As a new generation of automotive materials， smart materials provide new research directions and design ideas for the realization of automotive lightweight and intelligent design goals. In order to further promote the research process of smart materials for automobiles， this paper reviews piezoelectric materials， magnetorheological materials and shape memory alloys respectively， describes the special properties of various materials， and systematically summarizes domestic and foreign research achievements in important research fields such as automotive energy recovery， structural vibration suppression， sensors， actuators， and safety protection. Finally， the challenges of commercialization of smart materials for automobiles are analyzed， and the directions of future research are pointed out.

A 3D model of Diesel Particulate Filter（DPF） was established by CFD software， the influence of soot and ash distribution on the airflow movement of dissymmetric DPF ducts was studied. A one-dimensional thermodynamic model of engine was used to analyze influence of the DPF carries size， wall thickness， and the inlet/outlet aperture ratio on exhaust noise under plateau environment. The results showed that： the flow velocity of DPF inlet port has been increases first and then decreases axially from the beginning end. The flow velocity of the DPF inlet port has been decreases radially from the center. The carrier diameter has significant impact on noise than the length under plateau environment. The carrier diameter of 190 mm has a better noise attenuation effect than the diameter of 170 mm， reaching more than 3 dB. The inlet/outlet aperture ratio is 1.1， the end of DPF noise is the lowest， the attenuation amplitude increases with the increase in carbon loading （0~8 g/L）， the noise attenuation effect is better more than 3 dB.

Based on the change rule of the length of the contact line of the helical gear， the deflection moment under different load distributions along the axial direction is analyzed， and a novel model of the helical gear considering the time-varying stiffness and the deflection stiffness is proposed. The response of an example helical gear system with different tooth widths and torques is studied and compared with the existing experimental data. The lumped-parameter model accounting for backlash is also introduced to analyze the influence of torque and backlash. The results reveal that the proposed model is effective and advanced for general gear systems， especially at low and medium speeds. As the tooth width increases， the effect of the deflection stiffness becomes more significant. With increasing torque， the meshing stiffness of the helical gear increases， and the response of the gear system under parametric excitation caused by time-varying stiffness also increases. As the unbalanced force increases， the motion of the gear system considering backlash develops from periodic motion into pseudo-periodic motion and then back into periodic motion.

There are many uncertainties in the contact of bolted joints. It is difficult to determine the reasonable interval of contact stiffness in existing models. Therefore， the range of contact stiffness of joint surface with uncertain micromorphology is obtained by using interval Estimation theory. Firstly， based on fractal theory to characterize the contour height of the micro-convex body of the combined surface， the structural function method and interval algorithm are used to solve the uncertainty interval of the fractal parameters of the combined surface. Then， the surface topography parameters are connected in parallel with the fractal parameters by moment spectroscopy. The Chebyshev envelope function is introduced to calculate the surface morphology parameters interval under the influence of uncertainty. Finally， the uncertainty interval of surface morphology parameters is introduced into the statistical model to establish the contact stiffness model of joint surface considering the uncertainty of surface morphology parameters. The range of joint surface contact stiffness is obtained， and the influence of surface morphology parameters on the joint surface contact stiffness is explored. The results indicate that the model can accurately predict the range of changes in the surface contact stiffness of bolted connections， which can provide guidance for the design of the joint surface.

The bonding neck between the extruded material filament is a key factor affecting the forming quality of fused filament fabrication products. Therefore， the novel method of using sinusoidal vibration to improve it is proposed， that is， using piezoelectric ceramic to apply longitudinal （along the melt flow direction） sinusoidal vibration to the extrusion liquefier to increase the bonding neck， and thereby to improve the built products′ forming quality. The vibration modification on the fused filament fabrication equipment was completed， based on which the testing samples were prepared. Each sample′s bonding neck was measured with electron microscope. The corresponding theoretical model was established， and the predictions were compared with the measurements. The test results show that using vibration can effectively increase the bonding neck， which further increases with the increasing vibration frequency or amplitude. The growth of the horizontal and longitudinal bonding neck is up to 21.4% and 22.3%， respectively. The difference between the predicted and measured results is 2%~12%， indicating that the proposed model is correct and reliable， and it can predict the bonding neck accurately.

