Photovoltaic panels are often affected by pollutants such as dust for a longer period of time， which leads to a decrease in their transmittance， resulting in the reduction of the photoelectric conversion efficiency and service life. Therefore， regular cleaning is required. To support the automation of the cleaning process by robots， a deep learning-based automatic recognition model for photovoltaic panel image was developed. A dataset of clean and dusty photovoltaic panel images was created using image enhancement techniques such as Mosaic. Considering the computational constraints of the cleaning robot， a decision was made to use the Residual Network （ResNet） as the baseline model， where a smaller-scale ResNet-18 was chosen， and modifications were made to the residual blocks within ResNet-18. Additionally， a channel attention mechanism with cross-scale feature fusion was introduced on top of the multi-path architecture to enhance model accuracy. Experimental validation showed that the improved model achieved a 2.1% increase in accuracy compared to that of previous methods. It outperformed other mainstream classification networks like EfficientNet-B0 and YOLOv5x-cls， reaching the highest accuracy of 94.6%. Additionally， model interpretability techniques were used to analyze the misclassifications made by each model， thereby determining the model's focus on different regions and features and understanding its decision-making process in specific misclassification cases. Finally， the trained model was deployed on a cleaning robot， with the robot’s CPU utilized for inference， with an average recognition time of 121 ms， achieving high-precision and efficient identification of dust on photovoltaic panels.

To solve the problems of difficult workpiece positioning and poor installation accuracy in the automated assembly process， a high-precision and high-efficiency visual guidance system was designed for automatic assembly of the plate-like parts with strong adaptability to point cloud quality. Firstly， the bounding boxes of point clouds were used to extract point cloud data ROI， and the preprocessing steps for denoising and down-sampling were performed. Subsequently， a method of using finite element mesh center points to create point cloud templates was proposed， and the PCA algorithm was enhanced with spindle and centroid correction to improve the algorithm’s tolerance for point cloud quality and achieve fast and accurate registration of the point cloud data. Finally， the effectiveness of the point cloud template creation method， the improved registration effect of the PCA algorithm， the algorithm’s tolerance for variations in point cloud quality， its robustness to different parts and the overall accuracy of the system were experimentally verified. Eventually， the average translation error of the system is 0.80mm， with the RMSE less than 0.089mm， and the average rotation error is 1.24mm， with the RMSE less than 0.116mm， indicating that the requirements of automated assembly were fully met. The experimental results show that the proposed method has superior comprehensive performance in terms of point cloud quality tolerance， registration accuracy and efficiency.

Aiming at the issue of low real-time performance and loss of details of traditional image segmentation algorithms， an intelligent optimization algorithm （MSAPSO） is proposed using the SFM chaotic mapping to improve particle swarms while combining it with sparrow search algorithm to realize the OTSU threshold segmentation. The first choice is to use the chaos phenomenon generated by SPM chaotic mapping to initialize the particle population， improve the randomness and traversal of particles， and then strengthen the global search ability of particles.Secondly， the adaptive particle swarm optimization is used to dynamically replace inertial weights to balance the local and global search capabilities. Then the sparrow algorithm idea is introduced to guide the particle swarm to search within the scope of the optimal solution， achieving faster convergence while escaping local extreme values； Finally， by testing different standard images and conducting comparative analysis with a variety of algorithms， the simulation results demonstrate that the proposed method has faster convergence speed and better threshold accuracy than that of the other group intelligence algorithms， indicating the feasibility and effectiveness of the proposed algorithm.

In order to solve the problem of inventory control difficulties caused by obvious seasonal changes in the market demand for argucultural equipment enterprises， a reserve inventory control model for agricultural equipment enterprises is constructed and the corresponding intelligent algorithm is proposed. The multi-cycle inventory control dynamic programming model of agricultural equipment enterprises under production with peak-and-valley patterns is established by adding reserve inventory demand as a decision variable. The newsvendor problem is expanded to multi-product case， the benefit function of dynamic programming stage is constructed， and the multi-cycle and multi-product reserve inventory control model is established in this paper. A combined solution algorithm of dynamic programming and discrete whale algorithm is proposed to realize the optimal decision of inventory control. Taking an agricultural equipment enterprise as an example， the effectiveness of the inventory control model and algorithm is verified.

During the automated disassembly of waste lithium battery modules， it is necessary to quickly identify the positions and orientations of large number of various threaded fasteners. In view of the current situation where the existing feature matching methods are difficult to adapt to the complex background environment around the fasteners and the deep learning methods cannot achieve the accurate positioning and posture recognition of the fastener center， a coarce-to-fine recognition strategy to combine two types of methods is implemented， one of which is based on the lightweight deep learning model SqueezeNet， while the other on the feature analysis of the fastener BLOB （Binary Large Object） in order to quickly realize the classification and accurate positioning of fasteners. On this basis， a region intersection approach is further proposed to accurately identify the head posture angles of various fasteners. The experimental results show that， compared with other existing recognition models， the proposed method not only achieves a higher coarse positioning accuracy （94.9%）， but also has a precision positioning error of the fastener center within 0.3mm and a head attitude angle error within 3°， respectively. Thus， it can well meet the application requirements of robot disassembly of fasteners.

