Printed circuit boards are versatile and highly printed, which can be widely used in various fields, and also provide new opportunities for the development of electronic information equipment. However, it is difficult to detect defects and faults during the production and use of printed circuit boards. In this paper, in view of the defects and fault detection of printed circuit boards, a deep learning detection method GSC YOLOv5 that integrates light-weight network and dual attention mechanism is proposed. First, GSC YOLOv5 is improved on the basis of YOLOv5. Ghost Conv and Ghost Bottleneck are used to realize the lightweight of the algorithm structure, reduce the number of parameters and floating point arithmetic (Flops) of the model. Second, the dual attention mechanism of Squeeze-and-Excitation Module (SE Module) and Convolutional Block Attention Module (CBAM) are introduced to optimize the performance of the algorithm, while improves the detection precision and real-time detection efficiency. Last, the experimental results show that compared with the original algorithm, GSC YOLOv5 reduces the amount of parameters and Flops by 50.38% and 55.52%, respectively, and compresses the model volume by 50.26%. Furthermore, the detection precision is increased by 2.41% and the recall rate is improved by 1.06%. At the same time, a real-times detection performance of $89.4~FPS$ is achieved, it improves by 65.18%. Therefore, the proposed algorithm is not only lightweight but also can achieve better performance, it can satisfy the detection requirements of printed circuit board.

Integrated energy system (IES) facilitates efficient energy conversion and utilization. However, the joint optimization of energy use and carbon emissions (CEs) remains a significant and widely recognized challenge in this field. In this article, to solve the problem, a novel decision-making framework is proposed with leveraging a large language model (LLM) to assist deep reinforcement learning (DRL). First, a dynamic priority trading strategy is designed based on real-time supply and demand, which is adjusted dynamically through a trading matrix. Furthermore, a bidirectional equilibrium pricing mechanism is designed to determine reasonable prices that balance the interests of trading parties. Finally, the powerful inference and analysis capabilities of the LLM are leveraged to optimize DRL algorithms through interactive iterations and feedback loops, thereby enhancing decision-making performance. The experimental results demonstrate that the improved algorithm outperforms the baseline algorithm in terms of cost control, CE limitation.

Since the underground transportation of coal mainly relies on the mine conveyor belt to complete, the mine conveyor belt with large pieces of coal will affect transportation safety. Therefore, to address the problem of real‐time monitoring of lump coal, the method Ghost‐ECA‐Bi FPN (GEB) YOLOv5 for lump coal in the process of mining conveyor belt transportation is proposed based on a lightweight neural network and multisource information fusion. First, the image preprocessing is performed by adaptive histogram equalization, which reduces the influence of coal dust, dust, and uneven lighting on target monitoring. Second, the redundancy of the convolution process is exploited, and a lightweight neural network GhostNet is introduced to optimize the feature extraction process. In addition, combined with the efficient channel attention mechanism, the 1D convolution enables local cross‐channel information interaction, which can solve the problem of imbalance between model complexity and performance. Finally, the feature information of the three stages is fused using a weighted bidirectional feature pyramid network to enhance the generalization ability of the model. The experimental results show that the improved GEB YOLOv5 algorithm has obvious advantages. In terms of model structure, the number of network layers reduces by 36.97%, and the number of model structure parameters and floating‐point operations reduce by 64.53% and 69.14%, respectively. Moreover, the model volume reduces from 92.7 M to 33.0 M. Regarding the monitoring performance, the precision and recall rates improve by 1.19% and 1.11%, respectively. Furthermore, the real‐time performance improves from 68.34 FPS to 110.70 FPS. It can be seen that the problem of the model performance against the model complexity is effectively solved in this experiment and the real‐time monitoring of lump coal is realized.

North of Shanxi, Datong Yunzhou District is the base for the cultivation of Hemerocallis citrina Baroni, which is the main production and marketing product driving the local economy. Hemerocallis citrina Baroni and other crops’ picking rules are different: the picking cycle is shorter, the frequency is higher, and the picking conditions are harsh. Therefore, in order to reduce the difficulty and workload of picking Hemerocallis citrina Baroni, this paper proposes the GGSC YOLOv5 algorithm, a Hemerocallis citrina Baroni maturity detection method integrating a lightweight neural network and dual attention mechanism, based on a deep learning algorithm. First, Ghost Conv is used to decrease the model complexity and reduce the network layers, number of parameters, and Flops. Subsequently, combining the Ghost Bottleneck micro residual module to reduce the GPU utilization and compress the model size, feature extraction is achieved in a lightweight way. At last, the dual attention mechanism of Squeeze-and-Excitation (SE) and the Convolutional Block Attention Module (CBAM) is introduced to change the tendency of feature extraction and improve detection precision. The experimental results show that the improved GGSC YOLOv5 algorithm reduced the number of parameters and Flops by 63.58% and 68.95%, respectively, and reduced the number of network layers by about 33.12% in terms of model structure. In the case of hardware consumption, GPU utilization is reduced by 44.69%, and the model size was compressed by 63.43%. The detection precision is up to 84.9%, which is an improvement of about 2.55%, and the real-time detection speed increased from 64.16 $FPS$ to 96.96 $FPS$, an improvement of about 51.13%.

During the transfer process for coal with the utilization of mining conveyor belts, large pieces of coal often affect the safety of transportation, so real-time monitoring of the transport process in lump coal is essential. Therefore, a real-time monitoring method GSB YOLOv5 is proposed. Firstly, the dataset's contrast is enhanced by adaptive histogram equalization, while its richness is improved by combining Mosaic multi-data enhancement. Secondly, it proposes the utilization of Ghost Net, which is a neural network with low computational requirements for lightweight extraction and fusion of features. This approach effectively minimizes the model computation. In addition, the combination of Squeeze-Excitation mechanism improves the extraction of model's feature capabilities. Finally, a feature pyramid with weighted bidirectional is employed to accomplish multi-source information fusion by effectively merging features at varying resolutions. The experimental findings demonstrate that the enhanced GSB YOLOv5 algorithm achieves a 35.256% significant reduction in network layers, while it has substantial reductions of 63.023% and 68.582% in parameters and floating point operations, respectively. Furthermore, the compressed model size is decreased from 92.7MB to 34.4MB. In addition, there are improvements of 1.421% and 1.460% in the detection precision and recall rate of the model, respectively, while the detection efficiency of the real-time has a significant boost from 68.34 FPS to 107.91 FPS. Automatic, fast and high precision monitoring of lump coal objects on conveyor belts in underground coal mines can be realized.

The carbon-factor accounting method is widely used for carbon emissions (CEs) evaluation and carbon quotas (CQs) allocation in integrated energy systems (IESs). However, its linear mapping model cannot capture the real-time influence of external and environmental factors, which weakens the constraint effect of CQs on CEs and limits the overall energy–carbon optimization capability. To address this issue, this article proposes a large language model (LLM)-assisted deep reinforcement learning (DRL) optimization method to enhance the constraint effect of CQs on CEs in IESs. First, a nonlinear CQ modeling method based on LLM semantic reasoning is proposed, breaking the dependence of the linear carbon-factor method on expert experience. Second, considering information including energy structure, market changes, policy orientation, and environmental constraints, an interpretable nonlinear CQ accounting method is designed based on LLM to enhance the constraint effect of CQs on CEs. Finally, a trigger mechanism is designed to achieve collaborative optimization through automatic interaction between LLM and DRL. Simulation results indicate that the optimized CQ mechanism enforces a more effective constraint on CE behaviors, enabling timelier response and enhanced energy–carbon optimization performance.