118results about How to "Reduce modeling difficulty" patented technology

The invention discloses a three-dimension crack expanded simulate method, based on embedded finite element modeling, which shows the non-continuity of structure based on structure solid model, while the crack expansion is realized by changing crack shape and position of solid continuously. The invention first builds a n embedded crack sub model of upper and lower crack surfaces, divides the whole crack structure into a non-crack body and a crack body, wherein, the crack body is composed of upper and lower six solid sub blocks, the crack surface is between the upper and lower faces of four sub blocks as the third, fourth, fifth and sixth, that the crack surface is contained in the crack body and the crack body is embedded in the non-crack body. The invention can avoid digit error in the crack expansion process, caused by unreasonably divided unit, to support the description of cut or hole-crack in projection.

The invention belongs to the field of business intelligence, and particularly relates to industry data-oriented index setting definition, management, storage and application of providing data analysis and support. The invention discloses an index-system-model-based agile business intelligence data construction method. An index calculation model and data storage function construction method for different industries and service fields is realized by using index data capable of distinguishing data characteristics as a center through dynamic extraction of the index data, index system configuration and attribute definition. The method comprises the following steps of: performing index system extraction by using a minimum granularity information unit as an index extraction principle, and then performing index attribute definition and setting. By the index-system-model-based agile business intelligence data construction method, the application of a business intelligence platform can be prevented from being constrained by specific industry and service, a basic data model for data analysis and application is quickly and effectively constructed, the construction problem of an on-line analytical processing (OLAP) model is solved, and a foundation is laid for efficiently constructing various data analysis and application models with high quality.

The invention provides a method and device for data mining process generating. The method includes the following steps that, the metadata and training data submitted by an input device are received, and the metadata includes output data types; the preset output data types of multiple algorithm nodes are matched with the output data types in the metadata, so as to determine the successfully matched algorithm nodes; all the forward and the backward nodes of each successfully matched algorithm node are recursively searched, so as to confirm the multiple data mining paths corresponding to each successfully matched algorithm node; multiple data mining processes are established according to the preset node parameter templates; and the multiple data mining processes are respectively verified according to the training data, ad the optimal data mining process in the multiple data mining processes is determined according to the verification result and the preset evaluation rule. As long as an ordinary user specifies the evaluation rule, the optimal data mining process can be automatically chosen, so as to reduce the difficulty of modeling.

The invention discloses a knowledge modeling method and system used for aided design of a mechanical product scheme. The method comprises the steps of firstly performing overall function scheme designknowledge modeling to build a function-scheme template tree knowledge model and a function scheme design example model; and secondly establishing function or scheme design modules, establishing association with a function-scheme template tree, building a check function scheme design model and a parameter reasonability scheme check rule, combining the design modules in the form of a reconfigurablemodule process or task process, building a scheme design process model, and realizing parameter value automatic transmission and design process guide functions. The system comprises a function schemeaided design module, a module process aided design module, a task process aided design module, a design module control module and a system main control module. According to the method and the system,the knowledge modeling difficulty can be lowered; and the knowledge modeling method and the corresponding aided design system which conform to thinking habits of engineers and are reusable are provided.

The invention provides a parallel association rule mining-based method for predicting running reliability of a power distribution network. The method comprises the steps of 1, extracting related data from multi-source heterogeneous power distribution big data according to running reliability assessment demands; 2, mining related factors which influence the running reliability by adopting a parallel association rule mining method, and building an ''influence factor-running reliability index relevance model''; 3, obtaining a main influence factor as an input of an artificial neural network, and building an ''influence factor-running reliability index quantitative calculation model'' based on historical running conditions and running reliability parameters; and 4, taking real-time running condition data as an input of the artificial neural network, and predicting a running reliability index value in a corresponding running condition. According to the method, the main factor which influences a reliability index is accurately located, so that the input data dimensions of an assessment model are reduced, and the modeling difficulty is lowered.

