diff --git a/01_知识库检索结果.json b/01_知识库检索结果.json
new file mode 100644
index 0000000..9e3908f
--- /dev/null
+++ b/01_知识库检索结果.json
@@ -0,0 +1,47 @@
+[
+  {
+    "query": "水稻稻瘟病怎么识别？",
+    "results": [
+      {
+        "id": "01",
+        "score": 0.5556,
+        "snippet": "水稻稻瘟病是由稻瘟病菌（Magnaporthe oryzae）引起的真菌性病害，是水稻生产中危害最大的病害之一。该病可侵染叶片、节、穗颈和谷粒，分别称为叶瘟、节瘟、穗颈瘟和谷粒瘟。叶瘟典型病斑为梭形，"
+      },
+      {
+        "id": "02",
+        "score": 0.2222,
+        "snippet": "小麦条锈病由条形柄锈菌（Puccinia striiformis f.sp. tritici）引起，主要危害叶片和叶鞘。夏孢子堆沿叶脉纵向排列成行，呈虚线状，鲜黄色，这是区别于叶锈病和秆锈病的重要特征"
+      }
+    ]
+  },
+  {
+    "query": "遥感技术怎么监测病虫害？",
+    "results": [
+      {
+        "id": "05",
+        "score": 0.75,
+        "snippet": "农业遥感技术利用卫星、无人机等平台获取作物光谱信息，通过分析可见光、近红外、热红外等波段数据，实现对作物生长状况、病虫害、水分胁迫等的无损监测。常用植被指数包括NDVI（归一化差异植被指数）、EVI（"
+      },
+      {
+        "id": "06",
+        "score": 0.4167,
+        "snippet": "智能农业病虫害预警系统通过部署田间物联网传感器，实时采集温度、湿度、光照、风速等环境参数，结合作物生长模型和病虫害发生规律，利用机器学习算法预测病虫害发生风险。当环境条件达到特定病虫害暴发阈值时，系统"
+      }
+    ]
+  },
+  {
+    "query": "智能灌溉系统如何工作？",
+    "results": [
+      {
+        "id": "06",
+        "score": 0.4545,
+        "snippet": "智能农业病虫害预警系统通过部署田间物联网传感器，实时采集温度、湿度、光照、风速等环境参数，结合作物生长模型和病虫害发生规律，利用机器学习算法预测病虫害发生风险。当环境条件达到特定病虫害暴发阈值时，系统"
+      },
+      {
+        "id": "10",
+        "score": 0.2727,
+        "snippet": "农业大数据平台整合了气象、土壤、遥感、市场等多源数据，通过数据融合和分析，为农业生产提供决策支持。平台通常包括数据采集层、数据存储层、数据处理层和应用服务层，支持数据可视化、统计分析、模型预测和智能推"
+      }
+    ]
+  }
+]
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diff --git a/02_多模态检索结果.json b/02_多模态检索结果.json
new file mode 100644
index 0000000..c654ec9
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+++ b/02_多模态检索结果.json
@@ -0,0 +1,22 @@
+{
+  "image": "disease_0001.jpg",
+  "size": "857x811",
+  "annotations": [
+    {
+      "病害类别": "棉花_枯萎病",
+      "边界框": [
+        491,
+        464,
+        262,
+        231
+      ],
+      "面积": 60522
+    }
+  ],
+  "knowledge": [
+    {
+      "query": "棉花_枯萎病",
+      "related_doc": "棉花枯萎病由尖孢镰刀菌萎蔫专化型（Fusarium oxysporum f.sp. vasinfectum）引起，是棉花上的毁灭性病害。典型症状为叶片黄色网纹状，后变褐干枯，维管束变褐。该病菌可在土壤中存活10年以上，主要通过带菌土壤和种子传播。"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/03_RAG问答结果.json b/03_RAG问答结果.json
new file mode 100644
index 0000000..9d0e242
--- /dev/null
+++ b/03_RAG问答结果.json
@@ -0,0 +1,42 @@
+[
+  {
