With new artificial intelligence (AI) technologies and application scenarios constantly emerging, AI technology has become widely used in astronomy and has promoted notable progress in related fields. A large number of papers have reviewed the application of AI technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status of and research hotspots in telescope intelligence from these papers. This paper combines the development history of AI technology and difficulties with critical telescope technologies, comprehensively introduces the development of and research hotspots in telescope intelligence, conducts a statistical analysis of various research directions in telescope intelligence, and defines the merits of these research directions. A variety of research directions are evaluated, and research trends in each type of telescope intelligence are indicated. Finally, according to the advantages of AI technology and trends in telescope development, potential future research hotspots in the field of telescope intelligence are given.

The imaging quality of a telescope directly affects the reliability of astronomical research. Through the monitoring and diagnosis of imaging quality, the cause of the deterioration of imaging quality can be found in time, which is essential for ensuring the peaking performance of the telescope and high-quality imaging. Moreover, these operations are complex and crucial for achieving high-quality imaging of future giant telescope systems involving active optics, adaptive optics, and other advanced techniques. We propose a three-component method based on cutting-edge artificial intelligence technology to real-time monitor and efficiently diagnose the telescope image quality. The first component, an image quality monitoring system, monitors and outputs the telescope’s image quality. The second component is a query system with a knowledge graph, which outputs the node chains as the possible cause of poor image quality based on the input. The third component, a final estimator, uses the node parameter, which contains historical fault data and real-time updated data from sensors, to give the probability of each node chain. We construct and test the system in the Large Sky Area Multi-Object Fiber Spectroscopy Telescope.

The surface accuracy of a large radio telescope’s primary reflector is easily affected by gravity and temperature change during observations. An active surface system is crucial to ensure the regular operation and high-quality data output of the radio telescope. We propose a real-time closed-loop active surface system including two components. The first component, a new type of photoelectric edge sensor, detects the angle change of the adjacent panels. The second component, the displacement actuator, adjusts the panels’ position and posture to compensate for the angle changes. So, over the entire observation, the closed-loop surface control system with these two components could actively maintain the primary reflector’s accuracy in real time. Using this approach, we constructed an experimental active surface system for the Xinjiang Qitai 110 m Radio Telescope (QTT) to test the maintenance of the surface accuracy. The angle measurement accuracy is better than 0.″2, and the positioning accuracy of the displacement actuator could achieve ±15 μm over the whole 50 mm stroke. The preliminary test results show that the accuracy requirements of the QTT’s primary reflector surface can be met using the active surface system we propose.

Shack–Hartmann wavefront sensors (SHWFSs) are crucial for detecting distortions in adaptive optics systems, but the accuracy of wavefront reconstruction is often hampered by low guide star brightness or strong atmospheric turbulence. This study introduces a new method of using the Vision Transformer model to process image information from SHWFSs. Compared with previous traditional methods, this model can assign a weight value to each subaperture by considering the position and image information of each subaperture of this sensor, and it can process to obtain wavefront reconstruction results. Comparative evaluations using simulated SHWFS light intensity images and corresponding deformable mirror command vectors demonstrate the robustness and accuracy of the Vision Transformer under various guide star magnitudes and atmospheric conditions, compared to convolutional neural networks (CNNs), represented in this study by Residual Neural Network (ResNet), which are widely used by other scholars. Notably, normalization preprocessing significantly improves the CNN performance (improving Strehl ratio by up to 0.2 under low turbulence) while having a varied impact on the Vision Transformer, improving its performance under a low turbulence intensity and high brightness (Strehl ratio up to 0.8) but deteriorating under a high turbulence intensity and low brightness (Strehl ratio reduced to about 0.05). Overall, the Vision Transformer consistently outperforms CNN models across all tested conditions, enhancing the Strehl ratio by an average of 0.2 more than CNNs.

Space-based and ground-based telescopes have extensively documented the impact of satellites on astronomical observations. With the proliferation of satellite mega-constellation programmes, their influence on astronomical observations has become undeniable. Quantifying the impact of satellites on telescopes is crucial. To this end, we enhanced the circular orbit model for satellites and introduced a methodology based on two-line element (TLE) orbit data. This involves constructing a satellite probability distribution model to evaluate the impact of satellites on telescopes. Using our method, we assessed the satellite impact on global grounded observatories. Our results indicate that the regions most severely affected by satellite interference at present are those near the equator, with latitudes of around $ and $ degrees experiencing the most significant impact from low-Earth-orbit satellites. Furthermore, we validated the reliability of our method using imaging data obtained from the focal surface acquisition camera of the LAMOST telescope.

Large-aperture telescopes are essential facilities for astronomical data acquisition, and their performance directly affects data quality and the reliability of scientific output and interpretable performance monitoring, together with human-readable status reporting, critically important. However, existing data-driven and rule-based methods remain limited in simultaneously providing human-readable outputs and clear interpretability, while rule-based approaches rely heavily on manual rule construction. To address this issue, T-Guardian is proposed, a vision-language framework for telescope performance monitoring that bridges the semantic gap between telescope images and status descriptions. The framework incorporates a Multi-scale Contrastive U-Block for cross-scale visual-text alignment, a residual attention path to preserve fine-grained visual details, and an Adapter module to map multimodal features into the language generation space. Experimental results show that T-Guardian outperforms traditional methods and general-purpose vision-language models, achieving a macro-F1 of 0.9857 for state classification and a BERTScore of 0.9343 for description generation, which confirms its effectiveness for interpretable and human-readable telescope performance monitoring.