The invention discloses a pellet production method based on prediction of compressive strength of pellets in a rotary kiln. The method comprises the following steps: 1) collecting related operation parameters of the rotary kiln; 2) establishing a motion model of the pellets in the rotary kiln; 3) measuring and calculating average residence time T of the pellets in the rotary kiln; 4) measuring flame length FL and the distribution of burning rate Rfuel of fuel in the flame length; 5) measuring the distribution of temperature of the pellets Tp along the kiln length direction z in the rotary kiln; 6) measuring a predicted value of the strength of the pellets; and 7) performing online control on process parameters of the rotary kiln according to the predicted value Qp of the compressive strength of the pellets. By using the method for online prediction of the thermal engineering state and the strength of the pellets in the rotary kiln, the transparency of the production process is increased, the control difficulty caused by information hysteresis is reduced, the risks of low finished product rate, accretion of the rotary kiln and the like caused by insufficient strength of the pellets can be reduced, and the thermal engineering operation of the rotary kiln is optimized in an online manner; and the energy consumption can be reduced and the production benefits can be improved.

The invention provides a microwave cavity filter intelligent debugging method based on a particle swarm optimization algorithm, and the method comprises the steps: firstly, randomly changing the length D of each coupling screw in an electromagnetic simulation model of a microwave cavity filter into a cavity, obtaining a corresponding dissipation parameter S, and constructing an original sample data set; secondly, preprocessing the data in the sample data set; based on a block modeling method and a BP neural network, performing training to obtain an electromechanical characteristic model of themicrowave cavity filter; and finally, based on the electromechanical characteristic model and a particle swarm optimization algorithm, debugging the microwave cavity filter to be adjusted. The adjustment amount of each coupling screw is determined through a particle swarm optimization algorithm, so that the value of the value function is continuously reduced until the output of the microwave cavity filter meets the preset performance index, and the debugging process of the microwave cavity filter is completed. The debugging method has the beneficial effects that the debugging difficulty is reduced, the debugging precision and the debugging speed are improved, and the practicability and the applicability are high.

The invention discloses a rotary exchange groove type multi-material exposure additive manufacturing device and method. The mask exposal technology is applied by the device to serve as a light-sensitive material forming technology, and meanwhile, a multi-partition rotary type material groove is applied to serve as a supply mechanism for machining different materials. During machining, a plurality of material portions of one part are subject to modeling, and then subject to layer shearing and mask generation treatment; a plurality of mask queues are guided into a control module, and the control module determines whether multiple materials are contained in layers with the same height; if the multiple materials are contained, the layers are subject to multi-material machining, and if the multiple materials are not contained, the layers are subject to single-material machining of corresponding materials; during machining of multiple materials on the same layer and machining of different materials in different layers, the cleaning step of machined parts is automatically executed, and therefore it is ensured that multiple materials are not polluted by one another. Through replacement of material grooves containing different light-sensitive materials and corresponding masks in the machining process, automatic precise machining of the parts containing the multiple different materials is achieved.

The invention discloses a temperature modeling method for a proton exchange membrane fuel cell (PEMFC) system based on a variation particle swarm and differential evolution hybrid algorithm. The PEMFC pile output performance is influenced by the temperature controlled by each module of an operation system, the method is combined with a PEMFC pile model based on the variation particle swarm and differential evolution hybrid algorithm, and the steps of resolving the optimal temperature operating parameter algorithm for each module of the pile when the electrochemical performance is optimal are proposed. The hybrid algorithm has good global and local searching and optimizing capacities, and the temperature control parameter for each module of the PEMFC system can be identified at high precision and controlled in real time, so that the electrochemical performance of the fuel cell system is improved.

The invention relates to a multi-time-scale square lithium battery SOC and SOT joint estimation method, and belongs to the technical field of battery management. The method is realized on the basis ofa control-oriented square lithium battery electro-thermal coupling model in combination with an advanced multi-time scale estimation algorithm. The method comprises the following steps of determiningheat production related parameters in an electric model in advance through experiments, acquiring other parameters of the electric model through online parameter identification, and then achieving SOC estimation at the current moment in combination with an observer; according to the SOC and the temperature value of the previous moment, determining the heat production rate of each discrete volumeunit of the battery, and obtaining the temperature distribution of the current moment; and then updating electric model parameters by utilizing the current temperature and the SOC value, determining the heat production rate of the discrete volume unit, and iteratively updating the SOC value and the SOT value in this way. The estimation precision and the calculation resource utilization condition can be well balanced, and strong-robustness, high-fault-tolerance, accurate and efficient lithium battery electric heating characteristic monitoring under different time scales is realized.

