The smart grid integrates network and information technologies with the grid to enhance the reliability, security, and efficiency of the power system. However, in a highly digitalized and interconnected environment, the smart grid faces increasingly complex and variable network security risks. This paper outlines the concept and architecture of the smart grid, indicating that its biggest difference from the traditional grid is bidirectional interactivity. Then, the security vulnerabilities and cyber attacks are summarized in terms of three categories, namely confidentiality attacks, integrity attacks, and availability attacks. Then, new strategies for enhancing the network security of smart grid, such as machine learning, blockchain, quantum computing are reviewed. Machine learning algorithms can improve the accuracy and sensitivity of power grid fault detection and attack identification. Blockchain technology provides solutions for identity verification, data security, and privacy protection through its decentralized and tamper-resistant features. Quantum computing has significant application potential in power grid fault diagnosis and data transmission security. Finally, the major challenges and future research directions are prsented.

Since the construction of smart grid platforms there has been substantial progress in the digital transformation of electricity consumption. This paper focuses on the application research on edge computing and blockchain in Smart Electricity. Firsly, the concept of edge computing in smart power consumption is expounded. Then the research and application of edge computing in power consumption information acquisition, smart power service system, and smart charging are introduced. Meanwhile the applications of blockchain in smart electricity are elaborated in terms of the concept of blockchain technology, electricity bill settlement, electricity trading, and electric vehicle charging. And through analysis and summary, key technical problems in the practical application of smart power consumption are presented through analysis and summary. Finally, the integration applications of edge computing and blockchain in smart power consumption, such as edge measurement oriented blockchain technology, cloud edge collaboration and blockchain integration applications, platform and data integration design, and multi-party reconciliation business application, are discussed.

Recently, the power industry has vigorously carried out the construction of smart grids; integration and application of new technologies has become a trend in the development of smart grids. Based on the actual business needs of the power grid side, this paper studies and designs an intelligent electricity platform by integrating edge computing and blockchain on a traditional platform, which changes the original centralized mode to decentralized mode, reduces cloud computing load, network pressure, and improves power data acquisition and efficient application capabilities by sinking acquisition and computing pressure, At the same time, edge computing is enabled by blockchain technology to achieve collaboration, security, trustworthiness and data sharing. This platform can achieve high real-time electricity cost calculation, electricity inspection, line loss analysis, voltage quality monitoring, and other lean electricity management needs, better serving the construction of modern power supply service systems and diverse interactive electricity systems.

In this paper, an architecture for fusing cloud-edge collaboration and blockchain is studied from theory, key technologies and specific architecture design. Particularly, it includes overall integration application research and design, cloudedge collaboration and blockchain interactive application design, blockchain management function design based on cloud-edge collaboration, smart contract management design based on cloud-edge collaboration, and blockchain data management design. Through feasibility study and by means of advantages of cloud-edge collaboration and blockchain integration, a general architecture that fuses the two is proposed, which can be applied to applications such as smart electricity consumption, smart connected vehicles, smart pipe networks, and industrial IoT Scenes.

With the surge of demand for edge computing, cloud edge collaboration has become an important evolution direction. One of the difficulties of cloud edge collaboration development lies in the deployment, management, update and upgrade of edge end and applications running on it. Container technology provides ideas for solving the above problem. Based on the lightweight, fast deployment, and easy portability characteristics of container technology, an efficient cloud edge application control method based on containerization was studied, including adding container design to the overall architecture of cloud edge collaboration and using container based application collaboration design; studying the cloud deployment update management mechanism; conducting research and implementation on the design content of container application deployment and updates, as well as container and application distribution. Experimental verification showed that this method could significantly improve system energy consumption, efficiency, computational latency, and cloud CPU utilization efficiency, thereby improving system update rate and increasing the number of available edge nodes on the server and data processing capacity. It could greatly optimize the cloud edge collaborative system and also greatly improve the overall system's computing power. The application of this technology can improve the problems of tight computing resources, heterogeneous edge devices, and complex and diverse service management requirements on the edge of the Power Grid smart electricity platform, achieving efficient application control of the Power Grid smart electricity cloud edge collaborative platform.

This paper first introduces various researches and solutions to solve the problem of blockchain storage at present. Secondly, combined with the deployment of blockchain on the edge side of the intelligent electricity platform, DHT distributed blockchain storage is adopted. At the same time, regular backup of historical side block data, multiple selection of consensus algorithms, and blockchain mirror cutting are used to realize the lightweight of side blockchain nodes. Finally, it is pointed out that with the enrichment and continuous development of application scenarios of the electricity internet of things, this topic still needs further research to find more optimized algorithm models and solutions.

