In order to study the influence of different ash grade lubricants on the durability of gasoline particle filter（GPF）， the effects of ash accumulation on engine torque， fuel consumption， back pressure， filtration efficiency， gas pollutant conversion efficiency and oxygen storage performance were analyzed based on the rapid aging test. The results show that the higher the ash grade of lubricating oil， the higher the ash content in GPF and the higher the ash trapping rate. Ash accumulation will decrease the engine dynamic performance and the catalytic performance of GPF， but a certain amount of ash can improve the filtration efficiency of GPF and reduce PN emissions.

Aiming at the problems of long scheduling time， low scheduling accuracy， and large power consumption in the current method for electric vehicle charging and discharging coordinated scheduling， a method for electric vehicle charging and discharging coordinated deployment using adaptive genetic algorithm is proposed. Firstly, through the charging prediction of electric vehicles, the charging facilities of electric vehicles are reasonably planned, and on this basis, the relevant constraints are put forward to establish the charging and discharging cooperative scheduling model of electric vehicles. Then, the model was solved by adaptive genetic particle swarm optimization algorithm.Finally， through the calculation results， the coordinated charging and discharging scheduling of electric vehicles is completed. The experimental results show that the method proposed in this paper has short scheduling time， high accuracy and low power consumption when performing electric vehicle charging and discharging coordinated scheduling.

Most fault diagnosis algorithms for rotating machinery are labeled and need to be set manually， an unsupervised fault diagnosis algorithm with adaptive parameters for unlabeled fault data by introducing twice anomaly recognitions and clustering algorithms was proposed.The method extracts and selects signal features by improving empirical wavelet transform and Laplace score algorithm, and adopts the unsupervised method of quadratic anomaly identification combined with improved fuzzy C-means clustering for fault identification. Through the verification of the fault data of the rotor system of the electric spindle, the diagnostic accuracy of the proposed method can reach 93%. Compared with the traditional unsupervised diagnostic method, it has good accuracy and robustness.

Aiming at the problem that the unbalanced distribution of data in the fault diagnosis of rolling bearings will reduce the model's diagnostic ability， a 1DCNN with Width Kernel of First Layer （WKFL-1DCNN） is proposed. WKFL-1DCNN firstly uses a larger kernel in first-layer to extract fault features， and adds a BN（Batch normalization） layer after the alternate convolution layer to adjust the data distribution； then uses the Class-Balanced loss function instead of the Cross-Entropy loss function to offset the impact of the data imbalanced distribution on the network. Experiments show that the improvement method in this paper can effectively improve the performance of WKFL-1DCNN in unbalanced fault diagnosis， and its fault diagnosis ability is better than other comparison algorithms.

Aiming at the key problem of prediction accuracy of eddy viscosity model in Reynolds averaged N-S equations for separation flow， a data-driven machine learning algorithm based on artificial intelligence is developed to overcome the problem that the traditional eddy viscosity model relying too much on empirical parameters for the separation boundary layer flow prediction， and the accuracy of numerical simulation is improved. According to the physical mechanism affecting turbulence evolution， the eddy viscosity model calculation results of separation flow are taken as the baseline， several variables characterized by the average flow state are selected as the input variables， the high fidelity database is constructed based on the calculation results of high-order Reynolds stress model， and the six variables decomposed from Reynolds stress are taken as output variables， the mapping relationship and prediction model from baseline flow field data to high fidelity data constructed by random forest regression algorithm are established. The results show that， the prediction accuracy of the model for the separation flow is significantly improved.

Ultrasonic impact is used to process the welding seam of aluminum alloy 2024-T4 friction stir welding. The residual stress， microstructure， microhardness and fatigue performance of the specimens before and after ultrasonic impact were compared and analyzed. The results show， after ultrasonic impact treatment， residual compressive stress is introduced into the surface of the specimen， and the average residual compressive stress can reach 263 MPa. The surface structure of the sample is refined after ultrasonic impact treatment， the depth of the deformation layer can reach 50~70 μm， and the surface hardness is increased from 175 HV to 235 HV. The fatigue life of the specimens treated by ultrasonic impact are significantly improved. The fatigue life of the ultrasonic impact specimens are 1.72~2 times that of the unimpacted specimens， and the location of fatigue crack initiation is transferred from the surface to the subsurface below the strengthening layer.

