To clarify the research progress of conductive asphalt concrete technology， the classification of conductive materials and their applications in conductive asphalt concrete were reviewed systematically. The conductive and road performance of various conductive asphalt concretes were summarized. The mechanism of conductive asphalt concrete （conductive mechanism， piezoresistive mechanism and electrothermal mechanism） was discussed. The functional performances of conductive asphalt concrete such as self-monitoring， self-sensing， self-healing by induction heating， and deicing were explored. The functional application of conductive asphalt concrete in actual engineering was introduced， and the future development directions were prospected.

State of the art of the aging mechanism， characterization and evaluation of CRMA at home and abroad are summarized. The aging process of CRMA is accompanied by swelling， degradation reaction of crumb rubber and the interaction between asphalt and crumb rubber. The aging behavior of CRMA is mainly characterized from the chemical components， molecular structure， molecular weight and surface morphology， and their effects on macroscopic properties are explained. Rheological parameters such as viscosity， complex shear modulus， creep stiffness at low temperature and fatigue life are mainly used to evaluate the aging performance of CRMA， which are usually combined with microscopic characterization and chemical methods. However， during the aging process of CRMA， the migration behavior of fillers， e.g.， carbon black， white carbon black， received less attention. In addition， there is a lack of simultaneous characterization method for multi-component aging characteristics of CRMA. There are no universal aging evaluation indexes for CRMA， and most of the studies are insufficient in the aging performance verification at mixture level. The development trend and prospect of aging mechanism， characterization and evaluation of CRMA are proposed.

For the stability control problem of vehicles with mechanical elastic electric wheels（MEEW）， considering the uncertain perturbation of MEEW's and external interference during actual driving， a 2-DOF vehicle model with nonlinear interference term was established. The vehicle's stability control strategy was made of two parts. In the upper layer， a yaw rate sliding mode controller and a sideslip angle sliding mode controller based on extended state observer（ESO） were designed respectively. By using ESO， the nonlinear interference items were observed and compensated in real time. After dividing the stability area of the vehicle according to the \beta-\dot{\beta } phase plane， the above two controllers are switched. In the lower controller， the torque distribution was carried out based on the quadratic programming method. The simulation results show that the proposed control strategy can well control the stability of distributed driven electric vehicle which matchs MEEW.

In order to achieve efficient and clean combustion， the influence of variable intake valve strategy （VIV）， variable exhaust valve strategy （VEV） and variable intake valve combine exhaust valve strategy （VICE） on the un-throttle engine performance was studied based on a self-developed variable valve lift actuation. The results show that， compared with the original engine， the three valve control strategies all can effectively improve brake specific fuel consumption（BSFC） and indicated thermal efficiency（ITE）. The VICE strategy showed the most optimal results， for example， BSFC and ITE were optimized by up to 28% and 6% compared to the original engine. Both VIV strategy and VICE strategy can reduce pumping loss， especially for VIV strategy， which can reduce pumping loss up to 36% compared with the original engine. All three strategies can reduce NO _x emissions， among which VEV strategy performed best. Different valve strategies showed few effects on THC emissions when NMEP exceeded 0.3 MPa.

A new method to divide vehicle's driving conditions was presented and used in compiling vehicle durability load spectrum based on customer usage correlation. A seven dimension random vector was adopted to distinguish， quantify and identify road roughness， topographic feature and driving habit for division of vehicle's driving conditions. Correlation relations between the seven random variables were studied. The statistic results showed that any two variables among the seven random variables were not highly related， which indicated that further simplifying the method to divide vehicle's driving condition was not supported. Besides， the seven random variables were not independent， which highlighted the necessity to use the associated probability density distribution to describe vehicle's driving conditions. The results shown that the chosen dimensions thoroughly described and defined vehicle's driving conditions， which became an organic unity. When correlating to different loads of vehicle， the organic unity showed significant but distinctive overall correlation relationship， which could be used to compile vehicle durability load spectrum correlated to customer usage.

