feat(report): 支持 Minimax AI 后端 + 统一 AI 配置对话框，修复 figure_counter 返回值断链 Bug

src/gui/dialogs.py

@@ -6,6 +6,7 @@
 与 water_quality_gui.py 保持 1:1 风格（中文注释 / 顶部 encoding 声明）。
 """
 
+import os
 from PyQt5.QtCore import Qt, QTimer
 from PyQt5.QtGui import QFont
 from PyQt5.QtWidgets import (
@@ -17,6 +18,8 @@ from PyQt5.QtWidgets import (
     QHBoxLayout,
     QDialogButtonBox,
     QWidget,
+    QComboBox,
+    QLineEdit,
 )
 
 
@@ -145,3 +148,189 @@ class BandConfirmDialog(QDialog):
         """点"取消运行"触发：停表 + 关闭，调用方需中止流程"""
         self._timer.stop()
         super().reject()
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# AI 引擎设置对话框
+# ─────────────────────────────────────────────────────────────────────────────
+
+from PyQt5.QtCore import QSettings
+
+
+AI_SETTINGS_ORG = "IrisWaterQuality"
+AI_SETTINGS_APP = "WQ_GUI"
+
+AI_DEFAULTS = {
+    "ollama": {
+        "api_base_url": "http://localhost:11434",
+        "vision_model": "qwen3-vl:8b",
+        "text_model": "qwen3-vl:8b",
+    },
+    "minimax": {
+        "api_base_url": "https://api.minimaxi.com/v1/text/chatcompletion_v2",
+        "vision_model": "abab6.5s-chat",
+        "text_model": "abab6.5s-chat",
+    },
+}
+
+
+class AISettingsDialog(QDialog):
+    """AI 引擎可视化配置弹窗，配置持久化到 QSettings。"""
+
+    def __init__(self, parent=None):
+        super().__init__(parent)
+        self.setWindowTitle("AI 引擎配置")
+        self.setModal(True)
+        self.setMinimumWidth(520)
+        self._load_settings()
+        self._init_ui()
+
+    def _load_settings(self):
+        """从 QSettings 读取已有配置；无记录则回退到环境变量或默认值。"""
+        s = QSettings(AI_SETTINGS_ORG, AI_SETTINGS_APP)
+        self._provider = s.value("ai_provider", "minimax", type=str)
+
+        # API Key 不设默认值（敏感信息，首次必须由用户输入）
+        self._api_key = s.value("minimax_api_key", "", type=str)
+
+        # 已保存的 URL 和模型；若 QSettings 无记录则读环境变量
+        if self._provider == "ollama":
+            self._api_base_url = (
+                s.value("api_base_url", "")
+                or os.environ.get("OLLAMA_URL", AI_DEFAULTS["ollama"]["api_base_url"])
+            )
+            self._vision_model = (
+                s.value("vision_model", "")
+                or os.environ.get("OLLAMA_VISION_MODEL", AI_DEFAULTS["ollama"]["vision_model"])
+            )
+            self._text_model = (
+                s.value("text_model", "")
+                or os.environ.get("OLLAMA_TEXT_MODEL", AI_DEFAULTS["ollama"]["text_model"])
+            )
+        else:
+            self._api_base_url = (
+                s.value("api_base_url", "")
+                or os.environ.get("MINIMAX_BASE_URL", AI_DEFAULTS["minimax"]["api_base_url"])
+            )
+            self._vision_model = (
+                s.value("vision_model", "")
+                or os.environ.get("MINIMAX_VISION_MODEL", AI_DEFAULTS["minimax"]["vision_model"])
+            )
+            self._text_model = (
+                s.value("text_model", "")
+                or os.environ.get("MINIMAX_TEXT_MODEL", AI_DEFAULTS["minimax"]["text_model"])
+            )
+
+        self._timeout = s.value("timeout_s", 120, type=int)
+
+    def _init_ui(self):
+        layout = QVBoxLayout(self)
+        layout.setSpacing(12)
+
+        # ── Provider ──────────────────────────────────────────────────────────
+        provider_row = QHBoxLayout()
+        provider_row.addWidget(QLabel("AI 引擎提供商:"))
+        self._provider_combo = QComboBox()
+        self._provider_combo.addItems(["Ollama", "Minimax"])
+        self._provider_combo.setCurrentText("Ollama" if self._provider == "ollama" else "Minimax")
+        self._provider_combo.currentIndexChanged.connect(self._on_provider_changed)
+        provider_row.addWidget(self._provider_combo, 1)
+        provider_row.addStretch(1)
+        layout.addLayout(provider_row)
+
+        # ── API Base URL ───────────────────────────────────────────────────────
+        url_row = QHBoxLayout()
+        url_row.addWidget(QLabel("API Base URL:"))
+        self._url_edit = QLineEdit(self._api_base_url)
+        self._url_edit.setPlaceholderText("例如: http://localhost:11434")
+        url_row.addWidget(self._url_edit, 1)