To address the current issue of low accuracy in the detection of steel surface defect， the CDB-YOLOv5s steel defect detection method is proposed with incorporation of attention mechanism. The CDB-YOLOv5s integrates the Convolutional Block Attention Module into the C3 module of the backbone extraction network to construct the C3CBAM module， which enhances the feature learning of the algorithm on the steel defect areas and suppresses the influence of image background on detection. The Deformable Convolutional Network is used to replace the conventional convolution in the neck networks to realize adaptive learning of receptive fields and improve feature extraction capability. Furthermore， in order to improve the feature fusion capability of the model to strengthen the detection of small target defects， the weighted bidirectional Feature Pyramid Network is used to replace the Path Aggregation Network. In view of the different concerns of classification and localization， the Decoupled Head structure is adopted in the output layer， with two parallel branches for classification and regression tasks respectively. Experimental results show that CDB-YOLOv5s exhibits excellent performance on the NEU-DET dataset， with its average accuracy reaching 79.2%， which is 5.1% higher than the original YOLOv5s. In comparison with other mainstream algorithms， the proposed method can detect the location of defects more accurately， which verifies the feasibility and effectiveness of the method.

Aiming at the problem that the feature point method has no obvious features in unstructured and complex scenes， which leads to the failure of feature extraction， an improved laser SLAM algorithm based on the feature extraction of cloth algorithm is proposed. Firstly， a new feature extraction method based on the cloth algorithm is proposed to extract the surface point cloud in the vertical direction as feature points， which can effectively reflect the vertical shape of the structure around the point cloud and contain more global point cloud information. Secondly， the 3DSC method is adopted as the descriptor of the feature points to determine the corresponding points of the neighbouring point clouds， and the improved RANSAC method is used to perform the coarse alignment， which is combined with the ICP method to perform the fine alignment to complete the pose estimation. The improved RANSAC method is used for coarse alignment and combined with the ICP method for fine alignment to complete the pose estimation and improve the alignment accuracy. Finally， the global descriptor LiDAR-Iris is used for loopback detection to reduce the cumulative error. The experimental results show that the proposed method is better than A-LOAM and LeGO-LOAM in terms of accuracy， low drift and stability， and improves the overall performance of the system.

Aiming at the incorrect and missed detection of safety helmets worn by mining workers， an improved detection method for safety helmets was proposed， including the adding of a small target detection layer on the basis of YOLOv5s to improve the network’s detection performance for small targets， the introduction of a new similarity metric for bounding boxes to reduce the sensitivity of the network to changes in the location of small targets， the reconstruction of the detection head of the model to accelerate network convergence and the rebuilt of the feature extraction module in the model to enhance the network’s ability to detect the occluded targets. The experimental results of this ablation experiment completed on the self-built dataset show that the improved model has a recognition accuracy improvement of 2.1% on average， 3.0% on average recall， and 1.9% on average precision compared to that of the original YOLOv5s model. The research shows that the improved model has good detection accuracy and is suitable for detecting the helmet wearing in complext situation， which will exert a positive significance for ensuring the safety of workers.

The accurate diagnosis of bearing fault types is crucial for improving equipment reliability and efficiency， and carries a great significance for conducting research in early diagnosis and prediction of fault. Currently， there are two main approaches in this research. One approach involves the manual extraction of fault features for classification， while the other utilizes the neural networks for diagnosis， which tends to lack the capability of adaptive parameter tuning， resulting in limited generalization performance. Therefore， this paper proposes the utilization of the genetic algorithm to optimize the convolutional neural network for fault diagnosis， where the 1d-CNN can extract subtle features from the bearing vibration signals and employ a genetic algorithm for adaptive parameter tuning， thus enhancing the diagnostic accuracy and generalization capability of the model. Experimental results demonstrate that the proposed method achieves an average diagnosis accuracy of 98.56%， outperforming the traditional methods， namely 1d-CNN， MLP， and SVM， by 3.26%， 10.45%， and 13.72%， respectively. These results highlight the superior performance and accuracy of the improved 1d-CNN in bearing fault diagnosis.

Detection of small-sized rod-end joint bearings in dense arrangements of small-sized rod-end joint bearings via machine vision faces challenges due to the lack of feature information and high variability of the spherical surfaces， leading to inaccurate identification which affects production efficiency. To address this， a deep learning based object detection algorithm is proposed. First， to enrich the semantic information in the network， a Space-to-depth Convolution （SPD-Conv） module without striding is introduced to improve to the Backbone network. A Multi-level Feature Fused SPD （MFSPD） module is then proposed to redesign the Neck network for enhanced feature extraction and detection accuracy on small objects. In the Head network， a P4 small objects detection branch with prior boxes generated by a weighted k-means algorithm on the dataset is added to increase feature-prior box matching and accelerate convergence. Next， a Confidence Propagation Cluster （CP-Cluster） post-processing method is applied to optimize predicted box confidence and detection speed. Finally， the algorithm performance on custom， T-LESS and COCO datasets is evaluated. The proposed detector achieves 96.8% and 93.6% mAP@.5 on custom and T-LESS datasets respectively， and 55.7% mAP on COCO， demonstrating remarkable improvements in detection accuracy and feature extraction capability.

