974 results about "Mechanical models" patented technology

The invention provides a twin power station collision visual simulation method and system based on physical simulation, and relates to the technical field of data processing, and the method comprises the steps: obtaining a three-dimensional geometric model of a twin power station, carrying out space division, extracting geometric parameters, obtaining material data, constructing a material correlation model and a mechanical model, and carrying out fusion to obtain a physical simulation model; acquiring initial collision condition parameters as boundary conditions, performing local grid encryption, constructing a nonlinear kinetic equation of a collision process, performing time domain solution, determining equivalent stress distribution and equivalent plastic strain distribution, and generating a collision result; judging whether plastic deformation or fracture failure occurs, generating full collision process physical field data, generating a collision animation frame sequence, generating a stress distribution cloud picture and a plastic strain cloud picture, constructing a fracture special effect, and comprehensively generating a collision visual scene.

The invention relates to the technical field of structure health monitoring, in particular to a breakwater structure health monitoring method based on a sensing network, which comprises the following steps: S1, collecting stress, acceleration, displacement and environmental parameter data of a breakwater; s2, preprocessing the sensing data acquired in the step S1; s3, integrating the data preprocessed in the S2 into a comprehensive data set reflecting the overall structure state of the breakwater; s4, analyzing and calculating the comprehensive data set in combination with a structural mechanical model of the breakwater so as to extract characteristic parameters; s5, constructing a breakwater health assessment model; s6, evaluating the real-time data based on the health evaluation model constructed in the S5, and calculating the health score of the breakwater; according to the invention, through real-time data acquisition based on the sensor network and the sparse Bayesian learning algorithm, comprehensive monitoring and intelligent early warning of the health state of the breakwater structure are realized, and the monitoring precision, the real-time performance and the risk prediction capability are effectively improved.

The invention relates to the technical field of intelligent early warning of digital twinning, and particularly discloses a slope multi-physics field fusion early warning decision-making system based on digital twinning, which is characterized in that physical monitoring data representing the macroscopic state of a slope and microscopic physical response signals reflecting internal damage evolution are synchronously acquired through a multi-modal data sensing module; space-time alignment, standardization and cross-modal fusion analysis are carried out through a damage eigenstate extraction module, and a unique eigendamage variable for quantitatively representing the real-time degradation degree of the material strength is interpreted; the twinborn self-evolution module takes the variable as a core observed quantity, and drives parameters and states of a slope mechanical model to be cooperatively and dynamically updated by adopting a data assimilation method, so that high-fidelity tracking of a digital model on physical reality is realized; and the prospective early warning decision module deduces a future spatio-temporal evolution path of the material strength parameters based on the calibrated model, and realizes graded early warning and intelligent decision support by combining Monte Carlo simulation and quantification of the instability risk probability.

The invention discloses a satellite orbit prediction method, device and equipment based on multivariate features. Comprising the following steps: determining a speed position parameter of a satellite at a prediction moment under an ICRS coordinate system based on an orbit mechanical model to obtain a first speed position parameter; converting the first speed position parameter into an ECEF coordinate system to obtain a second speed position parameter; time features, satellite state features and space environment features at the current moment are obtained, the satellite state features are in an ECEF coordinate system, and the space environment features are in a geodetic coordinate system; performing feature fusion on the time feature, the satellite state feature, the space environment feature and the second speed position parameter to obtain an input feature, and inputting the input feature to an orbit correction model; evaluating the error of the second speed position parameter based on an orbit correction model to obtain a target error prediction value; and adjusting the second speed position parameter based on the target error prediction value to obtain a target speed position parameter of the satellite at the prediction moment.

The invention discloses a macroscopic progressive damage intelligent evaluation method driven by multi-scale microscopic damage observation, and the method comprises the steps: selecting a plurality of representative materials to carry out a microscopic mechanical test, and observing the damage evolution process under different loading conditions through MicroCT to obtain a microscopic mechanical test result; simulating a crack state based on a micro-mechanical model to reproduce a micro-mechanical test result, and performing material parameter correction of simulation calculation by taking test data as a constraint to obtain a micro-numerical modeling calculation result; and determining a representative volume element (RVE) scale based on a microscopic numerical modeling calculation result, and describing a macroscopic damage behavior by utilizing a continuous damage mechanical model so as to obtain evolution relation data of a macroscopic damage variable, stress and strain. According to the method, the damage behaviors of metal and composite material structures under complex working conditions can be accurately predicted, and the safety and reliability of aircraft structures can be improved.

