import matplotlib.pyplot as plt<\/span><\/p>\n import numpy as np<\/span><\/p>\n \u00a0<\/span><\/p>\n class YieldAnalyzer:<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0"""\u826f\u7387\u5206\u6790\u5de5\u5177：Pareto\u56fe\u751f\u6210\u548cTop\u635f\u5931\u56e0\u5b50\u6392\u5e8f<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u4e3b\u8981\u529f\u80fd：<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a01. \u4ece\u6d4b\u8bd5\u6570\u636e\u4e2d\u7edf\u8ba1\u5404\u7c7b\u7f3a\u9677\u6570\u91cf<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a02. \u751f\u6210Pareto\u56fe\u8bc6\u522b\u4e3b\u8981\u635f\u5931\u56e0\u5b50<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a03. \u8f93\u51faTop\u635f\u5931\u56e0\u5b50\u6392\u5e8f\u62a5\u544a<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u4e3a\u4ec0\u4e48\u8fd9\u6837\u8bbe\u8ba1：<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0– Pareto\u5206\u6790\u662fYE\u6700\u5e38\u7528\u7684\u65b9\u6cd5，\u96c6\u4e2d\u7cbe\u529b\u89e3\u51b3\u4e3b\u8981\u77db\u76fe<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0– \u81ea\u52a8\u5316\u751f\u6210\u62a5\u544a\u8282\u7701\u624b\u5de5\u5236\u56fe\u65f6\u95f4<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0– \u53ef\u6269\u5c55\u6dfb\u52a0\u66f4\u591a\u5206\u6790\u529f\u80fd\u5982\u76f8\u5173\u6027\u5206\u6790<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0"""<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0def __init__(self, data_path):<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0self.df = pd.read_csv(data_path)<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0self.defect_types = self.df['defect_type'].unique()<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0def calculate_defect_counts(self):<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0"""\u7edf\u8ba1\u5404\u7c7b\u578b\u7f3a\u9677\u6570\u91cf"""<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0defect_counts = self.df.groupby('defect_type').size()<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0defect_counts = defect_counts.sort_values(ascending=False)<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return defect_counts<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0def generate_pareto(self, output_path='pareto.png'):<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0"""\u751f\u6210Pareto\u5206\u6790\u56fe"""<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0counts = self.calculate_defect_counts()<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0total = counts.sum()<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cumulative = counts.cumsum() \/ total * 100<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0plt.rcParams['font.sans-serif'] = ['SimHei']<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0fig, ax1 = plt.subplots(figsize=(10, 6))<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax1.bar(range(len(counts)), counts.values, color='#409EFF')<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax1.set_xlabel('\u7f3a\u9677\u7c7b\u578b')<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax1.set_ylabel('\u7f3a\u9677\u6570\u91cf', color='#409EFF')<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax1.set_xticks(range(len(counts)))<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax1.set_xticklabels(counts.index, rotation=45, ha='right')<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax2 = ax1.twinx()<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax2.plot(range(len(counts)), cumulative.values, <\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'o-', color='#E6A23C', linewidth=2)<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax2.set_ylabel('\u7d2f\u8ba1\u5360\u6bd4(%)', color='#E6A23C')<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0ax2.axhline(y=80, color='#E6A23C', linestyle='–', alpha=0.5)<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0plt.title('\u826f\u7387\u635f\u5931Pareto\u5206\u6790')<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0plt.tight_layout()<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0plt.savefig(output_path, dpi=150)<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0plt.close()<\/span><\/p>\n \u00a0<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0def get_top_losses(self, top_n=5):<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0"""\u83b7\u53d6Top\u635f\u5931\u56e0\u5b50"""<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0counts = self.calculate_defect_counts()<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0total = counts.sum()<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0results = []<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0for i, (defect, count) in enumerate(counts.head(top_n).items()):<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0results.append({'rank': i+1, 'defect_type': defect,<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'count': count, 'percentage': count\/total*100})<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return results<\/span><\/p>\n \u00a0<\/span><\/p>\n # \u4f7f\u7528\u793a\u4f8b<\/span><\/p>\n if __name__ == "__main__":<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0analyzer = YieldAnalyzer("cp_test_data.csv")<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0analyzer.generate_pareto("pareto.png")<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0top5 = analyzer.get_top_losses(5)<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0for item in top5:<\/span><\/p>\n \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0print(f"{item['rank']}. {item['defect_type']}: {item['percentage']:.1f}%")<\/span><\/p>\n \u4e94\u3001\u6548\u679c\u5bf9\u6bd4：YE vs PIE vs PE<\/p>\n \u5728FAB\u4e2d，YE\u3001PIE\u3001PE\u662f\u4e09\u4e2a\u6838\u5fc3\u5de5\u7a0b\u5c97\u4f4d，\u804c\u8d23\u6709\u6240\u91cd\u53e0\u4f46\u4fa7\u91cd\u70b9\u4e0d\u540c：<\/p>\n \u5c97\u4f4d<\/p>\n<\/td>\n \u826f\u7387\u6539\u5584\u8d21\u732e<\/p>\n<\/td>\n \u6570\u636e\u5206\u6790\u80fd\u529b<\/p>\n<\/td>\n \u6c9f\u901a\u534f\u8c03<\/p>\n<\/td>\n<\/tr>\n YE\u826f\u7387\u5de5\u7a0b\u5e08<\/p>\n<\/td>\n \u4e3b\u5bfc\u5206\u6790\u5b9a\u4f4d<\/p>\n<\/td>\n \u6838\u5fc3\u80fd\u529b<\/p>\n<\/td>\n \u8de8\u90e8\u95e8\u534f\u8c03<\/p>\n<\/td>\n<\/tr>\n PIE\u5de5\u827a\u5de5\u7a0b\u5e08<\/p>\n<\/td>\n \u5b9e\u65bd\u5de5\u827a\u4f18\u5316<\/p>\n<\/td>\n \u4e2d\u7b49\u8981\u6c42<\/p>\n<\/td>\n \u6280\u672f\u5bf9\u63a5<\/p>\n<\/td>\n<\/tr>\n PE\u4ea7\u54c1\u5de5\u7a0b\u5e08<\/p>\n<\/td>\n \u8ddf\u8fdb\u4ea7\u54c1\u95ee\u9898<\/p>\n<\/td>\n \u4e2d\u7b49\u8981\u6c42<\/p>\n<\/td>\n \u5ba2\u6237\u6c9f\u901a<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u4ece\u8fd9\u4e2a\u5bf9\u6bd4\u53ef\u4ee5\u770b\u51faYE\u7684\u72ec\u7279\u4ef7\u503c：<\/p>\n YE\u662f\u6570\u636e\u5206\u6790\u7684\u4e13\u5bb6，\u64c5\u957f\u4ece\u6d77\u91cf\u6570\u636e\u4e2d\u53d1\u73b0\u89c4\u5f8b\u3001\u5b9a\u4f4d\u95ee\u9898\u3002\u8fd9\u662fYE\u7684\u6838\u5fc3\u7ade\u4e89\u529b\u3002<\/p>\n