## 6 Platform Application Validation
### Verification of Path Optimization in the Platform  
Taking the fault point of the wheat combine harvester as the research object, data related to the maintenance service vehicle and the faulty agricultural machinery were obtained from (Weichai Lovol Heavy Industry Co., Ltd., Weifang City, China). Firstly, the maintenance path of the wheat combine harvester was planned using the platform. The planning process combined the GPS positioning system with the maintenance process of the service vehicle. The position information was fed back in real time by the information system, and the maintenance operation tracking diagram is shown in Figure 11.  
The Dijkstra algorithm and the A* algorithm were used to optimize the path of the map, and the results when using the two algorithms were simulated and compared. There are two main performance indicators for the purposes of comparison: search time and search accuracy. The map takes the location of each service vehicle and the faulty agricultural machinery as the node, and the time spent in each search is shown in Table 3.  
As can be seen from Table 3, when the number of nodes is small, the search speed of the Dijkstra algorithm shows little difference with that of the A* algorithm, but when the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture** & **Cross-Platform** & **Safety** & **Maintain** & **Responding Speed** \\ \hline B/S & good & poor & easy & slow \\ C/S & poor & good & difficulty & quick \\ Layer 2 C/S & good & poor & easy & faster \\ Layer 3 C/S & good & good & easy & faster \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of four architectures.  
Figure 11: Route planning route map for the operation and maintenance service vehicle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node** & **Dijkstra’s Algorithm** & **A* Algorithm** \\ \hline
32 & 4 s & 4 s \\
40 & 7 s & 6 s \\
63 & 16 s & 8 s \\
77 & 24 s & 11 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of search time between the Dijkstra algorithm and the A* algorithm.  
number of nodes is large, the Dijkstra algorithm is slower than the A* algorithm, and with increasing numbers of nodes, the difference in speed becomes greater.  
The Dijkstra algorithm and A* algorithm are used for path planning. For a map with \(N\) nodes, other than the starting node, the remaining nodes are all to be planned. The number of nodes to be planned is \(N-1\). Since each node to be planned corresponds to a shortest path, \(N-1\) shortest path should be planned for each map with \(N\) nodes. The accuracy function is:  
\[F=\frac{X}{N-1} \tag{1}\]  
where \(X\) represents the number of shortest paths obtained by the algorithm, and \(N\) represents the number of nodes.  
The search accuracy of the two algorithms is compared in Table 4.  
Table 4 shows that when the number of nodes is small, the accuracy of the A* algorithm is high, but it decreases with increasing numbers of nodes. However, the Dijkstra algorithm, no matter how the number of nodes changes, maintains an accuracy rate greater than 96%. Although the search time is long, the data are reliable. For the cross-regional operation and maintenance service platform for combine harvestesters, the Dijkstra algorithm is more reliable and suitable. After adopting the the operation path optimization strategy calculated by this algorithm, there was a reduction in the maintenance service operation path, a shortening of the time required, and a reduction in cost.