| With the development of industrial technology and the requirement high resolution telescopes for astronomical observation,the aperture of telescopes are becoming bigger and bigger,and also cameras and other devices are becoming more and more complex.On the other hand,in order to get better image quality,more and more telescopes are chosen to build in special environments such as plateau,mountain,Antarctica and space.These two factors cause the telescope to put forward higher requirements for its control system,especially when the telescope is under the unattended condition and with satellite networks,it requires that the telescope be able to achieve full autonomous observation.As the aperture of the telescope increases,the amount of data increases with the exponential level,such as the 2.5 meter diameter WFST's large focal plane survey data,and the FAST sky survey data.These large telescopes have a large amount of data observed daily and have higher accuracy requirements.Some traditional astronomical data processing algorithms are not satisfactory in terms of efficiency and effectiveness.In recent years,the development of computer hardware technology and the introduction of deep learning algorithms have made more choices in the algorithm of astronomical data processing.At present,there are few large optical telescopes in China,only LAMOST telescopes and some 1-2 meter optical telescopes.Many telescopes still use manual or semi manual observation modes,with observers providing observation targets,and then sending device commands manually.The observation control system of LAMOST telescope is based on CORBA,and has made a certain component architecture design.However,due to various restrictions in practice,it still needs a large number of personnel to participate.RTS2 is the only existing open source telescope control framework currently.It was designed for fully autonomous observation control system,although there are defects in the framework.With the construction of more large optical telescopes and polar telescopes,the requirement of autonomous observation and control of telescopes is becoming more and more urgent.This paper first introduces the current development of large telescopes and Antarctica telescopes from China and all around the world,as well as some existing telescope control and data processing technology.We have developed two telescope autonomous observation and control frameworks.One is the telescope observation and control framework based on RTS2 and EPICS,the other is the telescope autonomous observation and control framework based on ZeroMQ.The two frameworks have developed the technology of combining hardware and software simultaneously.The key component of the focal plane system,the astronomical camera,is combined with software and hardware.The upper layer can obtain the information of the underlying hardware in many aspects,and the control of the device layer is closely cooperated with the firmware inside the hardware,which makes the whole system work seamlessly and improves the stability and fault diagnosis ability of the system.This is especially important for autonomous observation and autonomous control of the whole telescope.Therefore,based on the requirement of autonomous observation and control,this paper completes a general SDK and application software for small camera control,which is easily integrated into the above two control frameworks through AreaDetector.At the same time,DAQ design and control system design for large focal plane mosaic camera are completed.For autonomous observation and control of telescopes,besides autonomous observation itself,another important technology is automatic and intelligent processing of astronomical big data,including online processing and offline processing.As computing power grows stronger,the boundaries between online and offline processing become blurred.With the increasing demand of astronomical survey,the amount of data is increasing rapidly,whether optical or radio telescopes.Based on the requirements of intelligent and automatic data processing in radio telescope,this paper designs an automatic data processing pipeline for the cosmic microwave background radiation data and fast off River hi data,which can flexibly combine various processes and algorithm modules with configuration files,and optimizes the performance of the pipeline to make the pipeline processing speed meeting the data processing requirements of FAST telescope.Cosmic microwave background(CMB)radiation is residual electromagnetic radiation from the early stage of the universe.Its observation is of great significance to the study of the early structure of the universe and the evolution of the universe.At present,there are many ground-based and space equipments for CMB observation,including Boomerang sounding balloon and Planck satellite.The CMB raw data contains a lot of foreground noise.The existing noise processing algorithms mainly use spectral analysis to reconstruct the foreground noise and then remove it.In this paper we found that the CMB foreground noise and 21cm neutral hydrogen spectrum are correlated.We enumerates three models to predict CMB foreground noise signal from the 21cm neutral hydrogen line,including a deep learning based model.The model is trained and tested on the Planck satellite data and HI4PI data,and the output of deep learning model is the best.The innovations of this paper are as follows:(1)Based on the overall needs of autonomous observation and control,two frameworks have been developed,including autonomous observation and control framework for telescope based on RTS2 and EPCIS,and autonomous observation and control framework of telescope based on ZeroMQ.Based on ZeroMQ,this paper implements the first common distributed telescope autonomous observation framework in China.The framework is designed with hierarchical structure,supporting automatic observation according to plan,rule-based fault monitoring,and Web based remote control interface and log visualization interface.(2)Based on the requirement of autonomous observation and control in the device layer,the astronomical camera,the key component of the focal plane system,has been integrated with hardware and software.The universal cross-platform camera control SDK and its application softwares have been completed.The camera control SDK can support both Linux and Windows systems,and has been tested successfully in the embedded ARM environment.The data acquisition system(DAQ)and control system design for the large focal plane mosaic camera are completed.The camera control system is optimized to the greatest extent based on the combination of hardware and software technology,and seamlessly integrated with the high level autonomous observation and control framework.(3)The automatic data processing pipeline is designed for the 21 cm neutral hydrogen atom spectrum data of fast radio telescope.For the need of intelligent and automatic processing of astronomical big data,a deep learning algorithm is proposed to predict the foreground noise of cosmic microwave background radiation through the 21cm neutral hydrogen atomic spectrum.The algorithm is tested on CMB data of Planck satellite and achieves good results. |
PDF Full Text Request