When the vehicle is idling， the large noise of the air conditioning refrigeration compressor is the low-frequency abnormal noise of the compressor. The idle noise of air conditioning refrigeration compressor has randomness and uncertainty， so its diagnosis is difficult. In order to find the compressor abnormality in time and provide a more comfortable driving and riding environment， the idle noise diagnosis algorithm of air conditioning refrigeration compressor based on wavelet packet extraction is studied. The characteristics of idle noise signal of air-conditioning refrigeration compressor are extracted by wavelet packet， and then the characteristics of idle noise signal of air-conditioning refrigeration compressor are classified by support vector machine （SVM）. According to the classification results of noise signal characteristics， the diagnosis of idle speed noise of air conditioning refrigeration compressor is realized. The experimental results show that the idle noise amplitude of air conditioning refrigeration compressor obtained by the proposed method is closer to the actual idle noise amplitude， and the area of ROC curve is large， which shows that the research algorithm has ideal accuracy for the diagnosis result of compressor idle noise.

An improved collaborative filtering recommendation model is proposed to recommend the new product service system for users quickly and accurately. Firstly， the target user neighbor set is determined. Aiming at the problem of data cold start in the traditional collaborative filtering algorithm， a method combining user attribute similarity and user experience similarity is proposed， and Jaccard coefficient， average score correction coefficient and popular coefficient are introduced to improve the accuracy of user experience similarity. Then， the similarity set of new product service system is determined. An improved Pearson cosine similarity algorithm based on product service system attributes is proposed to solve the problem that the traditional project-based collaborative filtering algorithm ignores the similarity constraints of project attributes， and BP neural network is used to obtain the objective weight of each attribute under different product service systems， which improves the reliability of attribute importance. Finally， the recommendation guideline to judge whether the new product service system can be recommended to target user is constructed. Taking the tractor service system recommendation as an example， the feasibility and effectiveness of recommendation model are verified.

The hygroscopic properties and properties of T800 carbon fiber/epoxy matrix composites were analyzed under water immersion conditions at different temperatures. The mass variation， moisture absorption rate variation， IR spectrum， surface morphology before and after water immersion， glass transition temperature， compressive strength and interlayer shear strength were analyzed. The results show that the temperature is higher， the rate of moisture absorption is faster， and the degree of Tg（glass transition temperature） reduction is larger. Cracks occur on the specimen surface and the interface between the fiber and the resin matrix is damaged. After water immersion， the inter-laminar shear strength and compressive strength of the sample are significantly reduced.

In view of the limitation of BPR （Bereau of Public Roads） function that it is not suitable for oversaturated traffic flow and does not meet the characteristics of urban intermittent flow， some improvements have been made. The paper redefines the traffic demand， and proposes a dynamic resistance function form of urban road intermittent flow that takes into account the influence of upstream and downstream signal timing. Based on the data obtained by VISSIM road section simulation， the SMOTE oversampling algorithm is used to balance the simulation data set， and the improved road resistance function is calibrated. Our research shows that adding three variables which include percentage of green wave \omega， green split for downstream research phase s_g， and steering factor s_l into the BPR function can simultaneously reflect the influence of signal timing and traffic demand on travel time. In the case of zero traffic flow， the zero-flow travel time is related with the percentage of green wave \omega. With the increase of \omega， the degree of coordination increases， and the zero-flow travel time decreases.

In order to study the setting conditions of exclusive phase at signalized intersections， the typical intersections in Harbin were selected as the research object， the traffic flow parameter data were obtained by video survey， and the running trajectories of motor vehicles and pedestrians were obtained by simulation with VISSIM and SSAM software. Considering the differences between different individuals in the same type of traffic conflict， the traffic conflict index TTC is introduced to improve the original model. The signalized intersection safety evaluation model based on risk analysis is constructed， and the setting conditions of signalized intersection exclusive phases are put forward. Using the setting condition of exclusive phase based on intersection risk degree， it is judged whether the selected intersection needs to be set with exclusive phase， and the comprehensive risk degree of intersection under different control schemes is compared to verify the rationality of exclusive phase setting. The safety evaluation model of signalized intersection based on risk degree analysis proposed in this paper can distinguish the risk degree of signalized intersection， and provide reference for whether to set exclusive phase at signalized intersection from the safety point of view.

