diff --git a/README.md b/README.md
index 3ff2548d7..b005bc189 100644
--- a/README.md
+++ b/README.md
@@ -155,6 +155,8 @@ MaiMBot是一个开源项目，我们非常欢迎你的参与。你的贡献，
- [🐳 Docker部署指南](docs/docker_deploy.md)
+- [🖥️群晖 NAS 部署指南](docs/synology_deploy.md)
+
### 配置说明
- [🎀 新手配置指南](docs/installation_cute.md) - 通俗易懂的配置教程，适合初次使用的猫娘
diff --git a/docs/fast_q_a.md b/docs/fast_q_a.md
index 92800bad2..abec69b40 100644
--- a/docs/fast_q_a.md
+++ b/docs/fast_q_a.md
@@ -38,6 +38,9 @@
### MongoDB相关问题
- 我应该怎么清空bot内存储的表情包 ❓
+>需要先安装`MongoDB Compass`，[下载链接](https://www.mongodb.com/try/download/compass)，软件支持`macOS、Windows、Ubuntu、Redhat`系统
+>以Windows为例，保持如图所示选项，点击`Download`即可，如果是其他系统，请在`Platform`中自行选择：
+>
>打开你的MongoDB Compass软件，你会在左上角看到这样的一个界面：
>
@@ -68,7 +71,9 @@
- 为什么我连接不上MongoDB服务器 ❓
>这个问题比较复杂，但是你可以按照下面的步骤检查，看看具体是什么问题
->
+
+
+>#### Windows
> 1. 检查有没有把 mongod.exe 所在的目录添加到 path。 具体可参照
>
> [CSDN-windows10设置环境变量Path详细步骤](https://blog.csdn.net/flame_007/article/details/106401215)
@@ -112,4 +117,173 @@
>MONGODB_HOST=127.0.0.1
>MONGODB_PORT=27017 #修改这里
>DATABASE_NAME=MegBot
->```
\ No newline at end of file
+>```
+
+
+Linux（点击展开）
+
+#### **1. 检查 MongoDB 服务是否运行**
+- **命令**：
+ ```bash
+ systemctl status mongod # 检查服务状态（Ubuntu/Debian/CentOS 7+）
+ service mongod status # 旧版系统（如 CentOS 6）
+ ```
+- **可能结果**：
+ - 如果显示 `active (running)`，服务已启动。
+ - 如果未运行，启动服务：
+ ```bash
+ sudo systemctl start mongod # 启动服务
+ sudo systemctl enable mongod # 设置开机自启
+ ```
+
+---
+
+#### **2. 检查 MongoDB 端口监听**
+MongoDB 默认使用 **27017** 端口。
+- **检查端口是否被监听**：
+ ```bash
+ sudo ss -tulnp | grep 27017
+ 或
+ sudo netstat -tulnp | grep 27017
+ ```
+- **预期结果**：
+ ```bash
+ tcp LISTEN 0 128 0.0.0.0:27017 0.0.0.0:* users:(("mongod",pid=123,fd=11))
+ ```
+ - 如果无输出，说明 MongoDB 未监听端口。
+
+
+---
+#### **3. 检查防火墙设置**
+- **Ubuntu/Debian（UFW 防火墙）**：
+ ```bash
+ sudo ufw status # 查看防火墙状态
+ sudo ufw allow 27017/tcp # 开放 27017 端口
+ sudo ufw reload # 重新加载规则
+ ```
+- **CentOS/RHEL（firewalld）**：
+ ```bash
+ sudo firewall-cmd --list-ports # 查看已开放端口
+ sudo firewall-cmd --add-port=27017/tcp --permanent # 永久开放端口
+ sudo firewall-cmd --reload # 重新加载
+ ```
+- **云服务器用户注意**：检查云平台安全组规则，确保放行 27017 端口。
+
+---
+
+#### **4. 检查端口占用**
+如果 MongoDB 服务无法监听端口，可能是其他进程占用了 `27017` 端口。
+- **检查端口占用进程**：
+ ```bash
+ sudo lsof -i :27017 # 查看占用 27017 端口的进程
+ 或
+ sudo ss -ltnp 'sport = :27017' # 使用 ss 过滤端口
+ ```
+- **结果示例**：
+ ```bash
+ COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME
+ java 1234 root 12u IPv4 123456 0t0 TCP *:27017 (LISTEN)
+ ```
+ - 输出会显示占用端口的 **进程名** 和 **PID**（此处 `PID=1234`）。
+
+- **解决方案**：
+ 1. **终止占用进程**（谨慎操作！确保进程非关键）：
+ ```bash
+ sudo kill 1234 # 正常终止进程
+ sudo kill -9 1234 # 强制终止（若正常终止无效）
+ ```
+ 2. **修改端口**：
+ 编辑麦麦目录里的`.env.dev`文件，修改端口号：
+ ```ini
+ MONGODB_HOST=127.0.0.1
+ MONGODB_PORT=27017 #修改这里
+ DATABASE_NAME=MegBot
+ ```
+
+
+##### **注意事项**
+- 终止进程前，务必确认该进程非系统关键服务（如未知进程占用，建议先排查来源），如果你不知道这个进程是否关键，请更改端口使用。
+
+
+
+
+macOS（点击展开）
+
+### **1. 检查 MongoDB 服务状态**
+**问题原因**：MongoDB 服务未启动
+**操作步骤**：
+```bash
+# 查看 MongoDB 是否正在运行（Homebrew 安装的默认服务名）
+brew services list | grep mongodb
+
+# 如果状态为 "stopped" 或 "error"，手动启动
+brew services start mongodb-community@8.0
+```
+✅ **预期结果**：输出显示 `started` 或 `running`
+❌ **失败处理**：
+- 若报错 `unrecognized service`，可能未正确安装 MongoDB，建议[重新安装](https://www.mongodb.com/docs/manual/tutorial/install-mongodb-on-os-x/#install-mongodb-community-edition)。
+
+---
+
+### **2. 检查端口是否被占用**
+**问题原因**：其他程序占用了 MongoDB 的默认端口（`27017`），导致服务无法启动或连接
+**操作步骤**：
+```bash
+# 检查 27017 端口占用情况（需 sudo 权限查看完整信息）
+sudo lsof -i :27017
+
+# 或使用 netstat 快速检测
+netstat -an | grep 27017
+```
+✅ **预期结果**：
+- 若无 MongoDB 运行，应无输出
+- 若 MongoDB 已启动，应显示 `mongod` 进程
+
+❌ **发现端口被占用**：
+#### **解决方案1：终止占用进程**
+1. 从 `lsof` 输出中找到占用端口的 **PID**（进程号）
+2. 强制终止该进程（谨慎操作！确保进程非关键）：
+ ```bash
+ kill -9 PID # 替换 PID 为实际数字（例如 kill -9 12345）
+ ```
+3. 重新启动 MongoDB 服务：
+ ```bash
+ brew services start mongodb-community@8.0
+ ```
+
+#### **解决方案2：修改端口**
+ 编辑麦麦目录里的`.env.dev`文件，修改端口号：
+ ```ini
+ MONGODB_HOST=127.0.0.1
+ MONGODB_PORT=27017 #修改这里
+ DATABASE_NAME=MegBot
+ ```
+
+---
+
+### **3. 检查防火墙设置**
+**问题原因**：macOS 防火墙阻止连接
+**操作步骤**：
+1. 打开 **系统设置 > 隐私与安全性 > 防火墙**
+2. 临时关闭防火墙测试连接
+3. 若需长期开放，添加 MongoDB 到防火墙允许列表（通过终端或 GUI）。
+
+
+---
+### **4. 重置 MongoDB 环境**
+***仅在以上步骤都无效时使用***
+**适用场景**：配置混乱导致无法修复
+```bash
+# 停止服务并删除数据
+brew services stop mongodb-community@8.0
+rm -rf /usr/local/var/mongodb
+
+# 重新初始化（确保目录权限）
+sudo mkdir -p /usr/local/var/mongodb
+sudo chown -R $(whoami) /usr/local/var/mongodb
+
+# 重新启动
