## 2 Related Work  
As satellite remote sensing technology advances, various satellite systems are being developed, significantly enhancing the comprehensive observation capabilities of Earth. The systems for acquiring remote sensing data are evolving steadily, leading to an exponential increase in the quantity and diversity of data, along with improvements in technical parameters like spatial, temporal, and spectral resolution [18]. At the same time, the frequency of updates for remote sensing data is increasing, enhancing their timeliness. Hence, it is urgent to provide users with intelligent and real-time remote sensing data services [19]. At present, the majority of existing remote sensing information services operate on query-based and subscription-based service models. In query-based services, users typically input criteria such as their area of interest and time to perform a search. The system subsequently returns retrieval results, enabling users to download the desired data. Conversely, in subscription-based services, users encapsulate their query conditions into resource orders and submit these to the remote sensing information system. When relevant remote sensing resources become available, the system automatically pushes them to the users. Although these two data service models facilitate the discovery of remote sensing resources to some extent, they are not without limitations, including passivity, latency, and a reliance on specialized knowledge. Moreover, there are increasing demands for enhanced precision and timeliness from various entities, including national agencies, industry-specific clients, and personalized users. Consequently, satellite information services are transitioning from reliance on large ground stations to vehicle-mounted, shipborne, and handheld terminals for data reception and transformation. It is evident that traditional processing and application models for satellite remote sensing data are insufficient to meet the current requirements for high timeliness, personalized data collection, and the delivery of information products. In recent years, research on intelligent distribution services for spatial information has made some progress. In order to fully explore users' potential demand for remote sensing images, the authors in [20] divided the remote sensing image coverage area into grid cells based on a unified grid system and proposed a spatio-temporal recommendation method for remote sensing data based on a probabilistic latent topic model-spatio-temporal periodic task model (STPT), in which the user retrieval behaviors of remote sensing images are represented as a mix of latent tasks, which act as a link between the user and the images, and each task is associated with a joint probability distribution of spatial, temporal and image features. Since the model adopts the minimum bounding rectangle as the filtering condition for spatial features and assumes that all user acquisitions of images are periodic, the model tends to return data that conform to the periodicity rule, which is less accurate and unsuitable for diverse users. The authors in [5] propose spatio-temporal embedded topic model (STET), which adopts the latent Dirichlet allocation (LDA) model to transform all user's data records into documents through spatial embedding, temporal embedding, and content embedding, and constructs the document topic model with spatio-temporal embedding to simulate the user's preferences, so as to fully utilize the spatial and temporal continuity features to recommend appropriate remote sensing images.