基于支持向量机的海洋环境监测数据动态集成与分析

Huatang Xue
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引用次数: 0

摘要

环境问题是世界性的问题。我们必须关注今天海洋面临的污染问题。它不仅改变了海洋的质量,而且对种植在海洋中的海鲜产品也有很大的影响。海洋环境监测的基本目的是全面、及时、准确地掌握人类活动对海洋环境影响的程度、效果和趋势。根据应用服务的实际需要,对适合海洋环境特点的时空分析模式及相关评价模型进行了探讨和研究,对适合系统建设的监测数据时空分析及相关评价技术进行了应用分析。通过合并不同性能格式的数据集,以统一的监控数据库格式为蓝本,动态进行数据格式转换、计量单位转换、监控参数标准化等处理技术。最后,将不同监控任务的相同监控元素动态合并成批量数据集,为数据质量控制和入库提供了前提条件。支持向量机的出现给这方面的研究带来了希望和便利。它有一套完善的理论知识。在这一套完善的理论基础上,可以达到良好的学习效果。数据质量主要通过完整性检查、台站基础信息质量控制和台站监测参数数据质量控制来保证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Integration and Analysis of Marine Environmental Monitoring Data Based on Support Vector Machine
Environmental problems are worldwide problems. We must pay attention to the pollution problems facing the ocean today. It not only changes the quality of the ocean, but also has a great impact on the seafood products planted in the ocean. The basic purpose of marine environmental monitoring is to comprehensively, timely and accurately grasp the level, effect and trend of the impact of human activities on the marine environment. According to the actual needs of application services, the spatio-temporal analysis mode and related evaluation model suitable for the characteristics of the marine environment are discussed and studied, and the time and space analysis of monitoring data suitable for system construction and related evaluation techniques are applied and analyzed. By merging the data sets with different performance formats and using the unified monitoring database format as the blueprint, the processing technologies such as data format conversion, measurement unit conversion and monitoring parameter standardization are dynamically carried out. Finally, the same monitoring elements of different monitoring tasks are dynamically merged into a data set in batches, which provides a prerequisite for data quality control and warehousing. The emergence of support vector machine brings hope and convenience to the research. It has a set of perfect theoretical knowledge. On the basis of this set of perfect theory, it can achieve good learning effect. The data quality is mainly guaranteed through completeness inspection, quality control of station basic information and quality control of station monitoring parameter data.
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