林麝生长健康度综合评价数据

本数据集的应用场景包括：
1.自用场景：养殖场通过实时监测与算法分析，可精准识别个体健康风险，指导兽医进行定向干预，降低死亡率。另可根据健康度指数筛选种源，提升种族健康度。
2.他用场景：本数据集可为科研机构提供标准化健康数据库，支撑林麝疾病预测模型开发。另本数据可为名贵中药材（麝香）质量控制提供溯源依据，助力药企建立「优质麝香」认证标准，提升溢价空间。1.数据采集。数据采集自申请人自有林麝养殖基地，采集：日期、个体编号、体重体长比、体重体长比标准值、近24h粪便干物质含量、近24h进食量、单位时间采食量、血清免疫球蛋白G浓度、采食持续时长、血清免疫球蛋白G浓度（mg/dL）等字段。去除异常值确保数据质量。
2.核心指标计算。① 体型系数 =（体重体长比 / 体重体长比标准值）×100；② 消化效率 =（1 - 近24h粪便干物质含量/近24h进食量）×100；③ 采食效能 = 单位时间采食量 × 采食持续时长修正系数（＜30min=0.8，30-60min=1.0，＞60min=0.9）
④ 免疫指数 = lg(免疫球蛋白浓度) ×20。
3.归一化评分处理。
各指标按养殖场历史数据正态分布：前10%记100分，后10%记40分，中间80%线性插值。
4.健康度指数计算。健康度指数 =体型系数×35% +消化效率×30% +采食效能×25% +免疫指数×10%。
5.健康判定：① 优秀：健康度指数≥80分且未触发生理阈值；② 正常：60≤健康度指数＜80分且未触发生理阈值；③预警：健康度指数＜60分或任意指标触发生理阈值。生理阈值为：体型系数评分＜60、消化效率评分＜50、采食效能评分＜50、免疫指数评分＜30。

Application scenarios of this dataset include:
1. Self-use scenarios: The forest musk deer breeding farm can accurately identify individual health risks through real-time monitoring and algorithmic analysis, guide targeted veterinary interventions, and reduce mortality rates. Additionally, it can screen breeding stock based on the health index to improve the overall health of the population.
2. Third-party use scenarios: This dataset can provide standardized health databases for research institutions, supporting the development of forest musk deer disease prediction models. Moreover, the data can provide traceability basis for quality control of the precious traditional Chinese medicinal material (musk), helping pharmaceutical companies establish "high-quality musk" certification standards and increase premium profit margins.

1. Data Collection. The data is collected from the applicant's own forest musk deer breeding base, including fields such as collection date, individual number, body weight to body length ratio, standard body weight to body length ratio, dry matter content of feces in the past 24 hours, food intake in the past 24 hours, food intake per unit time, serum immunoglobulin G concentration, feeding duration, serum immunoglobulin G concentration (mg/dL), etc. Outliers are removed to ensure data quality.
2. Core Index Calculation. ① Body shape coefficient = (body weight to body length ratio / standard body weight to body length ratio) × 100; ② Digestive efficiency = (1 - dry matter content of feces in the past 24 hours / food intake in the past 24 hours) × 100; ③ Feeding efficiency = food intake per unit time × feeding duration correction coefficient (＜30min = 0.8, 30-60min = 1.0, ＞60min = 0.9); ④ Immunity index = lg(immunoglobulin concentration) × 20.
3. Normalized Scoring Processing. Each index follows the normal distribution of the farm's historical data: the top 10% are scored 100, the bottom 10% are scored 40, and the middle 80% are linearly interpolated.
4. Health Index Calculation. Health index = body shape coefficient × 35% + digestive efficiency × 30% + feeding efficiency × 25% + immunity index × 10%.
5. Health Judgment. ① Excellent: Health index ≥ 80 points and no physiological threshold triggered; ② Normal: 60 ≤ Health index < 80 points and no physiological threshold triggered; ③ Warning: Health index < 60 points or any index triggers the physiological threshold. The physiological thresholds are: body shape coefficient score < 60, digestive efficiency score < 50, feeding efficiency score < 50, immunity index score < 30.

搜集汇总

背景与挑战

背景概述

该数据集包含875条林麝生长健康度综合评价数据，每日更新，数据格式为xlsx。数据集通过多个指标（如体重体长比、粪便干物质含量、进食量等）计算健康度指数，用于养殖场健康监测和科研机构疾病预测模型开发。