【妹子花名】:舒淇 【老师车牌】: @shuqi1234 【验证留名】:八级大狂风 【修车水费】:5 【人照得分】: 9 【颜值得分】: 9 【身材得分】: 9 【服务得分】: 9 【态度得分】: 9 【环境得分】: 9 【综合得分】: 9 【验证时间】:2026-9-22 【过程描述】:姐姐到电梯接的,见面眼前一亮,挺高的。身材也可以,进屋陪洗,全程不用自己动手,还有毒龙,老师会功夫😂,倒立口,各种口,非常舒服。后入也挺舒服,值得二刷。
❤1

Channel
@zhengzhougkb01
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
25,601subscribers
-596 since we began measuring on 12 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1002262648830 |
|---|---|
| Type | Channel |
| Username | @zhengzhougkb01 |
| Description | 🎫个人/中介/会所/外围 认证中心 只做最真实的娱乐资源,真实即可认证 认证通过即可公示! 认证联系: @TShengdi_bot 郑州交流群: @zhengzhoujlq |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 12 August 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 28 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/zhengzhougkb01 |
Other / unclassifiable — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 73% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 18 Sept 2026, 19:39 | 25,601 | +131 |
| 16 Sept 2026, 10:38 | 25,470 | -73 |
| 14 Sept 2026, 18:41 | 25,543 | -10 |
| 13 Sept 2026, 07:58 | 25,553 | -237 |
| 11 Sept 2026, 08:58 | 25,790 | -117 |
| 8 Sept 2026, 14:35 | 25,907 | -170 |
| 5 Sept 2026, 05:17 | 26,077 | -78 |
| 3 Sept 2026, 07:45 | 26,155 | -118 |
| 2 Sept 2026, 04:34 | 26,273 | -51 |
| 1 Sept 2026, 07:28 | 26,324 | -74 |
| 31 Aug 2026, 06:17 | 26,398 | -60 |
| 30 Aug 2026, 06:26 | 26,458 | -103 |
| 29 Aug 2026, 03:34 | 26,561 | +10 |
| 28 Aug 2026, 01:14 | 26,551 | +62 |
| 26 Aug 2026, 23:27 | 26,489 | +29 |
| 25 Aug 2026, 20:13 | 26,460 | -48 |
| 24 Aug 2026, 21:52 | 26,508 | +47 |
| 23 Aug 2026, 09:12 | 26,461 | +19 |
| 21 Aug 2026, 21:04 | 26,442 | +16 |
| 20 Aug 2026, 16:57 | 26,426 | first reading |
35 posts held, back to 3 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 43 pages of Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 22 September 2026 |
|---|---|
| Posts held | 35 (3 August 2026 – 22 September 2026) |
| Views total | 21,203 |
| Reactions total | 42 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 23 Sept 2026, 06:50 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Lifetime counters from Telegram’s own channel header, read 23 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Measured directly from 14 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
115 reactions across 35 posts, in 6 distinct kinds. The most used accounts for 47.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 55 | 47.8% | |
| 🔥 | 30 | 26.1% | |
| 👍 | 24 | 20.9% | |
| 🥰 | 4 | 3.48% | |
| 👌 | 1 | 0.87% | |
| 😁 | 1 | 0.87% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 35 of the 35 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 115 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 35 most recent posts we hold, published 3 August 2026 to 22 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
