Since the 21st century, new quality productivity driven by innovation in information and network technologies has become a fundamental engine for industrial upgrading and high-quality economic development. Advanced productivity has shifted from traditional factor-driven models to systemic innovations characterized by digitalization, intelligence, and green development, underpinned by new infrastructure and a highly skilled workforce. Cultivating new quality productivity is a systems engineering spanning technology, industry, institutions, and society, requiring innovation-driven development and deeper reform. Through the combined momentum of policy guidance and market mechanisms, it is crucial to accelerate the integration of digital technologies with the real economy. Meanwhile, it is necessary to strengthen the government's institutional provision and public-private partnership collaboration, build an open and mutually beneficial global innovation ecosystem, and also pay attention to the social impact of technological changes, ensuring social harmony through education dissemination and skill reformation. Only through the concerted efforts of multiple parties can a solid modern industrial system be built and the stable and long-term development of the economy and society be achieved.

Corresponding to the process of industrial development, design has entered the 4.0+ era. As an important part of it, innovative design 4.0+ has become an urgently needed innovative design method for enterprises. Based on the background of normalized changes and the characteristics of design 4.0+, this paper firstly proposed that the innovative design 4.0+ should be a high-level innovative design. However, achieving this type of design had the two challenges of defining goals and agile innovation, while also meeting market-driven enterprise needs. To address the above challenges and demands, based on the analysis of typical literature on invention classification in TRIZ (Teoriya Resheniya Izobreatatelskikh Zadatch), it was proposed that the goal of innovative design 4.0+ was to create market-driven high-level inventions and innovative products. Then, the agile invention process in C-TRIZ was introduced into the design process to achieve agile innovation design, and high-level innovative opportunities were determined by establishing a change-driven innovation opportunity identification model. Finally, a market-driven agile innovation design process model was constructed, and an AI-assisted cross-domain knowledge search path for the high-level invention process in the model was proposed. The market-driven agile innovation design process model is an innovation design 4.0+ process model, providing enterprise designers with a feasible solution for design process innovation.

To implement effective prevention and control of hazards faced by cranes at the source of design, it is necessary to address core issues existing in active bridge cranes, such as incomplete hazard source identification, lack of quantitative assessment systems, and limitations of risk assessment models. Therefore, a hazard level assessment method for bridge cranes based on FFT-BN (fuzzy fault tree-Bayesian network) model is proposed, and a dedicated system platform is developed. Focusing on the structure and components of bridge cranes, a refined hazard source identification process was established through systematic failure analysis to achieve comprehensive coverage of potential risks. An expert evaluation quantitative system was constructed, standard quantitative indicators were designed, and hazard sources were quantitatively characterized. A hazard level assessment model based on FFT-BN was proposed, which combined the failure logic analysis capability of FFT with the uncertainty reasoning advantage of BN, to achieve dynamic quantitative assessment and level classification of complex risks while improving the model accuracy and efficiency. A dedicated hazard level assessment system platform for bridge cranes was developed, realizing the intelligent innovation of the assessment process and significantly improving the application efficiency in engineering practice. Taking the in-service QD40 t-22.5 m-9 m general bridge crane as an example, the engineering feasibility and scenario applicability of the proposed method were verified, providing effective solutions and tool support for the improvement of equipment intrinsic safety and active prevention of accidents.

In response to the problems of low efficiency in manual detection, poor reliability of sensor monitoring, and scarce label data in the wear monitoring of hobs of the full section tunnel boring machine, a prediction method based on graph Laplacian regularization (GLR) deep learning model was proposed. Based on a high-altitude tunnel excavation project, an efficient data preprocessing system was constructed: the excavation cycle dynamic extraction method was used to accurately identify and eliminate non excavation and empty push section data, the quartile method was used to eliminate outliers, the SG (Savitzky-Goloy) filtering for noise reduction was combined, then the data quality was improved. Furthermore, By integrating GLR and deep learning technologies, a data manifold structure was constructed using k-NN (k-nearest neighbor) graph. The Laplacian matrix was used to constrain the smoothness of adjacent sample prediction, generating pseudo-labels with high confidence to expand the training set, and combining with long short-term memory (LSTM), deep neural network (DNN) and convolutional neural networks (CNN) to develop three prediction models: GLR-LSTM, GLR-DNN, and GLR-CNN. The experimental results showed that the GLR-LSTM model had the best prediction performance. Compared with traditional small-sample machine learning methods such as ridge regression, support vector regression and gradient boosting regression tree, the prediction accuracy of the GLR-LSTM model improved significantly. This method can accurately predict the real-time wear rate of the hobs by only requiring the operating parameters such as the TBM cutterhead torque and the total propulsion force, providing technical support for reducing the opening inspection and optimizing maintenance decisions.

