[{"data":1,"prerenderedAt":6031},["ShallowReactive",2],{"best-practices:data-analysis-advanced":3,"content-manifest":3386},{"id":4,"title":5,"body":6,"category":3368,"date":3369,"description":3370,"difficulty":3371,"extension":3372,"meta":3373,"navigation":493,"path":3374,"seo":3375,"source":3369,"sourceName":3369,"sourceType":3376,"stem":3377,"tags":3378,"updatedAt":3384,"__hash__":3385},"bestPractices/best-practices/data-analysis-advanced.md","数据分析助手",{"type":7,"value":8,"toc":3325},"minimark",[9,12,16,20,87,90,94,304,307,459,462,591,593,596,874,877,1043,1046,1211,1213,1216,1356,1359,1442,1445,1703,1705,1708,1889,1892,2019,2022,2276,2278,2281,2462,2464,2561,2564,2672,2675,2678,2962,2965,3011,3014,3017,3040,3043,3063,3066,3086,3089,3092,3095,3168,3171,3174,3232,3235,3238,3296,3299,3321],[10,11,5],"h1",{"id":5},[13,14,15],"p",{},"本文介绍如何使用 OpenClaw 构建智能数据分析助手，让数据分析更高效、更智能。",[17,18,19],"h2",{"id":19},"核心场景",[21,22,23,39],"table",{},[24,25,26],"thead",{},[27,28,29,33,36],"tr",{},[30,31,32],"th",{},"场景",[30,34,35],{},"描述",[30,37,38],{},"价值",[40,41,42,54,65,76],"tbody",{},[27,43,44,48,51],{},[45,46,47],"td",{},"报表生成",[45,49,50],{},"自动生成数据报表",[45,52,53],{},"节省 80% 报表制作时间",[27,55,56,59,62],{},[45,57,58],{},"数据可视化",[45,60,61],{},"智能图表推荐与生成",[45,63,64],{},"降低可视化门槛",[27,66,67,70,73],{},[45,68,69],{},"异常检测",[45,71,72],{},"自动发现数据异常",[45,74,75],{},"及时预警风险",[27,77,78,81,84],{},[45,79,80],{},"智能洞察",[45,82,83],{},"AI 驱动的数据解读",[45,85,86],{},"发现隐藏价值",[17,88,89],{"id":89},"数据连接",[91,92,93],"h3",{"id":93},"支持的数据源",[95,96,101],"pre",{"className":97,"code":98,"language":99,"meta":100,"style":100},"language-json shiki shiki-themes github-light github-dark","{\n  \"data_sources\": {\n    \"databases\": [\n      \"PostgreSQL\",\n      \"MySQL\",\n      \"MongoDB\",\n      \"Redis\"\n    ],\n    \"warehouses\": [\n      \"Snowflake\",\n      \"BigQuery\",\n      \"Redshift\",\n      \"ClickHouse\"\n    ],\n    \"files\": [\n      \"CSV\",\n      \"Excel\",\n      \"JSON\",\n      \"Parquet\"\n    ],\n    \"apis\": [\n      \"REST API\",\n      \"GraphQL\",\n      \"Webhook\"\n    ]\n  }\n}\n","json","",[102,103,104,113,123,132,142,150,158,164,170,178,186,194,202,208,213,221,229,237,245,251,256,264,272,280,286,292,298],"code",{"__ignoreMap":100},[105,106,109],"span",{"class":107,"line":108},"line",1,[105,110,112],{"class":111},"sVt8B","{\n",[105,114,116,120],{"class":107,"line":115},2,[105,117,119],{"class":118},"sj4cs","  \"data_sources\"",[105,121,122],{"class":111},": {\n",[105,124,126,129],{"class":107,"line":125},3,[105,127,128],{"class":118},"    \"databases\"",[105,130,131],{"class":111},": [\n",[105,133,135,139],{"class":107,"line":134},4,[105,136,138],{"class":137},"sZZnC","      \"PostgreSQL\"",[105,140,141],{"class":111},",\n",[105,143,145,148],{"class":107,"line":144},5,[105,146,147],{"class":137},"      \"MySQL\"",[105,149,141],{"class":111},[105,151,153,156],{"class":107,"line":152},6,[105,154,155],{"class":137},"      \"MongoDB\"",[105,157,141],{"class":111},[105,159,161],{"class":107,"line":160},7,[105,162,163],{"class":137},"      \"Redis\"\n",[105,165,167],{"class":107,"line":166},8,[105,168,169],{"class":111},"    ],\n",[105,171,173,176],{"class":107,"line":172},9,[105,174,175],{"class":118},"    \"warehouses\"",[105,177,131],{"class":111},[105,179,181,184],{"class":107,"line":180},10,[105,182,183],{"class":137},"      \"Snowflake\"",[105,185,141],{"class":111},[105,187,189,192],{"class":107,"line":188},11,[105,190,191],{"class":137},"      \"BigQuery\"",[105,193,141],{"class":111},[105,195,197,200],{"class":107,"line":196},12,[105,198,199],{"class":137},"      \"Redshift\"",[105,201,141],{"class":111},[105,203,205],{"class":107,"line":204},13,[105,206,207],{"class":137},"      \"ClickHouse\"\n",[105,209,211],{"class":107,"line":210},14,[105,212,169],{"class":111},[105,214,216,219],{"class":107,"line":215},15,[105,217,218],{"class":118},"    \"files\"",[105,220,131],{"class":111},[105,222,224,227],{"class":107,"line":223},16,[105,225,226],{"class":137},"      \"CSV\"",[105,228,141],{"class":111},[105,230,232,235],{"class":107,"line":231},17,[105,233,234],{"class":137},"      \"Excel\"",[105,236,141],{"class":111},[105,238,240,243],{"class":107,"line":239},18,[105,241,242],{"class":137},"      \"JSON\"",[105,244,141],{"class":111},[105,246,248],{"class":107,"line":247},19,[105,249,250],{"class":137},"      \"Parquet\"\n",[105,252,254],{"class":107,"line":253},20,[105,255,169],{"class":111},[105,257,259,262],{"class":107,"line":258},21,[105,260,261],{"class":118},"    \"apis\"",[105,263,131],{"class":111},[105,265,267,270],{"class":107,"line":266},22,[105,268,269],{"class":137},"      \"REST API\"",[105,271,141],{"class":111},[105,273,275,278],{"class":107,"line":274},23,[105,276,277],{"class":137},"      \"GraphQL\"",[105,279,141],{"class":111},[105,281,283],{"class":107,"line":282},24,[105,284,285],{"class":137},"      \"Webhook\"\n",[105,287,289],{"class":107,"line":288},25,[105,290,291],{"class":111},"    ]\n",[105,293,295],{"class":107,"line":294},26,[105,296,297],{"class":111},"  }\n",[105,299,301],{"class":107,"line":300},27,[105,302,303],{"class":111},"}\n",[91,305,306],{"id":306},"数据源配置",[95,308,310],{"className":97,"code":309,"language":99,"meta":100,"style":100},"{\n  \"connections\": {\n    \"main_db\": {\n      \"type\": \"postgresql\",\n      \"host\": \"${DB_HOST}\",\n      \"port\": 5432,\n      \"database\": \"production\",\n      \"username\": \"${DB_USER}\",\n      \"password\": \"${DB_PASSWORD}\"\n    },\n    \"analytics\": {\n      \"type\": \"bigquery\",\n      \"project_id\": \"my-project\",\n      \"credentials\": \"${GOOGLE_CREDENTIALS}\"\n    }\n  }\n}\n",[102,311,312,316,323,330,343,355,367,379,391,401,406,413,424,436,446,451,455],{"__ignoreMap":100},[105,313,314],{"class":107,"line":108},[105,315,112],{"class":111},[105,317,318,321],{"class":107,"line":115},[105,319,320],{"class":118},"  \"connections\"",[105,322,122],{"class":111},[105,324,325,328],{"class":107,"line":125},[105,326,327],{"class":118},"    \"main_db\"",[105,329,122],{"class":111},[105,331,332,335,338,341],{"class":107,"line":134},[105,333,334],{"class":118},"      \"type\"",[105,336,337],{"class":111},": ",[105,339,340],{"class":137},"\"postgresql\"",[105,342,141],{"class":111},[105,344,345,348,350,353],{"class":107,"line":144},[105,346,347],{"class":118},"      \"host\"",[105,349,337],{"class":111},[105,351,352],{"class":137},"\"${DB_HOST}\"",[105,354,141],{"class":111},[105,356,357,360,362,365],{"class":107,"line":152},[105,358,359],{"class":118},"      \"port\"",[105,361,337],{"class":111},[105,363,364],{"class":118},"5432",[105,366,141],{"class":111},[105,368,369,372,374,377],{"class":107,"line":160},[105,370,371],{"class":118},"      \"database\"",[105,373,337],{"class":111},[105,375,376],{"class":137},"\"production\"",[105,378,141],{"class":111},[105,380,381,384,386,389],{"class":107,"line":166},[105,382,383],{"class":118},"      \"username\"",[105,385,337],{"class":111},[105,387,388],{"class":137},"\"${DB_USER}\"",[105,390,141],{"class":111},[105,392,393,396,398],{"class":107,"line":172},[105,394,395],{"class":118},"      \"password\"",[105,397,337],{"class":111},[105,399,400],{"class":137},"\"${DB_PASSWORD}\"\n",[105,402,403],{"class":107,"line":180},[105,404,405],{"class":111},"    },\n",[105,407,408,411],{"class":107,"line":188},[105,409,410],{"class":118},"    \"analytics\"",[105,412,122],{"class":111},[105,414,415,417,419,422],{"class":107,"line":196},[105,416,334],{"class":118},[105,418,337],{"class":111},[105,420,421],{"class":137},"\"bigquery\"",[105,423,141],{"class":111},[105,425,426,429,431,434],{"class":107,"line":204},[105,427,428],{"class":118},"      \"project_id\"",[105,430,337],{"class":111},[105,432,433],{"class":137},"\"my-project\"",[105,435,141],{"class":111},[105,437,438,441,443],{"class":107,"line":210},[105,439,440],{"class":118},"      \"credentials\"",[105,442,337],{"class":111},[105,444,445],{"class":137},"\"${GOOGLE_CREDENTIALS}\"\n",[105,447,448],{"class":107,"line":215},[105,449,450],{"class":111},"    }\n",[105,452,453],{"class":107,"line":223},[105,454,297],{"class":111},[105,456,457],{"class":107,"line":231},[105,458,303],{"class":111},[91,460,461],{"id":461},"自然语言查询",[95,463,467],{"className":464,"code":465,"language":466,"meta":100,"style":100},"language-python shiki shiki-themes github-light github-dark","async def nl_to_sql(question, schema):\n    \"\"\"自然语言转 SQL\"\"\"\n    prompt = f\"\"\"\n    将以下问题转换为 SQL 查询：\n\n    问题：{question}\n\n    数据库结构：\n    {schema}\n\n    要求：\n    1. 生成标准 SQL\n    2. 添加必要的 JOIN\n    3. 考虑性能优化\n    4. 添加注释说明\n\n    返回 JSON：\n    {{\n        \"sql\": \"SELECT ...