1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473# coding=utf-8
"""
统计分析模块
提供新闻统计和分析功能：
- calculate_news_weight: 计算新闻权重
- format_time_display: 格式化时间显示
- count_word_frequency: 统计词频
"""
from typing import Dict, List, Tuple, Optional, Callable
from trendradar.core.frequency import matches_word_groups
def calculate_news_weight(
title_data: Dict,
rank_threshold: int,
weight_config: Dict,
) -> float:
"""
计算新闻权重，用于排序
Args:
title_data: 标题数据，包含 ranks 和 count
rank_threshold: 排名阈值
weight_config: 权重配置 {RANK_WEIGHT, FREQUENCY_WEIGHT, HOTNESS_WEIGHT}
Returns:
float: 计算出的权重值
"""
ranks = title_data.get("ranks", [])
if not ranks:
return 0.0
count = title_data.get("count", len(ranks))
# 排名权重：Σ(11 - min(rank, 10)) / 出现次数
rank_scores = []
for rank in ranks:
score = 11 - min(rank, 10)
rank_scores.append(score)
rank_weight = sum(rank_scores) / len(ranks) if ranks else 0
# 频次权重：min(出现次数, 10) × 10
frequency_weight = min(count, 10) * 10
# 热度加成：高排名次数 / 总出现次数 × 100
high_rank_count = sum(1 for rank in ranks if rank <= rank_threshold)
hotness_ratio = high_rank_count / len(ranks) if ranks else 0
hotness_weight = hotness_ratio * 100
total_weight = (
rank_weight * weight_config["RANK_WEIGHT"]
+ frequency_weight * weight_config["FREQUENCY_WEIGHT"]
+ hotness_weight * weight_config["HOTNESS_WEIGHT"]
)
return total_weight
def format_time_display(
first_time: str,
last_time: str,
convert_time_func: Callable[[str], str],
) -> str:
"""
格式化时间显示（将 HH-MM 转换为 HH:MM）
Args:
first_time: 首次出现时间
last_time: 最后出现时间
convert_time_func: 时间格式转换函数
Returns:
str: 格式化后的时间显示字符串
"""
if not first_time:
return ""
# 转换为显示格式
first_display = convert_time_func(first_time)
last_display = convert_time_func(last_time)
if first_display == last_display or not last_display:
return first_display
else:
return f"[{first_display} ~ {last_display}]"
def count_word_frequency(
results: Dict,
word_groups: List[Dict],
filter_words: List[str],
id_to_name: Dict,
title_info: Optional[Dict] = None,
rank_threshold: int = 3,
new_titles: Optional[Dict] = None,
mode: str = "daily",
global_filters: Optional[List[str]] = None,
weight_config: Optional[Dict] = None,
max_news_per_keyword: int = 0,
sort_by_position_first: bool = False,
is_first_crawl_func: Optional[Callable[[], bool]] = None,
convert_time_func: Optional[Callable[[str], str]] = None,
quiet: bool = False,
) -> Tuple[List[Dict], int]:
"""
统计词频，支持必须词、频率词、过滤词、全局过滤词，并标记新增标题
Args:
results: 抓取结果 {source_id: {title: title_data}}
word_groups: 词组配置列表
filter_words: 过滤词列表
id_to_name: ID 到名称的映射
title_info: 标题统计信息（可选）
rank_threshold: 排名阈值
new_titles: 新增标题（可选）
mode: 报告模式 (daily/incremental/current)
global_filters: 全局过滤词（可选）
weight_config: 权重配置
max_news_per_keyword: 每个关键词最大显示数量
sort_by_position_first: 是否优先按配置位置排序
is_first_crawl_func: 检测是否是当天第一次爬取的函数
convert_time_func: 时间格式转换函数
quiet: 是否静默模式（不打印日志）
Returns:
Tuple[List[Dict], int]: (统计结果列表, 总标题数)
"""
# 默认权重配置
if weight_config is None:
weight_config = {
"RANK_WEIGHT": 0.4,
"FREQUENCY_WEIGHT": 0.3,
"HOTNESS_WEIGHT": 0.3,
}
# 默认时间转换函数
if convert_time_func is None:
convert_time_func = lambda x: x
# 默认首次爬取检测函数
if is_first_crawl_func is None:
is_first_crawl_func = lambda: True
# 如果没有配置词组，创建一个包含所有新闻的虚拟词组
if not word_groups:
print("频率词配置为空，将显示所有新闻")
word_groups = [{"required": [], "normal": [], "group_key": "全部新闻"}]
filter_words = [] # 清空过滤词，显示所有新闻
is_first_today = is_first_crawl_func()
# 确定处理的数据源和新增标记逻辑
if mode == "incremental":
if is_first_today:
# 增量模式 + 当天第一次：处理所有新闻，都标记为新增
results_to_process = results
all_news_are_new = True
else:
# 增量模式 + 当天非第一次：只处理新增的新闻
results_to_process = new_titles if new_titles else {}
all_news_are_new = True
elif mode == "current":
# current 模式：只处理当前时间批次的新闻，但统计信息来自全部历史
if title_info:
latest_time = None
for source_titles in title_info.values():
for title_data in source_titles.values():