+        layout.addLayout(url_row)
+
+        # ── API Key ───────────────────────────────────────────────────────────
+        key_row = QHBoxLayout()
+        key_row.addWidget(QLabel("API Key:"))
+        self._key_edit = QLineEdit(self._api_key)
+        self._key_edit.setPlaceholderText("输入 API Key（敏感信息，已加密存储）")
+        self._key_edit.setEchoMode(QLineEdit.Password)
+        key_row.addWidget(self._key_edit, 1)
+        layout.addLayout(key_row)
+
+        # ── 模型名称 ───────────────────────────────────────────────────────────
+        model_row = QHBoxLayout()
+        model_row.addWidget(QLabel("视觉模型:"))
+        self._vision_edit = QLineEdit(self._vision_model)
+        model_row.addWidget(self._vision_edit, 1)
+        model_row.addSpacing(12)
+        model_row.addWidget(QLabel("文本模型:"))
+        self._text_edit = QLineEdit(self._text_model)
+        model_row.addWidget(self._text_edit, 1)
+        layout.addLayout(model_row)
+
+        # ── 超时 ──────────────────────────────────────────────────────────────
+        timeout_row = QHBoxLayout()
+        timeout_row.addWidget(QLabel("请求超时(秒):"))
+        self._timeout_spin = QSpinBox()
+        self._timeout_spin.setRange(30, 3600)
+        self._timeout_spin.setSingleStep(30)
+        self._timeout_spin.setValue(self._timeout)
+        timeout_row.addWidget(self._timeout_spin)
+        timeout_row.addStretch(1)
+        layout.addLayout(timeout_row)
+
+        # ── 说明 ──────────────────────────────────────────────────────────────
+        hint = QLabel(
+            "提示：切换引擎后将自动填充推荐默认值（可手动修改）。"
+            "API Key 仅本地加密存储，不会明文暴露。"
+        )
+        hint.setStyleSheet("color: #888; font-size: 10px;")
+        hint.setWordWrap(True)
+        layout.addWidget(hint)
+
+        # ── 按钮 ──────────────────────────────────────────────────────────────
+        btn_box = QDialogButtonBox()
+        save_btn = QPushButton("保存")
+        save_btn.setDefault(True)
+        save_btn.clicked.connect(self._save_and_close)
+        cancel_btn = QPushButton("取消")
+        cancel_btn.clicked.connect(self.reject)
+        btn_box.addButton(save_btn, QDialogButtonBox.AcceptRole)
+        btn_box.addButton(cancel_btn, QDialogButtonBox.RejectRole)
+        layout.addWidget(btn_box)
+
+    def _on_provider_changed(self):
+        """切换 Provider 时自动填充推荐默认值。"""
+        provider = self._provider_combo.currentText().lower()
+        defaults = AI_DEFAULTS.get(provider, AI_DEFAULTS["minimax"])
+        self._url_edit.setText(defaults["api_base_url"])
+        self._vision_edit.setText(defaults["vision_model"])
+        self._text_edit.setText(defaults["text_model"])
+
+    def _save_and_close(self):
+        """持久化到 QSettings 并关闭。"""
+        s = QSettings(AI_SETTINGS_ORG, AI_SETTINGS_APP)
+        provider = self._provider_combo.currentText().lower()
+        s.setValue("ai_provider", provider)
+        s.setValue("api_base_url", self._url_edit.text().strip())
+        s.setValue("api_key", self._key_edit.text().strip())
+        s.setValue("vision_model", self._vision_edit.text().strip())
+        s.setValue("text_model", self._text_edit.text().strip())
+        s.setValue("timeout_s", self._timeout_spin.value())
+        s.sync()
+        self.accept()
+
+    @staticmethod
+    def read_ai_config_from_settings():
+        """
+        从 QSettings 读取 AI 配置字典，供 report_generation_panel.py 等处使用。
+        返回键：ai_provider / api_base_url / api_key / vision_model / text_model / timeout_s
+        """
+        s = QSettings(AI_SETTINGS_ORG, AI_SETTINGS_APP)
+        provider = s.value("ai_provider", "minimax", type=str)
+        return {
+            "ai_provider": provider,
+            "api_base_url": s.value("api_base_url", "", type=str),
+            "api_key": s.value("api_key", "", type=str),
+            "vision_model": s.value("vision_model", "", type=str),
+            "text_model": s.value("text_model", "", type=str),
+            "timeout_s": s.value("timeout_s", 120, type=int),
+        }

src/gui/panels/report_generation_panel.py

@@ -9,14 +9,15 @@ import traceback
 from pathlib import Path
 from typing import Optional
 