\",\n        \"explanation\": \"查询说明\",\n        \"estimated_rows\": 1000\n    }}\n    \"\"\"\n\n    return await generate_response(prompt)\n","python",[102,468,469,474,479,484,489,495,500,504,509,514,518,523,528,533,538,543,547,552,557,562,567,572,577,582,586],{"__ignoreMap":100},[105,470,471],{"class":107,"line":108},[105,472,473],{},"async def nl_to_sql(question, schema):\n",[105,475,476],{"class":107,"line":115},[105,477,478],{},"    \"\"\"自然语言转 SQL\"\"\"\n",[105,480,481],{"class":107,"line":125},[105,482,483],{},"    prompt = f\"\"\"\n",[105,485,486],{"class":107,"line":134},[105,487,488],{},"    将以下问题转换为 SQL 查询：\n",[105,490,491],{"class":107,"line":144},[105,492,494],{"emptyLinePlaceholder":493},true,"\n",[105,496,497],{"class":107,"line":152},[105,498,499],{},"    问题：{question}\n",[105,501,502],{"class":107,"line":160},[105,503,494],{"emptyLinePlaceholder":493},[105,505,506],{"class":107,"line":166},[105,507,508],{},"    数据库结构：\n",[105,510,511],{"class":107,"line":172},[105,512,513],{},"    {schema}\n",[105,515,516],{"class":107,"line":180},[105,517,494],{"emptyLinePlaceholder":493},[105,519,520],{"class":107,"line":188},[105,521,522],{},"    要求：\n",[105,524,525],{"class":107,"line":196},[105,526,527],{},"    1. 生成标准 SQL\n",[105,529,530],{"class":107,"line":204},[105,531,532],{},"    2. 添加必要的 JOIN\n",[105,534,535],{"class":107,"line":210},[105,536,537],{},"    3. 考虑性能优化\n",[105,539,540],{"class":107,"line":215},[105,541,542],{},"    4. 添加注释说明\n",[105,544,545],{"class":107,"line":223},[105,546,494],{"emptyLinePlaceholder":493},[105,548,549],{"class":107,"line":231},[105,550,551],{},"    返回 JSON：\n",[105,553,554],{"class":107,"line":239},[105,555,556],{},"    {{\n",[105,558,559],{"class":107,"line":247},[105,560,561],{},"        \"sql\": \"SELECT ...\",\n",[105,563,564],{"class":107,"line":253},[105,565,566],{},"        \"explanation\": \"查询说明\",\n",[105,568,569],{"class":107,"line":258},[105,570,571],{},"        \"estimated_rows\": 1000\n",[105,573,574],{"class":107,"line":266},[105,575,576],{},"    }}\n",[105,578,579],{"class":107,"line":274},[105,580,581],{},"    \"\"\"\n",[105,583,584],{"class":107,"line":282},[105,585,494],{"emptyLinePlaceholder":493},[105,587,588],{"class":107,"line":288},[105,589,590],{},"    return await generate_response(prompt)\n",[17,592,47],{"id":47},[91,594,595],{"id":595},"报表模板",[95,597,599],{"className":97,"code":598,"language":99,"meta":100,"style":100},"{\n  \"report_templates\": {\n    \"daily_sales\": {\n      \"name\": \"每日销售报表\",\n      \"schedule\": \"0 8 * * *\",\n      \"sections\": [\n        {\n          \"title\": \"销售概览\",\n          \"type\": \"summary\",\n          \"metrics\": [\"total_sales\", \"order_count\", \"avg_order_value\"]\n        },\n        {\n          \"title\": \"销售趋势\",\n          \"type\": \"chart\",\n          \"chart_type\": \"line\",\n          \"dimensions\": [\"date\"],\n          \"metrics\": [\"sales\"]\n        },\n        {\n          \"title\": \"商品排行\",\n          \"type\": \"table\",\n          \"dimensions\": [\"product_name\"],\n          \"metrics\": [\"sales\", \"quantity\"],\n          \"order_by\": \"sales DESC\",\n          \"limit\": 10\n        }\n      ]\n    }\n  }\n}\n",[102,600,601,605,612,619,631,643,650,655,667,679,704,709,713,724,735,747,760,771,775,779,790,801,812,827,839,849,854,859,864,869],{"__ignoreMap":100},[105,602,603],{"class":107,"line":108},[105,604,112],{"class":111},[105,606,607,610],{"class":107,"line":115},[105,608,609],{"class":118},"  \"report_templates\"",[105,611,122],{"class":111},[105,613,614,617],{"class":107,"line":125},[105,615,616],{"class":118},"    \"daily_sales\"",[105,618,122],{"class":111},[105,620,621,624,626,629],{"class":107,"line":134},[105,622,623],{"class":118},"      \"name\"",[105,625,337],{"class":111},[105,627,628],{"class":137},"\"每日销售报表\"",[105,630,141],{"class":111},[105,632,633,636,638,641],{"class":107,"line":144},[105,634,635],{"class":118},"      \"schedule\"",[105,637,337],{"class":111},[105,639,640],{"class":137},"\"0 8 * * *\"",[105,642,141],{"class":111},[105,644,645,648],{"class":107,"line":152},[105,646,647],{"class":118},"      \"sections\"",[105,649,131],{"class":111},[105,651,652],{"class":107,"line":160},[105,653,654],{"class":111},"        {\n",[105,656,657,660,662,665],{"class":107,"line":166},[105,658,659],{"class":118},"          \"title\"",[105,661,337],{"class":111},[105,663,664],{"class":137},"\"销售概览\"",[105,666,141],{"class":111},[105,668,669,672,674,677],{"class":107,"line":172},[105,670,671],{"class":118},"          \"type\"",[105,673,337],{"class":111},[105,675,676],{"class":137},"\"summary\"",[105,678,141],{"class":111},[105,680,681,684,687,690,693,696,698,701],{"class":107,"line":180},[105,682,683],{"class":118},"          \"metrics\"",[105,685,686],{"class":111},": [",[105,688,689],{"class":137},"\"total_sales\"",[105,691,692],{"class":111},", ",[105,694,695],{"class":137},"\"order_count\"",[105,697,692],{"class":111},[105,699,700],{"class":137},"\"avg_order_value\"",[105,702,703],{"class":111},"]\n",[105,705,706],{"class":107,"line":188},[105,707,708],{"class":111},"        },\n",[105,710,711],{"class":107,"line":196},[105,712,654],{"class":111},[105,714,715,717,719,722],{"class":107,"line":204},[105,716,659],{"class":118},[105,718,337],{"class":111},[105,720,721],{"class":137},"\"销售趋势\"",[105,723,141],{"class":111},[105,725,726,728,730,733],{"class":107,"line":210},[105,727,671],{"class":118},[105,729,337],{"class":111},[105,731,732],{"class":137},"\"chart\"",[105,734,141],{"class":111},[105,736,737,740,742,745],{"class":107,"line":215},[105,738,739],{"class":118},"          \"chart_type\"",[105,741,337],{"class":111},[105,743,744],{"class":137},"\"line\"",[105,746,141],{"class":111},[105,748,749,752,754,757],{"class":107,"line":223},[105,750,751],{"class":118},"          \"dimensions\"",[105,753,686],{"class":111},[105,755,756],{"class":137},"\"date\"",[105,758,759],{"class":111},"],\n",[105,761,762,764,766,769],{"class":107,"line":231},[105,763,683],{"class":118},[105,765,686],{"class":111},[105,767,768],{"class":137},"\"sales\"",[105,770,703],{"class":111},[105,772,773],{"class":107,"line":239},[105,774,708],{"class":111},[105,776,777],{"class":107,"line":247},[105,778,654],{"class":111},[105,780,781,783,785,788],{"class":107,"line":253},[105,782,659],{"class":118},[105,784,337],{"class":111},[105,786,787],{"class":137},"\"商品排行\"",[105,789,141],{"class":111},[105,791,792,794,796,799],{"class":107,"line":258},[105,793,671],{"class":118},[105,795,337],{"class":111},[105,797,798],{"class":137},"\"table\"",[105,800,141],{"class":111},[105,802,803,805,807,810],{"class":107,"line":266},[105,804,751],{"class":118},[105,806,686],{"class":111},[105,808,809],{"class":137},"\"product_name\"",[105,811,759],{"class":111},[105,813,814,816,818,820,822,825],{"class":107,"line":274},[105,815,683],{"class":118},[105,817,686],{"class":111},[105,819,768],{"class":137},[105,821,692],{"class":111},[105,823,824],{"class":137},"\"quantity\"",[105,826,759],{"class":111},[105,828,829,832,834,837],{"class":107,"line":282},[105,830,831],{"class":118},"          \"order_by\"",[105,833,337],{"class":111},[105,835,836],{"class":137},"\"sales DESC\"",[105,838,141],{"class":111},[105,840,841,844,846],{"class":107,"line":288},[105,842,843],{"class":118},"          \"limit\"",[105,845,337],{"class":111},[105,847,848],{"class":118},"10\n",[105,850,851],{"class":107,"line":294},[105,852,853],{"class":111},"        }\n",[105,855,856],{"class":107,"line":300},[105,857,858],{"class":111},"      ]\n",[105,860,862],{"class":107,"line":861},28,[105,863,450],{"class":111},[105,865,867],{"class":107,"line":866},29,[105,868,297],{"class":111},[105,870,872],{"class":107,"line":871},30,[105,873,303],{"class":111},[91,875,876],{"id":876},"自动报表生成",[95,878,880],{"className":464,"code":879,"language":466,"meta":100,"style":100},"async def generate_report(template_id, date_range):\n    \"\"\"生成报表\"\"\"\n    template = await get_template(template_id)\n\n    report_sections = []\n    for section in template.sections:\n        if section.type == \"summary\":\n            data = await fetch_summary_metrics(section.metrics, date_range)\n            content = await format_summary(data)\n\n        elif section.type == \"chart\":\n            data = await fetch_chart_data(section, date_range)\n            chart = await generate_chart(data, section.chart_type)\n            content = chart\n\n        elif section.type == \"table\":\n            data = await fetch_table_data(section, date_range)\n            content = await format_table(data)\n\n        report_sections.append({\n            \"title\": section.title,\n            \"content\": content\n        })\n\n    # 生成洞察\n    insights = await generate_insights(report_sections)\n\n    return {\n        \"title\": template.name,\n        \"date_range\": date_range,\n        \"sections\": report_sections,\n        \"insights\": insights\n    }\n",[102,881,882,887,892,897,901,906,911,916,921,926,930,935,940,945,950,954,959,964,969,973,978,983,988,993,997,1002,1007,1011,1016,1021,1026,1032,1038],{"__ignoreMap":100},[105,883,884],{"class":107,"line":108},[105,885,886],{},"async def generate_report(template_id, date_range):\n",[105,888,889],{"class":107,"line":115},[105,890,891],{},"    \"\"\"生成报表\"\"\"\n",[105,893,894],{"class":107,"line":125},[105,895,896],{},"    template = await get_template(template_id)\n",[105,898,899],{"class":107,"line":134},[105,900,494],{"emptyLinePlaceholder":493},[105,902,903],{"class":107,"line":144},[105,904,905],{},"    report_sections = []\n",[105,907,908],{"class":107,"line":152},[105,909,910],{},"    for section in template.sections:\n",[105,912,913],{"class":107,"line":160},[105,914,915],{},"        if section.type == \"summary\":\n",[105,917,918],{"class":107,"line":166},[105,919,920],{},"            data = await fetch_summary_metrics(section.metrics, date_range)\n",[105,922,923],{"class":107,"line":172},[105,924,925],{},"            content = await format_summary(data)\n",[105,927,928],{"class":107,"line":180},[105,929,494],{"emptyLinePlaceholder":493},[105,931,932],{"class":107,"line":188},[105,933,934],{},"        elif section.type == \"chart\":\n",[105,936,937],{"class":107,"line":196},[105,938,939],{},"            data = await fetch_chart_data(section, date_range)\n",[105,941,942],{"class":107,"line":204},[105,943,944],{},"            chart = await generate_chart(data, section.chart_type)\n",[105,946,947],{"class":107,"line":210},[105,948,949],{},"            content = chart\n",[105,951,952],{"class":107,"line":215},[105,953,494],{"emptyLinePlaceholder":493},[105,955,956],{"class":107,"line":223},[105,957,958],{},"        elif section.type == \"table\":\n",[105,960,961],{"class":107,"line":231},[105,962,963],{},"            data = await fetch_table_data(section, date_range)\n",[105,965,966],{"class":107,"line":239},[105,967,968],{},"            content = await format_table(data)\n",[105,970,971],{"class":107,"line":247},[105,972,494],{"emptyLinePlaceholder":493},[105,974,975],{"class":107,"line":253},[105,976,977],{},"        report_sections.append({\n",[105,979,980],{"class":107,"line":258},[105,981,982],{},"            \"title\": section.title,\n",[105,984,985],{"class":107,"line":266},[105,986,987],{},"            \"content\": content\n",[105,989,990],{"class":107,"line":274},[105,991,992],{},"        })\n",[105,994,995],{"class":107,"line":282},[105,996,494],{"emptyLinePlaceholder":493},[105,998,999],{"class":107,"line":288},[105,1000,1001],{},"    # 生成洞察\n",[105,1003,1004],{"class":107,"line":294},[105,1005,1006],{},"    insights = await generate_insights(report_sections)\n",[105,1008,1009],{"class":107,"line":300},[105,1010,494],{"emptyLinePlaceholder":493},[105,1012,1013],{"class":107,"line":861},[105,1014,1015],{},"    return {\n",[105,1017,1018],{"class":107,"line":866},[105,1019,1020],{},"        \"title\": template.name,\n",[105,1022,1023],{"class":107,"line":871},[105,1024,1025],{},"        \"date_range\": date_range,\n",[105,1027,1029],{"class":107,"line":1028},31,[105,1030,1031],{},"        \"sections\": report_sections,\n",[105,1033,1035],{"class":107,"line":1034},32,[105,1036,1037],{},"        \"insights\": insights\n",[105,1039,1041],{"class":107,"line":1040},33,[105,1042,450],{},[91,1044,1045],{"id":1045},"报表分发",[95,1047,1049],{"className":97,"code":1048,"language":99,"meta":100,"style":100},"{\n  \"distribution\": {\n    \"channels\": [\"email\", \"slack\", \"feishu\"],\n    \"recipients\": {\n      \"email\": [\"manager@company.com\", \"team@company.com\"],\n      \"slack\": [\"#sales-reports\"],\n      \"feishu\": [\"oc_xxx\"]\n    },\n    \"format\": [\"pdf\", \"excel\", \"html\"],\n    \"schedule\": {\n      \"daily\": \"08:00\",\n      \"weekly\": \"Monday 09:00\",\n      \"monthly\": \"1st 10:00\"\n    }\n  }\n}\n",[102,1050,1051,1055,1062,1084,1091,1108,1120,1132,1136,1158,1165,1177,1189,1199,1203,1207],{"__ignoreMap":100},[105,1052,1053],{"class":107,"line":108},[105,1054,112],{"class":111},[105,1056,1057,1060],{"class":107,"line":115},[105,1058,1059],{"class":118},"  \"distribution\"",[105,1061,122],{"class":111},[105,1063,1064,1067,1069,1072,1074,1077,1079,1082],{"class":107,"line":125},[105,1065,1066],{"class":118},"    \"channels\"",[105,1068,686],{"class":111},[105,1070,1071],{"class":137},"\"email\"",[105,1073,692],{"class":111},[105,1075,1076],{"class":137},"\"slack\"",[105,1078,692],{"class":111},[105,1080,1081],{"class":137},"\"feishu\"",[105,1083,759],{"class":111},[105,1085,1086,1089],{"class":107,"line":134},[105,1087,1088],{"class":118},"    \"recipients\"",[105,1090,122],{"class":111},[105,1092,1093,1096,1098,1101,1103,1106],{"class":107,"line":144},[105,1094,1095],{"class":118},"      \"email\"",[105,1097,686],{"class":111},[105,1099,1100],{"class":137},"\"manager@company.com\"",[105,1102,692],{"class":111},[105,1104,1105],{"class":137},"\"team@company.com\"",[105,1107,759],{"class":111},[105,1109,1110,1113,1115,1118],{"class":107,"line":152},[105,1111,1112],{"class":118},"      \"slack\"",[105,1114,686],{"class":111},[105,1116,1117],{"class":137},"\"#sales-reports\"",[105,1119,759],{"class":111},[105,1121,1122,1125,1127,1130],{"class":107,"line":160},[105,1123,1124],{"class":118},"      \"feishu\"",[105,1126,686],{"class":111},[105,1128,1129],{"class":137},"\"oc_xxx\"",[105,1131,703],{"class":111},[105,1133,1134],{"class":107,"line":166},[105,1135,405],{"class":111},[105,1137,1138,1141,1143,1146,1148,1151,1153,1156],{"class":107,"line":172},[105,1139,1140],{"class":118},"    \"format\"",[105,1142,686],{"class":111},[105,1144,1145],{"class":137},"\"pdf\"",[105,1147,692],{"class":111},[105,1149,1150],{"class":137},"\"excel\"",[105,1152,692],{"class":111},[105,1154,1155],{"class":137},"\"html\"",[105,1157,759],{"class":111},[105,1159,1160,1163],{"class":107,"line":180},[105,1161,1162],{"class":118},"    \"schedule\"",[105,1164,122],{"class":111},[105,1166,1167,1170,1172,1175],{"class":107,"line":188},[105,1168,1169],{"class":118},"      \"daily\"",[105,1171,337],{"class":111},[105,1173,1174],{"class":137},"\"08:00\"",[105,1176,141],{"class":111},[105,1178,1179,1182,1184,1187],{"class":107,"line":196},[105,1180,1181],{"class":118},"      \"weekly\"",[105,1183,337],{"class":111},[105,1185,1186],{"class":137},"\"Monday 09:00\"",[105,1188,141],{"class":111},[105,1190,1191,1194,1196],{"class":107,"line":204},[105,1192,1193],{"class":118},"      \"monthly\"",[105,1195,337],{"class":111},[105,1197,1198],{"class":137},"\"1st 10:00\"\n",[105,1200,1201],{"class":107,"line":210},[105,1202,450],{"class":111},[105,1204,1205],{"class":107,"line":215},[105,1206,297],{"class":111},[105,1208,1209],{"class":107,"line":223},[105,1210,303],{"class":111},[17,1212,58],{"id":58},[91,1214,1215],{"id":1215},"智能图表推荐",[95,1217,1219],{"className":464,"code":1218,"language":466,"meta":100,"style":100},"async def recommend_chart(data_profile):\n    \"\"\"推荐最佳图表类型\"\"\"\n    prompt = f\"\"\"\n    根据数据特征推荐最佳可视化方式：\n\n    数据特征：\n    - 维度：{data_profile.dimensions}\n    - 指标：{data_profile.metrics}\n    - 数据量：{data_profile.row_count}\n    - 时间跨度：{data_profile.time_span}\n\n    推荐要求：\n    1. 选择最直观的图表类型\n    2. 说明推荐理由\n    3. 提供配置建议\n\n    返回 JSON：\n    {{\n        \"chart_type\": \"line\",\n        \"reason\": \"适合展示时间序列趋势\",\n        \"config\": {{\n            \"x\": \"date\",\n            \"y\": [\"sales\", \"profit\"],\n            \"colors\": [\"#4CAF50\", \"#2196F3\"]\n        }}\n    }}\n    \"\"\"\n\n    return await generate_response(prompt)\n",[102,1220,1221,1226,1231,1235,1240,1244,1249,1254,1259,1264,1269,1273,1278,1283,1288,1293,1297,1301,1305,1310,1315,1320,1325,1330,1335,1340,1344,1348,1352],{"__ignoreMap":100},[105,1222,1223],{"class":107,"line":108},[105,1224,1225],{},"async def recommend_chart(data_profile):\n",[105,1227,1228],{"class":107,"line":115},[105,1229,1230],{},"    \"\"\"推荐最佳图表类型\"\"\"\n",[105,1232,1233],{"class":107,"line":125},[105,1234,483],{},[105,1236,1237],{"class":107,"line":134},[105,1238,1239],{},"    根据数据特征推荐最佳可视化方式：\n",[105,1241,1242],{"class":107,"line":144},[105,1243,494],{"emptyLinePlaceholder":493},[105,1245,1246],{"class":107,"line":152},[105,1247,1248],{},"    数据特征：\n",[105,1250,1251],{"class":107,"line":160},[105,1252,1253],{},"    - 维度：{data_profile.dimensions}\n",[105,1255,1256],{"class":107,"line":166},[105,1257,1258],{},"    - 指标：{data_profile.metrics}\n",[105,1260,1261],{"class":107,"line":172},[105,1262,1263],{},"    - 数据量：{data_profile.row_count}\n",[105,1265,1266],{"class":107,"line":180},[105,1267,1268],{},"    - 时间跨度：{data_profile.time_span}\n",[105,1270,1271],{"class":107,"line":188},[105,1272,494],{"emptyLinePlaceholder":493},[105,1274,1275],{"class":107,"line":196},[105,1276,1277],{},"    推荐要求：\n",[105,1279,1280],{"class":107,"line":204},[105,1281,1282],{},"    1. 选择最直观的图表类型\n",[105,1284,1285],{"class":107,"line":210},[105,1286,1287],{},"    2. 说明推荐理由\n",[105,1289,1290],{"class":107,"line":215},[105,1291,1292],{},"    3. 