数据集概览
| 数据集名称 | 智慧城市解决方案数据集 |
| 数据总量 | 15,200 条（方案文档 + 技术参数 + 实施案例 + 招投标信息） |
| 覆盖领域 | 智慧交通、智慧环保、智慧城管、智慧社区、数字政府、城市大脑、智慧应急 |
| 时间跨度 | 2018 - 2026 年 |
| 数据来源 | 政府采购网、招标平台、企业公开方案、智慧城市试点总结报告 |
| 标注方式 | 行业分析师标注 + 技术专家交叉审核 |
| 格式输出 | JSON / CSV / Markdown / PDF摘要 |
| 更新频率 | 月度增量更新 |
数据维度详细说明
| 字段名称 | 数据类型 | 取值范围/说明 |
|---|---|---|
| solution_id | String(32) | 方案___UNIQUE_TECH___专属ID___UNIQUE_TECH___ |
| domain | Enum | SMART_TRAFFIC | SMART_ENV | SMART_CITY_MGMT | DIGITAL_GOV | CITY_BRAIN | SMART_EMERGENCY |
| city_tier | Enum | TIER1 | TIER2 | TIER3 | COUNTY |
| budget_range | Enum | <500万 | 500-2000万 | 2000-5000万 | 5000万-1亿 | >1亿 |
| tech_stack | Array | IoT | 5G | AI | BigData | Cloud | Edge | Blockchain | DigitalTwin |
| key_vendors | Array | 参与厂商列表（脱敏） |
| implementation_stage | Enum | PLANNING | BIDDING | DEPLOYING | OPERATING | COMPLETED |
| kpi_metrics | Object | {metric_name: {target_value, actual_value, unit}} |
| data_sources | Array | [{source_type, volume, frequency, format}] |
| roi_analysis | Object | {investment, annual_saving, payback_period_months} |
脱敏 JSON 数据样例
[
{
"solution_id": "SMC-2024-GD-SZ-0301",
"domain": "SMART_TRAFFIC",
"city_tier": "TIER1",
"budget_range": ">1亿",
"tech_stack": ["IoT", "5G", "AI", "BigData", "Edge"],
"key_vendors": ["华为", "海康威视", "商汤科技"],
"implementation_stage": "OPERATING",
"kpi_metrics": {
"avg_congestion_reduction": {"target_value": 15, "actual_value": 18.2, "unit": "%"},
"emergency_response_time": {"target_value": 8, "actual_value": 6.5, "unit": "min"}
},
"data_sources": [
{"source_type": "交通摄像头", "volume": 12000, "frequency": "daily", "format": "视频流"},
{"source_type": "地磁传感器", "volume": 500000, "frequency": "realtime", "format": "时序流"}
],
"roi_analysis": {"investment": 28000, "annual_saving": 5200, "payback_period_months": 65}
},
{
"solution_id": "SMC-2023-ZJ-HZ-0147",
"domain": "CITY_BRAIN",
"city_tier": "TIER1",
"budget_range": "5000万-1亿",
"tech_stack": ["Cloud", "AI", "BigData", "DigitalTwin"],
"key_vendors": ["阿里云", "新华三"],
"implementation_stage": "DEPLOYING",
"kpi_metrics": {
"gov_service_online_rate": {"target_value": 95, "actual_value": 88.5, "unit": "%"},
"data_sharing_coverage": {"target_value": 90, "actual_value": 76.3, "unit": "%"}
},
"data_sources": [
{"source_type": "民生数据中台", "volume": 2000000, "frequency": "daily", "format": "结构化"}
],
"roi_analysis": {"investment": 8500, "annual_saving": 1200, "payback_period_months": 85}
},
{
"solution_id": "SMC-2024-HN-CS-0082",
"domain": "SMART_ENV",
"city_tier": "TIER2",
"budget_range": "2000-5000万",
"tech_stack": ["IoT", "AI", "Cloud"],
"key_vendors": ["平安智慧城市", "聚光科技"],
"implementation_stage": "OPERATING",
"kpi_metrics": {
"air_quality_monitoring": {"target_value": 98, "actual_value": 99.2, "unit": "%"},
"pollution_source_traceability": {"target_value": 85, "actual_value": 82.7, "unit": "%"}
},
"data_sources": [
{"source_type": "环境监测站", "volume": 365000, "frequency": "hourly", "format": "时序"},
{"source_type": "卫星遥感", "volume": 52, "frequency": "weekly", "format": "栅格"}
],
"roi_analysis": {"investment": 3200, "annual_saving": 480, "payback_period_months": 80}
},
{
"solution_id": "SMC-2025-SC-CD-0023",
"domain": "SMART_CITY_MGMT",
"city_tier": "TIER2",
"budget_range": "500-2000万",
"tech_stack": ["IoT", "AI", "Cloud"],
"key_vendors": ["数字政通", "辰安科技"],
"implementation_stage": "BIDDING",
"kpi_metrics": {
"incident_response_time": {"target_value": 30, "actual_value": null, "unit": "min"},
"citizen_satisfaction": {"target_value": 90, "actual_value": null, "unit": "%"}
},
"data_sources": [
{"source_type": "城管终端", "volume": 8000, "frequency": "daily", "format": "图文"}
],
"roi_analysis": {"investment": 1500, "annual_saving": 300, "payback_period_months": 60}
},
{
"solution_id": "SMC-2022-JS-NJ-0198",
"domain": "DIGITAL_GOV",
"city_tier": "TIER1",
"budget_range": "2000-5000万",
"tech_stack": ["Cloud", "AI", "Blockchain"],
"key_vendors": ["华为云", "浪潮"],
"implementation_stage": "COMPLETED",
"kpi_metrics": {
"one_stop_service_rate": {"target_value": 90, "actual_value": 94.3, "unit": "%"},
"document_processing_time": {"target_value": 3, "actual_value": 1.8, "unit": "day"}
},
"data_sources": [
{"source_type": "民生服务", "volume": 15000000, "frequency": "daily", "format": "结构化"}
],
"roi_analysis": {"investment": 4200, "annual_saving": 980, "payback_period_months": 51}
}
]
AI 训练适用场景
智慧城市规划方案生成
适用模型：LLM（GPT-4o / Claude）+ RAG。基于历史方案库和城市特征自动生成定制化智慧城市规划方案。
城市运行态势预测
适用模型：TFT / Autoformer / PatchTST。基于多维城市运行数据训练交通、环境、能耗等时序预测模型。
智慧城市 ROI 评估
适用模型：XGBoost + SHAP。基于历史项目投入产出数据训练智慧城市项目ROI预测与评估模型。
数字孪生城市建模
适用模型：NeRF / 3D Gaussian Splatting + Cesium。基于城市多源数据训练三维城市场景重建和可视化模型。