diff --git a/.gitignore b/.gitignore
index a37cb82..42ccdd0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,7 @@
-__pycache__/
-build/
-dist/
-other/
-*.egg-info/
-*.pdf
-*.spec
+__pycache__/
+build/
+dist/
+other/
+*.egg-info/
+*.pdf
+*.spec
diff --git a/.python-version b/.python-version
index e4fba21..3d2704b 100644
--- a/.python-version
+++ b/.python-version
@@ -1 +1 @@
-3.12
+3.12
diff --git a/README.md b/README.md
index 63d59f8..4c8810d 100644
--- a/README.md
+++ b/README.md
@@ -1,165 +1,167 @@
-# EVA 值班排班工具
-
-## 环境配置
-
-本项目使用 `uv` 进行包管理。
-
-安装项目中所需要的所有包的最新版本。其中 `pyside6` 是一个前端库，`pyinstaller` 用于打包项目，`ortools` 是一个高效的组合优化求解器，`pandas` 用于处理表格数据：
-```bash
-uv sync
-```
-
-## 项目结构
-```
-EVA_duty_arrange_tool/
-    ├─ main.py                // 主函数，定义了前端界面和组件的回调函数
-    ├─ solve.py               // 定义了值班排班问题的求解函数
-    ├─ utils.py               // 定义了读取、写入 excel 的函数
-    ├─ pics                   // 储存了说明文档中用到的图片
-    │  ├─ *.jpg/*.png
-    ├─ *.md                   // 说明文档
-    ├─ *.xlsx                 // 测试用例
-```
-
-## 项目运行 & 打包
-项目运行方式：
-```bash
-uv run main.py
-```
-
-本项目使用 `pyinstaller` 工具进行打包。如果要打包，请确保能够正常运行项目。打包命令如下：
-```bash
-uv run pyinstaller --onefile --windowed --name=EVA_duty_arrange_tool main.py
-```
-
-## 数学原理
-本项目将值班排班问题建模为了一个[组合优化](https://zh.wikipedia.org/wiki/%E7%BB%84%E5%90%88%E4%BC%98%E5%8C%96)问题，使用 Google 的 OR-Tools 求解器中的 SCIP 求解器进行求解。
-
-### 问题描述
-
-在本问题中，优化目标是一个涉及多个方面的量化指标：
-- **目标1**：每一班同学数量的平均程度
-- **目标2**：每一班技术部老人数量接近期望值
-- **目标3**：每一班技术部小朋友数量接近期望值
-- **目标4**：每一班人资部小朋友数量接近期望值
-- **目标5**：每一班各部门人数的平均程度
-
-在本问题中，主要约束是：
-1. 让每位同学每周的班次数符合意愿（特别地，选择3次的同学可以安排2-3次）
-2. 让每位同学只在自己有空的时间段值班
-3. 每班次的总人数在指定范围内
-4. 每班次技术部老人数量在指定范围内
-5. 每班次老人总数在指定范围内
-6. 每班次小朋友总数在指定范围内
-
-### 数学建模
-
-设一共有 $n$ 位同学，$m$ 个值班的班次，协会共有 $t=5$ 个部门（电脑部、电器部、人资部、财外部、文宣部），定义以下符号：
-
-**决策变量：**
-- $x_{ij} \in \{0,1\}, \quad i=1,2,\dots,n,\quad j=1,2,\dots,m$ 表示第 $i$ 位同学是否值第 $j$ 班
-
-**已知参数：**
-- $N_i \in \mathbb{N}, \quad i=1,2,\dots,n$ 表示第 $i$ 个同学每周愿意值班次数
-- $v_{ij} \in \{0,1\}, \quad i=1,2,\dots,n,\quad j=1,2,\dots,m$ 表示第 $i$ 位同学是否有空值第 $j$ 班
-- $\text{old}_i \in \{0,1\}, \quad i=1,2,\dots,n$ 表示第 $i$ 位同学是否是老人（非小朋友）
-- $d_{ik}\in\{0,1\}, \quad i=1,2,\dots,n,\quad k=1,2,\dots,t$ 表示第 $i$ 位同学是否属于第 $k$ 个部门
-- $\text{tech}_i = d_{i1} + d_{i2} \in \{0,1\}$ 表示第 $i$ 位同学是否属于技术部（电脑部或电器部）
-- $\text{hr}_i = d_{i3} \in \{0,1\}$ 表示第 $i$ 位同学是否属于人资部
-
-**派生量：**
-- $M_j = \sum_{i=1}^{n} x_{ij}, \quad j=1,2,\dots,m$ 表示第 $j$ 班次实际安排的值班人数
-- $\bar{M} = \frac{\sum_{i=1}^{n}N_i}{m}$ 表示平均每班的人数
-
-### 优化目标
-
-为了处理多目标优化问题，我们采用**线性加权法**将多个目标组合成单一目标函数。引入归一化系数 $m_1=2.5, m_2=3, m_3=4, m_4=1, m_5=8$ 和权重 $w_1, w_2, w_3, w_4, w_5$，总目标函数为：
-
-$$\min \quad Z = w_1 \cdot \frac{X_1}{m_1 \cdot M} + w_2 \cdot \frac{X_2}{m_2 \cdot M} + w_3 \cdot \frac{X_3}{m_3 \cdot M} + w_4 \cdot \frac{X_4}{m_4 \cdot M} + w_5 \cdot \frac{X_5}{m_5 \cdot M}$$
-
-各目标定义如下：
-
-**目标1：均衡班次人数**
-
-$$X_1 = \sum_{j=1}^{m} a_j^{(1)}$$
-
-其中辅助变量 $a_j^{(1)} \in [0,+\infty)$ 满足：
-$$a_j^{(1)} \ge M_j - \bar{M}, \quad a_j^{(1)} \ge \bar{M} - M_j, \quad \forall j$$
-
-> 注：在组合优化问题中只能定义线性约束，不能直接使用绝对值。因此引入辅助变量 $a_j^{(1)}$ 并添加两个不等式约束，使得在优化过程中 $a_j^{(1)}$ 自动收敛到 $|M_j - \bar{M}|$。
-
-**目标2：每班技术部老人数量接近期望值**
-
-期望每班有 $m_2 = 3$ 位技术部老人，定义缺口：
-
-$$X_2 = \sum_{j=1}^{m} a_j^{(2)}$$
-
-其中辅助变量 $a_j^{(2)} \in [0,+\infty)$ 满足：
-$$a_j^{(2)} \ge \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i - m_2$$
-$$a_j^{(2)} \ge m_2 - \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i, \quad \forall j$$
-
-**目标3：每班技术部小朋友数量接近期望值**
-
-期望每班有 $m_3 = 4$ 位技术部小朋友，定义缺口：
-
-$$X_3 = \sum_{j=1}^{m} a_j^{(3)}$$
-
-其中辅助变量 $a_j^{(3)} \in [0,+\infty)$ 满足：
-$$a_j^{(3)} \ge \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{tech}_i - m_3$$
-$$a_j^{(3)} \ge m_3 - \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{tech}_i, \quad \forall j$$
-
-**目标4：每班人资部小朋友数量接近期望值**
-
-期望每班有 $m_4 = 1$ 位人资部小朋友，定义缺口：
-
-$$X_4 = \sum_{j=1}^{m} a_j^{(4)}$$
-
-其中辅助变量 $a_j^{(4)} \in [0,+\infty)$ 满足：
-$$a_j^{(4)} \ge \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{hr}_i - m_4$$
-$$a_j^{(4)} \ge m_4 - \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{hr}_i, \quad \forall j$$
-
-**目标5：均衡各部门人数分布**
-
-对每个班次 $j$ 和每个部门 $k$，定义该班次该部门的人数偏差：
-
-$$X_5 = \sum_{j=1}^{m} \sum_{k=1}^{t} a_{jk}^{(5)}$$
-
-其中 $\bar{M}_j = \frac{M_j}{t}$ 表示第 $j$ 班次各部门的平均人数，$C_{jk} = \sum_{i=1}^{n} x_{ij} \cdot d_{ik}$ 表示第 $j$ 班次第 $k$ 部门的实际人数，辅助变量 $a_{jk}^{(5)} \in [0,+\infty)$ 满足：
-
-$$a_{jk}^{(5)} \ge C_{jk} - \bar{M}_j, \quad a_{jk}^{(5)} \ge \bar{M}_j - C_{jk}, \quad \forall j,k$$
-
-### 约束条件
-
-**基本约束：**
-
-1. **意愿班次约束**：每位同学的值班次数必须符合其意愿
-   $$\sum_{j=1}^{m}x_{ij} = N_i, \quad \forall i \text{ where } N_i \neq 3$$
-   $$2 \le \sum_{j=1}^{m}x_{ij} \le 3, \quad \forall i \text{ where } N_i = 3$$
-
-2. **时间可行性约束**：只在有空的时间段排班
-   $$x_{ij} \le v_{ij}, \quad \forall i,j$$
-
-**班次人数约束：**
-
-3. **总人数约束**：每班次人数在 $[n_{\min}, n_{\max}]$ 范围内
-   $$n_{\min} \le \sum_{i=1}^{n} x_{ij} \le n_{\max}, \quad \forall j$$
-
-4. **技术部老人约束**：每班次技术部老人数在范围内
-   $$n_{\text{tech\_old\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i \le n_{\text{tech\_old\_max}}, \quad \forall j$$
-
-5. **老人总数约束**：每班次老人总数在范围内
-   $$n_{\text{old\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \le n_{\text{old\_max}}, \quad \forall j$$
-
-6. **小朋友总数约束**：每班次小朋友总数在范围内
-   $$n_{\text{new\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \le n_{\text{new\_max}}, \quad \forall j$$
-
-### 求解器
-
-以上完成了整个排班问题的建模。本项目使用 Google OR-Tools 中的 **SCIP 求解器**（Solving Constraint Integer Programs）进行求解。SCIP 是一个强大的混合整数规划（MIP）求解器，特别适合处理这类组合优化问题。
-
-OR-Tools 支持 `C++`、`Python`、`C#`、`Java` 等多种语言，并提供跨平台支持。在本项目中，我们使用其 Python 接口 `ortools.linear_solver.pywraplp` 进行建模和求解。当找到最优解时，求解器会输出详细的目标值和各个优化指标的统计信息。
-
-## 维护指南
-- 如果你想更改 Excel 的读取、写入相关的功能，应该修改 `utils.py` 中的相关函数。
-- 如果你想更改软件的前端界面，应该修改 `main.py` 中 `MyWidget` 这个类相关的代码。
+# EVA 值班排班工具
+
+## 环境配置
+
+本项目使用 `uv` 进行包管理。
+
+安装项目中所需要的所有包的最新版本。其中 `pyside6` 是一个前端库，`pyinstaller` 用于打包项目，`ortools` 是一个高效的组合优化求解器，`pandas` 用于处理表格数据：
+```bash
+uv sync
+```
+
+## 项目结构
+```
+EVA_duty_arrange_tool/
+    ├─ main.py                // 主函数，定义了前端界面和组件的回调函数
+    ├─ solve.py               // 定义了值班排班问题的求解函数
+    ├─ utils.py               // 定义了读取、写入 excel 的函数
+    ├─ pics                   // 储存了说明文档中用到的图片
+    │  ├─ *.jpg/*.png
+    ├─ *.md                   // 说明文档
+    ├─ *.xlsx                 // 测试用例
+```
+
+## 项目运行 & 打包
+项目运行方式：
+```bash
+uv run main.py
+```
+
+本项目使用 `pyinstaller` 工具进行打包。如果要打包，请确保能够正常运行项目。打包命令如下：
+```bash
+uv run pyinstaller --onefile --windowed --name=EVA_duty_arrange_tool --hidden-import=ortools --collect-all ortools --additional-hooks-dir=. main.py
+```
+
+> **注意**：由于 `ortools` 库包含大量 DLL 依赖,需要使用 `--collect-all ortools` 参数来收集所有必要的动态链接库。`--additional-hooks-dir=.` 参数指定使用项目根目录下的 `hook-ortools.py` 文件来确保正确打包。
+
+## 数学原理
+本项目将值班排班问题建模为了一个[组合优化](https://zh.wikipedia.org/wiki/%E7%BB%84%E5%90%88%E4%BC%98%E5%8C%96)问题，使用 Google 的 OR-Tools 求解器中的 SCIP 求解器进行求解。
+
+### 问题描述
+
+在本问题中，优化目标是一个涉及多个方面的量化指标：
+- **目标1**：每一班同学数量的平均程度
+- **目标2**：每一班技术部老人数量接近期望值
+- **目标3**：每一班技术部小朋友数量接近期望值
+- **目标4**：每一班人资部小朋友数量接近期望值
+- **目标5**：每一班各部门人数的平均程度
+
+在本问题中，主要约束是：
+1. 让每位同学每周的班次数符合意愿（特别地，选择3次的同学可以安排2-3次）
+2. 让每位同学只在自己有空的时间段值班
+3. 每班次的总人数在指定范围内
+4. 每班次技术部老人数量在指定范围内
+5. 每班次老人总数在指定范围内
+6. 每班次小朋友总数在指定范围内
+
+### 数学建模
+
+设一共有 $n$ 位同学，$m$ 个值班的班次，协会共有 $t=5$ 个部门（电脑部、电器部、人资部、财外部、文宣部），定义以下符号：
+
+**决策变量：**
+- $x_{ij} \in \{0,1\}, \quad i=1,2,\dots,n,\quad j=1,2,\dots,m$ 表示第 $i$ 位同学是否值第 $j$ 班
+
+**已知参数：**
+- $N_i \in \mathbb{N}, \quad i=1,2,\dots,n$ 表示第 $i$ 个同学每周愿意值班次数
+- $v_{ij} \in \{0,1\}, \quad i=1,2,\dots,n,\quad j=1,2,\dots,m$ 表示第 $i$ 位同学是否有空值第 $j$ 班
+- $\text{old}_i \in \{0,1\}, \quad i=1,2,\dots,n$ 表示第 $i$ 位同学是否是老人（非小朋友）
+- $d_{ik}\in\{0,1\}, \quad i=1,2,\dots,n,\quad k=1,2,\dots,t$ 表示第 $i$ 位同学是否属于第 $k$ 个部门