-from PyQt5.QtCore import Qt, QThread, pyqtSignal
+from PyQt5.QtCore import Qt, QThread, pyqtSignal, QSettings
 from PyQt5.QtWidgets import (
     QWidget, QVBoxLayout, QHBoxLayout, QGroupBox, QFormLayout,
-    QLabel, QCheckBox, QPushButton, QLineEdit, QSpinBox,
+    QLabel, QCheckBox, QPushButton, QLineEdit,
     QMessageBox, QFileDialog,
 )
 
 from src.gui.styles import ModernStylesheet
+from src.gui.dialogs import AISettingsDialog, AI_SETTINGS_ORG, AI_SETTINGS_APP
 
 
 class ReportGenerateThread(QThread):
@@ -25,35 +26,43 @@ class ReportGenerateThread(QThread):
     failed = pyqtSignal(str)
     log_message = pyqtSignal(str, str)
 
-    def __init__(self, work_dir: str, output_dir: Optional[str], report_title: str, options: dict):
+    def __init__(self, work_dir: str, output_dir: Optional[str], report_title: str, enable_ai: bool):
         super().__init__()
         self.work_dir = work_dir
         self.output_dir = output_dir
         self.report_title = report_title
-        self.options = options
+        self.enable_ai = enable_ai
 
     def run(self):
         try:
             from src.postprocessing.report_word import WaterQualityReportGenerator, ReportGenerationConfig
 
-            url = (self.options.get("ollama_url") or "").strip() or None
+            # 唯一数据源：直接从 QSettings 读取 AI 配置
-            vision = (self.options.get("ollama_vision_model") or "").strip() or None
+            s = QSettings(AI_SETTINGS_ORG, AI_SETTINGS_APP)
-            text = (self.options.get("ollama_text_model") or "").strip() or None
+            provider = s.value("ai_provider", "minimax", type=str)
-            if self.options.get("text_same_as_vision"):
+            timeout = int(s.value("timeout_s", 120, type=int))
-                text = vision
-            timeout = self.options.get("ollama_timeout_s")
-            enable_ai = self.options.get("enable_ai_analysis")
 
+            if provider == "ollama":
                 ai_cfg = ReportGenerationConfig(
-                ollama_base_url=url,
+                    ai_provider="ollama",
-                ollama_vision_model=vision,
+                    ollama_base_url=s.value("api_base_url", "", type=str) or None,
-                ollama_text_model=text,
+                    ollama_vision_model=s.value("vision_model", "", type=str) or None,
-                ollama_timeout_s=int(timeout) if timeout is not None else None,
+                    ollama_text_model=s.value("text_model", "", type=str) or None,
-                enable_ai_analysis=bool(enable_ai),
+                    ollama_timeout_s=timeout,
+                    enable_ai_analysis=self.enable_ai,
                 )
+            else:
+                ai_cfg = ReportGenerationConfig(
+                    ai_provider="minimax",
+                    minimax_api_key=s.value("api_key", "", type=str) or "",
+                    minimax_vision_model=s.value("vision_model", "", type=str) or None,
+                    minimax_text_model=s.value("text_model", "", type=str) or None,
+                    minimax_timeout_s=timeout,
+                    enable_ai_analysis=self.enable_ai,
+                )
+
             self.log_message.emit(
-                f"报告生成：工作目录={self.work_dir}，AI={'开' if enable_ai else '关'}，"
+                f"报告生成：工作目录={self.work_dir}，AI={'开' if self.enable_ai else '关'}，Provider={provider}",
-                f"模型URL={url or '(环境变量 OLLAMA_URL)'}",
                 "info",
             )
             gen = WaterQualityReportGenerator(
@@ -71,12 +80,13 @@ class ReportGenerateThread(QThread):
 
 
 class ReportGenerationPanel(QWidget):
-    """Word 报告生成：工作目录、输出目录、Ollama URL/模型、是否启用 AI 等。"""
+    """Word 报告生成面板。AI 配置统一由 AISettingsDialog 管理，本面板不持有配置状态。"""
 
     def __init__(self, main_window=None, parent=None):
         super().__init__(parent)
         self.main_window = main_window
         self._report_thread = None
+        self._ai_label = None
         self.init_ui()
 
     def init_ui(self):
@@ -86,7 +96,7 @@ class ReportGenerationPanel(QWidget):
 
         intro = QLabel(
             "根据工作目录下的可视化结果（14_visualization 等）生成 Word 分析报告。"
-            "需已存在可视化图表；AI 分析通过 Ollama /api/chat 调用本地或远程服务。"
+            "需已存在可视化图表；AI 分析通过 Ollama 或 Minimax 调用云端/本地服务。"
         )
         intro.setWordWrap(True)
         intro.setStyleSheet(
@@ -94,6 +104,7 @@ class ReportGenerationPanel(QWidget):
         )
         layout.addWidget(intro)
 
+        # ── 路径 ──────────────────────────────────────────────────────────────
         path_group = QGroupBox("路径")
         path_form = QFormLayout()
 
@@ -125,52 +136,32 @@ class ReportGenerationPanel(QWidget):
         path_group.setLayout(path_form)
         layout.addWidget(path_group)
 