The invention discloses a deep learning-based intelligent identification method and system for rock and soil mechanical parameters, and aims to accurately identify the elastic modulus, strength parameter and permeability coefficient of rock and soil and support the decision and design of engineering projects under complex geological conditions. And starting from data demand analysis, determining a parameter identification target and planning a field test layout. A basic data set is constructed by comprehensively collecting and integrating multi-source data including laboratory and field test data, remote sensing information and the like. The data is pre-processed and analyzed to remove noise and explore associations between the data. The method comprises the following core steps: constructing a rock-soil mechanical model by using a deep learning algorithm, and realizing high-precision inversion of parameters by defining and optimizing an objective function. And verifying an inversion result and carrying out uncertainty analysis. According to the method, an analysis result is fed back to engineering design adjustment and decision support, and scheme optimization is achieved. The model and strategy are dynamically adjusted through continuous monitoring and real-time data, and safety and high efficiency of engineering implementation are guaranteed.

The invention relates to the technical field of medical simulation training, in particular to an anesthesia puncture virtual simulation operation training and analysis system which comprises a force feedback calibration unit, a force feedback adjustment unit and a force feedback output unit. A theoretical force feedback value is calculated based on a human tissue mechanical model library, tissue levels and characteristics are accurately considered, in a deviation correction link, a force perception deviation value is periodically recalculated, data are processed by applying a sliding window averaging method, overall deviation and local deviation are distinguished and correspondingly adjusted, and the overall deviation is uniformly corrected; functional fine adjustment is locally set according to puncture depth sections, time-dependent force feedback of tissue viscoelasticity is considered, components are calculated through convolution integral and synthesized into final values with elasticity and inertia components, force feedback accuracy is improved comprehensively, individual differences and operation details of trainees are fitted, and anesthesia puncture training quality and effect are improved efficiently.

The invention relates to the technical field of hydraulic fracturing, in particular to a method for evaluating effectiveness of fracture parameters of artificial fracturing of a horizontal well. The method comprises the following steps that information collection based on ground process parameters and underground dynamic parameters is conducted on the horizontal well artificial fracturing construction process, and fracturing working condition data are obtained; according to the fracturing working condition data, the fracture expansion of the horizontal well is monitored in real time, digital conversion processing is conducted, and real-time fracture monitoring data is generated; performing micro-seismic signal analysis on the real-time crack monitoring data, and performing crack network space distribution calculation to generate crack network distribution data; performing reservoir stress field dynamic evolution analysis according to the fracture network distribution data to generate stress field distribution data; and carrying out propping agent migration trajectory tracking according to the stress field distribution data to generate propping agent distribution data. According to the method, the reservoir stress field dynamic evolution analysis and the proppant migration mechanical model are combined, and the effectiveness of the fracture parameters can be evaluated more accurately.

The invention relates to the technical field of underground engineering and geotechnical mechanics, and provides a marine facies soft soil tunnel construction disturbance induced surrounding rock energy dissipation grading evaluation method which comprises the following steps: step 1, establishing a marine facies soft soil energy dissipation constitutive model based on thermodynamics; 2, determining mechanical parameters of the constitutive model; 3, establishing a tunnel construction mechanical model, embedding the constitutive model in the step 1 and the mechanical parameters in the step 2 into the tunnel construction mechanical model, and outputting an energy subitem output quantity in real time; step 4, establishing a damage unit identification system; 5, arranging a monitoring system to obtain monitoring data and feeding the monitoring data back to the numerical model for verification; and step 6, performing graded early warning according to the closeness degree of the structural unit to the instability failure energy threshold value. According to the scheme, a surrounding rock damage grading system is constructed through an energetic perspective, rapid risk identification is realized in combination with multi-source monitoring data, and a new theoretical and engineering means is provided for safe construction of the marine soft soil tunnel.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

