For the front-end, large-scale data acquisition is usually realized through paging request mechanism, the efficiency of this mechanism is limited by the browser mechanism, server processing capability and network conditions. In order to achieve efficient processing and display of a large number of geographic data nodes in the front-end big data visualization platform, the scheme proposes a data loading optimization mechanism based on paging request mechanism, which comprises three modules: a detector, a record library and a recommender. The detector is used to detect request conditions in specific environments, the record library stores the detection results, and the recommender returns appropriate paging request results. The scheme not only improves the data processing efficiency, but also enhances the user interaction experience. The scheme can be used in the field of data visualization in various industries, especially for the rapid visualization of geographic data.

With the rapid development of communication technologies, the need for accurate and efficient complaint auxiliary analysis (CAA) among mobile network optimization personnel is growing. However, most existing research solutions focus only on text data and structured data, with few incorporating user complaint speech data. To address this, the authors propose a CAA scheme for mobile network based on multi-modal generative Large Language Model (LLM). By integrating speech, text, and other multi-modal data, the proposed scheme aims to accurately understand and efficiently analyze user complaint information. The proposed approach consists of three key components. First, the Whisper model is employed to transform user complaint speech data into structured textual representations. Subsequently, a self-constructed domain-specific dictionary is integrated with an Attention-based mechanism to train a Key Information Extraction (KIE) model, thereby enhancing its semantic comprehension performance. Finally, keyword-based knowledge derived from knowledge graphs is combined with expert-defined rules to train a Generative Programs and Recommendations (GPR) model, enabling the system to deliver more accurate and professional Customer Assistance Automation (CAA) solutions. Experimental evaluations demonstrate that the proposed framework exhibits superior generalization capabilities.

The next generation of mobile communication enables the realization of emerging technologies such as smart cities, smart industries and Internet of vehicles. However, the realization of these technologies requires the mobile communication network to have sensing capability of high precision. Starting from the model of integrated sensing and communication (ISAC) system, the processing of wireless signal sending and receiving and frequency domain radar are discussed, including interpolation, target detection, distance and velocity estimation. Then, according to the characteristics of integrated sensing and communication technology, the application scenarios of ISAC are explored. Next, starting from the application scenario requirements of ISAC, the key indicators of sensing are analyzed, and the performance of sensing capabilities is systematically depicted. Finally, based on 5G-A millimeter wave, the remote detection and accurate tracking capabilities of integrated sensing and communication technology in the low-altitude UAV scene are tested and verified. Through the on-site test, technical support is provided for future integrated communication-sensing applications, and the development of communication technology is promoted towards a more efficient and intelligent direction.