+- $\text{tech}_i = d_{i1} + d_{i2} \in \{0,1\}$ 表示第 $i$ 位同学是否属于技术部（电脑部或电器部）
+- $\text{hr}_i = d_{i3} \in \{0,1\}$ 表示第 $i$ 位同学是否属于人资部
+
+**派生量：**
+- $M_j = \sum_{i=1}^{n} x_{ij}, \quad j=1,2,\dots,m$ 表示第 $j$ 班次实际安排的值班人数
+- $\bar{M} = \frac{\sum_{i=1}^{n}N_i}{m}$ 表示平均每班的人数
+
+### 优化目标
+
+为了处理多目标优化问题，我们采用**线性加权法**将多个目标组合成单一目标函数。引入归一化系数 $m_1=2.5, m_2=3, m_3=4, m_4=1, m_5=8$ 和权重 $w_1, w_2, w_3, w_4, w_5$，总目标函数为：
+
+$$\min \quad Z = w_1 \cdot \frac{X_1}{m_1 \cdot M} + w_2 \cdot \frac{X_2}{m_2 \cdot M} + w_3 \cdot \frac{X_3}{m_3 \cdot M} + w_4 \cdot \frac{X_4}{m_4 \cdot M} + w_5 \cdot \frac{X_5}{m_5 \cdot M}$$
+
+各目标定义如下：
+
+**目标1：均衡班次人数**
+
+$$X_1 = \sum_{j=1}^{m} a_j^{(1)}$$
+
+其中辅助变量 $a_j^{(1)} \in [0,+\infty)$ 满足：
+$$a_j^{(1)} \ge M_j - \bar{M}, \quad a_j^{(1)} \ge \bar{M} - M_j, \quad \forall j$$
+
+> 注：在组合优化问题中只能定义线性约束，不能直接使用绝对值。因此引入辅助变量 $a_j^{(1)}$ 并添加两个不等式约束，使得在优化过程中 $a_j^{(1)}$ 自动收敛到 $|M_j - \bar{M}|$。
+
+**目标2：每班技术部老人数量接近期望值**
+
+期望每班有 $m_2 = 3$ 位技术部老人，定义缺口：
+
+$$X_2 = \sum_{j=1}^{m} a_j^{(2)}$$
+
+其中辅助变量 $a_j^{(2)} \in [0,+\infty)$ 满足：
+$$a_j^{(2)} \ge \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i - m_2$$
+$$a_j^{(2)} \ge m_2 - \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i, \quad \forall j$$
+
+**目标3：每班技术部小朋友数量接近期望值**
+
+期望每班有 $m_3 = 4$ 位技术部小朋友，定义缺口：
+
+$$X_3 = \sum_{j=1}^{m} a_j^{(3)}$$
+
+其中辅助变量 $a_j^{(3)} \in [0,+\infty)$ 满足：
+$$a_j^{(3)} \ge \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{tech}_i - m_3$$
+$$a_j^{(3)} \ge m_3 - \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{tech}_i, \quad \forall j$$
+
+**目标4：每班人资部小朋友数量接近期望值**
+
+期望每班有 $m_4 = 1$ 位人资部小朋友，定义缺口：
+
+$$X_4 = \sum_{j=1}^{m} a_j^{(4)}$$
+
+其中辅助变量 $a_j^{(4)} \in [0,+\infty)$ 满足：
+$$a_j^{(4)} \ge \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{hr}_i - m_4$$
+$$a_j^{(4)} \ge m_4 - \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{hr}_i, \quad \forall j$$
+
+**目标5：均衡各部门人数分布**
+
+对每个班次 $j$ 和每个部门 $k$，定义该班次该部门的人数偏差：
+
+$$X_5 = \sum_{j=1}^{m} \sum_{k=1}^{t} a_{jk}^{(5)}$$
+
+其中 $\bar{M}_j = \frac{M_j}{t}$ 表示第 $j$ 班次各部门的平均人数，$C_{jk} = \sum_{i=1}^{n} x_{ij} \cdot d_{ik}$ 表示第 $j$ 班次第 $k$ 部门的实际人数，辅助变量 $a_{jk}^{(5)} \in [0,+\infty)$ 满足：
+
+$$a_{jk}^{(5)} \ge C_{jk} - \bar{M}_j, \quad a_{jk}^{(5)} \ge \bar{M}_j - C_{jk}, \quad \forall j,k$$
+
+### 约束条件
+
+**基本约束：**
+
+1. **意愿班次约束**：每位同学的值班次数必须符合其意愿
+   $$\sum_{j=1}^{m}x_{ij} = N_i, \quad \forall i \text{ where } N_i \neq 3$$
+   $$2 \le \sum_{j=1}^{m}x_{ij} \le 3, \quad \forall i \text{ where } N_i = 3$$
+
+2. **时间可行性约束**：只在有空的时间段排班
+   $$x_{ij} \le v_{ij}, \quad \forall i,j$$
+
+**班次人数约束：**
+
+3. **总人数约束**：每班次人数在 $[n_{\min}, n_{\max}]$ 范围内
+   $$n_{\min} \le \sum_{i=1}^{n} x_{ij} \le n_{\max}, \quad \forall j$$
+
+4. **技术部老人约束**：每班次技术部老人数在范围内
+   $$n_{\text{tech\_old\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i \le n_{\text{tech\_old\_max}}, \quad \forall j$$
+
+5. **老人总数约束**：每班次老人总数在范围内
+   $$n_{\text{old\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \le n_{\text{old\_max}}, \quad \forall j$$
+
+6. **小朋友总数约束**：每班次小朋友总数在范围内
+   $$n_{\text{new\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \le n_{\text{new\_max}}, \quad \forall j$$
+
+### 求解器
+
+以上完成了整个排班问题的建模。本项目使用 Google OR-Tools 中的 **SCIP 求解器**（Solving Constraint Integer Programs）进行求解。SCIP 是一个强大的混合整数规划（MIP）求解器，特别适合处理这类组合优化问题。
+
+OR-Tools 支持 `C++`、`Python`、`C#`、`Java` 等多种语言，并提供跨平台支持。在本项目中，我们使用其 Python 接口 `ortools.linear_solver.pywraplp` 进行建模和求解。当找到最优解时，求解器会输出详细的目标值和各个优化指标的统计信息。
+
+## 维护指南
+- 如果你想更改 Excel 的读取、写入相关的功能，应该修改 `utils.py` 中的相关函数。
+- 如果你想更改软件的前端界面，应该修改 `main.py` 中 `MyWidget` 这个类相关的代码。
 - 如果你想更换排班问题的建模方式、更换求解器、增减限制条件，应该修改 `solve.py` 中的相关代码。
\ No newline at end of file
diff --git a/hook-ortools.py b/hook-ortools.py
new file mode 100644
index 0000000..4b9e5b8
--- /dev/null
+++ b/hook-ortools.py
@@ -0,0 +1,5 @@
+from PyInstaller.utils.hooks import collect_data_files, collect_dynamic_libs
+
+# 收集 ortools 的所有数据文件和动态链接库
+datas = collect_data_files('ortools')
+binaries = collect_dynamic_libs('ortools')
diff --git a/main.py b/main.py
index dcebe28..57150ae 100644
--- a/main.py
+++ b/main.py
@@ -1,402 +1,402 @@
-from sys import exit
-from PySide6 import QtCore, QtWidgets, QtGui
-from PySide6.QtGui import QFont
-from utils import read_excel, save_to_excel
-from solve import solve_program
-from datetime import datetime
-from traceback import format_exc
-
-# 约束条件配置
-CONSTRAINTS_CONFIG = [
-    ("每班次人数：", "5", "8"),
-    ("每班次电脑或电器的老人数：", "", "2"),
-    ("每班次老人数：", "1", ""),
-    ("每班次小朋友数：", "2", "")
-]
-
-# 权重条件配置
-WEIGHTS_CONFIG = [
-    ("目标 1 权重：每班人数平均程度", "1.0"),
-    ("目标 2 权重：每班技术部老人", "1.0"),
-    ("目标 3 权重：每班技术部小朋友", "1.0"),
-    ("目标 4 权重：每班人资部小朋友", "0.5"),
-    ("目标 5 权重：每班部门平均程度", "0.5")
-]
-
-# 参数调优顺序和尝试值
-PARAM_CONFIGS = [
-    ('num_min', [5, 4, 3, 2, 1]),
-    ('num_max', [8, 9, 10, 11, 12, 13, 14, 15]),
-    ('num_old_min', [2, 1, 0]),
-    ('num_old_max', [3, 4, 5, 6, 7, 8]),
-    ('num_new_min', [1, 0]),
-    ('num_tech_old_min', [1, 0]),
-    ('num_tech_old_max', [1, 2, 3, 4, 5, 6, 7, 8]),
-]
-
-# 工作线程类，用于在后台执行求解任务
-class SolverThread(QtCore.QThread):
-    # 定义信号用于线程与主界面通信
-    log_signal = QtCore.Signal(str)  # 发送日志消息
-    finished_signal = QtCore.Signal(object, dict)  # 发送求解结果 (vars, params)
-    error_signal = QtCore.Signal(str)  # 发送错误消息
-    
-    def __init__(self, preference_mat, depart_mat, want_num_array, is_new_array, 
-                 auto_mode, manual_params=None, parent=None):
-        super().__init__(parent)
-        self.preference_mat = preference_mat
-        self.depart_mat = depart_mat
-        self.want_num_array = want_num_array
-        self.is_new_array = is_new_array
-        self.auto_mode = auto_mode
-        self.manual_params = manual_params or {}
-        
-    def run(self):
-        try:
-            vars = None
-            final_params = {}
-            
-            if self.auto_mode:
-                self.log_signal.emit("自动调参开始...")
-                
-                auto_params = {
-                    'num_min': None, 'num_max': None,
-                    'num_tech_old_min': None, 'num_tech_old_max': None,
-                    'num_old_min': None, 'num_old_max': None,
-                    'num_new_min': None, 'num_new_max': None,
-                    'weights': self.manual_params.get('weights', [1.0, 1.0, 1.0, 0.5, 0.5])
-                }
-                
-                # 逐步优化参数
-                for param_name, try_values in PARAM_CONFIGS:
-                    value = None
-                    vars = None
-                    for try_value in try_values:
-                        self.log_signal.emit(f"尝试 {param_name} = {try_value}...")
-                        test_params = auto_params.copy()
-                        test_params[param_name] = try_value
-                        vars = solve_program(
-                            preference_mat=self.preference_mat,
-                            depart_mat=self.depart_mat,
-                            want_num_array=self.want_num_array,
-                            is_new_array=self.is_new_array,
-                            **test_params
-                        )
-                        if vars:
-                            value = try_value
-                            self.log_signal.emit(f"✓ {param_name} = {try_value} 成功")
-                            break
-                        else:
-                            self.log_signal.emit(f"✗ {param_name} = {try_value} 失败")
-                    if value:
-                        auto_params[param_name] = value
-                    else:
-                        break
-                
-                final_params = auto_params
-                
-            else:
-                self.log_signal.emit("计算最优解中...")
