19dfe5b09e
Rename utility files to use English names for better cross-platform compatibility: - utils/离散备料计划维护数据提取.py → utils/discrete_material_plan_extractor.py - utils/离散备料计划维护数据清理.py → utils/discrete_material_plan_cleaner.py Update all import statements across the codebase and documentation references. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
402 lines
11 KiB
Markdown
402 lines
11 KiB
Markdown
# 进度回调机制详解
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## 概述
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`_report_progress` 是一个基于**回调函数模式**的进度报告系统，用于后台任务（数据提取）和 GUI 主线程之间的线程安全通信。
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---
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## 架构设计
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### 系统架构图
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```mermaid
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flowchart TB
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subgraph BG["后台线程 (Background Thread)"]
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Extractor["DiscreteMaterialPlanExtractor"]
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Report["_report_progress()"]
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ProgressInfo["ProgressInfo 对象"]
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end
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subgraph Boundary["线程边界 (Thread Boundary)"]
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Callback["progress_callback()"]
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end
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subgraph FG["主线程 (Main/GUI Thread)"]
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Calc["ProgressCalculator<br/>计算总体百分比"]
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Queue["queue.Queue<br/>线程安全队列"]
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Poll["_poll_progress_queue()<br/>每50ms轮询"]
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GUI["GUI 组件<br/>progress_bar<br/>status_label"]
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end
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Extractor -->|"调用"| Report
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Report -->|"创建"| ProgressInfo
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ProgressInfo -->|"触发"| Callback
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Callback -->|"计算"| Calc
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Calc -->|"put"| Queue
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Queue -->|"get"| Poll
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Poll -->|"更新"| GUI
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style Callback fill:#ff9,stroke:#333,stroke-width:2px
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style Queue fill:#9f9,stroke:#333,stroke-width:2px
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style Boundary fill:#ddd,stroke:#333,stroke-dasharray: 5 5
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```
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### 组件职责
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| 组件 | 职责 | 位置 |
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|------|------|------|
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| `DiscreteMaterialPlanExtractor` | 执行数据提取任务 | 后台线程 |
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| `_report_progress()` | 报告进度到回调 | 后台线程 |
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| `ProgressInfo` | 进度信息数据结构 | 跨线程 |
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| `progress_callback()` | GUI 提供的回调函数 | 主线程定义，后台调用 |
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| `ProgressCalculator` | 计算总体进度百分比 | 主线程 |
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| `queue.Queue` | 线程安全的消息队列 | 主线程 |
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| `_poll_progress_queue()` | 轮询队列并更新 GUI | 主线程 |
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---
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## 数据流程
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### 完整时序图
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```mermaid
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sequenceDiagram
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participant Bg as 后台线程<br/>(Extractor)
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participant Report as _report_progress()
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participant Callback as progress_callback()
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participant Calc as ProgressCalculator
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participant Queue as 进度队列
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participant Poll as _poll_progress_queue()
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participant GUI as GUI 组件
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Bg->>Report: _report_progress('download', 2, 3, '下载中')
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Report->>Report: 创建 ProgressInfo 对象
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Report->>Callback: progress_callback(progress_info)
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Note over Callback: 主线程定义的函数<br/>在后台线程中执行
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Callback->>Calc: calculate_overall_percent(progress_info)
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Note over Calc: download 阶段<br/>stage_offset=10%<br/>current=2, total=3<br/>weight=65%<br/>result = 10 + 67%×65 = 54%
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Calc-->>Callback: 返回 54
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Callback->>Queue: put((54, '下载中'))
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Note over Queue: 线程安全队列<br/>缓冲区
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loop 每 50ms
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Poll->>Queue: get_nowait()
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Queue-->>Poll: (54, '下载中')
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Poll->>GUI: progress_bar['value'] = 54
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Poll->>GUI: status_label['text'] = '下载中'
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Poll->>Poll: after(50ms, 继续轮询)
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end
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```
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### 进度计算逻辑
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```mermaid
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flowchart LR
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A[ProgressInfo<br/>stage=download<br/>current=2<br/>total=3] --> B[ProgressCalculator]
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subgraph Calc["计算过程"]
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direction TB
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B --> C["计算阶段内进度<br/>2/3 × 100 = 67%"]
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C --> D["查找阶段权重<br/>download = 65%"]
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D --> E["查找阶段偏移<br/>offset = 10%"]
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E --> F["总体进度<br/>10 + 67%×65 = 54%"]
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end
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F --> G["更新进度条<br/>54%"]
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```
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---
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## 阶段权重分配
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### 进度阶段划分
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```mermaid
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pie title 各阶段权重分布
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"登录 (5%)" : 5
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"查询 (5%)" : 5
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"下载 (65%)" : 65
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"注销 (5%)" : 5
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"转换 (15%)" : 15
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"完成 (5%)" : 5