In order to more accurately describe the car-following interaction behavior of adjacent vehicles on the road， a car-following model is constructed by using its similarity with the thermodynamic performance of some microscopic particles. Through the system similarity analysis, the vehicle is likened to a microscopic particle in a long and narrow pipeline, and the complex vehicle following interaction is simplified into a dynamic process in which the following vehicle constantly seeks to keep a safe distance from the preceding vehicle. The expressions of the safe distance under different conditions are established. Through the mathematical derivation of Lennard-Jones potential function which is suitable for the thermodynamic analysis of inert gas system, the influence of its variables on the potential energy is clarified, and the problems existing in the existing molecular following model are analyzed. The vehicle interaction potential function was constructed by referring to the Lennard-Jones potential function, and the influence of the road wall potential generated by the lane boundary was considered. The vehicle following model based on the Lennard-Jones potential was proposed. The simulation test results show that： Compared with the existing molecular car-following model and IDM model, the mean absolute error and root mean square error of the vehicle acceleration results obtained by this model are lower than the actual data, which proves that the car-following model based on Lennard-Jones potential has a better fitting effect on the real vehicle's car-following behavior

In order to improve the safety of vehicle lane changing on highway in snowy weather， an environment adaptive automatic driving strategy for high-speed safe lane changing of intelligent vehicle was proposed. Based on the typical case of high-speed lane changing traffic accidents， the dynamic mechanism of vehicle instability caused by environment was analyzed， and the lane changing trajectory model was constructed by using Gaussian distribution. Then taking the space-time situation and stability of safe lane changing as constraints， by using decision tree and hierarchical weighted score， a method of cooperative safe lane changing decision and path planning for different driving modes was proposed. Based on the hardware in the loop simulation test platform of intelligent vehicle built by Prescan/Simulink， the traffic accident scene of high-speed lane change of snowy vehicles was used to verify the automatic driving strategy of vehicles on the low attachment road. The simulation results show that the proposed safe lane change decision and path planning mechanism， with vehicle road cooperation， can better ensure the safety and the environmental adaptability of intelligent vehicles， which can provide a reference for the development of intelligent driving decision system.

To investigate the nonlinear impact of built environment on residents' commuting time， a case study was conducted in Nanjing by using a Gradient Boosting Decision Tree. The model has considered the impact of socio-demographics， trip characteristics， and built environment at traffic analysis zone. The result shows that trip characteristics have the largest cumulative contribution on commuting time as high as 52%， followed by built environment factors（38.88%）. Significant nonlinear impacts of built environment have been presented. The nonlinear thresholds of land-use mix and residential ratio are identified. This study expects to provide supportive scientific references for urban future planning and design at the micro-level.

An instant demand-responsive scheme for the customized bus（CB） is proposed， which enables vehicles to flexibly adjust routes according to the immediate needs of passengers during operation. A node insertion algorithm is developed to consider practical issues， such as late passenger handling and profit calculation. The implementation of "passenger waiting for bus" mechanism and consideration of passenger acceptable waiting time reduce the occurrence of passenger's bus-missing. In addition， strictly preventing the active rejection of loading can also ensure the fairness of CB service. The proposed model and algorithm are verified by a numerical example based on the CB network of Xiong'an district， Hebei province. The results show that the instant demand-responsive scheme can effectively deal with the situation of passengers being late under the premise of guaranteeing the CB system profit and meanwhile satisficing passengers' service time window.

Based on the complex network theory， considering the actual operation characteristics of subway and public transportation， a weighted composite network is constructed， and two new robustness evaluation indexes， "travel success rate" and " detour coefficient"， are proposed， as well as two new attack methods， "A" and "B"， which were more closer to the reality. Through simulation experiments， the changes in the evaluation indexes （network efficiency， maximum connectivity subgraph rate， detour coefficient， and travel success rate） of the composite weighted network under three attack models with two types of attack strategies， namely， deliberate attack and random attack， were analyzed， and the robustness characteristics of the composite weighted network against the above three attack models were also analyzed under deliberate attack and random attack， respectively. The results showed that under deliberate attacks， the robustness of the composite network against the new attack modes A and B were higher than that of the traditional attack mode in terms of detour accessibility， and the robustness of the composite network against the new attack mode A was higher than that against the traditional attack mode and the new mode B with respect to the travel success rate. The robustness of the composite network to the three attack modes under random attacks， in terms of bypassing coefficient， showed alternating robustness. And the robustness to the three was： new attack mode A>new attack mode B>traditional attack mode when it came to the travel success rate.

