智慧渔业水产养殖数据集

本数据集为通过在鱼塘内设置多处不同类型的传感器装置，从而检测出多种与水体状态相关的监测指标，然后再由人工对检测出的指标进行清洗、归类后，形成可分析的数据集。针对水位、水温、水质等参数，来分析养殖环境的稳定性和适宜性；通过流速和水压数据，对洪水、污染事故等进行预测分析；通过各种水体指标的数据变化并结合时间进行数据模型建立，实现对水产养殖的规范管理和科学决策。

This dataset is developed by deploying multiple heterogeneous sensors in fish ponds to detect various monitoring indicators associated with water body conditions, followed by manual cleaning and categorization of the detected indicators to generate an analyzable dataset. Parameters such as water level, water temperature and water quality are utilized to assess the stability and suitability of the aquaculture environment. Flow velocity and water pressure data are employed to perform predictive analysis of floods, pollution accidents and other similar emergencies. By analyzing data fluctuations of diverse water body indicators and integrating temporal information to build data models, standardized management and evidence-based scientific decision-making for aquaculture can be achieved.

搜集汇总

背景与挑战

背景概述

该数据集通过鱼塘内多处传感器采集水位、水温、水质、流速和水压等多种水体参数，经过人工清洗和归类处理，形成结构化数据。它主要用于分析水产养殖环境的稳定性和适宜性，预测洪水或污染事故，并支持通过数据模型建立实现养殖管理的规范化和科学决策，具有多参数监测和智能分析的特点。