YE\u662f\u8de8\u90e8\u95e8\u534f\u8c03\u7684\u67a2\u7ebd，\u826f\u7387\u95ee\u9898\u5f80\u5f80\u6d89\u53ca\u5de5\u827a\u3001\u8bbe\u5907\u3001\u6750\u6599\u7b49\u591a\u4e2a\u90e8\u95e8，YE\u9700\u8981\u534f\u8c03\u5404\u65b9\u8d44\u6e90，\u63a8\u52a8\u95ee\u9898\u89e3\u51b3\u3002<\/p>\n YE\u662f\u95ee\u9898\u95ed\u73af\u7684owner，\u4ece\u53d1\u73b0\u5f02\u5e38\u5230\u6839\u56e0\u5b9a\u4f4d\u5230\u6539\u5584\u9a8c\u8bc1，YE\u8d1f\u8d23\u5168\u7a0b\u8ddf\u8e2a，\u786e\u4fdd\u95ee\u9898\u771f\u6b63\u89e3\u51b3\u3002<\/p>\n \u4e0d\u540c\u5c97\u4f4d\u6ca1\u6709\u4f18\u52a3\u4e4b\u5206，\u53ea\u662f\u5206\u5de5\u4e0d\u540c\u3002YE\u66f4\u9002\u5408\u559c\u6b22\u6570\u636e\u5206\u6790\u3001\u5584\u4e8e\u6c9f\u901a\u534f\u8c03\u7684\u4eba\u3002PIE\u66f4\u9002\u5408\u559c\u6b22\u94bb\u7814\u5de5\u827a\u6280\u672f\u7684\u4eba\u3002PE\u66f4\u9002\u5408\u559c\u6b22\u8ddf\u4ea7\u54c1\u6253\u4ea4\u9053\u7684\u4eba\u3002<\/p>\n \u516d\u3001\u5b9e\u65bd\u5efa\u8bae：YE\u5de5\u7a0b\u5e08\u5165\u95e8\u6307\u5357<\/p>\n \u5982\u679c\u4f60\u5bf9YE\u611f\u5174\u8da3，\u60f3\u8fdb\u5165\u8fd9\u4e2a\u5c97\u4f4d，\u6211\u6709\u51e0\u70b9\u5efa\u8bae：<\/p>\n \u7b2c\u4e00，\u6253\u597d\u6570\u636e\u5206\u6790\u57fa\u7840\u3002<\/p>\n YE\u65e5\u5e38\u5de5\u4f5c\u79bb\u4e0d\u5f00\u6570\u636e\u5206\u6790，Excel\u662f\u57fa\u672c\u529f，Python\u662f\u52a0\u5206\u9879\u3002\u5b66\u4f1a\u7528\u6570\u636e\u8bf4\u8bdd，\u5b66\u4f1a\u7528\u56fe\u8868\u8868\u8fbe\u5206\u6790\u7ed3\u8bba\u3002\u63a8\u8350\u5b66\u4e60Pandas\u3001Matplotlib\u7b49\u5e38\u7528\u5e93\u3002<\/p>\n \u7b2c\u4e8c，\u719f\u6089FAB\u751f\u4ea7\u6d41\u7a0b\u3002<\/p>\n \u4e0d\u4e86\u89e3\u5de5\u827a\u6d41\u7a0b，\u5c31\u4e0d\u77e5\u9053\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u5efa\u8bae\u5148\u719f\u6089\u5404\u5de5\u5e8f\u7684\u57fa\u672c\u539f\u7406，\u77e5\u9053\u6bcf\u4e2a\u5de5\u5e8f\u53ef\u80fd\u5f15\u5165\u4ec0\u4e48\u7f3a\u9677，\u77e5\u9053\u5173\u952e\u53c2\u6570\u6709\u54ea\u4e9b\u3002<\/p>\n \u7b2c\u4e09，\u638c\u63e1\u5e38\u7528\u7684\u5206\u6790\u5de5\u5177\u3002<\/p>\n SPC\u3001Pareto\u3001\u76f8\u5173\u6027\u5206\u6790\u662fYE\u7684\u4e09\u5927\u6cd5\u5b9d\u3002\u7406\u89e3\u8fd9\u4e9b\u5de5\u5177\u7684\u539f\u7406\u548c\u5e94\u7528\u573a\u666f，\u77e5\u9053\u4ec0\u4e48\u60c5\u51b5\u4e0b\u7528\u4ec0\u4e48\u5de5\u5177\u3002<\/p>\n \u7b2c\u56db，\u57f9\u517b\u7cfb\u7edf\u601d\u7ef4\u3002<\/p>\n \u826f\u7387\u95ee\u9898\u5f80\u5f80\u662f\u4e00\u679c\u591a\u56e0，\u4e0d\u80fd\u53ea\u770b\u5230\u4e00\u4e2a\u56e0\u7d20\u5c31\u4e0b\u7ed3\u8bba\u3002\u5b66\u4f1a\u7efc\u5408\u591a\u65b9\u9762\u8bc1\u636e，\u5b66\u4f1a\u6392\u9664\u6cd5，\u5b66\u4f1a\u9a8c\u8bc1\u5047\u8bbe\u3002<\/p>\n \u7b2c\u4e94，\u63d0\u5347\u6c9f\u901a\u80fd\u529b\u3002<\/p>\n YE\u9700\u8981\u548c\u5de5\u827a\u5de5\u7a0b\u5e08\u8ba8\u8bba\u6280\u672f\u7ec6\u8282，\u9700\u8981\u548c\u8bbe\u5907\u5de5\u7a0b\u5e08\u534f\u8c03\u8bbe\u5907\u95ee\u9898，\u9700\u8981\u5411\u7ba1\u7406\u5c42\u6c47\u62a5\u5206\u6790\u7ed3\u8bba\u3002\u6c9f\u901a\u80fd\u529b\u76f4\u63a5\u5f71\u54cd\u95ee\u9898\u89e3\u51b3\u6548\u7387\u3002<\/p>\n \u7b2c\u516d，\u4fdd\u6301\u5b66\u4e60\u7684\u70ed\u60c5\u3002<\/p>\n \u6280\u672f\u5728\u8fdb\u6b65，\u65b0\u4ea7\u54c1\u65b0\u5de5\u827a\u4e0d\u65ad\u6d8c\u73b0，YE\u9700\u8981\u6301\u7eed\u5b66\u4e60\u3002\u5173\u6ce8\u884c\u4e1a\u52a8\u6001，\u5b66\u4e60\u65b0\u7684\u5206\u6790\u65b9\u6cd5，\u79ef\u7d2f\u4e0d\u540c\u4ea7\u54c1\u7684\u5206\u6790\u7ecf\u9a8c\u3002<\/p>\n \u4e03\u3001\u8fdb\u9636\u65b9\u5411：AI\u826f\u7387\u9884\u6d4b\u4e0e\u5927\u6570\u636e\u5206\u6790<\/p>\n 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