The invention discloses a planning operation joint optimization method and system for an electricity-gas hybrid system based on distributed multiple scenes. The method comprises the steps: collectingelectricity purchasing power, gas source point gas inflow and t-moment net load fluctuation power parameters in different scenes; establishing an electricity-gas hybrid system capacity optimization problem model comprising a capacity configuration layer sub-problem model and a system operation layer sub-problem model; decoupling an electricity-gas hybrid system planning operation joint optimization problem into a configuration layer sub-problem and operation layer sub-problems in n scenes by adopting an alternating direction multiplier method with an adaptive step length, and carrying out iterative solution; and obtaining an optimal configuration result and an operation strategy set of the electricity-gas hybrid system. According to the method, indexes such as the net load fluctuation ratio are adopted to evaluate the operation flexibility of the power distribution network, the flexibility influence is considered in the optimization target, and the multi-target optimization problem ofconsidering both economy and flexibility in the capacity optimization of the electricity-gas hybrid system is effectively solved.

The invention discloses an electromechanical transient modeling method for an LCC-MMC hybrid direct current grid. A constructed electromechanical transient model of the LCC-MMC hybrid direct current grid takes the dynamic characteristics of a MMC direct current side model into account, so the accuracy of the model is higher. In addition, the method adopts a state space expression for unified combination solving of an algebraic-differential mathematical model of a direct current network and a converter station, and an algebraic-differential equation of a controller, so that the modeling difficulty is low, and the implementation is convenient. The model constructed by the method can be used for transient stability analysis of the LCC-MMC hybrid direct current grid, and has important application in planning, designing and running of the grid.

Note: for the problem of characters, expressions of the same meaning need to adopt the same character, and expressions of different meanings need to adopt different characters; and for the problem ofnames of terms, for expression of the same meaning, the names of terms should be unified. The invention discloses a shield tunnel segment design parameter optimization method, and relates to the technical field of engineering construction. The shield tunnel segment design parameter optimization method comprises the following steps: performing parameter value combination by utilizing an orthogonaltest table and segment design parameters, and forming a plurality of working condition design schemes; calculating to obtain the minimum safety coefficient Kmin of the shield tunnel segment under eachworking condition design scheme; training a BP neural network according to the segment design parameters under the working condition design scheme and the corresponding Kmin; according to the segmentdesign parameters, the Kmin and the mapping relationship between the segment design parameters and the Kmin, solving an optimal solution of the segment design parameters by adopting a genetic algorithm; and adjusting the optimal solution of the segment design parameters. The shield tunnel segment design parameter optimization method is high in operability and reliability for the actual shield tunnel segment design, overcomes the defect that an existing shield tunnel segment design is optimized through experience, can optimize multiple design parameter variables at the same time within a larger range, and can guarantee that design parameter values in a design scheme are optimal, so as to guarantee safety construction of the design scheme.

The invention relates to a BIM modeling method based on variable cross-section rigid frame bridge continuous beam cantilever casting construction. The BIM modeling method based on variable cross-section rigid frame bridge continuous beam cantilever casting construction comprises the steps of A, extracting the related property value of a main beam; B, extracting the corresponding coordinate value of the mileage pile number of each node according to road center line; C, establishing a file of main beam segmental node information according to the values obtained in the step A and B; D, establishing a main beam parametric family file according to the shape and design parameters of the cross section of the main beam; E, integrating an BIM model of a variable cross-section continuous beam, whichsatisfies cantilever casting construction segment, in a software according to the file of main beam segmental node information, which is established in the step C, and the main beam parametric familyfile, which is established in the step D. The BIM modeling method based on variable cross-section rigid frame bridge continuous beam cantilever casting construction has the advantages that the BIM model of the variable cross-section continuous beam, which satisfies cantilever casting construction segment, is automatically established by inputting parameters; the reverse modeling efficiency and accuracy are greatly enhanced; and the method can be used again in the follow-up similar projects so that the modeling time and difficulty are greatly reduced.

The invention aims to provide a variable working condition emission performance prediction method for a combustion chamber of a gas turbine, and the method comprises the following steps: carrying outmesh generation and numerical simulation calculation of a combustion chamber model through FLUENT software for some typical working conditions; dividing the combustion chamber into five characteristic areas including a head mixing area, a main combustion area, a wall surface cooling area, a backflow area and a mixing area according to the combustion process, temperature distribution and flow field analysis; according to the divided feature regions, establishing a chemical reactor network (CRN) model for the combustion chamber of the gas turbine by utilizing Chemkin-Pro software; determining the effective volume of the feature region by adopting an infinitesimal method; and finding out calculation parameters of the reactor under other working conditions by adopting a Newton interpolation polynomial method, and predicting the emission performance under unknown working conditions. A method of combining fluent numerical simulation with a chemical reaction network is utilized, and the NOxemission performance of the combustion chamber of the gas turbine at different fuel-air ratios under the design working condition and the bias working condition is rapidly predicted.