+brew services start mongodb-community@8.0
+```
+
+
\ No newline at end of file
diff --git a/docs/linux_deploy_guide_for_beginners.md b/docs/linux_deploy_guide_for_beginners.md
index 1f1b0899f..f254cf665 100644
--- a/docs/linux_deploy_guide_for_beginners.md
+++ b/docs/linux_deploy_guide_for_beginners.md
@@ -2,7 +2,7 @@
## 事前准备
-为了能使麦麦不间断的运行，你需要一台一直开着的主机。
+为了能使麦麦不间断的运行，你需要一台一直开着的服务器。
### 如果你想购买服务器
华为云、阿里云、腾讯云等等都是在国内可以选择的选择。
@@ -12,6 +12,8 @@
### 如果你不想购买服务器
你可以准备一台可以一直开着的电脑/主机，只需要保证能够正常访问互联网即可
+**下文将统称它们为`服务器`**
+
我们假设你已经有了一台Linux架构的服务器。举例使用的是Ubuntu24.04，其他的原理相似。
## 0.我们就从零开始吧
@@ -120,6 +122,7 @@ sudo apt install python-is-python3
```
## 3.MongoDB的安装
+*如果你是参考[官方文档](https://www.mongodb.com/zh-cn/docs/manual/administration/install-on-linux/#std-label-install-mdb-community-edition-linux)进行安装的，可跳过此步*
``` bash
cd /moi/mai
@@ -156,6 +159,7 @@ sudo systemctl enable mongod
curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && sudo bash napcat.sh
```
执行后，脚本会自动帮你部署好QQ及Napcat
+*注：如果你已经手动安装了Napcat和QQ，可忽略此步*
成功的标志是输入``` napcat ```出来炫酷的彩虹色界面
@@ -225,7 +229,8 @@ bot
└─ bot_config.toml
```
-你要会vim直接在终端里修改也行，不过也可以把它们下到本地改好再传上去：
+你可以使用vim、nano等编辑器直接在终端里修改这些配置文件，但如果你不熟悉它们的操作，也可以使用带图形界面的编辑器。
+如果你的麦麦部署在远程服务器，也可以把它们下载到本地改好再传上去
### step 5 文件配置
@@ -244,7 +249,7 @@ bot
- [⚙️ 标准配置指南](./installation_standard.md) - 简明专业的配置说明，适合有经验的用户
-**step # 6** 运行
+### step 6 运行
现在再运行
diff --git a/docs/manual_deploy_linux.md b/docs/manual_deploy_linux.md
index 653284bf5..5a8806771 100644
--- a/docs/manual_deploy_linux.md
+++ b/docs/manual_deploy_linux.md
@@ -24,9 +24,6 @@
---
-## 一键部署
-请下载并运行项目根目录中的run.sh并按照提示安装，部署完成后请参照后续配置指南进行配置
-
## 环境配置
### 1️⃣ **确认Python版本**
diff --git a/docs/pic/compass_downloadguide.png b/docs/pic/compass_downloadguide.png
new file mode 100644
index 000000000..06a08b52d
Binary files /dev/null and b/docs/pic/compass_downloadguide.png differ
diff --git a/script/run_thingking.bat b/script/run_thingking.bat
index a134da6fe..0806e46ed 100644
--- a/script/run_thingking.bat
+++ b/script/run_thingking.bat
@@ -1,5 +1,5 @@
-call conda activate niuniu
-cd src\gui
-start /b python reasoning_gui.py
+@REM call conda activate niuniu
+cd ../src\gui
+start /b ../../venv/scripts/python.exe reasoning_gui.py
exit
diff --git a/src/gui/reasoning_gui.py b/src/gui/reasoning_gui.py
index a93d80afd..43f692d58 100644
--- a/src/gui/reasoning_gui.py
+++ b/src/gui/reasoning_gui.py
@@ -6,6 +6,8 @@ import time
from datetime import datetime
from typing import Dict, List
from typing import Optional
+sys.path.insert(0, sys.path[0]+"/../")
+sys.path.insert(0, sys.path[0]+"/../")
from src.common.logger import get_module_logger
import customtkinter as ctk
diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py
index 38450f903..aebe1e7db 100644
--- a/src/plugins/chat/bot.py
+++ b/src/plugins/chat/bot.py
@@ -296,7 +296,7 @@ class ChatBot:
return
raw_message = f"[戳了戳]{global_config.BOT_NICKNAME}" # 默认类型
- if info := event.raw_info:
+ if info := event.model_extra["raw_info"]:
poke_type = info[2].get("txt", "戳了戳") # 戳戳类型，例如“拍一拍”、“揉一揉”、“捏一捏”
custom_poke_message = info[4].get("txt", "") # 自定义戳戳消息，若不存在会为空字符串
raw_message = f"[{poke_type}]{global_config.BOT_NICKNAME}{custom_poke_message}"
diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py
index cc53db623..0d63e7afc 100644
--- a/src/plugins/chat/utils.py
+++ b/src/plugins/chat/utils.py
@@ -260,9 +260,15 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
# print(f"处理前的文本: {text}")
- # 统一将英文逗号转换为中文逗号
- text = text.replace(",", "，")
- text = text.replace("\n", " ")
+ # 检查是否为西文字符段落
+ if not is_western_paragraph(text):
+ # 当语言为中文时，统一将英文逗号转换为中文逗号
+ text = text.replace(",", "，")
+ text = text.replace("\n", " ")
+ else:
+ # 用"|seg|"作为分割符分开
+ text = re.sub(r"([.!?]) +", r"\1\|seg\|", text)
+ text = text.replace("\n", "\|seg\|")
text, mapping = protect_kaomoji(text)
# print(f"处理前的文本: {text}")
@@ -285,21 +291,29 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
for sentence in sentences:
parts = sentence.split("，")
current_sentence = parts[0]
- for part in parts[1:]:
- if random.random() < split_strength:
+ if not is_western_paragraph(current_sentence):
+ for part in parts[1:]:
+ if random.random() < split_strength:
+ new_sentences.append(current_sentence.strip())
+ current_sentence = part
+ else:
+ current_sentence += "，" + part
+ # 处理空格分割
+ space_parts = current_sentence.split(" ")
+ current_sentence = space_parts[0]
+ for part in space_parts[1:]:
+ if random.random() < split_strength:
+ new_sentences.append(current_sentence.strip())