The invention relates to a railway bridge post-earthquake traffic safety probability evaluation method and device, which are applied to the technical field of traffic safety, and the method comprises the steps: obtaining an earthquake-induced damage value set of each component through a probability distribution function of different material parameters of a railway track-bridge system; the method comprises the following steps: acquiring a mapping relation between the earthquake-induced damage of a key component and track irregularity through a balance differential equation of a bridge and railway track structural mechanical model, and acquiring earthquake-induced track random irregularity samples of different components based on an earthquake-induced damage value set of each component and the mapping relation between the earthquake-induced damage of the key component and track irregularity; constructing a power spectrum of the track irregularity caused by vibration of different components; establishing a rapid prediction model of the driving performance indexes on the axle after the earthquake through the earthquake-induced track irregularity sample and the coupling dynamic response result; through a Monte Carlo method, based on the earthquake-induced track irregularity power spectrum and the rapid prediction model, the overrun probability and the confidence interval of the driving safety on the axle after the earthquake are rapidly and accurately obtained.

The invention discloses a whole-span bridge hoisting construction method. The method comprises the steps that prefabricated bridge components are transported to a construction site; in the construction preparation stage, a foundation monitoring mechanical model of a construction site is built, and the optimal position and operation parameters of a crane are determined; before hoisting, a laser emitting device is installed on a bridge component, a laser receiving device is installed at the front end of a crane suspension arm, and a plurality of laser reflection targets are arranged on the periphery of a preset installation position of a pier; in the lifting and translation processes, the laser positioning system monitors the spatial position and attitude information of the bridge component and the suspension arm in real time, and transmits the data to the control center in real time; and the control center compares the actual position deviation with the target position deviation in real time according to the preset hoisting path and the in-position precision requirement, and when the deviation exceeds the threshold value range, a control instruction is sent to a power system and a hydraulic control system of the crane. According to the method, the position and posture of the bridge component can be accurately monitored in real time, the action of the crane is timely and accurately regulated and controlled according to the deviation, and the construction precision and safety are improved.

The invention discloses a tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, and belongs to the field of tunnel-landslide masses, and the method comprises the following steps: S1, extracting a feature parameter set and an abnormal event mark set; s2, constructing a three-dimensional geological-mechanical model; s3, space-time multi-scale modeling of multi-physics field coupling is carried out; s4, outputting a stability prediction level by using the constructed ISSA-LSTM-GARCH combined prediction model; s5, generating an early warning in combination with an association rule engine; and S6, knowledge updating and systematic evolution of closed-loop optimization are carried out. According to the tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, multi-source monitoring data and a multi-physical field coupling model are fused, landslide mass stability under the influence of tunnel engineering is accurately and comprehensively analyzed through high-precision positioning and parameter inversion optimization, and powerful support is provided for engineering decision making.

The invention provides a shield tunnel construction period segment floating amount prediction method, system and device and a medium, and belongs to the technical field of tunnel detection. The method comprises the following steps: acquiring an exploration data set and an actual measurement data set of a shield tunnel section; the exploration data set is input into a shield tunnel mechanical model to be solved, and relevant mechanical parameter values of the shield tunnel section are obtained; determining a physical control equation for reflecting the deformation characteristics of the shield tunnel section according to the Timoshensincaro beam model and related mechanical parameter values thereof; according to the actually measured data set and the physical control equation, training a neural network model to obtain a segment floating quantity prediction model; and obtaining positions of a plurality of segments to be detected in the shield tunnel section and inputting the positions into the segment floating quantity prediction model for analysis to obtain predicted floating quantities of the plurality of segments to be detected. According to the method, a physical mechanism conforming to tunnel construction can be fused into the neural network model to effectively constrain the prediction process, and the reliability of the prediction result of the segment floating amount can be improved.

The invention discloses a roadway surrounding rock deformation monitoring method based on an optical fiber sensor, and the method comprises the steps: laying on the surface of roadway surrounding rock through employing a distributed optical fiber sensing technology, and obtaining original optical signal data; processing backscattered light data in the original optical signal data through a Brillouin optical time domain analysis algorithm, extracting frequency deviation of an optical signal, and obtaining strain distribution data; according to the strain distribution data, in combination with a pre-established surrounding rock mechanical model, performing initial inversion calculation by adopting a finite element method to obtain initial three-dimensional deformation distribution; on the basis of the initial three-dimensional deformation distribution, updating dynamic boundary conditions of the surrounding rock mechanical model by acquiring real-time geological parameter data to obtain an optimized mechanical model; carrying out inversion calculation on the strain distribution data again by adopting an optimized mechanical model to generate final three-dimensional deformation distribution; and based on the final three-dimensional deformation distribution, performing comparison judgment through a preset deformation threshold, determining the stability state of the surrounding rock, and generating an early warning signal.