This paper proposes a detection method based on an improved YOLOv5s model for the problem of slight cracks in wind turbine blades that are difficult to detect. The method mainly includes three improvements. First, in the backbone network part, ASPP (atrous spatial pyramid pooling) is used instead of SPP (spatial pyramid pooling) to adapt to targets of different sizes and proportions. Second, SE (squeeze and excitation) attention modules are inserted into the backbone network to increase the network's sensitivity to small defects; and SIoU-Loss is used to replace the original CIoU-Loss to further improve the accuracy and training speed of the new network. Finally, a comparison experiment is conducted using a self-built dataset. Experimental results show that the mAP value of the improved YOLOv5s model is 94.29%, which is 7.03 percentage points higher than that of the YOLOv5s model, and its detection accuracy has advantages over other mainstream models. The detection speed is 42.78f/s. This method has good performance and effect in detecting defects inside wind turbine blades.

This paper aims to solve the problem of accurate detection of various types of defects in the complex internal cavity structure of wind turbine blades. An improved SSD (single shot multibox detector) algorithm is thus proposed and three aspects of improvement are made: 1) in terms of network framework, the base network of SSD is changed from VGG-16 to ResNet101 to optimize the input features for the regression and classification tasks of predicting bounding boxes; 2) an FCSE attention module is added to make the model pay more attention to important features and improve its detection accuracy; 3) the loss function is improved by adding a hyperparameter to control the smooth region, making the model more robust. Through comparative experiments on a self-built wind turbine blade internal cavity dataset, the improved SSD model achieves an mAP value of 83.6%, which is 9.4 percentage points higher than that of the original SSD model, and has advantages over other mainstream models based on SSD framework in detection accuracy, while greatly reducing the model parameter quantity, lowering the model complexity and storage requirements, and achieving a detection speed of 31.6 f/s, meeting the detection speed needs in practical production.

This paper introduces the research progress of polyurethane foam changed by carbon nano-materials in recent years. The enhancement methods are divided into two categories according to the different ways of introducing carbon nano-materials, namely the internal doping method and the external protective layer method. The action mechanism and modification effect of the two methods are discussed respectively. The shortcomings of polyurethane foam modified by carbon nano-materials and the prospect for the future are discussed. Simple modification operation and reduction of packing agglomeration should be the focus of future research.

Long-term repetitive activities or incorrect postures make muscles bear continuous stress and the incidence of chronic injury of human muscle tissue is high. In this paper, the research status of muscle tissue morphological model, biophysical model, chronic injury analysis and data collection are reviewed. Muscle shape model is mainly based on line segment, volume and surface modeling methods, muscle dynamic modeling methods are worthy of further exploration. Muscle biophysical properties are based on muscle biological tests, and more efforts are needed to accurately describe the biophysical properties of muscle groups. The model of chronic muscle injury is mainly based on MRI data, and simulation experiments are carried out with the help of simulation platform and algorithm, which can further enhance simplification and optimization of calculation methods. A variety of measuring equipment such as myoelectrometer and depth sensor can be used for acquisition of physical quantities such as muscle deformation and force, and more innovations are expected in data fusion methods and algorithms. Finally, when studying chronic injury of muscle tissue, correct description of the morphology and mechanical properties of muscle tissue is the key to accurate results.

3,5-difluoro-2,4,6-trinitroanisole（DFTNAN）is a promising melt-cast carrier explosive that is expected to replace TNT. The compatibility of 3, 5-difluoro-2, 4, 6-trinitroanisole as a new melt-cast explosive carrier with a number of high explosives (RDX, HMX, CL-20 and TKX-50) and functional additives（powdered aluminum and ammonium perchlorate）was studied at the mass ratio of 1:1 by using DSC and VST methods. The detonation properties of DFTNAN/ RDX, DFTNAN/HMX, DFTNAN/CL-20, DFTNAN/Al and DFTNAN/AP mixtures were calculated. Experimental results of compatibility indicated that there were differences in the results of compatibility determination due to the differences in the test principles between DSC method and VST method. Through comprehensive analysis, DFTNAN showed good compatibility with RDX, HMX, CL-20, AP and Al except TKX-50. The results of detonation performance calculation exhibited that the addition of AP can effectively improve the oxygen balance of DFTNA. Among RDX, HMX and AP, the detonation energy of DFTNN/CL-20 mixed system was the biggest, with detonation velocity being 8899 m·s-1, detonation pressure 39.6 GPa, and detonation heat 6442 kJ·kg-1, which are 4.0 %, 32.9 % and 0.6 % bigger than those of pure DFTNAN, respectively.