水库藻分类监测数据

采集的水库水质、气象、剖面藻分类数据，可用于：
（1）分析水库藻类浓度随时间以及水深的变化趋势，研究藻类的时空变化规律；
（2）研究水体水质、气象等参数对水体藻类的影响，分析水质和气象对水体藻类的影响机制；
（3）为藻浓度预测模型构建提供数据基础，支撑预测预报水库水华的爆发。通过实时上传的监测数据计算水体藻分类浓度，实现水体藻类的预警，具体过程如下：
（1）数据采集：定时检测水库水质及气象参数，定时检测水体剖面藻类荧光参数（荧光参数1-荧光参数9），其中，荧光参数数据（荧光参数1-荧光参数9）使用自研设备自行采集。
（2）数据处理：根据检测藻类荧光参数（荧光参数1-荧光参数9）和不同藻类的荧光光谱特征系数，利用非负最小二乘法得到的不同门类藻属叶绿素a浓度（剖面绿藻、剖面蓝藻、剖面硅甲藻、剖面隐藻）。
（3）将计算得到的不同门类藻属叶绿素a浓度（剖面绿藻、剖面蓝藻、剖面硅甲藻、剖面隐藻）与系统中预先设置的预警值（蓝藻阈值、绿硅隐藻阈值）相比较，如果剖面蓝藻大于蓝藻阈值，或者剖面绿藻、剖面硅甲藻、剖面隐藻大于绿硅隐藻阈值，则预警，1为预警，0为不预警，实现水体藻类的在线预警及报警。
（4）可根据采集所得的数据按天、月、年等时间维度进行趋势分析，形成水质统计分析数据。
补充数据结构单位：
7：电导率：86.8(uS/cm)
9：湿度：95%
23：Power：25V
25：气象风向：117o
32：累计用电量：2772AH
34：充电电流：0A
35：负载电流：2A
36：累计发电量：4090AH
37：太阳总辐射：0W/m2

This dataset collects reservoir water quality, meteorological, and vertical profile algae classification data, which can be used for the following purposes:
(1) Analyze the temporal and depth-related variation trends of reservoir algae concentration, and study the spatiotemporal variation rules of algae;
(2) Investigate the impacts of water quality, meteorological and other parameters on aquatic algae, and analyze the influencing mechanisms of water quality and meteorology on aquatic algae;
(3) Provide a data basis for the construction of algae concentration prediction models, and support the prediction and early warning of reservoir algal blooms.
The specific process of realizing aquatic algae early warning by calculating algae classification concentration from real-time uploaded monitoring data is as follows:
(1) Data collection: Regularly detect reservoir water quality and meteorological parameters, as well as water vertical profile algae fluorescence parameters (Fluorescence Parameter 1 to Fluorescence Parameter 9). The fluorescence parameter data (Fluorescence Parameter 1 to 9) are collected by self-developed equipment.
(2) Data processing: Based on the detected algae fluorescence parameters (Fluorescence Parameter 1 to 9) and the fluorescence spectral characteristic coefficients of different algae, the chlorophyll-a concentrations of different algal phyla (vertical profile Chlorophyta, vertical profile Cyanobacteria, vertical profile Diatoms, vertical profile Cryptophytes) are obtained using the non-negative least squares method.
(3) Early warning judgment: Compare the calculated chlorophyll-a concentrations of different algal phyla (vertical profile Chlorophyta, vertical profile Cyanobacteria, vertical profile Diatoms, vertical profile Cryptophytes) with the pre-set early warning thresholds in the system (cyanobacteria threshold, green-silicon-cryptophytes threshold). If the vertical profile Cyanobacteria concentration exceeds the cyanobacteria threshold, or the vertical profile Chlorophyta, Diatoms, and Cryptophytes concentrations exceed the green-silicon-cryptophytes threshold, an early warning is triggered, where 1 represents an early warning and 0 represents no early warning, realizing online early warning and alarm for aquatic algae.
(4) Trend analysis and statistics: Conduct trend analysis on the collected data according to time dimensions such as daily, monthly, and annual, and generate water quality statistical analysis data.

Supplementary data structure units:
7: Conductivity: 86.8 (uS/cm)
9: Humidity: 95%
23: Power: 25V
25: Meteorological wind direction: 117°
32: Cumulative power consumption: 2772 AH
34: Charging current: 0 A
35: Load current: 2 A
36: Cumulative power generation: 4090 AH
37: Total solar radiation: 0 W/m²

搜集汇总

背景与挑战

背景概述

水库藻分类监测数据是一个由杭州绿洁科技股份有限公司提供的企业数据集，包含575条记录，每日更新。数据集涵盖了水质、气象和藻类浓度等多方面的监测数据，适用于藻类时空变化规律研究、水质和气象对藻类影响机制分析以及藻浓度预测模型构建。