+ current_sentence = part
+ else:
+ current_sentence += " " + part
+ else:
+ # 处理分割符
+ space_parts = current_sentence.split("\|seg\|")
+ current_sentence = space_parts[0]
+ for part in space_parts[1:]:
new_sentences.append(current_sentence.strip())
current_sentence = part
- else:
- current_sentence += "，" + part
- # 处理空格分割
- space_parts = current_sentence.split(" ")
- current_sentence = space_parts[0]
- for part in space_parts[1:]:
- if random.random() < split_strength:
- new_sentences.append(current_sentence.strip())
- current_sentence = part
- else:
- current_sentence += " " + part
new_sentences.append(current_sentence.strip())
sentences = [s for s in new_sentences if s] # 移除空字符串
sentences = recover_kaomoji(sentences, mapping)
@@ -308,10 +322,12 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
sentences_done = []
for sentence in sentences:
sentence = sentence.rstrip("，,")
- if random.random() < split_strength * 0.5:
- sentence = sentence.replace("，", "").replace(",", "")
- elif random.random() < split_strength:
- sentence = sentence.replace("，", " ").replace(",", " ")
+ # 西文字符句子不进行随机合并
+ if not is_western_paragraph(current_sentence):
+ if random.random() < split_strength * 0.5:
+ sentence = sentence.replace("，", "").replace(",", "")
+ elif random.random() < split_strength:
+ sentence = sentence.replace("，", " ").replace(",", " ")
sentences_done.append(sentence)
logger.debug(f"处理后的句子: {sentences_done}")
@@ -347,7 +363,11 @@ def random_remove_punctuation(text: str) -> str:
def process_llm_response(text: str) -> List[str]:
# processed_response = process_text_with_typos(content)
- if len(text) > 100:
+ # 对西文字符段落的回复长度设置为汉字字符的两倍
+ if len(text) > 100 and not is_western_paragraph(text) :
+ logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
+ return ["懒得说"]
+ elif len(text) > 200 :
logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
return ["懒得说"]
# 处理长消息
@@ -509,3 +529,13 @@ def recover_kaomoji(sentences, placeholder_to_kaomoji):
sentence = sentence.replace(placeholder, kaomoji)
recovered_sentences.append(sentence)
return recovered_sentences
+
+
+def is_western_char(char):
+ """检测是否为西文字符"""
+ return len(char.encode('utf-8')) <= 2
+
+def is_western_paragraph(paragraph):
+ """检测是否为西文字符段落"""
+ return all(is_western_char(char) for char in paragraph if char.isalnum())
+
\ No newline at end of file
diff --git a/src/plugins/memory_system/memory_manual_build.py b/src/plugins/memory_system/memory_manual_build.py
index b575f455e..4b5d3b155 100644
--- a/src/plugins/memory_system/memory_manual_build.py
+++ b/src/plugins/memory_system/memory_manual_build.py
@@ -10,6 +10,12 @@ from pathlib import Path
import matplotlib.pyplot as plt
import networkx as nx
from dotenv import load_dotenv
+sys.path.insert(0, sys.path[0]+"/../")
+sys.path.insert(0, sys.path[0]+"/../")
+sys.path.insert(0, sys.path[0]+"/../")
+sys.path.insert(0, sys.path[0]+"/../")
+sys.path.insert(0, sys.path[0]+"/../")
+from src.common.logger import get_module_logger
import jieba
# from chat.config import global_config
@@ -17,7 +23,6 @@ import jieba
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
sys.path.append(root_path)
-from src.common.logger import get_module_logger # noqa: E402
from src.common.database import db # noqa E402
from src.plugins.memory_system.offline_llm import LLMModel # noqa E402
diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py
index 975bcaf7b..5ad69ff25 100644
--- a/src/plugins/models/utils_model.py
+++ b/src/plugins/models/utils_model.py
@@ -274,6 +274,7 @@ class LLM_request:
raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}")
response.raise_for_status()
+ reasoning_content = ""
# 将流式输出转化为非流式输出
if stream_mode:
@@ -303,6 +304,8 @@ class LLM_request:
accumulated_content += delta_content
# 检测流式输出文本是否结束
finish_reason = chunk["choices"][0].get("finish_reason")
+ if delta.get("reasoning_content", None):
+ reasoning_content += delta["reasoning_content"]
if finish_reason == "stop":
chunk_usage = chunk.get("usage", None)
if chunk_usage:
@@ -314,7 +317,6 @@ class LLM_request:
except Exception as e:
logger.exception(f"解析流式输出错误: {str(e)}")
content = accumulated_content
- reasoning_content = ""
think_match = re.search(r"(.*?)", content, re.DOTALL)
if think_match:
reasoning_content = think_match.group(1).strip()
diff --git a/src/plugins/personality/can_i_recog_u.py b/src/plugins/personality/can_i_recog_u.py
new file mode 100644
index 000000000..715c9ffa0
--- /dev/null