In order to optimize the taxi travel fare， the fare structure of cruise taxi mode and online car-hailing mode was taken as the research object. Based on the analysis of the cruising behavior， the probability statement of taxi cruising direction selection and cruising matching results under different modes was determined， and the equilibrium conditions of multi-mode taxi service network was established based on the balance of supply and demand and the balance of service time. The control threshold was used to transform the equilibrium condition into inequality condition as constraint， and a multi-objective fare structure optimization model under uncertain demand was constructed. The non-dominated sorting genetic algorithm （NSGA-II） was introduced to transform the constraint conditions including the equilibrium conditions into the external penalty function and the adaptive mechanism of the balance control threshold decreasing with the iteration state was set. Through the case analysis of A city， after the optimization of the existing fare structure， per-unit fares for cruise mode and online car-hailing mode increase by 18.15% and 9.75% respectively， the corresponding traffic volume decreased by about 3%~4%. The empty rate decreased by about 6%~7%， the average of passenger waiting time decreased by 4.73% and 6.41 %， and the comprehensive index of taxi drivers' labor value increased by 10.86% and 4.15%， respectively. The validity of the model is verified. The analysis found that in the current fare structure， there is a large optimization space for the basic rate of cruise taxi （including base fare， starting mileage and distance fare ） and the no-occupancy surcharge， and the overall optimization space of online ride-hailing fare structure is relatively small.

Taking traditional car travelers in Xi'an as the research object， a questionnaire combining the revealed preference and stated preference was designed to obtain the travel mode choice behavior of car travelers in specific scenarios. The CHAID decision tree method was used to divide the categories of traditional car travelers. The fixed parameter logit and random parameter logit model were used to establish travel mode choice models of different groups under the driving restriction policy（DRP）， and both two models were compared. The results show that traditional car travelers can be divided into four categories， namely young travelers with only one car， middle-aged travelers with only one car， elderly travelers with only one car， and travelers with multiple cars. The fitting effect of random parameter logit models outperform those of fixed parameter logit models. The low-income young and middle-aged travelers with only one car are more likely to choose public transport under the DRP. Improving the service level of public transport is helpful to attract travelers with only one car to choose public transport on restricted days. Uncongested road is helpful to attract travelers with two or more cars to choose public transport on restricted days. The research conclusions can provide theoretical support for traffic management authorities to formulate differentiated DRPs.

Considering the similarity in residents ′ commuting behavior living in nearby zones， several multilevel Bayesian models are employed to examine the impacts of built environment at traffic analysis zone （TAZ） levels on commuting mode choice. Bayesian models can capture the spatial autocorrelation of commuting behavior by incorporating adjacency matrixes to represent the spatial relationships between TAZs. The results show that the spatial autocorrelation significantly exists in residents′ commuting behavior. In addition， the model incorporating a centroid distance adjacent matrix has the best performance among the compared models. After controlling for socioeconomic characteristics at the individual levels， built environment characteristics are important factors of car commuting. Specifically， land use mix， public transit station density and intersection density have negative impacts on commuting by car. These results suggest that increasing the number of public transit stations， promoting more balanced land use and optimizing road network designs are important for car commuting reductions. The findings suggest that the optimization of built environment is important for encouraging low-carbon travel.

