333 lines
11 KiB
Python
333 lines
11 KiB
Python
"""
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Task 3 - Step 5: 日历排程生成
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=============================
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输入:
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- 04_reschedule.xlsx (站点访问次数 + 配对访问明细)
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输出: 05_calendar.xlsx (365天的完整排程)
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排程逻辑:
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1. 生成所有访问事件（单站点 + 双站点）
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2. 为每个事件计算理想日期（均匀分布）
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3. 贪心分配到365天，每天2个事件槽位
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4. 优先安排双站点访问（时间较长）
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约束:
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- 每天恰好2个访问事件
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- 同一站点的访问尽量均匀分布
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"""
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import pandas as pd
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import numpy as np
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from collections import defaultdict
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# ============================================
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# 参数设置
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# ============================================
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INPUT_FILE = '04_reschedule.xlsx'
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OUTPUT_FILE = '05_calendar.xlsx'
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DAYS_PER_YEAR = 365
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EVENTS_PER_DAY = 2
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TOTAL_EVENTS = DAYS_PER_YEAR * EVENTS_PER_DAY # 730
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# ============================================
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# 读取数据
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# ============================================
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print("=" * 60)
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print("Task 3 - Step 5: 日历排程生成")
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print("=" * 60)
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# 读取站点访问次数
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df_sites = pd.read_excel(INPUT_FILE, sheet_name='sites_schedule')
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print(f"\n读取站点数据: {len(df_sites)} 个站点")
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# 读取配对访问
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df_pairs = pd.read_excel(INPUT_FILE, sheet_name='pair_visits')
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print(f"读取配对数据: {len(df_pairs)} 对")
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# ============================================
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# 生成访问事件
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# ============================================
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print(f"\n" + "-" * 40)
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print("生成访问事件")
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print("-" * 40)
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events = []
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event_id = 0
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# 1. 生成单站点访问事件
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for _, row in df_sites.iterrows():
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site_id = row['site_id']
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site_name = row['site_name']
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k_single = int(row['k_single_final'])
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for visit_num in range(k_single):
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# 计算理想日期（均匀分布）
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ideal_day = (visit_num + 0.5) * DAYS_PER_YEAR / k_single
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events.append({
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'event_id': event_id,
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'event_type': 'single',
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'site_i_id': site_id,
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'site_j_id': None,
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'site_i_name': site_name,
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'site_j_name': None,
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'visit_num': visit_num + 1,
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'total_visits': k_single,
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'ideal_day': ideal_day,
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'priority': 0 # 单站点优先级较低
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})
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event_id += 1
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# 2. 生成双站点访问事件
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for _, row in df_pairs.iterrows():
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site_i_id = row['site_i_id']
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site_j_id = row['site_j_id']
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site_i_name = row['site_i_name']
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site_j_name = row['site_j_name']
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k_dual = int(row['k_dual'])
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for visit_num in range(k_dual):
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ideal_day = (visit_num + 0.5) * DAYS_PER_YEAR / k_dual
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events.append({
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'event_id': event_id,
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'event_type': 'dual',
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'site_i_id': site_i_id,
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'site_j_id': site_j_id,
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'site_i_name': site_i_name,
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'site_j_name': site_j_name,
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'visit_num': visit_num + 1,
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'total_visits': k_dual,
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'ideal_day': ideal_day,
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'priority': 1 # 双站点优先级较高
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})
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event_id += 1
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df_events = pd.DataFrame(events)
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print(f"总访问事件数: {len(df_events)}")
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print(f" - 单站点: {(df_events['event_type'] == 'single').sum()}")
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print(f" - 双站点: {(df_events['event_type'] == 'dual').sum()}")
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# ============================================
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# 贪心排程算法
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# ============================================
