The application scenarios of large-scale equipment data communication in industrial sites requires a data communication solution with low latency， large capacity and high speed. This paper compares mainstream cellular IoT technologies， proposes an industrial site data collection technology solution based on the 5G lite technology RedCap， desigs a data communication terminal based on 5G RedCap technology， and verifies its feasibility through testing. The data communication terminal based on 5G RedCap technology can well meet the needs of industrial site data communication， is in a valuble position to be promoted and applied in the field of IIoT， and will push forward the development and evolution of cellular IoT towards end-network collaboration.

To ensure the safety and stability of the power transmission systems and achieve comprehensive inspection and maintenance of newly constructed power transmission towers， this paper proposes a centrally symmetric quadrupedal humanoid climbing robot designed for existing foot pegs used by maintenance workers to climb towers. Each limb is configured with 3-1-2 arrangement. At the end of each limb， a large-tolerance， semi-enclosed hook-type gripping tool is designed specifically for the foot pegs. This tool features high tolerance， eliminating the need for precise end-effector positioning and enabling rapid engagement with the foot pegs. Humanoid climbing gait planning method is developed， facilitating the robot's full-range climbing of the power transmission tower by quickly hooking and gripping the foot pegs using the hook-type tool. Targeting a 40-meter-high self-supporting transmission tower， the robot's full-range climbing dynamics model and simulations were completed. Simulation results demonstrate that the proposed robot configuration can achieve humanoid full-range climbing of the tower， with a climbing time from the base to the top of less than 30 minutes， matching the efficiency of maintenance personnel. This provides a feasible solution for robotic maintenance applications in power transmission towers.

To address the challenges of frequent bearing failures under complex working conditions， as well as the low real-time performance and strong dependence on manual feature extraction in traditional diagnostic methods， this paper proposes a bearing fault diagnosis method based on a deep learning model combining a Multi-Scale Convolutional Neural Network （MSCNN） and Long Short-Term Memory （LSTM）， and develops an intelligent bearing health management system. The system adopts an end-to-end diagnostic workflow， directly taking raw time-domain vibration signals as input. It extracts hierarchical local features across different frequency domains through MSCNN， and captures the temporal evolution of fault characteristics using LSTM， thereby achieving high-accuracy automated fault classification. To enhance the interpretability of diagnostic results and support intelligent maintenance decisions， the system integrates the Chinese large language model iFLYTEK Spark， which generates natural language diagnostic reports and maintenance suggestions through standardized prompts. The system is deployed on a domestically developed Phytium quad-core processor platform， ensuring full autonomy and reliability of both hardware and software components for industrial applications. Experimental results show that the proposed system achieves an average classification accuracy of 98.46% on the CWRU bearing dataset， and 96.73% on the AITHE bearing fault dataset， demonstrating strong robustness and cross-dataset generalization under complex and noisy conditions. With real-time visualization of diagnostic results and maintenance recommendations through a human-machine interface （HMI）， this system provides a reliable and intelligent solution for equipment health management and predictive maintenance.

Detection on the surface defect of Pouch Cells is a key procedure of the production process. Aiming at the problems of low detection accuracy and difficult imaging of large-sized batteries in the existing detection methods， a detection method based on photometric stereo imaging and deep learning was proposed. Firstly， a Multi-Source Time-Sharing Exposure Imaging System （MSTIS） was established by combining photometric stereo and line scan camera imaging technology. After obtaining the surface images of batteries under different light sources through time-sharing exposure， photometric stereo calculation was conducted to obtain the curvature map with 3D information. Then， to solve the problem of missed detection of minor target and multi-scale defects， the YOLOv8 algorithm was improved. An edge information enhancement module （EIEM） was developed using a dual-channel convolution structure， which incorporated Sobel convolution and conventional convolution to improve feature edge extraction capabilities. The semantic and detail information fusion method （SDI） was integrated with the bidirectional feature pyramid module to boost the recognition accuracy of tiny defects. A lightweight shared convolution detection head was also implemented to reduce the algorithm's computational load.The experimental results show that the average detection accuracy of this method reaches 94.2% and the detection speed reaches 116 FPS， which can effectively detect the surface defects of pouch cells.

To solve the problems of small molding size and low printing efficiency of traditional desktop-level single-nozzle FDM 3D printer， a medium-sized FDM multi-nozzles collaborative 3D printer is designed and built. The printer adopts a Cartesian （XYZ） structure and is equipped with three side-by-side composite printing nozzles， and the materials can be selectively extruded according to the demand. The control system is divided into three parts according to the functions： main motion control module，embedded auxiliary measurement and control module and upper computer software module，while the software and hardware of these three parts are developed.Two printing modes of multi-nozzles synchronous forming and multi-nozzles stackable co-filling are designed and the corresponding path planning algorithms are proposed.After printing verification， compared with single-nozzle printing， the synchronous forming efficiency of the composite multi-nozzles printer is increased by 3 times， whereas the stackable co-filling printing time is reduced by 41%. The printing efficiency is significantly improved under the premise of ensuring the printing quality.

