Pharmaceutical manufacturers generate thousands of batch manufacturing records (BMRs) each year under FDA 21 CFR Part 211 and EU GMP rules. These long documents combine tables, calculations, images, and handwritten notes, and are usually digitized by hand with hours of expert review per record. We present an AI workflow that converts unstructured BMRs into structured JSON using token based chunking, parallel large language model extraction, and a fixed schema that covers 11 content types while preserving the original Group-Phase-Step hierarchy. The system applies three layers of validation (JSON syntax, structural integrity of classes and references, and pharmaceutical compliance checks aligned with GMP) and reports coverage metrics for text, tables, images, and calculations. On three real BMRs between 15 and 66 pages, it achieves composite confidence scores in the low to high eighties while reducing processing time from hours to minutes on a single GPU. This enables practical, human in the loop BMR digitization at scale and unlocks historical manufacturing data for downstream analysis.

翻译：在FDA 21 CFR Part 211和欧盟GMP法规要求下，制药企业每年生成数千份批生产记录。这些冗长文档包含表格、计算、图像和手写注释，通常需人工数字化处理，每份记录需经数小时专家审核。本文提出一种AI工作流，通过基于令牌的文本分块、并行大语言模型信息提取，以及覆盖11种内容类型并保持原始"组-阶段-步骤"层级结构的固定模式，将非结构化BMR转换为结构化JSON。该系统实施三层验证（JSON语法校验、类别与引用的结构完整性检查、符合GMP标准的制药合规性验证），并报告文本、表格、图像和计算的覆盖度指标。在15至66页的三份真实BMR测试中，系统获得80%中高段的综合置信度评分，同时在单GPU上将处理时间从数小时缩短至数分钟。这实现了可规模化、人机协同的BMR数字化实践，并为下游分析释放了历史生产数据价值。