Aiming at the problem of weld defect images affected by high noise and interference fringes in TOFD (time of flight diffraction) ultrasonic detection technology, as well as the challenge of feature information loss and computational efficiency imbalance faced by current deep learning models in processing such images, an innovative defect identification model integrating convolutional neural network (CNN) and Transformer architecture is proposed, named MHLFNet (multi-scale high-low focused network). By introducing a multi-scale feature fusion (MSFF) module, this model significantly enhanced the ability of capturing local information. At the same time, a high-low focused linear (HLFL) module was designed, which used the adjustable allocation ratio to dynamically allocate the attention for the high and low frequency information of feature images, and adopted focused linear attention instead of traditional multi-head self-attention, effectively reducing the computational complexity while enhancing the diversity of attention mechanisms and the feature expression ability. In order to verify the performance of MHLFNet, a TOFD weld defect image dataset was constructed, and a systematic experimental evaluation was conducted. The results showed that MHLFNet achieved an accuracy of 98.6% in the weld defect identification task, and performed excellently in terms of model parameters, floating-point operations, and inference time. In visual analysis and identification validation, MHLFNet demonstrates excellent identification capabilities for high-risk defects (such as lack of fusion and cracks), proving its reliability and engineering value in industrial inspection.

In response to the requirements of biomimetic swimming propulsion technology, a frog-inspired swimming robot based on a bistable structure is designed. By constructing a linkage-spring-cable composite mechanism, a bistable actuator with rapid energy transition characteristics was proposed. The device utilized an elastic deformation energy storage mechanism of springs to achieve millisecond-level switching between stable states under periodic torque input, generating efficient propulsion through hydrodynamic reaction forces via paddle blades. Subsequently, through ADAMS dynamics simulations and prototype experiments, the influence laws of key parameters such as the paddle blade swing angle amplitude, the spring stiffness, and the spring compression on the response ability and motion performance of the robot were analyzed. Results showed that increasing the stiffness of the main spring k? could enhance the robot's energy storage and release capabilities, and enhance the swing output force, enabling the robot to achieve an average propulsion speed of 43.33 cm/s (1.73 body lengths per second) within 0.3 s after activation. Increasing the compression of the spring k₂ could improve propulsion force, with experiments recording a maximum instantaneous propulsion force of 2.14 N (2.58 times body weight). Tests demonstrated that when the actuation cycle of the bistable actuator was 0.5 s, the robot achieved a stable swimming speed of 22.5 cm/s. The designed bistable actuator provides a new paradigm for high-power-density actuation in biomimetic underwater robots, enhancing the application potential of robots in disaster rescue and ecological monitoring fields.

This study aims to address the challenge of rapid and accurate localization of brick plastered surfaces under partial occlusion caused by mud splashes in construction environments. A stereo-vision-based localization method suitable for complex working conditions was proposed. The contour information of the plastered surface was extracted using the YOLOv11 instance segmentation model. By combining the contour features with the stereo depth images, a depth restoration model combining confidence-layered mapping and gradient prediction was developed to mitigate the loss of depth information caused by mud occlusion, as well as contour edge blurring resulting from stereo imaging. The restored depth map was then converted into a high-quality point cloud, from which the plastered surface three-dimensional space was precisely localized by extracting the corner points of the plastered surface through plane fitting and minimum bounding rectangle. The experimental results showed that under no occlusion working condition, the average localization errors in the X, Y, and Z directions of the plastered surface after depth restoration decreased by 17.8%, 16.1%, and 12.6%, respectively. Under mild and moderate occlusion working conditions, the average localization errors of the three axes decreased by 23.8%, 21.2%, and 25.1% respectively. When the occlusion rate was less than 25%, the maximum error was controlled within 5 mm, meeting the precision requirements for the robotic end operation. The proposed method has the advantages of rapid and accurate localization under the working conditions of multiple camera postures and moderate occlusion, showing strong potential for engineering applications and providing reliable visual perception technology support for automated plastering operations.