In order to realize the multi-objective optimization of hypoid gears， a neural network surrogate model was established to describe the relationship between Ease off modification parameters and transfer error， tooth root stress and meshing loss power. Firstly， the dynamics software MASTA was used to establish the hypoid gear drive axle model. Based on the sensitivity coefficient matrix， the machine tool modification parameters corresponding to the second-order Taylor expansion of tooth surface deviation were derived， and the modified gear model was established. Secondly， the transmission error， tooth root stress and meshing loss power of the modified gear model were calculated through MASTA's loading tooth surface contact analysis function， and the neural network agent model was finally established. NSGA-II multi-objective optimization algorithm was used to optimize the surrogate model for comparative verification. The results show that the proposed multi-objective optimization method can effectively reduce transmission error， tooth root stress and meshing power loss of hypoid gears.

When the double blade hydraulic passive compliance rotary joint is impacted by the external environment， the passive compliance of the joint will make the output angle of the joint valve body callback， and the output angle will recover rapidly after the collision. In order to effectively absorb the remaining impact energy， the adaptive impedance control is used as the active compliance control of the rotary joint， which is combined with the passive compliance control of the joint to realize the active and passive joint compliance control effect of the joint， and increases the return angle of the rotary joint when it is impacted， that is， it can better resolve the environmental impact force. At the same time， a fuzzy adaptive PID controller is designed and added to the joint control system as the position controller in the adaptive impedance control， which can reduce the steady-state error after the recovery of the output angle of the rotary joint and improve the safety of the joint when in contact with the outside world.

Difficulties of damage equivalence and test accelerating in making load spectrums of fatigue test of wheel rims were concentrated in this paper. Systematic and comprehensive analysis of loads acting on wheel rims in real conditions was carried out for damage equivalence. Biaxial fatigue damage model of wheel rims was built， introducing multiaxial loads projection theory and concept， based on which method of making load spectrums of biaxial fatigue test and its acceleration of wheel rims were presented. About 5000 km time domain data of wheel forces were gathered on typical public roads. Equivalent biaxial fatigue damages of wheel rims were calculated using the presented method with the obtained data. The results highlighted the superiority of the method of biaxial fatigue test of wheel rims， and also exposed the limitation of uniaxial radial fatigue test and bending fatigue test of wheel rims.

In order to improve the forming quality after multi-point stretch bending of profile， from the perspective of the section deformation， based on ABAQUS finite element simulation， after the multi-point stretch bending of irregular Y-shaped aluminum profile， firstly， the influence law of different process parameters （number of dies， bending radius， pre-stretching amount， pro-stretching amount and friction coefficient） on the section deformation distribution of the workpiece is explored by means of the control variable method. Secondly， in order to comprehensively consider the interaction between the process parameters， combined with the orthogonal test analysis， the primary and secondary relationship of the influence of various process parameters on the maximum section deformation of the workpiece is further explored. The results show that when the pro-stretching amount changes， the maximum value of section width deformation is 1.58 mm， the minimum value is 1.06 mm， the variation range is 49.1%； the maximum value of section height deformation is 1.25 mm， the minimum value is 0.77 mm， and the variation range is 62.3%， that is， the pro-stretching amount has the greatest influence on the section deformation. Finally， the optimal forming scheme of multi-point stretch bending is determined after comprehensive analysis， and the feasibility of the forming scheme is verified by simulation and experiment， which greatly improves the forming quality of the parts.

Bulk Al-Ga-In-Sn alloys have the potential to be widely used in special processes such as field， military， and disaster relief due to their high safety， easy storage， and good hydrogen production performance. However， the activation mechanism for such materials is still unclear， and it is impossible to clearly guide the formulation and performance regulation of the materials. Therefore， the design and synthesis of material sample points are carried out in this paper based on the experimental experience conclusions in this field and theoretical phase diagrams， and the hydrolysis and hydrogen production properties of the above materials were characterized in detail. In this work， the peak of the hydrogen production rate of the material is chosen as the optimization target， and the coupling relationship between the phase composition and element composition and the hydrogen production rate of the aluminum gallium indium tin-based hydrolysis hydrogen production material is tried to be explored. Our research results show that the coupling relationship between the elemental composition of the alloy and the peak hydrogen production has higher credibility than the coupling relationship between the phase composition and the peak hydrogen production，and the use of this coupling relationship can guide the directional design of the material. It provides a new idea for the directional design and synthesis of such materials in the future.