In order to improve the predictive accuracy of coagulant dosage in water plants， an improved sparrow search algorithm （ISSA） is proposed to optimize the eXtreme Gradient Boosting（XGBoost） model for coagulant dosage prediction. Firstly， the Sobol sequence， the two-sample learning strategy and the Cauchy-Gaussian mutation strategy were combined with the sparrow search algorithm. Secondly， a coagulation dosing prediction model was established using the improved sparrow search algorithm to optimize the main hyperparameters in the XGBoost model. This model took influent flow rate， turbidity， temperature， pH， and oxygen consumption as inputs， and coagulant dosage as output. The model was then trained and tested through 20 repeated experiments using the production data from a water plant in Guilin. The results indicate that the Improved Sparrow Search Algorithm optimized XGBoost model achieved an average root mean square error （RMSE） of 0.4895 mg/L and an average coefficient of determination （R2） of 0.893， validating the model's high predictive accuracy and stability.

Aiming at the current problems in the detection of surface defects in cigarrete such as single detectable type， slow inspection speed and low accuracy， a machine vision based cigarette defect inspection method was designed. The category variance of the maximum inter-class variance method （OTSU method） is used as the judgment basis to calculate the optimal segmentation threshold， and its edge is optimized in combinaton with the Canny operator. After the region of interest （ROI） is divided， the morphology-based Blob region analysis algorithm is utilized to extract defects and determine whether to eliminate them. A cigarette defect detection system is constructed as well using a conveying module， an image acquisition module， a logic operation module and a removal module. Experimental verification shows that under the actual processing conditions， the system can effectively detect defects such as creases， damages， dirt， misaligned teeth on tipping paper， and bursting on the surface of cigarettes， with an accuracy rate of up to 98%， which can meet the online detection requirements of industrial production lines.

While the adoption of two-vector model predictive current control （MPCC） by the doubly-fed induction generator （DFIG） extends the range of alternative voltage vectors compared with that of a single vector MPCC， the number of rolling optimization times is doubled， resulting in an increase in computational complexity and thus affecting its practicability. To address this problem， a two-vector based fast predictive current control （FPCC） is proposed on the basis of the two-vector MPCC strategy. Firstly， the voltage plane is divided into six sectors by the active voltage vector and the synthesized voltage vector trajectory， while each sector is further divided into six sub-regions； Secondly， by judging the sector where the reference voltage vector is located， the first and second optimal voltage vectors for the next step are selected； Finally， the new voltage vector is synthesized to realize the operation control of the system. Simulation and experimental results show that the proposed method maintains the effectiveness of two-vector MPCC while significantly reducing the control complexity.

The improvement in people’s living standards directly leads to an increase in the volume of household garbage， making the classification of garbage crucial for the reuse of recyclable resources and the management of environmental pollution. This paper proposes a garbage recognition and classification method based on YOLOv5s， and addresses the diverse characteristics of garbage， including its various types， sizes and shapes. The algorithm is enhanced in three key areas： attention mechanism， network structure and loss function. SE attention mechanism is added to the network to address the issue of low accuracy in the YOLOv5s model， while an efficient BiFPN structure is also brought in to improve the model’s recognition capabilities for various scales of garbage. Additionally， SIOU_Loss is used as the boundary box loss function to enhance the model’s ability to recognize garbage targets in case of occlusion and to accelerate the training speed. The performance of the improved model is verified by a self-made data set containing 2，000 images and 2，291 target boxes. The experimental results indicate that the improved YOLOv5s network model increases recognition precision of garbage by 7.71% and the recall rate by 9.69%， which can provide reference for the related research on the recognition of garbage classification by robot..

Using the actual production data of sintering machine of an iron and steel enterprise， a prediction model for the low temperature reduction degradation index （RDI） of the sinter was constructed based on the one-dimensional convolutional neural network model. Firstly， the actual production data was preprocessed， and the optimal feature parameter combination was then determined by random forest feature selection and K-fold cross-validation. Secondly， the structure and parameters of one-dimensional convolutional neural network were trained and adjusted， while the final one-dimensional convolutional neural network prediction model was established. Finally， compared with the prediction results based on MLP neural network， linear regression model and random forest model， the one-dimensional convolutional neural network model has a better performance in the average relative error and hit rate of the prediction. In order to further improve the prediction accuracy of the model， Markov chain was adopted to correct the prediction results. The goodness of fit R2 of the final model reached 0.8478 and the hit rate reached 94.7% within the error range of 2.5%， basically achieving the purpose of real-time prediction of the sinter RDI.

