林麝生长状态监测预警数据

本数据集的应用场景包括：
1.自用场景：本数据集可实现实时预警，通过G值、R值、ΔW等多维度指标，快速定位营养不良、消化异常或运动不足个体，缩短疾病响应时间。另可根据不同林麝个体的G值、R值、ΔW历史记录，筛选生长状态优异个体用于种源优化。
2.他用场景：可向自然保护区或科研机构提供林麝生长基线数据（如W7、F7等），支撑林麝生长研究。另可基于本数据集和算法规则形成数据产品，集成称重传感器、活动监测项圈等设备，形成“监测-分析-干预”闭环解决方案，并向中小型养殖场提供实时监测服务（含G/R/ΔW预警模块）。1.数据采集。数据采集自申请人自有林麝养殖基地，包括：采集时间、圈舍区号、林麝编号、实时体重W、日均进食量F、活动频率A、前7日体重均值W7、前7日进食均值F7、相邻区活动均值An等数据字段，剔除异常值保障数据质量。
2.动态特征值计算及预警判定。
生长偏离度：G=0.4×|W-W7|/W7 + 0.3×|F-F7|/F7 + 0.3×|A-An|/An。G值按以下规则进行预警判定：G≤0.1为正常，0.1<G≤0.2为亚健康预警，G>0.2为异常预警。
3. 行为关联分析及预警判定。
​ ①进食-活动比R计算：R=F/（1000×（A+1）），R值按以下规则判定：R＜0.1为消化异常预警，0.1≤R≤1为正常，R＞1为运动不足预警。
②体重加速度ΔW计算：ΔW=（W-W7）/W7×100％，ΔW值按以下规则判定：ΔW<-5%为营养不良预警，-5%≤ΔW≤10%百分号为正常，ΔW＞10%是生长异常预警。

The application scenarios of this dataset are as follows:
1. Self-use scenario: This dataset enables real-time early warning. By leveraging multi-dimensional indicators including G value, R value, ΔW and others, it can quickly identify individuals with malnutrition, digestive disorders or insufficient exercise, shortening disease response time. Additionally, it can screen individuals with excellent growth status for breeding stock optimization based on the historical records of G value, R value and ΔW of different forest musk deer individuals.
2. Third-party use scenario: It can provide growth baseline data of forest musk deer (e.g., W7, F7, etc.) to nature reserves or research institutions, supporting research on forest musk deer growth. In addition, based on this dataset and algorithm rules, data products can be developed, integrated with equipment such as weighing sensors and activity monitoring collars to form a closed-loop "monitoring-analysis-intervention" solution, and provide real-time monitoring services (including G/R/ΔW early warning modules) for small and medium-sized breeding farms.

1. Data Collection
The data is collected from the applicant's own forest musk deer breeding base, including data fields such as collection time, pen zone number, forest musk deer individual number, real-time weight W, daily average feed intake F, activity frequency A, 7-day moving average weight W7, 7-day moving average daily feed intake F7, and activity average of adjacent zones An. Outliers are removed to ensure data quality.

2. Dynamic Characteristic Value Calculation and Early Warning Judgment
Growth deviation degree: G = 0.4×|W - W7|/W7 + 0.3×|F - F7|/F7 + 0.3×|A - An|/An. Early warning judgment for G value is conducted according to the following rules: G ≤ 0.1 is normal; 0.1 < G ≤ 0.2 is sub-health early warning; G > 0.2 is abnormal early warning.

3. Behavior Correlation Analysis and Early Warning Judgment
① Feed-intake to Activity Ratio R Calculation: R = F / (1000 × (A + 1)). The R value is judged according to the following rules: R < 0.1 is digestive disorder early warning; 0.1 ≤ R ≤ 1 is normal; R > 1 is insufficient exercise early warning.
② Weight Change ΔW Calculation: ΔW = (W - W7)/W7 × 100%. The ΔW value is judged according to the following rules: ΔW < -5% is malnutrition early warning; -5% ≤ ΔW ≤ 10% is normal; ΔW > 10% is abnormal growth early warning.

搜集汇总

背景与挑战

背景概述

该数据集为林麝生长状态监测预警数据，包含831条记录，每日更新，涵盖林麝的体重、进食量、活动频率等多维度指标，通过算法规则进行生长偏离度、进食-活动比等预警判定，适用于养殖场实时监测和科研机构研究。