> For the complete documentation index, see [llms.txt](https://docs.din.lol/din-cook-data-for-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.din.lol/din-cook-data-for-ai/chinese/din-cao-zuo-yu-jie-shao/comprehensive-network-architecture/exploring-din-an-in-depth-technical-overview/data-flow-of-ai.md).
# AI 数据流
数据流是代表AI工程生命周期中数据移动顺序的机器学习模式。
首先，如图1所示，数据分层处理，以便进行存储、训练等准备工作。\
然后，数据通过处理层进行存储、优化，并为机器学习模型和应用的使用做准备。从功能角度来看，数据随后被不同的机器学习功能组使用，具体如下：
Data Flow and Functional Groups in AI
上图中每一层的详细信息如下：
**数据来源**
数据来源包括：
* 公司内部数据库
* 公司内部文件
* 网站
* 公开数据
* 智能手机应用
* 物联网设备
* 商业数据聚合商
* 销售点
* 企业内部流程数据流
* 社交媒体
* 数据流
**数据捕获**
数据捕获机制包括：
* 网站抓取
* 网站和智能手机聊天对话
* 网站和智能手机表单提交
* 物联网设备接口
* 商业数据聚合商数据流
* 企业内部流程数据流
**数据管道**
数据管道流程包括：
* 数据摄取
* 数据临时存储
* 数据订阅
* 数据发布
**数据库**
数据库包括：
* 数据湖
* 关系型数据库
* 文档型数据库
* 图形数据库
**ETL流程**
ETL流程包括：
* 提取功能：从选定的数据源中提取数据
* 转换功能：规范化、正则化、聚合
* 加载功能：将数据保存为模型处理可用的格式
**模型**
模型类型示例包括：
* 人工神经网络
* 决策树
* 概率图模型
* 聚类分析
* 高斯过程
* 回归分析
**应用**
应用示例包括：
* 医疗诊断
* 自动驾驶车辆
* 聊天机器人对话
* 图像识别
* 人脸识别
* 产品推荐
* 客户流失预测
* 恶意软件检测
* 搜索优化
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.din.lol/din-cook-data-for-ai/chinese/din-cao-zuo-yu-jie-shao/comprehensive-network-architecture/exploring-din-an-in-depth-technical-overview/data-flow-of-ai.md?ask=&goal=
```
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.