A scalable deep learning framework is proposed to leverage multi-source data in bus passenger flow prediction. First， on the basis of four external factors， three internal factors are introduced as explanatory variables of passenger flow of a bus route. The variance reduction method is used to verify the linkage relationship between internal factors and external factors， and the necessity of capturing the linkage relationship of multi-source data. Then， by utilizing the advantages of convolutional operations of convolutional neural networks in handling two-dimensional data， the influential factors of passenger flow are visualized and an hourly passenger flow subdivision matrix is constructed to adapt to convolutional operations， such that the linkage between multi-source data is captured. To further improve the prediction performance， the matrix structure optimization problem is transformed into a Traveling Salesman Problem， and the surrogate-based optimization technique is used to efficiently optimize the structure of hourly passenger flow subdivision matrix. Finally， the data from Route-281 bus in Guangzhou， China was used as an example for validation. The results show that， through optimizing the structure of hourly passenger flow subdivision matrix， the prediction accuracy can be effectively improved， and optimal usage of data resources can be achieved. The independent effect of internal factors was not significant， whereas the combined effect of external and internal factors is significant. In terms of prediction accuracy，the proposed method has certain advantages compared to the results only considering external factors and other deep learning models.

To improve the safety of vehicles driving on the superhighway， a virtual track system model of the superhighway based on high precision map is established and analyzed. The virtual track system is composed of high precision map subsystem， positioning subsystem， cloud server subsystem and track keeping subsystem. When the autonomous vehicle is driving on the virtual track， the frequency of the vehicle positioning information will affect the response speed of the vehicle. The minimum positioning return frequency is calculated based on the constraint that the vehicle does not deviate from the virtual track and the lane change safety.When the autonomous vehicle is running on the virtual track， the frequency of vehicle positioning information return affects the response speed of the vehicle. The minimum positioning return frequency is calculated based on the constraint that the vehicle does not deviate from the virtual track and the safety of lane change. If the angle between the center line of the body and the tangent line of the track and the sum of the deflection angle of the front wheel or the lateral deviation distance exceeds the threshold value， the vehicle may deviate from the track， which will trigger the track deviation warning system， and the vehicle will take the maximum safe deflection angle of the front wheel as the reference to the corrected driving trajectory. The research results show that when the vehicle speed is 100， 120， 140， 160， 180 km/h， the system positioning and return frequency is kept above 49， 58， 68， 78， 87 Hz can ensure the vehicle running in the virtual track.

To realize the classification and recognition of entering and leaving stops′ driving styles， based on the entering and leaving stops data in the natural driving process of pure electric bus， 14 driving behavior characterization indexes were selected， the dimension of the indexes is reduced by principal component analysis， and a K-means clustering model was established to cluster the entering and leaving stops segments into three categories. Taking economy， dynamic and comfort as the three dimensions of semantic interpretation， the three types were interpreted as high energy consumption & aggressive style， general style and energy-saving & comfort style. A three-layer BP neural network model was established to realize the on-line recognition of driving style. The model verification showed that the evaluation index values of the recognition model are near 0， and the average recognition rate of the model is 93.52%， which can better realize the driving style recognition of any entering and leaving stops segment.

In order to solve the safety and efficiency problems caused by the mixed operation of automatic cars （AC）， human-driving cars and human-driving trucks on freeways in the future， the method of setting an AC dedicated lane on six-lane freeways and its impact on traffic flow are studied. The separation mode and the entrances and exits layout method for inside AC dedicated lane are proposed. The models of vehicles， driving behaviors and freeway sections with entrance and exit ramps are established based on VISSIM. Through simulation experiments in which the freeway design speed is 120 km/h and the truck ratio is 0.2， the effects of AC penetration rate and AC safe headway on traffic flow characteristics after setting an AC dedicated lane are studied， and the effects of the AC dedicated lane on the operation of each vehicle type and each lane are discussed. The results show that the influence of AC penetration rate on traffic flow characteristics is significant and nonlinear； There is an optimal AC penetration rate， which can maximize the capacity， critical density and average speed of the freeway with an AC dedicated lane； The influence of AC safe headway on traffic flow characteristics is nonlinear； The smaller the AC safe headway， the more significant the improvement effect on freeway capacity； The AC penetration rate has significant impacts on the flow rate and average speed of each vehicle type and each lane， especially on the flow rate of AC and inside lane.