+++ b/src/plugins/personality/can_i_recog_u.py
@@ -0,0 +1,351 @@
+"""
+基于聊天记录的人格特征分析系统
+"""
+
+from typing import Dict, List
+import json
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+import sys
+import random
+from collections import defaultdict
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime
+import matplotlib.font_manager as fm
+
+current_dir = Path(__file__).resolve().parent
+project_root = current_dir.parent.parent.parent
+env_path = project_root / ".env.prod"
+
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+from src.plugins.personality.scene import get_scene_by_factor, PERSONALITY_SCENES # noqa: E402
+from src.plugins.personality.questionnaire import FACTOR_DESCRIPTIONS # noqa: E402
+from src.plugins.personality.offline_llm import LLMModel # noqa: E402
+from src.plugins.personality.who_r_u import MessageAnalyzer # noqa: E402
+
+# 加载环境变量
+if env_path.exists():
+ print(f"从 {env_path} 加载环境变量")
+ load_dotenv(env_path)
+else:
+ print(f"未找到环境变量文件: {env_path}")
+ print("将使用默认配置")
+
+class ChatBasedPersonalityEvaluator:
+ def __init__(self):
+ self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
+ self.scenarios = []
+ self.message_analyzer = MessageAnalyzer()
+ self.llm = LLMModel()
+ self.trait_scores_history = defaultdict(list) # 记录每个特质的得分历史
+
+ # 为每个人格特质获取对应的场景
+ for trait in PERSONALITY_SCENES:
+ scenes = get_scene_by_factor(trait)
+ if not scenes:
+ continue
+ scene_keys = list(scenes.keys())
+ selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
+
+ for scene_key in selected_scenes:
+ scene = scenes[scene_key]
+ other_traits = [t for t in PERSONALITY_SCENES if t != trait]
+ secondary_trait = random.choice(other_traits)
+ self.scenarios.append({
+ "场景": scene["scenario"],
+ "评估维度": [trait, secondary_trait],
+ "场景编号": scene_key
+ })
+
+ def analyze_chat_context(self, messages: List[Dict]) -> str:
+ """
+ 分析一组消息的上下文，生成场景描述
+ """
+ context = ""
+ for msg in messages:
+ nickname = msg.get('user_info', {}).get('user_nickname', '未知用户')
+ content = msg.get('processed_plain_text', msg.get('detailed_plain_text', ''))
+ if content:
+ context += f"{nickname}: {content}\n"
+ return context
+
+ def evaluate_chat_response(
+ self, user_nickname: str, chat_context: str, dimensions: List[str] = None) -> Dict[str, float]:
+ """
+ 评估聊天内容在各个人格维度上的得分
+ """
+ # 使用所有维度进行评估
+ dimensions = list(self.personality_traits.keys())
+
+ dimension_descriptions = []
+ for dim in dimensions:
+ desc = FACTOR_DESCRIPTIONS.get(dim, "")
+ if desc:
+ dimension_descriptions.append(f"- {dim}：{desc}")
+
+ dimensions_text = "\n".join(dimension_descriptions)
+
+ prompt = f"""请根据以下聊天记录，评估"{user_nickname}"在大五人格模型中的维度得分（1-6分）。
+
+聊天记录：
+{chat_context}
+
+需要评估的维度说明：
+{dimensions_text}
+
+请按照以下格式输出评估结果，注意，你的评价对象是"{user_nickname}"（仅输出JSON格式）：
+{{
+ "开放性": 分数,
+ "严谨性": 分数,
+ "外向性": 分数,
+ "宜人性": 分数,
+ "神经质": 分数
+}}
+
+评分标准：
+1 = 非常不符合该维度特征
+2 = 比较不符合该维度特征
+3 = 有点不符合该维度特征
+4 = 有点符合该维度特征
+5 = 比较符合该维度特征
+6 = 非常符合该维度特征
+
+如果你觉得某个维度没有相关信息或者无法判断，请输出0分
+
+请根据聊天记录的内容和语气，结合维度说明进行评分。如果维度可以评分，确保分数在1-6之间。如果没有体现，请输出0分"""
+
+ try:
+ ai_response, _ = self.llm.generate_response(prompt)
+ start_idx = ai_response.find("{")
+ end_idx = ai_response.rfind("}") + 1
+ if start_idx != -1 and end_idx != 0:
+ json_str = ai_response[start_idx:end_idx]
+ scores = json.loads(json_str)
+ return {k: max(0, min(6, float(v))) for k, v in scores.items()}
+ else:
+ print("AI响应格式不正确，使用默认评分")
+ return {dim: 0 for dim in dimensions}
+ except Exception as e:
+ print(f"评估过程出错：{str(e)}")
+ return {dim: 0 for dim in dimensions}
+
+ def evaluate_user_personality(self, qq_id: str, num_samples: int = 10, context_length: int = 5) -> Dict:
+ """
+ 基于用户的聊天记录评估人格特征
+
+ Args:
+ qq_id (str): 用户QQ号
+ num_samples (int): 要分析的聊天片段数量
+ context_length (int): 每个聊天片段的上下文长度
+
+ Returns:
+ Dict: 评估结果
+ """
+ # 获取用户的随机消息及其上下文
+ chat_contexts, user_nickname = self.message_analyzer.get_user_random_contexts(
+ qq_id, num_messages=num_samples, context_length=context_length)
+ if not chat_contexts:
+ return {"error": f"没有找到QQ号 {qq_id} 的消息记录"}
+
+ # 初始化评分
+ final_scores = defaultdict(float)
+ dimension_counts = defaultdict(int)
+ chat_samples = []
+
+ # 清空历史记录
+ self.trait_scores_history.clear()
+
+ # 分析每个聊天上下文
+ for chat_context in chat_contexts:
+ # 评估这段聊天内容的所有维度
+ scores = self.evaluate_chat_response(user_nickname, chat_context)
+
+ # 记录样本
+ chat_samples.append({
+ "聊天内容": chat_context,
+ "评估维度": list(self.personality_traits.keys()),
+ "评分": scores
+ })
+
+ # 更新总分和历史记录
