用python实现上述过程，详细写出算法的过程，其中算法的参数用常见的默认值。

下面是使用Python实现上述过程的伪代码，包括布谷鸟算法、粒子群算法和DV-HOP算法的过程。请注意，这只是一个伪代码示例，具体的实现可能还需要根据具体情况进行调整和改进。

import random
import math

# 定义节点类
class Node:
    def __init__(self, node_type):
        self.x = 0
        self.y = 0
        self.node_type = node_type

# 初始化传感器节点
sensor_nodes = []
for _ in range(70):
    node = Node("unknown")
    sensor_nodes.append(node)
for _ in range(30):
    node = Node("anchor")
    sensor_nodes.append(node)

# 布谷鸟算法
def cuckoo_search(sensor_nodes):
    max_iter = 100  # 最大迭代次数
    population_size = 100  # 种群大小
    pa = 0.25  # 鸟巢更新概率
    
    for _ in range(max_iter):
        for node in sensor_nodes:
            if node.node_type == "unknown":
                # 随机更新节点位置
                step_size = 0.1  # 步长
                node.x += random.uniform(-step_size, step_size)
                node.y += random.uniform(-step_size, step_size)
            
            # 更新节点类型
            if random.random() < pa:
                node.node_type = "anchor"

# 粒子群算法
def particle_swarm(sensor_nodes):
    max_iter = 100  # 最大迭代次数
    population_size = 100  # 种群大小
    c1 = 2  # 学习因子 c1
    c2 = 2  # 学习因子 c2
    
    for _ in range(max_iter):
        for node in sensor_nodes:
            if node.node_type == "unknown":
                # 初始化粒子速度
                velocity_x = random.uniform(-1, 1)
                velocity_y = random.uniform(-1, 1)
                
                # 更新节点位置
                node.x += c1 * random.random() * (node.best_x - node.x) + c2 * random.random() * (global_best_x - node.x) + velocity_x
                node.y += c1 * random.random() * (node.best_y - node.y) + c2 * random.random() * (global_best_y - node.y) + velocity_y
            
            # 更新节点类型
            if random.random() < pa:
                node.node_type = "anchor"

# DV-HOP算法
def dv_hop(sensor_nodes):
    anchor_nodes = [node for node in sensor_nodes if node.node_type == "anchor"]
    anchor_distances = {}  # 锚节点之间的距离字典
    
    for i in range(len(anchor_nodes)):
        for j in range(i+1, len(anchor_nodes)):
            anchor1 = anchor_nodes[i]
            anchor2 = anchor_nodes[j]
            distance = math.sqrt((anchor1.x - anchor2.x)**2 + (anchor1.y - anchor2.y)**2)
            anchor_distances[(i, j)] = distance
    
    for node in sensor_nodes:
        if node.node_type == "unknown":
            hop_distances = []
            for i, anchor in enumerate(anchor_nodes):
                distance = math.sqrt((node.x - anchor.x)**2 + (node.y - anchor.y)**2)
                hop_distances.append(distance)
            
            # 估计节点之间的跳数关系
            hop_count = []
            for i in range(len(anchor_nodes)):
                for j in range(i+1, len(anchor_nodes)):
                    if anchor_distances[(i, j)] != 0:
                        hop_count.append(hop_distances[i] / anchor_distances[(i, j)])
            
            # 推断未知节点位置
            node.x = sum(hop_count) / len(hop_count) * anchor_distances[(0, 1)]
            node.y = sum(hop_count) / len(hop_count) * anchor_distances[(0, 2)]
    
    # 更新节点类型
    for node in sensor_nodes:
        if node.node_type == "unknown":
            node.node_type = "anchor"

# 主函数
def main():
    # 布谷鸟算法定位节点
    cuckoo_search(sensor_nodes)
    
    # 粒子群算法优化节点定位
    particle_swarm(sensor_nodes)
    
    # DV-HOP算法推断未知节点位置
    dv_hop(sensor_nodes)
    
    # 绘制节点分布图
    draw_node_distribution(sensor_nodes)
    
    # 计算节点定位误差和节点本地化成功率
    localization_error = calculate_localization_error(sensor_nodes)
    localization_success_rate = calculate_localization_success_rate(sensor_nodes)
    
    print("节点定位误差：", localization_error)
    print("节点本地化成功率：", localization_success_rate)

if __name__ == '__main__':
    main()

在上述代码中，你可以根据需要添加绘制节点分布图和计算节点定位误差、节点本地化成功率的具体实现。同时，也可以根据你的需求调整算法参数和细节。