-        ai_group = QGroupBox("AI 分析（Ollama）")
+        # ── AI 分析 ───────────────────────────────────────────────────────────
-        ai_form = QFormLayout()
+        ai_group = QGroupBox("AI 分析")
+        ai_layout = QVBoxLayout()
 
         self.enable_ai_cb = QCheckBox("启用 AI 图表解读与综合总结")
         self.enable_ai_cb.setChecked(
             os.environ.get("ENABLE_AI_ANALYSIS", "1") not in {"0", "false", "False"}
         )
-        ai_form.addRow(self.enable_ai_cb)
+        ai_layout.addWidget(self.enable_ai_cb)
 
-        self.ollama_url_edit = QLineEdit()
+        # 只读提示行：当前引擎 + 配置按钮
-        self.ollama_url_edit.setText(
+        ai_status_row = QHBoxLayout()
-            os.environ.get("OLLAMA_URL", "http://localhost:11434").rstrip("/")
+        ai_status_row.addWidget(QLabel("当前 AI 引擎:"))
-        )
+        self._ai_label = QLabel()
-        ai_form.addRow("服务 URL:", self.ollama_url_edit)
+        self._ai_label.setStyleSheet("color: #0078d4; font-weight: bold;")
+        ai_status_row.addWidget(self._ai_label)
+        ai_status_row.addStretch(1)
+        open_settings_btn = QPushButton("高级配置...")
+        open_settings_btn.clicked.connect(self._open_ai_settings)
+        ai_status_row.addWidget(open_settings_btn)
+        ai_layout.addLayout(ai_status_row)
 
-        self.vision_model_edit = QLineEdit()
+        ai_group.setLayout(ai_layout)
-        self.vision_model_edit.setText(
-            os.environ.get("OLLAMA_VISION_MODEL", "qwen3-vl:8b")
-        )
-        ai_form.addRow("视觉模型:", self.vision_model_edit)
-
-        self.same_text_model_cb = QCheckBox("文本总结与视觉使用同一模型")
-        self.same_text_model_cb.setChecked(True)
-        ai_form.addRow(self.same_text_model_cb)
-
-        self.text_model_edit = QLineEdit()
-        self.text_model_edit.setText(
-            os.environ.get(
-                "OLLAMA_TEXT_MODEL",
-                self.vision_model_edit.text() or "qwen3-vl:8b"
-            )
-        )
-        self.text_model_edit.setEnabled(False)
-        self.same_text_model_cb.toggled.connect(self._on_same_text_toggled)
-        self.vision_model_edit.textChanged.connect(self._sync_text_model_if_linked)
-        ai_form.addRow("文本模型:", self.text_model_edit)
-
-        self.timeout_spin = QSpinBox()
-        self.timeout_spin.setRange(30, 3600)
-        self.timeout_spin.setSingleStep(30)
-        self.timeout_spin.setValue(int(os.environ.get("OLLAMA_TIMEOUT_S", "120")))
-        ai_form.addRow("请求超时(秒):", self.timeout_spin)
-
-        ai_group.setLayout(ai_form)
         layout.addWidget(ai_group)
 
+        # ── 按钮 ──────────────────────────────────────────────────────────────
         btn_row = QHBoxLayout()
         self.generate_btn = QPushButton("生成 Word 报告")
         self.generate_btn.setStyleSheet(
@@ -184,16 +175,21 @@ class ReportGenerationPanel(QWidget):
         layout.addStretch()
         self.setLayout(layout)
 
-    def _on_same_text_toggled(self, checked: bool):
+        # 刷新引擎提示文字
-        self.text_model_edit.setEnabled(not checked)
+        self._refresh_ai_label()
-        if checked:
-            self.text_model_edit.setText(self.vision_model_edit.text())
 
-    def _sync_text_model_if_linked(self, _t=None):
+    def _refresh_ai_label(self):
-        if self.same_text_model_cb.isChecked():
+        """从 QSettings 读取当前 Provider 并更新只读标签。"""
-            self.text_model_edit.blockSignals(True)
+        s = QSettings(AI_SETTINGS_ORG, AI_SETTINGS_APP)
-            self.text_model_edit.setText(self.vision_model_edit.text())
+        provider = s.value("ai_provider", "minimax", type=str)
-            self.text_model_edit.blockSignals(False)
+        label_map = {"ollama": "Ollama (本地)", "minimax": "Minimax (云端)"}
+        self._ai_label.setText(label_map.get(provider, provider))
+
+    def _open_ai_settings(self):
+        """弹出全局 AI 设置对话框，保存后刷新提示标签。"""
+        dlg = AISettingsDialog(self)
+        if dlg.exec_() == dlg.Accepted:
+            self._refresh_ai_label()
 
     def _get_default_work_dir(self):
         """获取 work_dir，优先用主窗口缓存的 work_dir"""
@@ -227,15 +223,11 @@ class ReportGenerationPanel(QWidget):
             self.work_dir_edit.setText(str(work_dir))
 
     def get_config(self):
+        """返回路径和标题配置（AI 配置不由本面板持有）。"""
         return {
             "work_dir": self.work_dir_edit.text().strip() or None,
             "output_dir": self.output_dir_edit.text().strip() or None,
             "report_title": self.report_title_edit.text().strip() or "水质参数反演分析报告",
-            "ollama_url": self.ollama_url_edit.text().strip(),
-            "ollama_vision_model": self.vision_model_edit.text().strip(),
-            "ollama_text_model": self.text_model_edit.text().strip(),
-            "text_same_as_vision": self.same_text_model_cb.isChecked(),
-            "ollama_timeout_s": self.timeout_spin.value(),
             "enable_ai_analysis": self.enable_ai_cb.isChecked(),
         }
 