+ for dimension, score in scores.items():
+ if score > 0: # 只统计大于0的有效分数
+ final_scores[dimension] += score
+ dimension_counts[dimension] += 1
+ self.trait_scores_history[dimension].append(score)
+
+ # 计算平均分
+ average_scores = {}
+ for dimension in self.personality_traits:
+ if dimension_counts[dimension] > 0:
+ average_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
+ else:
+ average_scores[dimension] = 0 # 如果没有有效分数，返回0
+
+ # 生成趋势图
+ self._generate_trend_plot(qq_id, user_nickname)
+
+ result = {
+ "用户QQ": qq_id,
+ "用户昵称": user_nickname,
+ "样本数量": len(chat_samples),
+ "人格特征评分": average_scores,
+ "维度评估次数": dict(dimension_counts),
+ "详细样本": chat_samples,
+ "特质得分历史": {k: v for k, v in self.trait_scores_history.items()}
+ }
+
+ # 保存结果
+ os.makedirs("results", exist_ok=True)
+ result_file = f"results/personality_result_{qq_id}.json"
+ with open(result_file, "w", encoding="utf-8") as f:
+ json.dump(result, f, ensure_ascii=False, indent=2)
+
+ return result
+
+ def _generate_trend_plot(self, qq_id: str, user_nickname: str):
+ """
+ 生成人格特质累计平均分变化趋势图
+ """
+ # 查找系统中可用的中文字体
+ chinese_fonts = []
+ for f in fm.fontManager.ttflist:
+ try:
+ if '简' in f.name or 'SC' in f.name or '黑' in f.name or '宋' in f.name or '微软' in f.name:
+ chinese_fonts.append(f.name)
+ except Exception:
+ continue
+
+ if chinese_fonts:
+ plt.rcParams['font.sans-serif'] = chinese_fonts + ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS']
+ else:
+ # 如果没有找到中文字体，使用默认字体，并将中文昵称转换为拼音或英文
+ try:
+ from pypinyin import lazy_pinyin
+ user_nickname = ''.join(lazy_pinyin(user_nickname))
+ except ImportError:
+ user_nickname = "User" # 如果无法转换为拼音，使用默认英文
+
+ plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
+
+ plt.figure(figsize=(12, 6))
+ plt.style.use('bmh') # 使用内置的bmh样式，它有类似seaborn的美观效果
+
+ colors = {
+ "开放性": "#FF9999",
+ "严谨性": "#66B2FF",
+ "外向性": "#99FF99",
+ "宜人性": "#FFCC99",
+ "神经质": "#FF99CC"
+ }
+
+ # 计算每个维度在每个时间点的累计平均分
+ cumulative_averages = {}
+ for trait, scores in self.trait_scores_history.items():
+ if not scores:
+ continue
+
+ averages = []
+ total = 0
+ valid_count = 0
+ for score in scores:
+ if score > 0: # 只计算大于0的有效分数
+ total += score
+ valid_count += 1
+ if valid_count > 0:
+ averages.append(total / valid_count)
+ else:
+ # 如果当前分数无效，使用前一个有效的平均分
+ if averages:
+ averages.append(averages[-1])
+ else:
+ continue # 跳过无效分数
+
+ if averages: # 只有在有有效分数的情况下才添加到累计平均中
+ cumulative_averages[trait] = averages
+
+ # 绘制每个维度的累计平均分变化趋势
+ for trait, averages in cumulative_averages.items():
+ x = range(1, len(averages) + 1)
+ plt.plot(x, averages, 'o-', label=trait, color=colors.get(trait), linewidth=2, markersize=8)
+
+ # 添加趋势线
+ z = np.polyfit(x, averages, 1)
+ p = np.poly1d(z)
+ plt.plot(x, p(x), '--', color=colors.get(trait), alpha=0.5)
+
+ plt.title(f"{user_nickname} 的人格特质累计平均分变化趋势", fontsize=14, pad=20)
+ plt.xlabel("评估次数", fontsize=12)
+ plt.ylabel("累计平均分", fontsize=12)
+ plt.grid(True, linestyle='--', alpha=0.7)
+ plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ plt.ylim(0, 7)
+ plt.tight_layout()
+
+ # 保存图表
+ os.makedirs("results/plots", exist_ok=True)
+ timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+ plot_file = f"results/plots/personality_trend_{qq_id}_{timestamp}.png"
+ plt.savefig(plot_file, dpi=300, bbox_inches='tight')
+ plt.close()
+
+def analyze_user_personality(qq_id: str, num_samples: int = 10, context_length: int = 5) -> str:
+ """
+ 分析用户人格特征的便捷函数
+
+ Args:
+ qq_id (str): 用户QQ号
+ num_samples (int): 要分析的聊天片段数量
+ context_length (int): 每个聊天片段的上下文长度
+
+ Returns:
+ str: 格式化的分析结果
+ """
+ evaluator = ChatBasedPersonalityEvaluator()
+ result = evaluator.evaluate_user_personality(qq_id, num_samples, context_length)
+
+ if "error" in result:
+ return result["error"]
+
+ # 格式化输出
+ output = f"QQ号 {qq_id} ({result['用户昵称']}) 的人格特征分析结果：\n"
+ output += "=" * 50 + "\n\n"
+
+ output += "人格特征评分：\n"
+ for trait, score in result["人格特征评分"].items():
+ if score == 0:
+ output += f"{trait}: 数据不足，无法判断 (评估次数: {result['维度评估次数'].get(trait, 0)})\n"
+ else:
+ output += f"{trait}: {score}/6 (评估次数: {result['维度评估次数'].get(trait, 0)})\n"
+
+ # 添加变化趋势描述
+ if trait in result["特质得分历史"] and len(result["特质得分历史"][trait]) > 1:
+ scores = [s for s in result["特质得分历史"][trait] if s != 0] # 过滤掉无效分数
+ if len(scores) > 1: # 确保有足够的有效分数计算趋势
+ trend = np.polyfit(range(len(scores)), scores, 1)[0]
+ if abs(trend) < 0.1:
+ trend_desc = "保持稳定"
+ elif trend > 0:
+ trend_desc = "呈上升趋势"
+ else:
+ trend_desc = "呈下降趋势"