To address issues such as low detection accuracy， slow speed， missed and false detections， and large model parameter sizes in complex scenarios from a UAV perspective， this paper proposes an improved RBGE-YOLO algorithm model. Firstly， RFAConv is introduced in the backbone network to replace the original Conv， enhancing the model's ability to extract and fuse image features. Secondly， the neck network is reconstructed using BiFPN-GLSA to improve feature fusion and spatial feature utilization efficiency. Thirdly， a dual-layer small target detection structure is designed to strengthen the feature information of small targets. Finally， the Inner-EIoU loss function is utilized to address the limitations of IoU. Experiments on the VisDrone2019 dataset show that RBGE-YOLO improves Precision， Recall， mAP@0.5， and mAP@0.5：0.95 by 4.7%， 2%， 3.6%， and 2.5%， respectively， compared to the original YOLOv8s， while reducing the number of parameters by 16.4%. This achieves model lightweighting while significantly enhancing detection performance.

The dexterous multi-fingered robotic hand， serving as a key end-effector， is pivotal for enabling robots to perform fine-grained grasping and compliant manipulation. Its advancement holds significant importance for promoting automation in manufacturing， enhancing the intelligence of service robots， and expanding applications in specialized environments. Focusing on humanoid multi-fingered dexterous hand technologies， this paper systematically reviews the current state-of-the-art and future trends. It begins by elucidating the fundamental concepts， system architecture， and typical characteristics of dexterous hands. This is followed by a comprehensive of research achievements from domestic and international teams and commercially available mainstream multi-fingered dexterous hand products， covering various degrees-of-freedom designs and their respective hardware and software implementations. Key technologies， including core hardware components， multi-modal sensory fusion， and control strategies， are critically analyzed. The paper subsequently summarizes practical applications across domains such as industrial assembly， daily life assistance， and operations in extreme environments. Current challenges， particularly in reliability， multi-modal coordination， generalization capability， human-robot safety， and integration and application， are identified. Finally， future research directions are prospected from multiple perspectives， including standard establishment， novel mechanical structures， advanced multi-modal perception and fusion， bionic evolution， and embodied intelligence， aiming to provide valuable insights for in-depth research and groundbreaking applications of dexterous hands.

Heat exchangers play a crucial role in improving the energy efficiency of industrial processes， reducing fuel consumption， and decreasing greenhouse gas emissions. This paper addresses the innovative design problem of heat exchangers with numerous parameters， variable structures and complex medium flow characteristics by proposing a generative design method for spiral tube heat exchangers. Firstly， it analyzes the design principles， the structural advantages， and the performance characteristics of spiral tube heat exchangers， introduces the application process of the generative design method， the design optimization logic， and the automated parametric model generation method. Then， through computational fluid dynamics simulation， it evaluates the thermal transfer efficiency and fluid dynamics performance advantages of the spiral tube heat exchangers. Finally， through structural mechanics simulation， it assesses the risk resistance performance advantages of the spiral tube structure under various operating conditions. The proposed generative design method achieves rapid optimization iteration of design solutions and rapid generation of heat exchanger models， providing the possibility for rapid exploration and design of high-performance spiral tube heat exchangers.

To address the problems of scattered knowledge systems in ship assembly and ineffective mining and utilization of massive process data， this paper proposes an automatic knowledge graph construction technology for the shipbuilding domain based on large language models. This method uses large language models to convert unstructured and semi-structured ship data into structured data to build a ship process corpus. It models ship ontology knowledge structure with the assistance of large language models， designs an instruction prompting framework for ship assembly domain， and achieves efficient entity-relationship extraction， to complete the automatic construction of knowledge graphs. Additionally， the method uses triple sets constructed by general large language model instruction prompts as fine-tuning training sets to further fine-tune specialized small language models， ensuring the security of specific private ship data while reducing computational resources. Experimental results show that this method outperforms traditional baseline models in key metrics such as accuracy， providing a new technical approach for knowledge management and intelligent upgrading in the shipbuilding domain.

As an important component of intelligent manufacturing and intelligent operation and maintenance systems， industrial inspection robots are playing a key role in various complex industrial scenarios. With the continuous progress of deep learning， multi-sensor fusion， and autonomous navigation technologies， industrial inspection robots have significantly been improved in terms of accuracy， efficiency， and adaptability. This article systematically reviews the concept， key technologies， and typical applications of industrial inspection robots， and focuses on analyzing the research status of core technologies such as perception and recognition， autonomous positioning and navigation， advanced control， and intelligent decision-making. It also assesses the maturity and industrialization progress of current technologies by combining practical applications in fields such as power， workshops， and special environments. Despite significant achievements in this field， challenges still exist in perception accuracy， dynamic environment adaptability， and task execution intelligence. The development of key technologies is expected to continue in the directions of multi-source data fusion， autonomous learning， and collaborative operation. The article aims to provide a systematic reference and guidance for future research and industrial development of industrial inspection robot technology.