The invention relates to the field of constructional engineering, in particular to a selection method for a hoisting system of a steel box girder and related products, and the selection method comprises the steps that a parameterized mechanical model is established according to parameters of the hoisting system, and hoisting point positions are variable parameters; the hoisting system comprises a steel box girder and a sling; based on the parameterized mechanical model, carrying out stress analysis in the hoisting process, and calculating the stress conditions of the sling and the steel box girder; by taking minimization of the maximum stress of the sling, the maximum stress of the steel box girder or the maximum deformation in the hoisting process as an optimization target, determining an optimal hoisting point position and matched sling parameters by utilizing an optimization algorithm; according to the method, by establishing the parameterized mechanical model of the hoisting system and carrying out nonlinear finite element analysis in the hoisting process, the sling stress, the steel box girder stress and the system deformation under different hoisting point positions and sling parameter combinations can be accurately calculated.

The invention discloses a subway depot upper cover building vibration response prediction method based on deep learning, and the method comprises the steps: constructing a feature library containing vibration signals and working condition data, generating enhanced data through a mechanical model, and fusing the enhanced data into a data set; a mixed deep learning model embedded with physical prior is constructed, and training and dual-objective parameter optimization are carried out; a prediction result is output after working conditions of real-time data are recognized through the lightweight model; parameters are finely adjusted through regular incremental learning, and transfer learning adaptation is carried out when working conditions suddenly change; verifying precision and rationality, and adjusting the weight of a regular term or suggesting to add a sensor; according to the method, measured data sparseness is made up by enhancing data fusion; the double-branch architecture overcomes the deep nonlinear mapping problem, and physical constraints are prevented from violating physical rules; the incremental learning reduces the cost, and the transfer learning solves the time-varying vibration capture problem; precision is improved through closed-loop verification, accurate real-time prediction of vibration response is achieved, and safety and comfort of a building are guaranteed.

The embodiment of the invention provides a bolt and bolt group monitoring method and device, and a bolt looseness detection system is constructed by combining an engineering mechanical model and a multi-source image analysis technology. According to the method, detection units are divided, a stress distribution model is established, median filtering and a bolt segmentation model are used for processing two-dimensional image data, depth image data are analyzed in combination with a binocular depth estimation algorithm based on parallax consistency check, and accurate recognition of the position and state of the bolt is achieved. The system judges transverse and longitudinal loosening states through comparative analysis of measured average angles and distances, and evaluates the influence of loosening propagation. According to the method, the defects of the traditional technology in the aspects of detection efficiency and precision are effectively overcome, and reliable technical support is provided for safety monitoring of the bolt connection structure.

The invention discloses an abnormal detection method and system for a splayed anti-loosening line of a rail train, and relates to the technical field of rail traffic detection, and the method comprises the steps: image collection and processing: shooting an image of a fixed point region containing the anti-loosening line, and preprocessing the image to obtain a target image; line integrity judgment: extracting a bolt, a bolt marking line and an anti-loosening line through an analysis model to form a target area graph, and judging whether the anti-loosening line is complete or not according to line segment continuity and form; performing line deformation depth analysis, and synchronously obtaining the position of an anti-loosening line intersection point and deformation data through the model in combination with the loosening angle of the bolt marking line when the line deformation is complete; performing line deformation safety judgment, setting a safety threshold group based on historical data, and comparing deformation data to output a signal; after line deformation abnormity judgment and comparison signal receiving, actual data are compared with theoretical data output by the mechanical model, and abnormal or normal signals are output. The anti-loose line full-dimension high-precision detection is achieved, automation and real-time performance are improved, and train operation safety is guaranteed.

