Search

In the cloud edge collaborative management and control software, due to the large amount of wireless mesh video data and high requirements for real-time, packet loss is easy to occur in the transmission process. How to improve the transmission reliability of wireless mesh video has always been a research hotspot. Aiming at the problem that the reliability of data transmission in cloud edge collaborative management and control software is not high, this paper proposes a wireless mesh network load balancing protocol NNP based on neural network prediction model_L2MPM. The protocol takes the length of MAC interface queue as the measure of traffic load, and then uses RBF neural network prediction model to predict the traffic load of nodes in mesh network. In order to avoid the congestion in the next time, we can improve the performance of the network according to the load of the next node. The simulation results show that: the protocol uses RBF neural network prediction model to achieve the node traffic load prediction, and then achieves load balancing. Experiments show that nnp-l2mpm protocol has good performance.

Three-dimensional laser scanning technology is a new surveying and mapping technology, also known as “real scene replication technology”, which can completely and accurately reconstruct scanned objects and quickly obtain original surveying and mapping data. Multi-site cloud data registration is an important link in 3D laser scanning data processing. The seven-parameter registration model based on control points with the same name is an algorithm model with high accuracy. Through piecewise low-order interpolation method, the curve smoothing processing of empty area model is realized, and then combined with the existing hierarchical detail model algorithm, the 3D model file of power equipment is loaded and rendered in the 3D scene. The purpose of this paper is to provide reference for the subsequent construction of power 3D geographic information platform.

The traditional method of conducting regular inspections for high-voltage transmission towers is mainly based on manual inspection. Workers need to board the tower for visual inspection during operation, which is not only unsafe, but also inefficient. With the gradual popularization of drones in the use of power industry, automated inspections based on artificial intelligence and image processing technology have become possible. In the complex background of aerial images, doing defect detection for key equipment components of high-voltage transmission lines is a challenging problem, and achieving the goal of target recognition of transmission line equipment is the basis of defect detection. Based on the deep learning technology, this paper researches the target recognition of transmission line equipment by using aerial image, focusing on insulator and anti-vibration hammer. The process is as follows: Firstly, the image data should be preprocessed. Secondly, the data should be marked and the data set should be divided. Then the two networks of Faster R-CNN and YOLOv3 are used for training. Finally, the trained model is evaluated.

The smart grid system is the physical power infrastructure integrated with many intelligent electronic devices. It supports the two-way flow of energy as well as information. The smart meters are important intelligent devices, which account for the largest portion among all terminals in the smart grid. In many application scenarios, the master station need high frequency total electricity consumption information of each low voltage transformer area. It requires frequent data interchange between smart meters and master station. However, the electric communication network cannot provide enough bandwidth resource to support such high frequency data interchange. Edge Computing Nodes (ECNs), which can be deployed at different low voltage transformer areas, can achieve the electricity data aggregation at the edge side. The communication resource can be saved since only aggregated electricity data will be uploaded to the master station. Traditional data aggregation scheme requires the plaintext from smart meters. From the viewpoint of security and privacy-preserving, it is not safe to implement the data decryption operation at edge side. Therefore, the data aggregation scheme with ciphertext is required at the edge side. In this paper, we propose a secure edge electricity data aggregation scheme based on homomorphic public key encryption techniques. The ECNs will implement the electricity data aggregation with the ciphertext from different smart meters, and the aggregated data will be transmitted to the master station through communication network. The mater station can use its public key to obtain the total electricity consumption of each low voltage transformer area.

In the process of recycling EOL (end of life) products, disassembly is an important stage. Moreover, there are several targets for disassembly: reuse, remanufacturing and material recycling. In order to improve the efficiency of disassembly, only the component which need to be reused or remanufactured need to be disassembled with nondestructive method. The rest components which recycled for material can be disassembled with partial destructive method. Namely, partial destructive disassembly is more practical than total disassembly within recycling process for EOL products. In this paper, the generally used partial destructive disassembly methods are analyzed. And several rules are defined for reconstructing the hybrid graph of EOL product. And then, an extended Floyd algorithm is proposed for searching optimized disassembly sequence. With this algorithm, the material properties of parts, connection properties between parts or components are taking into account. By ranking the cost of several possible disassembly path, the optimal disassembly sequence is obtained for target component. At last, a soybean milk machine is used as a study case to verify the former approach.

Cost model is a key issue in the disassembly process planning, because the optimized disassembly sequence is determined by ranking several possible disassembly operations. In this study, a fuzzy cost model for disassembly processes was developed based on the fuzzy clustering method. The objective was to solve the problems in the practical applications of currently used quantitative models of disassembly cost model, which is based on the change times of the tools operation or the disassembly time. The sample data were obtained through the disassembly tests of the typical EOL (end-of-life) products. Following this , the transitive closure operations were performed after standardization and normalization. Dynamic clustering was carried out on the basis of above results, and appropriate clustering results were selected to construct the membership function of fuzzy costs. This study also proposed a method of proportional interpolation to expand the directly built membership function to the uncovered discourse domain, resulting in more practical fuzzy cost models. Finally, the disassembly process of a general reducer was adopted as an example to verify the feasibility of the above method.

In order to reduce the dependence on oil, develop clean energy, alleviate environmental pollution and other issues, the electric car, as the representative of the new energy industry, has been greatly supported by the government and achieved vigorous development. Blockchain is an innovative revolution over the internet finance; however, the current trading mode involves the third party. As a result, the cost has increased while accompanied with reduced efficiency. In this article, in respect of the problem between different current charging piles not to achieve unified charge payment, we use the consortium blockchain to establish a n unified payment channel without involvement of the third party. It can solve the problems such as great inconvenience to users caused by different charges and payment methods in different charging pile operators, and thus promote the rapid popularization of electric vehicles.