提供配置建议\n",[105,1294,1295],{"class":107,"line":223},[105,1296,494],{"emptyLinePlaceholder":493},[105,1298,1299],{"class":107,"line":231},[105,1300,551],{},[105,1302,1303],{"class":107,"line":239},[105,1304,556],{},[105,1306,1307],{"class":107,"line":247},[105,1308,1309],{},"        \"chart_type\": \"line\",\n",[105,1311,1312],{"class":107,"line":253},[105,1313,1314],{},"        \"reason\": \"适合展示时间序列趋势\",\n",[105,1316,1317],{"class":107,"line":258},[105,1318,1319],{},"        \"config\": {{\n",[105,1321,1322],{"class":107,"line":266},[105,1323,1324],{},"            \"x\": \"date\",\n",[105,1326,1327],{"class":107,"line":274},[105,1328,1329],{},"            \"y\": [\"sales\", \"profit\"],\n",[105,1331,1332],{"class":107,"line":282},[105,1333,1334],{},"            \"colors\": [\"#4CAF50\", \"#2196F3\"]\n",[105,1336,1337],{"class":107,"line":288},[105,1338,1339],{},"        }}\n",[105,1341,1342],{"class":107,"line":294},[105,1343,576],{},[105,1345,1346],{"class":107,"line":300},[105,1347,581],{},[105,1349,1350],{"class":107,"line":861},[105,1351,494],{"emptyLinePlaceholder":493},[105,1353,1354],{"class":107,"line":866},[105,1355,590],{},[91,1357,1358],{"id":1358},"图表类型指南",[21,1360,1361,1374],{},[24,1362,1363],{},[27,1364,1365,1368,1371],{},[30,1366,1367],{},"数据类型",[30,1369,1370],{},"推荐图表",[30,1372,1373],{},"适用场景",[40,1375,1376,1387,1398,1409,1420,1431],{},[27,1377,1378,1381,1384],{},[45,1379,1380],{},"时间序列",[45,1382,1383],{},"折线图、面积图",[45,1385,1386],{},"趋势分析",[27,1388,1389,1392,1395],{},[45,1390,1391],{},"对比分析",[45,1393,1394],{},"柱状图、条形图",[45,1396,1397],{},"类别比较",[27,1399,1400,1403,1406],{},[45,1401,1402],{},"占比分析",[45,1404,1405],{},"饼图、环形图",[45,1407,1408],{},"构成分析",[27,1410,1411,1414,1417],{},[45,1412,1413],{},"分布分析",[45,1415,1416],{},"直方图、箱线图",[45,1418,1419],{},"数据分布",[27,1421,1422,1425,1428],{},[45,1423,1424],{},"关联分析",[45,1426,1427],{},"散点图、气泡图",[45,1429,1430],{},"相关性",[27,1432,1433,1436,1439],{},[45,1434,1435],{},"地理数据",[45,1437,1438],{},"地图、热力图",[45,1440,1441],{},"区域分析",[91,1443,1444],{"id":1444},"交互式图表",[95,1446,1448],{"className":97,"code":1447,"language":99,"meta":100,"style":100},"{\n  \"chart_config\": {\n    \"type\": \"line\",\n    \"data\": {\n      \"labels\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\"],\n      \"datasets\": [\n        {\n          \"label\": \"销售额\",\n          \"data\": [100, 120, 150, 130, 180],\n          \"borderColor\": \"#4CAF50\"\n        }\n      ]\n    },\n    \"options\": {\n      \"responsive\": true,\n      \"plugins\": {\n        \"tooltip\": {\n          \"enabled\": true\n        },\n        \"zoom\": {\n          \"enabled\": true\n        }\n      },\n      \"scales\": {\n        \"y\": {\n          \"beginAtZero\": true\n        }\n      }\n    }\n  }\n}\n",[102,1449,1450,1454,1461,1472,1479,1511,1518,1522,1534,1566,1576,1580,1584,1588,1595,1607,1614,1621,1631,1635,1642,1650,1654,1659,1666,1673,1682,1686,1691,1695,1699],{"__ignoreMap":100},[105,1451,1452],{"class":107,"line":108},[105,1453,112],{"class":111},[105,1455,1456,1459],{"class":107,"line":115},[105,1457,1458],{"class":118},"  \"chart_config\"",[105,1460,122],{"class":111},[105,1462,1463,1466,1468,1470],{"class":107,"line":125},[105,1464,1465],{"class":118},"    \"type\"",[105,1467,337],{"class":111},[105,1469,744],{"class":137},[105,1471,141],{"class":111},[105,1473,1474,1477],{"class":107,"line":134},[105,1475,1476],{"class":118},"    \"data\"",[105,1478,122],{"class":111},[105,1480,1481,1484,1486,1489,1491,1494,1496,1499,1501,1504,1506,1509],{"class":107,"line":144},[105,1482,1483],{"class":118},"      \"labels\"",[105,1485,686],{"class":111},[105,1487,1488],{"class":137},"\"Jan\"",[105,1490,692],{"class":111},[105,1492,1493],{"class":137},"\"Feb\"",[105,1495,692],{"class":111},[105,1497,1498],{"class":137},"\"Mar\"",[105,1500,692],{"class":111},[105,1502,1503],{"class":137},"\"Apr\"",[105,1505,692],{"class":111},[105,1507,1508],{"class":137},"\"May\"",[105,1510,759],{"class":111},[105,1512,1513,1516],{"class":107,"line":152},[105,1514,1515],{"class":118},"      \"datasets\"",[105,1517,131],{"class":111},[105,1519,1520],{"class":107,"line":160},[105,1521,654],{"class":111},[105,1523,1524,1527,1529,1532],{"class":107,"line":166},[105,1525,1526],{"class":118},"          \"label\"",[105,1528,337],{"class":111},[105,1530,1531],{"class":137},"\"销售额\"",[105,1533,141],{"class":111},[105,1535,1536,1539,1541,1544,1546,1549,1551,1554,1556,1559,1561,1564],{"class":107,"line":172},[105,1537,1538],{"class":118},"          \"data\"",[105,1540,686],{"class":111},[105,1542,1543],{"class":118},"100",[105,1545,692],{"class":111},[105,1547,1548],{"class":118},"120",[105,1550,692],{"class":111},[105,1552,1553],{"class":118},"150",[105,1555,692],{"class":111},[105,1557,1558],{"class":118},"130",[105,1560,692],{"class":111},[105,1562,1563],{"class":118},"180",[105,1565,759],{"class":111},[105,1567,1568,1571,1573],{"class":107,"line":180},[105,1569,1570],{"class":118},"          \"borderColor\"",[105,1572,337],{"class":111},[105,1574,1575],{"class":137},"\"#4CAF50\"\n",[105,1577,1578],{"class":107,"line":188},[105,1579,853],{"class":111},[105,1581,1582],{"class":107,"line":196},[105,1583,858],{"class":111},[105,1585,1586],{"class":107,"line":204},[105,1587,405],{"class":111},[105,1589,1590,1593],{"class":107,"line":210},[105,1591,1592],{"class":118},"    \"options\"",[105,1594,122],{"class":111},[105,1596,1597,1600,1602,1605],{"class":107,"line":215},[105,1598,1599],{"class":118},"      \"responsive\"",[105,1601,337],{"class":111},[105,1603,1604],{"class":118},"true",[105,1606,141],{"class":111},[105,1608,1609,1612],{"class":107,"line":223},[105,1610,1611],{"class":118},"      \"plugins\"",[105,1613,122],{"class":111},[105,1615,1616,1619],{"class":107,"line":231},[105,1617,1618],{"class":118},"        \"tooltip\"",[105,1620,122],{"class":111},[105,1622,1623,1626,1628],{"class":107,"line":239},[105,1624,1625],{"class":118},"          \"enabled\"",[105,1627,337],{"class":111},[105,1629,1630],{"class":118},"true\n",[105,1632,1633],{"class":107,"line":247},[105,1634,708],{"class":111},[105,1636,1637,1640],{"class":107,"line":253},[105,1638,1639],{"class":118},"        \"zoom\"",[105,1641,122],{"class":111},[105,1643,1644,1646,1648],{"class":107,"line":258},[105,1645,1625],{"class":118},[105,1647,337],{"class":111},[105,1649,1630],{"class":118},[105,1651,1652],{"class":107,"line":266},[105,1653,853],{"class":111},[105,1655,1656],{"class":107,"line":274},[105,1657,1658],{"class":111},"      },\n",[105,1660,1661,1664],{"class":107,"line":282},[105,1662,1663],{"class":118},"      \"scales\"",[105,1665,122],{"class":111},[105,1667,1668,1671],{"class":107,"line":288},[105,1669,1670],{"class":118},"        \"y\"",[105,1672,122],{"class":111},[105,1674,1675,1678,1680],{"class":107,"line":294},[105,1676,1677],{"class":118},"          \"beginAtZero\"",[105,1679,337],{"class":111},[105,1681,1630],{"class":118},[105,1683,1684],{"class":107,"line":300},[105,1685,853],{"class":111},[105,1687,1688],{"class":107,"line":861},[105,1689,1690],{"class":111},"      }\n",[105,1692,1693],{"class":107,"line":866},[105,1694,450],{"class":111},[105,1696,1697],{"class":107,"line":871},[105,1698,297],{"class":111},[105,1700,1701],{"class":107,"line":1028},[105,1702,303],{"class":111},[17,1704,69],{"id":69},[91,1706,1707],{"id":1707},"异常检测配置",[95,1709,1711],{"className":97,"code":1710,"language":99,"meta":100,"style":100},"{\n  \"anomaly_detection\": {\n    \"metrics\": [\n      {\n        \"name\": \"daily_revenue\",\n        \"method\": \"zscore\",\n        \"threshold\": 3,\n        \"lookback_days\": 30\n      },\n      {\n        \"name\": \"conversion_rate\",\n        \"method\": \"isolation_forest\",\n        \"sensitivity\": 0.95\n      },\n      {\n        \"name\": \"error_rate\",\n        \"method\": \"moving_average\",\n        \"window\": 7,\n        \"threshold\": 2\n      }\n    ]\n  }\n}\n",[102,1712,1713,1717,1724,1731,1736,1748,1760,1772,1782,1786,1790,1801,1812,1822,1826,1830,1841,1852,1864,1873,1877,1881,1885],{"__ignoreMap":100},[105,1714,1715],{"class":107,"line":108},[105,1716,112],{"class":111},[105,1718,1719,1722],{"class":107,"line":115},[105,1720,1721],{"class":118},"  \"anomaly_detection\"",[105,1723,122],{"class":111},[105,1725,1726,1729],{"class":107,"line":125},[105,1727,1728],{"class":118},"    \"metrics\"",[105,1730,131],{"class":111},[105,1732,1733],{"class":107,"line":134},[105,1734,1735],{"class":111},"      {\n",[105,1737,1738,1741,1743,1746],{"class":107,"line":144},[105,1739,1740],{"class":118},"        \"name\"",[105,1742,337],{"class":111},[105,1744,1745],{"class":137},"\"daily_revenue\"",[105,1747,141],{"class":111},[105,1749,1750,1753,1755,1758],{"class":107,"line":152},[105,1751,1752],{"class":118},"        \"method\"",[105,1754,337],{"class":111},[105,1756,1757],{"class":137},"\"zscore\"",[105,1759,141],{"class":111},[105,1761,1762,1765,1767,1770],{"class":107,"line":160},[105,1763,1764],{"class":118},"        \"threshold\"",[105,1766,337],{"class":111},[105,1768,1769],{"class":118},"3",[105,1771,141],{"class":111},[105,1773,1774,1777,1779],{"class":107,"line":166},[105,1775,1776],{"class":118},"        \"lookback_days\"",[105,1778,337],{"class":111},[105,1780,1781],{"class":118},"30\n",[105,1783,1784],{"class":107,"line":172},[105,1785,1658],{"class":111},[105,1787,1788],{"class":107,"line":180},[105,1789,1735],{"class":111},[105,1791,1792,1794,1796,1799],{"class":107,"line":188},[105,1793,1740],{"class":118},[105,1795,337],{"class":111},[105,1797,1798],{"class":137},"\"conversion_rate\"",[105,1800,141],{"class":111},[105,1802,1803,1805,1807,1810],{"class":107,"line":196},[105,1804,1752],{"class":118},[105,1806,337],{"class":111},[105,1808,1809],{"class":137},"\"isolation_forest\"",[105,1811,141],{"class":111},[105,1813,1814,1817,1819],{"class":107,"line":204},[105,1815,1816],{"class":118},"        \"sensitivity\"",[105,1818,337],{"class":111},[105,1820,1821],{"class":118},"0.95\n",[105,1823,1824],{"class":107,"line":210},[105,1825,1658],{"class":111},[105,1827,1828],{"class":107,"line":215},[105,1829,1735],{"class":111},[105,1831,1832,1834,1836,1839],{"class":107,"line":223},[105,1833,1740],{"class":118},[105,1835,337],{"class":111},[105,1837,1838],{"class":137},"\"error_rate\"",[105,1840,141],{"class":111},[105,1842,1843,1845,1847,1850],{"class":107,"line":231},[105,1844,1752],{"class":118},[105,1846,337],{"class":111},[105,1848,1849],{"class":137},"\"moving_average\"",[105,1851,141],{"class":111},[105,1853,1854,1857,1859,1862],{"class":107,"line":239},[105,1855,1856],{"class":118},"        \"window\"",[105,1858,337],{"class":111},[105,1860,1861],{"class":118},"7",[105,1863,141],{"class":111},[105,1865,1866,1868,1870],{"class":107,"line":247},[105,1867,1764],{"class":118},[105,1869,337],{"class":111},[105,1871,1872],{"class":118},"2\n",[105,1874,1875],{"class":107,"line":253},[105,1876,1690],{"class":111},[105,1878,1879],{"class":107,"line":258},[105,1880,291],{"class":111},[105,1882,1883],{"class":107,"line":266},[105,1884,297],{"class":111},[105,1886,1887],{"class":107,"line":274},[105,1888,303],{"class":111},[91,1890,1891],{"id":1891},"异常检测算法",[95,1893,1895],{"className":464,"code":1894,"language":466,"meta":100,"style":100},"async def detect_anomalies(metric, data):\n    \"\"\"检测数据异常\"\"\"\n    if metric.method == \"zscore\":\n        # Z-Score 方法\n        mean = np.mean(data)\n        std = np.std(data)\n        anomalies = [\n            (i, v) for i, v in enumerate(data)\n            if abs((v - mean) / std) > metric.threshold\n        ]\n\n    elif metric.method == \"isolation_forest\":\n        # 孤立森林\n        clf = IsolationForest(contamination=0.1)\n        predictions = clf.fit_predict(data.reshape(-1, 1))\n        anomalies = [(i, data[i]) for i, p in enumerate(predictions) if p == -1]\n\n    elif metric.method == \"moving_average\":\n        # 移动平均\n        ma = moving_average(data, metric.window)\n        deviations = np.abs(data - ma)\n        threshold = np.mean(deviations) * metric.threshold\n        anomalies = [(i, data[i]) for i, d in enumerate(deviations) if d > threshold]\n\n    return anomalies\n",[102,1896,1897,1902,1907,1912,1917,1922,1927,1932,1937,1942,1947,1951,1956,1961,1966,1971,1976,1980,1985,1990,1995,2000,2005,2010,2014],{"__ignoreMap":100},[105,1898,1899],{"class":107,"line":108},[105,1900,1901],{},"async def detect_anomalies(metric, data):\n",[105,1903,1904],{"class":107,"line":115},[105,1905,1906],{},"    \"\"\"检测数据异常\"\"\"\n",[105,1908,1909],{"class":107,"line":125},[105,1910,1911],{},"    if metric.method == \"zscore\":\n",[105,1913,1914],{"class":107,"line":134},[105,1915,1916],{},"        # Z-Score 方法\n",[105,1918,1919],{"class":107,"line":144},[105,1920,1921],{},"        mean = np.mean(data)\n",[105,1923,1924],{"class":107,"line":152},[105,1925,1926],{},"        std = np.std(data)\n",[105,1928,1929],{"class":107,"line":160},[105,1930,1931],{},"        anomalies = [\n",[105,1933,1934],{"class":107,"line":166},[105,1935,1936],{},"            (i, v) for i, v in enumerate(data)\n",[105,1938,1939],{"class":107,"line":172},[105,1940,1941],{},"            if abs((v - mean) / std) > metric.threshold\n",[105,1943,1944],{"class":107,"line":180},[105,1945,1946],{},"        ]\n",[105,1948,1949],{"class":107,"line":188},[105,1950,494],{"emptyLinePlaceholder":493},[105,1952,1953],{"class":107,"line":196},[105,1954,1955],{},"    elif metric.method == \"isolation_forest\":\n",[105,1957,1958],{"class":107,"line":204},[105,1959,1960],{},"        # 孤立森林\n",[105,1962,1963],{"class":107,"line":210},[105,1964,1965],{},"        clf = IsolationForest(contamination=0.1)\n",[105,1967,1968],{"class":107,"line":215},[105,1969,1970],{},"        predictions = clf.fit_predict(data.reshape(-1, 1))\n",[105,1972,1973],{"class":107,"line":223},[105,1974,1975],{},"        anomalies = [(i, data[i]) for i, p in enumerate(predictions) if p == -1]\n",[105,1977,1978],{"class":107,"line":231},[105,1979,494],{"emptyLinePlaceholder":493},[105,1981,1982],{"class":107,"line":239},[105,1983,1984],{},"    elif metric.method == \"moving_average\":\n",[105,1986,1987],{"class":107,"line":247},[105,1988,1989],{},"        # 移动平均\n",[105,1991,1992],{"class":107,"line":253},[105,1993,1994],{},"        ma = moving_average(data, metric.window)\n",[105,1996,1997],{"class":107,"line":258},[105,1998,1999],{},"        deviations = np.abs(data - ma)\n",[105,2001,2002],{"class":107,"line":266},[105,2003,2004],{},"        threshold = np.mean(deviations) * metric.threshold\n",[105,2006,2007],{"class":107,"line":274},[105,2008,2009],{},"        anomalies = [(i, data[i]) for i, d in enumerate(deviations) if d > threshold]\n",[105,2011,2012],{"class":107,"line":282},[105,2013,494],{"emptyLinePlaceholder":493},[105,2015,2016],{"class":107,"line":288},[105,2017,2018],{},"    return anomalies\n",[91,2020,2021],{"id":2021},"异常告警",[95,2023,2025],{"className":97,"code":2024,"language":99,"meta":100,"style":100},"{\n  \"workflow\": {\n    \"id\": \"anomaly-alert\",\n    \"name\": \"异常告警\",\n    \"trigger\": {\n      \"type\": \"event\",\n      \"event\": \"anomaly.detected\"\n    },\n    \"steps\": [\n      {\n        \"id\": \"analyze\",\n        \"type\": \"ai\",\n        \"prompt\": \"分析以下数据异常：\\n指标：{{event.metric}}\\n异常值：{{event.value}}\\n正常范围：{{event.normal_range}}\\n\\n给出可能原因和建议措施。\"\n      },\n      {\n        \"id\": \"alert\",\n        \"type\": \"action\",\n        \"action\": \"sendAlert\",\n        \"params\": {\n          \"channel\": \"slack\",\n          \"severity\": \"{{event.severity}}\",\n          \"message\": \"⚠️ 数据异常告警\\n\\n指标：{{event.metric}}\\n异常值：{{event.value}}\\n分析：{{analyze.result}}\"\n        }\n      }\n    ]\n  }\n}\n",[102,2026,2027,2031,2038,2050,2062,2069,2080,2090,2094,2101,2105,2117,2129,2161,2165,2169,2180,2191,2203,2210,2221,2233,2256,2260,2264,2268,2272],{"__ignoreMap":100},[105,2028,2029],{"class":107,"line":108},[105,2030,112],{"class":111},[105,2032,2033,2036],{"class":107,"line":115},[105,2034,2035],{"class":118},"  \"workflow\"",[105,2037,122],{"class":111},[105,2039,2040,2043,2045,2048],{"class":107,"line":125},[105,2041,2042],{"class":118},"    \"id\"",[105,2044,337],{"class":111},[105,2046,2047],{"class":137},"\"anomaly-alert\"",[105,2049,141],{"class":111},[105,2051,2052,2055,2057,2060],{"class":107,"line":134},[105,2053,2054],{"class":118},"    \"name\"",[105,2056,337],{"class":111},[105,2058,2059],{"class":137},"\"异常告警\"",[105,2061,141],{"class":111},[105,2063,2064,2067],{"class":107,"line":144},[105,2065,2066],{"class":118},"    \"trigger\"",[105,2068,122],{"class":111},[105,2070,2071,2073,2075,2078],{"class":107,"line":152},[105,2072,334],{"class":118},[105,2074,337],{"class":111},[105,2076,2077],{"class":137},"\"event\"",[105,2079,141],{"class":111},[105,2081,2082,2085,2087],{"class":107,"line":160},[105,2083,2084],{"class":118},"      \"event\"",[105,2086,337],{"class":111},[105,2088,2089],{"class":137},"\"anomaly.detected\"\n",[105,2091,2092],{"class":107,"line":166},[105,2093,405],{"class":111},[105,2095,2096,2099],{"class":107,"line":172},[105,2097,2098],{"class":118},"    \"steps\"",[105,2100,131],{"class":111},[105,2102,2103],{"class":107,"line":180},[105,2104,1735],{"class":111},[105,2106,2107,2110,2112,2115],{"class":107,"line":188},[105,2108,2109],{"class":118},"        \"id\"",[105,2111,337],{"class":111},[105,2113,2114],{"class":137},"\"analyze\"",[105,2116,141],{"class":111},[105,2118,2119,2122,2124,2127],{"class":107,"line":196},[105,2120,2121],{"class":118},"        \"type\"",[105,2123,337],{"class":111},[105,2125,2126],{"class":137},"\"ai\"",[105,2128,141],{"class":111},[105,2130,2131,2134,2136,2139,2142,2145,2147,2150,2152,2155,2158],{"class":107,"line":204},[105,2132,2133],{"class":118},"        \"prompt\"",[105,2135,337],{"class":111},[105,2137,2138],{"class":137},"\"分析以下数据异常：",[105,2140,2141],{"class":118},"\\n",[105,2143,2144],{"class":137},"指标：{{event.metric}}",[105,2146,2141],{"class":118},[105,2148,2149],{"class":137},"异常值：{{event.value}}",[105,2151,2141],{"class":118},[105,2153,2154],{"class":137},"正常范围：{{event.normal_range}}",[105,2156,2157],{"class":118},"\\n\\n",[105,2159,2160],{"class":137},"给出可能原因和建议措施。