In order to achieve the efficient interchange and drive the economic development of cities along The Belt and Road， it is necessary to determine the optimal locations for distribution centers. Considering the attraction of the economic development between cities and the factors of investment and operation cost， the utility function for distribution center locating based on the field theory and topological analysis method is established. The parameters are calibrated by using the entropy weight method（Technique for order preference by similarity to an ideal solution，TOPSIS） and the optimal location scheme is determined with the utility maximization theory. The results indicate that the optimal locating scheme is determined with the model， by taking into account the attraction of topological transportation network and socio-economic as well as the minimum total cost. The proposed method can be applied to the siting of distribution centers， which is conducive to improving the transportation network layout， enhancing the transportation efficiency， reducing the transportation cost， advancing the resource balance and economic radiation between cities along The Belt and Road， and promoting the coordinated development of regions.

A target decision-making behavior model of passengers in transfer corridor is proposed. In this model， firstly， the visual area of each passenger is simulated and the direct effects of exits and staff on passenger's target decision-making is modeled. Secondly， a series of psychological parameters are introduced in the model to reflect the influence of passengers' complex psychological factors on target decision-making. Detailed simulations on pedestrian evacuation processes are designed and conducted in the context of the transfer corridors of a subway station. The results show that the total evacuation time is sensitive to model parameters and the number and location of the staff. It is found that the totalevacuation time is shorter if passengers in transfer corridor evacuate from the nearest exit； The more familiar the passengers are with the environment， the shorter the evacuation time is achieved； Reasonable placement of staff can greatly improve evacuation efficiency.

To explore the influencing factors and interaction mechanism of urban residents' low-carbon travel intention after implementing the driving restriction policy， the multiple indicators multiple cause （MIMIC） model is developed to construct urban residents' low-carbon travel intention model under the driving restriction policy， concerning the theory of planned behavior and technology acceptance model. The revealed preference（RP） survey was used to design the questionnaire， and 637 valid samples was obtained through network survey and field survey. Finally， the empirical test was conducted by the MIMIC model. The results show that policy attitude， policy effect perception and problem perception have a direct and significant impact on travel intention. Policy attitude and policy effect perception are mediating variables， and policy effect perception and subjective norms could indirectly affect travel intention through policy attitude， and problem perception could indirectly affect travel intention through policy effect perception. The total effect value of potential variables on urban residents' low-carbon travel intention after implementing the driving restriction policy rank from large to small as policy effect perception， policy attitude， problem perception， and subjective norms. Socio-economic attributes are moderating variables， which can indirectly affect travel intention by adjusting travelers' psychological perception factors. The research conclusions could provide the theoretical support for traffic management authorities to improve the efficiency of driving restriction policy and guide residents to low-carbon travel.

Through questionnaire survey and cross-section traffic flow parameter survey， the characteristics of electric bicycle travel， traffic flow and speed are analyzed. Aiming at four kinds of decision-making behaviors of electric bike， such as normal riding， occupying motorway riding， interleaving between vehicles and reverse riding， a decision-making behavior model of electric bike riding based on Elman neural network and multiple logistic regression was established. Finally， taking Futian District of Shenzhen as an example， this paper makes an empirical study. The results show that： the prediction effect of electric bicycle riding decision-making behavior model based on Elman neural network is significantly better than that based on multiple logistic regression， with an average prediction accuracy of 91.62%， which is 9.93% higher than that of multiple logistic regression model； the established multiple logistic regression model can reveal the relationship between influencing factors and riding decision-making behavior. The relationship can provide theoretical support for the formulation of strategies and policies to reduce the unsafe riding behavior of electric bikes.

To study the mechanical properties of high-strength steel reinforced concrete columns under large eccentric loading， and to determine the stress state and the compressive strength level of a new developed 630 MPa ribbed high-strength steel， 17 concrete columns reinforced with 630 MPa high-strength steel tested under large eccentric compression were fabricated， and the failure pattern， lateral deflection， strain of steel and concrete， and the bearing capacity were analyzed. The results show that the typical failure pattern of 630 MPa high-strength steel reinforced concrete column with large eccentric compression is the same as that of ordinary reinforced concrete column. The ultimate compressive strain of concrete compression zone is greater than 0.0033 in the current code， which is conducive to give full play to the compressive strength of 630 MPa high strength reinforcement in eccentric compression member， so that the compressive strength reaches the same value as the tensile strength. The test value of bearing capacity of each specimen is much higher than its design value， with an average value of the ratio of 1.887. It is reasonable and safe to design and calculate the 630 MPa high strength reinforced concrete column members by using the current specification. The tensile and compressive strength design values as 545 MPa of the 630 MPa high-strength steel in column member subjected to eccentricity is put forward， providing an experimental basis for the compilation of technical specification for high-strength bar in concrete structures and the promotion of 630 MPa high-strength steel in engineering application.

