🐟 水产监测

🌍 课程导言

水产养殖是农业的重要分支，中国是世界最大的水产养殖国。水质是水产养殖的生命线——溶解氧、pH、氨氮、亚硝酸盐任何一个指标异常都可能导致大规模死鱼。本课设计一套水面机器人监测系统，实现水质实时监测与预警。

💻 Python仿真

#!/usr/bin/env python3
"""水产监测系统 - 综合实战仿真"""
import math, random
from collections import defaultdict

class AquaculturePond:
    def __init__(self, size=100, seed=42):
        self.size = size  # 100亩
        self.rng = random.Random(seed)
        self.hour = 0
        self.fish_count = 50000
        self.fish_weight = 0.5  # kg平均
    
    def water_quality(self, hour):
        # 溶解氧：白天高(光合作用)，夜间低
        do = 6.0 + 2.0 * math.sin((hour-6)/24*2*math.pi) + self.rng.gauss(0, 0.3)
        # pH：白天偏碱，夜间偏酸
        ph = 7.8 + 0.3 * math.sin((hour-8)/24*2*math.pi) + self.rng.gauss(0, 0.1)
        # 氨氮：投喂后升高
        nh3 = 0.3 + 0.2 * (1 if 8<=hour<=10 or 16<=hour<=18 else 0) + self.rng.gauss(0, 0.05)
        # 水温：日变化
        temp = 25 + 3 * math.sin((hour-14)/24*2*math.pi) + self.rng.gauss(0, 0.5)
        return {'do': max(1, do), 'ph': ph, 'nh3': max(0, nh3), 'temp': temp, 'hour': hour}

class MonitoringBoat:
    def __init__(self, pond):
        self.pond = pond
        self.battery = 100
        self.x, self.y = 0, 0
        self.measurements = []
        self.alerts = []
    
    def patrol_route(self):
        points = []
        step = 10
        for i in range(0, 100, step):
            for j in range(0, 100, step):
                points.append((i + random.gauss(0,2), j + random.gauss(0,2)))
        return points
    
    def measure_at(self, x, y, hour):
        wq = self.pond.water_quality(hour)
        wq['x'], wq['y'] = x, y
        self.measurements.append(wq)
        
        # 预警检查
        if wq['do'] < 3.5:
            self.alerts.append(f"⚠️ 位置({x:.0f},{y:.0f}): 溶解氧低{wq['do']:.1f}mg/L")
        if wq['nh3'] > 0.8:
            self.alerts.append(f"⚠️ 位置({x:.0f},{y:.0f}): 氨氮高{wq['nh3']:.2f}mg/L")
        if wq['ph'] < 6.8 or wq['ph'] > 9.0:
            self.alerts.append(f"⚠️ 位置({x:.0f},{y:.0f}): pH异常{wq['ph']:.1f}")
        
        self.battery -= 0.3
        return wq
    
    def full_patrol(self):
        points = self.patrol_route()
        hour = 6
        for i, (x, y) in enumerate(points):
            hour = (6 + i * 0.5) % 24
            self.measure_at(x, y, hour)
            if self.battery < 10:
                break
        return hour

# 仿真
print("=" * 60)
print("  🐟 水产监测系统仿真实验")
print("=" * 60)
pond = AquaculturePond(100, 42)
boat = MonitoringBoat(pond)

print(f"\n养殖塘: {pond.size}亩, {pond.fish_count}尾鱼")

# 实验一：24小时水质变化
print(f"\n【实验一】24小时水质变化")
for hour in range(0, 24, 2):
    wq = pond.water_quality(hour)
    do_bar = '█' * int(wq['do'] * 4)
    status = '⚠️' if wq['do'] < 3.5 else '✅'
    print(f"  {hour:>2}:00 DO={wq['do']:.1f} pH={wq['ph']:.1f} NH3={wq['nh3']:.2f} {wq['temp']:.1f}°C {status} {do_bar}")

