养殖团头鲂投喂分析数据

养殖团头鲂投喂分析数据面向规模化水产养殖场的投喂管理方法研究。该分析综合溶氧、水温、pH、氨氮、气象条件与鱼群活性等多源数据，研究饲料投喂策略对团头鲂生长速度的影响规律，实现科学化、精准化投喂管理。主要应用于日常精准投喂、风险预警应急、成本控制优化、数据驱动决策、合规质量追溯等场景，通过多参数融合、实时监测、数据分析等技术手段，提升饲料利用率、降低养殖成本、保障养殖安全。对养殖行业而言，既避免饲料浪费与残饵污染，降低养殖成本，又能依托数据异常预警水质恶化、鱼群应激等风险，减少病害损失；更可将有效经验转化为标准化体系，推动团头鲂养殖从 “凭经验” 的粗放模式向精细化转型。对社会来说，精准投喂减少资源消耗与环境承载压力，契合绿色农业理念；科学投喂提升团头鲂品质与养殖成功率，既降低应急用药需求保障食品安全，又稳定市场供应；同时降本增效直接提升养殖户收益，带动产业链发展，为乡村振兴注入产业动力。1、数据采集：通过企业对鱼塘进行数据检测与采集，使用水质传感器对鱼塘进行采集数据，整理后获得原始数据字段：养殖池编号、鱼类、记录时间、周数、季节、活跃度、溶氧、水温、pH、氨氮、鱼群活性评分、摄食活跃度评分、鱼群聚集度、平均体重、放养密度、历史残饵率。

2、算法规则：对鱼类投喂量在基础投喂量计算的基础上，结合环境参数修正因子、鱼群行为修正因子和残饵率修正，采用如下综合算法： 基础日投喂率：根据平均体重设定：0.2–0.5 kg：1.8%–2.5%/日，0.5–1.0 kg：1.2%–1.8%/日，1.0–2.0 kg：0.8%–1.2%/日； 日总投喂量 = 平均体重(kg/尾) × 放养密度(尾/亩) × 基础日投喂率； 环境修正因子 = 1 - (0.35 × |溶氧 - 溶氧最优值| + 0.30 × |水温 - 水温最优值| + 0.20 × |pH - pH最优值| + 0.15 × |氨氮 - 氨氮最优值|)； 溶氧最优值：6.0 mg/L，权重：0.35； 水温最优值：26.0 ℃，权重：0.30； pH最优值：7.0，权重：0.20； 氨氮最优值：0.1 mg/L，权重：0.15； 行为修正因子 = 0.4 × 鱼群活性评分/100 + 0.4 × 摄食活跃度评分/100 + 0.2 × 鱼群聚集度/100； 历史残饵率修正系数:根据历史残饵率设定（< 3%：1.10，3%–5%：1.00，> 5%：0.80）； 当日投喂次数根据季节设定：春季/秋季3次（8:30、11:30、16:30），夏季4次（7:00、9:30、17:30、20:00），冬季1-2次（中午时段）; 最终单次投喂量 = (日总投喂量 × 环境修正因子 × 行为修正因子 × 历史残饵率修正系数) / 当日投喂次数。

3、数据分析： 通过算法规则获得最终单次投喂量去进行投喂，不仅能显著减少饲料浪费，更能精准匹配鱼类不同生长阶段、不同环境下的进食需求，保障其健康生长；从源头可降低历史残饵率，减少残饵分解产生的有机污染物积累，避免水体溶氧消耗、氨氮超标等问题，有效缓解因过量投喂导致的水质恶化，优化下一次测量数据，更精确的得到最终单次投喂量数据，为智慧养殖的降本增效与绿色生态发展提供了可落地的技术路径。

Megalobrama amblycephala feeding analysis dataset is developed for research on feeding management methods for large-scale aquaculture farms. This analysis integrates multi-source data including dissolved oxygen, water temperature, pH, ammonia nitrogen, meteorological conditions and fish group activity, to investigate the impact of feed feeding strategies on the growth rate of Megalobrama amblycephala, and enable scientific and precise feeding management.

This dataset is mainly applied in scenarios such as daily precise feeding, risk warning and emergency response, cost control and optimization, data-driven decision-making and compliance quality traceability. Through technical means such as multi-parameter fusion, real-time monitoring and data analysis, it can improve feed utilization rate, reduce aquaculture costs and ensure aquaculture safety.