-                vars = solve_program(
-                    preference_mat=self.preference_mat,
-                    want_num_array=self.want_num_array,
-                    depart_mat=self.depart_mat,
-                    is_new_array=self.is_new_array,
-                    **self.manual_params
-                )
-                final_params = self.manual_params
-            
-            # 发送完成信号
-            self.finished_signal.emit(vars, final_params)
-            
-        except Exception as e:
-            self.error_signal.emit(format_exc())
-
-class MyWidget(QtWidgets.QWidget):
-    def __init__(self):
-        super().__init__()
-        
-        self.solver_thread = None  # 用于存储求解线程
-
-        self.main_layout = QtWidgets.QVBoxLayout(self)
-
-        bold_font = QFont()
-        bold_font.setBold(True)
-
-        thin_font = QFont()
-        thin_font.setBold(False)
-        
-        # 选择文件部分
-        self._excel_dir = None
-        self.group_box_1 = QtWidgets.QGroupBox("Step 1. 选择问卷星导出的表格文件")
-        self.group_box_1.setFont(bold_font)
-
-        self.openfile_layout = QtWidgets.QVBoxLayout()
-        self.button_openfile = QtWidgets.QPushButton("选择文件")
-        self.button_openfile.setFont(thin_font)
-        self.button_openfile.clicked.connect(self.open_file)
-        self.label_openfile = QtWidgets.QLabel("未选择文件")
-        self.label_openfile.setFont(thin_font)
-
-        self.openfile_layout.addWidget(self.button_openfile)
-        self.openfile_layout.addWidget(self.label_openfile)
-        self.group_box_1.setLayout(self.openfile_layout)
-        self.main_layout.addWidget(self.group_box_1)
-
-        # 限制条件部分
-        self.group_box_2 = QtWidgets.QGroupBox("Step 2. 输入限制条件，权重参数均已归一化")
-        self.group_box_2.setFont(bold_font)
-        self.cond_layout_overall = QtWidgets.QVBoxLayout()
-        self.group_box_2.setLayout(self.cond_layout_overall)
-
-        # 创建权重输入框
-        self.weight_widgets = {}
-
-        for i, (label_text, value) in enumerate(WEIGHTS_CONFIG, 1):
-            layout, edit = self._create_weight_row(label_text, value, thin_font)
-            self.weight_widgets[f'layout_{i}'] = layout
-            self.weight_widgets[f'edit_{i}'] = edit
-            self.cond_layout_overall.addLayout(layout)
-
-        # 自动模式开关
-        self._auto_mode = True
-        self.cond_layout_0 = QtWidgets.QVBoxLayout()
-        self.auto_switch = QtWidgets.QCheckBox("自动调参模式")
-        self.auto_switch.setTristate(False)  # 禁用三态模式
-        self.auto_switch.setChecked(True)
-        self.auto_switch.stateChanged.connect(self.on_switch_toggled)
-        self.auto_switch.setFont(thin_font)
-
-        self.cond_layout_0.addWidget(self.auto_switch)
-        self.cond_layout_overall.addLayout(self.cond_layout_0)
-
-        # 创建限制条件输入框
-        self.constraint_widgets = {}
-
-        for i, (label_text, min_value, max_value) in enumerate(CONSTRAINTS_CONFIG, 1):
-            layout, min_edit, max_edit = self._create_constraint_row(label_text, min_value, max_value, thin_font)
-            self.constraint_widgets[f'layout_{i}'] = layout
-            self.constraint_widgets[f'min_{i}'] = min_edit
-            self.constraint_widgets[f'max_{i}'] = max_edit
-            self.cond_layout_overall.addLayout(layout)
-
-        self.main_layout.addWidget(self.group_box_2)
-
-        # 处理文件部分
-        self.group_box_3 = QtWidgets.QGroupBox("Step 3. 获取最优结果")
-        self.group_box_3.setFont(bold_font)
-
-        self.button_solve = QtWidgets.QPushButton("开始排班！")
-        self.button_solve.setFont(thin_font)
-        self.text_solve = QtWidgets.QTextEdit(self)
-        self.text_solve.setFont(thin_font)
-        self.text_solve.setReadOnly(True)  # 设置为只读模式
-
-        # 创建 QScrollArea 并将 QTextEdit 添加进去
-        self.scroll_area = QtWidgets.QScrollArea(self)
-        self.scroll_area.setWidget(self.text_solve)  # 将 QTextEdit 设置为 QScrollArea 的内容
-        self.scroll_area.setWidgetResizable(True)   # 允许 QTextEdit 根据 QScrollArea 的大小自动调整
-        
-        self.button_solve.clicked.connect(self.magic)
-
-        self.solve_layout = QtWidgets.QVBoxLayout()
-        self.solve_layout.addWidget(self.scroll_area)
-        self.solve_layout.addWidget(self.button_solve)
-        self.group_box_3.setLayout(self.solve_layout)
-        self.main_layout.addWidget(self.group_box_3)
-
-    def _create_constraint_row(self, label_text, min_value, max_value, font):
-        """创建一行限制条件输入框"""
-        layout = QtWidgets.QHBoxLayout()
-        
-        label = QtWidgets.QLabel(label_text, self)
-        label.setFont(font)
-        
-        # 创建输入框的辅助函数
-        def create_input(value):
-            edit = QtWidgets.QLineEdit(self)
-            edit.setFont(font)
-            edit.setValidator(QtGui.QIntValidator())
-            edit.setPlaceholderText("无限制")
-            if value:
-                edit.setText(value)
-            edit.setEnabled(False)
-            return edit
-        
-        min_edit = create_input(min_value)
-        to_label = QtWidgets.QLabel("到", self)
-        to_label.setFont(font)
-        max_edit = create_input(max_value)
-        
-        layout.addWidget(label)
-        layout.addWidget(min_edit)
-        layout.addWidget(to_label)
-        layout.addWidget(max_edit)
-        
-        return layout, min_edit, max_edit
-
-    def _create_weight_row(self, label_text, value, font):
-        """创建一行权重输入框"""
-        layout = QtWidgets.QHBoxLayout()
-
-        label = QtWidgets.QLabel(label_text, self)
-        label.setFont(font)
-
-        edit = QtWidgets.QLineEdit(self)
-        edit.setFont(font)
-        edit.setValidator(QtGui.QDoubleValidator(0.0, 1.0, 2, self))
-        edit.setText(str(value))
-
-        layout.addWidget(label)
-        layout.addWidget(edit)
-
-        return layout, edit
-
-    def open_file(self):
-        file_path, _ = QtWidgets.QFileDialog.getOpenFileName(self, "打开文件", "", "所有文件 (*.*);;文本文件 (*.txt)")
-        if file_path:
-            self.label_openfile.setText(f"选中文件: {file_path}")
-            self._excel_dir = file_path
-
-    def on_switch_toggled(self, state):
-        enabled = state == 0
-        self._auto_mode = not enabled
-        
-        for i in range(1, len(CONSTRAINTS_CONFIG) + 1):
-            self.constraint_widgets[f'min_{i}'].setEnabled(enabled)
-            self.constraint_widgets[f'max_{i}'].setEnabled(enabled)
-
-    @QtCore.Slot()
-    def magic(self):
-        try:
-            self.text_solve.append(f"******** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ********")
-
-            # 读取文件
-            if self._excel_dir is None:
-                self.text_solve.append("请先选择文件！")
-                return
-
-            # 禁用按钮，防止重复点击
-            self.button_solve.setEnabled(False)
-            self.button_solve.setText("计算中...")
-
-            self.text_solve.append("开始排班...")
-            self.text_solve.append("读取文件中...")
-            self.all_data, self.index_to_name_dict, preference_mat, depart_mat, want_num_array, is_new_array = read_excel(self._excel_dir)
-            self.text_solve.append("读取文件成功！")
-        
-            # 计算并打印统计信息
-            is_tech_array = [depart_mat[i][0] == 1 or depart_mat[i][1] == 1 for i in range(len(depart_mat))]
-            stu_num = len(self.index_to_name_dict)
-            class_num = len(preference_mat[0])
-            tech_sum = sum(want_num_array[i] for i in range(len(preference_mat)) 
-                          if not is_new_array[i] and is_tech_array[i])
-            all_sum = sum(want_num_array)
-            min_prefer = min(sum(preference_mat[i][j] for i in range(len(preference_mat))) 
-                           for j in range(class_num))
-            
-            self.text_solve.append("信息统计：")
-            self.text_solve.append(f"\t学生总人数：{stu_num}")
-            self.text_solve.append(f"\t班次总数：{class_num}")
-            self.text_solve.append(f"\t电脑部或电器部的所有老人的意愿班次之和：{tech_sum}")
-            self.text_solve.append(f"\t所有人的意愿班次之和：{all_sum}")
-            self.text_solve.append(f"\t平均每班人数：{all_sum/20}")
-            self.text_solve.append(f"\t所有班次中最少拥有意愿数：{min_prefer}")
-
-            def get_weight_value(key):
-                text = self.weight_widgets[key].text()
-                return float(text) if text else 0.0
-
-            def get_constraint_value(key):
-                text = self.constraint_widgets[key].text()
-                return int(text) if text else None
-            
-            # 准备参数
-            params = {
-                'num_min': get_constraint_value('min_1'),
-                'num_max': get_constraint_value('max_1'),
-                'num_tech_old_min': get_constraint_value('min_2'),
-                'num_tech_old_max': get_constraint_value('max_2'),
-                'num_old_min': get_constraint_value('min_3'),
-                'num_old_max': get_constraint_value('max_3'),
-                'num_new_min': get_constraint_value('min_4'),
-                'num_new_max': get_constraint_value('max_4'),
-                'weights': [get_weight_value(f'edit_{i}') for i in range(1, len(WEIGHTS_CONFIG) + 1)]
-            }
-
-            # 创建并启动求解线程
-            self.solver_thread = SolverThread(
-                preference_mat, depart_mat, want_num_array, is_new_array,
-                self._auto_mode, params, self
-            )
-            
-            # 连接信号
-            self.solver_thread.log_signal.connect(self.on_log_message)
-            self.solver_thread.finished_signal.connect(self.on_solve_finished)
-            self.solver_thread.error_signal.connect(self.on_solve_error)
-            
-            # 启动线程
-            self.solver_thread.start()
-            
-        except Exception:
-            self.text_solve.append("程序出现严重错误，请联系开发者解决问题！！！")
-            self.text_solve.append(f"Error Details:\n{format_exc()}")
-            self.button_solve.setEnabled(True)
-            self.button_solve.setText("开始排班！")
-    
-    @QtCore.Slot(str)
-    def on_log_message(self, message):
-        """接收线程发送的日志消息并实时显示"""
-        self.text_solve.append(message)
-    
-    @QtCore.Slot(object, dict)
-    def on_solve_finished(self, vars, params):
-        """求解完成后的回调"""
-        try:
-            if vars is None:
-                if self._auto_mode:
-                    self.text_solve.append("自动求解失败！请尝试手动调参！")
-                else:
-                    self.text_solve.append("在目前限制条件下无解！请尝试更改限制条件！")
-            else:
-                if self._auto_mode:
-                    self.text_solve.append("自动计算成功！")
-                    self.text_solve.append("参数：")
-                    self.text_solve.append(f"\t每班人数：[{params['num_min']}, {params['num_max']}]")
-                    self.text_solve.append(f"\t每班电脑或电器的老人数：[{params['num_tech_old_min']}, {params['num_tech_old_max']}]")
-                    self.text_solve.append(f"\t每班老人数：[{params['num_old_min']}, {params['num_old_max']}]")
-                    self.text_solve.append(f"\t每班小朋友数：[{params['num_new_min']}, inf]")
-                else:
-                    self.text_solve.append("计算最优解成功！")
-                
-                # 保存结果到 excel
-                self.text_solve.append("保存结果中...")