The invention discloses a thermal error prediction method for a spindle system of a high-speed precision machine tool under an inclined working condition, and develops a comprehensive thermal error compensation method for the spindle system of the high-speed precision machine tool running under the inclined working condition by utilizing error mechanism analysis. The thermal error is modeled as an exponential function which depends on the steady-state thermal error, the time constant of the thermal error balance process and the working time, derivation of the exponential function is supported by error mechanism analysis, and the exponential function is used for accurately identifying the time constant of the steady-state thermal error and the time constant of the thermal error balance process through thermal-structural interaction. Influences of inclined working conditions are incorporated into a quasi-static mechanical model of the bearing, and the problems of bearing heating and changes of axial and radial rigidity are solved. The invention further discloses a thermal error compensation system.

The invention provides a thermal runaway prediction method and device, equipment and a storage medium. The method comprises the following steps: respectively constructing an electrochemical model, a thermal model, a mechanical model and a physical information neural network; converting the electrochemical model, the thermal model and the mechanical model into a loss function of the physical information neural network; training the physical information neural network to be trained according to a thermal runaway training data set to reduce the loss of the loss function; if the loss of the loss function is smaller than a preset loss threshold value, obtaining the trained physical information neural network; and inputting internal parameters of the battery into the trained physical information neural network for prediction to obtain a thermal runaway prediction result. By applying the technical scheme of the invention, the problem of relatively low solving efficiency caused by large calculation amount of a finite element method in solving the thermal runaway process mechanism model of the battery in the prior art can be solved.

The invention relates to computer simulation, and discloses an internal stray light multi-physical field simulation method, device and equipment and a medium. The method comprises the following steps: generating a to-be-tested system model according to assembly data of an actual mechanical model; dividing grids for each structural surface of the to-be-tested system model, and performing finite element analysis solution according to thermal characteristics, stress characteristics, boundary conditions and loads to obtain a final refractive index steady-state value, a temperature field steady-state model and a deformation stress model; importing the temperature field steady-state model and the deformation stress model into ray tracing software; light rays are reversely emitted from imaging surfaces of the thermal field steady-state model and the deformation stress model through light ray tracing software, and a key optical surface is screened through reverse light ray tracing; and carrying out sampling processing on the key optical surface by adopting forward ray tracing, and carrying out forward ray tracing on the temperature field steady-state model and the deformation stress model so as to obtain an internal stray light simulation result and a path analysis result. Therefore, the accuracy of internal stray light analysis is improved.

The invention discloses a multi-factor coupling action slope dangerous rock mass instability starting speed determination method which comprises the following steps: establishing a dangerous rock mass generalization mechanical model, simplifying a dangerous rock mass main control structural plane into two parts, namely a through section and a non-through main control structural plane, and controlling dangerous rock mass instability damage by a rock mass corresponding to the dangerous rock mass main control structural plane; carrying out stress analysis on the dangerous rock mass and the locking section rock mass based on the external load effect and the generalized mechanical model of the dangerous rock mass; the external load effect comprises horizontal and vertical earthquake force, frost heaving force, fracture water pressure or excess pore water pressure; determining elastic strain energy released by potential energy reduction in crack propagation of the main control structural plane of the dangerous rock mass based on a multi-factor coupling effect; and on the basis of the assumption that elastic strain energy released by reduction of potential energy in crack propagation of the main control structural plane of the dangerous rock mass is instantaneously released and converted into kinetic energy of dangerous rock mass movement, the instability starting speed of slope dangerous rock mass severe movement under the multi-factor coupling effect is determined. The invention further discloses a system, electronic equipment and a computer readable storage medium.

The invention discloses a deep anisotropic coal and rock mass in-situ stress recovery test method. The method comprises the following steps: step 1, preparing an anisotropic coal and rock mass sample; 2, carrying out an initial stress loading test; step 3, stress recovery test; and step 4, construction of a deep coal and rock mass anisotropy mechanical model. The method can solve the problem that the anisotropic characteristics and in-situ stress recovery process of the deep coal and rock mass cannot be accurately simulated in the prior art, and can accurately restore the mechanical behaviors of the deep coal and rock mass under different stress paths by combining an indoor test and a numerical simulation technology. And a scientific basis is provided for deep mine engineering design and disaster prevention and control.