The invention relates to an avionics system dynamic reconfiguration modeling method based on AltaRica, which is used for avionics system task reliability modeling. On the basis of analyzing the characteristics of the avionics system, the invention proposes that the task reliability of the system is modeled based on the AltaRica model with the finite state machine as the theoretical basis. This method can realize the integrated modeling of system function and fault logic. Through the extended definitions of fault state and trigger event, it can describe the jump and influence relationship of each component working state under the condition of fault occurrence and fault-tolerant design, and truly reflect the design characteristics of avionics system. The invention models the complex dynamicreconstruction process, facilitates the analysis of the task reliability of the dynamic reconstruction process, combines the AltaRica model with the dynamic reconstruction process of the avionics system, and creates a new process modeling method.

The invention belongs to the field of rolling process fault diagnosis, and particularly relates to a variable-control-limit online fault diagnosis method for the IPCA rolling process. According to theonline fault diagnosis method, faults in the rolling process are identified and diagnosed online through real-time data, therefore, the production continuity and stability requirements are met, the maintenance time is shortened, the product yield in the steel rolling process is increased, and the product quality in the steel rolling process is improved. A diagnosis model for normal historical training data and the statistic control limit are updated online through an incremental principal component analysis (IPCA) method, online data which are diagnosed and do not have faults are expanded tothe diagnosis model to adapt to the current production state in the rolling process, and the fault diagnosis accurate rate is increased.

The invention provides a continuous stretch-draw integrated robot driven by multiple sections of ropes. The continuous stretch-draw integrated robot comprises a base, a plurality of basic units, a plurality of connecting mechanisms and three driving mechanisms, wherein the center of each basic unit is provided with an elastic connecting piece, and the elastic connecting pieces are elastically connected with the basic units; the three driving mechanisms are arranged front and back; and each driving mechanism comprises a plurality of ropes and a plurality of driving units for driving the ropes to move, and the front ends of the ropes are connected with the driving units. The continuous stretch-draw integrated robot driven by the multiple sections of ropes integrates three deformation modes of stretching, bending and twisting, and has flexible movement capability. The robot body is simple and ingenious in structure and has good reconfigurability. On the basis of the basic units, the connecting mechanisms and the base, springs and the ropes are connected to form the body structure of the robot, the modeling difficulty of kinetic analysis is reduced while assembling is easy, the model accuracy is high, and a better control effect can be achieved.

The invention provides a motion control method for the workpiece table of a lithography machine, comprising the following steps: (S1) setting the motion servo system of the lithography machine workpiece table into a three-ring PID control structure; (S2) introducing a dual feedforward in the three-loop PID control structure to achieve control of the motion servo system. According to the motion control method for workpiece table of lithography machine, the design and modeling difficulty of the motion servo system are reduced, the model uncertainty caused by the disturbance is tracked, and the steady-state tracking error is reduced, which improve the positioning and tracking accuracy. The invention not only ensures the stability and corresponding rapidity of the motion servo system, but alsomakes the output trajectory and speed of the motion servo system reproduce the planning trajectory and the planning speed to the maximum extent at any time. The method of the invention is flexible, prone to control, and is strong in promotion value and practical value.

The invention provides a three-dimensional terraced field modeling method based on TIN, belonging to the field of three-dimensional terraced field modeling. At present, all the terraced field modelingmethods are based on DEM (digital elevation model) of a regular grid, which have the problems that it is difficult to control the terraced field shape, and the model precision varies with the DEM precision, so it is difficult to extract earthwork and optimize the terraced field model. In order to solve the above problems, the technical scheme of the invention mainly comprises the following steps:creating an empty TIN surface to obtain a three-dimensional original terrain surface; obtaining the terrain clone surface; obtaining the average slope of the terrace clone surface in each terraced field area, and finding out the height difference H of terraced field step; taking H as a parameter, extracting a new contour with a height difference H from the terrain clone surface; obtaining a reference contour; generating upper and lower step sidelines until all datum contours are processed; creating an empty TIN surface and adding the lines delimiting the terrace area as boundary lines to theterrace surface to obtain a three-dimensional terrace surface.

