{
  "schemaVersion": 1,
  "id": "1.1.5",
  "title": "智能交通系统的数据采集、处理和审核流程设计",
  "durationMinutes": 30,
  "category": "业务数据处理与结果分析",
  "scenario": "某智能交通系统希望通过车辆的行驶数据，利用人工智能技术进行交通流量预测和拥堵预警。你作为人工智能训练师，需要设计一套全面的业务数据采集、处理和审核流程，确保数据在进入交通流量分析系统之前经过严格的采集、清洗、审核和预处理。这里提供一个车辆行驶数据集（vehicle_traffic_data.csv），包含以下字段：\n\nVehicleID: 车辆ID\n\nDriverName: 驾驶员姓名\n\nAge: 年龄\n\nGender: 性别（Male/Female）\n\nSpeed: 车速（km/h）\n\nTravelDistance: 行驶距离（km）\n\nTravelTime: 行驶时间（min）\n\nTrafficEvent: 交通事件（Normal, Accident, Traffic Jam, Breakdown）\n\n你作为人工智能训练师，根据提供的vehicle_traffic_data.csv数据集和Python代码框架（1.1.5.ipynb），完成以下数据的采集、处理和审核任务，确保数据的准确性和可靠性。请按照以下要求完成任务，确保结果准确并保存相应的截图。\n\n（1）数据采集：\n\n通过运行Python代码（1.1.5.ipynb），从本地文件vehicle_traffic_data.csv中读取数据，并将数据加载到DataFrame中。显示前5行数据截图以JPG的格式保存，命名为“1.1.5-1”。\n\n（2）数据清洗与预处理： \n\n通过运行Python代码（1.1.5.ipynb）对数据进行清洗和预处理，具体要求如下：\n\n处理缺失值：对缺失值进行删除。\n\n数据类型转换：确保每个字段的数据类型正确。\n\n处理异常值：删除不合理的年龄、车速、行驶距离和行驶时间。\n\n清洗后的数据保存为新文件cleaned_vehicle_traffic_data.csv。\n\n（3）数据合理性审核： 通过运行Python代码审核以下字段的合理性：\n\n年龄：应在18到70岁之间。\n\n车速：应在0到200 km/h之间。\n\n行驶距离：应在1到1000 km之间。\n\n行驶时间：应在1到1440分钟（24小时）之间。\n\n对不合理的数据进行标记，并将审核结果截图以JPG的格式保存，命名为“1.1.5-2”。\n\n（4）数据统计：\n\n通过运行Python代码（1.1.5.ipynb），完成以下数据统计任务：\n\n统计每种交通事件的发生次数。\n\n统计不同性别的平均车速、行驶距离和行驶时间。\n\n统计不同年龄段的驾驶员数（18-25岁、26-35岁、36-45岁、46-55岁、56-65岁、65岁以上）。\n\n将统计结果分别截图以JPG的格式保存，分别命名为“1.1.5-3”、“1.1.5-4”、“1.1.5-5”。",
  "skillRequirements": "（1）能结合人工智能技术要求和业务特征，设计整套业务数据采集流程；\n\n（2）能结合人工智能技术要求和业务特征，设计整套业务数据处理流程；\n\n（3）能结合人工智能技术要求和业务特征，设计整套业务数据审核流程；",
  "qualityIndicators": "（1）数据完整性：数据无缺失，每项记录完整。\n\n（2）数据合理性：所有数值在合理范围内，无异常点。\n\n（3）数据一致性：字段类型正确，数据格式统一。\n\n（4）分析准确性：统计结果反映真实数据分布，无偏差。",
  "tasks": [
    {
      "id": "code-fill",
      "type": "code-fill",
      "title": "补全代码任务",
      "instructions": "依据公开题面和附件补全代码空位；仅检查完成度与提交格式，不公开标准内容。",
      "codeBlocks": [
        "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 1. 数据采集\n# 从本地文件中读取数据  2分\ndata = _____________\nprint(\"数据采集完成，已加载到DataFrame中\")\n\n# 打印数据的前5条记录 2分\nprint(_____________)",
        "# 2. 数据清洗与预处理\n# 处理缺失值（删除）  2分\ndata = _____________\n\n# 数据类型转换\ndata_____________ = _____________(int)       #Age数据类型转换为int 1分\ndata_____________ = _____________(float)     #Speed数据类型转换为float 1分\ndata_____________ = _____________(float)     #TravelDistance数据类型转换为float 1分\ndata_____________ = _____________(float)     #TravelTime数据类型转换为float 1分\n\n# 处理异常值  2分\ndata = data[(_____________(18, 70))  & \n            (_____________(0, 200)) & \n            (_____________(1, 1000)) & \n            (_____________(1, 1440))]\n\n# 保存清洗后的数据  1分\n_____________('cleaned_vehicle_traffic_data.csv', index=False)\nprint(\"数据清洗完成，已保存为 'cleaned_vehicle_traffic_data.csv'\")",
        "# 3. 数据合理性审核\n# 审核字段合理性 1分\nunreasonable_data = data[~((_____________(18, 70)) & \n                           (_____________(0, 200)) & \n                           (_____________(1, 1000)) & \n                           (_____________(1, 1440)))]\nprint(\"不合理的数据:\\n\", unreasonable_data)\n\n# 4. 数据统计\n# 统计每种交通事件的发生次数  2分\ntraffic_event_counts = _____________\nprint(\"每种交通事件的发生次数:\\n\", traffic_event_counts)\n\n# 统计不同性别的平均车速、行驶距离和行驶时间  2分\ngender_stats = data._____________._____________\nprint(\"不同性别的平均车速、行驶距离和行驶时间:\\n\", gender_stats)\n\n# 统计不同年龄段的驾驶员数  5分\nage_bins = [18, 26, 36, 46, 56, 66, np.inf]\nage_labels = ['18-25', '26-35', '36-45', '46-55', '56-65', '65+']\ndata['AgeGroup'] = _____________(_____________,_____________,_____________, right=False)\nage_group_counts = _____________\nprint(\"不同年龄段的驾驶员数:\\n\", age_group_counts)"