\"\n",[105,2162,2163],{"class":107,"line":210},[105,2164,1658],{"class":111},[105,2166,2167],{"class":107,"line":215},[105,2168,1735],{"class":111},[105,2170,2171,2173,2175,2178],{"class":107,"line":223},[105,2172,2109],{"class":118},[105,2174,337],{"class":111},[105,2176,2177],{"class":137},"\"alert\"",[105,2179,141],{"class":111},[105,2181,2182,2184,2186,2189],{"class":107,"line":231},[105,2183,2121],{"class":118},[105,2185,337],{"class":111},[105,2187,2188],{"class":137},"\"action\"",[105,2190,141],{"class":111},[105,2192,2193,2196,2198,2201],{"class":107,"line":239},[105,2194,2195],{"class":118},"        \"action\"",[105,2197,337],{"class":111},[105,2199,2200],{"class":137},"\"sendAlert\"",[105,2202,141],{"class":111},[105,2204,2205,2208],{"class":107,"line":247},[105,2206,2207],{"class":118},"        \"params\"",[105,2209,122],{"class":111},[105,2211,2212,2215,2217,2219],{"class":107,"line":253},[105,2213,2214],{"class":118},"          \"channel\"",[105,2216,337],{"class":111},[105,2218,1076],{"class":137},[105,2220,141],{"class":111},[105,2222,2223,2226,2228,2231],{"class":107,"line":258},[105,2224,2225],{"class":118},"          \"severity\"",[105,2227,337],{"class":111},[105,2229,2230],{"class":137},"\"{{event.severity}}\"",[105,2232,141],{"class":111},[105,2234,2235,2238,2240,2243,2245,2247,2249,2251,2253],{"class":107,"line":266},[105,2236,2237],{"class":118},"          \"message\"",[105,2239,337],{"class":111},[105,2241,2242],{"class":137},"\"⚠️ 数据异常告警",[105,2244,2157],{"class":118},[105,2246,2144],{"class":137},[105,2248,2141],{"class":118},[105,2250,2149],{"class":137},[105,2252,2141],{"class":118},[105,2254,2255],{"class":137},"分析：{{analyze.result}}\"\n",[105,2257,2258],{"class":107,"line":274},[105,2259,853],{"class":111},[105,2261,2262],{"class":107,"line":282},[105,2263,1690],{"class":111},[105,2265,2266],{"class":107,"line":288},[105,2267,291],{"class":111},[105,2269,2270],{"class":107,"line":294},[105,2271,297],{"class":111},[105,2273,2274],{"class":107,"line":300},[105,2275,303],{"class":111},[17,2277,80],{"id":80},[91,2279,2280],{"id":2280},"自动洞察生成",[95,2282,2284],{"className":464,"code":2283,"language":466,"meta":100,"style":100},"async def generate_insights(data, context=None):\n    \"\"\"生成数据洞察\"\"\"\n    prompt = f\"\"\"\n    分析以下数据，生成洞察报告：\n\n    数据摘要：\n    {data.summary}\n\n    关键指标：\n    {data.key_metrics}\n\n    趋势信息：\n    {data.trends}\n\n    {f'背景信息：{context}' if context else ''}\n\n    要求：\n    1. 识别关键变化和趋势\n    2. 分析可能原因\n    3. 提出行动建议\n    4. 使用简洁专业的语言\n\n    输出格式：\n    ## 关键发现\n    - 发现 1\n    - 发现 2\n\n    ## 原因分析\n    - 分析 1\n    - 分析 2\n\n    ## 行动建议\n    - 建议 1\n    - 建议 2\n    \"\"\"\n\n    return await generate_response(prompt)\n",[102,2285,2286,2291,2296,2300,2305,2309,2314,2319,2323,2328,2333,2337,2342,2347,2351,2356,2360,2364,2369,2374,2379,2384,2388,2393,2398,2403,2408,2412,2417,2422,2427,2431,2436,2441,2447,2452,2457],{"__ignoreMap":100},[105,2287,2288],{"class":107,"line":108},[105,2289,2290],{},"async def generate_insights(data, context=None):\n",[105,2292,2293],{"class":107,"line":115},[105,2294,2295],{},"    \"\"\"生成数据洞察\"\"\"\n",[105,2297,2298],{"class":107,"line":125},[105,2299,483],{},[105,2301,2302],{"class":107,"line":134},[105,2303,2304],{},"    分析以下数据，生成洞察报告：\n",[105,2306,2307],{"class":107,"line":144},[105,2308,494],{"emptyLinePlaceholder":493},[105,2310,2311],{"class":107,"line":152},[105,2312,2313],{},"    数据摘要：\n",[105,2315,2316],{"class":107,"line":160},[105,2317,2318],{},"    {data.summary}\n",[105,2320,2321],{"class":107,"line":166},[105,2322,494],{"emptyLinePlaceholder":493},[105,2324,2325],{"class":107,"line":172},[105,2326,2327],{},"    关键指标：\n",[105,2329,2330],{"class":107,"line":180},[105,2331,2332],{},"    {data.key_metrics}\n",[105,2334,2335],{"class":107,"line":188},[105,2336,494],{"emptyLinePlaceholder":493},[105,2338,2339],{"class":107,"line":196},[105,2340,2341],{},"    趋势信息：\n",[105,2343,2344],{"class":107,"line":204},[105,2345,2346],{},"    {data.trends}\n",[105,2348,2349],{"class":107,"line":210},[105,2350,494],{"emptyLinePlaceholder":493},[105,2352,2353],{"class":107,"line":215},[105,2354,2355],{},"    {f'背景信息：{context}' if context else ''}\n",[105,2357,2358],{"class":107,"line":223},[105,2359,494],{"emptyLinePlaceholder":493},[105,2361,2362],{"class":107,"line":231},[105,2363,522],{},[105,2365,2366],{"class":107,"line":239},[105,2367,2368],{},"    1. 识别关键变化和趋势\n",[105,2370,2371],{"class":107,"line":247},[105,2372,2373],{},"    2. 分析可能原因\n",[105,2375,2376],{"class":107,"line":253},[105,2377,2378],{},"    3. 提出行动建议\n",[105,2380,2381],{"class":107,"line":258},[105,2382,2383],{},"    4. 使用简洁专业的语言\n",[105,2385,2386],{"class":107,"line":266},[105,2387,494],{"emptyLinePlaceholder":493},[105,2389,2390],{"class":107,"line":274},[105,2391,2392],{},"    输出格式：\n",[105,2394,2395],{"class":107,"line":282},[105,2396,2397],{},"    ## 关键发现\n",[105,2399,2400],{"class":107,"line":288},[105,2401,2402],{},"    - 发现 1\n",[105,2404,2405],{"class":107,"line":294},[105,2406,2407],{},"    - 发现 2\n",[105,2409,2410],{"class":107,"line":300},[105,2411,494],{"emptyLinePlaceholder":493},[105,2413,2414],{"class":107,"line":861},[105,2415,2416],{},"    ## 原因分析\n",[105,2418,2419],{"class":107,"line":866},[105,2420,2421],{},"    - 分析 1\n",[105,2423,2424],{"class":107,"line":871},[105,2425,2426],{},"    - 分析 2\n",[105,2428,2429],{"class":107,"line":1028},[105,2430,494],{"emptyLinePlaceholder":493},[105,2432,2433],{"class":107,"line":1034},[105,2434,2435],{},"    ## 行动建议\n",[105,2437,2438],{"class":107,"line":1040},[105,2439,2440],{},"    - 建议 1\n",[105,2442,2444],{"class":107,"line":2443},34,[105,2445,2446],{},"    - 建议 2\n",[105,2448,2450],{"class":107,"line":2449},35,[105,2451,581],{},[105,2453,2455],{"class":107,"line":2454},36,[105,2456,494],{"emptyLinePlaceholder":493},[105,2458,2460],{"class":107,"line":2459},37,[105,2461,590],{},[91,2463,1391],{"id":1391},[95,2465,2467],{"className":464,"code":2466,"language":466,"meta":100,"style":100},"async def compare_periods(current, previous):\n    \"\"\"期间对比分析\"\"\"\n    changes = calculate_changes(current, previous)\n\n    prompt = f\"\"\"\n    对比分析两个时期的数据：\n\n    当前期间：{current.period}\n    对比期间：{previous.period}\n\n    变化情况：\n    {format_changes(changes)}\n\n    请分析：\n    1. 主要变化点\n    2. 变化原因推测\n    3. 需要关注的指标\n    \"\"\"\n\n    return await generate_response(prompt)\n",[102,2468,2469,2474,2479,2484,2488,2492,2497,2501,2506,2511,2515,2520,2525,2529,2534,2539,2544,2549,2553,2557],{"__ignoreMap":100},[105,2470,2471],{"class":107,"line":108},[105,2472,2473],{},"async def compare_periods(current, previous):\n",[105,2475,2476],{"class":107,"line":115},[105,2477,2478],{},"    \"\"\"期间对比分析\"\"\"\n",[105,2480,2481],{"class":107,"line":125},[105,2482,2483],{},"    changes = calculate_changes(current, previous)\n",[105,2485,2486],{"class":107,"line":134},[105,2487,494],{"emptyLinePlaceholder":493},[105,2489,2490],{"class":107,"line":144},[105,2491,483],{},[105,2493,2494],{"class":107,"line":152},[105,2495,2496],{},"    对比分析两个时期的数据：\n",[105,2498,2499],{"class":107,"line":160},[105,2500,494],{"emptyLinePlaceholder":493},[105,2502,2503],{"class":107,"line":166},[105,2504,2505],{},"    当前期间：{current.period}\n",[105,2507,2508],{"class":107,"line":172},[105,2509,2510],{},"    对比期间：{previous.period}\n",[105,2512,2513],{"class":107,"line":180},[105,2514,494],{"emptyLinePlaceholder":493},[105,2516,2517],{"class":107,"line":188},[105,2518,2519],{},"    变化情况：\n",[105,2521,2522],{"class":107,"line":196},[105,2523,2524],{},"    {format_changes(changes)}\n",[105,2526,2527],{"class":107,"line":204},[105,2528,494],{"emptyLinePlaceholder":493},[105,2530,2531],{"class":107,"line":210},[105,2532,2533],{},"    请分析：\n",[105,2535,2536],{"class":107,"line":215},[105,2537,2538],{},"    1. 主要变化点\n",[105,2540,2541],{"class":107,"line":223},[105,2542,2543],{},"    2. 变化原因推测\n",[105,2545,2546],{"class":107,"line":231},[105,2547,2548],{},"    3. 需要关注的指标\n",[105,2550,2551],{"class":107,"line":239},[105,2552,581],{},[105,2554,2555],{"class":107,"line":247},[105,2556,494],{"emptyLinePlaceholder":493},[105,2558,2559],{"class":107,"line":253},[105,2560,590],{},[91,2562,2563],{"id":2563},"预测分析",[95,2565,2567],{"className":464,"code":2566,"language":466,"meta":100,"style":100},"async def forecast(metric, historical_data, periods=7):\n    \"\"\"预测分析\"\"\"\n    # 时间序列预测\n    model = Prophet()\n    model.fit(historical_data)\n    future = model.make_future_dataframe(periods=periods)\n    forecast = model.predict(future)\n\n    # 生成预测报告\n    prompt = f\"\"\"\n    基于历史数据预测未来 {periods} 天的 {metric}：\n\n    历史趋势：{summarize_trend(historical_data)}\n    预测结果：{summarize_forecast(forecast)}\n\n    请提供：\n    1. 预测解读\n    2. 置信度说明\n    3. 