The invention relates to the technical field of heat exchanger fault diagnosis, and discloses a heat exchanger early fault diagnosis method based on a BP neural network. The method comprises the stepsof: A) collecting key parameters of a heat exchanger in a normal working state; B) dividing the acquired key parameters into a training set, a verification set and a test set; C) constructing a BP neural network model; D) obtaining a mean value [mu] and a standard deviation [sigma] of a test output error; E) predicting the outlet data of the heat exchanger by using the trained BP neural network model to obtain predicted output of the outlet data of the heat exchanger; and F) performing fault diagnosis, and determining whether the predicted output error is continuously out of a normal error range within the error determination time length T. According to the method, the BP neural network model is established with no need for fault samples, the model can be established only by adopting thedata of the heat exchanger in the normal working state, then the fault of the heat exchanger is diagnosed through the determination standard, and the fault diagnosis accuracy is high.

The invention provides a cruising system with controllable voice input and a vehicle, and belongs to the field of vehicles. The cruising system comprises a voice recognizing system and a cruising system control unit, wherein the voice recognizing system is used for recognizing and analyzing voice information of a driver of the vehicle; the recognizing result is sent to the cruising system control unit; according to the result which is analyzed and recognized by the voice recognizing system, the cruising system control unit controls vehicle speed of the vehicle to start cruising. The cruising system is based on controllable voice input, so that button resources of the vehicle are saved, and the modeling difficulty degree of a steering wheel is reduced; and besides, the vehicle speed for cruising of the vehicle is set through the voice input, a driver does not need to adjust the buttons by manual operation, the control is convenient, the driving safety is effectively guaranteed, and the driving comfortableness and the technological content of whole vehicle are improved. The invention further provides the vehicle, no-button control in aspects of the vehicle cruising, temperature of an air conditioner of the vehicle, volume of a loudspeaker, and the like is performed based on the controllable voice input, and the driving comfortableness and the driving soundness of the vehicle are improved.

The invention relates to a heat transfer technology and provides a shell-and-tube heat exchanger and a method for machining plate holes in the shell-and-tube heat exchanger. According to the technology, heat exchange tubes are distributed in a radial topological mode in cooperation with circular-ring-shaped and disk-shaped baffle plates which are arranged alternately, so that shell-pass fluid uniformly flows through all the heat exchange tubes in the radial direction, and the utilization rate of the heat exchange tubes is increased. The shell-and-tube heat exchanger mainly comprises a barrel body, tube plates, tube boxes, the heat exchange tubes and the baffle plates. The baffle plates comprise the circular-ring-shaped baffle plates and the disk-shaped baffle plates, the circular-ring-shaped baffle plates and the disk-shaped baffle plates are arranged alternately, and the total number of the baffle plates is an odd number. The heat exchange tubes are arranged on concentric circles with different diameters with the symmetrical axis of the heat exchanger as the center and are distributed evenly in the circumferential direction. The spacing between the heat exchange tubes is not a definite value, and the minimum spacing is 1.25 times larger than or equal to the outer diameter of each heat exchange tube.

The invention relates to an aspect-oriented interlock system security demand formalized modeling and verification method. The method comprises the following steps: 1) establishing an object-oriented model of a signal device, wherein the model comprises a route class, an annunciator class, a zone class, a turnout class and corresponding subclasses; 2) establishing a station yard graph information extraction model, extracting corresponding device information, route information and topological information according to an input station yard graph source file and a corresponding data structure, and establishing mapping with the established object-oriented model; 3) performing aspect-oriented security demand formalized modeling; and 4) establishing a formalized model and an interface model of a universal application. Compared with the prior art, the method has the advantages that the modeling efficiency is improved, the security demand is easier to maintain, and the like.

The invention discloses a product reliability analysis method and device. The method comprises the steps that failure and maintenance historical information of various types of parts of a plurality ofsamples of a to-be-analyzed product is acquired, the failures of the parts capable of being completely maintained are modeled into complete maintenance failure statistic models, and the failures of the parts capable of being subjected to minimum maintenance are modeled into minimum maintenance failure statistic models; the complete maintenance failure statistic models or the minimum maintenance failure statistic models, obtained through modeling, of the parts of various types are detected; the detected complete maintenance failure statistic models or the minimum maintenance failure statisticmodels of the parts of various types are used for Monte-Carlo simulation of the to-be-analyzed product; simulation data obtained through Monte-Carlo simulation is subjected to statistic analysis, andthe mean time between failures of the to-be-analyzed product and unconditional failure intensity of the to-be-analyzed product in each fixed time interval are calculated. By means of the method, the modeling difficulty during reliability analysis can be reduced.