-                save_dir = f"result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
-                save_to_excel(vars, self.all_data, self.index_to_name_dict, save_dir)
-                self.text_solve.append(f"保存结果成功！保存路径：{save_dir}")
-        
-        except Exception:
-            self.text_solve.append("保存结果时出现错误！")
-            self.text_solve.append(f"Error Details:\n{format_exc()}")
-        finally:
-            # 恢复按钮状态
-            self.button_solve.setEnabled(True)
-            self.button_solve.setText("开始排班！")
-    
-    @QtCore.Slot(str)
-    def on_solve_error(self, error_msg):
-        """处理求解过程中的错误"""
-        self.text_solve.append("程序出现严重错误，请联系开发者解决问题！！！")
-        self.text_solve.append(f"Error Details:\n{error_msg}")
-        self.button_solve.setEnabled(True)
-        self.button_solve.setText("开始排班！")
-
-if __name__ == "__main__":
-    app = QtWidgets.QApplication([])
-
-    widget = MyWidget()
-    widget.setWindowTitle("EVA 值班排班软件")
-    widget.resize(600, 600)
-    widget.show()
-
+from sys import exit
+from PySide6 import QtCore, QtWidgets, QtGui
+from PySide6.QtGui import QFont
+from utils import read_excel, save_to_excel
+from solve import solve_program
+from datetime import datetime
+from traceback import format_exc
+
+# 约束条件配置
+CONSTRAINTS_CONFIG = [
+    ("每班次人数：", "5", "8"),
+    ("每班次电脑或电器的老人数：", "", "2"),
+    ("每班次老人数：", "1", ""),
+    ("每班次小朋友数：", "2", "")
+]
+
+# 权重条件配置
+WEIGHTS_CONFIG = [
+    ("目标 1 权重：每班人数平均程度", "1.0"),
+    ("目标 2 权重：每班技术部老人", "1.0"),
+    ("目标 3 权重：每班技术部小朋友", "1.0"),
+    ("目标 4 权重：每班人资部小朋友", "0.5"),
+    ("目标 5 权重：每班部门平均程度", "0.5")
+]
+
+# 参数调优顺序和尝试值
+PARAM_CONFIGS = [
+    ('num_min', [5, 4, 3, 2, 1]),
+    ('num_max', [8, 9, 10, 11, 12, 13, 14, 15]),
+    ('num_old_min', [2, 1, 0]),
+    ('num_old_max', [3, 4, 5, 6, 7, 8]),
+    ('num_new_min', [1, 0]),
+    ('num_tech_old_min', [1, 0]),
+    ('num_tech_old_max', [1, 2, 3, 4, 5, 6, 7, 8]),
+]
+
+# 工作线程类，用于在后台执行求解任务
+class SolverThread(QtCore.QThread):
+    # 定义信号用于线程与主界面通信
+    log_signal = QtCore.Signal(str)  # 发送日志消息
+    finished_signal = QtCore.Signal(object, dict)  # 发送求解结果 (vars, params)
+    error_signal = QtCore.Signal(str)  # 发送错误消息
+    
+    def __init__(self, preference_mat, depart_mat, want_num_array, is_new_array, 
+                 auto_mode, manual_params=None, parent=None):
+        super().__init__(parent)
+        self.preference_mat = preference_mat
+        self.depart_mat = depart_mat
+        self.want_num_array = want_num_array
+        self.is_new_array = is_new_array
+        self.auto_mode = auto_mode
+        self.manual_params = manual_params or {}
+        
+    def run(self):
+        try:
+            vars = None
+            final_params = {}
+            
+            if self.auto_mode:
+                self.log_signal.emit("自动调参开始...")
+                
+                auto_params = {
+                    'num_min': None, 'num_max': None,
+                    'num_tech_old_min': None, 'num_tech_old_max': None,
+                    'num_old_min': None, 'num_old_max': None,
+                    'num_new_min': None, 'num_new_max': None,
+                    'weights': self.manual_params.get('weights', [1.0, 1.0, 1.0, 0.5, 0.5])
+                }
+                
+                # 逐步优化参数
+                for param_name, try_values in PARAM_CONFIGS:
+                    value = None
+                    vars = None
+                    for try_value in try_values:
+                        self.log_signal.emit(f"尝试 {param_name} = {try_value}...")
+                        test_params = auto_params.copy()
+                        test_params[param_name] = try_value
+                        vars = solve_program(
+                            preference_mat=self.preference_mat,
+                            depart_mat=self.depart_mat,
+                            want_num_array=self.want_num_array,
+                            is_new_array=self.is_new_array,
+                            **test_params
+                        )
+                        if vars:
+                            value = try_value
+                            self.log_signal.emit(f"✓ {param_name} = {try_value} 成功")
+                            break
+                        else:
+                            self.log_signal.emit(f"✗ {param_name} = {try_value} 失败")
+                    if value:
+                        auto_params[param_name] = value
+                    else:
+                        break
+                
+                final_params = auto_params
+                
+            else:
+                self.log_signal.emit("计算最优解中...")
+                vars = solve_program(
+                    preference_mat=self.preference_mat,
+                    want_num_array=self.want_num_array,
+                    depart_mat=self.depart_mat,
+                    is_new_array=self.is_new_array,
+                    **self.manual_params
+                )
+                final_params = self.manual_params
+            
+            # 发送完成信号
+            self.finished_signal.emit(vars, final_params)
+            
+        except Exception as e:
+            self.error_signal.emit(format_exc())
+
+class MyWidget(QtWidgets.QWidget):
+    def __init__(self):
+        super().__init__()
+        
+        self.solver_thread = None  # 用于存储求解线程
+
+        self.main_layout = QtWidgets.QVBoxLayout(self)
+
+        bold_font = QFont()
+        bold_font.setBold(True)
+
+        thin_font = QFont()
+        thin_font.setBold(False)
+        
+        # 选择文件部分
+        self._excel_dir = None
+        self.group_box_1 = QtWidgets.QGroupBox("Step 1. 选择问卷星导出的表格文件")
+        self.group_box_1.setFont(bold_font)
+
+        self.openfile_layout = QtWidgets.QVBoxLayout()
+        self.button_openfile = QtWidgets.QPushButton("选择文件")
+        self.button_openfile.setFont(thin_font)
+        self.button_openfile.clicked.connect(self.open_file)
+        self.label_openfile = QtWidgets.QLabel("未选择文件")
+        self.label_openfile.setFont(thin_font)
+
+        self.openfile_layout.addWidget(self.button_openfile)
+        self.openfile_layout.addWidget(self.label_openfile)
+        self.group_box_1.setLayout(self.openfile_layout)
+        self.main_layout.addWidget(self.group_box_1)
+
+        # 限制条件部分
+        self.group_box_2 = QtWidgets.QGroupBox("Step 2. 输入限制条件，权重参数均已归一化")
+        self.group_box_2.setFont(bold_font)
+        self.cond_layout_overall = QtWidgets.QVBoxLayout()
+        self.group_box_2.setLayout(self.cond_layout_overall)
+
+        # 创建权重输入框
+        self.weight_widgets = {}
+
+        for i, (label_text, value) in enumerate(WEIGHTS_CONFIG, 1):
+            layout, edit = self._create_weight_row(label_text, value, thin_font)
+            self.weight_widgets[f'layout_{i}'] = layout
+            self.weight_widgets[f'edit_{i}'] = edit
+            self.cond_layout_overall.addLayout(layout)
+
+        # 自动模式开关
+        self._auto_mode = True
+        self.cond_layout_0 = QtWidgets.QVBoxLayout()
+        self.auto_switch = QtWidgets.QCheckBox("自动调参模式")
+        self.auto_switch.setTristate(False)  # 禁用三态模式
+        self.auto_switch.setChecked(True)
+        self.auto_switch.stateChanged.connect(self.on_switch_toggled)
+        self.auto_switch.setFont(thin_font)
+
+        self.cond_layout_0.addWidget(self.auto_switch)
+        self.cond_layout_overall.addLayout(self.cond_layout_0)
+
+        # 创建限制条件输入框
+        self.constraint_widgets = {}
+
+        for i, (label_text, min_value, max_value) in enumerate(CONSTRAINTS_CONFIG, 1):
+            layout, min_edit, max_edit = self._create_constraint_row(label_text, min_value, max_value, thin_font)
+            self.constraint_widgets[f'layout_{i}'] = layout
+            self.constraint_widgets[f'min_{i}'] = min_edit
+            self.constraint_widgets[f'max_{i}'] = max_edit
+            self.cond_layout_overall.addLayout(layout)
+
+        self.main_layout.addWidget(self.group_box_2)
+
+        # 处理文件部分
+        self.group_box_3 = QtWidgets.QGroupBox("Step 3. 获取最优结果")
+        self.group_box_3.setFont(bold_font)
+
+        self.button_solve = QtWidgets.QPushButton("开始排班！")
+        self.button_solve.setFont(thin_font)
+        self.text_solve = QtWidgets.QTextEdit(self)
+        self.text_solve.setFont(thin_font)
+        self.text_solve.setReadOnly(True)  # 设置为只读模式
+
+        # 创建 QScrollArea 并将 QTextEdit 添加进去
+        self.scroll_area = QtWidgets.QScrollArea(self)
+        self.scroll_area.setWidget(self.text_solve)  # 将 QTextEdit 设置为 QScrollArea 的内容
+        self.scroll_area.setWidgetResizable(True)   # 允许 QTextEdit 根据 QScrollArea 的大小自动调整
+        
+        self.button_solve.clicked.connect(self.magic)
+
+        self.solve_layout = QtWidgets.QVBoxLayout()
+        self.solve_layout.addWidget(self.scroll_area)
+        self.solve_layout.addWidget(self.button_solve)
+        self.group_box_3.setLayout(self.solve_layout)
+        self.main_layout.addWidget(self.group_box_3)
+
+    def _create_constraint_row(self, label_text, min_value, max_value, font):
+        """创建一行限制条件输入框"""
+        layout = QtWidgets.QHBoxLayout()
+        
+        label = QtWidgets.QLabel(label_text, self)
+        label.setFont(font)
+        
+        # 创建输入框的辅助函数
+        def create_input(value):
+            edit = QtWidgets.QLineEdit(self)
+            edit.setFont(font)
+            edit.setValidator(QtGui.QIntValidator())
+            edit.setPlaceholderText("无限制")
+            if value:
+                edit.setText(value)
+            edit.setEnabled(False)
+            return edit
+        
+        min_edit = create_input(min_value)
+        to_label = QtWidgets.QLabel("到", self)
+        to_label.setFont(font)
+        max_edit = create_input(max_value)
+        
+        layout.addWidget(label)
+        layout.addWidget(min_edit)
+        layout.addWidget(to_label)
+        layout.addWidget(max_edit)
+        
+        return layout, min_edit, max_edit
+
+    def _create_weight_row(self, label_text, value, font):
+        """创建一行权重输入框"""
+        layout = QtWidgets.QHBoxLayout()
+
+        label = QtWidgets.QLabel(label_text, self)
+        label.setFont(font)
+
+        edit = QtWidgets.QLineEdit(self)
+        edit.setFont(font)
+        edit.setValidator(QtGui.QDoubleValidator(0.0, 1.0, 2, self))
+        edit.setText(str(value))
+
+        layout.addWidget(label)
+        layout.addWidget(edit)
+
+        return layout, edit
+
+    def open_file(self):
+        file_path, _ = QtWidgets.QFileDialog.getOpenFileName(self, "打开文件", "", "所有文件 (*.*);;文本文件 (*.txt)")
+        if file_path:
+            self.label_openfile.setText(f"选中文件: {file_path}")
+            self._excel_dir = file_path
+
+    def on_switch_toggled(self, state):
+        enabled = state == 0
+        self._auto_mode = not enabled
+        
+        for i in range(1, len(CONSTRAINTS_CONFIG) + 1):
+            self.constraint_widgets[f'min_{i}'].setEnabled(enabled)
+            self.constraint_widgets[f'max_{i}'].setEnabled(enabled)
+
+    @QtCore.Slot()
+    def magic(self):
+        try:
+            self.text_solve.append(f"******** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ********")
+
+            # 读取文件
+            if self._excel_dir is None:
+                self.text_solve.append("请先选择文件！")
+                return
+
+            # 禁用按钮，防止重复点击
+            self.button_solve.setEnabled(False)
+            self.button_solve.setText("计算中...")
+
+            self.text_solve.append("开始排班...")
+            self.text_solve.append("读取文件中...")
