龙游县乌猪胴体肌肉品质分析数据

生产过程中，整合分析猪胴体肌肉的肉色评分，ph1，ph24，滴水损失率，大理石纹，肌内脂肪含量等相关数据，观察品质指标嫩度的变化，用于评估预测猪猪胴体肌肉品质情况，便于针对养殖户及时提供技术支持，确保猪的质量和产量，确保整体品质。为统筹生产销售计划提供数据支持，降低生产成本，提高产量，增加行业利润。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，实现性别，肉色评分，ph1，ph24，滴水损失，大理石纹，肌内脂肪含量，嫩度等多维数据的全方位感知传输；2、利用数据收集整理分析技术对采集到的肉色评分（指标数值2.9±0.77），ph1（指标数值6.48±0.23），ph24（指标数值5.85±0.13），滴水损失（指标数值7.75±2.55），大理石纹（指标数值2.8±0.62），肌内脂肪含量（指标数值2.52±1.04）数据进行处理和合并，完成数据预处理以提高数据质量并实现多源数据融合，调控分析嫩度（指标数值5.13±1.2）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，构建猪胴体肌肉品质模型；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。3、为乌猪养殖环境提供数据支撑，提高乌猪质量，实现养殖模式可复制。

During pork production, relevant data including meat color score, pH1, pH24, drip loss percentage, marbling score, and intramuscular fat content of porcine carcass muscles are integrated and analyzed, with changes in the quality indicator tenderness observed, to evaluate and predict the muscle quality of porcine carcasses. This facilitates timely provision of technical support for pig farmers, ensuring the quality and yield of pigs and overall product quality. It also provides data support for coordinating production and sales plans, reducing production costs, increasing output, and boosting industry profits.
1. Adopt testing instruments to establish an on-demand indicator detection system, build a data platform with a hierarchical distributed system architecture, and realize comprehensive perception and transmission of multi-dimensional data including sex, meat color score, pH1, pH24, drip loss, marbling score, intramuscular fat content, and tenderness;
2. Use data collection, organization and analysis technologies to process and merge the collected data: meat color score (metric value: 2.9±0.77), pH1 (metric value: 6.48±0.23), pH24 (metric value: 5.85±0.13), drip loss (metric value: 7.75±2.55), marbling score (metric value: 2.8±0.62), and intramuscular fat content (metric value: 2.52±1.04). Complete data preprocessing to improve data quality and achieve multi-source data fusion, analyze and regulate the changes in tenderness data (metric value: 5.13±1.2). Leveraging the advantages of artificial intelligence and deep learning in data mining and information feature extraction, learn the complex high-dimensional correlations between multi-source parameters from massive experimental sample data, and combine with object sequence analysis methods to construct a porcine carcass muscle quality model. All data mentioned in the above algorithms are reflected in the data structure, and deep reinforcement learning methods and optimal control are adopted to realize parameter control and optimization in the digital space.
3. Provide data support for the breeding environment of black pigs, improve the quality of black pigs, and realize replicable breeding models.