In high-precision assembly tasks, robotic arms need to avoid static obstacles. However, existing obstacle avoidance methods often interrupt the image-based visual servoing (IBVS) process, which affects task continuity and may induce motion oscillations, thereby reducing positioning accuracy. To address this issue, a real-time visual servoing obstacle avoidance method based on dynamic feature point remapping is proposed. This method constructed a multi-objective optimization model with field-of-view constraints using the geometric parameters of the smallest extensible cuboid of obstacles to solve an optimal avoidance path. Subsequently, the direction vector of the obstacle avoidance path was mapped into the image space, where guiding feature points were dynamically generated from real-time feature points to drive the IBVS controller to achieve obstacle avoidance. To improve motion smoothness, a path smoothing transition mechanism based on the Sigmoid function was designed, and an obstacle avoidance distance-driven adaptive gain function was introduced to dynamically optimize the system convergence rate. Finally, the global stability of the closed-loop system was proven through Lyapunov stability analysis. The axle-hole positioning experiments demonstrated that, under different initial poses, the method could generate feasible obstacle avoidance paths in real time, while the motion trajectories of the robotic arm end-effector and the feature points remained smooth without abrupt changes, achieving fast and high-precision convergence with an uninterrupted IBVS process throughout the task. The proposed method realizes a unified closed-loop integration of obstacle avoidance planning and IBVS control, which can provide a practical solution for real-time obstacle avoidance of robotic arms in high-precision assembly tasks requiring safety, continuity and accuracy.

Due to the frequent population movement and diverse travel demands within the urban, the metro passenger capacity is unevenly distributed. To explore the complex impacts of the randomness and imbalance of passenger capacity distribution on the economy and the availability of metro vehicle maintenance strategies, a multi-objective optimization method for component maintenance strategies considering dynamic passenger capacity is proposed. Firstly, an accelerated failure model was introduced to develop a component equivalent service life conversion method between random passenger capacity and baseline passenger capacity, leading to the construction of a component failure rate model under the influence of dynamic passenger capacity. Secondly, two maintenance approaches of primary maintenance and advanced maintenance were selected to construct a reliability evolution model for metro vehicle components under different levels of imperfect maintenance. Finally, considering the differentiated train downtime penalty costs caused by uneven passenger capacity distribution and using component maintenance cycles and types as decision variables for maintenance strategies, a maintenance model was established with maintenance cost and maintenance time as optimization objectives, and the optimal maintenance plan was solved using the genetic algorithm. Case study analysis showed that the proposed preventive maintenance strategy could coordinate the train downtime with passenger flow distribution, avoiding high downtime penalty costs caused by the maintenance tasks falling into high passenger flow intervals. Compared with the maintenance strategy that did not consider passenger capacity impact, it could reduce maintenance costs by 9.1% and shorten maintenance time by 4.1%. The research results can provide certain references for improving metro vehicle component maintenance strategies under the influence of relevant factors.

High-altitude wind power exhibits significant potential for large-scale exploitation. The gearbox, as a critical component of the land-based umbrella ladder type high-altitude wind power air-to-ground energy conversion device, has transmission stability and transmission efficiency as its critical performance indicators. An optimization method combining macro-parameter optimization and micro-geometric modification was proposed. In terms of macro-parameter optimization, based on NSGA-II (non-dominated sorting genetic algorithm-II), with transmission reliability and efficiency as the optimization goals, the design parameters of the gearbox were optimized for multiple objectives. Based on the optimized parameters, the transmission reliability and efficiency of the gearbox were analyzed using the Romax software. The results showed that both the transmission reliability and efficiency of the gearbox were improved after optimization. Furthermore, in response to uneven load distribution and edge loading on gear tooth flanks, a comprehensive modification strategy combining tooth profile modification and helix modification was proposed to optimize the gearbox at the microscopic level. After the comprehensive optimization of macro-parameter optimization and micro-geometric modification, the reliability of the gearbox increased from 96.353% to 99.473% and the transmission efficiency increased from 97.62% to 99.10% after 10 years of operation. This study provides theoretical support and technical reference for high-efficiency operation of the land-based umbrella ladder type high-altitude wind power air-to-ground energy conversion mechanism, and lays a solid foundation for the subsequent engineering application and operation.

Basalt fiber-reinforced polymer (BFRP) has excellent mechanical properties and melt-recyclability, with broad application prospects in automotive lightweight field. For the aluminum crashworthy device of a certain vehicle, a multi-objective optimization design of BFRP/Al hybrid crashworthy device is carried out. Firstly, mechanical tests were conducted on BFRP laminates, and a finite element model of the crashworthy device was established using HyperMesh software. Subsequently, training samples for the surrogate model were generated via Latin hypercube sampling. Key design parameters were identified through sensitivity analysis, and the prediction accuracy of the surrogate model for response indicators was enhanced by a space-filling sampling method based on the weighted Euclidean distance. Finally, with the objectives of minimizing peak load, total mass and maximum crossbeam displacement of the crashworthy device, the MOPSO (multi-objective particle swarm optimization) algorithm was employed to obtain the Pareto frontier, and the optimal design parameter combination was determined based on the entropy weight-TOPSIS (technique for order preference by similarity to an ideal solution) method. The results demonstrated that the optimized crashworthy device achieved reductions of 36.15% in peak load and 12.23% in total mass, exhibiting significantly improved crashworthiness while meeting the lightweight target. The proposed method can provide a systematic solution for the lightweight design of BFRP/Al hybrid crashworthy devices.