风险提示\n    \"\"\"\n\n    return await generate_response(prompt)\n",[102,2568,2569,2574,2579,2584,2589,2594,2599,2604,2608,2613,2617,2622,2626,2631,2636,2640,2645,2650,2655,2660,2664,2668],{"__ignoreMap":100},[105,2570,2571],{"class":107,"line":108},[105,2572,2573],{},"async def forecast(metric, historical_data, periods=7):\n",[105,2575,2576],{"class":107,"line":115},[105,2577,2578],{},"    \"\"\"预测分析\"\"\"\n",[105,2580,2581],{"class":107,"line":125},[105,2582,2583],{},"    # 时间序列预测\n",[105,2585,2586],{"class":107,"line":134},[105,2587,2588],{},"    model = Prophet()\n",[105,2590,2591],{"class":107,"line":144},[105,2592,2593],{},"    model.fit(historical_data)\n",[105,2595,2596],{"class":107,"line":152},[105,2597,2598],{},"    future = model.make_future_dataframe(periods=periods)\n",[105,2600,2601],{"class":107,"line":160},[105,2602,2603],{},"    forecast = model.predict(future)\n",[105,2605,2606],{"class":107,"line":166},[105,2607,494],{"emptyLinePlaceholder":493},[105,2609,2610],{"class":107,"line":172},[105,2611,2612],{},"    # 生成预测报告\n",[105,2614,2615],{"class":107,"line":180},[105,2616,483],{},[105,2618,2619],{"class":107,"line":188},[105,2620,2621],{},"    基于历史数据预测未来 {periods} 天的 {metric}：\n",[105,2623,2624],{"class":107,"line":196},[105,2625,494],{"emptyLinePlaceholder":493},[105,2627,2628],{"class":107,"line":204},[105,2629,2630],{},"    历史趋势：{summarize_trend(historical_data)}\n",[105,2632,2633],{"class":107,"line":210},[105,2634,2635],{},"    预测结果：{summarize_forecast(forecast)}\n",[105,2637,2638],{"class":107,"line":215},[105,2639,494],{"emptyLinePlaceholder":493},[105,2641,2642],{"class":107,"line":223},[105,2643,2644],{},"    请提供：\n",[105,2646,2647],{"class":107,"line":231},[105,2648,2649],{},"    1. 预测解读\n",[105,2651,2652],{"class":107,"line":239},[105,2653,2654],{},"    2. 置信度说明\n",[105,2656,2657],{"class":107,"line":247},[105,2658,2659],{},"    3. 风险提示\n",[105,2661,2662],{"class":107,"line":253},[105,2663,581],{},[105,2665,2666],{"class":107,"line":258},[105,2667,494],{"emptyLinePlaceholder":493},[105,2669,2670],{"class":107,"line":266},[105,2671,590],{},[17,2673,2674],{"id":2674},"数据问答",[91,2676,2677],{"id":2677},"自然语言问答",[95,2679,2681],{"className":97,"code":2680,"language":99,"meta":100,"style":100},"{\n  \"workflow\": {\n    \"id\": \"data-qa\",\n    \"name\": \"数据问答\",\n    \"steps\": [\n      {\n        \"id\": \"understand\",\n        \"type\": \"ai\",\n        \"prompt\": \"理解用户问题，提取关键信息：\\n问题：{{user.question}}\\n\\n返回：\\n- 意图\\n- 时间范围\\n- 指标\\n- 维度\"\n      },\n      {\n        \"id\": \"query\",\n        \"type\": \"action\",\n        \"action\": \"executeDataQuery\",\n        \"params\": {\n          \"intent\": \"{{understand.intent}}\",\n          \"time_range\": \"{{understand.time_range}}\",\n          \"metrics\": \"{{understand.metrics}}\",\n          \"dimensions\": \"{{understand.dimensions}}\"\n        }\n      },\n      {\n        \"id\": \"answer\",\n        \"type\": \"ai\",\n        \"prompt\": \"根据查询结果回答用户问题：\\n\\n问题：{{user.question}}\\n\\n数据：{{query.result}}\\n\\n要求：\\n1. 直接回答问题\\n2. 提供数据支撑\\n3. 必要时添加可视化建议\"\n      }\n    ]\n  }\n}\n",[102,2682,2683,2687,2693,2704,2715,2721,2725,2736,2746,2785,2789,2793,2804,2814,2825,2831,2843,2855,2866,2875,2879,2883,2887,2898,2908,2946,2950,2954,2958],{"__ignoreMap":100},[105,2684,2685],{"class":107,"line":108},[105,2686,112],{"class":111},[105,2688,2689,2691],{"class":107,"line":115},[105,2690,2035],{"class":118},[105,2692,122],{"class":111},[105,2694,2695,2697,2699,2702],{"class":107,"line":125},[105,2696,2042],{"class":118},[105,2698,337],{"class":111},[105,2700,2701],{"class":137},"\"data-qa\"",[105,2703,141],{"class":111},[105,2705,2706,2708,2710,2713],{"class":107,"line":134},[105,2707,2054],{"class":118},[105,2709,337],{"class":111},[105,2711,2712],{"class":137},"\"数据问答\"",[105,2714,141],{"class":111},[105,2716,2717,2719],{"class":107,"line":144},[105,2718,2098],{"class":118},[105,2720,131],{"class":111},[105,2722,2723],{"class":107,"line":152},[105,2724,1735],{"class":111},[105,2726,2727,2729,2731,2734],{"class":107,"line":160},[105,2728,2109],{"class":118},[105,2730,337],{"class":111},[105,2732,2733],{"class":137},"\"understand\"",[105,2735,141],{"class":111},[105,2737,2738,2740,2742,2744],{"class":107,"line":166},[105,2739,2121],{"class":118},[105,2741,337],{"class":111},[105,2743,2126],{"class":137},[105,2745,141],{"class":111},[105,2747,2748,2750,2752,2755,2757,2760,2762,2765,2767,2770,2772,2775,2777,2780,2782],{"class":107,"line":172},[105,2749,2133],{"class":118},[105,2751,337],{"class":111},[105,2753,2754],{"class":137},"\"理解用户问题，提取关键信息：",[105,2756,2141],{"class":118},[105,2758,2759],{"class":137},"问题：{{user.question}}",[105,2761,2157],{"class":118},[105,2763,2764],{"class":137},"返回：",[105,2766,2141],{"class":118},[105,2768,2769],{"class":137},"- 意图",[105,2771,2141],{"class":118},[105,2773,2774],{"class":137},"- 时间范围",[105,2776,2141],{"class":118},[105,2778,2779],{"class":137},"- 指标",[105,2781,2141],{"class":118},[105,2783,2784],{"class":137},"- 维度\"\n",[105,2786,2787],{"class":107,"line":180},[105,2788,1658],{"class":111},[105,2790,2791],{"class":107,"line":188},[105,2792,1735],{"class":111},[105,2794,2795,2797,2799,2802],{"class":107,"line":196},[105,2796,2109],{"class":118},[105,2798,337],{"class":111},[105,2800,2801],{"class":137},"\"query\"",[105,2803,141],{"class":111},[105,2805,2806,2808,2810,2812],{"class":107,"line":204},[105,2807,2121],{"class":118},[105,2809,337],{"class":111},[105,2811,2188],{"class":137},[105,2813,141],{"class":111},[105,2815,2816,2818,2820,2823],{"class":107,"line":210},[105,2817,2195],{"class":118},[105,2819,337],{"class":111},[105,2821,2822],{"class":137},"\"executeDataQuery\"",[105,2824,141],{"class":111},[105,2826,2827,2829],{"class":107,"line":215},[105,2828,2207],{"class":118},[105,2830,122],{"class":111},[105,2832,2833,2836,2838,2841],{"class":107,"line":223},[105,2834,2835],{"class":118},"          \"intent\"",[105,2837,337],{"class":111},[105,2839,2840],{"class":137},"\"{{understand.intent}}\"",[105,2842,141],{"class":111},[105,2844,2845,2848,2850,2853],{"class":107,"line":231},[105,2846,2847],{"class":118},"          \"time_range\"",[105,2849,337],{"class":111},[105,2851,2852],{"class":137},"\"{{understand.time_range}}\"",[105,2854,141],{"class":111},[105,2856,2857,2859,2861,2864],{"class":107,"line":239},[105,2858,683],{"class":118},[105,2860,337],{"class":111},[105,2862,2863],{"class":137},"\"{{understand.metrics}}\"",[105,2865,141],{"class":111},[105,2867,2868,2870,2872],{"class":107,"line":247},[105,2869,751],{"class":118},[105,2871,337],{"class":111},[105,2873,2874],{"class":137},"\"{{understand.dimensions}}\"\n",[105,2876,2877],{"class":107,"line":253},[105,2878,853],{"class":111},[105,2880,2881],{"class":107,"line":258},[105,2882,1658],{"class":111},[105,2884,2885],{"class":107,"line":266},[105,2886,1735],{"class":111},[105,2888,2889,2891,2893,2896],{"class":107,"line":274},[105,2890,2109],{"class":118},[105,2892,337],{"class":111},[105,2894,2895],{"class":137},"\"answer\"",[105,2897,141],{"class":111},[105,2899,2900,2902,2904,2906],{"class":107,"line":282},[105,2901,2121],{"class":118},[105,2903,337],{"class":111},[105,2905,2126],{"class":137},[105,2907,141],{"class":111},[105,2909,2910,2912,2914,2917,2919,2921,2923,2926,2928,2931,2933,2936,2938,2941,2943],{"class":107,"line":288},[105,2911,2133],{"class":118},[105,2913,337],{"class":111},[105,2915,2916],{"class":137},"\"根据查询结果回答用户问题：",[105,2918,2157],{"class":118},[105,2920,2759],{"class":137},[105,2922,2157],{"class":118},[105,2924,2925],{"class":137},"数据：{{query.result}}",[105,2927,2157],{"class":118},[105,2929,2930],{"class":137},"要求：",[105,2932,2141],{"class":118},[105,2934,2935],{"class":137},"1. 直接回答问题",[105,2937,2141],{"class":118},[105,2939,2940],{"class":137},"2. 提供数据支撑",[105,2942,2141],{"class":118},[105,2944,2945],{"class":137},"3. 必要时添加可视化建议\"\n",[105,2947,2948],{"class":107,"line":294},[105,2949,1690],{"class":111},[105,2951,2952],{"class":107,"line":300},[105,2953,291],{"class":111},[105,2955,2956],{"class":107,"line":861},[105,2957,297],{"class":111},[105,2959,2960],{"class":107,"line":866},[105,2961,303],{"class":111},[91,2963,2964],{"id":2964},"常见问题示例",[21,2966,2967,2977],{},[24,2968,2969],{},[27,2970,2971,2974],{},[30,2972,2973],{},"问题",[30,2975,2976],{},"处理方式",[40,2978,2979,2987,2995,3003],{},[27,2980,2981,2984],{},[45,2982,2983],{},"\"昨天销售额多少\"",[45,2985,2986],{},"查询 → 直接回答",[27,2988,2989,2992],{},[45,2990,2991],{},"\"本周和上周对比\"",[45,2993,2994],{},"查询 → 对比分析",[27,2996,2997,3000],{},[45,2998,2999],{},"\"为什么今天订单少了\"",[45,3001,3002],{},"查询 → 归因分析",[27,3004,3005,3008],{},[45,3006,3007],{},"\"预测下月销量\"",[45,3009,3010],{},"预测 → 趋势解读",[17,3012,3013],{"id":3013},"最佳实践",[91,3015,3016],{"id":3016},"数据质量",[3018,3019,3020,3028,3034],"ol",{},[3021,3022,3023,3027],"li",{},[3024,3025,3026],"strong",{},"数据验证"," - 定期检查数据完整性",[3021,3029,3030,3033],{},[3024,3031,3032],{},"异常处理"," - 建立异常数据处理流程",[3021,3035,3036,3039],{},[3024,3037,3038],{},"版本控制"," - 保留历史数据版本",[91,3041,3042],{"id":3042},"报表设计",[3018,3044,3045,3051,3057],{},[3021,3046,3047,3050],{},[3024,3048,3049],{},"目标明确"," - 每个报表有清晰目标",[3021,3052,3053,3056],{},[3024,3054,3055],{},"简洁直观"," - 避免信息过载",[3021,3058,3059,3062],{},[3024,3060,3061],{},"行动导向"," - 提供可操作的建议",[91,3064,3065],{"id":3065},"安全合规",[3018,3067,3068,3074,3080],{},[3021,3069,3070,3073],{},[3024,3071,3072],{},"数据脱敏"," - 