Product cycle model

博客介绍了产品周期模型包含规划、设计、实施、稳定和发布五个阶段,同时指出测试人员应在规划阶段开始时就参与到产品周期中,涉及信息技术领域的产品开发流程。

There are five phrases in product cycle model.

 

1. Planning Phrase

2. Design Phrase

3. Implementation Phrase

4. Stabilization Phrase

5. Release Phrase

 

Testers should attend the product cycle when the Planning Phrase beginning.

WITH date_calculations AS ( SELECT BUILDING_NO, CELL_NO, MACHINE_ID, DURATION, PRODUCT_MODEL_NAME, TARGET_UPTIME, STATUS, LOG_TYPE, CUR_LOG_DATE, TRUNC(CUR_LOG_DATE - 8.5/24) AS CDATE, CASE WHEN CUR_LOG_DATE BETWEEN TRUNC(CUR_LOG_DATE) + 8.5/24 AND TRUNC(CUR_LOG_DATE) + 20.5/24 THEN 'D' ELSE 'N' END AS SHIFT, CASE WHEN CUR_LOG_DATE BETWEEN TRUNC(CUR_LOG_DATE) + 6/24 AND TRUNC(CUR_LOG_DATE) + 7/24 THEN 'Y' WHEN NEXT_LOG_DATE BETWEEN TRUNC(NEXT_LOG_DATE) + 6/24 AND TRUNC(NEXT_LOG_DATE) + 7/24 THEN 'Y' WHEN CUR_LOG_DATE BETWEEN TRUNC(CUR_LOG_DATE) + 18/24 AND TRUNC(CUR_LOG_DATE) + 19/24 THEN 'Y' WHEN NEXT_LOG_DATE BETWEEN TRUNC(NEXT_LOG_DATE) + 18/24 AND TRUNC(NEXT_LOG_DATE) + 19/24 THEN 'Y' WHEN CUR_LOG_DATE <= TRUNC(CUR_LOG_DATE) + 6/24 AND NEXT_LOG_DATE >= TRUNC(NEXT_LOG_DATE) + 7/24 THEN 'Y' WHEN CUR_LOG_DATE <= TRUNC(CUR_LOG_DATE) + 18/24 AND NEXT_LOG_DATE >= TRUNC(NEXT_LOG_DATE) + 19/24 THEN 'Y' ELSE 'N' END AS REST_TIME FROM table WHERE TRUNC(CUR_LOG_DATE - 8.5/24) >= SYSDATE - 90 AND DURATION<100 ), work_cycle AS ( SELECT BUILDING_NO, CELL_NO, MACHINE_ID, CDATE, SHIFT, CASE MAX(STATUS) WHEN -1 THEN '试做' WHEN 11 THEN '试模' WHEN 10 THEN '保养' WHEN 1 THEN '试做' WHEN 0 THEN '量产' END AS REPORT_STATUS FROM date_calculations WHERE LOG_TYPE = 'WORK_CYCLE' AND REST_TIME = 'N' --AND DURATION < 100 GROUP BY BUILDING_NO, CELL_NO, MACHINE_ID, CDATE, SHIFT ), auto_cycle AS ( SELECT BUILDING_NO, CELL_NO, MACHINE_ID, CDATE, SHIFT, CASE WHEN SUM(DURATION) >= 3600 THEN 'Y' ELSE 'N' END AS OVER_ONE_HOUR FROM date_calculations WHERE LOG_TYPE = 'AUTO_CYCLE' AND REST_TIME = 'N' --AND DURATION < 100 GROUP BY BUILDING_NO, CELL_NO, MACHINE_ID, CDATE, SHIFT ) SELECT S.CDATE, S.SHIFT, S.BUILDING_NO, S.CELL_NO, S.MACHINE_ID, S.PRODUCT_MODEL_NAME, REPLACE(S.PRODUCT_MODEL_NAME, ' ', '') AS "机种_去空", B.REPORT_STATUS, SUM(S.DURATION) AS DURATION, MAX(S.TARGET_UPTIME) AS TARGET_UPTIME, COUNT(S.DURATION) AS 产能 FROM date_calculations S JOIN auto_cycle A ON S.MACHINE_ID = A.MACHINE_ID AND S.CDATE = A.CDATE AND S.SHIFT = A.SHIFT LEFT JOIN work_cycle B ON S.MACHINE_ID = B.MACHINE_ID AND S.CDATE = B.CDATE AND S.SHIFT = B.SHIFT WHERE S.LOG_TYPE = 'AUTO_CYCLE' AND S.REST_TIME = 'N' AND A.OVER_ONE_HOUR = 'Y' GROUP BY S.CDATE, S.SHIFT, S.BUILDING_NO, S.CELL_NO, S.MACHINE_ID, S.PRODUCT_MODEL_NAME, B.REPORT_STATUS 上述代码中添加计算每班上下班时间差,如上班时间差,当shift=D时,CUR_LOG_DATE-8:30:00,ELSE CUR_LOG_DATE-20:30:00,下班时间差,当shift=D时,NEXT_LOG_DATE-20:30:00 ELSE NEXT_LOG_DATE-8:30:00
12-18
【多变量输入超前多步预测】基于CNN-BiLSTM的光伏功率预测研究(Matlab代码实现)内容概要:本文介绍了基于CNN-BiLSTM模型的多变量输入超前多步光伏功率预测方法,并提供了Matlab代码实现。该研究结合卷积神经网络(CNN)强大的特征提取能力与双向长短期记忆网络(BiLSTM)对时间序列前后依赖关系的捕捉能力,构建了一个高效的深度学习预测模型。模型输入包含多个影响光伏发电的气象与环境变量,能够实现对未来多个时间步长的光伏功率进行精确预测,适用于复杂多变的实际应用场景。文中详细阐述了数据预处理、模型结构设计、训练流程及实验验证过程,展示了该方法相较于传统模型在预测精度和稳定性方面的优势。; 适合人群:具备一定机器学习和深度学习基础,熟悉Matlab编程,从事新能源预测、电力系统分析或相关领域研究的研发人员与高校研究生。; 使用场景及目标:①应用于光伏电站功率预测系统,提升电网调度的准确性与稳定性;②为可再生能源并网管理、能量存储规划及电力市场交易提供可靠的数据支持;③作为深度学习在时间序列多步预测中的典型案例,用于科研复现与教学参考。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注数据归一化、CNN特征提取层设计、BiLSTM时序建模及多步预测策略的实现细节,同时可尝试引入更多外部变量或优化网络结构以进一步提升预测性能。
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