Query 2021-2025 CUMCM school awards and 2026 forecasts, or assess a student's preparation distance from provincial and national awards using school history and prior modeling experience.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use the bundled workbook as the only source for school counts. Two modes: 学校画像 and 备赛进度评估.
Run python scripts/query_school.py --school "学校名称" --region "赛区".
ok: output 2021-2025 first/second prizes, totals, 2026 forecast, frequent advisor and appearances.ambiguous: show candidates and ask for confirmation.not_found: output: “非常遗憾，按当前榜单口径，您所在的高校过去5年没有获得国奖成绩。对于这类学校，本年度可能获得国奖的经验概率为6.81%。” State that 6.81% is an owner-supplied school-level heuristic, not an individual probability or official result.First query the school. Then ask only for missing information: 是否参加过其他数模竞赛、次数和名称、是否获奖及等级、完整模拟/论文次数、目标奖项. Output 学校基础、个人准备度、目标差距、下一步行动.
$bzd-review-paper when available to review the previous paper, then complete 1-2 simulations.Preserve workbook values. Historical advisor frequency is correlation, not quota control. Do not use “预定、分配、保证”. Data ends in 2025 and 2026 is forecast. End every assessment with: “这是一项基于学校历史与个人经历的经验评估，不是官方概率，也不能承诺具体奖项。”
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Review a user-provided Chinese mathematical-modeling abstract without requiring the problem statement or full paper, identify concrete writing defects, and prioritize revisions. Use when the user asks to check, diagnose, or improve a modeling-paper abstract; do not claim coverage or factual consistency that cannot be verified without the problem or paper.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate or review mathematical-modeling competition AI tool usage statements and the anonymous AI工具使用详情.pdf. Use when users provide real AI usage records, or when checking a paper, statement, details PDF, code, and AI records for completeness, anonymity, and consistency.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review a mathematical-modeling paper's model-assumption section against the complete problem statement. Diagnose whether each assumption is necessary, evidence-based, compatible with the task, appropriately simplified, and usable by the later model. Use for assumption review, not automatic full-paper review or unsupported assumption generation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Query the bundled BZD mathematical-modeling dictionary for a user-selected model, explain its assumptions, inputs, outputs, limitations and validation, then judge whether it fits the current competition problem and data. Use when a user asks whether a proposed model is appropriate, requests model feasibility review, or wants dictionary-based alternatives for a modeling task.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review the model establishment, numerical solution, result analysis, model validation, and sensitivity or robustness sections of a mathematical-modeling paper against the complete problem. Use for core-model-section diagnosis, not automatic full-paper scoring or unsupported recomputation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.