龙游县乌猪屠宰性能分析数据

养殖过程中，整合分析体高，胴体长，背膘厚等相关数据，通过对比数据波动信息，记录猪屠宰性能，用于评估屠宰情况，便于针对养殖户及时提供技术支持，将数据进行处理和合并，调控分析与屠宰性能有关的数据变化，利用人工智能和深度学习在数据挖掘及信息特征提取上的优势，从海量实验样本数据中学习多源参数间的复杂高维关联，结合对象序列分析方法，构建猪屠宰性能深度网络模型.，指导产业升级。1、采用检测仪器，建立指标按需检测体系，构建数据平台，分层分布式系统结构，输入屠宰日龄，宰前活重，总重，胴体长，皮重，骨重，肥肉重，瘦肉重，实现多维数据的全方位感知传输；2、输出计算皮率，骨率，肥肉率，瘦肉率，其中项目的合理数值区间分别为：背膘厚（指标数值36.77±6.61），皮率（指标数值8.78±2.3），骨率（指标数值8.72±1.22），肥肉率（指标数值31.06±6.1），瘦肉率（指标数值51.44±5.98）。将数据进行处理和合并，调控分析与屠宰性能有关的数据变化，利用人工智能和深度学习在数据挖掘及信息特征提取上的优势，从海量实验样本数据中学习多源参数间的复杂高维关联，结合对象序列分析方法，构建猪屠宰性能深度网络模型；以上算法中提到的数据均在数据结构中体现，并采用深度强化学习方法和优化控制在数字空间实现参数的控制与优化。

During the pig breeding and slaughtering process, relevant data such as body height, carcass length and backfat thickness are integrated and analyzed. By comparing data fluctuation information, pig slaughter performance is recorded to evaluate slaughter conditions, so as to provide timely technical support for farmers. After processing and consolidating the data, we regulate and analyze the changes in data related to slaughter performance. Leveraging the advantages of artificial intelligence and deep learning in data mining and information feature extraction, we learn the complex high-dimensional correlations among multi-source parameters from massive experimental sample data, and combine with object sequence analysis methods to construct a deep network model for pig slaughter performance, so as to guide industrial upgrading. 1. Adopt testing instruments to establish an on-demand indicator detection system, build a data platform with a layered distributed system architecture. Input parameters including slaughter age, live weight before slaughter, total weight, carcass length, skin weight, bone weight, fat weight and lean meat weight, to realize comprehensive perception and transmission of multi-dimensional data. 2. Calculate and output skin percentage, bone percentage, fat percentage and lean meat percentage. The reasonable value ranges of each indicator are as follows: backfat thickness: 36.77±6.61, skin percentage: 8.78±2.3, bone percentage: 8.72±1.22, fat percentage: 31.06±6.1, lean meat percentage: 51.44±5.98. After processing and consolidating the data again, regulate and analyze the changes in data related to slaughter performance. Leveraging the advantages of artificial intelligence and deep learning in data mining and information feature extraction, learn the complex high-dimensional correlations among multi-source parameters from massive experimental sample data, and combine with object sequence analysis methods to construct a deep network model for pig slaughter performance. All data mentioned in the above algorithms are reflected in the data structure, and deep reinforcement learning and optimal control are adopted to realize parameter control and optimization in the digital space.

搜集汇总

数据集介绍

特点

该数据集包含601条龙游县乌猪的屠宰性能数据，涵盖个体号、性别、屠宰日龄、宰前活重等16个字段，应用于养殖过程中的屠宰性能评估和技术支持，结合人工智能和深度学习进行数据分析和模型构建。