+            self.all_data, self.index_to_name_dict, preference_mat, depart_mat, want_num_array, is_new_array = read_excel(self._excel_dir)
+            self.text_solve.append("读取文件成功！")
+        
+            # 计算并打印统计信息
+            is_tech_array = [depart_mat[i][0] == 1 or depart_mat[i][1] == 1 for i in range(len(depart_mat))]
+            stu_num = len(self.index_to_name_dict)
+            class_num = len(preference_mat[0])
+            tech_sum = sum(want_num_array[i] for i in range(len(preference_mat)) 
+                          if not is_new_array[i] and is_tech_array[i])
+            all_sum = sum(want_num_array)
+            min_prefer = min(sum(preference_mat[i][j] for i in range(len(preference_mat))) 
+                           for j in range(class_num))
+            
+            self.text_solve.append("信息统计：")
+            self.text_solve.append(f"\t学生总人数：{stu_num}")
+            self.text_solve.append(f"\t班次总数：{class_num}")
+            self.text_solve.append(f"\t电脑部或电器部的所有老人的意愿班次之和：{tech_sum}")
+            self.text_solve.append(f"\t所有人的意愿班次之和：{all_sum}")
+            self.text_solve.append(f"\t平均每班人数：{all_sum/20}")
+            self.text_solve.append(f"\t所有班次中最少拥有意愿数：{min_prefer}")
+
+            def get_weight_value(key):
+                text = self.weight_widgets[key].text()
+                return float(text) if text else 0.0
+
+            def get_constraint_value(key):
+                text = self.constraint_widgets[key].text()
+                return int(text) if text else None
+            
+            # 准备参数
+            params = {
+                'num_min': get_constraint_value('min_1'),
+                'num_max': get_constraint_value('max_1'),
+                'num_tech_old_min': get_constraint_value('min_2'),
+                'num_tech_old_max': get_constraint_value('max_2'),
+                'num_old_min': get_constraint_value('min_3'),
+                'num_old_max': get_constraint_value('max_3'),
+                'num_new_min': get_constraint_value('min_4'),
+                'num_new_max': get_constraint_value('max_4'),
+                'weights': [get_weight_value(f'edit_{i}') for i in range(1, len(WEIGHTS_CONFIG) + 1)]
+            }
+
+            # 创建并启动求解线程
+            self.solver_thread = SolverThread(
+                preference_mat, depart_mat, want_num_array, is_new_array,
+                self._auto_mode, params, self
+            )
+            
+            # 连接信号
+            self.solver_thread.log_signal.connect(self.on_log_message)
+            self.solver_thread.finished_signal.connect(self.on_solve_finished)
+            self.solver_thread.error_signal.connect(self.on_solve_error)
+            
+            # 启动线程
+            self.solver_thread.start()
+            
+        except Exception:
+            self.text_solve.append("程序出现严重错误，请联系开发者解决问题！！！")
+            self.text_solve.append(f"Error Details:\n{format_exc()}")
+            self.button_solve.setEnabled(True)
+            self.button_solve.setText("开始排班！")
+    
+    @QtCore.Slot(str)
+    def on_log_message(self, message):
+        """接收线程发送的日志消息并实时显示"""
+        self.text_solve.append(message)
+    
+    @QtCore.Slot(object, dict)
+    def on_solve_finished(self, vars, params):
+        """求解完成后的回调"""
+        try:
+            if vars is None:
+                if self._auto_mode:
+                    self.text_solve.append("自动求解失败！请尝试手动调参！")
+                else:
+                    self.text_solve.append("在目前限制条件下无解！请尝试更改限制条件！")
+            else:
+                if self._auto_mode:
+                    self.text_solve.append("自动计算成功！")
+                    self.text_solve.append("参数：")
+                    self.text_solve.append(f"\t每班人数：[{params['num_min']}, {params['num_max']}]")
+                    self.text_solve.append(f"\t每班电脑或电器的老人数：[{params['num_tech_old_min']}, {params['num_tech_old_max']}]")
+                    self.text_solve.append(f"\t每班老人数：[{params['num_old_min']}, {params['num_old_max']}]")
+                    self.text_solve.append(f"\t每班小朋友数：[{params['num_new_min']}, inf]")
+                else:
+                    self.text_solve.append("计算最优解成功！")
+                
+                # 保存结果到 excel
+                self.text_solve.append("保存结果中...")
+                save_dir = f"result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
+                save_to_excel(vars, self.all_data, self.index_to_name_dict, save_dir)
+                self.text_solve.append(f"保存结果成功！保存路径：{save_dir}")
+        
+        except Exception:
+            self.text_solve.append("保存结果时出现错误！")
+            self.text_solve.append(f"Error Details:\n{format_exc()}")
+        finally:
+            # 恢复按钮状态
+            self.button_solve.setEnabled(True)
+            self.button_solve.setText("开始排班！")
+    
+    @QtCore.Slot(str)
+    def on_solve_error(self, error_msg):
+        """处理求解过程中的错误"""
+        self.text_solve.append("程序出现严重错误，请联系开发者解决问题！！！")
+        self.text_solve.append(f"Error Details:\n{error_msg}")
+        self.button_solve.setEnabled(True)
+        self.button_solve.setText("开始排班！")
+
+if __name__ == "__main__":
+    app = QtWidgets.QApplication([])
+
+    widget = MyWidget()
+    widget.setWindowTitle("EVA 值班排班软件")
+    widget.resize(600, 600)
+    widget.show()
+
     exit(app.exec())
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 5e0c2da..3164b68 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,13 +1,13 @@
-[project]
-name = "eva-duty-arrange-tool"
-version = "2.0.0"
-description = "EVA 值班排班工具"
-readme = "README.md"
-requires-python = ">=3.12"
-dependencies = [
-    "openpyxl>=3.1.5",
-    "ortools>=9.14.6206",
-    "pandas>=2.3.3",
-    "pyinstaller>=6.16.0",
-    "pyside6>=6.10.0",
-]
+[project]
+name = "eva-duty-arrange-tool"
+version = "2.0.0"
+description = "EVA 值班排班工具"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "openpyxl>=3.1.5",
+    "ortools>=9.14.6206",
+    "pandas>=2.3.3",
+    "pyinstaller>=6.16.0",
+    "pyside6>=6.10.0",
+]
diff --git a/solve.py b/solve.py
index 58625a2..8640083 100644
--- a/solve.py
+++ b/solve.py
@@ -1,210 +1,210 @@
-from ortools.linear_solver import pywraplp
-
-def solve_program(preference_mat:list, depart_mat:list, want_num_array:list, is_new_array:list,
-                  num_min=None, num_max=None,
-                  num_tech_old_min=None, num_tech_old_max=None,
-                  num_old_min=None, num_old_max=None,
-                  num_new_min=None, num_new_max=None,
-                  weights=[1.0, 1.0, 1.0, 0.5, 0.5]
-                  ):
-
-    is_old_array = [not is_new for is_new in is_new_array]
-    is_tech_array = [depart_mat[i][0] == 1 or depart_mat[i][1] == 1 for i in range(len(depart_mat))]
-    is_hr_array = [depart_mat[i][2] == 1 for i in range(len(depart_mat))]
-    
-    # 使用 SCIP 求解器求解组合优化问题
-    solver = pywraplp.Solver.CreateSolver("SCIP")
-    if not solver:
-        return
-
-    N = len(preference_mat)     # 学生人数
-    M = len(preference_mat[0])  # 班次数
-    avg_num = sum(want_num_array) / M
-    print(f"平均人数：{avg_num}")
-
-    # 各个目标的期望值/归一化系数
-    m = [2.5, 3, 4, 1, 8]  # [班次人数偏差, 技术部老人, 技术部小朋友, 人资部小朋友, 部门均衡]
-
-    # 创建决策变量：variables[i][j] 表示第 i 个学生是否选择第 j 个班次
-    variables = [[solver.IntVar(0.0, 1.0, f"choice_{i}_{j}") 
-                  for j in range(M)] for i in range(N)]
-    
-    # 创建辅助变量用于优化目标1（均衡班次人数）
-    aux_vars_x1 = [solver.NumVar(0.0, solver.infinity(), f"aux_x1_{j}") 
-                   for j in range(M)]
-    
-    # 创建辅助变量用于优化目标2（每班次技术部老人数量缺口）
-    aux_vars_x2 = [solver.NumVar(0.0, solver.infinity(), f"aux_x2_{j}") 
-                   for j in range(M)]
-    
-    # 创建辅助变量用于优化目标3（每班次技术部小朋友数量缺口）
-    aux_vars_x3 = [solver.NumVar(0.0, solver.infinity(), f"aux_x3_{j}") 
-                   for j in range(M)]
-    
-    # 创建辅助变量用于优化目标4（每班次人资部小朋友数量缺口）
-    aux_vars_x4 = [solver.NumVar(0.0, solver.infinity(), f"aux_x4_{j}") 
-                   for j in range(M)]
-    
-    # 创建辅助变量用于优化目标5（均衡各部门人数）
-    # 如果提供了部门信息，则创建相应的辅助变量
-    aux_vars_x5 = []
-    num_departments = 5  # 电脑部、电器部、人资部、财外部、文宣部
-    # 为每个班次的每个部门创建辅助变量
-    aux_vars_x5 = [[solver.NumVar(0.0, solver.infinity(), f"aux_x5_{j}_{k}") 
-                    for k in range(num_departments)] for j in range(M)]
-    
-    print("Number of variables =", solver.NumVariables())
-
-    # 约束：每个同学意愿一定要满足
-    for i in range(N):
-        for j in range(M):
-            solver.Add(variables[i][j] <= preference_mat[i][j])
-
-    # 约束：辅助变量 X1 用于计算与平均人数的偏差（绝对值）
-    for j in range(M):
-        actual_num = sum(variables[i][j] for i in range(N))
-        solver.Add(aux_vars_x1[j] >= actual_num - avg_num)
-        solver.Add(aux_vars_x1[j] >= avg_num - actual_num)
-    
-    # 约束：辅助变量 X2 用于计算每班次技术部老人数量超出期望值的部分
-    # 辅助变量表示超出期望值(期望每班有m2个技术部老人,超过即计入)
-    # 目标是让尽可能多的班次达到但不超过这个期望值
-    tech_old_target = m[1]  # 使用归一化系数作为期望值
-    for j in range(M):
-        tech_old_count = sum(variables[i][j] * is_old_array[i] * is_tech_array[i] for i in range(N))
-        # 辅助变量捕获超出部分和不足部分
-        solver.Add(aux_vars_x2[j] >= tech_old_count - tech_old_target)
-        solver.Add(aux_vars_x2[j] >= tech_old_target - tech_old_count)
-    
-    # 约束：辅助变量 X3 用于计算每班次技术部小朋友数量超出期望值的部分
-    tech_new_target = m[2]  # 使用归一化系数作为期望值
-    for j in range(M):
-        tech_new_count = sum(variables[i][j] * is_new_array[i] * is_tech_array[i] for i in range(N))
-        solver.Add(aux_vars_x3[j] >= tech_new_count - tech_new_target)
-        solver.Add(aux_vars_x3[j] >= tech_new_target - tech_new_count)
-    
-    # 约束：辅助变量 X4 用于计算每班次人资部小朋友数量超出期望值的部分
-    hr_new_target = m[3]  # 使用归一化系数作为期望值
-    for j in range(M):
-        hr_new_count = sum(variables[i][j] * is_new_array[i] * is_hr_array[i] for i in range(N))
-        solver.Add(aux_vars_x4[j] >= hr_new_count - hr_new_target)
-        solver.Add(aux_vars_x4[j] >= hr_new_target - hr_new_count)
-    
-    # 约束：辅助变量 X5 用于计算各部门人数与平均值的偏差（绝对值）
-    if aux_vars_x5 is not None:
-        for j in range(M):
-            # 计算第 j 班次的总人数
-            shift_total = sum(variables[i][j] for i in range(N))
-            # 计算平均每个部门应有的人数
-            avg_dept = shift_total / num_departments
-            
-            # 对每个部门 k
-            for k in range(num_departments):
-                # 计算第 j 班次中第 k 个部门的实际人数
-                dept_count = sum(variables[i][j] * depart_mat[i][k] for i in range(N))
-                # 添加绝对值约束
-                solver.Add(aux_vars_x5[j][k] >= dept_count - avg_dept)
-                solver.Add(aux_vars_x5[j][k] >= avg_dept - dept_count)
-
-    # 约束：满足每位同学的意愿班次数
-    for i in range(N):
-        total_shifts = sum(variables[i])
-        if want_num_array[i] == 3:
-            solver.Add(total_shifts >= 2)
-            solver.Add(total_shifts <= 3)
-        else:
-            solver.Add(total_shifts == want_num_array[i])
-
-    # 添加约束的辅助函数
-    def add_shift_constraint(array, min_val, max_val):
-        for j in range(M):
-            shift_count = sum(variables[i][j] * array[i] for i in range(N))
-            if min_val is not None:
-                solver.Add(shift_count >= min_val)
-            if max_val is not None:
-                solver.Add(shift_count <= max_val)
-
-    # 约束：每个班次的总人数
-    add_shift_constraint([1] * N, num_min, num_max)
-    
-    # 约束：每班次电脑或电器的老人数量
-    tech_old_array = [is_old_array[i] * is_tech_array[i] for i in range(N)]
-    add_shift_constraint(tech_old_array, num_tech_old_min, num_tech_old_max)
-    
-    # 约束：每班次老人数量
-    add_shift_constraint(is_old_array, num_old_min, num_old_max)
-    
-    # 约束：每班次小朋友数量
-    add_shift_constraint(is_new_array, num_new_min, num_new_max)
-
-    # 构建多目标优化函数
-    m1, m2, m3, m4, m5 = m[0], m[1], m[2], m[3], m[4]
-
-    # X1: 每班次人数与平均人数的偏差
-    x1 = sum(aux_vars_x1)
-    obj_x1 = x1 / (m1 * M)  # 归一化处理，防止数值过大
-
-    # X2: 每班次技术部老人数量的缺口总和（优化目标是让每班都达到目标值）
-    x2 = sum(aux_vars_x2)
-    obj_x2 = x2 / (m2 * M)  # 归一化处理，缺口越小越好
-    
-    # X3: 每班次技术部小朋友数量的缺口总和
-    x3 = sum(aux_vars_x3)
-    obj_x3 = x3 / (m3 * M)  # 归一化处理，缺口越小越好
-
-    # X4: 每班次人资部小朋友数量的缺口总和
-    x4 = sum(aux_vars_x4)
-    obj_x4 = x4 / (m4 * M)  # 归一化处理，缺口越小越好
-
-    # X5: 每班次各部门人数的平均程度
-    obj_x5 = 0
-    if aux_vars_x5:
-        x5 = sum(sum(aux_vars_x5[j]) for j in range(M))
-        obj_x5 = x5 / (m5 * M)  # 归一化处理
-
-    # 线性加权组合所有目标
-    total_objective = (
-        weights[0] * obj_x1 + weights[1] * obj_x2 + weights[2] * obj_x3 + weights[3] * obj_x4 + weights[4] * obj_x5
-    )
-    
-    print(f"优化目标权重: X1={weights[0]}, X2={weights[1]}, X3={weights[2]}, X4={weights[3]}, X5={weights[4]}")
-    solver.Minimize(total_objective)
-
-    # 求解
-    status = solver.Solve()
-
-    if status == pywraplp.Solver.OPTIMAL:
-        print("Optimal solution found:")
-        variables_return = [[variables[i][j].solution_value() for j in range(M)] 
-                           for i in range(N)]
-        aux_vars_x1_return = sum([aux_vars_x1[j].solution_value() for j in range(M)]) / M
-        
-        print(f"Optimized objective value: {solver.Objective().Value()}")
-        print(f"X1 (每班次人数平均偏差): {aux_vars_x1_return}")
-        
-        # 输出各个优化目标的详细信息
-        tech_old_array = [is_old_array[i] * is_tech_array[i] for i in range(N)]
-        tech_new_array = [is_new_array[i] * is_tech_array[i] for i in range(N)]
-        hr_new_array = [is_new_array[i] * is_hr_array[i] for i in range(N)]
-        
-        x2_value = sum(sum(variables[i][j].solution_value() * tech_old_array[i] for i in range(N)) for j in range(M)) / M
-        x3_value = sum(sum(variables[i][j].solution_value() * tech_new_array[i] for i in range(N)) for j in range(M)) / M
-        x4_value = sum(sum(variables[i][j].solution_value() * hr_new_array[i] for i in range(N)) for j in range(M)) / M
-        
-        aux_vars_x2_return = sum([aux_vars_x2[j].solution_value() for j in range(M)]) / M
-        aux_vars_x3_return = sum([aux_vars_x3[j].solution_value() for j in range(M)]) / M
-        aux_vars_x4_return = sum([aux_vars_x4[j].solution_value() for j in range(M)]) / M
-
-        print(f"X2 (每班平均技术部老人): {x2_value}, 缺口平均: {aux_vars_x2_return}")
-        print(f"X3 (每班平均技术部小朋友): {x3_value}, 缺口平均: {aux_vars_x3_return}")
-        print(f"X4 (每班平均人资部小朋友): {x4_value}, 缺口平均: {aux_vars_x4_return}")
-
-        if aux_vars_x5:
-            x5_value = sum(sum(aux_vars_x5[j][k].solution_value() for k in range(num_departments)) for j in range(M))
-            print(f"X5 (部门人数偏差): {x5_value}")
-        
-        return variables_return
-    else:
-        print("No solution found.")