Aiming at the problem that traditional convolutional neural networks cannot effectively extract global features and some deep learning models are more complex， this paper proposes a lightweight gearbox fault diagnosis method based on multi-scale convolutional neural networks and feature fusion ViT. Firstly， a multi-scale feature extraction module is constructed， which captures the feature information of the data from multiple scales by multi-scale convolutional neural network using different scale convolution kernels， and fully exploits the local features of the input information. Then， the feature fusion ViT module is designed， which utilises an improved multi-attention mechanism to capture the global features of the fault information， and further constructs the D-MLP to reduce the number of parameters in the model using depth-separable convolution. Finally， the experimental validation is given using gearbox data from Southeast University， and the results show that， compared with the comparison methods， the proposed method has high fault diagnosis accuracy and good generalization ability under complex conditions such as variable operating conditions and variable noise.

In the industrial software ecosystem， Computer-Aided Design（CAD） interfaces play a pivotal role. This study outlines the composition and collaborative mechanisms of the industrial software ecosystem， reviews the evolutionary trajectory of CAD interface technologies， and summarizes their core roles within the ecosystem from the perspectives of data transmission， functional integration， and innovation-driven development.Building on this foundation， an in-depth analysis of the application bottlenecks and challenges faced by CAD interfaces is conducted， including data interface standards， the depth of system integration， and the convergence with emerging technologies. Furthermore，future development trends for CAD interfaces are explored， emphasizing key directions such as data standardization and semantic enrichment， multi-user collaborative design with real-time interaction， and the deep integration of artificial intelligence technologies. This work aims to provide theoretical insights and practical guidance for the research and application of CAD interfaces within the industrial software ecosystem.

To solve the dynamic job shop scheduling problem in scenarios with variable job and machine quantities， a solution approach called Dense-D3QN， combining DenseNet， a densely connected convolutional network， with Dueling Double Deep Q-Learning （D3QN） is proposed. The disjunctive graph model is utilized to construct a single-objective job shop scheduling model aiming to minimize the maximum processing time， representing the scheduling state in the form of multi-dimensional matrices while designing a dense-sparse reward function. To validate the effectiveness of the proposed algorithm， both public benchmarks and real data are used to construct common and actual scheduling environments. The Dense-D3QN model is trained and tested in the common environment. In the actual environment， the Dense-D3QN model is trained and tested in both static and dynamic settings. The experimental results demonstrate that the Dense-D3QN model is more capable of handling dynamic job shop scheduling problems with variable scales.

By analyzing the existing gangue sorting manipulator claw and its use， it is concluded that the existing claw has a large weight， is susceptive to wear and tear as well as higher cost of the overall replacement. A method is hence adopted to install replaceable wear-resistant shims and to select lighter quality materials for improvement. The finger force analysis is made before and after the improvement through the Ansys Workbench， and the improved finger effect proves to be better， verifying the feasibility of the installation of replaceable wear-resistant shims， while determining the replaceable wear-resistant shims material being 20CrMnSi， and finger base material being TC4. The fatigue life analysis is made for the finger before and after the improvement using fatigue analysis tools， and the conclusion is drawn that the fatigue life of improved finger matrix is longer， and the replaceable wear-resistant shims begin to fail after being used 4.3794×105 times， and are therefore needed to be replaced after about two months of use.

To address the need for both independent control of Continuous Damping Control （CDC） dampers and coordinated control of the entire vehicle semi-active suspension system， while also improving upon the issues present in traditional semi-active suspension controller software design such as challenges in meeting real-time requirements and low CPU utilization in bare-metal development environments， this study proposes an innovative approach. Initially， the study establishes separate models for the seven-degree-of-freedom semi-active suspension system and the forward-inverse models of CDC dampers. Building upon the skyhook control strategy， the study integrates a vehicle-coordinated parallel fuzzy controller based on the Mamdani fuzzy control method. Subsequently， by transplanting the FreeRTOS-SMP multicore real-time operating system and utilizing the Infineon AURIX series 32-bit triple-core microcontroller TC275 as the main control chip， the study designs the software and hardware system for the CDC damper control unit. Furthermore， the study conducts task scheduling verification of the multicore real-time operating system and validates the effectiveness of the designed control unit and proposed strategy through hardware-in-the-loop testing using typical random road surfaces to demonstrate the improvement in overall ride comfort of the vehicle.

