{"is_fixed_template": false, "template_name": "lineChart", "template_doc": "## lineChart.html — 折线图通用模板\n\n### 适用场景\n用于展示任何需要折线图可视化的趋势数据分析场景，如碳排放趋势、温度变化、产量趋势、成本变化等。\n\n### 所需 JSON 数据格式\n\n```json\n{\n  \"page_title\": \"碳排放趋势分析\",\n  \"data\": {\n    \"text_values\": {\n      \"PAGE_TITLE\": \"碳排放趋势分析\",\n      \"CHART_TITLE\": \"碳排放趋势\",\n      \"STAT_TITLE\": \"统计数据\",\n      \"STAT_LABEL_1_1\": \"当月\",\n      \"STAT_VALUE_1_1\": \"4441.5 t\",\n      \"STAT_LABEL_1_2\": \"上月同期\",\n      \"STAT_VALUE_1_2\": \"1050 t\",\n      \"STAT_LABEL_1_3\": \"趋势\",\n      \"STAT_VALUE_1_3\": \"3391.5 % ↑\",\n      \"STAT_VALUE_1_3_CLASS\": \"up\",\n      \"STAT_LABEL_2_1\": \"当年\",\n      \"STAT_VALUE_2_1\": \"5491.5 t\",\n      \"STAT_LABEL_2_2\": \"去年同期\",\n      \"STAT_VALUE_2_2\": \"1040.5 t\",\n      \"STAT_LABEL_2_3\": \"趋势\",\n      \"STAT_VALUE_2_3\": \"4451 % ↑\",\n      \"STAT_VALUE_2_3_CLASS\": \"up\",\n      \"TABLE_TITLE\": \"各月碳排放明细\",\n      \"TABLE_HEADER_1\": \"月份\",\n      \"TABLE_HEADER_2\": \"碳排放量（t）\",\n      \"TABLE_HEADER_3\": \"环比变化\",\n      \"TABLE_HEADER_4\": \"同比变化\",\n      \"TABLE_ROWS\": \"<tr><td>1月</td><td>285.6</td><td><span class=\\\"tag-down\\\">-3.2% ↓</span></td><td><span class=\\\"tag-up\\\">+5.1% ↑</span></td></tr><tr><td>2月</td><td>268.3</td><td><span class=\\\"tag-down\\\">-6.1% ↓</span></td><td><span class=\\\"tag-down\\\">-2.3% ↓</span></td></tr>\",\n      \"SUGGESTION_TITLE\": \"减排建议\",\n      \"ANALYSIS_SUMMARY\": \"全年碳排放量合计 3,556.7 t，较去年同期上涨约 7.2%。其中 3月（312.5t）、5月（325.4t）、8月（318.6t）为全年排放高峰月份，6月（289.2t）和9月（295.3t）排放相对较低。下半年整体排放水平高于上半年，年末两月维持高位运行。\",\n      \"OPTIMIZATION_SUGGESTIONS\": \"<li>· 3月、5月、8月碳排放明显偏高，建议重点排查该时段高耗能设备运行情况，优化生产调度计划，避免高峰时段非必要能耗。</li><li>· 全年排放整体呈上升趋势，建议加快推进可再生能源替代，提高光伏、风电等清洁能源使用比例，从源头降低碳排放总量。</li><li>· 参考6月、9月低排放时段的运营经验，总结节能措施并推广至全年，争取将年均碳排放控制在目标范围内。</li><li>· 建议对高排放月份开展专项能源审计，识别主要排放源，制定针对性的节能降碳方案并跟踪落实效果。</li>\"\n    },\n    \"js_variables\": {\n      \"CHART_X_AXIS_DATA\": [\"1月\", \"2月\", \"3月\", \"4月\", \"5月\", \"6月\", \"7月\", \"8月\", \"9月\", \"10月\", \"11月\", \"12月\"],\n      \"CHART_SERIES_DATA\": [285.6, 268.3, 312.5, 298.7, 325.4, 289.2, 301.8, 318.6, 295.3, 308.9, 327.2, 325.3],\n      \"CHART_Y_AXIS_NAME\": \"t\",\n      \"CHART_SERIES_NAME\": \"碳排放量\"\n    }\n  }\n}\n```\n\n### 字段说明\n\n| 字段 | 类型 | 说明 |\n|------|------|------|\n| PAGE_TITLE | 字符串 | 页面标题，显示在浏览器标签页 |\n| CHART_TITLE | 字符串 | 折线图卡片标题 |\n| STAT_TITLE | 字符串 | 统计数据卡片标题 |\n| STAT_LABEL_1_1 | 字符串 | 左侧统计块第1行标签 |\n| STAT_VALUE_1_1 | 字符串 | 左侧统计块第1行数值 |\n| STAT_LABEL_1_2 | 字符串 | 左侧统计块第2行标签 |\n| STAT_VALUE_1_2 | 字符串 | 左侧统计块第2行数值 |\n| STAT_LABEL_1_3 | 字符串 | 左侧统计块第3行标签 |\n| STAT_VALUE_1_3 | 字符串 | 左侧统计块第3行数值（含箭头符号） |\n| STAT_VALUE_1_3_CLASS | 字符串 | 左侧统计块第3行样式类，可选值：\"up\"（红色上升）、\"down\"（绿色下降）、\"\"（默认） |\n| STAT_LABEL_2_1 | 字符串 | 右侧统计块第1行标签 |\n| STAT_VALUE_2_1 | 字符串 | 右侧统计块第1行数值 |\n| STAT_LABEL_2_2 | 字符串 | 右侧统计块第2行标签 |\n| STAT_VALUE_2_2 | 字符串 | 右侧统计块第2行数值 |\n| STAT_LABEL_2_3 | 字符串 | 右侧统计块第3行标签 |\n| STAT_VALUE_2_3 | 字符串 | 右侧统计块第3行数值（含箭头符号） |\n| STAT_VALUE_2_3_CLASS | 字符串 | 右侧统计块第3行样式类，可选值：\"up\"（红色上升）、\"down\"（绿色下降）、\"\"（默认） |\n| TABLE_TITLE | 字符串 | 明细表格卡片标题 |\n| TABLE_HEADER_1 | 字符串 | 表格第1列表头 |\n| TABLE_HEADER_2 | 字符串 | 表格第2列表头 |\n| TABLE_HEADER_3 | 字符串 | 表格第3列表头 |\n| TABLE_HEADER_4 | 字符串 | 表格第4列表头 |\n| TABLE_ROWS | 字符串（HTML） | 表格数据行的完整 HTML，每行格式：`<tr><td>值1</td><td>值2</td><td><span class=\"tag-up/tag-down\">值3</span></td><td><span class=\"tag-up/tag-down\">值4</span></td></tr>` |\n| SUGGESTION_TITLE | 字符串 | 建议卡片标题 |\n| ANALYSIS_SUMMARY | 字符串 | 数据总结文本，2-4句话概述数据情况 |\n| OPTIMIZATION_SUGGESTIONS | 字符串（HTML） | 优化建议列表的完整 HTML，每条建议格式：`<li>· 建议内容</li>` |\n| CHART_X_AXIS_DATA | 数组 | 折线图 X 轴数据，字符串数组，如 `[\"1月\", \"2月\", \"3月\"]` |\n| CHART_SERIES_DATA | 数组 | 折线图 Y 轴数据，数值数组，与 X 轴数据一一对应 |\n| CHART_Y_AXIS_NAME | 字符串 | Y 轴单位名称，如 \"t\"、\"kWh\"、\"℃\" |\n| CHART_SERIES_NAME | 字符串 | 图表系列名称，显示在 tooltip 中 |\n\n### 图表特性\n\n折线图包含以下特性：\n- 平滑曲线显示\n- 区域填充（渐变色）\n- 圆点标记\n- 鼠标悬停显示详细数据\n\n### 注意事项\n\n1. **TABLE_ROWS** 和 **OPTIMIZATION_SUGGESTIONS** 字段需要填入完整的 HTML 代码\n2. **STAT_VALUE_X_X_CLASS** 字段用于控制数值颜色：\n   - `\"up\"` - 红色加粗（表示上升/增长）\n   - `\"down\"` - 绿色加粗（表示下降/减少）\n   - `\"\"` - 默认颜色\n3. **CHART_X_AXIS_DATA** 和 **CHART_SERIES_DATA** 数组长度必须一致\n4. 表格标签样式：\n   - `tag-up` - 红色背景（表示上升）\n   - `tag-down` - 绿色背景（表示下降）\n\n---\n", "rows": [{"cycle_count": 3.5, "stat_date": "2024-11-08 16:00:00", "daily_charge_energy": 381.885, "daily_discharge_energy": 380.91499999999996}, {"cycle_count": 3.0, "stat_date": "2024-11-09 16:00:00", "daily_charge_energy": 294.45000000000005, "daily_discharge_energy": 378.455}, {"cycle_count": 2.0, "stat_date": "2024-11-10 16:00:00", "daily_charge_energy": 305.81, "daily_discharge_energy": 