紧固件数字库-PEM-01螺钉编码管理数据
收藏浙江省数据知识产权登记平台2024-10-22 更新2024-10-24 收录
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本数据有助于紧固件PEM-01螺钉产品属性管理及产品唯一简化编码生成和管理,实现产品的唯一性匹配。压铆螺钉产品属性繁多有如:产品类目、产品标准名称、产品标准代号、产品规格(由直径、长度、牙距、牙型组成)、材质、强度、材料、表面处理等12个属性构成,本数据通过将产品12个属性名称和属性值进行统一规范的管理,产品描述的所有属性值多的长达50个文字以上,很难在后续的ERP、WMS、MES等系统中调用和描述,并且无法进行产品唯一性的匹配和管理,为此将产品的12个属性值通过类似于Base64 的自定义编码(仅包含字母和数字一共33个)的方式转换成12~19位长的产品唯一编码实现产品的唯一性匹配和检索,并且通过产品的唯一性编码生成一个对于的产品二维码,快速的进行扫码识别进行后续系统平台的调用。1、数据采集:
通过国家发行的紧固件标准手册、螺纹公差、机械性能、材质手册等,获得紧固件品类产品的属性数据和参数,从本单位开发运营的“工业零部件数字库-【紧固件】标准底层系统”上采集紧固件产品的属性数据,包括:标准名称、标准代号、产品规格(细化为直径、长度、牙距、牙型等四项)、材质、强度、材料、材料化学成分、材料机械性能、表面处理等并生成属性对应的ID代号。
2、算法加工方法:
第一步:从工业零部件数字库-紧固件中获取产品数据,包括品类、标准【标准名称和标准代号】、规格(由直径、长度、牙距、牙型组成)以及材质、强度、材料、表色等数据及属性对应的ID代号。
第二步:将获取的品类、标准、规格3个属性的的ID号,使用33位自定义编码(仅含字母和数字:H1VGSCNXQTF6L7MR2UB04ZP89JE5Y3DKWA)转换成特定的短编码。
第三步:将获取的材质、强度、材料、表色属性值字符串通过哈希算法得到哈希值,再将哈希值使用33位自定义编码转换成特定的短编码。
第四步:将步骤二、三转换后获得的,品类、标准、规格及其他属性的自定义短码通过横杠连接在一起形成产品的唯一编码。
This dataset facilitates attribute management, generation and management of the unique simplified coding for PEM-01 fastener screws, enabling unique product matching. Self-clinching screws feature up to 12 core attributes, including product category, official standard name, standard code, product specification (composed of diameter, length, pitch and thread profile), material quality, strength, base material, surface treatment and others.
Previously, although unified standardized management was implemented for the 12 attribute names and their corresponding values, individual attribute descriptions could exceed 50 characters in length, making it difficult to invoke and describe in subsequent systems such as ERP, WMS and MES, and failing to support unique product matching and management. To address this issue, the 12 attribute values are converted into a 12 to 19-character unique product code via a custom encoding similar to Base64 (comprising only 33 alphanumeric characters: H1VGSCNXQTF6L7MR2UB04ZP89JE5Y3DKWA), to realize unique product matching and retrieval. Additionally, a corresponding product QR code is generated based on the unique product code, enabling rapid scanning and recognition for invocation by subsequent system platforms.
1. Data Collection:
Attribute data and parameters of fastener products are sourced from nationally published fastener standard manuals, thread tolerance specifications, mechanical property manuals and material manuals. Meanwhile, attribute data of fastener products are collected from the "Industrial Parts Digital Library - [Fasteners] Standard Underlying System" developed and operated by our unit, including standard name, standard code, product specification (refined into four sub-items: diameter, length, pitch and thread profile), material quality, strength, base material, material chemical composition, material mechanical properties, surface treatment, and ID codes corresponding to each attribute are generated.
2. Algorithm Processing Method:
Step 1: Retrieve product data from the Industrial Parts Digital Library - Fasteners, including category, standard [official standard name and standard code], specification (composed of diameter, length, pitch and thread profile), as well as data such as material quality, strength, base material, surface color and ID codes corresponding to each attribute.
Step 2: Convert the ID numbers of the three attributes (category, standard and specification) into specific short codes using the 33-character custom encoding (comprising only alphanumeric characters: H1VGSCNXQTF6L7MR2UB04ZP89JE5Y3DKWA).
Step 3: Convert the attribute value strings of material quality, strength, base material and surface color into hash values via a hash algorithm, then convert the hash values into specific short codes using the 33-character custom encoding.
Step 4: Connect the custom short codes of category, standard, specification and other attributes obtained from Steps 2 and 3 with hyphens to form the final unique product code.
提供机构:
杭州优工品科技有限公司
创建时间:
2024-08-27
搜集汇总
数据集介绍

特点
该数据集为紧固件PEM-01螺钉的编码管理数据,包含产品ID、类目、标准、规格等12个属性,通过自定义编码和哈希算法生成唯一编码,便于产品唯一性匹配和检索。数据规模为1101条,每周更新,适用于ERP、WMS、MES等系统的调用和管理。
以上内容由遇见数据集搜集并总结生成



