To ensure the stable operation of substations, staff members are frequently required to enter operational areas. However, due to the lack of proactive monitoring mechanisms, accidental intrusions into live zones often lead to electric shock incidents. To address this issue, this study proposes an intelligent method that integrates keypoint detection, ranging, and early warning into a unified framework. First, a Channel-aware Inception Depthwise Convolution (CIDW) feature extraction module is proposed to enhance the capability of small-object feature extraction. Second, a Context-Dynamic Attention (CDA) feature enhancement module is developed to improve the model’s ability to capture fine-grained details in critical regions. Third, a Context-Enhanced Mechanism (CEM) module is designed to improve the feature pyramid network, thereby strengthening the model’s perception of high-resolution features. To overcome the challenges that monocular cameras cannot directly obtain threedimensional information and that long-distance pixels exhibit nonlinear errors, an innovative Long-distance Error Correction (LEC) model is proposed. By combining an inverse coordinate transformation formula, the LEC model enables high-precision 3D ranging. Experimental results demonstrate that the proposed detector outperforms existing algorithms in terms of detection accuracy, speed, and lightweight design. The ranging accuracy reaches the millimeter level at short distances, with long-distance errors controlled within approximately 5 cm, making it an effective safety monitoring approach for the perception layer of intelligent substation IoT systems.

The distribution of software to small devices, such as smartphones, requires significant resources in terms of network bandwidth, server storage, and energy consumption during the upload and download process. In addition, software distribution platforms like Apple's App Store and Google's Play Store impose restrictions on maximum software size, emphasizing the importance of code size reduction. While traditional compression tools can help minimize redundancies within files, this paper demonstrates the presence of considerable redundancies across different files that are not addressed by existing methods. To remedy this, we propose a novel approach to reduce code size by compressing instructions at the intermediate representation (IR) layer, which is supported as a distribution format by Apple. Our method identifies and extracts common sub-strings among instructions across all IR files, creating a shared dictionary. When combined with conventional compression tools for packaging, this technique achieves an average mobile software size reduction of 24.49% compared to using zip compression alone, thereby alleviating software distribution costs for small devices.

The precise location of insulators in infrared images is of great significance for insulator condition monitoring and fault diagnosis. Due to the characteristics of insulators themselves and the use of handheld infrared cameras, insulators usually appear in infrared images with different aspect ratios and main axis orientations. Therefore, it is very important and necessary to make full use of the prior knowledge of the insulator itself to accurately locate it. However, most of the existing methods use axial horizontal detection boxes to detect insulators, which cannot take into account the characteristics of the insulator well. When there are large overlapping areas of two horizontal detection boxes, the nonmaximum suppression algorithm may lead to missed detection of the object. To further improve the accuracy of the detection algorithm, this article makes full use of the prior features carried by the insulator itself, and optimizes faster region-based convolutional neural networks (R-CNN) from five aspects: rectangular box representation, feature extraction, candidate box generation, anchor design, and feature alignment. An oriented detection model for infrared images of insulators is constructed. Comparative experiments with a variety of mainstream detection methods were carried out on the constructed infrared dataset. The results show that the proposed method is superior to other models in detection accuracy. When the intersection and union ratio is 0.5, the average precision reaches 95.08%. In addition, it can also effectively predict the shape and angle information of insulators in complex scenes, laying a beneficial foundation for subsequent diagnosis automation tasks.

With the widespread development of cloud computing, cluster systems are becoming increasingly complex, system logs is an universal and effective approach for automatic system management and troubleshooting. Log event classification as an effective preprocessing method for log analysis, which is helpful for system administrators to locate or predict components’ have errors or failures.In this paper, we design and implement an automatic log classification system based on deep CNN (Convolutional Neural Network) models, and take advantage of the feature engineering and learning algorithm to improve classification performance. First, in the feature engineering step, to address the problem of that the original unstructured event logs are unsuitable for numerical calculation in deep CNN models, we propose a novel and effective log preprocessing method, which include building categories dictionary libraries, filtering abundant information, generating numerical semantic feature vectors by calculating and combining the semantic similarity values for filtered log events. Additionally, in the learning step, we measure a series of deep CNN algorithms with varied hyper-parameter combinations by using standard evaluation metrics, and the results of our study reveal the advantages and potential capabilities of the proposed deep CNN models for log classification tasks on cluster systems. The optimal classification precision of our approach is 98.14%, which surpasses the popular traditional machine learning methods, and it can also be applied to other large-scale system logs with good accuracy. Just like the experiment results, different choices of learning algorithm do result in performance numbers varying, and subsequently careful feature engineering enables promoting performances, thus both of approaches contribute to best learning model finding.