The invention relates to a serial production line buffer zone capacity allocation method based on availability evaluation. The serial production line buffer zone capacity allocation method specifically comprises: 1) determining the number of serial production line devices and the total capacity of buffer zones; 2) decomposing a serial production line into a plurality of secondary subsystems; 3) determining the state law of secondary subsystem devices; 4) determining the change law of the states of buffer zones of the secondary subsystems; 5) determining an availability evaluation model of thesecondary subsystems; 6) making the secondary subsystems equivalent to a virtual device, and forming new secondary subsystems; 7) performing multiple iterations to determine the availability evaluation model of the serial production line; and 8) using the improved genetic algorithm to solve the model. The invention can reasonably allocate the capacity of the buffer zones of the serial production line and improve the productivity of the serial production line.

The invention relates to a method for reconstructing a vehicle interior noise signal, comprising the following steps: 1) decomposing and analyzing a signal; decomposing and analyzing the source signalto obtain three stable signal component categories, namely, a high-frequency component, an intermediate-frequency component and a low-frequency component; 2) component fitness calculation: respectively train that BP neural network model and taking the performing weight and the threshold value of the BP neural network model as component fitness value to obtain the optimal component fitness value;3) performing Signal reconstruction model: According to the categories of input signal components, the BP network is trained by assigning the fitness value of the optimal component to the noise reconstruction as the initial weight and threshold. After convergence, the corresponding reconstruction algorithm model of each signal component is obtained, and the noise signal of the occupant's ear sideis reconstructed by reconstruction superposition according to the reconstruction algorithm model. Compared with the prior art, the invention has the advantages of reducing the non-stationarity of thesignal and the difficulty of modeling, improving the reconstruction accuracy and the like.

The invention discloses a vertical malicious crawler traffic identification method based on deep learning, and belongs to the technical field of the Internet. According to the invention, deep learningis used for flow identification of website access behaviors; identity recognition is carried out on flow with malicious crawler behaviors through classification of access behavior characteristics, and the method comprises three parts of encoding website access flow into a three-dimensional vector, carrying out recognition training through a three-dimensional convolutional neural network, and finally establishing a classification recognition model for malicious crawler flow through optimization of network parameters. The website access flow is classified and identified through deep learning, the malicious crawler flow identification accuracy of the website is improved, and the website can deploy the corresponding security policy according to the identification result so as to improve the website performance and reduce the website redundant load.

The invention discloses a finite element modeling method for a binding type generator stator end. The method comprises the steps of, on the basis of a three-dimensional model of the stator end, performing middle line extraction and / or middle surface extraction processing on stator end parts of the three-dimensional model; performing gridding processing on the stator end parts to build corresponding grid models; and connecting the grid models of the parts to build a whole stator end finite element model. For the problems that the finite element model in a process of performing dynamics analysison a binding type fixed structure of the generator stator end by adopting a numerical method is difficult to build reasonably and the modeling efficiency is relatively low, on the basis of a principle that beam and shell units is same in degree of freedom, a line rod beam unit finite element model and a bracket and pressing plate shell unit finite element model are built by using middle line extraction or middle surface extraction, and a connection mode of node sharing is adopted, so that the use of contact units is reduced, the modeling efficiency is effectively improved while the modeling difficulty is lowered and the analysis precision is guaranteed, the calculation scale is reduced, and the implementation is easy.

The invention provides a finite element modeling method for an automobile exhaust pipe silencer. The method comprises the steps of performing finite element modeling on a turnup portion of a drum body, turnup portions at the drum body and an end cover, an air intake pipe, an intake pipe, a separating plate, an exit pipe, an end cover, a welding portion of the air intake pipe and the drum body, a welding portion of the exit pipe and the end cover, a welding portion of the inner pipe and the separating plate, a welding portion of the drum body and the separating plate and the drum body and obtaining corresponding grids; creating materials and attributes for the grids and assigning the created materials and attributes to the corresponding grids; finally adding mass points, building rigid units and performing mass correction on a whole automobile exhaust pipe silencer. The method has the advantages of accurate modeling, fewer grids, higher grid quality and easy implementation and is suitable for the field of computer-aided engineering.