+ output += f" 变化趋势: {trend_desc} (斜率: {trend:.2f})\n"
+
+ output += f"\n分析样本数量：{result['样本数量']}\n"
+ output += f"结果已保存至：results/personality_result_{qq_id}.json\n"
+ output += "变化趋势图已保存至：results/plots/目录\n"
+
+ return output
+
+if __name__ == "__main__":
+ # 测试代码
+ # test_qq = "" # 替换为要测试的QQ号
+ # print(analyze_user_personality(test_qq, num_samples=30, context_length=20))
+ # test_qq = ""
+ # print(analyze_user_personality(test_qq, num_samples=30, context_length=20))
+ test_qq = "1026294844"
+ print(analyze_user_personality(test_qq, num_samples=30, context_length=30))
diff --git a/src/plugins/personality/renqingziji_with_mymy.py b/src/plugins/personality/renqingziji_with_mymy.py
new file mode 100644
index 000000000..511395e51
--- /dev/null
+++ b/src/plugins/personality/renqingziji_with_mymy.py
@@ -0,0 +1,196 @@
+"""
+The definition of artificial personality in this paper follows the dispositional para-digm and adapts a definition of
+personality developed for humans [17]:
+Personality for a human is the "whole and organisation of relatively stable tendencies and patterns of experience and
+behaviour within one person (distinguishing it from other persons)". This definition is modified for artificial
+personality:
+Artificial personality describes the relatively stable tendencies and patterns of behav-iour of an AI-based machine that
+can be designed by developers and designers via different modalities, such as language, creating the impression
+of individuality of a humanized social agent when users interact with the machine."""
+
+from typing import Dict, List
+import json
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+import sys
+
+"""
+第一种方案：基于情景评估的人格测定
+"""
+current_dir = Path(__file__).resolve().parent
+project_root = current_dir.parent.parent.parent
+env_path = project_root / ".env.prod"
+
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+from src.plugins.personality.scene import get_scene_by_factor, PERSONALITY_SCENES # noqa: E402
+from src.plugins.personality.questionnaire import FACTOR_DESCRIPTIONS # noqa: E402
+from src.plugins.personality.offline_llm import LLMModel # noqa: E402
+
+# 加载环境变量
+if env_path.exists():
+ print(f"从 {env_path} 加载环境变量")
+ load_dotenv(env_path)
+else:
+ print(f"未找到环境变量文件: {env_path}")
+ print("将使用默认配置")
+
+
+class PersonalityEvaluator_direct:
+ def __init__(self):
+ self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
+ self.scenarios = []
+
+ # 为每个人格特质获取对应的场景
+ for trait in PERSONALITY_SCENES:
+ scenes = get_scene_by_factor(trait)
+ if not scenes:
+ continue
+
+ # 从每个维度选择3个场景
+ import random
+
+ scene_keys = list(scenes.keys())
+ selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
+
+ for scene_key in selected_scenes:
+ scene = scenes[scene_key]
+
+ # 为每个场景添加评估维度
+ # 主维度是当前特质，次维度随机选择一个其他特质
+ other_traits = [t for t in PERSONALITY_SCENES if t != trait]
+ secondary_trait = random.choice(other_traits)
+
+ self.scenarios.append(
+ {"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
+ )
+
+ self.llm = LLMModel()
+
+ def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
+ """
+ 使用 DeepSeek AI 评估用户对特定场景的反应
+ """
+ # 构建维度描述
+ dimension_descriptions = []
+ for dim in dimensions:
+ desc = FACTOR_DESCRIPTIONS.get(dim, "")
+ if desc:
+ dimension_descriptions.append(f"- {dim}：{desc}")
+
+ dimensions_text = "\n".join(dimension_descriptions)
+
+
+ prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（1-6分）。
+
+场景描述：
+{scenario}
+
+用户回应：
+{response}
+
+需要评估的维度说明：
+{dimensions_text}
+
+请按照以下格式输出评估结果（仅输出JSON格式）：
+{{
+ "{dimensions[0]}": 分数,
+ "{dimensions[1]}": 分数
+}}
+
+评分标准：
+1 = 非常不符合该维度特征
+2 = 比较不符合该维度特征
+3 = 有点不符合该维度特征
+4 = 有点符合该维度特征
+5 = 比较符合该维度特征
+6 = 非常符合该维度特征
+
+请根据用户的回应，结合场景和维度说明进行评分。确保分数在1-6之间，并给出合理的评估。"""
+
+ try:
+ ai_response, _ = self.llm.generate_response(prompt)
+ # 尝试从AI响应中提取JSON部分
+ start_idx = ai_response.find("{")
+ end_idx = ai_response.rfind("}") + 1
+ if start_idx != -1 and end_idx != 0:
+ json_str = ai_response[start_idx:end_idx]
+ scores = json.loads(json_str)
+ # 确保所有分数在1-6之间
+ return {k: max(1, min(6, float(v))) for k, v in scores.items()}
+ else:
+ print("AI响应格式不正确，使用默认评分")
+ return {dim: 3.5 for dim in dimensions}
+ except Exception as e:
+ print(f"评估过程出错：{str(e)}")
+ return {dim: 3.5 for dim in dimensions}
+
+
+def main():
+ print("欢迎使用人格形象创建程序！")
+ print("接下来，您将面对一系列场景（共15个）。请根据您想要创建的角色形象，描述在该场景下可能的反应。")
+ print("每个场景都会评估不同的人格维度，最终得出完整的人格特征评估。")
+ print("评分标准：1=非常不符合，2=比较不符合，3=有点不符合，4=有点符合，5=比较符合，6=非常符合")
+ print("\n准备好了吗？按回车键开始...")
