用Python代码来编写基于深度优先和广度优先解决八数码问题，保证在Spyder上执行

在Python中，我们可以使用`heapq`库来实现广度优先搜索(BFS)和`deque`队列来实现深度优先搜索(DFS)来解决八数码问题，也称为数独问题。这里我会给出简单的DFS和BFS解决方案：

**深度优先搜索 (DFS) 示例**:

from collections import deque

def dfs(board, row, col, num):
    directions = [(0, -1), (-1, 0), (0, 1), (1, 0),
                  (-1, -1), (1, -1), (-1, 1), (1, 1)]
    
    if row < 0 or row >= len(board) or \
       col < 0 or col >= len(board[0]) or \
       board[row][col] != 0 or board[row][col] == num:
        return True

    board[row][col] = num
    for dx, dy in directions:
        new_row, new_col = row + dx, col + dy
        if dfs(board, new_row, new_col, num):
            return True
    
    board[row][col] = 0
    return False

# 示例用法
board = [[5, 3, 0, 0, 7, 0, 0, 0],
         [6, 0, 0, 1, 9, 5, 0, 0],
         [0, 9, 8, 0, 0, 0, 0, 6],
         [8, 0, 0, 0, 6, 0, 0, 3],
         [4, 0, 0, 8, 0, 3, 0, 0],
         [7, 0, 0, 0, 2, 0, 6, 0],
         [0, 6, 0, 0, 0, 0, 2, 8],
         [0, 0, 0, 4, 1, 9, 0, 5]]
solution = dfs(board.copy(), 0, 0, 1)

**广度优先搜索 (BFS) 示例**:

import heapq

def bfs(board, start_row, start_col):
    queue = []
    heapq.heappush(queue, (0, start_row, start_col))
    visited = set()

    while queue:
        depth, row, col = heapq.heappop(queue)

        if (row, col) in visited:
            continue

        if board[row][col] == 0:
            break
        
        visited.add((row, col))

        for dr, dc in [(0, -1), (-1, 0), (0, 1), (1, 0)]:
            new_row, new_col = row + dr, col + dc
            if 0 <= new_row < len(board) and 0 <= new_col < len(board[0]) and board[new_row][new_col] == 0:
                heapq.heappush(queue, (depth+1, new_row, new_col))

    return bool(queue)

solution = bfs(board.copy(), 0, 0)

在这个例子中，我们假设`board`是一个二维列表，表示数独游戏的状态。函数`dfs`和`bfs`分别尝试从给定的位置填充数字1，直到找到解决方案或无法继续。