Search

This article develops a spacecraft component-level vacuum thermal test intelligent control system, which can control the thermal vacuum chamber to automatically complete the entire test process including establishing a vacuum environment, setting temperature control parameters, switching test conditions, and executing shutdown procedures. It is the first to complete the one-key boot function in similar domestic laboratories. This system combines thermostat, parameter identification and self-tuning control technology to complete the temperature control of the test piece. It solves the technical problems of complex thermal characteristics of test piece, large time delay, and overshoot in temperature control. The system also has the function of automatic conversion of test condition and provides a guarantee for the smooth development of the vacuum thermal test of the spacecraft and its components.

With the rapid development of the 5thgeneration of wireless network communication technology, a high-speed data rate, broader bandwidth, and low-latency wireless network has already come to the truth. Meanwhile, Edge Computing which put data processing at the edge of the network has already been well developed, and it has changed the computing mode tremendously. And also, Artificial Intelligence (AI) has made a great breakthrough from Computer Vision, Unmanned Vehicle to Natural Language Processing. Nevertheless, with the increasing model size and model depth, the training data set and communication delay has become the bottleneck of AI popularization. As zillions of bytes of data are being generated at the network edge, and also lots of AI applications are being utilized by enormous customers lied at the edge, the combination of Edge Computing and AI is imperative. This paper has done a deep investigation into the new field about the confluence of Edge Computing and AI, aiming of discussing the concept, architecture, and research ideas on Edge Intelligence. To the end, this paper provides the road-map for future researching work.

With the rapid development of integrated energy system and microgrid technologies, aiming at the operational safety problems caused by the uncertainty of the sources and loads, this paper proposed a robust optimal scheduling model of the multiple integrated energy microgrids considering sources and loads uncertainty. In order to minimize the total operating cost of the multiple microgrids, the interconnection lines were introduced into the multiple microgrids for joint operation, on the basis of renewable energy outputs and multi-energy loads prediction, the uncertainty of sources and loads were characterized and the established model were solved. The results of an example showed that the proposed method can effectively improve the economy of multiple microgrids and reduce the risk caused by the uncertainty of sources and loads, thus verified the effectiveness and feasibility of the proposed method.

In the operation of energy internet, the problem of uneven regional energy distribution which caused by the different social, economic, geographical and environmental factors has existed for a long time, and has become the most complex and intractable problem to solve at the Multi-space energy allocation. To alleviate the problems of uneven regional energy distribution, combined with the rapid development of block data, in this paper, an energy internet efficient regional optimal allocation method based on block data is explicitly proposed. Especially, by means of considering the difference of social, economic, geographical and environmental factors in each region, a concept of dynamic spatial energy allocation adjustment is proposed in this paper. Energy utilization rates and a new objective functional is proposed to support regional energy allocation strategy which adjusting dynamically to optimize the energy utilization ratio when the regional energy allocation is found to be unreasonable. The traversal algorithm method is applied in the strategy to ensure the comprehensiveness and stability of adjustment scheme selection in practical application. A fractional steps computing solution that simultaneously emphasizes the insufficient computational capabilities of hardware and out of memory error is implemented to deal with the huge amount of block data caused by complex energy internet. As a result, a promising approach is introduced for the energy internet efficient regional optimal allocation adjustment.