The advancement of Industry 4.0 necessitates the deployment of intelligent， low-cost robotic systems on edge devices. However， the high computational complexity of Deep Reinforcement Learning （RL） algorithms presents a major obstacle to their implementation on resource-constrained platforms such as the Raspberry Pi. To overcome this challenge， this paper introduces a lightweight RL framework tailored for industrial robot sorting tasks. The core contributions are threefold： First， we propose a joint compression method combining Gradient Sensitivity-guided structured Pruning （GS-Pruning） with hierarchical quantization， which reduces model size by over 90% and achieves real-time inference below 35 ms on a Raspberry Pi while preserving policy accuracy. Second， we design a Dynamic Weight Adaptive Reward function （DWAR） that balances sorting efficiency， motion stability， and energy consumption， successfully suppressing robotic arm jitter and cutting average energy use by 18.1%. Third， we construct an end-to-end deployment system， RPi-EdgeRL， featuring a multi-threaded pipeline and a safety watchdog to guarantee stable and efficient autonomous operation. Experiments conducted on a FR3 collaborative robot validate our framework， achieving a 93.5% success rate in complex sorting tasks and confirming the feasibility and superiority of this low-cost， high-efficiency solution for real-world industrial applications.

Based on human-machine coupling modeling and biomechanical analysis， a shoulder-elbow rehabilitation assistive device featuring 5 degrees-of-freedom （DoF） rotational joints and 3-DoF sliding adjustments was developed. Motion capture experiments were conducted to obtain personalized scaled musculoskeletal models and reproduce upper limb rehabilitation movements through inverse kinematics. Utilizing Hill-type muscle models and the Computed Muscle Control （CMC） algorithm， the study analyzed muscle forces and energy consumption during rehabilitation training. Results demonstrated significant reductions in muscle forces for primary movers under assistive support： the long head of biceps brachii showed a 51.34% average force reduction， while the lateral head of triceps brachii exhibited 49.05% decrease. Energy consumption decreased by 30.74% and 36.56% in the long and short heads of biceps brachii respectively， with peak reductions exceeding 40%， indicating sustained unloading effects during elbow motion. Secondary muscles including the posterior deltoid and medial head of triceps brachii maintained moderate 10% reductions， balancing unloading requirements with joint stability to prevent over-intervention. The analysis confirms that the rehabilitation assistive device effectively reduces muscular burden and energy expenditure during training， mitigates muscle overload risks， and provides efficient assistance for patient rehabilitation.

Industrial robots， with their high flexibility and large working range， have gradually become another important processing equipment besides CNC machine tools in national strategic fields such as aerospace and maritime industry in China. The dynamic characteristics of the robot end are dominated by its joints. To improve the dynamic performance of the robot， it is necessary to start with its weak joints， make improvements and innovations on the basis of the existing series form， and explore new high-stiffness driving methods and robot configurations. A 2-RPR robot for milling large propellers is proposed， which includes a six-axis robot main body and a double electric cylinder branch chain. The translation of the electric cylinder drives the rotation of the robot joint， thereby improving the stiffness of the whole robot. In order to meet the processing space requirements of large propellers， the length parameters of each link of the robot are optimized based on the genetic algorithm to realize the optimization of the working space of the whole robot， so that the working space meets the processing range of a single blade and has the maximum utilization rate.

At present， close range manual handles are still used at many bridge crane workplaces. To address the potential safety risks faced by operators， a remote path planning bridge crane experimental device is designed to improve the stability and gripping efficiency of the bridge crane and determine the mechanical structure scheme of the device； By studying the working characteristics of the experimental platform in three-dimensional space， the three-dimensional path planning of the device is designed； the ant colony algorithm is improved by storing pheromones on path nodes， and a search method that combines layer by layer advancement with grid plane method is used， optimizing path nodes using pruning algorithm， and updating pheromones using a combination of global and local path planning. Through the above improvement strategies， the improved ant colony algorithm obtained through simulation in MATLAB software has 3 fewer iterations， 46 fewer inflection points， 22.6874 s shorter algorithm time， and 4.4043 units shorter shortest path compared to the traditional ant colony algorithm. Finally， the operating system of the experimental platform is designed， and an experimental prototype is built. The results show that the device meets the actual work requirements， verifying the feasibility and effectiveness of improving ant colony algorithm.

The quality of screw tightening greatly affects the safety of mechanical products， whereas traditional diagnosis approaches are time-consuming and imprecise， and the implementation of effective fault diagnosis， therefore， bears significant engineering value. In this paper， an innovative method of fault diagnosis for screw-tightening based on LSTM and Expert knowledge is proposed. Firstly， tightening process curve under specific failure mode was studied and several expert knowledge rules were established. Secondly， a data pre-processing algorithm was established based on the characteristics of sequential data such as noise clipping， stage segmentation， fitting and sampling to improve the quality of data. After that， the feature vector obtained through LSTM was used as the input of the expert knowledge model to obtain the expert knowledge vector， and the two vectors were combined as the input of the classifier. Finally， compared with SVM and LSTM， the results show that the method has higher diagnostic accuracy in multiple failure modes.