Aiming at the problem of low accuracy of traditional visual inspection of the magnetic tile surface defects， an improved YOLOv7 recognition method for the magnetic tile surface defect is proposed. Firstly， the limited defect image data is augmented using the image processing technology， while the surface defect data set of magnetic tile was made. Secondly， CIoU in the original YOLOv7 model was replaced by WIoU to enhance the attention to the common quality anchor frame. Meanwhile， the global attention module GAM is added to the main part of the model so that the model can capture the important features of the image in multiple dimensions and improve the detection accuracy. Then， the parameter count and computational load of the improved model are reduced via partial convolution PConv. Finally， through experimental comparison and analysis， it is shown that the average accuracy of the improved model is 4% higher than that of the original model， the recall rate is 10.4% higher， and the single image processing time is 52.6 ms. At the same time， when compared with several current mainstream algorithms， the improved YOLOv7 model is shown to have a better effect and greater application value for the magnetic tile surface defect detection.

Aiming at the difficuties in measuing the amount of coal powder discharged from the traditional ball mill system， a prediction model is established for the powder output of ball mill based on the long and short term memory neutral network （LATM） in order to accurately predict the amount of powder discharged from the system and reduce the model error， while a modelling prediction method based on the improrved Whale Algorithm （WOA） to optimize the LSTM neural network is proposed. Firstly， each operating parameter of the coal mill is screened and processed and the model inputs is determined to improve the integration efficiency of the raw data， and secondly， the improved whale optimization algorithm is applied to optimize the LSTM structure and construct the IWOA-LSTM prediction model， and at the same time， a comparative experiment with the LSTM model， the WOA-BP and the WOA-LSTM model is conducted to verify the results. The study illustrates that the IWOA-LSTM prediction model has the highest accuracy with an average relative error of only 1.564%.

Underwater garbage identification is crucial to the cleaning work carried out by underwater robots. In this paper， an underwater garbage recognition algorithm based on YOLOv8-MHSA-DCN is studied. Aiming at the low quality of underwater image caused by complex underwater lighting environment， a multi-scale fusion based underwater image enhancement algorithm is proposed， which combines the white balance algorithm and gamma correction algorithm to improve the underwater image quality. In terms of recognition algorithm， MHSA attention mechanism is added based on Transformer network to enrich feature and semantic information. Common convolution is replaced by deep deformable convolution in backbone network to enhance feature extraction capability. The experimental results on the data set show that the average detection accuracy of the proposed algorithm is increased from 81.8% to 83.3%， and the calculation time is only 5.6ms， indicating a better comprehensive performance than the original model.

To address the shortcomings of the detection methods for the surface texture defects of existing industrial products， such as low detection accuracy and large model volume， a method based on multi-scale residual feature matching is proposed. Firstly， the VGG16 model is used as the backbone network to establish a two-branch model composed of the actual feature extraction network and the normal feature reconstruction network， then the model is modifies by pruning the redundant network layers and incorporating a multi-scale residual feature extraction module. Subsequently， the method uses the transfer learning strategy to enable the model to obtain good initial feature extraction capability. Finally， the method fine-tunes the model with outlier sample enhancement and dynamic learning rate to obtain the best texture defect detection model. The experimental results show that the proposed method can effectively achieve the multi-scale residual feature extraction and the matching of texture defect images， so as to achieve high detection accuracy in the texture defect detection. Meanwhile， the proposed model has a small footprint， which is beneficial for practical engineering applications.

Aiming at the problems of slow search speed， low tracking accuracy and large steady-state oscillation in the conventional perturb and observe algorithm， a observation method for fuzzy variable step disturbance is proposed based on black widow optimization algorithm to construct the maximum power point tracking control system of microbial fuel cell and realize the maximum power point tracking control of microbial fuel cell. The black widow optimization algorithm conducts real-time optimization of the main parameters of the fuzzy logic controller to improve the control accuracy and adaptability of the fuzzy controller； The fuzzy controller adjusts the search step of the perturb and observe algorithm in real-time based on the changes in power and voltage， and the PWM regulator adjusts the duty cycle of the Boost converter according to the output instructions of the fuzzy controller， so that the internal and external resistances of the microbial fuel cell achieve real-time matching. The simulation results show that the maximum power point tracking speed of the fuzzy variable step perturb and observe algorithm based on the black widow optimization algorithm is 71.7% faster than that of the conventional perturb and observe algorithm and 64.6% faster than that of the variable step perturb and observe algorithm. Meanwhile， the tracking error is reduced by 5.9% compared with that of the conventional perturb and observe algorithm and 5.0% compared with that of the variable step perturb and observe algorithm. The improved fuzzy perturb and observe algorithm based on black widow optimization algorithm greatly accelerates the tracking speed， improves the tracking accuracy， reduces the steady state oscillation， and can effectively weaken the impact of interference. It is an effective method to achieve maximum power tracking of microbial fuel cells.