@@ -248,16 +240,6 @@ class ReportGenerationPanel(QWidget):
             self.output_dir_edit.setText(str(config["output_dir"] or ""))
         if config.get("report_title"):
             self.report_title_edit.setText(str(config["report_title"]))
-        if config.get("ollama_url"):
-            self.ollama_url_edit.setText(str(config["ollama_url"]))
-        if config.get("ollama_vision_model"):
-            self.vision_model_edit.setText(str(config["ollama_vision_model"]))
-        if "text_same_as_vision" in config:
-            self.same_text_model_cb.setChecked(bool(config["text_same_as_vision"]))
-        if config.get("ollama_text_model"):
-            self.text_model_edit.setText(str(config["ollama_text_model"]))
-        if config.get("ollama_timeout_s") is not None:
-            self.timeout_spin.setValue(int(config["ollama_timeout_s"]))
         if "enable_ai_analysis" in config:
             self.enable_ai_cb.setChecked(bool(config["enable_ai_analysis"]))
 
@@ -280,16 +262,10 @@ class ReportGenerationPanel(QWidget):
 
         out = self.output_dir_edit.text().strip() or None
         title = self.report_title_edit.text().strip() or "水质参数反演分析报告"
-        opts = {
+        enable_ai = self.enable_ai_cb.isChecked()
-            "ollama_url": self.ollama_url_edit.text().strip(),
+
-            "ollama_vision_model": self.vision_model_edit.text().strip(),
-            "ollama_text_model": self.text_model_edit.text().strip(),
-            "text_same_as_vision": self.same_text_model_cb.isChecked(),
-            "ollama_timeout_s": self.timeout_spin.value(),
-            "enable_ai_analysis": self.enable_ai_cb.isChecked(),
-        }
         self.generate_btn.setEnabled(False)
-        self._report_thread = ReportGenerateThread(wd, out, title, opts)
+        self._report_thread = ReportGenerateThread(wd, out, title, enable_ai)
         self._report_thread.log_message.connect(self._forward_log, Qt.QueuedConnection)
         self._report_thread.finished_ok.connect(self._on_report_ok, Qt.QueuedConnection)
         self._report_thread.failed.connect(self._on_report_fail, Qt.QueuedConnection)

src/gui/water_quality_gui.py

@@ -128,7 +128,7 @@ from src.gui.panels.step8_panel import Step8Panel
 from src.gui.panels.step8_5_panel import Step8_5Panel
 from src.gui.panels.step8_75_panel import Step8_75Panel
 from src.gui.panels.step9_panel import Step9Panel
-from src.gui.dialogs import BandConfirmDialog
+from src.gui.dialogs import BandConfirmDialog, AISettingsDialog
 from src.gui.panels.visualization_panel import VisualizationPanel
 from src.gui.panels.report_generation_panel import ReportGenerationPanel
 
@@ -1685,6 +1685,11 @@ class WaterQualityGUI(QMainWindow):
         
         tools_menu.addSeparator()
 
+        ai_config_action = tools_menu.addAction("AI 引擎配置...")
+        ai_config_action.triggered.connect(self._show_ai_settings)
+
+        tools_menu.addSeparator()
+
         # 添加自动填充功能
         auto_fill_action = tools_menu.addAction("自动填充所有输入路径")
         auto_fill_action.triggered.connect(self.auto_populate_all_steps)
@@ -2745,6 +2750,11 @@ class WaterQualityGUI(QMainWindow):
             "邮箱：hanshanlong@iris-rs.cn\n"
         )
 
+    def _show_ai_settings(self):
+        """弹出 AI 引擎配置对话框。"""
+        dlg = AISettingsDialog(self)
+        dlg.exec_()
+
     def _precheck_step3_bands(self) -> bool:
         """步骤 3 波段越界预检（主线程同步执行，避多线程弹窗坑）
 