Aiming at the efficiency improvement and layout optimization of the assembly line， combined with the advantages of the two-sided line and the U-type line， a collaborative optimization model of two-sided U-type assembly line is proposed. A new design with P-l partition layout is defined to simplify the balancing system of two-sided U-type assembly line balancing problem. Considering the task orientation and time sequencing in cross-workstation， to minimize the number of positions as the primary objective and minimize the number of workstations as the secondary objective， the mathematical model of two-sided U-type assembly line balance multi- constraint is established. And a heuristic algorithm based on multi-level priority rules is proposed to solve the model. 20 classic examples are calculated. The results of the heuristic algorithm are compared with the results of the modified particle swarm optimization algorithm and the bi-objective integer programming model method. The number of optimal results accounts for 95%， which verifies that the collaborative optimization model and algorithm are reasonable， and provides a useful reference for the modeling and solution of two-sided U-type assembly line balancing.

The seismic performance of building double-beam-column joints is analyzed， in order to accurately obtain the changing characteristics of the building structure when earthquake occurs， and improve the seismic safety of the building. The seismic failure process is divided into four stages： initial concrete cracking， yield of beam column stirrups， maximum node bearing capacity， and core area failure. The building damage standard is set， and the damage form of beam-column joints is clarified； the seismic performance of joints is affected by factors such as concrete strength， horizontal stirrups， and beam ends. The basic assumptions of the nodes in the loading process are set， and the deformation displacement of the double beam itself， the displacement caused by the column deformation， and the deformation displacement of the core area are calculated separately， and the three displacements are added to obtain the overall displacement. In the experimental analysis， the load-displacement hysteresis curve is used to study the joint degradation stiffness. The experimental results show that the stiffness gradually decreases with the increase of the displacement； through the maximum load characteristic value and the ductility coefficient， it is proved that the seismic performance of the double beam-column joint is higher than that of the single beam-Column node； the seismic performance of the core area under the node is the weakest， which is easy to cause building deformation， but the seismic performance can be effectively improved by strengthening the concrete strength.

In this paper， through the secondary development of the finite element numerical simulation software， the numerical simulation was refined to model and designed the working conditions， which provide the basic data of the optimal combination of key parameters， and verified its accuracy through field tests. On this basis， the parameters affecting compaction quality were fitted by multiple nonlinear regression method. Then， the construction parameters corresponding to the maximum compaction quality can be obtained by linear search method. The results show that the rolling speed of 4 m/s， rolling passes for 2 times， the corresponding minimum final compaction degree， is 0.878， at the same time， the rolling speed of 1 m/s， rolling passes for 4 times， the corresponding final compaction degree is the largest， 0.955. The conclusions are as follows： the combination of construction parameters to achieve the optimal compaction degree is： rolling speed 1.3 m/s， rolling times 4 times.

The pavement performance and modification mechanism of resin-based high-viscosity asphalt and elastomer-based high-viscosity asphalt were analyzed using various methods including transmission electron microscope scanning（TEM）， rheological tests， and regular performance tests. The multiple stress creep recovery test（MSCR）， temperature sweep test， and dynamic frequency sweep test were conducted on the two high-viscosity asphalts at different contents（3%，5%，8%） with the rheometer. The results of the dynamic frequency sweep were fitted using the Cross model and the Carreau model respectively to determine the zero shear viscosity（ZSV） of the asphalt at 60°C. Furthermore， grey correlation analysis was applied to assess the relationship between the test indexes of the high-viscosity asphalt binder and its rutting stability. The findings indicate that the resin-based high-viscosity modified asphalt is primarily composed of internally formed crystals that can enhance viscosity， while the elastomer-based modified asphalt exhibits a three-dimensional elastic structure created by the modifier and base asphalt to increase viscosity. The resin-based high-viscosity modified asphalt appears a favorable modification effect in the linear elastic interval， but in the nonlinear elastic interval， its modification effect is inferior to that of the elastomer-based high-viscosity asphalt due to crystal melting. The ZSV fitting value obtained from the Cross model is higher than that from the Carreau model. The softening point and creep recovery rate of the high-viscosity asphalt exhibit a significant correlation with the rutting stability of the asphalt mixtures， which is appropriate to evaluate the high-temperature pavement performance. However， the non-recoverable creep compliance and phase angle show a relatively weak correlation.

