林麝粪便的健康指数日常分析数据

本数据集的应用场景包括：
1.自用场景：本数据集为养殖场基于健康指数对个体进行分级管理提供数据支持（如优秀个体优先育种、预警个体隔离观察），结合寄生虫检测与皮质醇代谢物浓度，针对性优化饲料配方、调节环境温湿度以降低应激性腹泻风险。
2.他用场景：①为科研机构关于林麝粪便理化指标-健康指数关联模型的构建提供数据支持。②该数据集可为第三方兽医服务机构开发粪便AI分析系统提供数据支持。1.数据采集。采集字段：数据采集自申请人自有林麝养殖基地，采集：日期、个体编号、近24h粪便重量、粪便颜色系数（1-正常/0.5-异常）、粪便形状系数（1-成型/0.5-松散）、粪便含水量（%）、pH值、短链脂肪酸浓度（mmol/kg）、寄生虫检测（0-无/1-有）、皮质醇代谢物浓度（ng/g）、近24h进食量、近24h饮水量。剔除含异常数据。
2.核心指标计算：①粪便形态指数=(颜色系数+形状系数)×50。②消化稳定性=(1-(粪便干物质量/进近24h食量))×100，其中粪便干物质量=近24h粪便重量×(1-粪便含水率)）。③水分代谢率指数=(粪便含水量/近24h饮水量)×100％，其中粪便含水量=近24h粪便重量×粪便含水率。④代谢健康值=log10(短链脂肪酸浓度)+(100-皮质醇浓度×0.2)。
3.归一化评分：各指标按养殖基地近三年数据建立正态分布模型：前20%记100分，后20%记20分，其余线性插值。
4.健康指数计算：健康指数=粪便形态指数评分×30%+消化稳定性评分×25%+水分代谢率指数评分×25%+代谢健康值评分×20%。
5.健康判定：①优秀：健康指数≥85且无寄生虫；②正常：70≤指数＜85且无寄生虫；③预警：指数＜70或有寄生虫。

Application scenarios of this dataset include:
1. Self-use scenarios: This dataset provides data support for graded management of individual forest musk deer based on health indices in the applicant's own breeding base (e.g., prioritizing breeding of excellent individuals, isolating and observing warning individuals). Combined with parasite detection and cortisol metabolite concentrations, it can help optimize feed formulas and adjust environmental temperature and humidity to reduce the risk of stress-induced diarrhea.
2. Third-party use scenarios: ① Providing data support for research institutions to construct correlation models between physical and chemical indicators of forest musk deer feces and health indices. ② Providing data support for third-party veterinary service institutions to develop fecal AI analysis systems.

1. Data Collection: The dataset is collected from the applicant's own forest musk deer breeding base. Collected fields include: collection date, individual ID, fecal weight within the past 24 hours, fecal color score (1 - normal / 0.5 - abnormal), fecal shape score (1 - formed / 0.5 - loose), fecal water content (%), pH value, short-chain fatty acid concentration (mmol/kg), parasite detection (0 - none / 1 - present), cortisol metabolite concentration (ng/g), feed intake within the past 24 hours, and water intake within the past 24 hours. Samples with abnormal data are excluded.
2. Core Index Calculation:
① Fecal Morphology Index = (color score + shape score) × 50.
② Digestive Stability = (1 - (fecal dry matter weight / feed intake within the past 24 hours)) × 100, where fecal dry matter weight = fecal weight within the past 24 hours × (1 - fecal water content percentage).
③ Water Metabolism Rate Index = (fecal water content / water intake within the past 24 hours) × 100%, where fecal water content = fecal weight within the past 24 hours × fecal water content percentage.
④ Metabolic Health Score = log₁₀(short-chain fatty acid concentration) + (100 - cortisol metabolite concentration × 0.2).
3. Normalized Scoring: A normal distribution model is established based on the past three years of data from the breeding base for each indicator: the top 20% are assigned a score of 100, the bottom 20% are assigned a score of 20, and the remaining values are linearly interpolated.
4. Health Index Calculation: Health Index = Fecal Morphology Index Score × 30% + Digestive Stability Score × 25% + Water Metabolism Rate Index Score × 25% + Metabolic Health Score × 20%.
5. Health Judgement:
① Excellent: Health Index ≥ 85 and no parasites detected.
② Normal: 70 ≤ Index < 85 and no parasites detected.
③ Warning: Index < 70 or parasites detected.

搜集汇总

背景与挑战

背景概述

该数据集专注于林麝的日常粪便分析，通过采集粪便重量、颜色、形状、含水率、pH值、短链脂肪酸浓度、寄生虫检测和皮质醇代谢物等多项指标，结合进食量和饮水量数据，计算粪便形态指数、消化稳定性、水分代谢率指数和代谢健康值，并归一化评分后综合得出健康指数与等级。数据集规模至少726条，每日更新，旨在为养殖场提供个体健康分级管理支持，例如优先育种或隔离预警，同时可用于科研模型构建和兽医AI系统开发，实现基于数据的精细化健康监测。