-        return None
+from ortools.linear_solver import pywraplp
+
+def solve_program(preference_mat:list, depart_mat:list, want_num_array:list, is_new_array:list,
+                  num_min=None, num_max=None,
+                  num_tech_old_min=None, num_tech_old_max=None,
+                  num_old_min=None, num_old_max=None,
+                  num_new_min=None, num_new_max=None,
+                  weights=[1.0, 1.0, 1.0, 0.5, 0.5]
+                  ):
+
+    is_old_array = [not is_new for is_new in is_new_array]
+    is_tech_array = [depart_mat[i][0] == 1 or depart_mat[i][1] == 1 for i in range(len(depart_mat))]
+    is_hr_array = [depart_mat[i][2] == 1 for i in range(len(depart_mat))]
+    
+    # 使用 SCIP 求解器求解组合优化问题
+    solver = pywraplp.Solver.CreateSolver("SCIP")
+    if not solver:
+        return
+
+    N = len(preference_mat)     # 学生人数
+    M = len(preference_mat[0])  # 班次数
+    avg_num = sum(want_num_array) / M
+    print(f"平均人数：{avg_num}")
+
+    # 各个目标的期望值/归一化系数
+    m = [2.5, 3, 4, 1, 8]  # [班次人数偏差, 技术部老人, 技术部小朋友, 人资部小朋友, 部门均衡]
+
+    # 创建决策变量：variables[i][j] 表示第 i 个学生是否选择第 j 个班次
+    variables = [[solver.IntVar(0.0, 1.0, f"choice_{i}_{j}") 
+                  for j in range(M)] for i in range(N)]
+    
+    # 创建辅助变量用于优化目标1（均衡班次人数）
+    aux_vars_x1 = [solver.NumVar(0.0, solver.infinity(), f"aux_x1_{j}") 
+                   for j in range(M)]
+    
+    # 创建辅助变量用于优化目标2（每班次技术部老人数量缺口）
+    aux_vars_x2 = [solver.NumVar(0.0, solver.infinity(), f"aux_x2_{j}") 
+                   for j in range(M)]
+    
+    # 创建辅助变量用于优化目标3（每班次技术部小朋友数量缺口）
+    aux_vars_x3 = [solver.NumVar(0.0, solver.infinity(), f"aux_x3_{j}") 
+                   for j in range(M)]
+    
+    # 创建辅助变量用于优化目标4（每班次人资部小朋友数量缺口）
+    aux_vars_x4 = [solver.NumVar(0.0, solver.infinity(), f"aux_x4_{j}") 
+                   for j in range(M)]
+    
+    # 创建辅助变量用于优化目标5（均衡各部门人数）
+    # 如果提供了部门信息，则创建相应的辅助变量
+    aux_vars_x5 = []
+    num_departments = 5  # 电脑部、电器部、人资部、财外部、文宣部
+    # 为每个班次的每个部门创建辅助变量
+    aux_vars_x5 = [[solver.NumVar(0.0, solver.infinity(), f"aux_x5_{j}_{k}") 
+                    for k in range(num_departments)] for j in range(M)]
+    
+    print("Number of variables =", solver.NumVariables())
+
+    # 约束：每个同学意愿一定要满足
+    for i in range(N):
+        for j in range(M):
+            solver.Add(variables[i][j] <= preference_mat[i][j])
+
+    # 约束：辅助变量 X1 用于计算与平均人数的偏差（绝对值）
+    for j in range(M):
+        actual_num = sum(variables[i][j] for i in range(N))
+        solver.Add(aux_vars_x1[j] >= actual_num - avg_num)
+        solver.Add(aux_vars_x1[j] >= avg_num - actual_num)
+    
+    # 约束：辅助变量 X2 用于计算每班次技术部老人数量超出期望值的部分
+    # 辅助变量表示超出期望值(期望每班有m2个技术部老人,超过即计入)
+    # 目标是让尽可能多的班次达到但不超过这个期望值
+    tech_old_target = m[1]  # 使用归一化系数作为期望值
+    for j in range(M):
+        tech_old_count = sum(variables[i][j] * is_old_array[i] * is_tech_array[i] for i in range(N))
+        # 辅助变量捕获超出部分和不足部分
+        solver.Add(aux_vars_x2[j] >= tech_old_count - tech_old_target)
+        solver.Add(aux_vars_x2[j] >= tech_old_target - tech_old_count)
+    
+    # 约束：辅助变量 X3 用于计算每班次技术部小朋友数量超出期望值的部分
+    tech_new_target = m[2]  # 使用归一化系数作为期望值
+    for j in range(M):
+        tech_new_count = sum(variables[i][j] * is_new_array[i] * is_tech_array[i] for i in range(N))
+        solver.Add(aux_vars_x3[j] >= tech_new_count - tech_new_target)
+        solver.Add(aux_vars_x3[j] >= tech_new_target - tech_new_count)
+    
+    # 约束：辅助变量 X4 用于计算每班次人资部小朋友数量超出期望值的部分
+    hr_new_target = m[3]  # 使用归一化系数作为期望值
+    for j in range(M):
+        hr_new_count = sum(variables[i][j] * is_new_array[i] * is_hr_array[i] for i in range(N))
+        solver.Add(aux_vars_x4[j] >= hr_new_count - hr_new_target)
+        solver.Add(aux_vars_x4[j] >= hr_new_target - hr_new_count)
+    
+    # 约束：辅助变量 X5 用于计算各部门人数与平均值的偏差（绝对值）
+    if aux_vars_x5 is not None:
+        for j in range(M):
+            # 计算第 j 班次的总人数
+            shift_total = sum(variables[i][j] for i in range(N))
+            # 计算平均每个部门应有的人数
+            avg_dept = shift_total / num_departments
+            
+            # 对每个部门 k
+            for k in range(num_departments):
+                # 计算第 j 班次中第 k 个部门的实际人数
+                dept_count = sum(variables[i][j] * depart_mat[i][k] for i in range(N))
+                # 添加绝对值约束
+                solver.Add(aux_vars_x5[j][k] >= dept_count - avg_dept)
+                solver.Add(aux_vars_x5[j][k] >= avg_dept - dept_count)
+
+    # 约束：满足每位同学的意愿班次数
+    for i in range(N):
+        total_shifts = sum(variables[i])
+        if want_num_array[i] == 3:
+            solver.Add(total_shifts >= 2)
+            solver.Add(total_shifts <= 3)
+        else:
+            solver.Add(total_shifts == want_num_array[i])
+
+    # 添加约束的辅助函数
+    def add_shift_constraint(array, min_val, max_val):
+        for j in range(M):
+            shift_count = sum(variables[i][j] * array[i] for i in range(N))
+            if min_val is not None:
+                solver.Add(shift_count >= min_val)
+            if max_val is not None:
+                solver.Add(shift_count <= max_val)
+
+    # 约束：每个班次的总人数
+    add_shift_constraint([1] * N, num_min, num_max)
+    
+    # 约束：每班次电脑或电器的老人数量
+    tech_old_array = [is_old_array[i] * is_tech_array[i] for i in range(N)]
+    add_shift_constraint(tech_old_array, num_tech_old_min, num_tech_old_max)
+    
+    # 约束：每班次老人数量
+    add_shift_constraint(is_old_array, num_old_min, num_old_max)
+    
+    # 约束：每班次小朋友数量
+    add_shift_constraint(is_new_array, num_new_min, num_new_max)
+
+    # 构建多目标优化函数
+    m1, m2, m3, m4, m5 = m[0], m[1], m[2], m[3], m[4]
+
+    # X1: 每班次人数与平均人数的偏差
+    x1 = sum(aux_vars_x1)
+    obj_x1 = x1 / (m1 * M)  # 归一化处理，防止数值过大
+
+    # X2: 每班次技术部老人数量的缺口总和（优化目标是让每班都达到目标值）
+    x2 = sum(aux_vars_x2)
+    obj_x2 = x2 / (m2 * M)  # 归一化处理，缺口越小越好
+    
+    # X3: 每班次技术部小朋友数量的缺口总和
+    x3 = sum(aux_vars_x3)
+    obj_x3 = x3 / (m3 * M)  # 归一化处理，缺口越小越好
+
+    # X4: 每班次人资部小朋友数量的缺口总和
+    x4 = sum(aux_vars_x4)
+    obj_x4 = x4 / (m4 * M)  # 归一化处理，缺口越小越好
+
+    # X5: 每班次各部门人数的平均程度
+    obj_x5 = 0
+    if aux_vars_x5:
+        x5 = sum(sum(aux_vars_x5[j]) for j in range(M))
+        obj_x5 = x5 / (m5 * M)  # 归一化处理
+
+    # 线性加权组合所有目标
+    total_objective = (
+        weights[0] * obj_x1 + weights[1] * obj_x2 + weights[2] * obj_x3 + weights[3] * obj_x4 + weights[4] * obj_x5
+    )
+    
+    print(f"优化目标权重: X1={weights[0]}, X2={weights[1]}, X3={weights[2]}, X4={weights[3]}, X5={weights[4]}")
+    solver.Minimize(total_objective)
+
+    # 求解
+    status = solver.Solve()
+
+    if status == pywraplp.Solver.OPTIMAL:
+        print("Optimal solution found:")
+        variables_return = [[variables[i][j].solution_value() for j in range(M)] 
+                           for i in range(N)]
+        aux_vars_x1_return = sum([aux_vars_x1[j].solution_value() for j in range(M)]) / M
+        
+        print(f"Optimized objective value: {solver.Objective().Value()}")
+        print(f"X1 (每班次人数平均偏差): {aux_vars_x1_return}")
+        
+        # 输出各个优化目标的详细信息
+        tech_old_array = [is_old_array[i] * is_tech_array[i] for i in range(N)]
+        tech_new_array = [is_new_array[i] * is_tech_array[i] for i in range(N)]
+        hr_new_array = [is_new_array[i] * is_hr_array[i] for i in range(N)]
+        
+        x2_value = sum(sum(variables[i][j].solution_value() * tech_old_array[i] for i in range(N)) for j in range(M)) / M
+        x3_value = sum(sum(variables[i][j].solution_value() * tech_new_array[i] for i in range(N)) for j in range(M)) / M
+        x4_value = sum(sum(variables[i][j].solution_value() * hr_new_array[i] for i in range(N)) for j in range(M)) / M
+        
+        aux_vars_x2_return = sum([aux_vars_x2[j].solution_value() for j in range(M)]) / M
+        aux_vars_x3_return = sum([aux_vars_x3[j].solution_value() for j in range(M)]) / M
+        aux_vars_x4_return = sum([aux_vars_x4[j].solution_value() for j in range(M)]) / M
+
+        print(f"X2 (每班平均技术部老人): {x2_value}, 缺口平均: {aux_vars_x2_return}")
+        print(f"X3 (每班平均技术部小朋友): {x3_value}, 缺口平均: {aux_vars_x3_return}")
+        print(f"X4 (每班平均人资部小朋友): {x4_value}, 缺口平均: {aux_vars_x4_return}")
+
+        if aux_vars_x5:
+            x5_value = sum(sum(aux_vars_x5[j][k].solution_value() for k in range(num_departments)) for j in range(M))
+            print(f"X5 (部门人数偏差): {x5_value}")
+        
+        return variables_return
+    else:
+        print("No solution found.")
