WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced that a big data analytical engine system based on GIS location was developed to improve the efficiency of data utilization.
GIS is a system for processing and managing geographic information through computer technology. It integrates various spatial data into a database and realizes the analytical, display, query and other operations of spatial data through various functional modules. GIS system adopts a series of software tools and technologies, which are widely used in the fields of urban planning, resource management, environmental monitoring, traffic scheduling and so on, and it has become an important tool for digital management and decision-making support.
In this context, WiMi’s big data analytical engine system not only integrates and analyzes spatial data using GIS technology, but also processes, mines, and analyzes huge amounts of data through big data technology, which will greatly enhance the efficiency of data utilization and facilitate more accurate data analysis and decision-making.
The system’s built-in big data engine employs advanced algorithms and technologies to quickly process massive data and extract effective information. At the same time, the engine also supports machine learning and data mining technology, which can automatically identify the correlation and regularity existing in the data, providing more comprehensive support for the user’s data analysis work. In addition, the system also supports multi-dimensional data visualization and analysis, and through the 3D visualization function, the data can be displayed in three-dimensional space to help users better understand the structure and laws of the data.
The core modules of the system include GIS system, big data storage and processing, data positioning, data mining and data visualization. The system can realize map display, spatial data management, map overlay and other functions by using GIS technology, which can support the processing and analysis of spatial data and location information. Big data storage and processing technology can realize large-scale data storage and processing, support complex data query and analysis operations, and also have high availability and fault tolerance. The location information of mobile devices or users is obtained through GPS, LBS and other technologies to realize the collection and tracking of location data, which can be used for path analysis, heat map drawing and other operations. Using data mining and machine learning technologies, large-scale location data can be analyzed to extract valuable information, such as user preferences and traffic hotspots. In addition, through visualization technology, the system can present the analysis results to users in a direct manner, such as maps, charts and other forms, to help users better understand the data analysis results.
WiMi’s GIS location-based big data analytical engine system has a wide range of application prospects, which organically combines GIS technology, big data technology and artificial intelligence technology to provide users with powerful and easy-to-use data analysis tools. With the development of the Internet, the scale and speed of data generation will become faster and faster, the launch of this system provides more possibilities for us to utilize the data, which can better serve data analysis and decision-making in various industries. Whether it is urban planning, transportation scheduling, environmental monitoring, resource management, decision-making in various industries requires a large amount of data support. However, how to effectively utilize these data for accurate analysis and decision-making is a challenge.
Traditional data processing methods often require manual intervention and are inefficient. The system, on the other hand, adopts an intelligent processing method and realizes automatic identification and analysis of data through machine learning and automation algorithms, which greatly improves the efficiency and accuracy of data processing. The system can also be upgraded and expanded as needed to meet more demands as user needs change and data size increases. It not only improves the efficiency and precision of data processing but also helps users better understand the structure and laws of data, which is a very important tool for decision makers to help them make more accurate and scientific decisions.
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