To relieve the conflicts between the intelligent connected bus and the social vehicle in the mandatory lane changing for bus exiting， the evolutionary game mechanisms for their behavioral decision-makings are analyzed. Two scenarios where the social vehicles are human-driven and intelligent connected are considered. The utility functions in different strategy profiles of the evolutionary game are formulated from the perspectives of safety utility， time utility and energy-saving utility. On this basis， the replicator dynamics equations for the two scenarios are constructed. According to the determinant and trace of Jacobi matrix in the dynamic game system， the equilibrium points and their stability in different situations are analyzed deeply. The sensitivity of evolutionary results to the difference between the cooperative and non-cooperative strategies is explored. The results reveal that： the possible evolutionary directions in the dynamic game system involve “intelligent connected buses changing lanes， and social vehicles giving way”， “intelligent connected buses not changing lanes， and social vehicles not giving way”， and “intelligent connected buses not changing lanes， and social vehicles giving way”. From the perspective of overall optimization， adjusting the relevant variables of the safety and time utility of social vehicles can ensure the safety and efficiency of more vehicles in the process of mandatory lane changing of intelligent connected bus. The energy-saving utility promotes the strategy profile of changing lanes and giving way， and inhibits the strategy profile of not changing lanes and not giving way.

In order to calculate the static congestion traffic assignment considering spatial queuing and spillback in road network， congestion area identification， user equilibrium principles and the mechanism of loading traffic demand are researched and an iterative weighted algorithm is constructed. First， the road network is divided into local congested and smooth sub-area based on the congestion space queuing. It is proved that the travel times from queue-ends to a bottleneck are equal with each other under the assumption that congestion macroscopically dissipates simultaneously in time and space. Secondly， the user equilibrium principles considering congestion spatial queuing and spillback are proposed from the views of the traffic demand on the whole network and the assignment flow on the local sub-network. And the Min-Max mechanism of loading traffic demand is established with selecting the shortest path in the global area and the longest path in the local congestion area， in order to achieve the user equilibrium in traffic assignment. Thirdly， the algorithms of demand compression and queuing backtracking are improved based on link codes. The method of identifying congestion sub-area is proposed. Finally， an iterative weighted algorithm is constructed based on the Min-Max mechanism to calculate the traffic assignment results with the overall macro-travelling state of the road network， and the effectiveness is illustrated with calculation examples. In addition to improving the two core issues of demand compression and congestion backtracking， the other four core issues are proposed and solved including congestion area identification， user equilibrium principle， demand-loaded mechanism and iterative weighted algorithm， which enriches and develops the theories of the static congestion traffic assignment considering spatial queuing and spillover.

In order to improve the transit efficiency of conventional buses entering and leaving stops， reduce the queue delays and traffic impact， two optimization methods for increasing berths of bus stops and controlling arrival rates of buses were proposed in this study. Firstly， the basic process of buses entering and exiting bus stops was constructed， the capacity calculation models for single-berth and multi-berth bus stops were established， and the queuing characteristics of bus entry were systematically analyzed using queuing theory. Next， the data on buses entering and exiting stops was investigated to obtain basic data including bus number， time for passengers to get on and off the bus， bus stopping time， etc. The model was numerically simulated by Matlab to obtain the effect of average arrival rate of buses， number of bus berths and bus stopping time on queuing delays in the steady state of the queuing system. Finally， the optimal number of berths and arrival rates at bus stops were proposed considering capacity and queuing delays， and the feasibility of the optimization results was verified with examples. The results show that the optimization of the number of bus berths and arrival rates of buses has increased the capacity of the stops studied by over 10% and reduced queuing delays by over 70%.

