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Time series prediction and early warning question answering system based on large language model and MCP.

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时序项目启动!!!

backend

启动命令

conda create -n tsr python=3.11 # 创建conda环境
conda activate tsr # 使用该环境
pip install -r requirements.txt # 下载相关包
python run.py # 启动服务命令

注意:

# backend/routes/chat.py下面的DEEPSEEK_API_KEY 需要改为自己的llm token。
DEEPSEEK_API_KEY = "my token"

# 修改mysql的配置 账号密码等 在backend/dao/database.py下面
SQLALCHEMY_DATABASE_URL = "mysql+pymysql://your-username:your-password@127.0.0.1/your-database-name"

数据库sql文件:

你可以在Google Drive下载本项目对应的sql文件。

模型下载:

你可以在Google Drive下载项目必须的相关算法,下载并解压命名为algorithm,然后将这整个文件夹放在backend目录下,即backend/algorithm

frontend

vue3 项目 npm管理依赖

npm install
npm run serve

首页

home

预测页面

forcast

异常检测页面

anomaly detection

智能问答助手页面

QA

时序助手使用示例

QA example

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