Facing the requirements for diverse starting and ending positions as well as velocity in the field of robotic motion control， this paper proposes a novel universal S-curve velocity planning algorithm aiming at adapting to arbitrarily specified starting and ending positions and velocity conditions. The paper first introduces the velocity-to-velocity S-curve velocity planning algorithm， then elaborates on the general 7-segment S-curve velocity planning algorithm. Based on this， a more versatile S-curve velocity planning algorithm is proposed. For different input parameters， this paper classifies the S-curve （s-t curve） into ten types and provides detailed segmented planning strategies for each type. Through simulation tests， it is verified that the algorithm not only excels in efficiency but also demonstrates outstanding performance in the smoothness and positional accuracy of the planned curve. Furthermore， tests conducted on actual robotic platforms further confirm that this algorithm can effectively reduce shocks and vibrations during robotic operations， significantly enhancing the operational performance of robots while demonstrating good practicality and broad application prospects.

In recent years， indigenously developed， aerospace-specific 3D structural design systems in China have undergone robust development， with notable achievements in the R&D of core components. However， with the widespread adoption of Large Language Models （LLMs）， establishing an effective interface between 3D structural design and AI-driven methodologies remains a central challenge. Furthermore， existing LLMs lack the capacity for precise reasoning over 3D geometry and complex physical fields， such as aerodynamics， which precludes their direct application in the intelligent design of aircraft structures. Among aerospace structural components， the aircraft wing is critical for generating lift. Its design process is highly complex， heavily reliant on expert experience， and tightly coupled with aerodynamic performance. Consequently， traditional design paradigms are characterized by lengthy iteration cycles and substantial costs. To address this challenge， this paper presents Airfoil-LLM， an intelligent design interface for the 3D modeling of aircraft wings， using the wing as a representative case study. Based on the Transformer architecture， this interface integrates natural language encoding with the decoding of CAD modeling sequences to enable intelligent and automated 3D wing generation. To support model training and validation， we have constructed a large-scale 3D wing design dataset. This comprehensive dataset comprises parameterized 3D CAD models， a wide spectrum of flight conditions from subsonic to supersonic regimes， key aerodynamic performance metrics， and multi-level textual descriptions. Experimental results demonstrate that Airfoil-LLM is capable of deeply comprehending textual descriptions ranging from simple geometric attributes to complex， coupled "geometry-performance" requirements. The system generates 3D models that align closely with the design targets in both geometric shape， achieving a maximum Intersection over Union （IoU） of 0.831， and aerodynamic performance.

Aiming at the pain point of slow traditional rescue response in water areas， this paper proposes a structural design scheme of an amphibious rescue equipment that integrates the functions of unmanned aerial vehicles （UAVs） and rescue boats. The UAV adopts a lightweight fuselage and NACA4412 airfoil aerodynamic design. Through the rotation of wings and elescopic mechanism of the counter-rotating propellers， a rapid cross-medium form switching can be achieved. Meanwhile， the dynamic performance is analyzed by establishing the mathematical model and state-space model of the drone， and a fuzzy PID controller is designed. MATLAB/Simulink is used to carry out dynamic response simulation verification for the mathematical model of the drone and the designed fuzzy PID controller. The results show that when the input is a square wave and a step signal， the fuzzy PID controller designed in this paper has a faster response speed and better stability compared with the traditional PID controller.

In order to adapt to the characteristics of complex electronic equipment， such as multi-variety， variable batch， multi-level blind matching and vertical interconnection， a six-degree of freedom heterogeneous assembly robot arm has been independently developed and applied to the assembly line of complex electronic equipment. Firstly， the structure composition， configuration advantages and problems in practical application of the heterogeneous six-axis manipulator are introduced. Secondly， the forward and inverse kinematics algorithm of the heterogeneous six-axis manipulator is established by using D-H parameter method， and the kinematics model of the heterogeneous six-axis manipulator is constructed. Then the calibration algorithm， trajectory planning algorithm， collision control algorithm and other methods of the heterogeneous six-axis manipulator are studied. Finally， the field calibration experiment and MATLAB simulation analysis are used to verify the motion planning method， which proves the rationality and practicability of the relevant methods.

In order to solve the problem of poor accuracy of current workpiece visual online inspection technology， a workpiece size inspection system based on binocular vision is proposed.The system is calibrated using the improved Zhang Zhengyou calibration method. By improving the filtering method， gradient calculation and threshold selection of the Canny edge detection algorithm， the accuracy of image edge detection is improved.Build a workpiece size detection system platform， take gear workpieces as the detection object， and conduct dimensional detection experiments.In order to verify the detection accuracy of the system， a 6D high-precision tracking measurement and fast scanning integrated system was used to conduct a dimensional detection comparison test on the gear workpiece.The results show that the workpiece size detection system designed in this article can detect the workpiece size accurately and efficiently. The average relative measurement error is 0.119%， which can meet the requirements of actual indust.