The disk-shaped settlement distribution of pile group may cause secondary stress， which possibly leads to overall tilting and cracking of the building. Therefore， it is necessary to optimize the design of the squeezed branch pile group. Based on this， we took an improved genetic algorithm to refine the method. The calculated top axial force of pile group was grouped according to the sizes， the number of bearing discs in each group of piles were used as the design variables. Then， a model with the minimum-number discs subject to the constraints of the bearing capacity and the maximum settlement and settlement difference was built. With the help of Python language， ABAQUS was developed to establish an automatic optimization platform to solve the above optimization model and obtain the best pile group arrangement. Result shows that the optimized pile group foundation can reduce the number of bearing discs while decreasing the differential settlement， leading to the uniform deformation of the raft slab and pile top forces， which are beneficial to use the structure securely.

Aiming at the problems of high measurement cost， complex data fusion， and few extracted features in traditional 3D motion parameter measurement methods， a non-contact measurement method of 3D motion parameters based on pose estimation is proposed. First of all， the use of high frame rate synchronous acquisition equipment to capture object motion video from different perspectives makes up for the shortcomings of blind spots that are prone to single-view capture targets. Secondly， using the 2D pose estimation model of the training target based on the deep transfer learning 2D pose estimation framework DeepLabCut， and using the model to estimate the 2D pose of the object from different perspectives， makes up for the shortcomings of the traditional vision method with fewer features. Third， the 2D pose estimation of objects in different directions is merged into a 3D pose estimation based on the principle of triangulation. Finally， according to the measurement requirements， the 3D motion parameters such as the displacement， speed， pose angle， frequency of the specified key points of the object can be calculated using the results of the 3D pose estimation. Taking the motion measurement of the planetary pendulum and the human hand as an example， we measured the 3D motion parameters of the two and analyzed the sources of error and the advantages and disadvantages of this paper. The experimental results show that the system measurement is accurate， and the visualization results correctly reflect the movement of the experimental subjects. This method provides a new idea for the non-contact measurement of 3D motion parameters of objects.

In order to reduce the amount of historical data of text data and improve the efficiency of text data compression and storage， a text data compression and storage algorithm based on time series model is proposed. The wavelet threshold denoising method is used to estimate and eliminate the error and noise of text data； from the perspective of text data features， the features are described in detail， and the combination and inheritance relationship between feature types are set to build a time series model. Convert the preprocessed text data into binary coded bytes with similar structure using the time series model， perform XOR operation to compress the redundant part in the result， and store the compressed data in the corresponding database， and finally complete the text Data compression storage. The simulation results show that the proposed algorithm can effectively improve the compression performance and obtain more satisfactory compression and storage results of text data.

At present， in the field of image data encryption， chaos theory encryption method is more prominent. The high randomness， unpredictability and extreme sensitivity of initial parameters of chaotic system provide favorable guarantee for image data security. Based on this kind of mapping， a large number of complex chaotic mapping models have been proposed by scholars at home and abroad. Compared with one-dimensional chaotic mapping， the security has been greatly improved， but the computational complexity has also increased. Therefore， this paper proposes a one-dimensional compound Logistic-Tent algorithm for chaotic mapping， which increases the mapping range of chaotic mapping， reduces the computational complexity and ensures the security of chaotic sequences. Combined with Fibonacci scrambling transform， this algorithm adopts scrambling - diffusion encryption process. Simulation experiments show that the scheme has high encryption rate， and can effectively defend against brute force cracking， entropy attack， difference attack and statistical image attack.

The unknown attack threat of the network will affect the security situation of the Internet of Things. In order to improve the identification effect， an automatic identification algorithm of the security situation of the Internet of Things based on deep neural network is proposed. Analyze the security threats of the Internet of Things through Trojan horse and DDoS attacks， obtain the security situation elements of the Internet of Things， extract the security situation characteristics of the Internet of Things by establishing the security state distribution model of the Internet of Things invaded by viruses， combine the deep neural network with the deep stack editor， train the extracted security situation characteristics， and use the final training results to determine the security situation performance of the Internet of Things， Complete the automatic identification of Internet of Things security situation and realize the design of automatic identification algorithm of Internet of Things security situation. The experimental results show that the algorithm has high accuracy and strong practicability through the comparison test of recognition effect， recall test， F₁ value test and recognition running time test.

