用确定性流程计算学生成本，自动标注高低中三档，确保可复现。
Deterministic Preprocessing and Interpretable Fuzzy Banding for Cost-per-Student Reporting from Extracted Records
- 基于规则的脚本处理数据库导出文件，生成可复现的计算记录。
- 按年份划分成本/学生比，用最小值、中位数、最大值定义高低中三档。
- 引入模糊隶属度权重辅助决策，适合教育预算与治理场景使用。
行政数据常以电子表格形式交换，并可能直接用于预算、工作量评估和治理讨论。当导出的表格成为决策参考快照时，可通过重新计算验证其准确性。本文实现了一个确定性的、基于规则的文件级工作流（cad_processor.py），读取因果学术数据库（CAD）导出文件，聚合包含成本与学生人数，计算学科-年度及学校-年度总成本，并推导出每生成本比率。输出包含四个表格：处理摘要（运行记录与计数器）、趋势分析（年度每生成本矩阵）、报告（宽格式学科级表格）和模糊带（每年锚点、隶属度权重与标签）。运行记录包含输入文件字节的SHA-256哈希，支持快照匹配复现。针对年度内解释，增加简单模糊分段层，将有限正值的每生成本比划分为低、中、高三档；锚点为各年度有限正比率的最小值、中位数和最大值；隶属度采用左肩、三角、右肩函数计算，按固定优先级（中→低→高）确定唯一归属。这些权重作为决策支持信号而非概率。附录提供证据链矩阵、可复现说明及术语表，关联声明与代码及文档产物。
原文摘要 · Abstract (English)
Administrative extracts are often exchanged as spreadsheets and may be read as reports in their own right during budgeting, workload review, and governance discussions. When an exported workbook becomes the reference snapshot for such decisions, the transformation can be checked by recomputation against a clearly identified input. A deterministic, rule-governed, file-based workflow is implemented in cad_processor.py. The script ingests a Casual Academic Database (CAD) export workbook and aggregates inclusive on-costs and student counts into subject-year and school-year totals, from which it derives cost-per-student ratios. It writes a processed workbook with four sheets: Processing Summary (run record and counters), Trend Analysis (schoolyear cost-per-student matrix), Report (wide subject-level table), and Fuzzy Bands (per-year anchors, membership weights, and band labels). The run record includes a SHA-256 hash of the input workbook bytes to support snapshot-matched recomputation. For within-year interpretation, the workflow adds a simple fuzzy banding layer that labels finite, positive school-year cost-per-student values as Low, Medium, or High. The per-year anchors are the minimum, median, and maximum of the finite, positive ratios. Membership weights are computed using left-shoulder, triangular, and right-shoulder functions, with deterministic tie-breaking in a fixed priority order (Medium, then Low, then High). These weights are treated as decision-support signals rather than probabilities. A worked example provides a reproducible calculation of a band assignment from the reported anchors and ratios. Supplementary material includes a claim-to-evidence matrix, a reproducibility note, and a short glossary that links selected statements to code and workbook artefacts.
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