After 2023, Japan suspended the updating of a new artificial intelligence strategy, and the fundamental cause lay in the disparity between technological development and strategic cognition. The technological breakthrough represented by “Generative AI”, not only surpassed the original expectations of Japan’s strategic planners but also profoundly transformed their strategic perception of artificial intelligence, thereby inducing significant alterations in their strategic organizational model and governance thinking. Nevertheless, the implementation of a strategy demands an organic integration with market practice. Currently, the most significant challenge for Japan’s artificial intelligence strategy is not how to understand artificial intelligence but the difficulty in aligning the Japanese strategy with the market.

Artificial intelligence has become a key focus for the GCC countries. In response, these nations have actively formulated policies that include establishing “Vision” strategies, strengthening technology regulation, and engaging in external cooperation. The motivations behind AI development in the GCC are driven by the need for economic diversification, inherent advantages, and the pursuit of data sovereignty. However, challenges remain, including geopolitical tensions, technology regulation, private sector application, and talent shortages. In the future, GCC countries should aim to address these challenges by minimizing the impact of geopolitical factors, enhancing regional cooperation, promoting private sector adoption, and strengthening talent development to ensure sustained progress in the field of artificial intelligence.

Electricity demand response is a strategy to balance supply and demand by adjusting or shifting electricity use periods, aiming to optimize the operational efficiency of the grid and reduce energy costs. With the development of big data technology, its application in the power system provides a new perspective and method. By analyzing a large amount of consumption data to predict power demand and adjust power supply in real time, the real-time and accuracy of response strategies can be improved. The integration of this technology provides a more efficient tool for power demand response management, making the power system more intelligent and adaptive. Based on this, this paper expounds the theoretical basis of power demand response, introduces the collection, processing and analysis methods of power data, optimizes management efficiency by simulating demand response strategies, and demonstrates how to optimize power demand response management by using big data analysis through empirical analysis, so as to improve the response efficiency and reliability of the entire power system.

Real-time cloud rendering is a new rendering method based on cloud computing, which fully integrates the three key concepts of “real-time”, “cloud” and “rendering”, which can make full use of cloud computing resources, improve rendering efficiency, reduce hardware threshold, and promote technological innovation and industrial upgrading. Based on this, this paper describes the development background of real-time cloud rendering, introduces the principle and key technical points of real-time cloud rendering, and deeply analyzes the development trend of real-time cloud rendering technology.

At present, China has carried out a series of policy and institutional arrangements for the custody of material evidence records. However, in practice, due to manual operation error prone, lack of information technology and low efficiency, there is a problem of incomplete connection between institutional arrangements and specific practice, which leads to the damage and loss of key material evidence in criminal cases, and has a great impact on the final litigation and trial of the case. In this regard, based on specific project research, Radio Frequency Identification (RFID) technology is successfully applied to the intelligent traceability management system of public security material evidence. Through technology enabling, the traceability management of material evidence is realized, and the standardization and transparency of material evidence management are improved.

The unmanned aerial vehicle traffic monitoring system uses the unmanned aerial vehicle for real-time traffic monitoring, which provides a new perspective and data support for traffic management. In this process, the fusion application of air pressure sensor and radar technology greatly enhances the operation efficiency and data accuracy of the unmanned aerial vehicle by providing accurate altitude and position information. Air pressure sensors can compensate for radar positioning errors in complex environments, while radar technology can track the dynamics of vehicles on the ground in real time. The combination of the two allows unmanned aerial vehicle to work steadily in various weather conditions and optimize their flight strategy. This paper explores the application of pneumatic sensor and radar fusion technology in unmanned aerial vehicle traffic monitoring, demonstrating the significant advantages of this technology combination in improving the efficiency and accuracy of unmanned aerial vehicle traffic monitoring.