In order to study the mechanical characteristics of ultra-thin asphalt overlay on old cement pavement of different joint load transfer capacity， the finite element model is established to analyze the stress at the center of joint at the bottom of asphalt overlay by ABAQUS. The results show that the first principal stress increases， and the maximum shear stress and equivalent stress decrease with the increase of ultra-thin overlay thickness. The three kinds of stresses increase linearly with the increase of the ultra-thin overlay modulus and vehicle load， and decrease with the increase of the joint load transfer capacity. The interlamination contact states have significant effects on the maximum shear stress of ultra-thin overlay. The order of the influence degree of factors are as follows： vehicle load， ultra-thin overlay modulus， joint load transfer capacity， interlamination contact state， ultra-thin overlay thickness. The increase of the joint load transfer capacity can significantly improve the fatigue crack life of the ultra-thin overlay， and the relationship between them is according to power function. The joint load transfer capacity of old cement pavement should be controlled according to the traffic load before ultra-thin overlay.

To research the influence of the structural details on the human-induced vibration and pedestrian comfort of the suspension footbridge， based on a practical suspension footbridge， two different structural models， one with vertical hanger system and the other with inclined hanger system， were built. The influence of different hanger systems on the free vibration and human-induced vibration were analyzed， and the pedestrian comfort was evaluated based on EN03 code. The results show that， the first-order vertical and lateral frequencies of the inclined hanger system model are lower than those of the vertical one， and both two hanger system model have low free vibration frequencies and corresponding high flexibility. When the pedestrian mass is considered， the free vibration frequencies and the maximum accelerator of the main girder under pedestrian load decrease with the increase of the pedestrian flow for both two models. Under the same pedestrian flow， the maximum accelerators according to different mode and free frequency are different， but the inclined hanger system model has lower lateral and vertical accelerators compared to the vertical one. The lateral comfort evaluation result is same for two models， but the vertical comfort evaluation is better for the inclined hanger system model. The inclined hangers can improve both the lateral and vertical rigidities of the suspension footbridge.

A magnetic resonance imaging（MRI） brain tumor image classification method based on sparrow search algorithm and scaled reconstruction residual capsule network was proposed. Firstly， for MRI brain tumor images with poor image quality， an image enhancement method based on sparrow search was taken to improve the image quality. Secondly， the capsule network was used to achieve better results on the small data volume and unbalanced medical dataset. Finally， in view of the gradient disappearance and gradient explosion problems of the capsule network for large size images， an improved residual network was used to extract the key features of large size images. Meanwhile， by using scaled reconstruction， the volume of model decreased while the calculation speed increased. The experimental results verify the effectiveness of the proposed method in the classification of small samples， low-quality， large size MRI brain tumor images.

The two-stage object detection algorithm has a slow inference speed， meanwhile， the light-weighted model has a poor performance on small datasets. In view of reasons mentioned above， an improved multi-object detection algorithm based on the algorithm YOLOv4 is proposed. Taking dishes detection as an example， multi-object detection is used to detect and classify the empty dishes. Similar characteristics of the plates' edge are key identity to each completed plate. In order to preserve the salient features， the attention mechanism and pooling methods are added. Furthermore， a light-weighted network can speed up the inference speed and a multi-scale fusion method is able to improve the precision of the model. It turns out that the algorithm proposed improved 4.25% than previous work. According to the comparison with the classic detectors such as Faster RCNN， the FPS is 8-9 times of the latter one. The algorithm in this paper improves the reduced accuracy that the light-weighted models caused， and a more convenient and quick deployment of mobile or embedded devices are able to complete tasks.

A novel cross-entropy-based loss is proposed， which is applied to a classical convolutional neural network to obtain a better embedding space. A convolutional feature aggregation algorithm based on the weighting of the center of the response value is designed to process the three-dimensional convolutional features obtained by the neural network. The algorithm obtains the spatial weighting coefficient of the feature map by calculating the current position response value and the Gaussian center. The algorithm reduces the loss of information when the 3D convolution feature map is reduced to a 1D image feature descriptor and realizes the enhancement of the target area. Finally， the obtained image feature descriptors are used for retrieval tasks. On the CUB-200-2011 dataset， the effectiveness of the loss function and the response value center weighting algorithm are respectively verified by ablation experiments. Compared with the current retrieval methods， higher precision and recall rates have been obtained on the four datasets of Paris6k， Oxford5k， CUB-200-2011 and CARS196.