敏感数据加密处理",[3021,3075,3076,3079],{},[3024,3077,3078],{},"访问控制"," - 按角色限制数据访问",[3021,3081,3082,3085],{},[3024,3083,3084],{},"审计日志"," - 记录所有数据操作",[17,3087,3088],{"id":3088},"常见问题",[91,3090,3091],{"id":3091},"查询速度慢",[13,3093,3094],{},"优化方案：",[95,3096,3098],{"className":97,"code":3097,"language":99,"meta":100,"style":100},"{\n  \"optimization\": {\n    \"use_cache\": true,\n    \"pre_aggregate\": true,\n    \"partition_tables\": true,\n    \"index_columns\": [\"date\", \"user_id\"]\n  }\n}\n",[102,3099,3100,3104,3111,3122,3133,3144,3160,3164],{"__ignoreMap":100},[105,3101,3102],{"class":107,"line":108},[105,3103,112],{"class":111},[105,3105,3106,3109],{"class":107,"line":115},[105,3107,3108],{"class":118},"  \"optimization\"",[105,3110,122],{"class":111},[105,3112,3113,3116,3118,3120],{"class":107,"line":125},[105,3114,3115],{"class":118},"    \"use_cache\"",[105,3117,337],{"class":111},[105,3119,1604],{"class":118},[105,3121,141],{"class":111},[105,3123,3124,3127,3129,3131],{"class":107,"line":134},[105,3125,3126],{"class":118},"    \"pre_aggregate\"",[105,3128,337],{"class":111},[105,3130,1604],{"class":118},[105,3132,141],{"class":111},[105,3134,3135,3138,3140,3142],{"class":107,"line":144},[105,3136,3137],{"class":118},"    \"partition_tables\"",[105,3139,337],{"class":111},[105,3141,1604],{"class":118},[105,3143,141],{"class":111},[105,3145,3146,3149,3151,3153,3155,3158],{"class":107,"line":152},[105,3147,3148],{"class":118},"    \"index_columns\"",[105,3150,686],{"class":111},[105,3152,756],{"class":137},[105,3154,692],{"class":111},[105,3156,3157],{"class":137},"\"user_id\"",[105,3159,703],{"class":111},[105,3161,3162],{"class":107,"line":160},[105,3163,297],{"class":111},[105,3165,3166],{"class":107,"line":166},[105,3167,303],{"class":111},[91,3169,3170],{"id":3170},"图表不直观",[13,3172,3173],{},"改进建议：",[95,3175,3177],{"className":97,"code":3176,"language":99,"meta":100,"style":100},"{\n  \"visualization_tips\": {\n    \"simplify\": \"减少图表元素\",\n    \"highlight\": \"突出关键数据\",\n    \"annotate\": \"添加必要注释\"\n  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如何把转录、向量检索和学习流程接成长链路。","/news/openclaw-memory-showcase-watch-2026-03-21",[5029,3405,3705,5066,5067],"vector-search","knowledge",{"title":5069,"description":5070,"path":5071,"category":4837,"date":3614,"updatedAt":3614,"sourceType":3431,"source":5072,"tags":5073},"OpenClaw 的 mention gating 正在从单一 @ 规则变成跨渠道触发层","官方最新 Groups 和 Group Messages 文档显示，mention gating 已经不再只服务 WhatsApp 群，而是在 Telegram、Slack、Discord、iMessage 等入口上逐步形成统一触发层。","/news/openclaw-mention-gating-watch-2026-03-25","https://docs.openclaw.ai/channels/group-messages",[4429,3617,3644,3646,3712],{"title":5075,"description":5076,"path":5077,"category":4837,"date":3596,"updatedAt":3596,"sourceType":3431,"source":5078,"tags":5079},"OpenClaw 的消息入口语义正在变清楚","官方最近围绕 Messages、WebChat 和 Channel Routing 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和控制面交接写成同一条管理面问题。","/news/openclaw-operator-browser-watch-2026-03-26",[4201,3698,3677,3756,3475],{"title":5103,"description":5104,"path":5105,"category":4837,"date":3624,"updatedAt":3624,"sourceType":3431,"source":5106,"tags":5107},"OpenClaw 正在把 Dashboard、WebChat 和 WebSocket 收成一套 operator surface","官方最近围绕 Dashboard、WebChat 和 Gateway Protocol 的文档更新，越来越清楚地把这些入口解释成同一套 operator surface 的不同层，而不是彼此割裂的功能页。","/news/openclaw-operator-surface-convergence-watch-2026-03-29","https://docs.openclaw.ai/gateway/protocol",[3460,3530,4039,4201,3571],{"title":5109,"description":5110,"path":5111,"category":4837,"date":3614,"updatedAt":3614,"sourceType":3431,"source":4948,"tags":5112},"OpenClaw 的 pairing CLI 正在从单条 approve 命令长成正式入口治理面","官方最近把 pairing CLI 的 list、clear、--account 和 --notify 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正在被官方主动连成一条生命周期链。","/news/openclaw-plugin-install-tracking-watch-2026-03-24","https://docs.openclaw.ai/cli/plugins",[3507,3652,4483,4484,4188],{"title":5126,"description":5127,"path":5128,"category":4837,"date":3515,"updatedAt":3515,"sourceType":3431,"source":5129,"tags":5130},"OpenClaw 工作流观察：OpenProse 与 Lobster 的上下层分工正在变清楚","基于 2026 年 3 月 24 日可见的官方 Lobster 文档，最近最值得关注的信号是 OpenProse 与 Lobster 的边界正在被官方主动写清，多智能体准备和确定性执行不再混成一层。","/news/openclaw-prose-lobster-watch-2026-03-24","https://docs.openclaw.ai/tools/lobster",[3870,3871,3451,3722,3508],{"title":5132,"description":5133,"path":5134,"category":4837,"date":3624,"updatedAt":3624,"sourceType":3431,"source":5135,"tags":5136},"OpenClaw 正在把 provider、模型和远程执行分成不同治理层","官方最近围绕 Models、Model Failover 和 node 的文档更新，正在把 provider auth、模型 allowlist、多模态能力和远程 node 执行拆成更清楚的治理层。","/news/openclaw-provider-capability-governance-watch-2026-03-29","https://docs.openclaw.ai/concepts/models",[3589,3523,3570,3591,3889],{"title":5138,"description":5139,"path":5140,"category":4837,"date":3624,"updatedAt":3624,"sourceType":3431,"source":5141,"tags":5142},"OpenClaw 的 provider 故障恢复正在变成一条更清楚的梯子","官方最近围绕 Model Failover、Gateway Troubleshooting、LiteLLM 和 Cloudflare AI Gateway 的文档更新，正在把模型故障从“直接换模型”收敛成更清楚的几层：能力条件、profile 轮转、统一网关和跨模型 fallback。","/news/openclaw-provider-recovery-ladder-watch-2026-03-29","https://docs.openclaw.ai/concepts/model-failover",[3589,4273,3523,3401,3423],{"title":5144,"description":5145,"path":5146,"category":4631,"date":3473,"updatedAt":3473,"sourceType":3431,"source":4825,"tags":5147},"OpenClaw 发布观察：2026.3.13 恢复型 Release 暴露出版本跟踪里最容易忽略的三层差异","基于 2026 年 3 月 21 日可见的 GitHub Releases 页面，整理 OpenClaw 2026.3.13 恢复型 Release 的关键信号，以及中文站在跟踪版本时最该注意的 npm 版本、Git tag 和 GitHub Release 名称差异。","/news/openclaw-release-watch-2026-03-21",[4186,4187,4189,4188],{"title":5149,"description":5150,"path":5151,"category":4837,"date":3596,"updatedAt":3596,"sourceType":3431,"source":4851,"tags":5152},"OpenClaw 的远程管理员面边界越来越清楚","官方最近围绕 Dashboard、Pairing、Gateway-owned pairing 与 nodes 的文档更新，正在把远程协作、浏览器入口和设备接入的边界拆得更清楚。","/news/openclaw-remote-admin-watch-2026-03-22",[3460,3756,3570,3475,4092],{"title":5154,"description":5155,"path":5156,"category":4837,"date":3614,"updatedAt":3614,"sourceType":3431,"source":5157,"tags":5158},"OpenClaw 的 routing precedence 正在成为更明确的排障坐标系","官方最近围绕 Channel Routing 的写法，正在把 peer、thread inheritance、guild、team、account 和 channel 等命中层级固定下来，让路由排障越来越像查规则，而不是猜模型。","/news/openclaw-routing-precedence-watch-2026-03-25","https://docs.openclaw.ai/channels/channel-routing",[3599,3598,5159,3403,3600],"threads",{"title":5161,"description":5162,"path":5163,"category":4837,"date":3576,"updatedAt":3576,"sourceType":3431,"source":5164,"tags":5165},"OpenClaw 把 SearXNG 推进 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入口的基础隔离建议。","/news/openclaw-secure-dm-mode-watch-2026-03-25",[3618,3424,4380,3756,4227],{"title":5180,"description":5181,"path":5182,"category":4837,"date":3515,"updatedAt":3515,"sourceType":3431,"source":5183,"tags":5184},"OpenClaw 安全观察：security audit 已经从提醒工具继续长成加固入口","基于 2026 年 3 月 24 日可见的官方安全文档，最近最值得关注的信号是 openclaw security audit 不再只是列问题，而是在形成 audit、deep、fix 三段式加固路径。","/news/openclaw-security-audit-fix-watch-2026-03-24","https://docs.openclaw.ai/gateway/security",[3424,4212,4214,4213,3423],{"title":5186,"description":5187,"path":5188,"category":5189,"date":3397,"updatedAt":3397,"sourceType":3376,"source":5190,"tags":5191},"OpenClaw 安全与合规话题升温：中文团队更在意可审计与本地数据边界","基于 2026 年 3 月 16 日安全方向临时稿，整理 OpenClaw 在中文环境下最受关注的安全议题，包括本地运行、合规要求、可审计性与金融等高敏行业采用。","/news/openclaw-security-compliance-watch-2026-03-16","安全观察","content/tmp/2026-03-16/openclaw-security-audit-milestone.md",[3424,5192,3788,3422],"compliance",{"title":5194,"description":5195,"path":5196,"category":4837,"date":3596,"updatedAt":3596,"sourceType":3431,"source":5170,"tags":5197},"OpenClaw 的安全运维边界继续细化","官方最近围绕 Secrets、approvals forwarding、trusted proxy 和 Control UI 降级开关的文档，正在把安全问题从抽象原则推向更可执行的运维边界。","/news/openclaw-security-ops-watch-2026-03-22",[3424,4510,3722,5198,3423],"proxy",{"title":5200,"description":5201,"path":5202,"category":4837,"date":4521,"updatedAt":4521,"sourceType":3431,"source":5203,"tags":5204},"OpenClaw 正在把 session transcript 从 JSONL 文件写成结构化会话事实层","官方最近新增的 Session Management Deep Dive 文档，把 session header、message tree、compaction entry 和 branch summary 这几层边界写得更清楚了。","/news/openclaw-session-jsonl-deep-dive-watch-2026-04-01","https://docs.openclaw.ai/reference/session-management-compaction",[3618,4524,3988,4523,3401],{"title":5206,"description":5207,"path":5208,"category":4837,"date":4521,"updatedAt":4521,"sourceType":3431,"source":5209,"tags":5210},"OpenClaw 的 sessions CLI 正在从会话列表命令长成 store 巡检入口","官方最近把 sessions CLI 的 scope、all-agents、active 和显式 store 路径写得更完整了，也让它更适合作为长期运维里的 session store 巡检面。","/news/openclaw-sessions-cli-watch-2026-04-01","https://docs.openclaw.ai/cli/sessions",[3555,3581,4530,4212,3423],{"title":5212,"description":5213,"path":5214,"category":4837,"date":3614,"updatedAt":3614,"sourceType":3431,"source":5215,"tags":5216},"OpenClaw 正在把 shared inbox 明确限定为协作硬化，而不是多租户承诺","官方最新 security audit 文档正在更明确地区分协作型 shared inbox 与互不信任多租户环境，并把 secure DM、sandbox 和 workspace-scoped access 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