In order to solve the problems of the chattering and signal derivation for the traditional sliding mode control（SMC） approach， a novel continuous non-singular terminal sliding approach is proposed to realize the angle control of electronic throttles（ET）. Based on the mathematical model of the ET system， its control performance is improved in the aspect of sliding surface and control law. During the design of the sliding surface， the finite-time convergence can be achieved by introducing the power index terms concerning the system states. Meanwhile， it removes the constraint condition of the controller parameter for the traditional SMC approach. During the design of the control law， a continuous control law can be achieved by introducing the low pass filtering in the premise of the system stability. Meanwhile， it solves the problem of signal derivation of system states. Comparative study and simulations with the traditional first-order SM and second-order SM proves the superiorities of the proposed approach in chattering elimination， fast response and control accuracy.

The vibration characteristics of the whole wheel tractor and the symmetrical parts of the structure were collected and analyzed by using the vibration test system， and the vibration similarity of the symmetrical structure was compared with the vibration intensity. The results show that the vibration characteristics of the symmetric parts of the front axle and the rear axle are symmetrical. The main vibration direction of the symmetric measurement points is the same， and the vibration similarity of the symmetric positions of the front axle and the rear axle is 93.91% and 94.29%， respectively. It is concluded that the vibration characteristics of the whole tractor and its symmetrical parts are affected by the working state of the whole tractor， the defects of parts and the assembly quality. The difference of the parts and assembly quality of the symmetrical parts of the tractor changes the vibration transmission path and affects the similarity of the vibration characteristics of the symmetrical parts. This paper presents a method for the comparison and evaluation of the vibration characteristics of the tractor symmetric structure， which can provide a fast-screening method for the on-line detection of tractor working state and the discovery of mechanical structure defects and faults.

The critical fragments whose average diameter is slightly larger than or equal to the diameter of the bit coring hole will produce two migration modes： hole bottom output and hole wall input in the process of coring drilling， which will lead to the change of drilling load. By establishing the cutting model， it is obtained that the influence of cutting speed on cutting force is small and can be ignored in low-speed cutting. The cutting test is carried out on the critical scale particles with different particle sizes， and the variation curve of cutting load with time is obtained. The cutting load is identified by support vector machine （SVM）. The results show that the recognition rate of homogeneous simulated lunar soil cutting load is 100%， and the recognition rate of simulated lunar soil cutting load with critical scale particles is more than 95.5%. The research results can provide a reference for the on-line identification technology of lunar unmanned drilling sampling.

Aiming at the current situation that the state strongly advocates farmers to plant forage Broussonetia papyrifera， but there is a lack of suitable harvesting machinery in China， this paper combines the harvesting characteristics of Broussonetia papyrifera， guided by the moving track of hob， the limit value of cutting stubble height of Broussonetia papyrifera and the requirements of conveying roller for stem grasping， and takes neat stubble without splitting， low complete harvest loss and smooth branch and leaf grasping and conveying as the technical requirements， a hob rotary mechanical harvesting header for Broussonetia papyrifera was developed. According to the box Behnken experimental design method， the regression model of the number of hobs， hob speed and machine forward speed on the consistency of stubble height and harvest loss rate is constructed. The response surface method is used to analyze various factors and their interaction laws， and the parameters of the regression model are optimized. The optimal parameter combination is obtained as the number of hobs 3， hob speed 889.673 r/min and machine forward speed 4.138 km/h， the consistency of stubble height was 96.482%， and the harvest loss rate was 0.686%. The verification test is carried out under the optimal parameter combination. The results show that the consistency of stubble height is 97.12%， the harvest loss rate is 0.67%， and the test value is close to the predicted value. This study can provide a reference for the design of mulberry harvester.