The accuracy of short-term traffic flow prediction is one of the important issues in the construction of smart cities, and it is an effective way to solve the problem of traffic congestion. Most previous studies could not effectively mine the potential relationship between the temporal and spatial dimensions of traffic data flow. Due to the large variability in the traffic flow data of road conditions, we analyzed it with “dynamic”, using a dynamic-aware graph neural network model for the hidden relationships between space-time in the deep learning segment. In this paper, we propose a dynamic perceptual graph neural network model for the temporal and spatial hidden relationships of deep learning segments. This model mixes temporal features and spatial features with graphs and expresses them. The temporal features and spatial features are connected to each other to learn potential relationships, so as to more accurately predict the traffic speed in the future time period, we performed experiments on real data sets and compared with some baseline models. The experiments show that the method proposed in this paper has certain advantages.

The contact window scheduling technology supports various space mission requirements of the satellite information network. The continuous emergence of new space missions challenges the scarce satellite contact window scheduling. In this paper, We propose a dynamically updated micro-cloud structure based on digital twin and multi-agent system technology. Then, we build an on-satellite autonomous scheduling model based on the micro-cloud structure and design an efficient solution algorithm to solve the maximum revenue problem of inefficient task scheduling caused by the dynamic change of task priority and satellite position over time. Simulation experiments show that the scheme proposed in this paper can significantly improve task scheduling revenue and resource utilization, and have specific enlightening significance for the innovation of satellite task scheduling methods in the future.

Multisatellite intelligent computing, aimed at autonomously generating resource allocation schemes without reliance on ground control, is a crucial technology for the intelligent and integrated Internet of Things (IoT) from space to ground. Existing work focuses on ground-based computation for scheduling schemes, and while some recent studies have adopted graph computing and distributed computing strategies on satellites, they have overlooked the frequent changes in satellite network topologies and the coupling relationships between various types of resources. The robustness advantage of game theory offers a possibility for reliable computation in resource allocation. We introduce a multisatellite intelligent negotiation mechanism based on cooperative game theory, which considers dynamic and autonomous negotiation methods for satellite agents with various types of resources. Subsequently, we propose an intelligent computing algorithm for resource allocation based on convex games and design heuristic strategies for adaptively updating resource allocation schemes. Our simulation results indicate that this method outperforms benchmark algorithms in different scenarios, reducing time consumption by at least 0.151%.

To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.

Satellite network resource management and scheduling technology are significant to constructing integrated information networks in heaven and earth. The difficulty in realizing this technology lies in improving resource utilization efficiency while ensuring the service quality of satellites and efficiently coordinating complex satellite network systems and services. This paper proposes a model, A Dynamic task scheduling method based on a UNified resource Management architecture(DUNM), based on the designed resource management architecture supported by dynamic scheduling algorithms to address the problems of low resource utilization, resource allocation, and task completion rate. First, with sufficient resources, the task execution time to complete a task is calculated based on the number of resources, task transmission time, task waiting time, etc. Secondly, based on the tasks assigned to satellites, the execution time of all functions with different transmission rates of communication links between satellites is calculated, and the total sum of all time consumption is analyzed. Finally, after simulation experiments and comparison with various baseline algorithms, about a 40% reduction in time to complete scheduled tasks and an almost 25% reduction in the average cost to finish a scheduling task, our method has higher scheduling efficiency and lower task completion revenue. It also guarantees a higher task completion rate while completing the tasks. Our approach attained a nearly 100% completion rate for scheduling tasks, which means that our algorithm can achieve the scheduling tasks faster and at high task revenue, thus improving the efficiency and economic efficiency of the whole system. Therefore, it validates the advantages of our method, such as high efficiency and high revenue.