In order to reduce the weight of the mechanism and improve the vibration characteristics, the structure with opening which has excellent characteristics has been widely used in many fields such as ship engineering and aerospace. In order to study the vibration modal characteristics of the flexible structure with openings, the analysis model is established based on the finite element analysis method. The vibration modal analysis of the flexible structure with opening is carried out and the low order modal frequencies and mode shapes are obtained. The modal analysis results of the finite element analysis software ANSYS are used to verify the correctness and validity of the modeling method proposed in this paper. The modal analyses under different opening parameters are carried out, such as various opening sizes, different number of openings, different distribution and various opening shapes. And various structural parameters are also analyzed, such as different thickness, aspect ratio, and constraint conditions. The influences of various factors on the vibration modal characteristics are analyzed in this paper, which provide a basis for the design of the flexible structure with opening and it is useful for the active vibration control of it.

Because of its powerful semantic processing function and fast analysis ability, knowledge graph has been widely used in search, question and answer, diagnosis, etc. in recent years. However, the existing technology can't automatically build a complete knowledge graph, and the relational reasoning technology is needed to fill in the missing relationships of knowledge graph. Based on the spacecraft fault knowledge graph, this paper proposes a relational reasoning method which combines representation learning and deep neural network, and takes the semantic information and position information of entities and relationships into the calculation range. Firstly, the representation learning model is used to process triples to obtain semantic vectors. Secondly, the principal component analysis method is used to reduce the dimension of semantic vector in order to reduce the difficulty of subsequent calculation. Then, according to the global position of the entity in the knowledge graph, the position vector of the entity is obtained via Boolean data markers. Finally, the semantic vector and position vector are spliced as the input of the deep neural network, and the relational prediction vector is output. This method effectively solves the problems of complex physical relations, scarce samples and incomplete knowledge base of spacecraft fault knowledge graph. Experimental results show that the prediction accuracy of this method is higher than that of single representation learning reasoning and path reasoning, and it can effectively improve the relationship of spacecraft fault knowledge graph.

The time tagged data are directly related to whether the task can be realized, which is very important for a satellite. The time tagged data are executed according to the timetable and often stored in on board computer for a long time. Fault tolerance design is needed to mitigate the influence of single event upsets. By function,the time tagged data which has different length can be classified as Time Tagged Telecommand, Time Tagged Data Block, and Relative Time Tagged Telecommand. An SEU mitigation method of time tagged details is proposed. Error correction coding and frame annotation for time tagged data of different length are done on the ground, and the information bits and the supervision bits are checked by the on board computer periodically or before the data to be used. The error correcting capability of the new method does not  rely on hardware and is better than the traditional three mode redundancy method, and the memory space can be saved greatly.

Flash equipment is used as the storage medium on spacecraft, and file system is needed to manage the storage equipment. In the space embedded system, the scale of data storage is becoming larger and larger, and the amount of data generated by the acquisition task will reach the level of TB. For the sake of security, the memory capacity of spacecraft embedded system is limited, and the real time requirement is very high. Common embedded system is different from aerospace embedded system in memory capacity and real time requirements, so it is not suitable for aerospace embedded system. In order to meet special requirements of limited spacecraft system resources, and to resolve the problem that the space occupied by file index structure in memory increases linearly with the amount of file data in YAFFS2 which is a commonly used embedded file system, a new index method called BLOF(low overhead and fast file management based on binary group) is proposed in which continuous FLASH pages are indexed uniformly by introducing binary group structure based on B+ tree, and the file read and file write algorithms according to the index structure are designed. So that the file system can implements fast file reading and writing, and low memory footprint. The experimental results show that compared with the YAFFS2 index method, BLOF is more effective in the following cases: 1) when the number of FLASH pages for storing file data reaches an average of six or more consecutive pages, BLOF takes less memory footprint than YAFFS2, and maximum saving is 55.55%; 2) when the file size is greater than 512KB, the file reading and writing time of the BLOF method is always less than that of YAFFS2, and the value of YAFFS2 file writing time minus BLOF file writing time increases linearly with the file size. BLOF method is more suitable for the situation where the file data is large and stored in a large number of continuous pages.