To address the challenges faced by mobile robots in overcoming obstacles in unstructured environments such as agricultural inspections and disaster rescue， this study proposes a design scheme for a four-wheeled mobile chassis that integrates a rocker-steering suspension with a crank-slider mechanism. First， kinematic and dynamic models of the walking mechanism were established to analyze the influence of key configuration parameters （e.g.， support wheel center distance， hinge distance） on terrain adaptability and load platform posture. A multi-objective optimization method was employed to determine the optimal parameter combination （L_F =200 mm，k₁=0.9）. Second， a three-dimensional virtual prototype was developed by integrating a crank-slider mechanism and symmetric frame design. Dynamic simulations conducted on the RecurDyn platform validated the chassis performance in traversing 18 mm speed bumps and 20 mm semi-cylindrical obstacles， showing pitch angle fluctuations within ±3°and peak torque demand ≤15 N·m. Finally， prototype tests demonstrated that the chassis can stably cross 90 mm speed bump-type obstacles under a 75 kg load， with a linear motion speed of 1.8 m/s and a path deviation of less than 20 mm/5 m. The results indicate that this design significantly enhances the terrain adaptability of mobile robots in unstructured environments， providing a reliable mobile platform for agricultural inspection， logistics， disaster rescue， and similar scenarios.

Electric vehicle charging and battery swapping stations face multiple operational challenges including low service efficiency， poor economic benefits， and weak grid interaction capability. A V2G coordinated scheduling model based on hierarchical deep reinforcement learning is proposed which effectively reduces decision complexity through a collaborative architecture of strategic and tactical layers， and significantly enhances system responsiveness. Empirical research demonstrates that the model exhibits substantial practical value in actual charging station environments， primarily reflected in reasonable growth of operational revenue， optimized energy utilization efficiency， significant improvement in service quality， and effective reduction in user waiting times. Compared with traditional scheduling methods， the SAC algorithm adopted in this study shows stronger adaptability and stability when facing complex decision environments， effectively responding to uncertainties such as traffic flow fluctuations and electricity price changes. The research results provide an implementable intelligent scheduling solution for electric vehicle charging and battery swapping stations， offering valuable technical reference for addressing actual operational issues in the industry， and contributing positively to the sustainable development of the electric vehicle industry.

To meet the requirements of characteristic processing of high-end brand cigarettes， such as loose moisture regain first and then vacuum moisture regain， and to solve the problems of low control accuracy of moisture content in discharge and large fluctuation of process quality due to the difference of moisture absorption of different grades of tobacco leaves， the aligned data were sliced according to the grade of cigarette packets， and the key factors affecting the proportion of moisture regain were screened out by correlation analysis. Based on multiple regression analysis and neural network algorithm， the control model of the proportion of water added was established to realize the prediction of the proportion of water added before production and the automatic optimization and adjustment in production. After the application of the model， the qualified rate of the standard deviation of moisture content of the three brands' vacuum moisture regain was increased from 52.33% to 80.57%， and the model optimization control effect was obvious. This method effectively improves the process quality control level of vacuum moisture regain， and at the same time， ensures the compliance of key indicators in subsequent processes.

In response to the challenges posed by complex microhole backgrounds in frames， a multitude of small and medium-sized defective targets， and the high degree of shape randomness encountered in mobile phone visual inspections， we have developed an enhanced YOLOv8-burr model based on YOLOv8 model. This model incorporates a lightweight global attention transformation module， which leverages packet convolution， within the network neck region. It also integrates a multi-scale feature extraction module into the backbone and employs a polarization self-attention mechanism along with a CARAFE operator in the network sampling stage. These innovations enable the model to harness global feature information and multi-layer channel details for more precise detection of small target defects. The experimental results show that the improved model has a size of 14.4M and can achieve 92.1% accuracy of microhole defect recognition， and in the category of "burr" defects that are difficult to identify， the accuracy has been improved by 10.4% compared with the original model before improved， which meets the identification accuracy requirements of the robot for the identification of microhole machining defects in the mobile phone frame.

In ABAQUS simulation analysis， the Young's modulus and Poisson's ratio are input as fixed values with temperature changes， which affects the reliability and accuracy of the results. Therefore， the temperature variation function is embedded into the ultrasonic vibration-assisted cutting （UVAC） simulation of titanium alloy TC4 by using the secondary development function of ABAQUS. The analysis results show that the secondary development carried out is practical and instructive. Compared with the ordinary cutting， the ultrasonic vibration-assisted cutting can effectively achieve chip breaking and reduce the cutting temperature， with the maximum reduction reaching 34%. With the increase of amplitude， the chip length gradually decreases， and the influence depth of amplitude on residual compressive stress shows a trend of first decreasing and then increasing； in contrast， the frequency has a smaller impact on the chip morphology. As the frequency increases， both the chip length and residual compressive stress decrease.