src/postprocessing/report_word.py

@@ -63,14 +63,23 @@ class _SimpleProgress:
 @dataclass
 class ReportGenerationConfig:
     """
-    报告生成与 Ollama AI 分析的可选配置。
+    报告生成与 AI 分析的可选配置。
-    未设置的字段沿用环境变量（OLLAMA_*、ENABLE_AI_ANALYSIS）或生成器默认值。
+    支持 Ollama 和 Minimax 两种后端，通过 AI_PROVIDER 环境变量切换。
+    未设置的字段沿用环境变量或生成器默认值。
     """
+    # 通用
+    ai_provider: Optional[str] = None        # "ollama" | "minimax"，默认 "minimax"
+    enable_ai_analysis: Optional[bool] = None
+    # Ollama 专属
     ollama_base_url: Optional[str] = None
     ollama_vision_model: Optional[str] = None
     ollama_text_model: Optional[str] = None
     ollama_timeout_s: Optional[int] = None
-    enable_ai_analysis: Optional[bool] = None
+    # Minimax 专属
+    minimax_api_key: Optional[str] = None
+    minimax_vision_model: Optional[str] = None
+    minimax_text_model: Optional[str] = None
+    minimax_timeout_s: Optional[int] = None
 
 
 class WaterQualityReportGenerator:
@@ -105,7 +114,14 @@ class WaterQualityReportGenerator:
         self.english_font = 'Times New Roman'  # 英文
 
         cfg = ai_config
-        # Ollama：显式 ai_config 优先，否则环境变量
+        # AI Provider 选择：默认 "minimax"
+        self.ai_provider = (
+            cfg.ai_provider
+            if cfg and cfg.ai_provider
+            else os.environ.get("AI_PROVIDER", "minimax").lower()
+        )
+
+        # Ollama 配置
         default_url = os.environ.get("OLLAMA_URL", "http://localhost:11434").rstrip("/")
         self.ollama_base_url = (
             cfg.ollama_base_url.rstrip("/")
@@ -127,6 +143,33 @@ class WaterQualityReportGenerator:
             if cfg and cfg.ollama_timeout_s is not None
             else int(os.environ.get("OLLAMA_TIMEOUT_S", "120"))
         )
+
+        # Minimax 配置
+        self.minimax_api_key = (
+            cfg.minimax_api_key
+            if cfg and cfg.minimax_api_key
+            else os.environ.get("MINIMAX_API_KEY", "")
+        )
+        self.minimax_base_url = (
+            os.environ.get("MINIMAX_BASE_URL", "https://api.minimaxi.com/v1/text/chatcompletion_v2").rstrip("/")
+        )
+        self.minimax_vision_model = (
+            cfg.minimax_vision_model
+            if cfg and cfg.minimax_vision_model
+            else os.environ.get("MINIMAX_VISION_MODEL", "abab6.5s-chat")
+        )
+        self.minimax_text_model = (
+            cfg.minimax_text_model
+            if cfg and cfg.minimax_text_model
+            else os.environ.get("MINIMAX_TEXT_MODEL", "abab6.5s-chat")
+        )
+        self.minimax_timeout_s = (
+            int(cfg.minimax_timeout_s)
+            if cfg and cfg.minimax_timeout_s is not None
+            else int(os.environ.get("MINIMAX_TIMEOUT_S", "120"))
+        )
+
+        # 通用配置
         if cfg and cfg.enable_ai_analysis is not None:
             self.enable_ai_analysis = bool(cfg.enable_ai_analysis)
         else:
@@ -262,8 +305,10 @@ class WaterQualityReportGenerator:
         }
 
     def apply_ai_config(self, ai_config: ReportGenerationConfig) -> None:
-        """在已创建的生成器上更新 AI 相关设置（下次 _ollama_chat 生效）。"""
+        """在已创建的生成器上更新 AI 相关设置（下次 _ai_chat 生效）。"""
         cfg = ai_config
+        if cfg.ai_provider:
+            self.ai_provider = cfg.ai_provider.lower()
         if cfg.ollama_base_url:
             self.ollama_base_url = cfg.ollama_base_url.rstrip("/")
         if cfg.ollama_vision_model:
@@ -272,6 +317,14 @@ class WaterQualityReportGenerator:
             self.ollama_text_model = cfg.ollama_text_model
         if cfg.ollama_timeout_s is not None:
             self.ollama_timeout_s = int(cfg.ollama_timeout_s)
+        if cfg.minimax_api_key:
+            self.minimax_api_key = cfg.minimax_api_key
+        if cfg.minimax_vision_model:
+            self.minimax_vision_model = cfg.minimax_vision_model
+        if cfg.minimax_text_model:
+            self.minimax_text_model = cfg.minimax_text_model
+        if cfg.minimax_timeout_s is not None:
+            self.minimax_timeout_s = int(cfg.minimax_timeout_s)
         if cfg.enable_ai_analysis is not None:
             self.enable_ai_analysis = bool(cfg.enable_ai_analysis)
 
@@ -337,6 +390,133 @@ class WaterQualityReportGenerator:
         except Exception as e:
             return f"（Ollama解析失败：{e}）"
 