+        return None
    
\ No newline at end of file
diff --git a/utils.py b/utils.py
index fc4f0d0..9f1278f 100644
--- a/utils.py
+++ b/utils.py
@@ -1,143 +1,143 @@
-import pandas as pd
-from datetime import datetime
-from random import choice
-
-# 对于表格每一列：
-col_timestamp = 1       # i = 1    -> 时间戳
-col_name = 6            # i = 6    -> 姓名
-col_department = 7      # i = 7    -> 部门编号
-col_member = 8          # i = 8    -> 老人 / 小朋友
-col_dutystart = 9       # i = 9    -> 值班开始时间
-col_dutyend = 28        # i = 28   -> 值班结束时间
-col_dutyfreq = 29       # i = 29   -> 想要值班的次数
-
-index_to_departments = {     # 部门编号和部门名称的对应关系
-    1: "电脑部",
-    2: "电器部",
-    3: "人资部",
-    4: "财外部",
-    5: "文宣部"
-}
-
-index_to_type = {            # 老人 / 小朋友和对应的编号
-    1: "老人",
-    2: "小朋友"
-}   
-
-def read_excel(file_path):
-    df = pd.read_excel(file_path)
-
-    # 待返回的所有信息。N 为学生人数，M 为班次数
-    index_to_name_dict = {}      # 长度为 N 的字典，包含数组下标和学生姓名的对应关系
-    preference_mat = []          # N x M 的二维数组，表示每位学生对每个班次的满意度
-    depart_mat = []              # N x D 的二维数组，表示每位学生所属的部门（D 为部门数量）
-    want_num_array = []          # 长度为 N 的数组，表示每位学生想要值班的次数
-    is_new_array = []            # 长度为 N 的数组，表示每位学生是否是小朋友
-
-    '''
-        考虑到原始填表信息中可能有某位同学多次提交的记录，先过滤一下冗余信息
-    '''
-    all_data = {}  # 储存过滤后的数据
-    time_format = "%Y/%m/%d %H:%M:%S"
-    for index, row in df.iterrows():
-        name = row.iloc[col_name]
-        time_this = datetime.strptime(row.iloc[col_timestamp], time_format)
-        if name not in all_data or time_this > datetime.strptime(all_data[name][col_timestamp], time_format):
-            all_data[name] = row.tolist()
-
-    for index, (name, info_list) in enumerate(all_data.items()):
-        index_to_name_dict[index] = name
-        preference_mat.append(info_list[col_dutystart:col_dutyend + 1])
-        depart_mat.append([1 if info_list[col_department] == i else 0 for i in range(1, 6)])
-        want_num_array.append(info_list[col_dutyfreq])
-        is_new_array.append(index_to_type[info_list[col_member]] == "小朋友")
-
-    return all_data, index_to_name_dict, preference_mat, depart_mat, want_num_array, is_new_array
-
-def save_to_excel(variables, all_data, index_to_name_dict, file_path):
-
-    # 收集每个班次的值班人员信息
-    all_result = []
-    max_single_class_num = 0
-    
-    for j in range(len(variables[0])):
-        on_duty_list = []
-        hr_new_index = []
-        none_tech_new_index = []
-        new_index = []
-        all_index = []
-        
-        for i in range(len(variables)):
-            if variables[i][j] == 1:
-                name = index_to_name_dict[i]
-                department = index_to_departments[all_data[name][col_department]]
-                member_type = index_to_type[all_data[name][col_member]]
-                
-                single_stu_info = {
-                    "name": name,
-                    "department": department,
-                    "type": member_type,
-                    "duty_monitor": False
-                }
-                on_duty_list.append(single_stu_info)
-                
-                idx = len(all_index)
-                all_index.append(idx)
-                if member_type == "小朋友":
-                    new_index.append(idx)
-                    if department in ["人资部", "财外部", "文宣部"]:
-                        none_tech_new_index.append(idx)
-                        if department == "人资部":
-                            hr_new_index.append(idx)
-
-        # 选择值班组长（优先级：人资部小朋友 > 非技术部小朋友 > 小朋友 > 所有人）
-        if hr_new_index:
-            duty_monitor_index = choice(hr_new_index)
-        elif none_tech_new_index:
-            duty_monitor_index = choice(none_tech_new_index)
-        elif new_index:
-            duty_monitor_index = choice(new_index)
-        else:
-            duty_monitor_index = choice(all_index)
-        on_duty_list[duty_monitor_index]["duty_monitor"] = True
-
-        on_duty_list.sort(key=lambda x: x["department"])
-        all_result.append(on_duty_list)
-        max_single_class_num = max(max_single_class_num, len(on_duty_list))
-    
-
-    from openpyxl import Workbook
-    from openpyxl.styles import PatternFill
-
-    wb = Workbook()
-    ws = wb.active if wb.active else wb.create_sheet(title="Sheet1")
-
-    # 设置表头
-    weekdays = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
-    for idx, day in enumerate(weekdays, start=2):
-        col = chr(ord('A') + idx - 1)
-        ws[f'{col}1'] = day
-        ws.column_dimensions[col].width = 25
-
-    # 设置班次标题和起始行
-    shift_names = ["第一班", "第二班", "第三班"]
-    start_index = []
-    for shift_idx, shift_name in enumerate(shift_names):
-        start_row = 2 + shift_idx * (max_single_class_num + 1)
-        end_row = start_row + max_single_class_num
-        ws.merge_cells(f'A{start_row}:A{end_row}')
-        ws[f'A{start_row}'] = shift_name
-        start_index.append(start_row)
-
-    # 填写值班人员信息
-    yellow_fill = PatternFill(fill_type='solid', start_color='FFFF00', end_color='FFFF00')
-    for duty_index, duty in enumerate(all_result):
-        col = chr(ord('B') + duty_index // 3)
-        for stu_index, stu in enumerate(duty):
-            row = start_index[duty_index % 3] + stu_index
-            cell = ws[f'{col}{row}']
-            cell.value = f"{stu['name']} {stu['department']} {stu['type']}"
-            if stu['duty_monitor']:
-                cell.fill = yellow_fill
-
+import pandas as pd
+from datetime import datetime
+from random import choice
+
+# 对于表格每一列：
+col_timestamp = 1       # i = 1    -> 时间戳
+col_name = 6            # i = 6    -> 姓名
+col_department = 7      # i = 7    -> 部门编号
+col_member = 8          # i = 8    -> 老人 / 小朋友
+col_dutystart = 9       # i = 9    -> 值班开始时间
+col_dutyend = 28        # i = 28   -> 值班结束时间
+col_dutyfreq = 29       # i = 29   -> 想要值班的次数
+
+index_to_departments = {     # 部门编号和部门名称的对应关系
+    1: "电脑部",
+    2: "电器部",
+    3: "人资部",
+    4: "财外部",
+    5: "文宣部"
+}
+
+index_to_type = {            # 老人 / 小朋友和对应的编号
+    1: "老人",
+    2: "小朋友"
+}   
+
+def read_excel(file_path):
+    df = pd.read_excel(file_path)
+
+    # 待返回的所有信息。N 为学生人数，M 为班次数
+    index_to_name_dict = {}      # 长度为 N 的字典，包含数组下标和学生姓名的对应关系
+    preference_mat = []          # N x M 的二维数组，表示每位学生对每个班次的满意度
+    depart_mat = []              # N x D 的二维数组，表示每位学生所属的部门（D 为部门数量）
+    want_num_array = []          # 长度为 N 的数组，表示每位学生想要值班的次数
+    is_new_array = []            # 长度为 N 的数组，表示每位学生是否是小朋友
+
+    '''
+        考虑到原始填表信息中可能有某位同学多次提交的记录，先过滤一下冗余信息
+    '''
+    all_data = {}  # 储存过滤后的数据
+    time_format = "%Y/%m/%d %H:%M:%S"
+    for index, row in df.iterrows():
+        name = row.iloc[col_name]
+        time_this = datetime.strptime(row.iloc[col_timestamp], time_format)
+        if name not in all_data or time_this > datetime.strptime(all_data[name][col_timestamp], time_format):
+            all_data[name] = row.tolist()
+
+    for index, (name, info_list) in enumerate(all_data.items()):
+        index_to_name_dict[index] = name
+        preference_mat.append(info_list[col_dutystart:col_dutyend + 1])
+        depart_mat.append([1 if info_list[col_department] == i else 0 for i in range(1, 6)])
+        want_num_array.append(info_list[col_dutyfreq])
+        is_new_array.append(index_to_type[info_list[col_member]] == "小朋友")
+
+    return all_data, index_to_name_dict, preference_mat, depart_mat, want_num_array, is_new_array
+
+def save_to_excel(variables, all_data, index_to_name_dict, file_path):
+
+    # 收集每个班次的值班人员信息
+    all_result = []
+    max_single_class_num = 0
+    
+    for j in range(len(variables[0])):
+        on_duty_list = []
+        hr_new_index = []
+        none_tech_new_index = []
+        new_index = []
+        all_index = []
+        
+        for i in range(len(variables)):
+            if variables[i][j] == 1:
+                name = index_to_name_dict[i]
+                department = index_to_departments[all_data[name][col_department]]
+                member_type = index_to_type[all_data[name][col_member]]
+                
+                single_stu_info = {
+                    "name": name,
+                    "department": department,
+                    "type": member_type,
+                    "duty_monitor": False