Under the guidance of the carbon peaking and carbon neutrality goals， China is building a new power system mainly based on renewable energy. As frequent peak and frequency adjustments of coal-fired units have become the norm， studying the energy consumption characteristics in the process of variable load transients is of great significance in maintaining the economic and stable operation of the new power system. This paper studied and established an analysis model for the variable load transient process of coal-fired units. It analyzed the energy consumption characteristics of a coal-fired unit in the variable load transient process under multi-interval and multi-rate， and revealed the fundamental reason for the change of energy consumption of the unit. The results show that when the unit is under variable loads within different load ranges， the increase in energy consumption during the load increasing process is jointly caused by the increase in exergy loss and the increase in exergy storage. During the load decreasing process， the increase in energy consumption is partly offset by the release of exergy storage， and the increase rate of coal consumption is significantly reduced. For example， when the load interval is reduced from 90% to 100% THA to 50% to 60%， the coal consumption rate increases by 14.2 and 7.8 g•（kW•h）-1 in the load up and load down process respectively. Therefore， the feed-forward coal feeding rate by varying the load interval during the load increasing process is conducive to improving the unit control effect. The higher the load change rate of the unit is， the higher the coal consumption rate will be during the load rise， while the coal consumption rate will be lower during the load fall. The reason for the above phenomenon is that the exergy storage changes in the opposite direction during load up and down. In addition， during the load decreasing process， the faster the exergy storage is utilized， the more the exergy loss will increase. Therefore， it is necessary to adjust the operating parameters through the distribution law of exergy storage release to reduce the exergy loss. In conclusion， the change in exergy storage is the key factor causing the difference in energy consumption during transient processes， and it is necessary to optimize the unit’s operating strategy using the change in exergy storage to improve the unit’s control effect. The above conclusions provide technical guidance for the unit’s transient operating strategy.

Different from the single quality attribute or cost attribute evaluation method which was often used to select the optimal scheme of rapid prototyping manufacturing， an attribute evaluation method which took into account the distance-shape factor was put forward， and the optimal scheme selection of rapid prototyping manufacturing was realized based on the new method of combining the resource and environment attributes of RP manufacturing process. Firstly， the evaluation system of a resource and environment attribute suitable for the RP manufacturing process was established， and the quantifiable indices was proposed. Secondly， a calculation model of attribute involving the distance-shape factor was established with the comprehensive entropy weight method being used to avoid the subjective and objective tendency of index weight. The TOPSIS method was combined with the grey correlation analysis method to avoid the disadvantages of traditional single method that emphasized merely the sequence distance analysis or the sequence shape analysis. Finally， taking the SLA rapid prototyping process as an example， the calculation model was used to evaluate the five manufacturing schemes of flange parts with different placement angles under the same working condition. The experimental results show that the model is feasible and practical， and can be used for comprehensive evaluation and decision of resource and environment attributes in the RP manufacturing process.

To realize the integration of airworthiness requirements and product development process of aviation elastomeric sealing structure， and to support the airworthiness design， the construction method for the airworthiness domain knowledge model of sealing structure is proposed. Firstly， the airworthiness requirements of sealing structure throughout the lifecycle were extracted from CCAR， advisory circulars and industry standards， and the knowledge composition and relationship in aviation sealing structure airworthiness domain were analyzed. Then， based on the system engineering of civil aircraft and system development， the airworthiness design process of sealing structure was established. On this basis， the framework of the airworthiness domain knowledge model was defined， and the airworthiness domain knowledge model was constructed using the “seven-step method + protégé software”. The example illustrates that the proposed method can support the rapid generation of airworthiness certification basis of sealing structure as well as the airworthiness design effectively.

With the rapid development of the chemical fiber industry in recent years， the degree of automation in logistics packaging has been increasing， while the demand for automatic packaging production lines of the enterprises has also been growing. Therefore， it is particularly important to improve the production output and efficiency of automatic packaging systems. With the wire pushing mechanism of the silk boxes being the core equipment in the chemical fiber automatic packaging system， it’s operational efficiency， reliability and convenience hence determine the operating efficiency of the automatic packaging line. It is， therefore， of great significance to investigate the control and scheduling of the pushing mechanism. This article provides a comprehensive summary of the process flow of the outbound and the online operations of silk boxes based on the characteristics of wire pushing mechanism operation，and details the key parameters as well as the control and scheduling process of the pushing machine. The optimization of the operation rhythm of the pushing machine has been achieved and the packaging on-line volume of the automatic packaging system has been increased with the optimization of motion in both the first and the last layer of silk ingots and the adding of the position verification function. It lays a solid foundation for highly efficient operation of the whole process of the automatic packaging system.

In this paper， a high-frequency electromagnetic noise suppression strategy for automotive electronic water pumps is proposed based on an improved random carrier space vector pulse width modulation （RCSVPWM） method. Firstly， a new random sequence generator is designed with the Xorshift algorithm as the core to generate random numbers with better uniformity to increase the weakening effect on the high-frequency harmonic amplitude. Secondly， a sawtooth wave periodic function is combined to disperse and concentrate a large number of harmonics appearing at the carrier frequency and its integer multiples. Thereafter， a multi-platform simulation model of automotive electronic water pump is built to compare and analyse the harmonic suppression effects of SVPWM， RCSVPWM and the improved RCSVPWM control strategy， and to verify the suppression ability of the improved RCSVPWM control strategy on high-frequency harmonics. Finally， the experimental platform of the automotive electronic water pump is built， where the operation results of the electronic water pump under the three control strategies are mutually verified with the simulation results. The harmonic expansion factor is reduced by 2.35， and the suppression effect of high-frequency harmonic amplitude is improved by 10.39%. It is verified that the improved RCSVPWM control strategy can significantly improve the high-frequency electromagnetic noise of the automotive electronic water pump without affecting the original control system.

