Traditional launch vehicle tests are recorded on paper, which is not convenient for the effective management of information. In order to improve the informationization level of launch vehicle test management and realize the secure share of integrative test information, a launch vehicle test information management system based on IETM (interactive electronic technology manual) is designed. The overall architecture of the system is constructed, and the working principle of the system is introduced. The crucial technology issues are discussed, such as the reliability design, interface design, secrecy and security design of the system, and the solutions are put forward. The practice demonstrates that the application of the system can improve the information level of the launch vehicle electric test management and the integrative test capability, and the efficiency of searching information is increased by more than quadrupled.
With the introduction of the national “carbon peaking and carbon neutrality” strategic goals and the accelerated construction of the new generation of power systems, cloud applications built on advanced IT technologies play an increasingly important role in meeting the needs of digital power business. In view of the characteristics of the current power industrial control system operation support cloud platform with wide coverage, large amount of log data, and low analysis intelligence, this paper proposes a cloud platform network security behavior audit method based on FP-Growth association rule algorithm, aiming at the uniqueness of the operating data of the cloud platform that directly interacts with the isolated system environment of power industrial control system. By using the association rule algorithm to associate and classify user behaviors, our scheme formulates abnormal behavior judgment standards, establishes an automated audit strategy knowledge base, and improves the security audit efficiency of power industrial control system operation support cloud platform. The intelligent level of log data analysis enables effective discovery, traceability and management of internal personnel operational risks.
This paper constructs a system dynamics simulation model, which is divided into five subsystems of economy, population, energy, environment and government. The empirical results of the model show that considering the spatial spillover effect, the energy subsystem and other modules in the system will have a phenomenon of offsetting the effect of policy implementation. Therefore, in the future, attention should be paid to the regional coordinated development of new energy vehicles. Joint implementation of policies within the region will be more conducive to the promotion of new energy vehicles and the alleviation of environmental pressure.
With the general upward trend in the incidence of stroke in the population, the use of rehabilitation robots to replace traditional rehabilitation methods is a work that has both important practical significance and research difficulty. The theoretical basis of the human-computer interaction of the rehabilitation robot is an important part of the research and development process of the rehabilitation robot. In order to investigate the dynamics of the human-machine co-integrated system with the two-degree-of-freedom lower limb rehabilitation robot, the walking dynamics simulation of the human-machine model under three weight reduction modes (30%, 50% and 70%) was established in ADAMS. The torque curves of hip and knee joints and foot-ground contact force curves during human-robot walking were obtained. The relationship between the various curves and human gait was analyzed to verify the validity of the kinetic model. This paper provides a theoretical basis for the construction of the control system and the selection of the interaction strategy.
With the rapid development of the information age, the application of artificial intelligence has gradually expanded to various fields, including image and video identification, speech recognition, fingerprint recognition, face recognition, UAV, robot, weather forecast, etc. Deep learning is a very active direction in the field of artificial intelligence. Recently, the state has introduced policies on promoting the classification of domestic waste, and vigorously advocates the classification of waste, while the traditional disposal method wastes manpower, material resources and time. Therefore, this paper mainly introduces the development of deep learning and the application of Python in deep learning, as well as the study of intelligent garbage classification algorithm based on Python deep learning.