Aiming at the problem that the research of video real-time target tracking based on deep neural network mainly focuses on the optimization of backbone network, and the design and training are relatively complex, a new apparent enhanced depth target tracking algorithm with apparent enhancement was proposed. Firstly, by introducing simple and easy-to-implement traditional apparent features, it is directly fused with deep semantic features, which enhances the discrimination ability of objects within a class.Secondly,through voting mechanisms and adaptive search modules, the robustness of tracking algorithm is enhanced. The test results on the VOT series data set show that compared with the benchmark algorithm, the average overlap expectation (EAO) of the proposed algorithm has been improved by 2%~4%, and the accuracy and robustness have reached or even partially exceeded the existing complex optimization algorithms.

A novel way of distributed coding schemes to reduce blockchain nodes' memory requirements and network load using (n,k,d) exact-regeneration MSR （ER-MSR） codes is presented. Our scheme has the same protocol for mining， broadcasting， and verification of blocks as Bitcoin-type blockchains. As for the difference， the full nodes encode data across multiple blocks and do not have to store all blocks. Specially， a group of k blocks only need to store one block. To exactly read a block data that is not stored locally， it can be realized by connecting to d helper nodes and downloading a small amount of data. While maintaining the integrity and availability of block， this scheme leads to a significant reduction in storage and recovery communication costs， and the scalability of the system is enhanced. It is proved that this system has enlarged overall storage capability and reduced the cost of network bandwidth when more nodes attend the blockchain.

In order to meet the requirements of low delay and low energy consumption of user terminal equipment for intensive task processing in edge computing environment， the time delay model， energy consumption model and offloading optimization model were designed， and an offloading scheme based on improved particle swarm optimization algorithm was given， and the excellent performance of the proposed scheme was verified by experiments.Firstly, the PSO is improved and an improved particle swarm optimization (UPSO) algorithm is proposed. Secondly, by combining UPSO with Genetic algorithm (GA), an improved particle swarm optimization algorithm (GA-UPSO) is proposed to solve the unloading decision problem in the single-user multi-task scenario. The simulation results on Matlab show that the proposed offloading scheme is superior to the offloading scheme based on genetic algorithm and the offloading scheme based on standard particle swarm optimization algorithm in terms of time delay and energy consumption.

In order to shorten the transmission delay of wireless body area network， a multi-path selection method based on genetic algorithm is proposed. The genetic algorithm is used to set the minimum congestion， maximum energy saving and maximum power as constraints. According to the load situation, the degree of network congestion is judged. By calculating the expected congestion duration path of the jumping nodes, the nodes with sufficient energy and complete jumping are selected as transmission points, the best transmission power node is calculated by using the function, and the multi-path route with the best comprehensive performance is obtained through adaptive adjustment by using the mapping relationship between genetic chromosomes and network nodes. Experiment results show that the proposed method has short transmission delay and can prolong the service life of nodes.

In the current design process of social network abnormal data mining algorithm， social network data features are not extracted， resulting in low abnormal data detection rate and long detection running time. Therefore， a social network abnormal data mining algorithm based on deep integrated learning is designed. Mining social network data， extracting social network data features according to the data mining results， using the extracted data features to build a deep integrated learning model， using the model to predict abnormal data， so as to obtain abnormal social network data， and realize abnormal social network data mining. Experimental results show that the algorithm has high accuracy and effectiveness and good practical application effect through abnormal data detection rate test and abnormal data detection running time test.

Due to the complicated and volatile traffic scenes， deep learning-based approaches and most of the deep reinforcement learning approaches cannot satisfy the requirements of real applications. To address these issues， a reinforcement learning-based approach based on multi-sensing and multi-constraint reward function under SAC framework（MSMC-SAC） is proposed. The inputs of the method include front images and LiDAR data， as well as the bird's-eye view information generated from the perception results. The multiple information input is coded by an encoding network to obtain the representation in latent space， and the reconstructed information is used as the input for reinforcement learning module， and a reward function considering various constraints such as transverse-longitudinal error， heading， smoothness， and driving speed is designed. The performance of the proposed method in some typical traffic scenarios is simulated and verified with CARLA. The multi-constraint reward mechanism is analyzed. The simulation results show that the presented approach can generate the driving policies in many traffic scenarios， and the performance is outperformed against the existing SOTA methods.

