Science operations planning expertise: A neglected component of mission design

In this paper, Science Operations Planning Expertise (SOPE) is defined as the expertise that is held by people who have the two following qualities. First they have both theoretical and practical experience in operations planning, in general, and in space science operations planning in particular. Second, they can be used, on request and at least, to provide with advice the teams that design and implement science operations systems in order to optimise the performance and productivity of the mission. However, the relevance and use of such SOPE early on during the Mission Design Phase (MDP) is not sufficiently recognised. As a result, science operations planning is often neglected or poorly assessed during the mission definition phases. This can result in mission architectures that are not optimum in terms of cost and scientific returns, particularly for missions that require a significant amount of science operations planning. Consequently, science operations planning difficulties and cost underestimations are often realised only when it is too late to design and implement the most appropriate solutions. In addition, higher costs can potentially reduce both the number of new missions and the chances of existing ones to be extended. Moreover, the quality, and subsequently efficiency, of SOPE can vary greatly. This is why we also believe that the best possible type of SOPE requires a structure similar to the ones of existing bodies of expertise dedicated to the data processing such as the International Planetary Data Alliance (IPDA), the Space Physics Archive Search and Extract (SPASE) or the Planetary Data System (PDS). Indeed, this is the only way of efficiently identifying science operations planning issues and their solutions as well as of keeping track of them in order to apply them to new missions. Therefore, this paper advocates for the need to allocate resources in order to both optimise the use of SOPE early on during the MDP and to perform, at least, a feasibility study of such a more structured SOPE.     

PDM软件在航空制造企业的适用性分析

针对有关PDM与ERP软件选型的争论,论述了我国航空制造企业管理模式的特殊性,阐明PDM与ERP软件的主体设计思想,对航空企业管理特点和数据管理需求进行了细致分析,说明在资金有限的情况下,我国的航空制造企业可以暂不引进PDM,直接引进ERP软件。ERP系统能够承接和管理部分产品设计数据,并解决企业管理信息化的主体问题。     

高职高专院校建设数字化校园过程中应注意的问题

21世纪以来,数字化和信息化成为现代社会的主要标志。高职高专院校为了提高自己的管理水平,跟上时代的步伐,也都在着手数字化校园的建设。本文从数字化校园的基本概念入手,介绍数字化校园的组成、建设内容、建设步骤以及建设过程中应注意的问题,并给出了数字化校园的基本模型。     

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.     

月球与深空探测频率规划研究

介绍国际电（ITU）和空间频率协调组（SFGC）关于月球与深空探测频率使用的一些规定和建议,研究国外月球与深空探测频率使用规划和发展趋势,探讨我国月球与深空探测频率使用需求,提出我国月球与深空探测频率建议和发展方向。     

Distributed online mission planning for multi-player space pursuit and evasion

There are three important roles in evasion conflict: pursuer, target and defender. Pur-suers’ mission is to access targets;targets’ mission is to escape from pursuers’ capture;defenders’ mission is to intercept pursuers who are potentially dangerous to targets. In this paper, a distributed online mission plan (DOMP) algorithm for pursuers is proposed based on fuzzy evaluation and Nash equilibrium. First, an integrated effectiveness evaluation model is given. Then, the details of collaborative mission planning which includes the co-optimization of task distributing, trajectory and corresponding maneuvering scheme are presented. Finally, the convergence and steadiness of DOMP are discussed with simulation results. Compared with centralized mission planning, DOMP is more robust and can greatly improve the effectiveness of pursuing. It can be applied to dynamic scenario due to its distributed architecture.     

通用飞机批产项目进度计划体系研究与构建

以某型通用飞机产品为例,阐述其批产项目进度计划体系构建过程,实现批产项目进度计划管理的规范化、精细化、信息化,并建立一套适用于通用航空制造企业高效率、低成本的批产项目进度计划管理方法.     

飞机修理厂人力资源管理与中长期规划匹配探析

人力资源是企业发展动力的源泉,是可持续发展的根本保障。本文根据中国通用航空发展的态势和飞机修理厂的现状与发展目标,对飞机修理厂提出了规划要求和适应发展需要的应对措施。     

基于非精确计算的空间探测相控阵雷达任务规划算法

针对空间探测相控阵雷达系统，提出了一种新的基于非精确计算模型的观测任务规划算法。首先，建立了目标观测的实时任务模型，并分析了观测任务所占用传感器的资源；其次，基于非精确计算模型，提出一种多任务并行的实时容错调度算法来解决观测任务规划问题，该算法综合考虑相控阵雷达的搜索任务与跟踪任务，来进行系统资源的分配。对于跟踪任务，算法结合目标的过境时间以及当前系统的负载情况，以此来确定雷达对该目标的观测时间段；最后给出了算法的评估方法。利用2886个低轨空间目标进行仿真验证，结果表明，基于非精确计算模型的任务规划算法，可显著提高系统调度成功率以及时间资源利用率．比传统方法更稳健。     

面向航天科研的绩效预算计划决策框架

航天科研需要投入大量的资金进行研制、开发、试验和定型。而预算的基础,最终在于绩效,即必须通过型号任务的完成,发射的成功,科研成果的积累,科研水平的提高,人才的培养等多个方面的综合测度来进行评价,也就是以提高国家的航天战略能力作为评估的目标。本文根据航天科研的特点,在平衡记分法的基础上,提出了面向航天科研的绩效预算计划决策框架,建立了一个航天科研的预算计划决策过程模型以及组织、项目、过程的决策模型。