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```
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### 阶段详情表
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| 阶段 | stage | 权重 | 进度范围 | 说明 |
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|------|-------|------|----------|------|
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| 登录 | `login` | 5% | 0-5% | ERP 系统登录 |
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| 查询 | `query` | 5% | 5-10% | 查询数据库获取订单号 |
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| 下载 | `download` | 65% | 10-75% | 批量下载数据（主要耗时） |
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| 注销 | `logout` | 5% | 75-80% | 退出 ERP 系统 |
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| 转换 | `convert` | 15% | 80-95% | 转换 Excel 格式并合并 |
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| 完成 | `complete` | 5% | 95-100% | 任务完成 |
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---
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## 代码实现
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### 1. 后台任务：报告进度
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```python
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# utils/discrete_material_plan_extractor.py
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def _report_progress(self, stage: str, current: int, total: int, message: str, **detail):
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"""
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报告进度
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Args:
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stage: 阶段标识 ('login', 'query', 'download', 等)
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current: 当前进度值 (1, 2, 3...)
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total: 总量 (3, 100...)
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message: 显示给用户的消息
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**detail: 额外信息 (如 batch_index=1)
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"""
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if self.progress_callback:
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try:
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from gui.progress import ProgressInfo
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progress_info = ProgressInfo(
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stage=stage,
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current=current,
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total=total,
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message=message,
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detail=detail
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)
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# 调用 GUI 提供的回调函数
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self.progress_callback(progress_info)
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except Exception:
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# 回调失败不影响主流程
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pass
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```
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### 2. GUI：设置回调
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```python
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# gui/data_extraction_tab.py
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def _extraction_worker(self, input_file: str, output_file: str):
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"""后台工作线程"""
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# 创建进度回调函数
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def progress_callback(progress_info: ProgressInfo):
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# 1. 计算总体进度百分比
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overall_percent = self.progress_calculator.calculate_overall_percent(progress_info)
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# 2. 放入队列（线程安全）
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self._update_progress(overall_percent, progress_info.message)
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# 将回调传递给提取器
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self.extractor.extract(
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production_id_file=input_file,
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output_file=output_file,
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progress_callback=progress_callback
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)
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```
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### 3. 线程安全：队列通信
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```python
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# gui/data_extraction_tab.py
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def _update_progress(self, value: int, message: str):
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"""后台线程调用，放入队列"""
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try:
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self.progress_queue.put_nowait((value, message))
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except:
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pass # 队列满时忽略
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def _poll_progress_queue(self):
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"""主线程轮询，更新 GUI"""
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try:
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while True:
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# 非阻塞获取队列中的消息
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progress_data = self.progress_queue.get_nowait()
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value, message = progress_data
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# 更新 GUI 组件
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self.progress_bar['value'] = value
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self.status_label.config(text=message)
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except queue.Empty:
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pass
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finally:
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# 继续轮询（每 50ms 检查一次）
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self.after(50, self._poll_progress_queue)
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```
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### 4. 进度计算器
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```python
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# gui/progress.py
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class ProgressCalculator:
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# 各阶段在总进度中的占比
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STAGE_WEIGHTS = {
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'login': 5, # 0-5%
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'query': 5, # 5-10%
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'download': 65, # 10-75%
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'logout': 5, # 75-80%
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'convert': 15, # 80-95%
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'complete': 5, # 95-100%
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}
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def calculate_overall_percent(self, progress: ProgressInfo) -> int:
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"""计算总体进度百分比"""
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stage = progress.stage
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if stage == 'complete':
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return 100
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# 计算阶段起始百分比
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stage_offset = self._stage_offsets[stage]
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# 计算阶段内的进度百分比
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stage_percent = progress.percent # current/total * 100
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# 计算该阶段的权重