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print(f"\n" + "-" * 40)
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print("执行贪心排程")
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print("-" * 40)
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# 按理想日期和优先级排序
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df_events_sorted = df_events.sort_values(
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['ideal_day', 'priority'],
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ascending=[True, False]
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).reset_index(drop=True)
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# 初始化日历槽位
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calendar = {day: [] for day in range(1, DAYS_PER_YEAR + 1)}
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# 记录每个站点最后访问的日期
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last_visit = defaultdict(lambda: -float('inf'))
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# 贪心分配
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assigned_day = []
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for idx, event in df_events_sorted.iterrows():
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ideal = event['ideal_day']
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site_i = event['site_i_id']
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site_j = event['site_j_id']
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# 寻找最佳可用日期
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best_day = None
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best_score = float('inf')
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# 搜索范围：理想日期附近
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search_start = max(1, int(ideal) - 30)
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search_end = min(DAYS_PER_YEAR, int(ideal) + 30)
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for day in range(search_start, search_end + 1):
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# 检查槽位是否可用
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if len(calendar[day]) >= EVENTS_PER_DAY:
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continue
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# 检查同一站点是否已在当天访问
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sites_on_day = set()
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for e in calendar[day]:
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sites_on_day.add(e['site_i_id'])
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if e['site_j_id'] is not None:
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sites_on_day.add(e['site_j_id'])
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if site_i in sites_on_day:
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continue
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if site_j is not None and site_j in sites_on_day:
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continue
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# 计算得分（理想日期偏差 + 间隔惩罚）
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day_diff = abs(day - ideal)
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# 间隔惩罚：鼓励与上次访问保持距离
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min_gap = min(
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day - last_visit[site_i],
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day - last_visit[site_j] if site_j is not None else float('inf')
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)
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gap_penalty = max(0, 7 - min_gap) * 2 # 7天内再次访问有惩罚
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score = day_diff + gap_penalty
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if score < best_score:
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best_score = score
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best_day = day
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# 如果附近没找到，扩大搜索范围
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if best_day is None:
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for day in range(1, DAYS_PER_YEAR + 1):
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if len(calendar[day]) < EVENTS_PER_DAY:
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sites_on_day = set()
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for e in calendar[day]:
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sites_on_day.add(e['site_i_id'])
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if e['site_j_id'] is not None:
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sites_on_day.add(e['site_j_id'])
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if site_i not in sites_on_day:
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if site_j is None or site_j not in sites_on_day:
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best_day = day
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break
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if best_day is None:
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print(f"警告: 无法分配事件 {event['event_id']}")
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best_day = 1 # 强制分配
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# 分配到日历
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calendar[best_day].append(event.to_dict())
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assigned_day.append(best_day)
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# 更新最后访问日期
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last_visit[site_i] = best_day
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if site_j is not None:
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last_visit[site_j] = best_day
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df_events_sorted['assigned_day'] = assigned_day
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# ============================================
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# 生成日历视图
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# ============================================
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print(f"\n" + "-" * 40)
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print("生成日历视图")
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print("-" * 40)
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calendar_rows = []
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for day in range(1, DAYS_PER_YEAR + 1):
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events_on_day = calendar[day]
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row = {'day': day}
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for slot in range(EVENTS_PER_DAY):
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if slot < len(events_on_day):
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e = events_on_day[slot]
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row[f'slot_{slot+1}_type'] = e['event_type']
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row[f'slot_{slot+1}_site_i'] = e['site_i_id']
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row[f'slot_{slot+1}_site_j'] = e['site_j_id']
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row[f'slot_{slot+1}_name_i'] = e['site_i_name']
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row[f'slot_{slot+1}_name_j'] = e['site_j_name']
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else:
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row[f'slot_{slot+1}_type'] = None
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row[f'slot_{slot+1}_site_i'] = None