+                }
+                on_duty_list.append(single_stu_info)
+                
+                idx = len(all_index)
+                all_index.append(idx)
+                if member_type == "小朋友":
+                    new_index.append(idx)
+                    if department in ["人资部", "财外部", "文宣部"]:
+                        none_tech_new_index.append(idx)
+                        if department == "人资部":
+                            hr_new_index.append(idx)
+
+        # 选择值班组长（优先级：人资部小朋友 > 非技术部小朋友 > 小朋友 > 所有人）
+        if hr_new_index:
+            duty_monitor_index = choice(hr_new_index)
+        elif none_tech_new_index:
+            duty_monitor_index = choice(none_tech_new_index)
+        elif new_index:
+            duty_monitor_index = choice(new_index)
+        else:
+            duty_monitor_index = choice(all_index)
+        on_duty_list[duty_monitor_index]["duty_monitor"] = True
+
+        on_duty_list.sort(key=lambda x: x["department"])
+        all_result.append(on_duty_list)
+        max_single_class_num = max(max_single_class_num, len(on_duty_list))
+    
+
+    from openpyxl import Workbook
+    from openpyxl.styles import PatternFill
+
+    wb = Workbook()
+    ws = wb.active if wb.active else wb.create_sheet(title="Sheet1")
+
+    # 设置表头
+    weekdays = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
+    for idx, day in enumerate(weekdays, start=2):
+        col = chr(ord('A') + idx - 1)
+        ws[f'{col}1'] = day
+        ws.column_dimensions[col].width = 25
+
+    # 设置班次标题和起始行
+    shift_names = ["第一班", "第二班", "第三班"]
+    start_index = []
+    for shift_idx, shift_name in enumerate(shift_names):
+        start_row = 2 + shift_idx * (max_single_class_num + 1)
+        end_row = start_row + max_single_class_num
+        ws.merge_cells(f'A{start_row}:A{end_row}')
+        ws[f'A{start_row}'] = shift_name
+        start_index.append(start_row)
+
+    # 填写值班人员信息
+    yellow_fill = PatternFill(fill_type='solid', start_color='FFFF00', end_color='FFFF00')
+    for duty_index, duty in enumerate(all_result):
+        col = chr(ord('B') + duty_index // 3)
+        for stu_index, stu in enumerate(duty):
+            row = start_index[duty_index % 3] + stu_index
+            cell = ws[f'{col}{row}']
+            cell.value = f"{stu['name']} {stu['department']} {stu['type']}"
+            if stu['duty_monitor']:
+                cell.fill = yellow_fill
+
     wb.save(file_path)
\ No newline at end of file
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index e7cd98d..b286e75 100644
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diff --git a/使用手册.md b/使用手册.md
index b81b46a..c17b5f5 100644
--- a/使用手册.md
+++ b/使用手册.md
@@ -1,100 +1,100 @@
-# EVA 值班排班工具使用手册 - v2.0.0
-
-## 前言
-这是浙江大学学生E志者协会“排班工具软件”的使用手册，将简要地介绍该软件的使用方法和注意事项。请注意，这是面向使用者而非开发者的手册，如果想了解该工具的开发流程和所使用的技术，请移步至[此仓库](https://git.zjueva.net/happywind/EVA_duty_arrange_tool)的说明手册。
-
-## 软件运行环境 & 基本情况
-- 本软件只能在 Windows 系统上运行。
-- 本软件在部分版本的 Win10 和 Win11 系统上均测试过，能够正常运行。但是不排除可能出现缺少相关 dll 文件而无法运行的情况，如果出现请联系开发者解决。
-- 本软件将排班问题建模为**多目标组合优化问题**，使用 Google OR-Tools 的 SCIP 求解器求解。
-- 优化目标包括：
-  1. 各班次人数尽可能平均
-  2. 每班技术部老人数量接近期望值（3人）
-  3. 每班技术部小朋友数量接近期望值（4人）
-  4. 每班人资部小朋友数量接近期望值（1人）
-  5. 每班各部门人数尽可能均衡
-- 在指定的限制条件下，若有解，则得到的一定是**全局最优解**。
-
-## 软件使用方法
-
-双击运行可执行文件，会看到如下软件界面：
-
-<div style="text-align: center;">
-    <img src="./pics/screen.jpg" style="width:50%;">
-</div>
-
-进行以下三个步骤：
-
-1. 选择待输入的问卷星结果 Excel 表格。对于选择的 Excel 表格，有以下**格式要求**：
-   - 第 1 行，即表头的名称不会影响软件运行，但需要保证每一列的数据的格式和含义正确
-   - 第 1 列为序号，不会用到，但需要有
-   - 第 2 列为提交答卷时间，格式为 "year/month/day hour:min:sec"
-   - 第 3 列为填表所用时间，不会用到，但需要有
-   - 第 4 列为来源，不会用到，但需要有
-   - 第 5 列为来源详情，不会用到，但需要有
-   - 第 6 列为 IP，不会用到，但需要有
-   - 第 7 列为姓名，这是每一位同学的唯一标识
-   - 第 8 列为部门序号，用一个数字表示，映射关系如下：
-        ```python
-        index_to_departments = {     # 部门编号和部门名称的对应关系
-            1: "电脑部",
-            2: "电器部",
-            3: "人资部",
-            4: "财外部",
-            5: "文宣部"
-        }
-        ```
-    - 第 9 列标识老人还是小朋友，用一个数字表示，映射关系如下：
-        ```python
-        index_to_type = {            # 老人/小朋友和对应的编号
-            1: "老人",
-            2: "小朋友"
-        }  
-        ```
-    - 第 10 ~ 29 列，表示对于每个班次的意愿（1 代表有时间，0 代表没时间）。因为从周一第一班到周日第二班一共有 20 班，所以总共有 20 列。
-    - 第 30 列，表示愿意排几班，用一个数字表示。
-  
-    **我们提供了一个名为“问卷星结果_样例输入.xlsx”的文件，以作为正确输入格式的参考，以及一个名为“问卷星结果_较极端案例.xlsx”的文件，以作为程序运行的测试。**
-    
-    **本软件可以自动处理同一名同学多次填写问卷的情况**，只要保证多次填写中“姓名”保持一致即可，软件将会自动选择最后一次填写的结果作为最终意愿。由于“姓名”是每位同学的唯一标识符，如果出现了某位同学“姓名”填错的情况，需要手动删除该姓名的记录。
-    
-    如果出现了**两位同学撞名的情况**，需要在填表时“姓名”后加上后缀以作区分，比如“王五1”和“王五2”，否则就会当作一个人来看待。
-
-2. 设置优化参数
-   
-   软件界面的 Step 2 部分包含两类参数：
-   
-   ### 2.1 权重参数（始终可设置）
-   
-   软件提供了 5 个优化目标的权重设置，用于调整各个目标的重要程度：
-   - **目标 1 权重**：每班人数平均程度（默认 1.0）
-   - **目标 2 权重**：每班技术部老人数量（默认 1.0）
-   - **目标 3 权重**：每班技术部小朋友数量（默认 1.0）
-   - **目标 4 权重**：每班人资部小朋友数量（默认 0.5）
-   - **目标 5 权重**：每班部门平均程度（默认 0.5）
-   
-   权重值范围建议为 0.0 - 5.0，权重越大表示该目标越重要。所有权重参数均已归一化处理。
-   
-   ### 2.2 约束条件（自动/手动模式）
-   
-   **自动调参模式（推荐）：**
-   - 勾选"自动调参模式"复选框
-   - 软件会自动尝试不同的约束参数组合，实时显示尝试过程
-   - 从严格到宽松逐步调整参数，找到第一个可行解
-   - 无需手动设置约束条件，约束输入框会被禁用
-   
-   **手动模式：**
-   - 取消勾选"自动调参模式"
-   - 可手动设置以下约束条件：
-     - **每班次人数**：最少和最多人数（建议 5-8 人）
-     - **每班次电脑或电器的老人数**：技术部老人数量范围（建议最多 2 人）
-     - **每班次老人数**：所有老人数量范围（建议最少 1 人）
-     - **每班次小朋友数**：所有小朋友数量范围（建议最少 2 人）
-   - 留空表示该项无限制
-   - **注意**：约束条件设置过严可能导致无解，需要适当放宽限制
-
-3. 开始排班并查看进度
-
-    点击"开始排班！"按钮开始计算：
-    - 输出结果以 Excel 表格形式保存，文件名为 `result_<当前时间戳>.xlsx`
-    - **在输出结果中，组长会被标黄**
+# EVA 值班排班工具使用手册 - v2.0.0
+
+## 前言
+这是浙江大学学生E志者协会“排班工具软件”的使用手册，将简要地介绍该软件的使用方法和注意事项。请注意，这是面向使用者而非开发者的手册，如果想了解该工具的开发流程和所使用的技术，请移步至[此仓库](https://git.zjueva.net/happywind/EVA_duty_arrange_tool)的说明手册。
+
+## 软件运行环境 & 基本情况
+- 本软件只能在 Windows 系统上运行。
+- 本软件在部分版本的 Win10 和 Win11 系统上均测试过，能够正常运行。但是不排除可能出现缺少相关 dll 文件而无法运行的情况，如果出现请联系开发者解决。
+- 本软件将排班问题建模为**多目标组合优化问题**，使用 Google OR-Tools 的 SCIP 求解器求解。
+- 优化目标包括：
+  1. 各班次人数尽可能平均
+  2. 每班技术部老人数量接近期望值（3人）
+  3. 每班技术部小朋友数量接近期望值（4人）
+  4. 每班人资部小朋友数量接近期望值（1人）
+  5. 每班各部门人数尽可能均衡
+- 在指定的限制条件下，若有解，则得到的一定是**全局最优解**。
+
+## 软件使用方法
+
+双击运行可执行文件，会看到如下软件界面：
+
+<div style="text-align: center;">
+    <img src="./pics/screen.jpg" style="width:50%;">
+</div>
+
+进行以下三个步骤：
+
+1. 选择待输入的问卷星结果 Excel 表格。对于选择的 Excel 表格，有以下**格式要求**：
+   - 第 1 行，即表头的名称不会影响软件运行，但需要保证每一列的数据的格式和含义正确
+   - 第 1 列为序号，不会用到，但需要有
+   - 第 2 列为提交答卷时间，格式为 "year/month/day hour:min:sec"
+   - 第 3 列为填表所用时间，不会用到，但需要有
+   - 第 4 列为来源，不会用到，但需要有
+   - 第 5 列为来源详情，不会用到，但需要有
+   - 第 6 列为 IP，不会用到，但需要有
+   - 第 7 列为姓名，这是每一位同学的唯一标识
+   - 第 8 列为部门序号，用一个数字表示，映射关系如下：
+        ```python
+        index_to_departments = {     # 部门编号和部门名称的对应关系
+            1: "电脑部",
+            2: "电器部",
+            3: "人资部",
+            4: "财外部",
+            5: "文宣部"
+        }
+        ```
+    - 第 9 列标识老人还是小朋友，用一个数字表示，映射关系如下：
+        ```python
+        index_to_type = {            # 老人/小朋友和对应的编号
+            1: "老人",
+            2: "小朋友"
+        }  
+        ```
+    - 第 10 ~ 29 列，表示对于每个班次的意愿（1 代表有时间，0 代表没时间）。因为从周一第一班到周日第二班一共有 20 班，所以总共有 20 列。
+    - 第 30 列，表示愿意排几班，用一个数字表示。
+  
+    **我们提供了一个名为“问卷星结果_样例输入.xlsx”的文件，以作为正确输入格式的参考，以及一个名为“问卷星结果_较极端案例.xlsx”的文件，以作为程序运行的测试。**
+    
+    **本软件可以自动处理同一名同学多次填写问卷的情况**，只要保证多次填写中“姓名”保持一致即可，软件将会自动选择最后一次填写的结果作为最终意愿。由于“姓名”是每位同学的唯一标识符，如果出现了某位同学“姓名”填错的情况，需要手动删除该姓名的记录。
+    
+    如果出现了**两位同学撞名的情况**，需要在填表时“姓名”后加上后缀以作区分，比如“王五1”和“王五2”，否则就会当作一个人来看待。
+
+2. 设置优化参数
+   
+   软件界面的 Step 2 部分包含两类参数：
+   
+   ### 2.1 权重参数（始终可设置）
+   
+   软件提供了 5 个优化目标的权重设置，用于调整各个目标的重要程度：
+   - **目标 1 权重**：每班人数平均程度（默认 1.0）
+   - **目标 2 权重**：每班技术部老人数量（默认 1.0）
+   - **目标 3 权重**：每班技术部小朋友数量（默认 1.0）
+   - **目标 4 权重**：每班人资部小朋友数量（默认 0.5）
+   - **目标 5 权重**：每班部门平均程度（默认 0.5）
+   
+   权重值范围建议为 0.0 - 5.0，权重越大表示该目标越重要。所有权重参数均已归一化处理。
+   
+   ### 2.2 约束条件（自动/手动模式）
+   
+   **自动调参模式（推荐）：**
+   - 勾选"自动调参模式"复选框
+   - 软件会自动尝试不同的约束参数组合，实时显示尝试过程
+   - 从严格到宽松逐步调整参数，找到第一个可行解
+   - 无需手动设置约束条件，约束输入框会被禁用
+   
+   **手动模式：**
+   - 取消勾选"自动调参模式"
+   - 可手动设置以下约束条件：
+     - **每班次人数**：最少和最多人数（建议 5-8 人）
+     - **每班次电脑或电器的老人数**：技术部老人数量范围（建议最多 2 人）
+     - **每班次老人数**：所有老人数量范围（建议最少 1 人）
+     - **每班次小朋友数**：所有小朋友数量范围（建议最少 2 人）
+   - 留空表示该项无限制
+   - **注意**：约束条件设置过严可能导致无解，需要适当放宽限制
+
+3. 开始排班并查看进度
+
+    点击"开始排班！"按钮开始计算：
+    - 输出结果以 Excel 表格形式保存，文件名为 `result_<当前时间戳>.xlsx`
+    - **在输出结果中，组长会被标黄**