+    "question": "水稻叶片有褐色病斑，是不是稻瘟病？",
+    "matched_qa_score": 0.8571,
+    "knowledge_context": [
+      "01",
+      "03"
+    ],
+    "answer": "稻瘟病的病斑呈典型的梭形（纺锤形），两端有延伸的褐色坏死线，中央灰白色，边缘褐色；而胡麻叶斑病的病斑较小，呈椭圆形或不规则形，暗褐色，无明显坏死线。可通过观察病斑形状、颜色和坏死线特征来区分。",
+    "source": "QA语料库"
+  },
+  {
+    "question": "无人机怎么监测作物病害？",
+    "matched_qa_score": 0.8333,
+    "knowledge_context": [
+      "05",
+      "09"
+    ],
+    "answer": "无人机病虫害监测流程：1) 任务规划：设定航线、飞行高度（一般30-100m）和重叠率；2) 数据采集：搭载多光谱/高光谱相机获取影像；3) 图像预处理：拼接、辐射校正、几何校正；4) 特征提取：计算植被指数，识别异常区域；5) 病害识别：利用训练好的模型进行分类检测；6) 生成病虫害分布图，指导精准施药。",
+    "source": "QA语料库"
+  },
+  {
+    "question": "小麦锈病怎么防治？",
+    "matched_qa_score": 0.6667,
+    "knowledge_context": [
+      "02",
+      "06"
+    ],
+    "answer": "这是小麦条锈病的典型症状。建议：1) 立即喷施三唑酮或丙环唑等药剂；2) 适当增施磷钾肥，提高植株抗病力；3) 注意田间通风透光，降低湿度；4) 严重地块应在发病初期及时防治，间隔7-10天喷施一次。",
+    "source": "QA语料库"
+  },
+  {
+    "question": "土壤湿度传感器数据怎么看？",
+    "matched_qa_score": 0.5385,
+    "knowledge_context": [
+      "06",
+      "07"
+    ],
+    "answer": "智能灌溉系统通过以下方式自动调节：1) 土壤湿度传感器实时监测含水量；2) 设定作物不同生育期的湿度阈值上下限；3) 当湿度低于下限自动启动灌溉，达到上限自动停止；4) 结合天气预报，如果即将降雨则减少灌溉量；5) 根据土壤类型（砂土/壤土/黏土）调整灌溉速率；6) 通过历史数据优化灌溉策略，实现节水增产。",
+    "source": "QA语料库"
+  }
+]
\ No newline at end of file
diff --git a/04_HDF5数据摘要.json b/04_HDF5数据摘要.json
new file mode 100644
index 0000000..6301732
--- /dev/null
+++ b/04_HDF5数据摘要.json
@@ -0,0 +1,42 @@
+{
+  "metadata": {
+    "bands": "Blue(450nm), Green(560nm), Red(650nm), RedEdge(730nm), NIR(840nm)",
+    "create_date": "2024-06-15",
+    "description": "多光谱作物影像数据集",
+    "resolution": "2.4cm/pixel",
+    "sensor": "DJI P4 Multispectral"
+  },
+  "scene_count": 20,
+  "scene_001": {
+    "attributes": {
+      "crop_type": "水稻",
+      "date": "2024-09-05",
+      "health_status": "中度病害",
+      "location": "长江中下游"
+    },
+    "multispectral_shape": [
+      5,
+      64,
+      64
+    ],
+    "ndvi_shape": [
+      64,
+      64
+    ],
+    "ndvi_range": [
+      -0.9997854232788086,
+      0.9994948506355286
+    ]
+  },
+  "labels_shape": [
+    20,
+    64,
+    64
+  ],
+  "classes": [
+    "健康",
+    "轻度病害",
+    "中度病害",
+    "重度病害"
+  ]
+}
\ No newline at end of file
diff --git a/05_多光谱数据可视化.png b/05_多光谱数据可视化.png
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Binary files /dev/null and b/05_多光谱数据可视化.png differ