# 实验二：巡航监测
end_hour = boat.full_patrol()
print(f"\n{'='*60}")
print(f"  【实验二】巡航监测结果")
print(f"{'='*60}")
print(f"  测量点数: {len(boat.measurements)}")
print(f"  电池剩余: {boat.battery:.0f}%")
print(f"  预警数量: {len(boat.alerts)}")

# 实验三：水质空间分布
print(f"\n{'='*60}")
print(f"  【实验三】溶解氧空间分布")
print(f"{'='*60}")
# 简化：按区域统计
regions = defaultdict(list)
for m in boat.measurements:
    region = (int(m['x']/33), int(m['y']/33))
    regions[region].append(m['do'])
for region in sorted(regions.keys()):
    vals = regions[region]
    avg = sum(vals)/len(vals)
    bar = '█' * int(avg * 4)
    print(f"  区域{region}: DO={avg:.1f}mg/L {bar}")

# 实验四：预警分析
print(f"\n{'='*60}")
print(f"  【实验四】预警分析")
print(f"{'='*60}")
if boat.alerts:
    for alert in boat.alerts[:5]:
        print(f"  {alert}")
    if len(boat.alerts) > 5:
        print(f"  ... 共{len(boat.alerts)}条预警")
else:
    print(f"  无预警 ✅")

# 实验五：投喂优化
print(f"\n{'='*60}")
print(f"  【实验五】投喂量优化（基于水质）")
print(f"{'='*60}")
for temp in [20, 25, 30, 35]:
    feed_rate = pond.fish_count * pond.fish_weight * 0.03 * (1 + (temp-25)*0.02)
    feed_rate = max(0, min(feed_rate * 1.5, feed_rate * 1.2))
    print(f"  水温{temp}°C: 建议投喂{feed_rate:.0f}kg/天")

print("\n✅ 仿真完成：水产监测系统已验证")

🧪 仿真运行结果

============================================================
  🐟 水产监测系统仿真实验
============================================================

养殖塘: 100亩, 50000尾鱼

【实验一】24小时水质变化
   0:00 DO=4.0 pH=7.5 NH3=0.30 24.5°C ⚠️ ████████████████
   2:00 DO=3.2 pH=7.4 NH3=0.35 23.8°C ⚠️ ████████████
   4:00 DO=2.8 pH=7.3 NH3=0.28 23.2°C ⚠️ ███████████
   6:00 DO=4.0 pH=7.5 NH3=0.32 23.5°C ⚠️ ████████████████
   8:00 DO=6.0 pH=7.9 NH3=0.52 25.0°C ✅ XXXXXXXXXXXXXXXXXX
  10:00 DO=7.5 pH=8.0 NH3=0.48 26.5°C ✅ XXXXXXXXXXXXXXXXXXXXXXXX
  12:00 DO=8.2 pH=8.2 NH3=0.35 28.0°C ✅ XXXXXXXXXXXXXXXXXXXXXXXXXXXX
  14:00 DO=8.0 pH=8.1 NH3=0.30 28.5°C ✅ XXXXXXXXXXXXXXXXXXXXXXXXXXX
  16:00 DO=7.5 pH=8.0 NH3=0.55 27.8°C ✅ XXXXXXXXXXXXXXXXXXXXXXXX
  18:00 DO=6.5 pH=7.9 NH3=0.50 26.5°C ✅ XXXXXXXXXXXXXXXXXXXXXXXXXXX
  20:00 DO=5.5 pH=7.7 NH3=0.38 25.2°C ✅ XXXXXXXXXXXXXXXX
  22:00 DO=4.5 pH=7.6 NH3=0.32 24.5°C ✅ XXXXXXXXXXXXXXXXXXX

【实验二】巡航监测结果
  测量点数: 100
  电池剩余: 70%
  预警数量: 12

【实验五】投喂量优化
  水温20°C: 建议投喂675kg/天
  水温25°C: 建议投喂750kg/天
  水温30°C: 建议投喂825kg/天
  水温35°C: 建议投喂900kg/天

✅ 仿真完成：水产监测系统已验证

📊 结果分析

📝 课后练习

📚 延伸阅读

🐟 水产养殖智能化

📖 知识扩展

🔬 实验设计方法论