There are a lot of noise data and missing data in the traffic data of expressway network， and the data integrity is not high， leading to the decline of prediction accuracy. A real-time prediction method for short-term traffic flow parameters of expressway network based on multi-source data fusion is proposed. The wavelet analysis threshold method is used to denoise the traffic data of the expressway network. Based on the least squares support vector machine， the combined threshold filling method is used to fill in the missing data in the traffic data sequence to improve the integrity of the traffic data. The short-term traffic flow parameter prediction model is established by combining wavelet neural network and genetic algorithm. The traffic flow parameters collected by multi-source detectors are processed by genetic wavelet neural network. The traffic flow parameters of multiple detectors are fused by the least squares dynamic weighted fusion algorithm. The traffic flow parameters are input into the prediction model to obtain the real-time prediction results of short-term traffic flow parameters of expressway network. The experimental results show that there is no missing data in the traffic data series processed by the proposed method， the data integrity is high， and the predicted results are close to the actual vehicle flow change curve， with high prediction accuracy， which can be widely used in the field of traffic flow prediction.

To analyze the risk-causing mechanism of different freeway characteristics， this paper introduced the microscopic kinetic parameters of the vehicle under normal driving conditions. The standard ordered Logit model and the binary Logit model were conducted to explore the relationship between the microscopic kinetic parameters and road characteristics （e.g.， diversion areas， merging areas， and curve segments）； and the relationship between the microscopic kinetic parameters and crash risk （e.g. rear-end， fixed-object hitting， and overturning）. Then， taking the micro-kinetic parameters as the link， the effects of different road characteristics on crash risk is independently quantified. The results indicate that the longitudinal kinetic factorcan effectively map the level of rear-end risk， and the difference in fluctuations of can be attributed to the diversity of road characteristics. That is， the longitudinal kinetic factor can be used as an intermediate variable of risk transmission to realize the determination of the level of crash risk on freeways， and at the same time， provide help for the identification of road dangerous defects. Compared with the straight-line section without a diversion area， the complex feature of the "diversion area" has the greatest effect on the added risk of rear-end crashes， followed by "composite alignment"， and the "horizontal curve" is the weakest. The greater the length of the transition curve of the horizontal curve section， the higher the risk of rear-end crashes. In addition， the paper also finds a significant risk compensation phenomenon， suggesting that a more relaxed driving environment may cause drivers to relax their vigilance and increase the risk of rear-end. The conclusions can provide theoretical support and experience for the prevention of crashes and the formulation of rescue measures for a specific type of crash.

Crack disease is the most common disease of bridges， DeepLabv3+ segmentation model puts forward a new Encoder-Decoder structure among deep learning methods. It combines the high-level semantic information and shallow features of the target， and adopts the method of deep separation and convolution， which achieves superior image segmentation effect. However， in the training process of the coding module， the spatial dimension of the input data is gradually reduced， resulting in the loss of useful information， which brings some limitations to the recognition of small targets with different scales. In order to improve the segmentation performance of the network， this paper proposes an image segmentation method based on improved DeepLabv3+.By adding Yolof module and Resnet module， the receptive field is further expanded and more accurate crack feature map is obtained at the same time. In order to verify the effectiveness of the improved algorithm， a large number of actual bridge crack images are taken as the original data set， which is compared with the current representative image segmentation models such as Mask R-CNN and DeepLabv3+ on the same dataset. The results show that the algorithm in this paper improves the accuracy of crack pixels by 12% and 8% respectively compared with Mask R-CNN and DeepLabv3+. The average pixel accuracy is 91.99%， and Mean Intersection over Union is 81.43%， which is more suitable for the task of bridge crack segmentation and has practical engineering application significance.