To address the inefficiency of traditional manual visual inventory counting and the high deployment costs of existing automated solutions in Automated Storage and Retrieval Systems （AS/RS）， this paper proposes an intra-warehouse visual inventory system based on modular visual devices. A retrofit-free stacker-accessible modular visual inventory device is designed. On the basis of a YOLOv8-powered visual inventory algorithm for multi-surface information fusion from a single view， the accurate counting of complex stack patterns （e.g.， non-full stacks and staggered stacks） is effectively solved by combining front pallet layer identification with top pallet layer counting. The system also features a non-intrusive integration architecture between the Warehouse Visual Stock System （WVSS） and the existing Warehouse Control System （WCS） via a database， enabling dynamic task scheduling and data closed-loop. Experimental results on four palletized cargo datasets demonstrate a stack quantity recognition accuracy of 96.3% with a processing time of 0.11 seconds per storage location. This solution provides a new engineering path for automated warehousing， characterized by high precision， low deployment cost， and minimal operational disruption.

The actual fault data of hydraulic cylinders is difficult to obtain， and the probabilities of various types of faults are different. Some faults deteriorate rapidly after occurrence， resulting in an uneven overall fault data set， making fault diagnosis difficult. Based on this， this paper proposes a variational autoencoder （STL-MATVAE） that combines time series decomposition and multi-head self-attention mechanism for data augmentation of hydraulic cylinder faults， aiming to generate virtual samples that are similar in features to the original data but have different distributions. This method extracts deep features through the multi-head self-attention mechanism in the encoder and uses residual connections to optimize the network structure to reduce gradient vanishing. The decoder introduces a time series decomposition component to enhance the temporal interpretability of the samples. The experiments show that the data generated by STL-MATVAE outperforms that of the generative adversarial network （GAN） in terms of distribution characteristics and diversity， significantly improving the diagnostic performance of various classifiers. This provides a reliable technical path for the fault diagnosis of hydraulic cylinders and opens up a new research direction for fault diagnosis in complex industrial scenarios with uneven data.

In order to reduce the monitoring and operating pressure of operators during deep peak shaving， an intelligent desulfurization control system is proposed to address the problems of poor measurement accuracy and large inertia and delay in the controlled objects that prevented long-term stable automatic operations. By using BP neural network， a mapping relationship is constructed between signals such as flue gas flow rate， SO₂ concentration in the raw flue gas and slurry pH to achieve soft measurement of slurry pH value； Replacing conventional PID with variable structure predictive control and combining it with more accurate and reasonable feedforward signals ensures the control effect of the desulfurization system under rapidly changing load and coal quality conditions. Later， utilizing the unit ICS system， the desulfurization intelligent control system is successfully applied to a 650 MW unit. The operation results show that after the system is put into operation， the SO₂ concentration at the outlet is stably controlled within 25 mg/m³， and the deviation between the pH value of the slurry and the set value is kept within 0.2， and there are no significant fluctuations during the variable load and pH meter flushing process. The desulfurization is automatically put into operation for a long time， effectively reducing the operating pressure of the operators.

Manual hammer riveting is widely used in the assembly of aircraft skin and truss. During the process of hammer riveting， the strong vibration and noise caused by the repeated impact of the riveting gun on the rivet seriously affects the physical and mental health of the operator and the precision of precision instruments. Therefore， a viscous damping vibration reduction fixture and a riveting gun sound insulation cover are designed to maximize the vibration and noise reduction effect and realize the efficient suppression of vibration and noise during the riveting process of thin-walled skin parts. The principle of viscous damping and sound absorption of perforated plate is deduced， the structural design of viscous damping vibration reduction fixture and sound insulation cover of riveting gun is carried out， and the modal test and hammer riveting experiment are carried out respectively to verify their effectiveness. The experimental results show that the vibration of thin-walled parts presents multi-modal characteristics， and the peak value of each mode of frequency response function is reduced by more than 52.87% after using the vibration reduction and noise reduction device. The maximum acceleration in the process of hammer riveting is reduced by 59.34%， and the adjustment time of sound pressure attenuation in hammer riveting is reduced by 32.45%. The sound pressure level of noise is decreased by 3.2dB compared with that of conventional hammer riveting， showing good vibration and noise reduction effect.

Query command on ship component is generally coded by database maintainers. When the maintainers lack professional background knowledge however， it would be difficult for them to understand query requirements and feedback query results. To solve this problem， database query for ship component based on large language mode and template sentence is proposed. First， the multi-discipline design model data and expertise knowledge are integrated， and the graph database is imported. Second， the Large language model is used to convert query requirements of natural language into template sentences， which are further converted into database query code and get the query result from database. This method does not require costly fine-tuning， but rather inherits the encoder capability of large language model. It replaces the decoder of transformer structure with ontology knowledge and template sentence to improve the controllability and accuracy of code generation. The test result of 408 natural language questions shows that the accuracy of the proposed method is as high as 90%， which can be applied to the ship operation and maintenance.