This article introduces an algorithm for orthogonal frequency divisition multiplexing（OFDM） timing synchronization—correlated peak slope detection algorithm. In a multipath channel environment， due to multipath reflection， the front part of the cyclic prefix that OFDM complies with will be affected by Inter Symbol Interference（ISI）. This paper innovatively proposes a time-domain timing synchronization algorithm. The algorithm determines the arrival position of the last path by detecting the slope of the correlation peak， and estimates the dispersion length of the multipath channel， so that the subsequent fast Fourier transform（FFT） window position avoids the ISI， and provides a reference value for the selection of filter coefficients for channel estimation. This algorithm is also very suitable for application in multipath channels with equal strength and two paths. Finally， the algorithm was simulated with MATLAB and compared with the classic algorithm， which proved that the algorithm can better avoid ISI in the case of low signal-to-noise ratio. In addition， for a low-cost IoT communication system similar to NB-IOT that uses OFDM technology， this article simplifies the algorithm， which can reduce the cost of implementing the baseband chip of the IoT terminal and reduce the cost of the terminal.

Rape crop has great potential as Fodder， which is a new way of using its tender， juicy， nutritious stem. In order to develop the mechanized harvesting technology for fodder rape crop， a study on discrete element simulation of the stem chopping for rape crop in the flowering stage was carried out. By utilizing the three-point bending method， the discrete element simulation parameters of fodder rape stem in the flowering stage were calibrated based on the Response Surface Methodology（RSM）. The relative error between the maximum bending failure force obtained in the simulation with the calibrated parameters and that obtained by the actual test was 0.12%， indicating the calibrated discrete element simulation parameters were accurate. As further use of the calibrated discrete element parameters， the discrete element simulation experiment of cutting process of the mechanized harvesting for fodder rape was carried out. The simulation results showed that the optimal working parameter combination of the chopping device was the feeding roller speed 467 r/min and the cutter-head speed 770 r/min. Under the optimal working parameter combination condition， the qualified rate of stem chopped length in the simulation and bench experiment was 89.52% and 89.86%， respectively.

In order to improve the working efficiency of the flax combine harvester in hilly areas， this study took the forward speed of the working machine， the rotating speed of threshing drum and the rotating speed of impurity suction fan as independent variables， and the threshing rate， the total loss rate and the impurity content rate of grains as response values. According to the Box-Behnken experimental design principle， the mathematical models between each factor and the response values were established by using the response surface analysis method of three factors and three levels， and the interaction of each factor was analyzed. The results showed that the order of impact of the threshing rate was the speed of the threshing cylinder， advanced machine， and centrifugal fan. The order of effects of the total loss rate was the speed of the developed device， centrifugal fan， and threshing cylinder， and the order of impact on the impurity rate was the speed of the centrifugal fan threshing cylinder and advanced machine. The optimal working parameters of the device were as follows： the advance speed of 0.65 m/s， the threshing cylinder speed of 830.3 r/min， and the centrifugal fan speed of 1154.39 r/min. The validation test showed that the average threshing rate was 96.33%， the total loss rate was 1.87%， and the impurity rate was 3.24%. The results show that the total loss and seed impurity rate of hilly area flax combine harvester could be reduced under the operation parameters， which could provide a specific reference for the design and experiment of hilly area flax combine harvester.

To solve the problems of low mechanization level， high labor intensity， long time consuming and low efficiency in the harvesting process of industrialized planting leafy vegetables， the representative Shanghaiqing was taken as the research object， a harvesting device with band saw cutting， screw tensioning and V-belt transmission， was designed for the production line of Shanghaiqing. Based on the theoretical research and simulation analysis of cutting process of Shanghaiqing roots， the main factors affecting the cutting effect was determined， and the prototype was trial-manufactured and orthogonal tests were carried out， the optimal combination of parameters was obtained： belt saw was no teeth， feed speed was 0.05 m/s and cutting speed was 400 r/min. The verification test results show that cutting effect is well and the end face is smooth， and the qualified rate is 88.9%. Therefore， the harvesting device meets the requirements of high efficiency and labor saving. This study can provide reference for the research and development of harvesting device in the assembly line production of industrialized planting leafy vegetables.