【妹子花名】:舒淇 【老师车牌】: @shuqi1234 【验证留名】:八级大狂风 【修车水费】:5 【人照得分】: 9 【颜值得分】: 9 【身材得分】: 9 【服务得分】: 9 【态度得分】: 9 【环境得分】: 9 【综合得分】: 9 【验证时间】:2026-9-22 【过程描述】:姐姐到电梯接的,见面眼前一亮,挺高的。身材也可以,进屋陪洗,全程不用自己动手,还有毒龙,老师会功夫😂,倒立口,各种口,非常舒服。后入也挺舒服,值得二刷。
❤1
#郑州 #包养 高颜值纯欲甜妹女大,月一万起 欣欣 客服:@Baoyxin 双向客服:@Baoyxin_bot 欣欣包养频道:@Xinyangai66
❤1👍1🔥1
【老 师 艺 名】#涵涵 【所 在 位 置】#郑州 #二七 【茶 位】#6P #12PP 【服 务 类 型】#全套 【联 系 方 式】@hanhan5567 【老 师 频 道】@hanhan55677 【双向机器人】@hanhan5567bot 备注: #个人老师 #视频认证
👍3❤1🔥1
【老 师 艺 名】#半夏 【所 在 位 置】#郑州 #金水 【茶 位】#6P #11PP 【服 务 类 型】#全套 【联 系 方 式】@bxwd9898 【老 师 频 道】@bxwd88888 【双向机器人】@bxwdbxwdBot 备注: #个人老师 #视频认证
❤5👍1🔥1
【学生留名】:feng 【出击时间】:9.16 【老师名字】:#素素 【位置地区】:#郑州 #金水 【出击消费】:600p 【颜值身材】:人照一致 秀气玲珑 前凸后翘 【服务内容】:舌吻舔蛋蛋六九 【上课体验】:现在是回味时间,整体的体验都是极好的。老师没有一点儿风尘味,笑起来很甜,说话也甜甜的,我觉得就是那种学生情人的感觉,好像回到学生时代一样,我是不喜欢胸部大的吓人的那种,老师的胸部刚好是我喜欢的类型,粉嫩,刚刚好,有感受到老师温热的嘴巴,整根被含的湿湿的,很喜欢这种感觉,满满的征服,很喜欢老师婀娜多姿的腰身,第一次后入很快就结束了,是在老师的娇喘声下成功发射,很棒的体验。晚安,好梦!素素老师!! 【优点缺点】:温柔可人儿 【老师电报】:@susu850 【满分打分】:9.5分(满分10分) 【温馨提醒】:报告仅供参考,请理性选择,切勿对号入座
❤2🔥1
【妹子花名】:舒淇 【老师车牌】: @shuqi1234 【验证留名】:Leeee 【修车水费】:5p 【人照得分】: 9.1 【颜值得分】: 9 【身材得分】: 10 【服务得分】: 10 【态度得分】: 10 【环境得分】: 8 【综合得分】: 9.3 【验证时间】:2026-09-16 【过程描述】:和老师约好时间准时到达,老师与照片基本一致到电梯口接的我可以认出来,虽然身高很高但是很温柔,是我喜欢的类型,服务过程中所有的服务都做了,尤其是毒龙,倒立深喉,观音坐莲,君临天下,360℃旋转做爱,刺激死我了,没几下就交枪了,还没爽够,下次还来,想出击的狼友可以试试舒淇老师,真牛逼
❤4👍1🔥1
【老 师 艺 名】#素素 【所 在 位 置】#郑州 #金水 【茶 位】#6P #12PP 【服 务 类 型】#全套 【联 系 方 式】@susu850 【老 师 频 道】@susss56 【双向机器人】@susu558bot 备注: #个人老师
🥰1
🎉🎉🎉 #郑州 摸摸唱ktv🎉🎉🎉 😀📱📱📱📱📱-📱📱📱 主打高颜值,每日出勤 70➕ 📱📱奶子随便摸丝袜随便撕😀 酒水原价 1️⃣2️⃣8️⃣8️⃣ 活动价格 优惠至 7️⃣📱📱 🥸😃妹子😉1100😘1200😂1300 通台不限时❤️❤️❤️❤️❤️❤️❤️ 🥰 @yushao520😀 ❤️❤️❤️@mmc935 ❤️❤️❤️❤️❤️❤️ 👻🥲🥲😔😋🤓😋😋🤓 唯一微信❤️❤️ ktv18768916196 唯一订房电话❤️❤️ 18768916196 🅿️ 点击这回到“会所外围”选择 #郑州
❤1👍1🔥1
【老 师 艺 名】#佳琪 【所 在 位 置】#郑州 #中原 【茶 位】#7P #13PP 【服 务 类 型】#全套 【联 系 方 式】@guifenga 【老 师 频 道】@tuanzhidq 【双向机器人】@wushongqq_bot 备注: #个人老师 #视频认证
❤1👍1🔥1
🤔兰亭集精选全套会所🤔 #郑州 ⚡️⚡️主打颜值 服务 环境 安全为一体的高端私密会所 ⚡️⚡️想玩会所找我,长期靠谱安全稳定 📹全裸艳舞 鸳鸯戏水 水磨推拿🥰 📹口舌调情 后口毒龙 胯下吞龙🥰 📹手推胸推 足推臀推 激情爱爱🥰 📹指划漫游 制服情趣 到店海选🥰 🫣🫣🫣🫣🫣🫣🫣🫣🫣🫣🫣 一直传承老莞式全方位整套会所服务 为男人打造尊享一条龙服务! 🥸🥸🥸🙁🤯😖😏🤬😳😳😳 🤬 到店海选 一对一服务 🤗 🥰全天妹妹在线🔞 学生🤗萝莉🤗少妇🤗嫩模 😲 可双飞👕过夜👕至尊体验 😲 ✨ 无需定金 零门槛 无上门(到店消费安全放心) ⚡️⚡️合作会所每天都有补充颜值高、活好的年轻技师 客服号: @gopd8888 双向
❤1👍1🔥1
【老 师 艺 名】#CoCo 【所 在 位 置】#郑州 #金水 【茶 位】#7P #14PP 【服 务 类 型】#舌吻 #69式 #丝袜 【联 系 方 式】@laomao0629 【老 师 频 道】@zzcoco3 【双向机器人】@zzcoco6_bot 备注: #个人老师 #视频认证
❤1👌1👍1
【老 师 艺 名】#露娜 【所 在 位 置】#郑州 #管城 【茶 位】#5P #10PP 【服 务 类 型】#全套 【联 系 方 式】@luna2568 【老 师 频 道】@lunakeshi568 【双向机器人】@xgj568_bot 备注: #个人老师 #视频认证
❤1🔥1🥰1
Showing the 12 most recent of 35 posts we hold for @zhengzhougkb01. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 18 September 2026 — this entry's latest reading, not the date you are reading this.
“郑州【资源榜】” (@zhengzhougkb01), 25,601 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/zhengzhougkb01.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.