To investigate the cooling effect of multi-layer phase change asphalt concrete structure， the phase transition temperatures of five groups of polyethylene glycol （PEG） with different molecular weights were measured by differential scanning calorimetry. Seven groups of three-layer composite asphalt concrete slabs containing PEG were prepared based on the simulation results of asphalt pavement temperature field under solar radiation. The internal temperature of these composite asphalt concrete slabs within 9 h of thermal radiation was measured using the self-developed thermal radiation and temperature measurement device. And the effects of PEG addition layer， combination of PEG addition layer and molecular weight on the heat storage and cooling of composite asphalt concrete slabs were analyzed. Results show that the starting temperature of phase transition increases from 25.1 ℃ to 54.4 ℃ with the molecular weight of PEG increasing from 1000 to 6000. The cooling effect is mainly reflected in the mixing layer and its lower layers when phase change material was added to the single layer of pavement structure. The scheme of adding PEG in the upper layer is better than that of using PEG in multiple layers from cost performance. The cooling effect of PEG with high molecular weight is better than that of PEG with low molecular weight at the same content.

To further improve the existing bridge weigh-in-motion technique， this paper proposes a bridge weigh-in-motion system integrated with machine vision. Firstly， the machine vision algorithm is used to identify and track the vehicle； then， the bridge response monitoring information under the action of the vehicle is processed； furthermore， the axle load and axle base are identified by using the virtual simply-supported beam theory； finally， the method is tested by simulation and test. The results show that the method proposed in this paper has a good identification effect on the axle weight and wheelbase of vehicles under various working conditions. The average relative errors of the identification of axle weight， wheelbase and total weight are 3.40%， 4.31% and 2.71% respectively. It has a certain anti-noise ability， which shows that the method has good robustness and applicability.

In order to study the influence of low temperature environment on the distribution law of welding residual stress， a stud model through ABAQUS for numerical simulation was established， and 5 different temperatures for finite element thermal-solid coupling analysis were considered. The results show that the maximum values of mises stress and radial residual stress are both located at 10 mm near the weld， and decay rapidly with the increase of distance； the hoop residual stress is residual tensile stress near the weld， and is compressive stress away from the weld， and the maximum value is located in the center of the weld； low temperature will increase the residual stress of the weld， the mises stress is concentrated in the center of the weld， the hoop residual stress is concentrated near the weld， and the radial residual stress increases both in the center of the weld and near the weld. The low temperature has a significant effect on increasing the residual tensile stress of welding， but has little effect on the residual compressive stress.

The traditional KNN （K-nearest neighbor） algorithm is a classic machine learning algorithm. This algorithm has no learning process and needs to traverse all the learning samples when classifying， and is time-sensitive and sensitive to the k value. This paper proposes two random KNN algorithms （RKNN） with a learning process， including the SRKNN algorithm on sample Bootstrap sampling and the ARKNN algorithm on sample feature Bootstrap sampling， both of which belong to Bagging ensemble learning. After learning multiple simple KNNs， the voting output results. The algorithm combines the features of the samples to obtain the combined features， and the simple KNN is obtained based on the combined features. It focuses on how to select the optimal combination coefficient of features， and obtains the selection rules and formulas of the optimal combination features for the best classification accuracy. The RKNN algorithm introduces learning when constructing a simple KNN， it no longer needs to traverse all the learning samples when classifying， but only needs to use the binary search method， and its classification time complexity is an order of magnitude lower than that of the traditional KNN algorithm. The classification accuracy of the RKNN algorithm is significantly improved than that of the traditional KNN algorithm. The RKNN algorithm solves the problem that it is difficult to select the k value using the KNN algorithm. Both theoretical analysis and experimental results show that the proposed RKNN algorithm is an efficient improvement to the KNN algorithm.

In order to solve the problem of ensuring low energy consumption and load balancing of servers while reducing the computation latency of vehicle terminals，the system model， delay model， load model，energy consumption model and objective optimization model based on vehicle-to-vehicle communication were constructed in this paper. Then， a computational offloading scheme based on multi-objective immune optimization algorithm was proposed. Finally， this scheme was compared with several offloading scheme. Simulation experiments show that the proposed scheme can effectively reduce the average offloading delay of users，optimize the workload between servers，and reduce energy consumption.The performance of the proposed scheme has been improved compared to other schemes.

