蓝藻和水华控制管理系统

Yuming Tang, Hong Liang, Shi Chen, Hongyu Song
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引用次数: 0

摘要

蓝藻是一大类单细胞原核生物,能够进行光合作用产生氧气。当蓝藻受到氮、磷等元素的刺激时,会引起水体富营养化,造成湖泊“水华”现象,严重危及人、畜、鱼虾的安全。蓝藻华的监测和管理一直受到湖泊管理单位的困扰。目前,与蓝藻华防治相关的产品功能非常单一。产品功能大致分为两大类,有的只注重监测环节,有的只注重打捞加工环节。没有一种产品可以很好地结合这两个环节。针对这种情况,本文设计开发了一套将监测与打捞环节有效结合起来的蓝藻防治处置管理系统。该系统包括运行报告子系统、监测数据管理系统、藻泥环保全过程管理系统和蓝藻实时监测系统四个子系统。WEB终端与移动终端之间的三维交互,使系统更加高效、便捷。该系统有两个创新点:创新一:将蓝藻华的控制和救助环节有效整合,共同构建为一个系统。创新点2:将机器学习中的K-means算法应用到图像分类中,用AI代替人工人工无人值勤,提高识别率,降低错误率。
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Cyanobacteria and Bloom Control Management System
Cyanobacteria are a large class of single-cell prokaryotes capable of oxygen-producing photosynthesis. When cyanobacteria are stimulated by nitrogen, phosphorus and other elements, it will cause eutrophication of the water body and cause the phenomenon of “bloom” in the lake, which seriously endangers the safety of humans, livestock, fish and shrimp. The monitoring and management of cyanobacteria blooms have been plagued by lake management units. At present, the product functions related to the prevention and control of cyanobacteria blooms are very single. The product functions are roughly divided into two categories, some of which focus only on the monitoring link and the other only focus on the salvage and processing link. There is no one product that can combine the two links well. In view of this situation, this article has designed and developed a set of cyanobacteria and algae prevention and control disposal management system, which effectively combines the monitoring and salvage links. The system includes four subsystems: operation report subsystem, monitoring data management system, algae mud environmental protection whole process management system and real-time cyanobacteria monitoring system. The three-dimensional interaction between the WEB terminal and the mobile terminal makes the system more efficient and convenient. The system has the following two innovations: Innovation one: Effectively integrate the control and salvage links of cyanobacteria blooms and jointly build them into a system. Innovation point 2: Apply the K-means algorithm in machine learning to image classification, and replace artificial artificial unattended with AI to improve the recognition rate and reduce the error rate.
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