# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 4 Resource Request Queue Reconstruction and Collaborative Scheduling Method
### Resource Cooperative Allocation Optimization
The network \(G\) includes two resources, two source nodes, three sinks, and the resource demand quantity on each node. On a given network \(G\), we design the following algorithm based on the spatial information network environment characteristics to solve the minimum time consumption problem for resource allocation. As shown in Algorithm 2, we use Algorithm 1 to calculate the maximum traffic value that the current satellite network can accommodate (lines 1-7). If a resource request operation is added to the source satellite set, all resource request queues of the current satellite set are updated (line 10). Meanwhile, the total resource demand of the network at the current moment is obtained. Suppose the total demand is within the accommodating range of the entire network. In that case, the gurobi solver (a widely used solver, we have obtained a license) is used to solve the optimal resource allocation route, and then the current unallocated resources are updated (lines 11-13). If no new resource requests are added, the optimal resource allocation plan is resolved using gurobi, then the currently unallocated resources are updated and returned to the current minimum time resource allocation plan (lines 18-20). As shown in Figure 6, the computational results of the above simple example depict the reconstruction of the satellite resource request queue and satellite resource coordination scheme as well as the final route planning for resource coordination. The example shows resource cooperation among satellites, where satellites accomplish resource request tasks through resource sharing. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Resources Type** & **Sources Number** & **Sinks Number** \\ \hline
1 & 2 & 4 \\
1 & 7 & \(-4\) \\
1 & 8 & \(-1\) \\
1 & 9 & \(-1\) \\
2 & 2 & 3 \\
2 & 7 & \(-1\) \\
2 & 8 & \(-4\) \\
2 & 9 & \(-1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Resource scheduling initial data sample. Figure 5: The initial state of spatial information network resource allocation schematic diagram. ```
Input: Given network \(G\big{(}V,E,s_{source},s_{target},U,I\big{)}\). Initialize the number of resource requirements \(d_{i}\), unit data transmission time \(U^{r}_{u,v}\), link bandwidth \(l^{\max}_{s,s^{\prime}}\), source \(s_{source}\) and sink \(s_{target}\), Output: Network flow allocation time \(Time\)
1 if\(s_{source}\
eq\varnothing\&s_{target}\
eq\varnothing\)then
2 superSourceNode.next() = \(s_{source}\) ;
3 superTargetNode.next() = \(s_{target}\) ;
4\(C^{*}\).add(superSourceNode);
5\(G^{*}\).add(superTargetNode);
6 MF=FordFulkerson(\(d_{s,s^{\prime}}^{\max}\));// Use the algorithm Ford-Fulkerson to calculate the maximum flow value MF allowed in the network \(G^{*}\); for each \(t<\tau\)do
7 if a new resource request queue is addedthen
8 UpdateQueue(\(d(s_{source})\));
9 if the resource demand at the current momentd<MFthen
10 Route=gurobi.optimize();// Use the gurobi solver to calculate the minimum time-consuming planning scheme. 11\(d\)-\(d_{Allocated}\)=\(d_{new}\);// Update unallocated resources. 12
13 else
14 MF=FordFulkerson();// Update the maximum flow capacity. 15
16 else
17 Route=gurobi.optimize();
18\(d\)-\(d_{Allocated}\)=\(d_{new}\);// Update unallocated resources. 19 return Route
```  
**Algorithm 2** Satellite resource allocation in dynamic network  
In summary, the optimization process of spatial information network resource scheduling is the process of source request queue redistribution and inter-satellite coordination, which ultimately minimizes the time consumed by the satellite resource scheduling process.