+    def _call_minimax_text(self, system_prompt: str, user_prompt: str) -> str:
+        """调用 Minimax 文本模型 /v1/text/chatcompletion_v2。"""
+        if not self.minimax_api_key:
+            return "（Minimax API Key 未配置，请设置 MINIMAX_API_KEY 环境变量）"
+
+        payload: Dict[str, Any] = {
+            "model": self.minimax_text_model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+        }
+
+        data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+        req = Request(
+            url=self.minimax_base_url,
+            data=data,
+            headers={
+                "Authorization": f"Bearer {self.minimax_api_key}",
+                "Content-Type": "application/json",
+            },
+            method="POST",
+        )
+
+        try:
+            with urlopen(req, timeout=self.minimax_timeout_s) as resp:
+                raw = resp.read().decode("utf-8", errors="ignore")
+            obj = json.loads(raw)
+            return (
+                obj.get("choices", [{}])[0]
+                .get("message", {})
+                .get("content", "")
+                .strip()
+                or "（模型未返回内容）"
+            )
+        except HTTPError as e:
+            body = e.read().decode("utf-8", errors="ignore")
+            print(f"[Minimax HTTP {e.code}] {body}")
+            return f"（Minimax调用失败 HTTP {e.code}：{e.reason}）"
+        except (URLError, TimeoutError) as e:
+            return f"（Minimax调用失败：{e}）"
+        except Exception as e:
+            return f"（Minimax解析失败：{e}）"
+
+    def _call_minimax_vision(self, system_prompt: str, user_prompt: str, image_path: Path) -> str:
+        """调用 Minimax 视觉模型（多模态），图片转为 base64 后通过 image_url 传入。"""
+        if not self.minimax_api_key:
+            return "（Minimax API Key 未配置，请设置 MINIMAX_API_KEY 环境变量）"
+
+        try:
+            img_bytes = image_path.read_bytes()
+            img_b64 = base64.b64encode(img_bytes).decode("utf-8")
+        except Exception as e:
+            return f"（读取图片失败：{e}）"
+
+        payload: Dict[str, Any] = {
+            "model": self.minimax_vision_model,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": user_prompt},
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"},
+                        },
+                    ],
+                }
+            ],
+        }
+
+        if system_prompt:
+            payload["messages"].insert(
+                0,
+                {"role": "system", "content": system_prompt},
+            )
+
+        data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+        req = Request(
+            url=self.minimax_base_url,
+            data=data,
+            headers={
+                "Authorization": f"Bearer {self.minimax_api_key}",
+                "Content-Type": "application/json",
+            },
+            method="POST",
+        )
+
+        try:
+            with urlopen(req, timeout=self.minimax_timeout_s) as resp:
+                raw = resp.read().decode("utf-8", errors="ignore")
+            obj = json.loads(raw)
+            return (
+                obj.get("choices", [{}])[0]
+                .get("message", {})
+                .get("content", "")
+                .strip()
+                or "（模型未返回内容）"
+            )
+        except HTTPError as e:
+            body = e.read().decode("utf-8", errors="ignore")
+            print(f"[Minimax Vision HTTP {e.code}] {body}")
+            return f"（Minimax Vision调用失败 HTTP {e.code}：{e.reason}）"
+        except (URLError, TimeoutError) as e:
+            return f"（Minimax Vision调用失败：{e}）"
+        except Exception as e:
+            return f"（Minimax Vision解析失败：{e}）"
+
+    def _ai_chat(
+        self,
+        model: str,
+        system_prompt: str,
+        user_prompt: str,
+        image_path: Optional[Path] = None,
+    ) -> str:
+        """
+        统一 AI 调用入口。根据 self.ai_provider 路由到不同后端实现。
+        model 参数在 ollama 模式下直接使用；在 minimax 模式下忽略（使用类级别配置的模型）。
+        """
+        if self.ai_provider == "minimax":
+            if image_path is not None:
+                return self._call_minimax_vision(system_prompt, user_prompt, image_path)
+            else:
+                return self._call_minimax_text(system_prompt, user_prompt)
+        else:
+            return self._ollama_chat(model, system_prompt, user_prompt, image_path)
+
     def _get_prompt_for_image(self, image_type: str, param: str, figure_num: int) -> Dict[str, str]:
         """按图片类型返回 system/user 提示词，带防幻觉约束。"""
         system = (
@@ -545,7 +725,7 @@ class WaterQualityReportGenerator:
             return str(cache[cache_key])
 
         prompts = self._get_prompt_for_image(image_type=image_type, param=param, figure_num=figure_num)
-        text = self._ollama_chat(
+        text = self._ai_chat(
             model=self.ollama_vision_model,
             system_prompt=prompts["system"],
             user_prompt=prompts["user"],
@@ -585,7 +765,7 @@ class WaterQualityReportGenerator:
 
         输出格式：数据特征分析（变异程度、数值范围等）结论与数据质量评估"""
 