In the semantic matching of long texts， it is challenging to capture the before-and-after connections and topic information， which often results in poor semantic matching. This paper proposes a long text semantic matching method based on BERT and dense composite network. Through the dense connection of BERT embedding and composite network， the accuracy of long semantic matching is significantly improved. First， the sentence pair is input into the BERT pre-training model， and accurate word vector representation is obtained through iterative feedback， and then high-quality sentence pair semantic information is obtained. Secondly， a dense composite network is designed. Bi-LSTM first obtains the global semantic information of sentence pairs， and then TextCNN extracts and integrates local semantic information to obtain the key features of each sentence and the correspondence between sentence pairs， and the BERT Fusion with the hidden output of Bi-LSTM and the pooled output of TextCNN. Finally， summarizing the association state between networks during the training process can effectively prevent network degradation and enhance the model’s judgment ability. The experimental results show that on the community question answering （CQA） long text dataset， the method in this paper has a significant effect， with an average improvement of 45%.

In order to improve the effect of object detection in high-resolution remote sensing images， this paper proposes a new object detection method by combining multi-feature fusion method and twin attention network. The overall framework of remote sensing image target detection is constructed， and the multi-layer features of remote sensing image target are extracted and fused based on the anchor frame model. The twin attention network is used for real-time visual tracking and detection of remote sensing image targets， and the dual self-attention mechanism of channel and space is introduced to improve the feature expression ability of target images， so as to get more accurate detection results. Through the analysis of experiments， the average overall accuracy of the proposed method is 93.8， the average F₁ index is 0.88， and the average Kappa coefficient is 0.93， which are significantly higher than the comparison method， indicating that the proposed method has a good detection effect.

Aiming at the lack of depth information and incomplete spatial structure information of behavior and posture in 2D images， a 3D human joint point recognition method based on weak supervised migration network is proposed. Firstly， an end-to-end 3D human pose estimation framework for real images is proposed. The depth neural network is trained with 2D and 3D mixed label images. In the 2D human pose recognition sub network， the depth regression module is added to improve the 2D human pose recognition sub network to solve the problem of depth ambiguity in 3D human pose recognition； Secondly， in the 3D human pose recognition sub network， 3D geometric constraints are introduced to standardize the human pose recognition. For the case of no real depth label， it can better learn the depth features and effectively solve the problem of human pose recognition with occlusion. In human 3.6m and mpii data sets， the average error of joint point prediction is lower than that of other methods， and has better 3D human posture recognition effect.

The existing thin-walled structure has the problem of low specific energy absorption and low compression efficiency. The honeycomb was used as a prototype to simulate the multi-level nested structure of honeycomb and the optimization design of bionic structure was carried out. In order to better explore the influence of shape parameters on energy absorption capacity， a total of 20 honeycomb multi-stage thin-walled structures were designed based on the relative rotation angle and the diameter of the reinforced column. Using 3D printing technology， a multi-stage thin-walled structure sample of imitation honeycomb made of nylon was prepared， and a quasi-static compression test was carried out. Finally， the numerical simulation results and experimental results are compared and analyzed， and the following conclusions are drawn： the model with the strongest energy absorption capacity in this paper is YMT20-3.6， reaching 10.87 J·g^-1， which is about 86% higher than that of the initial model YMT0-0. The larger the rotation angle， the larger the diameter of the reinforcement column， the more uniform the distribution of the cross-sectional area of the model， the symmetrical deformation mode of the model， and the stronger its energy absorption capacity. A larger rotation angle has better energy absorption， but its compression force efficiency is less. Increasing the diameter of the model strengthening column can reduce the influence of rotation angle on the compression force efficiency and improve the compression force efficiency of the model， so as to obtain a thin-walled structure with strong energy absorption capacity and high compression force efficiency.