+ input()
+
+ evaluator = PersonalityEvaluator_direct()
+ final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
+ dimension_counts = {trait: 0 for trait in final_scores.keys()}
+
+ for i, scenario_data in enumerate(evaluator.scenarios, 1):
+ print(f"\n场景 {i}/{len(evaluator.scenarios)} - {scenario_data['场景编号']}:")
+ print("-" * 50)
+ print(scenario_data["场景"])
+ print("\n请描述您的角色在这种情况下会如何反应：")
+ response = input().strip()
+
+ if not response:
+ print("反应描述不能为空！")
+ continue
+
+ print("\n正在评估您的描述...")
+ scores = evaluator.evaluate_response(scenario_data["场景"], response, scenario_data["评估维度"])
+
+ # 更新最终分数
+ for dimension, score in scores.items():
+ final_scores[dimension] += score
+ dimension_counts[dimension] += 1
+
+ print("\n当前评估结果：")
+ print("-" * 30)
+ for dimension, score in scores.items():
+ print(f"{dimension}: {score}/6")
+
+ if i < len(evaluator.scenarios):
+ print("\n按回车键继续下一个场景...")
+ input()
+
+ # 计算平均分
+ for dimension in final_scores:
+ if dimension_counts[dimension] > 0:
+ final_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
+
+ print("\n最终人格特征评估结果：")
+ print("-" * 30)
+ for trait, score in final_scores.items():
+ print(f"{trait}: {score}/6")
+ print(f"测试场景数：{dimension_counts[trait]}")
+
+ # 保存结果
+ result = {"final_scores": final_scores, "dimension_counts": dimension_counts, "scenarios": evaluator.scenarios}
+
+ # 确保目录存在
+ os.makedirs("results", exist_ok=True)
+
+ # 保存到文件
+ with open("results/personality_result.json", "w", encoding="utf-8") as f:
+ json.dump(result, f, ensure_ascii=False, indent=2)
+
+ print("\n结果已保存到 results/personality_result.json")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/plugins/personality/who_r_u.py b/src/plugins/personality/who_r_u.py
new file mode 100644
index 000000000..5ea502b82
--- /dev/null
+++ b/src/plugins/personality/who_r_u.py
@@ -0,0 +1,155 @@
+import random
+import os
+import sys
+from pathlib import Path
+import datetime
+from typing import List, Dict, Optional
+
+current_dir = Path(__file__).resolve().parent
+project_root = current_dir.parent.parent.parent
+env_path = project_root / ".env.prod"
+
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+from src.common.database import db # noqa: E402
+
+class MessageAnalyzer:
+ def __init__(self):
+ self.messages_collection = db["messages"]
+
+ def get_message_context(self, message_id: int, context_length: int = 5) -> Optional[List[Dict]]:
+ """
+ 获取指定消息ID的上下文消息列表
+
+ Args:
+ message_id (int): 消息ID
+ context_length (int): 上下文长度（单侧，总长度为 2*context_length + 1）
+
+ Returns:
+ Optional[List[Dict]]: 消息列表，如果未找到则返回None
+ """
+ # 从数据库获取指定消息
+ target_message = self.messages_collection.find_one({"message_id": message_id})
+ if not target_message:
+ return None
+
+ # 获取该消息的stream_id
+ stream_id = target_message.get('chat_info', {}).get('stream_id')
+ if not stream_id:
+ return None
+
+ # 获取同一stream_id的所有消息
+ stream_messages = list(self.messages_collection.find({
+ "chat_info.stream_id": stream_id
+ }).sort("time", 1))
+
+ # 找到目标消息在列表中的位置
+ target_index = None
+ for i, msg in enumerate(stream_messages):
+ if msg['message_id'] == message_id:
+ target_index = i
+ break
+
+ if target_index is None:
+ return None
+
+ # 获取目标消息前后的消息
+ start_index = max(0, target_index - context_length)
+ end_index = min(len(stream_messages), target_index + context_length + 1)
+
+ return stream_messages[start_index:end_index]
+
+ def format_messages(self, messages: List[Dict], target_message_id: Optional[int] = None) -> str:
+ """
+ 格式化消息列表为可读字符串
+
+ Args:
+ messages (List[Dict]): 消息列表
+ target_message_id (Optional[int]): 目标消息ID，用于标记
+
+ Returns:
+ str: 格式化的消息字符串
+ """
+ if not messages:
+ return "没有消息记录"
+
+ reply = ""
+ for msg in messages:
+ # 消息时间
+ msg_time = datetime.datetime.fromtimestamp(int(msg['time'])).strftime("%Y-%m-%d %H:%M:%S")
+
+ # 获取消息内容
+ message_text = msg.get('processed_plain_text', msg.get('detailed_plain_text', '无消息内容'))
+ nickname = msg.get('user_info', {}).get('user_nickname', '未知用户')
+
+ # 标记当前消息
+ is_target = "→ " if target_message_id and msg['message_id'] == target_message_id else " "
+
+ reply += f"{is_target}[{msg_time}] {nickname}: {message_text}\n"
+
+ if target_message_id and msg['message_id'] == target_message_id:
+ reply += " " + "-" * 50 + "\n"
+
+ return reply
