记录一个错误Error resolving template [pi_*****_secret_******]

 Eror resoving template [pi_*****_secret_******], template hight not exist or might ,snot be accessible by any of the configured Template Resolvers

这是在对接stipe支付时返回给前端客户端密钥时报的错误:

            在网页上搜索了很久都是再说模板文件路径错误:需要确认模板文件是否存在,并且其路径是否正确。通常模板文件的路径应该是classpath:/templates/index.html。需要检查配置文件中的路径信息是否正确。模板引擎配置错误:需要检查配置文件中是否存在错误,特别是prefix和suffix等属性的设置是否正确。可能需要根据实际情况修改配置信息等等.

        但是试过之后都不管用最后问了公司的老程序员后发现应该在调用方法中加入@ResponseBody注解或者是类名上面加  @RestController

        因为单独的@Controller   默认返回的  页面如果接口是 rest接口   就需要单独方法加@ResponseBody 如果整个类都是  rest接口  就可以在类名上换成  @RestController

        

### A* Algorithm for Grid-Based Traveling Salesman Problem The **Traveling Salesman Problem (TSP)** involves finding the shortest possible route that visits each node exactly once and returns to the starting point. When applied to a grid-based system, it becomes necessary to incorporate pathfinding algorithms such as **A*** into solving TSP. #### Explanation of A* Algorithm The A* algorithm is an informed search algorithm used primarily for pathfinding and graph traversal. It combines features from Dijkstra's algorithm and greedy best-first search by using both cost-to-come \(g(n)\) and heuristic estimates \(h(n)\)[^3]. For any given node \(n\), the total estimated cost \(f(n)\) is calculated as: \[ f(n) = g(n) + h(n) \] Where: - \(g(n)\): Cost from start node to current node. - \(h(n)\): Estimated cost from current node to goal. In the context of TSP on grids, A* can be adapted to find optimal paths between pairs of cities/nodes while considering constraints like obstacles or restricted movement directions. #### Implementation Steps Overview To implement A* for solving TSP on a grid: 1. Define your grid environment where nodes represent potential locations/cities. 2. Use A* repeatedly to compute pairwise distances between all city pairs within the grid. 3. Construct a complete weighted graph based on these computed distances. 4. Apply a standard TSP solver (e.g., dynamic programming approach) to determine the minimal tour covering all cities. Below is Python pseudocode illustrating key components involved in implementing A* tailored towards resolving TSP problems constrained onto regular lattices/grids. ```python import heapq def astar(start, end, grid): open_set = [] closed_set = set() # Priority queue initialization with tuple structure: (priority, position) heapq.heappush(open_set, (0, start)) came_from = {} g_score = {start: 0} f_score = {start: heuristic_estimate(start, end)} while open_set: _, current = heapq.heappop(open_set) if current == end: return reconstruct_path(came_from, current) closed_set.add(current) neighbors = get_neighbors(grid, current) tentative_gscore = g_score[current] + distance_between(current, neighbor) for neighbor in neighbors: if neighbor in closed_set and tentative_gscore >= g_score.get(neighbor, float('inf')): continue if tentative_gscore < g_score.get(neighbor, float('inf')): came_from[neighbor] = current g_score[neighbor] = tentative_gscore f_score[neighbor] = tentative_gscore + heuristic_estimate(neighbor, end) if neighbor not in [node[1] for node in open_set]: heapq.heappush(open_set, (f_score[neighbor], neighbor)) return None def heuristic_estimate(node_a, node_b): """Manhattan Distance Heuristic""" return abs(node_a[0]-node_b[0]) + abs(node_a[1]-node_b[1]) def reconstruct_path(came_from, current_node): path = [] while current_node in came_from.keys(): path.append(current_node) current_node = came_from[current_node] return list(reversed(path)) def solve_tsp_with_astar(nodes_list, grid): n = len(nodes_list) dist_matrix = [[astar(nodes_list[i], nodes_list[j], grid) for j in range(n)] for i in range(n)] dp_table = [[float('inf')] * (1 << n) for _ in range(n)] dp_table[0][1] = 0 for mask in range(1<<n): for u in range(n): if ((mask >> u) & 1)==0 :continue; prev_mask=mask^(1<<u); for v in range(n): if((prev_mask>>v)&1)!=0 : dp_table[u][mask]=min(dp_table[u][mask], dp_table[v][prev_mask]+dist_matrix[v][u]) min_cost=float('inf') last=None final_mask=(1<<n)-1 for k in range(n): if(min_cost>dp_table[k][final_mask]): min_cost=dp_table[k][final_mask]; last=k; result=[last] curr_mask=final_mask; while(curr_mask!=1): next_city=-1; temp_min=float('inf'); for m in range(n): if(((curr_mask>>(m))&1)==0 ):continue ; if(temp_min>(dp_table[m][curr_mask^(1<<last)])+dist_matrix[last][m]): temp_min=dp_table[m][curr_mask^(1<<last)]; next_city=m; result.append(next_city); tmp=curr_mask; curr_mask=tmp^(1<<last); last=next_city; result.reverse(); return result,min_cost ``` This script demonstrates how one might integrate A* searches iteratively among multiple points defined upon discrete spatial arrangements represented through matrices—facilitating resolution strategies pertinent specifically toward tackling instances involving combinatorial optimization challenges posed under conditions adhered strictly according lattice structures imposed inherently due physical limitations encountered real-world scenarios encompassing robotics navigation systems amongst others[^4].
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