光伏组件串焊机虚焊分析应用数据
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通过对组件产线串焊机设备进行数据采集，分析设备工艺参数数据、物料数据、EL检测数据，截取前后两次质检参数发生变化的数据作为样本，将样本以时间维度按照参数变化情况进行分段，每一时间段内物料相同、工艺参数相同，统计电池片虚焊率、虚焊分布情况以及影响因素。利用大数据分析平台对组件串焊机数据采集后进行数据挖掘、提取、分析和可视化的过程。1、数据收集：采用kepware工业采集软件收集：包括设备灯管功率、高温加热上限时间、高温态冷却时间等关键数据。2、数据清洗：对收集的原始数据进行预处理，包括去重数据、处理缺失数据、纠正错误数据等；3、数据集成：对多个数据源进行整合，形成一个完整数据仓；4、模型选择：根据数据量以及变化量等因素选择随机森林等算法模型；5、模型训练：确认模型后，利用已有的数据进行模型训练，计算模型参数和系数，并对数据的准确性进行预评估，以及模型的参数增加或减少变量进行调整优化，使之达到预期效果；6、数据应用：通过模型优化算法，寻找虚焊位置、形态和工艺参数关系，找到一组使虚焊率尽可能低的工艺配方组合，并通过图表、推荐配方等方式给出优化曲线路径，如应用大数据分析模型推荐参数，实现良率的提升，计算公式：NG率=NG/OK。通过以上规则描述，可以对组件串焊机工艺环节建立可靠的大数据分析算法模型，并通过平台的分析后，给出最佳推荐工艺配方，为生产降本提质增效。
This dataset is developed by collecting data from solar module string welding machines on production lines, analyzing equipment process parameters, material data, and Electroluminescence (EL) inspection data. Samples are extracted when quality inspection parameters change between two consecutive time points, then segmented along the time axis based on parameter variations, with each time segment featuring consistent materials and process parameters. The false soldering rate of solar cells, the distribution of false soldering, and their influencing factors are statistically calculated. The overall workflow includes data collection from string welding machines, followed by data mining, extraction, analysis, and visualization via a big data analysis platform. 1. Data Collection: Use Kepware industrial data acquisition software to collect key data including equipment lamp power, high-temperature heating upper limit time, high-temperature state cooling time, and other critical metrics. 2. Data Cleaning: Preprocess the collected raw data, including deduplication, handling missing values, correcting erroneous data, and other preprocessing operations. 3. Data Integration: Integrate data from multiple sources to build a complete data warehouse. 4. Model Selection: Select algorithm models such as Random Forest based on factors including data volume and data variation magnitude. 5. Model Training: After confirming the selected model, train it using the available dataset, calculate model parameters and coefficients, conduct preliminary evaluation of data accuracy, and adjust and optimize the model by adding or removing variables to achieve the expected performance. 6. Data Application: Use the optimized model algorithm to identify the relationships between false soldering positions, morphologies and process parameters, and find a set of process formula combinations that minimize the false soldering rate. Optimized solution paths are provided via charts, recommended formulas and other means, such as recommending process parameters through the big data analysis model to improve production yield. The calculation formula is: NG Rate = NG / OK. Based on the above workflow, a reliable big data analysis algorithm model can be established for the solar module string welding process. The platform will then provide the optimal recommended process formulas, helping to reduce production costs, improve product quality and enhance production efficiency.