last_time = title_data.get("last_time", "")
if last_time:
if latest_time is None or last_time > latest_time:
latest_time = last_time
# 只处理 last_time 等于最新时间的新闻
if latest_time:
results_to_process = {}
for source_id, source_titles in results.items():
if source_id in title_info:
filtered_titles = {}
for title, title_data in source_titles.items():
if title in title_info[source_id]:
info = title_info[source_id][title]
if info.get("last_time") == latest_time:
filtered_titles[title] = title_data
if filtered_titles:
results_to_process[source_id] = filtered_titles
print(
f"当前榜单模式：最新时间 {latest_time}，筛选出 {sum(len(titles) for titles in results_to_process.values())} 条当前榜单新闻"
)
else:
results_to_process = results
else:
results_to_process = results
all_news_are_new = False
else:
# 当日汇总模式：处理所有新闻
results_to_process = results
all_news_are_new = False
total_input_news = sum(len(titles) for titles in results.values())
filter_status = (
"全部显示"
if len(word_groups) == 1 and word_groups[0]["group_key"] == "全部新闻"
else "频率词过滤"
)
print(f"当日汇总模式：处理 {total_input_news} 条新闻，模式：{filter_status}")
word_stats = {}
total_titles = 0
processed_titles = {}
matched_new_count = 0
if title_info is None:
title_info = {}
if new_titles is None:
new_titles = {}
for group in word_groups:
group_key = group["group_key"]
word_stats[group_key] = {"count": 0, "titles": {}}
for source_id, titles_data in results_to_process.items():
total_titles += len(titles_data)
if source_id not in processed_titles:
processed_titles[source_id] = {}
for title, title_data in titles_data.items():
if title in processed_titles.get(source_id, {}):
continue
# 使用统一的匹配逻辑
matches_frequency_words = matches_word_groups(
title, word_groups, filter_words, global_filters
)
if not matches_frequency_words:
continue
# 如果是增量模式或 current 模式第一次，统计匹配的新增新闻数量
if (mode == "incremental" and all_news_are_new) or (
mode == "current" and is_first_today
):
matched_new_count += 1
source_ranks = title_data.get("ranks", [])
source_url = title_data.get("url", "")
source_mobile_url = title_data.get("mobileUrl", "")
# 找到匹配的词组（防御性转换确保类型安全）
title_lower = str(title).lower() if not isinstance(title, str) else title.lower()
for group in word_groups:
required_words = group["required"]
normal_words = group["normal"]
# 如果是"全部新闻"模式，所有标题都匹配第一个（唯一的）词组
if len(word_groups) == 1 and word_groups[0]["group_key"] == "全部新闻":
group_key = group["group_key"]
word_stats[group_key]["count"] += 1
if source_id not in word_stats[group_key]["titles"]:
word_stats[group_key]["titles"][source_id] = []
else:
# 原有的匹配逻辑
if required_words:
all_required_present = all(
req_word.lower() in title_lower
for req_word in required_words
)
if not all_required_present:
continue
if normal_words:
any_normal_present = any(
normal_word.lower() in title_lower
for normal_word in normal_words
)
if not any_normal_present:
continue
group_key = group["group_key"]
word_stats[group_key]["count"] += 1
if source_id not in word_stats[group_key]["titles"]:
word_stats[group_key]["titles"][source_id] = []
first_time = ""
last_time = ""
count_info = 1
ranks = source_ranks if source_ranks else []
url = source_url
mobile_url = source_mobile_url
# 对于 current 模式，从历史统计信息中获取完整数据
if (
mode == "current"
and title_info
and source_id in title_info
and title in title_info[source_id]
):
info = title_info[source_id][title]
first_time = info.get("first_time", "")
last_time = info.get("last_time", "")
count_info = info.get("count", 1)
if "ranks" in info and info["ranks"]:
ranks = info["ranks"]
url = info.get("url", source_url)
mobile_url = info.get("mobileUrl", source_mobile_url)
elif (
title_info
and source_id in title_info
and title in title_info[source_id]
):
info = title_info[source_id][title]
first_time = info.get("first_time", "")
last_time = info.get("last_time", "")