Propose a method for sudden leakage warning of hazardous chemical storage tanks based on Internet of Things technology. Based on Internet of Things technology to monitor hazardous chemical storage tanks, design anomaly detection and correction algorithms based on pattern matching, preprocess monitoring data, and apply extension theory to provide safety warnings for sudden tank leakage accidents. The experimental results show that the design method can effectively achieve monitoring and early warning of sudden leaks in hazardous chemical storage tanks.

This article analyzes how the EU Artificial Intelligence Act achieves a balance between the two key objectives of the EU's AI strategy: independent innovation and effective regulation. The AI Act promotes innovation through measures such as risk-based classification, regulatory sandboxes, and the unification of EU AI market. It builds a legal framework centered on EU values to strengthen regulatory coordination, achieving effective oversight while bolstering its extraterritorial influence. The AI Act also enables mutual reinforcement between innovation and regulation. The Act offers insights for China’s AI governance, and its future development merits attention.

In order to improve the accuracy and efficiency of fault detection of Internet of Things devices, this paper designs a fault detection system of Internet of Things devices based on intelligent optimization algorithm. The data acquisition layer is responsible for the efficient collection of heterogeneous data from multiple sources, the edge computing layer uses Particle Swarm Optimization (PSO) algorithm for feature selection and fault detection, and the cloud computing layer performs global fault decision fusion. Cooperate with the management to schedule tasks, and realize dynamic collaboration and resource optimization between edge and cloud. The experiment verifies that the system is superior to the traditional method in different working conditions, and provides a feasible and effective solution for the fault detection of Internet of Things equipment.

This paper designs and implements a wireless radio frequency communication system based on the Internet of Things. The system is constructed with an STM32F407ZGT6 microcontroller, CC1101 wireless communication module, and BME280 environmental sensor to build a prototype. By changing the node spacing and the number of obstacles, the performance of the system under different scenarios is evaluated. The experimental results show that the system has good anti-interference capabilities and stability, with a high data transmission success rate, low transmission latency, and reasonable system power consumption. These characteristics make the system an efficient and reliable wireless communication solution for Internet of Things applications, which has important theoretical significance and practical value.

The application of intelligent routing algorithms in data center networks. Firstly, the basic theory and methods of intelligent routing algorithms are elaborated, and the network architecture and routing requirements of data centers are analyzed. Subsequently, the practical application of intelligent routing algorithms in data centers was focused on, including how to implement intelligent routing algorithms, optimize traffic management in data centers, enhance network fault recovery capabilities, achieve efficient network fault tolerance, and how to perform real-time routing adjustments and dynamic optimization. Through experimental analysis, this article verifies the effectiveness and advantages of intelligent routing algorithms in practical applications, providing theoretical support and practical guidance for the intelligent upgrading of data center networks in the future.

The development of the digital economy requires the combined enhancement of transportation capacity and computing power. The release of computing power requires a high-performance transport network characterized by large bandwidth, wide connectivity, and low latency. The government also puts forward requirements for the green and low-carbon development of the information and communication industry, and it is imperative to carry out the evolution of the simplified transport network. This article analyzes the current situation and problems of the transport network, proposes ideas and solutions for the evolution of the simplified transport network, and creates a high-quality, high-performance, low-carbon minimalist transmission network through the integration and evolution of Synchronous Digital Hierarchy (SDH) networks and Virtual Container-Optical Transport Network (VC-OTN) as well as the integration and evolution of Packet Transport Network (PTN) networks and Slicing Packet Network (SPN).

The rapid development of unmanned aerial vehicle has given rise to many illegal flight phenomena, and timely detection and real-time tracking of unmanned aerial vehicle is particularly important. It is difficult for unmanned aerial vehicle to be effectively detected by traditional radar system in long distance or low signal to noise ratio environment. Infrared imaging technology is an ideal method for unmanned aerial vehicle detection and tracking because of its high precision, strong anti-interference ability and excellent nonlinear non-Gaussian particle filter algorithm. Aiming at the problem of unmanned aerial vehicle real-time online detection and tracking of dim targets in complex background and low signal to noise ratio environment, an improved particle filter algorithm is proposed. In the stage of image preprocessing, the background suppression algorithm based on morphological filtering is improved by combining non-local mean filtering. Aiming at particle resampling, a hybrid resampling algorithm is proposed by combining the ideas of three traditional resampling algorithms. After a series of experimental tests and analysis, it is verified that the improved algorithm can significantly improve the tracking accuracy and stability of unmanned aerial vehicle target tracking in the context of low signal to noise ratio and dynamic changes.