When the hook mass or the cable length between the load and the hook becomes non-negligible， the double pendulum effect that may cause on the bridge crane can lead to the performance degradation of all control methods based on the single pendulum assumption. For this reason， an adaptive bounded tracking control method based on trajectory planning for double-swing bridge cranes is proposed in this paper. Firstly， an S-shaped curve is planned as a displacement reference trajectory， and a swing suppression link is introduced into the trajectory to define a new coupled system localization error term. After that， a new energy storage function is constructed based on the total energy of the system， and a swing suppression equation is derived and established on this basis to design the adaptive tracking controller. The introduced error constraint function ensures that the system coupled tracking error is always within the set upper and lower boundary conditions. Finally， the stability of the system is proved using Barbalat's theorem and Lyapunov's method. A series of simulations and experiments show that the method can not only accurately drive the cart to reach the specified position， but also effectively inhibit the oscillation of the load and the hook， and is robust to the parameter changes of the bridge crane and external disturbances.

A modified Smith-IFT control method is proposed to tackle the problem of model uncertainty or disturbances in the Smith compensation control method for first-order time-delay systems in industrial processes. The Smith controller C_{\mathrm{1}} is first designed by optimizing the {\mathrm{H}}_{\mathrm{\infty }} norm of the sensitivity function using the process model to enhance the output performance of system. Meanwhile， The iterative feedback tuning （IFT） method is adopted to design the data-driven controller C_{\mathrm{2}} and update the estimated model and controller C_{\mathrm{1}} to reduce the influence of model uncertainty or disturbances on the system. Taking pulp concentration control in an actual industrial process as an example， the simulations verify the effectiveness of the proposed method. Compared to the other two methods， the modified Smith-IFT control method has advantages such as small overshoot， short settling time and strong robustness.

The traditional direct torque control （DTC） tends to be susceptible to some factors such as speed overshoot， large torque ripple and poor anti-interference during the startup and the sudden load of the heavy duty AGV （automated guided vehical）. In order to improve the control performance of direct torque control for heavy-duty motors， a direct torque control strategy was designed based on the linear active disturbance rejection combined with improved sliding mode control. Firstly， an improved linear active disturbance rejection controller was adopted to replace the PI control structure to address the instability of speed tracking in the traditional PI control speed loop. Secondly， an original synovial controller was improved to tackle the problem of large torque ripple in the torque loop under heavy load. Finally， the control model was built by Matlab/Simulink， and the improved fusion control strategy was verified to reduce the torque and flux fluctuation of heavy-duty AGV motor by 56.12% and 21.8%， respectively. The speed fluctuation under sudden load was reduced by 50.2%， and the speed overshoot at start-up was eliminated， all of which improved the ride comfort of heavy-duty AGV.

Aircraft surface damage is one of the important hidden dangers threatening the flight safety. In order to ensure the continuous airworthiness， airline and periodic inspections of aircraft surface are required. At present， most of the inspection tasks still rely on manual visual inspections， which， however， does create problems such as low operational efficiency， poor safety， difficulties in ensuring inspection coverage ratio， strong subjectivity， misdiagnosis and missed detections. To solve these problems， an intelligent UGV （Unmanned Ground Vehicle） system that can automatically collect fuselage surface images is designed， and a CPP （Coverage Path Planning） algorithm that can cover a specified area is particularly proposed. The UGV is equipped with the functions of autonomous map building， localization， global path/local trajectory planning， and automatic control of elevation height and PTZ（Pan-Tilt-Zoom Camera）， etc. According to the planning results of CPP for UGV position， elevation height and PTZ， the whole system is able to operate automatically. The CPP algorithm is designed to satisfy the photographic constraints such as shooting distance， shooting inclination and overlap rate， while the acquired aircraft skin images can be used for image stitching and damage identification. In order to solve the problem of high center of mass caused by the introduction of elevation device， the CPP algorithm adopts the viewpoint projection merging strategy and designs a safe and stable motion mode. The simulation and experimental results show the effectiveness of the proposed scheme， which can not only realize the full coverage of the designated fuselage area but also ensure the quality of the captured images.

Gripping and handling robots are widely used in manufacturing sector. In order to mimic the bending envelope grasping behavior of elephant trunks in nature， a modular elephant trunk-imitating robot based on the combined drive of cable and shape memory alloy springs was designed. The structural design of the elephant trunk robot and the bending deformation regulation mechanism were described. The positive kinematic model of the elephant trunk robot was established based on the D-H method， and the Monte Carlo method was used to derive the workspace of the robot. The kinematic simulation analysis of the elephant trunk robot was carried out by using Adams， adjusting the size of the tensile spring stiffness between the modules， and verifying the feasibility of the elephant trunk robot as a holding mechanism through the simulation results of the opening and closing end tension angle. A prototype elephant trunk robot was built to complete bending experiments under different tension mappings of spring equivalent stiffness， and the polymorphic bending envelope performance of the elephant trunk robot was verified.

