2a4950d8a0
소형 건설업체(100억 미만)를 위한 AI 기반 토목공사 통합관리 플랫폼 Backend (FastAPI): - SQLAlchemy 모델 13개 (users, projects, wbs, tasks, daily_reports, reports, inspections, quality, weather, permits, rag, settings) - API 라우터 11개 (auth, projects, tasks, daily_reports, reports, inspections, weather, rag, kakao, permits, settings) - Services: Claude AI 래퍼, CPM Gantt 계산, 기상청 API, RAG(pgvector), 카카오 Skill API - Alembic 마이그레이션 (pgvector 포함) - pytest 테스트 (CPM, 날씨 경보) Frontend (Next.js 15): - 11개 페이지 (대시보드, 프로젝트, Gantt, 일보, 검측, 품질, 날씨, 인허가, RAG, 설정) - TanStack Query + Zustand + Tailwind CSS 인프라: - Docker Compose (PostgreSQL pgvector + backend + frontend) - 한국어 README 및 설치 가이드 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
112 lines
3.4 KiB
Python
112 lines
3.4 KiB
Python
"""
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CPM (Critical Path Method) calculation for Gantt chart.
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"""
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from datetime import date, timedelta
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from typing import NamedTuple
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import uuid
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class TaskNode(NamedTuple):
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id: uuid.UUID
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planned_start: date | None
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planned_end: date | None
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duration_days: int
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def compute_cpm(tasks: list, dependencies: list) -> dict[uuid.UUID, dict]:
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"""
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Compute CPM forward/backward pass.
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Returns dict: task_id -> {early_start, early_finish, late_start, late_finish, total_float, is_critical}
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"""
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if not tasks:
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return {}
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# Build adjacency maps
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task_map = {t.id: t for t in tasks}
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successors: dict[uuid.UUID, list[uuid.UUID]] = {t.id: [] for t in tasks}
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predecessors: dict[uuid.UUID, list[uuid.UUID]] = {t.id: [] for t in tasks}
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for dep in dependencies:
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successors[dep.predecessor_id].append(dep.successor_id)
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predecessors[dep.successor_id].append(dep.predecessor_id)
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def get_duration(task) -> int:
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if task.planned_start and task.planned_end:
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return max(1, (task.planned_end - task.planned_start).days + 1)
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return 1
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# Topological sort (Kahn's algorithm)
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in_degree = {t.id: len(predecessors[t.id]) for t in tasks}
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queue = [t.id for t in tasks if in_degree[t.id] == 0]
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topo_order = []
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while queue:
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node = queue.pop(0)
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topo_order.append(node)
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for succ in successors[node]:
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in_degree[succ] -= 1
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if in_degree[succ] == 0:
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queue.append(succ)
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# Forward pass: compute Early Start (ES) and Early Finish (EF)
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es: dict[uuid.UUID, int] = {} # days from project start
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ef: dict[uuid.UUID, int] = {}
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for tid in topo_order:
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task = task_map[tid]
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dur = get_duration(task)
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if not predecessors[tid]:
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es[tid] = 0
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else:
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es[tid] = max(ef[p] for p in predecessors[tid])
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ef[tid] = es[tid] + dur
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if not ef:
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return {}
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project_duration = max(ef.values())
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# Backward pass: compute Late Finish (LF) and Late Start (LS)
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lf: dict[uuid.UUID, int] = {}
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ls: dict[uuid.UUID, int] = {}
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for tid in reversed(topo_order):
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task = task_map[tid]
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dur = get_duration(task)
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if not successors[tid]:
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lf[tid] = project_duration
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else:
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lf[tid] = min(ls[s] for s in successors[tid])
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ls[tid] = lf[tid] - dur
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# Compute float and critical path
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result = {}
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# Find an actual project start date
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project_start = None
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for t in tasks:
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if t.planned_start:
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if project_start is None or t.planned_start < project_start:
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project_start = t.planned_start
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if not project_start:
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project_start = date.today()
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for tid in topo_order:
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total_float = ls[tid] - es[tid]
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is_critical = total_float == 0
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early_start_date = project_start + timedelta(days=es[tid])
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early_finish_date = project_start + timedelta(days=ef[tid] - 1)
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late_start_date = project_start + timedelta(days=ls[tid])
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late_finish_date = project_start + timedelta(days=lf[tid] - 1)
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result[tid] = {
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"early_start": early_start_date,
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"early_finish": early_finish_date,
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"late_start": late_start_date,
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"late_finish": late_finish_date,
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"total_float": total_float,
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"is_critical": is_critical,
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}
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return result, project_duration
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