To address the high torque issue of gas control valves during opening and closing, a multi-factor analysis and structural optimization design study is conducted, and a low-torque optimization approach integrating topology optimization, response surface methodology, and non-dominated sorting genetic algorithm II (NSGA-II) is proposed. By establishing a theoretical opening/closing torque model of the control valve, it was clarified that mechanical friction torque was the dominant influencing factor, and the coupling effect of medium-induced force, spring preload, and Glyd ring compression ratio on torque and sealing performance was analyzed in detail. In the structural optimization process, the valve seat shape was reconstructed through topology optimization to reduce the effective medium-acting area and frictional resistance. Subsequently, a multi-objective optimization model with mechanical friction torque and leakage rate as objectives was constructed based on the response surface regression model, and the torque and sealing performance were simultaneously optimized by combining the NSGA-II. The experimental results showed that under a medium pressure of 5.2 MPa, the mechanical friction torque of the optimized control valve was reduced by 71.8%, validating the accuracy and feasibility of the proposed optimization approach. The research results provide a theoretical basis for high-performance design and localization of gas control valves.

To address the issue of low transmission torque in radial magnetic coupling variable stiffness joints, a variable stiffness joint based on axial magnetic coupling is designed. Firstly, based on a simplified magnetic pole unit model, an analytical formula for magnetic coupling torque was established to conduct the preliminary design of permanent magnet dimensions. Then, a three-dimensional simulation model of the permanent magnet and the yoke iron was established using Maxwell software, and the torque and axial magnetic force characteristics were analyzed by scanning load angle, air gap thickness, and changing the number of magnetic pole pairs. Finally, a variable stiffness joint prototype was fabricated using permanent magnets with appropriate structural parameters, and an experimental system was established. The torque and variable stiffness performance of the prototype were tested to validate the effectiveness of its design. The results showed that when the air gap thickness was less than 10 mm, the theoretical model and the simulation model were relatively consistent in the torque prediction, with a maximum relative error of 11.6%, while the maximum relative error of axial magnetic force was 13.7%. Increasing the number of magnetic pole pairs could significantly enhance the joint torque, but it would reduce the rotation range of the joint. In addition, due to the increase in leakage flux, the decay rate of the joint torque became faster as the air gap thickness increased. Experimental tests showed that the designed variable stiffness joint prototype achieved a torque transmission of 6.43 N·m and an active stiffness adjustment range exceeding five-fold, with a relative error between the actual operating data and the simulation results within 7.8%. The proposed method can provide theoretical support for the optimization design of axial magnetic coupling variable stiffness joints.

To address the problems of pressure pulsation, power fluctuation and flow instability caused by the trapped-oil phenomenon when Roots pumps transport high-viscosity hydraulic oil, it is necessary to clarify the trapped-oil mechanism and compare the differences in trapped-oil characteristics induced by different rotor profiles, and propose targeted mitigation measures, thus providing theoretical support for the structural optimization and working condition adaptation of Roots pumps. Firstly, according to whether there was a circular arc transition at the top and root of the rotor profile, it was divided into full working type (such as circular arc profile) and non-full working type (such as involute profile). At the same time, the rotor profile was constructed through parametric modeling, the key structural parameters were defined, and a unified mathematical equation was established. Then, the three-dimensional geometric model of the Roots pump was generated using Siemens NX software, and the CFD (computational fluid dynamics) simulation model was built by Pumplinx software to analyze the operating characteristics of the pump with hydraulic oil as the medium. Finally, square relief grooves were machined at the rotor root, and the differences in trapped-oil characteristics between the pumps with and without relief grooves were compared. The simulation results showed that significant trapped-oil phenomenon occurred in both types of Roots pumps: the maximum pressure increase of the pumps with involute and circular arc profiles was 113.3% and 68.7%, respectively, and no cavitation occurred. After opening the relief grooves, the instantaneous pressure fluctuation amplitude of the pump with involute profile was reduced by 29.5%, and the maximum pressure increase was decreased to 50.4%, whereas the average output flow rate was reduced by 1.2%. In summary, the trapped-oil hazard of non-full working rotor profiles was more serious, and the relief grooves at the rotor root could effectively suppress the trapped-oil phenomenon, but it would lead to a slight reduction in the output flow rate. The research results provide a theoretical basis and technical references for the engineering application of Roots pumps in the high-viscosity liquid transportation field.