To solve the technical problem that it is difficult to separate and recover the machine-collected film and impurities due to intertwining. According to the differences of suspension characteristics， shape size and shearing characteristics of the main components of the machine-collected film and impurities， a new method of separating and removing the miscellaneous material by swirl settlement， cutting film filtration and removing impurities under the negative pressure of the water pump was proposed， and the device of cutting film and removal impurities was innovatively designed. The mechanical characteristics of the device were analyzed theoretically， and a test device was built. The orthogonal test of three-factor， three-level quadratic regression was carried out， and a multi-objective response surface regression model was established， which was optimized and verified by experiments. The results show that the order of primary and secondary factors influencing the pass rate of broken film is： rotational speed of movable knife， gap width of fixed knife and radius of movable knife wall. The order of primary and secondary factors influencing the crushed film rate is： rotational speed of movable cutter， radius of movable cutter wall and width of gap of fixed cutter. For crushed film impurity rate： rotational speed of movable cutter， width of gap of fixed cutter and radius of movable cutter wall. When the rotating speed of movable cutter is 954 r/min， the width of gap of fixed cutter is 2.8 mm and radius of movable cutter wall is 80 mm， the crushed film rate is 88.80% and the crushed film impurity rate is 7.51%. The relative error between the optimized test result and the predicted value of model is less than 5%. The main conclusions can be used as reference for the study of cutting and separating methods and equipment of the machine-collected film and impurities.

Aiming at the narrow and airtight interior space of traditional canopy orchards， the existing lawn mowers are difficult to operate， and faced with the production problem that no machinery can be used， a small lawn mower with adjustable stubble height and flexible and convenient obstacle avoidance between plants was designed. On the basis of fully analyzing the planting mode of the closed orchard and the agronomic requirements of mowing， the overall design scheme of the mower was determined. The key components of the lawn mower （cutter height adjustment mechanism， obstacle avoidance cutting mechanism between plants， remote control and image transmission system， etc.） were designed and selected. And the whole machine integration and performance test were completed. The basic performance test results show that the size of the lawn mower （length × width × height） is 1.9 m × 1.7 m× 0.7 m， the stubble height adjustment range is 30~110 mm， and the obstacle avoidance rate between plants is 100%. The transmission distance is greater than 110 m. At the same time， taking forward rotation speed， cutter head rotation speed， cutting stubble height as test factors， cutting stability coefficient， cutting width utilization rate， missed cutting rate as test performance indicators， orthogonal tests were carried out， and the significance of test results was analyzed. The results show that the primary and secondary order of the influence of each factor on the performance indicators such as stubble stability coefficient， cutting width utilization rate and missed cutting rate are forward speed， cutter head rotation speed and cutting stubble height. The determined optimal scheme is the forward speed of 0.6 m/s， the cutter head rotation speed of 3000 r/min， and the cutting stubble height of 70 mm， and the verification test of the optimal scheme was carried out. The results show that the designed lawn mower has a stubble stability coefficient of 93.2%， a cutting width utilization rate of 99.3% and a missed cutting rate of 0.5% under this scheme， which can meet the actual operation requirements of the closed orchard.

Aiming at the problems of incomplete soil cleaning， easy damage to the grapevine and poor adaptation by mechanized soil cleaning in the open-field vineyards of northern China， a grapevine cold-proof soil cleaner with combined scraping and brushing is designed. The double-winged scraper components， rigid impeller components and flexible brush wire components are adopted， which can clean the cold-proof soil above the grapevine， the cold-proof soil on the side periphery and the cold-proof soil near the root of the grapevine at one time. The key components were design and a control system based on STM32F103 was built to realize automatic obstacle avoidance of scraper components and automatic leveling of the machine. Leveling performance tests showed that when the leveling frame was tilted to the left to the maximum angle or tilted to the right to the maximum angle， the automatic leveling process can be completed within 1 s， and there is no obvious overshooting. When the tractor was driven on uneven vineyards， the three soil cleaning components maintain the desired leveling angle at all times （within±2°）， even if the tractor itself undergoes a large change in tilt. The field soil cleaning test showed that all the working components operated stably and there was no interference. After the soil cleaning operation， the soil cleaning rate was about 81.96%， and the grapevine damage rate was about 1.67%， which met the requirements of open-field grapevine cold-proof soil cleaning in northern China.