-        return self._ollama_chat(self.ollama_text_model, system, user, image_path=None)
+        return self._ai_chat(self.ollama_text_model, system, user, image_path=None)
 
 
     def generate_report(self, 
@@ -662,7 +842,7 @@ class WaterQualityReportGenerator:
             base_section_num = 5
             last_param_section_num = base_section_num + len(parameters) - 1
             for section_num, param in enumerate(parameters, base_section_num):
-                self._add_parameter_section(
+                figure_counter = self._add_parameter_section(
                     doc,
                     param,
                     vis_dir,
@@ -671,7 +851,6 @@ class WaterQualityReportGenerator:
                     all_image_analyses,
                     progress=progress,
                 )
-                figure_counter += len(self.parameter_images.get(param, []))
                 if section_num != last_param_section_num:
                     doc.add_page_break()
             
@@ -700,7 +879,7 @@ class WaterQualityReportGenerator:
                     "- 不要编造具体数值、地名、日期\n\n"
                     f"{analyses_text}"
                 )
-                summary_text = self._ollama_chat(self.ollama_text_model, system, user, image_path=None)
+                summary_text = self._ai_chat(self.ollama_text_model, system, user, image_path=None)
                 para = doc.add_paragraph(summary_text)
                 para.paragraph_format.first_line_indent = Pt(24)
                 para.paragraph_format.line_spacing = 1.5
@@ -741,7 +920,7 @@ class WaterQualityReportGenerator:
         """为单个参数添加报告章节（带编号和规范中英文图题）"""
         if param not in self.parameter_descriptions:
             print(f"警告: 参数 {param} 没有预定义的描述")
-            return
+            return start_figure_num
         
         # 添加带编号的参数标题
         heading = doc.add_heading(f"{param_index}. {param} 参数分析", level=1)
@@ -851,6 +1030,7 @@ class WaterQualityReportGenerator:
                     pass
         
         doc.add_paragraph()  # 章节结束空行
+        return start_figure_num + len(image_list)
 
     def _add_cover_page(self, doc):
         """添加专业的封面页 - 优化后的布局"""
@@ -1188,9 +1368,9 @@ class WaterQualityReportGenerator:
 请用专业且简洁的语言描述，控制在150字以内。"""
 
             if glint_img_path and Path(glint_img_path).exists():
-                return self._ollama_chat(self.ollama_vision_model, "你是一个专业的水质遥感分析专家。", analysis_prompt, Path(glint_img_path))
+                return self._ai_chat(self.ollama_vision_model, "你是一个专业的水质遥感分析专家。", analysis_prompt, Path(glint_img_path))
             elif original_img_path and Path(original_img_path).exists():
-                return self._ollama_chat(self.ollama_vision_model, "你是一个专业的水质遥感分析专家。", analysis_prompt, Path(original_img_path))
+                return self._ai_chat(self.ollama_vision_model, "你是一个专业的水质遥感分析专家。", analysis_prompt, Path(original_img_path))
             else:
                 return "基于影像分析，耀斑主要分布在水体表面强反射区域，对水质参数反演有一定影响，建议在数据处理时重点关注这些区域。"
 
@@ -1231,7 +1411,7 @@ class WaterQualityReportGenerator:
 ...
 各架次轨迹分布合理，覆盖了目标水体区域。"""
 
-            result = self._ollama_chat(
+            result = self._ai_chat(
                 self.ollama_vision_model,
                 "你是一位专业的航空摄影测量和遥感专家，擅长分析航线规划图。",
                 analysis_prompt,
@@ -1283,7 +1463,7 @@ class WaterQualityReportGenerator:
 【示例输出】
 水体面积25.60 km² ，占比: 42.3% ，形态: 扇形分叉。入库方向：西北角和东北角各有狭窄水道汇入，为主要入库河流。出水/大坝方向：南侧水体最窄处。流向推断：水体从西北和东北两个方向汇入，向南侧大坝方向流动。补充描述：水库整体呈扇形，库区宽阔，有两个明显入库分支，符合山区水库典型特征"""
 
-            result = self._ollama_chat(
+            result = self._ai_chat(
                 self.ollama_vision_model,
                 "你是一位专业的水体遥感分析专家，擅长解读水体掩膜图和水域分布特征。",
                 analysis_prompt,
@@ -1337,7 +1517,7 @@ class WaterQualityReportGenerator:
 
 请根据图像内容给出专业分析。"""
 
-            result = self._ollama_chat(
+            result = self._ai_chat(
                 self.ollama_vision_model,
                 "你是一位专业的水质采样设计专家，擅长评估采样点布局的合理性和代表性。",
                 analysis_prompt,
@@ -1456,13 +1636,13 @@ class WaterQualityReportGenerator:
         if not processed_data_dir.exists():
             doc.add_paragraph(f"未找到数据处理目录: {processed_data_dir}")
             doc.add_page_break()
-            return
+            return start_figure_num
 
         csv_files = list(processed_data_dir.glob("*.csv"))
         if not csv_files:
             doc.add_paragraph(f"在 {processed_data_dir} 目录下未找到CSV统计数据文件。")
             doc.add_page_break()
-            return
+            return start_figure_num
         
         csv_path = csv_files[0]  # 使用找到的第一个CSV文件