AI小龙虾（OpenClaw）与MongoDB集成场景

智能养殖监控系统

// MongoDB文档结构示例
{
  _id: ObjectId("..."),
  lobster_id: "LC-2024-001",
  species: "Procambarus clarkii",
  timestamp: ISODate("2024-01-15T10:30:00Z"),
  sensor_data: {
    temperature: 22.5,  // 水温
    pH: 7.2,            // pH值
    oxygen_level: 6.8,  // 溶氧量
    ammonia: 0.02       // 氨氮含量
  },
  health_status: "HEALTHY",
  ai_predictions: {
    growth_rate: 0.15,
    disease_risk: 0.03,
    harvest_date: ISODate("2024-03-20")
  }
}

行为分析场景

// 小龙虾行为模式分析
{
  lobster_id: "LC-2024-001",
  behavior_logs: [
    {
      time_window: "00:00-06:00",
      activity_level: "LOW",
      feeding_count: 2,
      movement_pattern: {
        avg_speed: 0.5,
        preferred_zones: ["zone_a", "zone_c"]
      }
    }
  ],
  ai_insights: {
    abnormal_behavior_detected: false,
    stress_indicator: 0.12,
    social_interaction_score: 0.8
  }
}

供应链追溯系统

// 全生命周期追溯
{
  batch_id: "BATCH-2024-Q1",
  farm_id: "FARM-001",
  breeding_phase: {
    start_date: ISODate("2024-01-01"),
    duration_days: 90,
    avg_weight: 45.2,  // 克
    mortality_rate: 0.05
  },
  processing: {
    facility: "PROCESSING-001",
    processing_date: ISODate("2024-03-30"),
    quality_grade: "A",
    size_category: "Large"
  },
  distribution: {
    retailer: "SEAFOOD-MART-001",
    delivery_date: ISODate("2024-04-02"),
    temperature_log: [4.0, 4.2, 3.9, 4.1]
  },
  quality_assurance: {
    lab_tests: ["重金属检测", "抗生素残留"],
    certification: "有机认证",
    ai_quality_score: 9.2
  }
}

AI预测模型数据存储

// 机器学习模型训练数据
{
  model_id: "growth_predictor_v3",
  training_data: {
    features: ["temperature", "feeding_freq", "population_density"],
    target: "weight_gain_30days",
    data_points: 250000,
    mongo_view: "lobster_training_view"
  },
  model_metrics: {
    accuracy: 0.89,
    mae: 3.2,
    last_trained: ISODate("2024-01-10")
  },
  predictions: [
    {
      lobster_id: "LC-2024-001",
      predicted_weight: 68.5,
      confidence: 0.92,
      recommended_action: "增加投喂频率"
    }
  ]
}

智能喂养优化

// 饲料管理文档
{
  feeding_schedule_id: "FS-001",
  lobster_group: ["LC-2024-001", "LC-2024-002", "LC-2024-003"],
  ai_recommendation: {
    feed_type: "高蛋白饲料",
    amount_grams: 250,
    frequency: "每天3次",
    time_windows: ["08:00", "14:00", "20:00"]
  },
  actual_feeding: [
    {
      timestamp: ISODate("2024-01-15T08:00:00Z"),
      amount: 240,
      consumption_rate: 0.95,
      waste_detected: 12
    }
  ],
  performance_metrics: {
    feed_conversion_ratio: 1.8,
    cost_per_kg: 15.2,
    growth_efficiency: 0.85
  }
}

聚合查询示例

// 1. 水质异常检测
db.sensor_data.aggregate([
  {
    $match: {
      timestamp: {
        $gte: ISODate("2024-01-15T00:00:00Z"),
        $lt: ISODate("2024-01-16T00:00:00Z")
      }
    }
  },
  {
    $group: {
      _id: "$sensor_id",
      avg_temperature: { $avg: "$sensor_data.temperature" },
      max_ammonia: { $max: "$sensor_data.ammonia" },
      anomaly_count: {
        $sum: {
          $cond: [
            { $gt: ["$sensor_data.ammonia", 0.05] },
            1,
            0
          ]
        }
      }
    }
  },
  { $match: { anomaly_count: { $gt: 0 } } }
])
// 2. 生长趋势分析
db.lobsters.aggregate([
  {
    $lookup: {
      from: "growth_measurements",
      localField: "lobster_id",
      foreignField: "lobster_id",
      as: "growth_history"
    }
  },
  {
    $project: {
      lobster_id: 1,
      current_weight: { $last: "$growth_history.weight" },
      growth_rate: {
        $divide: [
          { $subtract: [
            { $last: "$growth_history.weight" },
            { $first: "$growth_history.weight" }
          ]},
          { $divide: [
            { $subtract: [
              { $last: "$growth_history.measurement_date" },
              { $first: "$growth_history.measurement_date" }
            ]},
            1000 * 60 * 60 * 24  // 转换为天数
          ]}
        ]
      }
    }
  }
])

变更流实时监控

// 实时异常告警
const pipeline = [
  {
    $match: {
      "operationType": "insert",
      "fullDocument.sensor_data.ammonia": { $gt: 0.05 }
    }
  }
];
const changeStream = db.sensor_data.watch(pipeline);
changeStream.on('change', (change) => {
  // 发送实时告警
  sendAlert({
    type: "水质异常",
    sensor_id: change.fullDocument.sensor_id,
    ammonia_level: change.fullDocument.sensor_data.ammonia,
    timestamp: change.fullDocument.timestamp
  });
});

地理空间查询

// 养殖池布局优化
{
  farm_id: "FARM-001",
  ponds: [
    {
      pond_id: "POND-01",
      location: {
        type: "Point",
        coordinates: [120.15, 30.28]
      },
      dimensions: {
        length: 50,
        width: 20,
        depth: 1.5
      },
      capacity: 5000,
      current_population: 4500
    }
  ],
  ai_recommendations: {
    optimal_pond_distribution: {
      recommended_spacing: 10,  // 米
      sunlight_exposure: "optimized",
      water_circulation_score: 0.92
    }
  }
}

性能优化建议

1. 索引策略：

   

   // 创建复合索引
   db.sensor_data.createIndex({
     "timestamp": -1,
     "sensor_id": 1,
     "location": 1
   })
   // TTL索引自动清理旧数据
   db.sensor_data.createIndex(
     { "timestamp": 1 },
     { expireAfterSeconds: 2592000 }  // 30天后自动删除
   )

2. 分片策略：

   // 按养殖场分片
   sh.shardCollection("aquaculture.sensor_data", { "farm_id": 1 })

3. 数据归档：

   // 使用MongoDB Atlas Online Archive自动归档历史数据
   {
     "archivingRule": {
       "criteria": {
         "timestamp": { $lt: "2023-12-01T00:00:00Z" }
       },
       "archiveAfterDays": 180
     }
   }

集成架构优势

1. 灵活的数据模型：适应小龙虾养殖中多变的监测需求
2. 实时分析：变更流支持即时异常检测和响应
3. 水平扩展：分片支持大规模传感器数据存储
4. AI友好：原生JSON格式便于机器学习训练数据准备
5. 地理空间分析：优化养殖池布局和物流规划

这种集成模式特别适合：

• 精准水产养殖管理
• 食品安全追溯
• 环境可持续性监控
• 供应链优化
• 科研数据分析

标签： OpenClaw MongoDB