This paper introduces the application of intelligent internet of things technology in the medical field, discusses how to simplify the remote pathological section examination process for superior medical institutions by using this technology, to improve the accuracy and recovery efficiency of pathological diagnosis, and then meet the demand of shortening the diagnosis cycle and reducing manpower in the medical process, and further promote the quality of pathological diagnosis.

The application of reinforcement learning algorithms in locomotion control for quadrupedal sprawling robots is relatively scarce, and the impact of robot joint configurations on the training of control policies remains unknown. This paper proposes a motion control method for quadruped sprawling robots based on reinforcement learning algorithms and research the influence of different initial joint angles on the convergence performance of locomotion control policies for quadrupedal sprawling robots. Experimental results indicate that when the robot’s joint initial angles approximate their limit values, the convergence effect of the control policies is poorer. By implementing the control policy, which has been trained with appropriate joint initial angles, onto a robot prototype, adaptive locomotion of the quadrupedal sprawling robot on grass, gravel, and hard ground is successfully achieved.

In recent years, the global artificial intelligence industry has continuously achieved breakthroughs in technological innovation, market expansion, and strategic computing power development. At the global level, a tripartite pattern dominated by the United States, China, and the United Kingdom has emerged. The United States has established a formidable industrial scale through supportive policies and suppression of China; China emphasizes security, with vibrant market competition but a shortage of computing power; and the United Kingdom focuses on its role in international governance, boasting strong capabilities in AI research. Geopolitics, algorithms and data, as well as talent shortages, pose major challenges. In the future, geopolitical influences will persist, while regulatory and labor issues are expected to improve.

Multi-sensor data fusion technology can integrate different sensor information, and use data fusion technology to make the target information acquisition more efficient and comprehensive. In order to improve the effect of data fusion, the model design method of fusion optimization algorithm is proposed, combining dynamic weight allocation, attention mechanism and conflict resolution strategy in the algorithm, and designed to evaluate the performance of the optimization algorithm. The experimental results show that the proposed optimization algorithm has better fusion performance and can achieve more accurate and reliable monitoring and prediction.

ASEAN Guide on AI Governance and Ethics, released in early 2024, aims to regulate the responsible design and deployment of artificial intelligence models by ASEAN member states. The guideline seeks to foster a consensus on AI governance within the region, establish international cooperation platforms both within and outside ASEAN, and enhance the region’s global influence in AI governance. The guideline emphasizes the “ASEAN-centered” approach to AI development and governance, primarily due to differences in the readiness of member states, varying governance of related issues within each country, and the disparities in societal understanding of AI. Due to the inconvenience of data flow within the region and the lack of synchronization in AI development as well as digital transformation among member states, ASEAN faces challenges in further developing a regional AI strategy. In the future, ASEAN will focus on areas such as the integration of artificial intelligence with education, the establishment of mechanisms for sharing best practices among member states, and conducting international cooperation with countries outside the region.

With the continuous development of cloud computing technology, virtual machine load balancing has become an important means to enhance system performance and resource utilization. This paper proposes a virtual machine load balancing method based on a genetic algorithm aimed at optimizing resource allocation in cloud computing environments through intelligent scheduling. By utilizing the selection, crossover, and mutation operations of the genetic algorithm, the proposed method iteratively optimizes virtual machine allocation strategies to achieve balanced system loads, reduced migration frequency, and improved resource utilization. The results show that the genetic algorithm-based load balancing approach significantly enhances resource utilization, effectively lowering system overhead. Particularly in high-load scenarios, the genetic algorithm optimized system response time.