+
+ def get_user_random_contexts(
+ self, qq_id: str, num_messages: int = 10, context_length: int = 5) -> tuple[List[str], str]: # noqa: E501
+ """
+ 获取用户的随机消息及其上下文
+
+ Args:
+ qq_id (str): QQ号
+ num_messages (int): 要获取的随机消息数量
+ context_length (int): 每条消息的上下文长度（单侧）
+
+ Returns:
+ tuple[List[str], str]: (每个消息上下文的格式化字符串列表, 用户昵称)
+ """
+ if not qq_id:
+ return [], ""
+
+ # 获取用户所有消息
+ all_messages = list(self.messages_collection.find({"user_info.user_id": int(qq_id)}))
+ if not all_messages:
+ return [], ""
+
+ # 获取用户昵称
+ user_nickname = all_messages[0].get('chat_info', {}).get('user_info', {}).get('user_nickname', '未知用户')
+
+ # 随机选择指定数量的消息
+ selected_messages = random.sample(all_messages, min(num_messages, len(all_messages)))
+ # 按时间排序
+ selected_messages.sort(key=lambda x: int(x['time']))
+
+ # 存储所有上下文消息
+ context_list = []
+
+ # 获取每条消息的上下文
+ for msg in selected_messages:
+ message_id = msg['message_id']
+
+ # 获取消息上下文
+ context_messages = self.get_message_context(message_id, context_length)
+ if context_messages:
+ formatted_context = self.format_messages(context_messages, message_id)
+ context_list.append(formatted_context)
+
+ return context_list, user_nickname
+
+if __name__ == "__main__":
+ # 测试代码
+ analyzer = MessageAnalyzer()
+ test_qq = "1026294844" # 替换为要测试的QQ号
+ print(f"测试QQ号: {test_qq}")
+ print("-" * 50)
+ # 获取5条消息，每条消息前后各3条上下文
+ contexts, nickname = analyzer.get_user_random_contexts(test_qq, num_messages=5, context_length=3)
+
+ print(f"用户昵称: {nickname}\n")
+ # 打印每个上下文
+ for i, context in enumerate(contexts, 1):
+ print(f"\n随机消息 {i}/{len(contexts)}:")
+ print("-" * 30)
+ print(context)
+ print("=" * 50)
diff --git a/src/plugins/remote/remote.py b/src/plugins/remote/remote.py
index 65d77cc2d..fdc805df1 100644
--- a/src/plugins/remote/remote.py
+++ b/src/plugins/remote/remote.py
@@ -79,22 +79,42 @@ class HeartbeatThread(threading.Thread):
self.interval = interval
self.client_id = get_unique_id()
self.running = True
+ self.stop_event = threading.Event() # 添加事件对象用于可中断的等待
+ self.last_heartbeat_time = 0 # 记录上次发送心跳的时间
def run(self):
"""线程运行函数"""
logger.debug(f"心跳线程已启动，客户端ID: {self.client_id}")
while self.running:
+ # 发送心跳
if send_heartbeat(self.server_url, self.client_id):
logger.info(f"{self.interval}秒后发送下一次心跳...")
else:
logger.info(f"{self.interval}秒后重试...")
-
- time.sleep(self.interval) # 使用同步的睡眠
+
+ self.last_heartbeat_time = time.time()
+
+ # 使用可中断的等待代替 sleep
+ # 每秒检查一次是否应该停止或发送心跳
+ remaining_wait = self.interval
+ while remaining_wait > 0 and self.running:
+ # 每次最多等待1秒，便于及时响应停止请求
+ wait_time = min(1, remaining_wait)
+ if self.stop_event.wait(wait_time):
+ break # 如果事件被设置，立即退出等待
+ remaining_wait -= wait_time
+
+ # 检查是否由于外部原因导致间隔异常延长
+ if time.time() - self.last_heartbeat_time >= self.interval * 1.5:
+ logger.warning("检测到心跳间隔异常延长，立即发送心跳")
+ break
def stop(self):
"""停止线程"""
self.running = False
+ self.stop_event.set() # 设置事件，中断等待
+ logger.debug("心跳线程已收到停止信号")
def main():
diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py
index d58211215..d6ba165ee 100644
--- a/src/plugins/schedule/schedule_generator.py
+++ b/src/plugins/schedule/schedule_generator.py
@@ -101,6 +101,9 @@ class ScheduleGenerator:
except json.JSONDecodeError:
logger.exception("解析日程失败: {}".format(schedule_text))
return False
+ except Exception as e:
+ logger.exception(f"解析日程发生错误:{str(e)}")
+ return False
def _parse_time(self, time_str: str) -> str:
"""解析时间字符串，转换为时间"""
@@ -158,7 +161,7 @@ class ScheduleGenerator:
def print_schedule(self):
"""打印完整的日程安排"""
if not self._parse_schedule(self.today_schedule_text):
- logger.warning("今日日程有误，将在下次运行时重新生成")
+ logger.warning("今日日程有误，将在两小时后重新生成")
db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
else:
logger.info("=== 今日日程安排 ===")
diff --git a/src/plugins/willing/mode_classical.py b/src/plugins/willing/mode_classical.py
index 75237a525..0f32c0c75 100644
--- a/src/plugins/willing/mode_classical.py
+++ b/src/plugins/willing/mode_classical.py
@@ -41,8 +41,8 @@ class WillingManager:
interested_rate = interested_rate * config.response_interested_rate_amplifier
- if interested_rate > 0.5:
- current_willing += interested_rate - 0.5
+ if interested_rate > 0.4:
+ current_willing += interested_rate - 0.3
if is_mentioned_bot and current_willing < 1.0:
current_willing += 1