For the aquaculture industry, this not only avoids feed waste and residual feed pollution, but also reduces breeding costs. Moreover, it can rely on data anomalies to warn of risks such as water quality deterioration and fish stress, thereby reducing disease losses. More importantly, it can transform effective breeding experience into a standardized system, promoting the transformation of Megalobrama amblycephala aquaculture from the "experience-based" extensive model to refined aquaculture.

For the society, precise feeding reduces resource consumption and environmental carrying pressure, which is consistent with the concept of green agriculture. Scientific feeding improves the quality of Megalobrama amblycephala and the success rate of aquaculture, which not only reduces the demand for emergency medication to ensure food safety, but also stabilizes market supply. At the same time, cost reduction and efficiency improvement directly increase the income of farmers, drive the development of the industrial chain, and inject industrial momentum into rural revitalization.

1. Data Collection: Enterprises conduct data detection and collection in fish ponds, using water quality sensors to collect relevant data. The sorted original data fields include: aquaculture pond number, fish species, recording time, week number, season, activity level, dissolved oxygen, water temperature, pH, ammonia nitrogen, fish group activity score, feeding activity score, fish group aggregation degree, average body weight, stocking density, historical residual feed rate.

2. Algorithm Rules: The fish feeding amount is calculated based on the basic feeding amount, combined with environmental parameter correction factor, fish group behavior correction factor and residual feed rate correction, using the following comprehensive algorithm:
Basic daily feeding rate: Set according to the average body weight: 0.2–0.5 kg: 1.8%–2.5% per day; 0.5–1.0 kg: 1.2%–1.8% per day; 1.0–2.0 kg: 0.8%–1.2% per day;
Total daily feeding amount = average body weight (kg per fish) × stocking density (fish per mu) × basic daily feeding rate;
Environmental correction factor = 1 - (0.35 × |dissolved oxygen - optimal dissolved oxygen value| + 0.30 × |water temperature - optimal water temperature value| + 0.20 × |pH - optimal pH value| + 0.15 × |ammonia nitrogen - optimal ammonia nitrogen value|);
Optimal dissolved oxygen value: 6.0 mg/L, weight: 0.35;
Optimal water temperature value: 26.0 ℃, weight: 0.30;
Optimal pH value: 7.0, weight: 0.20;
Optimal ammonia nitrogen value: 0.1 mg/L, weight: 0.15;
Behavior correction factor = 0.4 × fish group activity score/100 + 0.4 × feeding activity score/100 + 0.2 × fish group aggregation degree/100;
Historical residual feed rate correction coefficient: Set according to the historical residual feed rate (<3%: 1.10, 3%–5%: 1.00, >5%: 0.80;
Daily feeding times are set according to the season: 3 times in spring/autumn (8:30, 11:30, 16:30), 4 times in summer (7:00, 9:30, 17:30, 20:00), 1-2 times in winter (at noon);
Final single feeding amount = (total daily feeding amount × environmental correction factor × behavior correction factor × historical residual feed rate correction coefficient) / daily feeding times.

3. Data Analysis: Using the final single feeding amount obtained through the above algorithm rules for feeding can not only significantly reduce feed waste, but also accurately match the feeding needs of fish in different growth stages and different environments, ensuring their healthy growth. It can reduce the historical residual feed rate from the source, reduce the accumulation of organic pollutants generated by the decomposition of residual feed, avoid problems such as water dissolved oxygen consumption and excessive ammonia nitrogen, effectively alleviate water quality deterioration caused by overfeeding, optimize the next measurement data, and obtain more accurate final single feeding amount data, providing a feasible technical path for cost reduction, efficiency improvement and green ecological development of intelligent aquaculture.

搜集汇总

背景与挑战

背景概述

该数据集是德清瓜山水产养殖有限公司登记的'养殖团头鲂投喂分析数据'，包含550条每周更新的企业数据，记录了团头鲂养殖过程中的水质参数、鱼群行为指标和投喂信息。数据集通过综合环境修正因子、行为修正因子和残饵率修正的算法，计算最终单次投喂量，用于研究精准投喂策略，以提升饲料利用率、降低养殖成本并推动养殖业向精细化、绿色化转型。