To address the issues of low efficiency， excessive redundancy， and the inability to dynamically avoid obstacles in indoor robot path planning when using the A^* algorithm， this paper proposes a fusion algorithm that combines an optimized A^* algorithm with the dynamic window approach. The proposed algorithm enhances the heuristic function by incorporating the distance from the parent node to the target node. It quantifies the obstacle information to dynamically adjust the weights of the heuristic function using an obstacle rate function. Additionally， it introduces a cost for turning to reduce unnecessary turns in the path and designs a strategy to remove redundant points， ensuring a globally optimal static path. It incorporates the offside angle， flexibly selects the key nodes of the A^* algorithm as local target points within the dynamic window to avoid the path getting trapped in local optima. The experimental results demonstrate that the improved fusion algorithm enhances the search efficiency， reduces the path length， resolves the issue of the dynamic window approach being trapped in local optima and enables the real-time obstacle avoidance.

Performing defect detection on power transmission and transformation equipment has become an important part of maintaining the stable operation of the power grid. Despite significant advancements in deep learning methods for defect detection in power equipment， there still exists challenges of a few-shot as the result of the scarcity of defect samples. To address this issue， a few-shot defect detection network for power transmission and transformation equipment is proposed based on meta-learning to improve the defect detection accuracy of power transmission and transformation equipment. The network utilizes DarkNet-53 as the backbone network of the detection framework and introduces multiple feature enhancement modules， including global information extraction， channel attention and cross-stage feature fusion to improve the backbone of the network and enhance the processing capability of data. By splitting the training set into support sets and query sets， a two-stage training process is conducted based on meta-learning algorithms. The meta-learning algorithm optimizes the parameter update strategy during the training stage to tackle the few-shot learning problem. The experimental results demonstrate that this method achieves a mean average precision （map） of 0.51 at IoU threshold 0.5， showing a significant improvement in the defect detection for power transmission and transformation equipment compared to the existing mainstream methods.

In order to improve the accuracy of fault diagnosis， a fault diagnosis model of coal mill is established on the basis of support vector machine （SVM）， while a fault diagnosis method based on whale algorithm （WOA） optimization support vector machine is proposed using the principal component analysis （PCA） feature selection. Firstly， the principal component analysis is used to extract features from the fault parameters of the coal mill to reduce the dimension of high-order raw data and to improve the efficiency of data classification and integration. Secondly， the whale optimization algorithm is used to optimize the parameters of the support vector machine， obtain the optimal model parameters and construct a fault diagnosis model. At the same time， the comparison of the particle swarm algorithm with the genetic algorithm optimization model is conducted for experimental verification. The results show that the classification accuracy of the WOA-SVM model is the highest， which can realize the accurate diagnosis of coal mill system faults in a short time， providing a practical reference for coal mill fault diagnosis.

Detection on bolt loosening is crucial for the safety of the normal operation of mechanical equipment or mechanical structure. There are drawbacks existed in the current visual detection methods of bolt loosening such as low efficiency and poor practicability and also certain limitations in the detection of mechanical systems with complex bolt distribution. Based on this situation， a visual detection method of multi-directional bolt loosening is proposed. For the visible front orientation of the bolt， the parameters of the camera are first calibrated， and the image captured by the camera is then converted to the HSV color space after being preprocessed such as filtering and noise reduction， The threshold of the specific channel is adjusted to obtain the ROI region， and finally，the rectangular ROI region is fitted into a straight line by the least squares method， and the slope of the line is converted into the bolt loosening angle to realize the detection of loose angle in the range of 0~180°， with the maximum absolute error being 0.98°. For the visible orientation on the side of the bolt， the ROI area is obtained after the image is processed， and the minimum external rectangle of the ROI area is then obtained by the rotation jamming method， Thereafter， the width of the minimum external rectangle is detected and the pixel width value is converted into the actual width value of the marking belt， which is then integrated into the conversion model of the marking belt length and the rotation angle of the bolt to obtain the actual bolt rotation angle， achieving the loose angle detection in the range of 0~300°， with the maximum absolute error of 3.59°， the minimum measurement absolute error of 0.2° and the average error of 1.77°. It can realize the non-contact detection of bolt angle during the working process of mechanical system， improve the detection efficiency and indicates a strong engineering applicability.

In order to improve the efficiency of cargo space allocation in automated warehouse and ensure its safe and stable operation， a cargo space optimization model is established to improve the efficiency of warehouse entry and exit， shelf stability， cargo correlation and surplus value of goods. The analytic hierarchy process is used to transform the multi-objective problem into a single-objective problem. An improved imperialist competitive algorithm is proposed for this model. The algorithm combines the advantages of imperialist competitive algorithm and genetic algorithm and designs a dynamic adjustment revolution rate formula and a natural disaster operator to enhance the diversity of colonies and empires. The experimental results show that the improved imperial competition algorithm has better convergence and search range， and effectively solves the problem of location allocation of different scales. The accuracy and stability of the solution are better than those of particle swarm optimization， genetic algorithm and standard imperial competition algorithm. It provides a theoretical basis and practical reference for improving the competitiveness of fast-selling enterprises and the efficiency of warehouse entry and exit.

