With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in significant economic losses. To prevent such occurrences, accurate estimation of the liquid height in gas well tubing is necessary. However, existing petroleum engineering models face numerous challenges in predicting liquid height, including complex theoretical solution steps and reliance on fundamental well parameters and extensive empirical data. The paper proposes an innovative blend of the Crayfish Optimization Algorithm (COA) with the eXtreme Gradient Boosting (XGBoost) methodology to forecast the liquid loading heights in gas wells. The COA is employed to optimize eight hyperparameters of the XGBoost, including the number of trees, maximum depth, minimum child weight, learning rate, minimum loss reduction, subsample, L1 regularization, and L2 regularization. After fine-tuning the hyperparameters, the XGBoost undergoes a retraining process, followed by an evaluation. Through comparative analysis with actual measurements from 32 wells in a gas field as well as support vector regression (SVR), XGBoost, random forest (RF), and PLATA (which predict liquid volume in the tubing and annulus), the proposed COA–XGBoost demonstrates a high degree of alignment with the measured values. It provides the most accurate predictions, with a mean relative error of only 2.25%. Compared with the traditional XGBoost, the COA–XGBoost reduced the mean relative error in predicting gas well tubing liquid loading height by 32.63%. Compared with the previous PLATA, the proposed model achieved a 3.52% decrease in mean relative error, enabling more accurate assessment of the severity of liquid loading in gas wells.

As a globally important agricultural commodity, the quality of tea is closely linked to its economic and cultural value. Recent advances in artificial intelligence and sensing technologies have facilitated the development of automated tea harvesting systems. Among the various factors affecting tea quality and flavor, leaf age serves as an important indicator of plant health and growth status. Accurate classification of tea leaf age is therefore essential for improving product consistency and optimizing harvest timing. However, existing automated approaches have primarily focused on bud detection or disease diagnosis, while the classification of tea leaf age has received comparatively less attention. To address this gap, a novel framework named SwinT-PGK is proposed, which integrates a Swin Transformer for feature extraction with a hybrid Principal Component Analysis–Genetic Algorithm (PCA–GA) for feature optimization, followed by a k-Nearest Neighbors (KNN) classifier. Under real-world plantation conditions, the method achieves a tea leaf age classification accuracy of 97.52%, using only a 26-dimensional feature vector. This represents a 2.71% improvement over the baseline Swin Transformer model, which relies on a 768-dimensional feature space. These results suggest that SwinT-PGK can effectively reduce feature dimensionality while maintaining high classification performance, offering a practical tool to support informed decision-making in tea cultivation and improve the overall consistency and quality of tea production.

Precise recognition of operator actions is crucial in industrial automation for enhancing production efficiency and ensuring safety standards. This study introduces a novel self-supervised pretraining framework using visual transformers to address the challenge of industrial event recognition. The framework incorporates an innovative Tube Masking strategy and leverages a comprehensive industrial dataset to effectively capture spatiotemporal features. Evaluation on our custom-built industrial dataset revealed a top-1 accuracy of 95%, demonstrating the model’s practical applicability in real-world industrial environments. To further assess the model’s generalization capabilities, it was tested on several public datasets, achieving top-1 accuracies of 92.8% on UCF101, 87.1% on HMDB51, and 90.2% on Kinetics400. These results highlight the robustness and versatility of our approach, paving the way for its application in diverse industrial scenarios and further research.

In modern manufacturing, cameras are widely used to record the full workflow of assembly line workers, enabling video-based operational analysis and management. However, these recordings are often excessively long, leading to high storage demands and inefficient processing. Existing keyframe extraction methods typically apply uniform strategies across all frames, which are ineffective in detecting subtle movements. To address this, we propose a keyframe extraction method tailored for assembly line videos, combining optical flow estimation with ORB-based visual features. Our approach adapts extraction strategies to actions with different motion amplitudes. Each video frame is first encoded into a feature vector using the ORB algorithm and a bag-of-visual-words model. Optical flow is then calculated using the DIS algorithm, allowing frames to be categorized by motion intensity. Adjacent frames within the same category are grouped, and the appropriate number of clusters, k, is determined based on the group’s characteristics. Keyframes are finally selected via k-means++ clustering within each group. The experimental results show that our method achieves a recall rate of 85.2%, with over 90% recall for actions involving minimal movement. Moreover, the method processes an average of 274 frames per second. These results highlight the method’s effectiveness in identifying subtle actions, reducing redundant content, and delivering high accuracy with efficient performance.

Accurate prediction of CO₂ injection profiles is essential for optimizing injection strategies in heterogeneous reservoirs. However, repeated compositional reservoir simulations under multiple geological and operational scenarios remain computationally prohibitive for rapid optimization and multi-scenario analysis. In addition, conventional machine learning models often struggle to jointly capture nonlinear temporal responses and static geological heterogeneity. This study proposes a deep learning surrogate framework for one-step-ahead prediction of layer-wise CO₂ injection profiles. A large-scale dataset was generated using the ECLIPSE compositional simulator under multiple geological and operational scenarios and converted into supervised time-series samples. The proposed framework integrates Bidirectional Long Short-Term Memory (Bi-LSTM), a self-attention mechanism, and Feature-wise Linear Modulation (FiLM). Bi-LSTM extracts temporal dependencies from historical injection-rate sequences, attention adaptively weights influential historical time steps, and FiLM incorporates static geological attributes by modulating learned temporal representations. Quantitative evaluations over five independent runs demonstrate that the proposed model outperforms conventional LSTM-based baselines, with an MAE of 14.28 ± 0.41 m³/d and an R² of 0.9915 ± 0.0024. Ablation studies further verify the complementary predictive contributions of all three core modules. The model also maintains stable predictive performance under different injection regimes, including continuous gas injection, Water-Alternating-Gas injection, and shut-in operations. This framework provides an efficient data-driven surrogate for accelerating multi-scenario evaluation and supporting layer-wise injection allocation optimization in CO₂-EOR and CCUS applications.