String key = String.format("DUKE_ATOMIC_ERR_NOTIFY_%d", System.currentTimeMillis() / 10_000); RedisAtomicLong redisAtomicLong = new RedisAtomicLong(key, redisTemplate.getConnectionFactory()); redisAtomicLong.expire(100, TimeUnit.SECONDS); long val = redisAtomicLong.getAndSet(1);解释getandset(v newvalue)的作用

`getAndSet(v)` 是 RedisAtomicLong 类的一个方法，它的作用是获取当前 RedisAtomicLong 实例的值，并设置一个新的值。具体地，该方法会执行以下操作：

1. 获取 RedisAtomicLong 实例的当前值。
2. 将 RedisAtomicLong 实例的值设置为 `newvalue`。
3. 返回 RedisAtomicLong 实例的原始值。

在你提供的代码中，`redisAtomicLong.getAndSet(1)` 的作用是获取 RedisAtomicLong 实例 `redisAtomicLong` 的当前值，并将其设置为 1。同时，它会返回 RedisAtomicLong 实例的原始值。这个方法常用于实现一些需要原子性操作的场景，例如实现分布式计数器。

相关问题

import networkx as nx import matplotlib.pyplot as plt # 输入数据 locations = [[125.330802,125.401931,125.326444,125.332284,125.322837,125.32563,125.334942,125.378548,125.386251,125.426883,125.42665,125.437111,125.453763,125.431396,125.430705,125.41968,125.437906,125.404171,125.385772,125.341942,125.341535,125.300812,125.307316,125.345642,125.331492,125.330322,125.284474,125.334851,125.30606,125.377211,125.381077,125.417041,125.41427,125.416371,125.432283,125.401676,125.403855,125.38582,125.426733,125.291], [43.917542,43.919075,43.905821,43.90266,43.900238,43.89703,43.888187,43.904508,43.892574,43.907904,43.896354,43.894605,43.889122,43.88774,43.882928,43.887149,43.8789,43.879647,43.883112,43.873763,43.861505,43.854652,43.876513,43.850479,43.833745,43.825044,43.812019,43.803154,43.793054,43.788869,43.824152,43.816805,43.801673,43.82893,43.83235,43.843713,43.854322,43.868372,43.871792,43.8306]] num_flights = 4 flight_capacity = [10, 10, 10, 10] # 将坐标转化为图 G = nx.Graph() for i in range(len(locations[0])): G.add_node(i+1, pos=(locations[0][i], locations[1][i])) for i in range(len(locations[0])): for j in range(i+1, len(locations[0])): dist = ((locations[0][i]-locations[0][j])**2 + (locations[1][i]-locations[1][j])**2)**0.5 G.add_edge(i+1, j+1, weight=dist) # 添加起点和终点 start_node = len(locations[0])+1 end_node = len(locations[0])+2 G.add_node(start_node, pos=(0, 0)) G.add_node(end_node, pos=(0, 0)) # 添加边和边权 for i in range(len(locations[0])): G.add_edge(start_node, i+1, weight=0) G.add_edge(i+1, end_node, weight=0) for f in range(num_flights): for i in range(len(locations[0])): G.add_edge(i+1, len(locations[0])+f*len(locations[0])+i+1, weight=0) G.add_edge(len(locations[0])+f*len(locations[0])+i+1, end_node, weight=0) # 添加航班容量的限制 for f in range(num_flights): for i in range(len(locations[0])): G.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+f*len(locations[0])+len(locations[0])+1, weight=-flight_capacity[f]) #创造路径规划模型 path_model = nx.DiGraph() for i in range(len(locations[0])): for f in range(num_flights): for j in range(len(locations[0])): if i != j: path_model.add_edge(len(locations[0])+flen(locations[0])+i+1, len(locations[0])+flen(locations[0])+j+1, weight=G[i+1][j+1]['weight']G[len(locations[0])+flen(locations[0])+i+1][len(locations[0])+f*len(locations[0])+j+1]['weight'])找出错误

在代码中，有两个地方出现了错误：

1. 在第 47 行，需要修改为 `len(locations[0])+f*len(locations[0])+len(locations[0])+1`，因为这是连接每个航班的起点和终点的边。

2. 在第 54 行，需要将代码拆分成两行，因为这是两个不同的操作。正确的代码应该是：

path_model.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+f*len(locations[0])+len(locations[0])+1, weight=-flight_capacity[f])
path_model.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+f*len(locations[0])+len(locations[0])+j+1, weight=G[i+1][j+1]['weight'])

这样就能够运行正确了。