枕包线设备良品率数据

枕包线设备良品率是指某车间线体类型为枕包线的设备在产线上最终通过测试的设备良品量占投入设备产量的比例，生产过程中的错误或缺陷可以导致生产成本上涨和产品质量下降。通过本算法得到的枕包线设备良品率数据，再将数据与FineBi可视化工具相结合。业务部门可以在任何时刻通过车间、设备描述等多维度分析车间生产过程中枕包线设备良品率情况，从而可以在生产过程中制定更优的枕包线设备生产规划措施，以提高枕包线设备的良品率，为企业降低成本增加效益。枕包线设备良品率数据的算法规则包括以下几个方面：1、数据采集：通过端口进入，从而获取枕包线设备的信息数据，完成信息数据的采集。2、数据校验：通过对导入的回流数量、人工剔废等基础数据数据进行比对和验证。3、数据处理：使用WebService的方式连接数据库，实现数据交互。关于日期、设备描述等字段通过唯一主键进行多表关联，搭建所需字段的SQL模型。为了提高枕包线设备良品率的准确性，故增加设备误剔废、人工剔废等基础数据获得每日枕包线设备良品量情况，通过FineBi计算各日期、设备描述等同一维度分组汇总结果值即使用公式K=1+1.6log(N^2/110)，N为记录数；再使用算法公式：设备良品率P=sum（a）/sum（b）*q*100%，其中设备良品量a=设备产量*w+回流数量*n-人工剔废*m，根据物料代码为中心点即满足最小二乘估计下的线性回归方程，物料代码每增加一个指定单位，基于设备良品量a相应地平均分别增加w、n 、m个对应的代数式单项式中的基础系数；b为设备产量；同理，q为设备良品量与设备产量占比的基础系数。通过该算法能更加清晰的分析到某时间段枕包线下的不同设备描述的设备良品率情况。

The qualified product yield rate of pillow packaging line equipment refers to the proportion of qualified equipment that finally passes the test of pillow packaging line equipment (with the production line type specified as pillow packaging line) in a given workshop, relative to the total input equipment output. Errors or defects occurring during the production process can lead to increased production costs and reduced product quality.
The qualified product yield data of pillow packaging line equipment obtained through this algorithm, when combined with the FineBi visualization tool, allows business departments to analyze the qualified product yield status of pillow packaging line equipment during workshop production from multiple dimensions such as workshop and equipment description at any time. This enables business departments to formulate optimized production planning measures for pillow packaging line equipment during production, thereby improving the qualified product yield of such equipment and reducing costs and increasing benefits for the enterprise.
The algorithm rules for the qualified product yield data of pillow packaging line equipment include the following aspects:
1. Data Collection: Obtain the information data of pillow packaging line equipment by accessing the corresponding port, thus completing the collection of information data.
2. Data Verification: Compare and verify the imported basic data such as returned quantity and manual waste rejection.
3. Data Processing: Connect to the database via the WebService protocol to realize data interaction. Associate multiple tables through unique primary keys for fields such as date and equipment description, and build an SQL model for the required fields.
To improve the accuracy of the qualified product yield rate of pillow packaging line equipment, basic data such as equipment false waste rejection and manual waste rejection are added to obtain the daily qualified product output of pillow packaging line equipment. The grouped summary results of the same dimensions such as each date and equipment description are calculated via FineBi using the formula $K=1+1.6log(N^2/110)$, where $N$ is the number of records. Then the algorithm formula is used: the equipment qualified product yield rate $P = frac{sum(a)}{sum(b)} imes q imes 100\%$, where the qualified product output $a = ext{equipment output} imes w + ext{returned quantity} imes n - ext{manual waste rejection} imes m$. Taking the material code as the center point, satisfying the linear regression equation under least squares estimation, when the material code increases by one specified unit, the qualified product output $a$ correspondingly increases by the basic coefficients in the corresponding algebraic monomials of $w$, $n$, and $m$ respectively on average; $b$ is the equipment output; similarly, $q$ is the basic coefficient of the proportion of qualified product output to equipment output. This algorithm enables a clearer analysis of the qualified product yield rate of different equipment descriptions under the pillow packaging line within a specific time period.

搜集汇总

数据集介绍

特点

该数据集记录了振德医疗用品股份有限公司的枕包线设备良品率数据，包含多个生产相关字段，每日更新，旨在通过数据分析优化生产规划，提高设备良品率。