养殖刺鲳预测最佳PH值数据
收藏资源简介：
将采集到的数据用来整理分析刺鲳养殖过程中的最佳PH值。在检测周期等数据为固定量条件下，调整养殖过程中的PH值，监测品质优化情形下数据的变化，以研究优化品质时的PH值数值，从而优化养殖方式，改进养殖环境方法，同时可将优化经验应用于外部环境，形成刺鲳养殖环境的多维度感知与控制，促进养殖产业向科学、高效、模式可移植的方向发展。为广大的刺鲳养殖企业提高了产品质量和生产效率提供技术支持。针对不同的养殖场将采集到的数据使用方差来预测刺鲳养殖的最佳PH值。该模型在检测周期，养殖环境内氨氮含量，亚硝酸盐含量，溶解氧，温度等数据为固定数值范围的条件下，收集每天三个时刻刺鲳养殖过程中的PH值，利用VAR函数计算三个PH值的方差大小，对比方差不同时，刺鲳整体品质的变化，品种外观提升则记为﹢，下降记为－；品种健康状况提升则记为﹢，下降记为－；将两项整合，出现共同提升则整体品质预测记为“↑”，共同下降则整体品质预测记为“↓”，出现一上升一下降情形时，整体品质预测记为“/"。通过收集PH值数据及其方差情况对整体品质的影响，确认刺鲳养殖提质情形下PH值的最佳数据范围，反推生产政策，从而为刺鲳养殖乃至水产养殖等其他行业提供品质优化的技术数据支持。
The collected data are used to organize and analyze the optimal pH value during the aquaculture process of Psenopsis anomala. Under the condition that fixed parameters such as detection cycle are kept constant, adjust the pH value in the farming process, monitor changes in relevant data during quality optimization, so as to determine the optimal pH value for quality improvement, thereby optimizing farming methods and aquaculture environment regulation approaches. Meanwhile, the accumulated optimization experience can be applied to external environments, establishing multi-dimensional perception and control systems for the Psenopsis anomala farming environment, and promoting the aquaculture industry to develop towards scientific, efficient and mode-transplantable directions. This work provides technical support for a wide range of Psenopsis anomala farming enterprises to improve product quality and production efficiency. For different farms, the collected data are used to predict the optimal pH value for Psenopsis anomala farming via variance analysis. Under the condition that fixed numerical ranges are set for parameters including detection cycle, ammonia nitrogen content, nitrite content, dissolved oxygen and temperature in the aquaculture environment, the model collects pH values at three time points per day during the Psenopsis anomala farming process, calculates the variance of the three pH values using the VAR function, and compares the changes in the overall quality of the cultured Psenopsis anomala stock under different variance levels. An improvement in the appearance of the cultured stock is marked as "+", while a decline is marked as "-"; an improvement in the health status of the stock is marked as "+", and a decline is marked as "-". By integrating the two scoring items, the overall quality prediction is marked as "↑" when both aspects improve, "↓" when both aspects decline, and "/" when one aspect improves while the other declines. By collecting the impacts of pH value data and their variance on the overall quality, the optimal pH value range for quality improvement in Psenopsis anomala farming is confirmed, and targeted production strategies are derived based on this range, thereby providing technical data support for quality optimization in Psenopsis anomala farming and even other aquaculture-related industries.