      ],
      "blanks": [
        {
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          "gradingMode": "completion-and-format-only"
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        {
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        {
          "id": "blank-17",
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          "gradingMode": "completion-and-format-only"
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        {
          "id": "blank-18",
          "block": 3,
          "placeholder": "_____________",
          "gradingMode": "completion-and-format-only"
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        {
          "id": "blank-19",
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          "placeholder": "_____________",
          "gradingMode": "completion-and-format-only"
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        {
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          "id": "blank-23",
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          "gradingMode": "completion-and-format-only"
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      ]
    },
    {
      "id": "1-1-response",
      "type": "rubric-response",
      "title": "数据处理结果分析",
      "instructions": "说明处理过程、结果证据和质量判断。",
      "sections": [
        {
          "id": "response",
          "label": "作答内容"
        }
      ],
      "rubric": [
        {
          "id": "criterion-1",
          "label": "处理流程完整且逻辑清晰",
          "weight": 25
        },
        {
          "id": "criterion-2",
          "label": "关键结果有附件或数据证据",
          "weight": 25
        },
        {
          "id": "criterion-3",
          "label": "结果符合技能要求与质量指标",
          "weight": 25
        },
        {
          "id": "criterion-4",
          "label": "能够解释异常、限制和改进方向",
          "weight": 25
        }
      ]
    },
    {
      "id": "artifact-submission",
      "type": "artifact-submission",
      "title": "选择结果文件",
      "instructions": "文件仅在本机选择并校验，不上传，也不持久化文件内容。",
      "items": [
        {
          "id": "artifact-1",
          "label": "提交文件：cleaned_vehicle_traffic_data.csv",
          "filename": "cleaned_vehicle_traffic_data.csv",
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          "filename": "1.1.5-1.jpg",
          "extensions": [
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          "label": "提交文件：1.1.5-2.jpg",
          "filename": "1.1.5-2.jpg",
          "extensions": [
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          "minCount": 1,
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          "id": "artifact-4",
          "label": "提交文件：1.1.5-3.jpg",
          "filename": "1.1.5-3.jpg",
          "extensions": [
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          "minCount": 1,
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  "grading": {
    "mode": "completion-and-format-only"
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  "review": {
    "status": "approved",
    "notice": "由公开题面通用生成，未使用答案树。",
    "conflicts": []
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  "contentVersion": "a486a298a409c193"
}