在A-star算法中，成本函数系数通常用来调整启发式函数和g值的权重，以便在搜索过程中更好地平衡最短路径和启发式函数的影响。以下是一个示例代码，展示了如何在A-star算法中使用成本函数系数：

import heapq

def heuristic(node, goal):
    # 启发式函数，估计从当前节点到目标节点的代价
    return abs(node[0] - goal[0]) + abs(node[1] - goal[1])

def astar(start, goal, cost_coefficient):
    # 使用堆来实现优先队列
    open_list = [(0, start)]  # (f值, 节点)
    closed_list = set()  # 已访问的节点集合
    g_values = {start: 0}  # 起始节点到每个节点的实际代价

    while open_list:
        f, current = heapq.heappop(open_list)  # 从优先队列中取出当前f值最小的节点
        closed_list.add(current)  # 将该节点加入已访问的节点集合

        if current == goal:
            # 找到目标节点，返回路径
            path = []
            while current != start:
                path.append(current)
                current = came_from[current]
            path.append(start)
            path.reverse()
            return path

        for neighbor in get_neighbors(current):
            if neighbor in closed_list:
                continue  # 跳过已访问过的节点

            # 计算从起始节点到该邻居节点的实际代价
            new_g = g_values[current] + get_cost(current, neighbor) * cost_coefficient

            if neighbor not in g_values or new_g < g_values[neighbor]:
                # 更新邻居节点的g值和f值
                g_values[neighbor] = new_g
                f = new_g + heuristic(neighbor, goal)
                heapq.heappush(open_list, (f, neighbor))
                came_from[neighbor] = current

    return None  # 未找到路径

# 示例函数，获取当前节点的邻居节点
def get_neighbors(node):
    # 返回当前节点的邻居节点列表
    pass

# 示例函数，计算从当前节点到邻居节点的代价
def get_cost(current, neighbor):
    # 返回从当前节点到邻居节点的代价
    pass

在上述示例代码中，cost_coefficient即为成本函数系数，通过将get_cost(current, neighbor)乘以cost_coefficient来调整实际代价。可以根据具体问题的需求，调整cost_coefficient的值来平衡最短路径和启发式函数的影响。