313.71500000000003}, {"cycle_count": 1.0, "stat_date": "2024-11-11 16:00:00", "daily_charge_energy": 485.37, "daily_discharge_energy": 257.43}, {"cycle_count": 2.5, "stat_date": "2024-11-12 16:00:00", "daily_charge_energy": 388.95500000000004, "daily_discharge_energy": 236.355}, {"cycle_count": 3.6666666666666665, "stat_date": "2024-11-13 16:00:00", "daily_charge_energy": 394.4066666666667, "daily_discharge_energy": 305.58666666666664}, {"cycle_count": 4.0, "stat_date": "2024-11-14 16:00:00", "daily_charge_energy": 448.115, "daily_discharge_energy": 344.93}, {"cycle_count": 3.0, "stat_date": "2024-11-17 16:00:00", "daily_charge_energy": 497.65, "daily_discharge_energy": 346.28}, {"cycle_count": 2.5, "stat_date": "2024-11-18 16:00:00", "daily_charge_energy": 523.86, "daily_discharge_energy": 261.445}, {"cycle_count": 1.0, "stat_date": "2025-02-06 16:00:00", "daily_charge_energy": 361.5, "daily_discharge_energy": 234.01}, {"cycle_count": 3.0, "stat_date": "2025-02-07 16:00:00", "daily_charge_energy": 216.6, "daily_discharge_energy": 238.83499999999998}, {"cycle_count": 3.75, "stat_date": "2025-04-24 16:00:00", "daily_charge_energy": 352.375, "daily_discharge_energy": 426.9175}, {"cycle_count": 2.75, "stat_date": "2025-04-25 16:00:00", "daily_charge_energy": 284.62, "daily_discharge_energy": 299.685}, {"cycle_count": 3.4285714285714284, "stat_date": "2025-04-26 16:00:00", "daily_charge_energy": 410.42142857142846, "daily_discharge_energy": 373.38142857142856}, {"cycle_count": 2.5714285714285716, "stat_date": "2025-04-27 16:00:00", "daily_charge_energy": 383.10714285714283, "daily_discharge_energy": 224.85285714285712}, {"cycle_count": 3.142857142857143, "stat_date": "2025-04-28 16:00:00", "daily_charge_energy": 465.84, "daily_discharge_energy": 360.16285714285715}, {"cycle_count": 4.285714285714286, "stat_date": "2025-05-11 16:00:00", "daily_charge_energy": 379.73, "daily_discharge_energy": 239.46285714285713}, {"cycle_count": 3.4285714285714284, "stat_date": "2025-05-13 16:00:00", "daily_charge_energy": 326.7814285714286, "daily_discharge_energy": 249.3742857142857}, {"cycle_count": 2.5714285714285716, "stat_date": "2025-05-14 16:00:00", "daily_charge_energy": 406.27571428571434, "daily_discharge_energy": 276.5628571428571}, 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