AI 寻优代码示例(2024-2026 实战)
2026-07 校准:3 个实战代码示例 — Python RL 冷机寻优 / Node-RED 群控 / MindSphere 集成。给研发岗直接复制。
1. Python RL 冷机寻优(2024-2026 主流)
# chiller_optimization_rl.py
# 2024-2026 主流:RL 冷机群控 + 故障预测
# 依赖: pip install stable-baselines3 gym numpy pandas
import gym
import numpy as np
import pandas as pd
from stable_baselines3 import PPO
class ChillerEnv(gym.Env):
"""冷机优化环境:动作=冷机组合 + 启停,观察=负荷 + 室外温度"""
def __init__(self, num_chillers=3):
super().__init__()
self.num = num_chillers
# 动作:每个冷机 0=关, 1=低负荷, 2=中负荷, 3=高负荷
self.action_space = gym.spaces.MultiDiscrete([4] * num_chillers)
# 观察:室外温度 + 负荷 + 当前每台负荷
self.observation_space = gym.spaces.Box(
low=0, high=100, shape=(3 + num_chillers,), dtype=np.float32
)
self.reset()
def reset(self):
self.t = 0
self.outdoor = 25
self.load = 0.5
self.ch_state = [0] * self.num
return np.array([self.outdoor, self.load] + self.ch_state, dtype=np.float32)
def step(self, action):
# 简化模型:每台冷机 COP 随负荷率变化
cop_per_ch = []
for i, a in enumerate(action):
if a == 0: # 关
cop_per_ch.append(0)
else:
load_rate = 0.33 * a
cop = 4.5 + 1.0 * load_rate - 0.5 * load_rate ** 2
cop_per_ch.append(cop)
# 总 COP
total_cop = np.mean(cop_per_ch) if cop_per_ch else 0
# 故障预测(简化)
fault_risk = max(action) * 0.01 # 高负荷 → 高故障
# 奖励 = COP - 故障风险 - 能耗
reward = total_cop * 10 - fault_risk * 100 - 1
# 时序更新
self.t += 1
self.outdoor = 25 + 10 * np.sin(self.t / 10)
self.load = 0.5 + 0.2 * np.cos(self.t / 8)
return (
np.array([self.outdoor, self.load] + list(action), dtype=np.float32),
reward,
self.t > 100,
{"cop": total_cop, "fault": fault_risk}
)
env = ChillerEnv(num_chillers=3)
model = PPO("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=10000)
model.save("chiller_rl_v1_2026.model")2. Node-RED 群控(2024-2026 主流)
// flows_2026.json - 冷站群控流程
{
"id": "chiller_plant_control_2026",
"type": "tab",
"label": "冷站群控 2026",
"disabled": false,
"info": "AI 寻优 + 故障预测"
}// 冷机选择器
[
{
"id": "load_predictor",
"type": "function",
"name": "负荷预测(AI)",
"func": "// 简化:用历史平均 + 实时调整\nconst currentLoad = msg.payload.load || 0;\nconst outdoorT = msg.payload.outdoor || 25;\nconst predicted = currentLoad * 1.15; // 简化预测\nreturn { payload: { load: predicted, outdoor: outdoorT, ts: Date.now() } };",
"outputs": 1
},
{
"id": "ai_optimizer",
"type": "function",
"name": "AI 群控寻优(2026)",
"func": "// 简化的 RL 群控:根据负荷分配冷机\nconst load = msg.payload.load || 0;\nconst numChillers = 3;\nlet actions = [0, 0, 0];\n\nif (load < 0.4) {\n actions = [1, 0, 0]; // 单台低负荷\n} else if (load < 0.7) {\n actions = [2, 1, 0]; // 一主一备\n} else if (load < 0.95) {\n actions = [3, 2, 0]; // 一主一中\n} else {\n actions = [3, 2, 1]; // 全开\n}\nreturn { payload: { actions, load, ts: Date.now() } };",
"outputs": 1
},
{
"id": "chiller_action",
"type": "mqtt out",
"topic": "hvac/chillers/control",
"name": "下发冷机指令",