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row[f'slot_{slot+1}_site_j'] = None
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row[f'slot_{slot+1}_name_i'] = None
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row[f'slot_{slot+1}_name_j'] = None
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calendar_rows.append(row)
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df_calendar = pd.DataFrame(calendar_rows)
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# ============================================
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# 统计和验证
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# ============================================
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print(f"\n排程统计:")
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# 每天事件数
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events_per_day = [len(calendar[d]) for d in range(1, DAYS_PER_YEAR + 1)]
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print(f" - 每天事件数: min={min(events_per_day)}, max={max(events_per_day)}, avg={np.mean(events_per_day):.2f}")
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# 理想日期偏差
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df_events_sorted['day_diff'] = abs(df_events_sorted['assigned_day'] - df_events_sorted['ideal_day'])
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print(f" - 理想日期偏差: avg={df_events_sorted['day_diff'].mean():.2f}, max={df_events_sorted['day_diff'].max():.0f}")
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# 访问间隔分析
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print(f"\n访问间隔分析:")
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site_visits = defaultdict(list)
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for _, event in df_events_sorted.iterrows():
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site_visits[event['site_i_id']].append(event['assigned_day'])
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# 只有双站点访问才有site_j_id
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if event['site_j_id'] is not None and not (isinstance(event['site_j_id'], float) and np.isnan(event['site_j_id'])):
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site_visits[event['site_j_id']].append(event['assigned_day'])
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gaps = []
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for site_id, days in site_visits.items():
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days_sorted = sorted(days)
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for i in range(1, len(days_sorted)):
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gaps.append(days_sorted[i] - days_sorted[i-1])
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if gaps:
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print(f" - 间隔范围: [{min(gaps)}, {max(gaps)}] 天")
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print(f" - 平均间隔: {np.mean(gaps):.1f} 天")
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print(f" - 中位数间隔: {np.median(gaps):.1f} 天")
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# ============================================
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# 保存结果
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# ============================================
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with pd.ExcelWriter(OUTPUT_FILE, engine='openpyxl') as writer:
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# Sheet 1: 日历视图
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df_calendar.to_excel(writer, sheet_name='calendar', index=False)
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# Sheet 2: 事件详情（含分配日期）
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df_events_sorted.to_excel(writer, sheet_name='events', index=False)
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# Sheet 3: 站点访问日期汇总
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site_schedule = []
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for site_id, days in site_visits.items():
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# 跳过None/NaN值
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if site_id is None or (isinstance(site_id, float) and np.isnan(site_id)):
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continue
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# 处理可能的类型不匹配
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site_row = df_sites[df_sites['site_id'] == int(site_id)]
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if len(site_row) > 0:
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site_name = site_row['site_name'].values[0]
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else:
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site_name = f"Site_{site_id}"
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days_sorted = sorted(days)
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site_schedule.append({
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'site_id': int(site_id),
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'site_name': site_name,
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'total_visits': len(days),
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'visit_days': ','.join(map(str, days_sorted)),
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'first_visit': days_sorted[0],
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'last_visit': days_sorted[-1]
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})
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df_site_schedule = pd.DataFrame(site_schedule)
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df_site_schedule.to_excel(writer, sheet_name='site_visits', index=False)
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# Sheet 4: 汇总统计
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summary = pd.DataFrame({
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'metric': ['total_days', 'total_events', 'single_events', 'dual_events',
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'avg_day_diff', 'max_day_diff', 'avg_gap', 'min_gap', 'max_gap'],
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'value': [DAYS_PER_YEAR, len(df_events),
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(df_events['event_type'] == 'single').sum(),
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(df_events['event_type'] == 'dual').sum(),
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df_events_sorted['day_diff'].mean(),
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df_events_sorted['day_diff'].max(),
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np.mean(gaps) if gaps else 0,
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min(gaps) if gaps else 0,
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max(gaps) if gaps else 0]
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})
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summary.to_excel(writer, sheet_name='summary', index=False)
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print(f"\n结果已保存至: {OUTPUT_FILE}")
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print(" - Sheet 'calendar': 365天日历视图")
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print(" - Sheet 'events': 事件详情")
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print(" - Sheet 'site_visits': 站点访问日期汇总")
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print(" - Sheet 'summary': 汇总统计")
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print("\n" + "=" * 60)
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