云切余钢网余料数据
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1.余料评估定级和交易推荐，通过分析余料材料类型、材质、形状、大小、板厚、重量、体积、余料重量/原钢板重等，评估钢板余料评价系数，为余料定级，为买家推荐定级更高的余料商品。2.市场价格分析，运用时间序列分析算法对不同材料类型、不同材质的余料价格进行分析和预测，帮助相关企业和机构了解市场价格走势，制定合理的采购、销售策略。例如，分析过去一年Q550D材质余料价格的变化趋势，预测未来几个月的价格走向，以便钢铁生产企业调整生产计划，余料交易平台制定促销活动。1、数据收集：从数据库中梳理出余料商品数据，筛选出企业名称、材料类型、材质、形状、X(长/半径/外径)（单位：mm）、Y(宽/内径/边长2)（单位：mm）、板厚（单位：mm）、单重（单位：吨）、单位、生产厂家、商品售卖库存、商品单价、录入日期、录入时间、录入机构、录入部门、是否删除、新增日期时间、上架价格、上架数量、库存数量、发货地址等字段； 2、数据加工： a. 先根据余料形状、X(长/半径/外径)（单位：mm）、Y(宽/内径/边长2)（单位：mm）、板厚（单位：mm）计算出余料体积（单位：m³）； b. 根据余料Y(宽/内径/边长2)（单位：mm）、板厚（单位：mm）计算出原钢板重（单位：吨），计算公式为原钢板重（单位：吨） = Y(宽/内径/边长2)（单位：mm） * 钢板长度（根据板厚确定钢板长度，板厚<=15mm，长度为6000mm；板厚>15mm且板厚<30mm，长度为8000mm；板厚>=30mm，长度为120000mm） * 板厚（单位：mm）÷ 1000000000 * 7.85。 c. 根据余料单重（单位：吨）、体积（单位：m³）、单重（单位：吨）/原钢板重（单位：吨）为余料分级，具体为“大”、“中”、“小”。当余料重量>=1吨时，单重（单位：吨）/原钢板重（单位：吨）>=0.5时，余料评为“大”；单重（单位：吨）/原钢板重（单位：吨）<0.1时，评为“小”；其他情况为“中”。当余料重量<1吨且体积>=1m³时，单重（单位：吨）/原钢板重（单位：吨）>=0.5时，评为“大”；单重（单位：吨）/原钢板重（单位：吨）<0.1时，评为“小”；其他情况为“中”。当余料重量<1吨且体积<1m³时,全部评为“小”。为每种余料分级设置一个余料评估系数，其中“大”系数为1；“中”系数为0.9；“小”系数为0.7。 3、数据应用：通过这样的分析流程，不仅能够更准确地为余料进行评级，同时还能够为买家推荐评级更高的余料商品，有效提升客户满意度。
1. Scrap Evaluation, Grading and Trading Recommendation: By analyzing the material type, material grade, shape, size, plate thickness, weight, volume, scrap weight/raw steel plate weight of steel plate scraps, evaluate the evaluation coefficient of steel plate scraps, grade the steel plate scraps, and recommend higher-graded scrap products to buyers. 2. Market Price Analysis: Use time series analysis algorithms to analyze and predict the prices of scraps of different material types and grades, helping relevant enterprises and institutions understand market price trends and formulate reasonable procurement and sales strategies. For example, analyze the price change trend of Q550D grade scraps over the past year, and predict the price trend in the coming months, so as to help steel manufacturing enterprises adjust production plans and scrap trading platforms formulate promotional activities. 1、Data Collection: Extract steel scrap product data from the database, and screen out fields including enterprise name, material type, material grade, shape, X (length/radius/outer diameter) (unit: mm), Y (width/inner diameter/side length 2) (unit: mm), plate thickness (unit: mm), single weight (unit: ton), unit, manufacturer, product sales inventory, unit product price, entry date, entry time, entry institution, entry department, deletion status, new date and time, listing price, listing quantity, inventory quantity, shipping address, etc. 2、Data Processing: a. First, calculate the volume of the scrap (unit: m³) based on the scrap shape, X (length/radius/outer diameter) (unit: mm), Y (width/inner diameter/side length 2) (unit: mm), and plate thickness (unit: mm); b. Calculate the raw steel plate weight (unit: ton) based on Y (width/inner diameter/side length 2) (unit: mm) and plate thickness (unit: mm). The calculation formula is: Raw Steel Plate Weight (ton) = Y (width/inner diameter/side length 2, mm) × Steel Plate Length (determined by plate thickness: 6000mm when plate thickness ≤ 15mm; 8000mm when 15mm < plate thickness < 30mm; 120000mm when plate thickness ≥ 30mm) × Plate Thickness (mm) ÷ 1000000000 × 7.85. c. Grade the scraps into three levels: "Large", "Medium" and "Small" based on the scrap single weight (unit: ton), volume (unit: m³) and the ratio of single weight to raw steel plate weight (single weight/raw steel plate weight). The specific grading rules are as follows: - When the scrap weight ≥ 1 ton: Grade as "Large" if single weight/raw steel plate weight ≥ 0.5; grade as "Small" if single weight/raw steel plate weight < 0.1; otherwise grade as "Medium". - When the scrap weight < 1 ton and volume ≥ 1 m³: Grade as "Large" if single weight/raw steel plate weight ≥ 0.5; grade as "Small" if single weight/raw steel plate weight < 0.1; otherwise grade as "Medium". - When the scrap weight < 1 ton and volume < 1 m³: All scraps are graded as "Small". Set an evaluation coefficient for each scrap grade: 1 for "Large", 0.9 for "Medium", and 0.7 for "Small". 3、Data Application: Through this analysis process, we can not only grade scraps more accurately, but also recommend higher-graded scrap products to buyers, effectively improving customer satisfaction.