"qos": "2"
}
]3. MindSphere 数据接入(2024-2026 主流)
# mindsphere_hvac_2026.py
# MindSphere 数字孪生 + AI 寻优集成
import mindsphere.client as ms
import json
client = ms.MindSphereAPIClient(
tenant="your_tenant",
auth=ms.Credentials("user", "password")
)
# 1. 设备孪生查询
asset_id = "chiller_unit_001_2026"
asset = client.assets.retrieve(asset_id)
print(f"Asset: {asset.name}, type: {asset.typeId}")
# 2. 实时数据读取
timeseries = client.timeseries.read(
entity_id=asset_id,
properties=["temperature_in", "temperature_out", "pressure_high", "power"],
limit=100,
sort="desc",
select="timestamp,value"
)
# 3. AI 寻优预测(2024-2026 主流)
def predict_optimization(asset_data):
"""基于历史数据 + 实时数据 + AI 寻优 → 调速 / 启停建议"""
avg_power = sum(d['value'] for d in asset_data if d['property'] == 'power') / len(asset_data)
avg_temp_out = sum(d['value'] for d in asset_data if d['property'] == 'temperature_out') / len(asset_data)
# 简化 AI 寻优
if avg_power > 1000 and avg_temp_out > 30:
return {"action": "reduce_load", "target_temp": 7, "expected_saving": "8%"}
elif avg_temp_out < 18:
return {"action": "free_cooling", "expected_saving": "15%"}
return {"action": "maintain", "expected_saving": "0%"}
optimization = predict_optimization(timeseries)
# 4. 推送到 MindSphere 事件流
event = client.events.create(
entity_id=asset_id,
body={
"timestamp": "2026-07-01T00:00:00Z",
"properties": {
"ai_optimization": optimization,
"source": "AI 寻优(2024-2026)"
}
}
)
print(f"Optimization event created: {event.id}")4. 故障预测示例(2024-2026 主流)
# fault_predict_2026.py
# 故障预测 + 健康管理
import pandas as pd
from sklearn.ensemble import IsolationForest
# 1. 加载历史数据(从 InfluxDB)
df = pd.read_csv("chiller_history.csv", parse_dates=["timestamp"])
# 字段: timestamp, current_A, voltage_V, temp_oil, vibration, power_kW
# 2. 训练异常检测模型
features = ["current_A", "voltage_V", "temp_oil", "vibration", "power_kW"]
model = IsolationForest(contamination=0.01, random_state=42)
model.fit(df[features])
# 3. 实时检测
new_data = df[features].tail(10)
predictions = model.predict(new_data)
anomalies = new_data[predictions == -1]
print(f"检测到 {len(anomalies)} 个异常点")
# 4. 推送到 MindSphere 事件
for idx, row in anomalies.iterrows():
event = client.events.create(
entity_id="chiller_001_2026",
body={
"timestamp": row["timestamp"].isoformat(),
"properties": {
"fault": "compressor_anomaly",
"severity": "high" if row["vibration"] > 5 else "medium",
"ai_source": "IsolationForest 2024-2026"
}
}
)
print(f"已推送事件: {event.id}")5. 写代码 / 部署必查清单(2024-2026 校准)
- Python 3.10+ / Node.js 18+
- stable-baselines3 / InfluxDB client / MindSphere SDK
- 数据 ≥ 6 月(故障预测 ≥ 1 年)
- 模型版本管理(MLflow / DVC)
- 实时流处理(Kafka / Flink)
- 模型监控 / 漂移告警
- 5 年 TCO 含运维
引用
- ✓ AI-暖通-寻优 4 大升级路径
- ✓ 数字孪生-MindSphere-实施 实施专题
- ✓ 能效-2026-2027 路径
- ✓ 暖通-2026更新 2024-2026 现状
- ✓ stable-baselines3 / InfluxDB / MindSphere 主流