The invention relates to the technical field of medical robot systems, and discloses a nerve plexus intelligent positioning anesthesia puncture robot system based on multi-modal fusion, which performs time-space synchronization on ultrasonic image flow and contact force data. The frequency domain analysis module extracts a main peak frequency by using optical flow field frequency domain transformation, and generates a probability mask for marking pulsatile tissues. The viscoelasticity verification module calculates phase lag parameters of visual deformation and contact force, identifies tissue rheological properties and calculates a dynamic damping coefficient. The deformation compensation module calculates a mechanical residual error based on a reference mechanical model, and inversely calculates the mechanical residual error into space deformation displacement of the tissue. The feedback control module combines the probability mask and the deformation displacement to generate a motion instruction. According to the invention, through multi-modal data fusion, precise identification of tiny blood vessels, dynamic identification of tissue attributes and real-time compensation of soft tissue conceding deformation are realized.

The invention provides an EMB sensorless clamping force estimation method based on multi-signal fusion and displacement compensation, and belongs to the technical field of vehicle braking, and the method comprises the steps: carrying out the modeling of an EMB system mechanical model; on the basis of the EMB system mechanical model established in the step S110, executing clamping force estimation; on the basis of the EMB system mechanical model established in the step S110, current-position information fusion is adopted to execute contact point detection; on the basis of the contact point detection result in the step S130, performing contact point detection position error compensation; on the basis of S140 position error compensation, a return stroke backlash compensation mechanism is introduced, the mechanical transmission gap error is further corrected, and the clamping force estimation precision is improved; and executing clamping force control scheme design based on the results of the steps. According to the method, a good clamping force estimation effect can be achieved, the system reliability is remarkably improved while the cost is reduced, and the method has good engineering application value.

The invention relates to a liquid lithium battery simulation method and device considering stress action and a storage medium in the technical field of lithium battery simulation, and the method comprises the following steps: constructing a mechanical model for describing the relationship between the deformation of electrode particles and environmental stress in an electrochemical reaction process, including the acting force of an electrolyte on the electrode particles; constructing an electrochemical model for describing the diffusion process of lithium ions in the electrode particles; coupling the electrochemical model with the mechanical model to obtain an electrochemical-mechanical coupling model; and simulating the performance parameters of the battery based on the electrochemical-force coupling model. The liquid electrolyte binding effect is introduced to reflect the stress change of the electrode material in the circulation process, and the electrochemical reaction and mechanical behaviors are coupled to simulate the concentration change of lithium ions at all positions in the electrode particles in the charging and discharging process, so that the model can accurately predict the performance and the service life of the battery.

The invention belongs to the technical field of civil engineering monitoring, and discloses an optical fiber strain sensor performance verification method and system for highway infrastructure monitoring, and the method comprises the steps: analyzing the influence rule of the parameter change of a coupling layer on the strain transmission efficiency of a measured sensor based on a layered material coupling mechanical model; based on the three-dimensional entity numerical model, analyzing an influence rule of layout process parameters of the tested sensor on the strain transmissibility; verifying the performance of the sensor in a laboratory environment by using a reduced scale model; on a real highway infrastructure or a constructed full-scale model, the performance of the sensor on an actual structure is verified through monitoring data. According to the invention, by constructing an integrity performance verification system from a theoretical model to numerical simulation and then to a reduced scale model test and entity structure verification, multi-dimensional and progressive performance verification of the performance of the optical fiber strain sensor for highway infrastructure monitoring is realized.

The invention is suitable for the technical field of plant modeling analysis, and provides a mature-period flexible wheat ear modeling method based on a discrete element method, and the method comprises the following steps: analyzing the geometric morphology and feature size parameters of a plurality of mature-period wheat ears of a variety to be modeled; determining geometric parameters of the wheat ear model and a filling ball arrangement mode; calculating to obtain coordinates of the filling ball; filling the filling balls, and establishing a geometric model of the wheat ears; measuring and calibrating wheat ear contact mechanical parameters and wheat ear bonding mechanical parameters; adding the mechanical parameters to the mechanical model; and inputting the geometric model and the mechanical model into discrete element software to obtain the flexible wheat ear model based on the discrete element method. The invention provides an efficient and accurate wheat ear modeling method, the calculation complexity is reduced while the model precision is ensured, the simulation efficiency is improved, and a more efficient simulation method is provided for research, development and optimization of wheat harvesting machinery.