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stage_weight = self.STAGE_WEIGHTS[stage]
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# 总进度 = 阶段偏移 + (阶段内进度 × 阶段权重 / 100)
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overall = stage_offset + int(stage_percent * stage_weight / 100)
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return min(overall, 100)
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```
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---
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## 设计要点
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### 1. 线程安全
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**问题**：Tkinter 不是线程安全的，后台线程不能直接操作 GUI。
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```mermaid
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flowchart LR
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A[后台线程] -->|"❌ 直接调用 GUI"| B[崩溃/未定义行为]
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A -->|"✅ 写入队列"| C[queue.Queue]
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C -->|"主线程读取"| D[GUI 更新]
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```
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**解决方案**：使用 `queue.Queue` 作为缓冲区。
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```python
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# 后台线程：只写入队列
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self.progress_queue.put_nowait((value, message))
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# 主线程：从队列读取并更新 GUI
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progress_data = self.progress_queue.get_nowait()
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self.progress_bar['value'] = progress_data[0]
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```
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### 2. 解耦设计
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```mermaid
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flowchart TB
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A[提取器] -->|"不需要知道 GUI"| B[回调接口]
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B -->|"由 GUI 提供"| C[实现]
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C -->|"可以替换"| D[测试回调<br/>日志回调<br/>GUI 回调]
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```
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**好处**：
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- 提取器代码不依赖 GUI
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- 易于测试（可以传入测试回调）
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- 灵活扩展（不同场景使用不同回调）
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### 3. 容错处理
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```python
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def _report_progress(self, ...):
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if self.progress_callback:
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try:
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# 调用回调
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self.progress_callback(progress_info)
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except Exception:
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# 回调失败不影响主流程
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pass
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```
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**保证**：进度报告失败不会中断数据提取任务。
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### 4. 准确的进度反映
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**问题**：不同阶段耗时差异大（登录 3 秒，下载 60 秒）
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**解决方案**：为每个阶段分配不同权重。
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```mermaid
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gantt
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title 数据提取各阶段耗时示例
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dateFormat X
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axisFormat %s
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section 任务
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登录 :0, 3
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查询 :3, 5
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下载第1批 :5, 25
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下载第2批 :25, 45
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下载第3批 :45, 65
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注销 :65, 68
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转换 :68, 72
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```
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---
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## 使用示例
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### 在提取器中报告进度
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```python
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# 下载批次
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for batch_index, order_ids_batch in enumerate(self.group_order_ids(order_ids, self.batch_size)):
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# 报告批次开始
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self._report_progress(
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'download',
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batch_index,
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total_batches,
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f'正在下载第 {batch_index + 1}/{total_batches} 批',
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batch_index=batch_index + 1
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)
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# 执行下载
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downloaded_file = self.download_batch(...)
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# 报告批次完成
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self._report_progress(
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'download',
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batch_index + 1,
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total_batches,
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f'第 {batch_index + 1} 批下载完成'
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)
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```
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### 在 GUI 中接收进度
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```python
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from gui.progress import ProgressInfo, ProgressCalculator
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class DataExtractionTab(ttk.Frame):
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def __init__(self, ...):
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self.progress_calculator = ProgressCalculator()
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self.progress_queue = queue.Queue()
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self._poll_progress_queue()
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def progress_callback(self, progress_info: ProgressInfo):
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"""后台任务调用的回调函数"""
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overall_percent = self.progress_calculator.calculate_overall_percent(progress_info)
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self._update_progress(overall_percent, progress_info.message)
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```
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---
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## 总结
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`_report_progress` 机制实现了：
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1. **线程安全**：通过队列跨线程通信
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2. **解耦设计**：提取器与 GUI 分离
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3. **准确反映**：权重分配适配实际耗时
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4. **容错能力**：回调失败不影响主流程
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5. **易于测试**：可注入测试回调
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这种模式适用于任何需要长时间运行任务并实时报告进度的场景。
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