To address the quality faults of PCB assembly products caused by the dynamic characteristics of risk factors in the SMT production process， this paper proposes a PCB assembly process fault analysis method based on Dynamic Fuzzy Bayesian Network （FDBN）. This method extracts fault factors from six aspects： human， machine， environment， management， material and technology， and conducts test analysis for AOI inspection personnel to ensure consistency in fault review between human and machine. Based on the original product defect history， the fault tree analysis （FTA） is used to analyze the causal relationship of faults， and the Bayesian network model is constructed through the transformation of the fault tree model. Meanwhile， an improved similarity aggregation method is introduced in the fuzzy set theory to construct a complete FDBN model for predicting the dynamic evolution of construction risk probability. Real-time information of case engineering is used as model input， and the results show that the built model accurately predicts the change of operation risk probability based on input evidence. At the same time， sensitivity analysis identifies key risk factors and ranks them accordingly. Finally， the combined theoretical verification further confirms the effectiveness of this method.

To solve the problem of accidents caused by damaged crane hooks in current industrial environment， and the low efficiency of crane loading and unloading， an improved YOLOv8n crane hook identification algorithm is proposed. Firstly， AKConv module is introduced to replace the Conv module in the backbone network. This module gives arbitrary parameters and shapes to the convolution kernel， providing rich choices between the convolution cores. Secondly， the ADown downsampling module is embedded in the backbone network， reducing the loss of feature information during the downsampling process. Finally， a CAFMAttention convolution attention fusion module is introduced to enhance the global and local feature extraction of hook recognition. Based on the experimental results， the improved YOLOv8n algorithm increases the precision， recall and mAP50 indicators by 4.6 %、4.2 % and 3.3 % respectively. The improved algorithm enables real time detection of hook positions， assisting operators in timely adjustment and decision-making， avoiding collisions or accidents， thereby improving safety in industrial environments. In addition， automatic hook recognition facilitates faster hook location identification while enabling precise cargo loading and unloading operations， consequently boosting work efficiency.

As the most important active safety system in the wire-controlled chassis assembly of vehicles at this stage， the pressure control precision and system reliability of the electro-hydraulic composite braking system will directly determine the chassis control capability and driving safety of new energy vehicles and intelligent driving vehicles. Therefore， in order to further improve the response speed and system reliability of the electro-hydraulic composite braking system， a piston displacement feed-forward control strategy was proposed based on the ANFIS algorithm and the P-V characteristics of the brake master cylinder， which further improved the response speed of the system and reduced the dependence of the control system on the pressure sensor to a certain extent. At the same time， in order to ensure the pressure control accuracy and system robustness of the electro-hydraulic composite braking system， a master cylinder pressure feedback control strategy was proposed based on the fuzzy PID control algorithm， which further improved the pressure tracking control accuracy. Finally， the performance of the proposed control strategy was verified by Simulink-AMESim joint simulation.

Tool wear prediction is of great significance for reducing costs， improving efficiency and ensuring machining quality. To address the challenges such as difficulties in extracting features related to tool wear information， low utilization rate of the extracted features， and low prediction precision and accuracy， under the circumstances of complex environmental noise and a low signal-to-noise ratio， a Multi-scale Sample Reconstruction （MSR） method for vibration signals was first proposed to mitigate the impact of noise on the prediction effect of subsequent models. Subsequently， an improved model was put forward， which was based on the integrated model of the Residual Network （ResNet） and the Bidirectional Long Short-Term Memory （BiLSTM） network. In this improved model， the Criss Cross Attention （CCA） mechanism was integrated into each residual layer， and a Stacked Bidirectional Long Short-Term Memory Network （SBILSTM） was adopted. By comparing this improved model with the ResNet-BiLSTM model as well as traditional deep learning models， the results demonstrated that this method significantly enhanced the prediction precision and accuracy of tool wear.

Interface debonding is one of the main failure modes of solid rocket motors （SRM） and a weak link limiting the lifespan of SRM. Accurate and reliable monitoring of the debonding damage is of great significance to ensure SRM reliability. However， the identification accuracy of debonding damage is still low. A debonding damage monitoring method based on probabilistic neural network （PNN） using electromechanical impedance is hence proposed in this paper. A PNN for disbonding monitoring is established， and the electromechanical impedance curve is directly used as input to realize the "end to end" identification of disbonding damage location. The proposed method is verified experimentally. The results show that the proposed method can achieve more than 90% damage monitoring accuracy under 12 training samples for each class. The sensor position has little influence on the monitoring accuracy， while overcoming the drawbacks of the existing method which relies on the artificially constructed damage index. The proposed method provides a technical basis for the SMR debonding monitoring.