of the "863" Project—"Study on Grid based on ocean data sharing and information service" (No. 2006AA09Z139). The following are the main works and innovations.1. The Architecture Design of Grid for Ocean Date Visualization (GODV)In order to meet the real-time and dynamic performance requirement, high precision and large-scale characteristics of ocean information visualization, the architecture of GODV is designed, which can serve the users as a remote ocean data visualization system. The Architecture of GODV is classified into four layers: Grid Portal, Visualization Service, Middleware and Visualization Resources.The application Grid Protal can receive the interactive information from the user, create a file description of visualization task, and display the result of the visualization results to the user. Visualization Service layer can respond to the task application of the Grid Portal, perform the specific visualization task by the Grid Resources. The visualization analysis services can parse the task description file with XML format and call other Visualization Services to finish the visualization task. Middleware performs the Grid Resource management of visualization grid system and its grid service deployment, registration, scheduling, service discovery and etc.. Visualization Resource layer provides various visualization resources for grid scheduling, including the visualization tools, the graphic hardware and etc. Each layer of GODV has the definite function, the relations between layers are less coupled, and produces a more scalable architecture.2. Scalable Data Format Converting services (DFC)As for the heterogeneous data format of the ocean information, DFC is designed and realized based on the analysis of common eight date format types of ocean information and using XML technology. This format converting services use the grid service with uniform data format converting based on the target and the source metadata to meet the ocean research with automatic data format conversion smoothly.3. A New Parallel Algorithm for Physical Feature-Dependent Topological Simplification of Planar Vector FieldThis paper introduces a new parallel algorithm for physical feature-sensitive topological simplification algorithm of planar vector fields. Based on the physical feature computation, the method can detect and selectively visualize the feature meeting the application-specified physical criterion form the parameter image by the image blending and segmentation technology. Since the feature analysis and the computation is complicated and time-consuming, a parallel method is used to improve the algorithm efficiency and meet the remote visualization. The experiment results show that a physical feature preserving topological simplification is achieved perfectly. Meanwhile, the selective visualization of physical feature of planar vector fields is an efficient and remarkable data compression is implemented as well. The method solves the difficulty in parallel computation on topological simplification and compression of flow fields at present.