This paper analyzes network information risk incidents and, combining the characteristics of blockchain technology, studies its application value in network information risk prevention and control. It examines practical application cases of blockchain in identity authentication, data integrity assurance, financial security, and network communication security, including Estonia's e-Residency system, India's Aadhaar project, and the applications of Ripple and JPMorgan Chase in cross-border payments. Practical evidence proves that the decentralized and immutable characteristics of blockchain provide strong technical support for network information risk prevention and control, effectively reducing risks such as identity fraud, data tampering, financial fraud, and communication hijacking. However, blockchain still faces challenges in its application, including performance bottlenecks, insufficient privacy protection, and lagging legal and regulatory frameworks. It is suggested that blockchain should integrate with big data, artificial intelligence, the Internet of Things, and privacy protection technologies to enhance data processing capabilities, privacy protection, and intelligent security analysis. This integration will help improve risk data identification, risk event early warning, and post-event risk management, thereby building a comprehensive network information risk prevention and control system and further enhancing the effectiveness of network information risk prevention and control.

In recent years, with the breakthroughs in generative artificial intelligence technology, AI has become a key approach and effective means for unlocking the value of data elements. The relationship between the big data industry and the AI industry has evolved from a one-way empowerment to a deep integration, giving birth to the data intelligence industry. The collaboration between data and intelligence has together become the core force in transforming the physical world and reshaping the digital world order. The booming development of the data intelligence industry has become the core engine driving innovation and growth in the digital economy. This paper focuses on the topic of data intelligence. It systematically clarifies the technical system of data intelligence, conducts an in-depth analysis of the current status and issues in specific areas of data intelligence, such as data, algorithms, and applications, and also provides an outlook on the future development trends in various fields of the data intelligence industry.

In warehouse logistics and post-disaster rescue, multi-UAV payload transport must navigate tight spaces, such as 1.2m × 0.8m aisles and collapsed pipelines as narrow as 0.6m. Traditional four-DOF (translation and scaling) trajectory planning struggles under such constraints. To overcome this, we propose an optimization-based framework that introduces rotational degrees of freedom, expanding the solution space to five dimensions. Using the MINCO transformation, we reformulate constrained formation adjustment into an unconstrained optimization problem via smooth mappings and penalty functions, enabling simultaneous obstacle avoidance and formation control. The GeoSafe algorithm further enhances safe passage by integrating iterative region expansion and semi-definite programming to maximize obstacle-free space. Extensive simulations and real-world experiments show our method’s superiority over sampling-based and IF-based approaches in narrow passage traversal, computational efficiency, and formation scalability.

Performance is a crucial indicator for the blockchain system evaluation. However, it is difficult to measure and comprehensively evaluate the performance of blockchain systems, particularly due to their complexity and diversity. Based on the comprehensive analysis of common blockchain systems and existing performance testing tools, this paper proposes a novel efficient and user-friendly performance testing tool for blockchain systems named TrustedBench. This tool supports all-round analysis and testing, and provides a powerful tool for understanding and analyzing the performance of blockchain systems. The architecture design, test execution process and important performance indicators of blockchain systems are introduced, and their calculation methods are further expounded. Finally, the overall results from the implementation of TrustedBench are presented and interpreted, revealing the operation and performance characteristics of some different blockchain products. With this tool, users can have a more comprehensive understanding of the performance of blockchain systems. Furthermore, TrustedBench effectively enhances the efficiency of the blockchain platform in many real-world scenarios.

Blockchain assessment is a key method to evaluate the quality of blockchain products. Blockchain assessment can effectively address the problem of application systems breaking down due to poor quality of blockchain product. A comprehensive and in-depth assessment is important for enhancing the quality of blockchain products. Blockchain assessment can detect bugs and weaknesses in blockchain products, then it can promote the product upgrades and evolution. Recently, research on blockchain assessment is fragmented with lacking systematization. Therefore, this paper proposes a blockchain assessment framework from both technical and application perspectives. This assessment framework includes the blockchain systems assessment, supporting service systems assessment, and business application systems assessment. For each class of blockchain products, corresponding assessment dimensions and criteria are put forward.

This paper reviewed current transactional database benchmark, especially TPC-C. There are some limitations to apply TPC-C into financial industry for benchmarking transactional database. In order to simulate real financial business, the project member has designed a benchmark named Databench-T, which is based on money transfer scenario. The model and workloads of the benchmark is introduced in detail in this paper. The benchmark is implemented by project members to provide a unified toolkit. To run a test, there are four steps: toolkit installation, data generation, workload execute and results display. All those steps are presented in the paper.

With the continuous emergence of cloud computing technology, cloud infrastructure software will become the mainstream application model in the future. Among the databases, relational databases occupy the largest market share. Therefore, the relational cloud database will be the main product of the combination of database technology and cloud computing technology, and will become an important branch of the database industry. This article explores the establishment of an evaluation system framework for relational databases, helping enterprises to select relational cloud database products according to a clear goal and path. This article can help enterprises complete the landing of relational cloud database projects.

In industry, analytic database is known to be difficult in testing and product selection. The traditional TPC-DS (transaction processing performance council-decisioin support) benchmark [1], which only has one retail scene, is not sufficient enough for database product selection of other business type. To deal with this drawback, an analytical database testing platform which can plug-in a variety of business scenes was developed. The telecommunication scene was also provided to explain the design concept, system architecture and scene development of this testing platform. The platform provides scene plug-in function which is easy to use without any programming background. Users can select and edit scenes according to their own business type to meet the requirements of analytical database testing and selection in different industries.

At present, data security has become a key area of attention from all aspects. The proliferation of data leakage incidents, the acceleration of the introduction of data security laws and regulations, and the rising cost of enterprise compliance punishment jointly urge all enterprises to step up to improve their own data security system. In the construction of data security, data desensitization is always a core technical ability. In view of the current situation and future development trend of data desensitization technology and products, this paper introduces the development of data desensitization technology in detail from desensitization algorithm, anonymization, static data desensitization and dynamic data desensitization. At the same time, from the core function, product form, supply side status and other aspects of the data desensitization technology are described. On this basis, the standardization direction of data desensitization products in terms of core functions, concept definition, performance and stability is proposed. Finally, the development trend of data desensitization technology and products in four aspects of regularization, anonymization, intellectualization and contextualization are put forward.

Sensor networks have emerged as a promising technology for large-scale, real-time monitoring in complex environments. This paper develops in-network algorithms for the detection and tracking of region-based targets and events. We deal with such issues as network topology, event boundary detection, region merging, and region simplification and smoothing. Simulation experiments confirmed that our algorithms are efficient and accurate.