双电机协同驱动：从汽车动力到分布式系统架构的启示

# 模拟定义两个服务实例（电机）的配置
# 实例A: 功率215kW, 角色: 前轴驱动
# 实例B: 功率300kW, 角色: 后轴驱动

# 模拟生成不同类型的负载请求（驾驶场景）
# 场景1: 急加速 - 高负载，需要双实例全力输出
# 场景2: 平稳巡航 - 低负载，单实例高效输出
# 场景3: 爬坡 - 中高负载，需要智能分配扭矩

# 使用一个简单的脚本模拟负载分发逻辑
cat << 'EOF' > load_scheduler.py
import random

class Motor:
    def __init__(self, id, max_power):
        self.id = id
        self.max_power = max_power
        self.current_load = 0

    def apply_load(self, load):
        if self.current_load + load <= self.max_power:
            self.current_load += load
            return True
        return False

class Coordinator:
    def __init__(self, motor_a, motor_b):
        self.motor_a = motor_a
        self.motor_b = motor_b

    def distribute_load(self, total_load, scenario):
        # 简单的负载分配策略示例
        if scenario == "acceleration":
            # 急加速：尝试平均分配或按功率比例分配
            load_a = total_load * 0.4
            load_b = total_load * 0.6
        elif scenario == "cruising":
            # 巡航：优先使用效率更高的电机（假设电机A效率更高）
            if self.motor_a.apply_load(total_load):
                return f"Motor A handles {total_load} load."
            else:
                self.motor_b.apply_load(total_load)
                return f"Motor B handles {total_load} load."
        else:
            # 爬坡：根据需求动态调整
            load_a = total_load * 0.3
            load_b = total_load * 0.7

        # 尝试应用负载
        if self.motor_a.apply_load(load_a) and self.motor_b.apply_load(load_b):
            return f"Both motors working: A({load_a}), B({load_b})"
        else:
            return "Load distribution failed, fallback to single motor."

# 初始化
motor_a = Motor("A", 215)
motor_b = Motor("B", 300)
coordinator = Coordinator(motor_a, motor_b)

# 模拟执行
print(coordinator.distribute_load(400, "acceleration"))
print(coordinator.distribute_load(100, "cruising"))
EOF

# 模拟性能监控命令输出
# 在实际系统中，这可能通过API或日志收集工具实现

cat << 'EOF' > monitor.sh
#!/bin/bash
# 模拟获取电机A的状态
echo "Motor A Status: Load=150kW, Temp=45C, Efficiency=92%"
# 模拟获取电机B的状态
echo "Motor B Status: Load=200kW, Temp=50C, Efficiency=94%"
# 模拟计算整体系统效率
echo "System Total Load: 350kW, Avg Efficiency: 93%"
EOF

# 执行监控脚本
bash monitor.sh

# 模拟一个简单的负载均衡策略调整逻辑
cat << 'EOF' > load_balancer_adjust.py
# 假设监控发现电机B温度过高，需要降低其负载
def adjust_strategy(motor_a, motor_b, temp_b):
    if temp_b > 60:  # 温度阈值
        # 将部分负载从电机B转移到电机A
        load_to_transfer = 30
        if motor_a.max_power - motor_a.current_load >= load_to_transfer:
            motor_a.current_load += load_to_transfer
            motor_b.current_load -= load_to_transfer
            print(f"Strategy adjusted: Transferred {load_to_transfer} load from B to A.")
        else:
            print("Cannot transfer load, Motor A is near capacity.")
    else:
        print("No strategy adjustment needed.")

# 模拟调整
adjust_strategy(motor_a, motor_b, 65)
EOF