To reduce the impact of temporary order insertion on the maximum completion time and the delivery time of flexible job shop scheduling through batch scheduling of jobs， a mathematical model for flexible job shop dynamic scheduling considering batch production is first established. Second， a three-layer chromosome coding scheme based on machine， process and batch is proposed. Then， a rescheduling method is used for the order insertion event. Finally， three local search neighborhood operations and the addition of nondominated sorting genetic algorithm in the selection operator are proposed to improve the optimization ability of the Memetic algorithm， and the improved Memetic algorithm is used to solve the model. Through the comparison of 6×8 examples， the maximum completion time of batch scheduling is reduced by 28.03% compared with that of non-batch scheduling， and the value of the early/delayed penalty function is reduced by 26.62%. Batch scheduling can effectively reduce the impact of job processing on the maximum completion time and the delivery time， with the optimal batch quantity of job being 2~3 batches.

在共享经济时代，物流服务行业建立稳定的联盟至关重要。因此，在物流企业联盟内部，如何确保公平的收益分配成为一大挑战。然而，由于联盟存在信息不完备的情况，一些传统的合作博弈单值解，例如Shapley值，可能并不适用于该问题，这些解法更适用于联盟的总体收益和各博弈者的参与水平都能够被准确估计的情形。研究了信息不完备的物流企业联盟的收益分配问题，并提出了一种相关的收益分配模型。为了证明所提出模型的适用性，进行了一个案例研究。结果表明，模糊Shapley值能合理地提升多方合作后的各方收益，有效地分配物流企业联盟的收益。

产业互联网是站在整个产业链视角，充分运用沉淀的产业数据，进行痛点分析和资源匹配，向全产业提供普惠式的综合服务和基础设施建设，提升产业链供应链的稳定性和现代化。在产业链发展中，上下游企业以及企业与终端用户间的业务对接和协同已经成为了一个关键问题。而传统的信息检索方式由于存在检索方式单一、检索内容固化等原因难以有效适应产业链中对相关产品和业务的个性化检索和信息咨询需求。随着人工智能的发展，智能客服已经成为一种新的解决产业链个性化信息检索和咨询问题的关键技术手段。然而，传统智能客服的适应能力相对有限，有时对特定问题的回答还需要在系统中进行预先定义，对于不断变化的业务语境难以灵活应对。为了提供一种面向产业链上下游企业的可以灵活适配目标企业业务需求的智能客服系统，提出了一种基于大语言模型（LLM）的领域自适应智能客服构建方法，该方法利用开源的大语言模型，使得中小企业可以针对具体下游任务进行垂域适配，结合私域知识库搭建低成本高质量的客户服务，同时也能避免敏感数据泄露问题，从而能够解决产业互联网中灵活地智能客服系统构建问题，降低产业互联网中中小企业低成本系统构建需求。

A scheduling energy-saving model aiming to minimize the completion time and the effect of total machine load on overall energy consumption is developed to reduce the impact of two dynamic disturbance events such as emergency order insertion and machine failure on energy consumption in the aluminum pot production workshops. To address the limitations of the Non-dominated Sorting Genetic Algorithm II （NSGA-II） in terms of search efficiency and Pareto solution quality for multi-objective optimization problems， an Improved Non-dominated Sorting Genetic Algorithm II （INSGA-II） is introduced. The INSGA-II algorithm incorporates double chain coding and fast non-dominated sorting techniques to enhance convergence. The initial Pareto solutions are considered as an external elite set， which is continuously updated through multiple cross mutations and multiple fast non-dominated sorting iterations to improve the quality of the Pareto solutions. The effectiveness and superiority of the INSGA-II algorithm are validated through examples and algorithm comparisons.

Accurate prediction and judgement of wind turbine operation status can provide the early warning of faults， maintain the stable operation of the wind turbine， realize the reasonable scheduling of wind power and guarantee the stability and the safety of power production. In this paper， a wind turbine state prediction method with multi-sensor data fusion is proposed. First， the processing and feature extraction methods of wind turbine multi-sensor data are studied， the multi-sensor data are cleaned by applying the quaternion method and Relief-F algorithm， and the multi-sensor data source is selected based on the data weights. Second， an information fusion algorithm based on BP Neural Network and D-S evidence theory is designed and verified in MATLAB， and the accuracy of wind turbine state prediction is 80.35% and 78.72% respectively. Next， based on the idea of two-layer fault-tolerant data fusion， the D-S evidence theory method is improved. The multi-sensor data fusion algorithm based on FTDF-TCR is designed and verified by applying the same sample dataset. Finally， as verified by experiments， the accuracy of wind turbine state prediction is 89.36%， an increase by 9.01% and 10.64% respectively， compared with that of the original algorithm， which shows that the accuracy of prediction has been effectively improved.