count_info = info.get("count", 1)
if "ranks" in info and info["ranks"]:
ranks = info["ranks"]
url = info.get("url", source_url)
mobile_url = info.get("mobileUrl", source_mobile_url)
if not ranks:
ranks = [99]
time_display = format_time_display(first_time, last_time, convert_time_func)
source_name = id_to_name.get(source_id, source_id)
# 判断是否为新增
is_new = False
if all_news_are_new:
# 增量模式下所有处理的新闻都是新增，或者当天第一次的所有新闻都是新增
is_new = True
elif new_titles and source_id in new_titles:
# 检查是否在新增列表中
new_titles_for_source = new_titles[source_id]
is_new = title in new_titles_for_source
word_stats[group_key]["titles"][source_id].append(
{
"title": title,
"source_name": source_name,
"first_time": first_time,
"last_time": last_time,
"time_display": time_display,
"count": count_info,
"ranks": ranks,
"rank_threshold": rank_threshold,
"url": url,
"mobileUrl": mobile_url,
"is_new": is_new,
}
)
if source_id not in processed_titles:
processed_titles[source_id] = {}
processed_titles[source_id][title] = True
break
# 最后统一打印汇总信息
if mode == "incremental":
if is_first_today:
total_input_news = sum(len(titles) for titles in results.values())
filter_status = (
"全部显示"
if len(word_groups) == 1 and word_groups[0]["group_key"] == "全部新闻"
else "频率词匹配"
)
print(
f"增量模式：当天第一次爬取，{total_input_news} 条新闻中有 {matched_new_count} 条{filter_status}"
)
else:
if new_titles:
total_new_count = sum(len(titles) for titles in new_titles.values())
filter_status = (
"全部显示"
if len(word_groups) == 1
and word_groups[0]["group_key"] == "全部新闻"
else "匹配频率词"
)
print(
f"增量模式：{total_new_count} 条新增新闻中，有 {matched_new_count} 条{filter_status}"
)
if matched_new_count == 0 and len(word_groups) > 1:
print("增量模式：没有新增新闻匹配频率词，将不会发送通知")
else:
print("增量模式：未检测到新增新闻")
elif mode == "current":
total_input_news = sum(len(titles) for titles in results_to_process.values())
if is_first_today:
filter_status = (
"全部显示"
if len(word_groups) == 1 and word_groups[0]["group_key"] == "全部新闻"
else "频率词匹配"
)
print(
f"当前榜单模式：当天第一次爬取，{total_input_news} 条当前榜单新闻中有 {matched_new_count} 条{filter_status}"
)
else:
matched_count = sum(stat["count"] for stat in word_stats.values())
filter_status = (
"全部显示"
if len(word_groups) == 1 and word_groups[0]["group_key"] == "全部新闻"
else "频率词匹配"
)
print(
f"当前榜单模式：{total_input_news} 条当前榜单新闻中有 {matched_count} 条{filter_status}"
)
stats = []
# 创建 group_key 到位置和最大数量的映射
group_key_to_position = {
group["group_key"]: idx for idx, group in enumerate(word_groups)
}
group_key_to_max_count = {
group["group_key"]: group.get("max_count", 0) for group in word_groups
}
for group_key, data in word_stats.items():
all_titles = []
for source_id, title_list in data["titles"].items():
all_titles.extend(title_list)
# 按权重排序
sorted_titles = sorted(
all_titles,
key=lambda x: (
-calculate_news_weight(x, rank_threshold, weight_config),
min(x["ranks"]) if x["ranks"] else 999,
-x["count"],
),
)
# 应用最大显示数量限制（优先级：单独配置 > 全局配置）
group_max_count = group_key_to_max_count.get(group_key, 0)
if group_max_count == 0:
# 使用全局配置
group_max_count = max_news_per_keyword
if group_max_count > 0:
sorted_titles = sorted_titles[:group_max_count]
stats.append(
{
"word": group_key,
"count": data["count"],
"position": group_key_to_position.get(group_key, 999),
"titles": sorted_titles,
"percentage": (
round(data["count"] / total_titles * 100, 2)
if total_titles > 0
else 0
),
}
)
# 根据配置选择排序优先级
if sort_by_position_first:
# 先按配置位置，再按热点条数
stats.sort(key=lambda x: (x["position"], -x["count"]))
else:
# 先按热点条数，再按配置位置（原逻辑）
stats.sort(key=lambda x: (-x["count"], x["position"]))
# 打印过滤后的匹配新闻数
matched_news_count = sum(len(stat["titles"]) for stat in stats if stat["count"] > 0)
if not quiet and mode == "daily":
print(f"当日汇总模式：处理 {total_titles} 条新闻，模式：频率词过滤")
print(f"频率词过滤后：{matched_news_count} 条新闻匹配")
return stats, total_titles