> For the complete documentation index, see [llms.txt](https://docs.docbits.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.docbits.com/chinese/infor-ji-cheng-yu-pei-zhi/importing-customer-master-data/m3/table-extraction-for-costing-element.md).
# 成本要素表格提取
前往 **Settings → Document Processing → Classification and Extraction**
**注意：** 要启用 **Costing Elements** 的表格提取，必须先将相关的 Costing Element 表导入 **DocBits**。如果未进行此配置，用户打开文档时会遇到以下错误：
***“成本要素配置不正确。请联系管理员进行配置。”***
## 从 M3 导入 Costing Elements 的步骤
1. **在 M3 中打开 PPS280：**
* 在 M3 主页中，按 `Command + R` 并搜索 **PPS280**。
* 选择结果中的任意一行或所有行。
2. **导出数据：**
* 在下一个菜单中，点击 **TOOLS → Export to Excel**。\\
* 选择 **Export All Rows**，然后点击 **EXPORT**。
3. **修改 Excel 文件：**\
打开下载的 Excel 文件，并进行以下修改：
* **重命名列：**
* A 列 → `costing_element`
* B 列 → `description`
* H 列 → `charge_operator`
* J 列 → `charge_type`
* K 列 → `distribution_method`
* L 列 → `distribution_type`
* N 列 → `charge_sequence_number`。
* 输入相应的序号。
* 如果留空，系统默认使用 **10**。
* **删除上方未列出的所有其他列**，无论这些列**是否包含数据或非空**。
4. **修改前后示例：**\
以下示例展示了应用所需修改前后的 Excel 文件：
* **修改前：**
* **修改后：**
5. **转换为 CSV：**
* 将修改后的 Excel 文件另存为 `.csv`
6. **上传到 DocBits：**
* 前往 **Settings → Document Processing → Master Data Lookup**
* 点击 imported 旁边的加号
* 填写所需信息
* **Data Type**：`costing_element`
* **File**：选择修改后的 `.csv` 文件
* **Delimiter**：`comma`
* **On Conflict**：选择冲突解决策略
* **Auto-generate ID**：确保已**勾选**此选项\\
---
# 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 by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://docs.docbits.com/chinese/infor-ji-cheng-yu-pei-zhi/importing-customer-master-data/m3/table-extraction-for-costing-element.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
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.
.png?alt=media)
.png?alt=media)
.png?alt=media)
.png?alt=media)
.png?alt=media)
.png?alt=media)
.png?alt=media)