35 results about "Macroscopic model" patented technology

The invention provides an aircraft tire shoulder heat generation optimization method and system based on interlayer pixel-level modeling, and relates to the technical field of aircraft tires, and the method comprises the steps: outputting an interlayer pixel-level model; extracting basic parameters of a tire shoulder area; tire shoulder heat generation simulation is executed, and a tire shoulder area temperature distribution cloud picture is output; positioning the highest temperature extreme value and the coordinate position of the high-temperature accumulation area; taking minimization of the maximum temperature extreme value as a heat generation temperature optimization target, taking a condition that the area of a high-temperature accumulation area is smaller than 15% of the total area of a tire shoulder area as a heat generation range constraint condition, and setting a parameter adjustment vector array of basic parameters of the tire shoulder area; executing particle swarm iterative optimization, and outputting tire shoulder optimization correlation parameters; and outputting the thermodynamic optimization parameters of the tire shoulder. According to the method, the technical problem that the durability and the critical speed of the tire are finally affected due to the fact that a macroscopic model is mostly adopted for calculation in an aviation tire shoulder heat generation optimization method in the prior art, and a detailed physical process between different hierarchical structures is ignored is solved.

The invention discloses a mine earthquake risk main control factor analysis method based on rock stratum macro-microscopic numerical simulation. The method comprises the following steps: firstly, collecting a rock sample and testing to obtain microscopic rock mechanical parameters; secondly, constructing a standard sample model based on discrete element numerical analysis software, and performing heterogeneity parameter assignment on the standard sample model by utilizing a Weibull function in combination with fractal dimensions and mesoscopic rock mechanical parameters, so as to realize accurate matching of rock mass mesoscopic-macroscopic mechanical properties; the mesoscopic rock mechanical parameters are converted into macroscopic rock mechanical parameters for discrete element analysis according to the conversion relation between the rock quality indexes and the rock strength, and high-fidelity reconstruction of a discrete element macroscopic model is achieved; and subsequently performing an orthogonal test and range analysis to quantify the macroscopic heterogeneous main control factors, thereby realizing priority ranking of each macroscopic heterogeneous main control factor as a key disaster-causing factor. Simulation can be carried out under the condition of considering the rock stratum heterogeneity, the result fits the actual rock stratum condition, and data support is provided for subsequent mine earthquake risk prevention and control treatment on the rock stratum.

The invention provides a geological unit attribute characterization method and system based on multi-scale cascade fusion, and belongs to the technical field of geotechnical engineering. The method comprises the steps that seismic wave data and sound wave data of a target area are acquired and preprocessed; performing parameter consistency conversion on the seismic wave data and the sound wave data by using the scale conversion coefficient; dividing the target area into grids of different scales based on the geological attribute change rate; on the basis of the divided grids, the converted seismic wave data and the converted sound wave data are input into a cascade fusion model, the converted seismic wave data serve as the basis of a macroscopic model, the sound wave data serve as local fine constraints, and attribute fusion of the seismic wave data and the sound wave data is achieved through a multi-scale coupling algorithm; and carrying out attribute prediction by using an attribute prediction algorithm to obtain a geological unit attribute representation graph. The synergistic effect of macroscopic and microscopic scales is effectively realized, and the problems of insufficient seismic wave and drilling sound wave data fusion and insufficient precision in the prior art are solved.

The application relates to the technical field of energy storage safety, and discloses a multi-scale multi-physical-field energy storage emergency space twin modeling method, which comprises the following steps: presetting macro low-dimensional and micro high-dimensional models in an edge computing device, measuring residual computing power, setting a concurrent threshold, calculating space-time gradient characteristics by using a macro model based on annular buffer data to mark a candidate region, calculating a thermodynamic urgency index, sorting the candidate region, and screening out an activated region, calling historical data to drive a micro model to perform super-real-time operation, completing internal state initialization, establishing a heterogeneous model physical boundary bidirectional coupling by using flux impedance matching, outputting a fault evolution prediction result, and generating a fixed-point emergency control instruction. By means of thermodynamic urgency scheduling and boundary coupling technology, the application solves the high-fidelity simulation problem under the condition of limited computing power on the edge side, and improves the prediction accuracy and response speed of the multi-scale multi-physical-field energy storage emergency space twin modeling.

The application provides a geological unit attribute characterization method and system based on multi-scale cascade fusion, and belongs to the technical field of geotechnical engineering. The method comprises the following steps: acquiring seismic wave data and acoustic wave data of a target region and performing pretreatment; performing parameter consistency conversion on the seismic wave data and the acoustic wave data by using a scale conversion coefficient; dividing the target region into grids of different scales based on a geological attribute change rate; inputting the converted seismic wave data and acoustic wave data into a cascade fusion model based on the divided grids; taking the converted seismic wave data as the basis of a macro model and taking the acoustic wave data as local fine constraints; and realizing attribute fusion of the seismic wave data and the acoustic wave data by using a multi-scale coupling algorithm; and performing attribute prediction by using an attribute prediction algorithm to obtain a geological unit attribute characterization map. The method effectively realizes the synergistic effect of macro and micro scales and solves the problems of insufficient fusion and insufficient precision of seismic wave and borehole acoustic wave data in the prior art.

The application discloses a load data determination method and device, equipment and a storage medium, comprising: obtaining initial parameter data of a steel skeleton light plate, and performing data processing on the initial parameter data to obtain target parameter data; according to the target parameter data, a macro model and a micro model for the steel skeleton light plate are established through a finite element network; the macro model and the micro model are coupled and iteratively processed, and when a coupling end condition is met, target load data is obtained based on the coupled macro model and micro model. The above technical solution realizes safe, reliable and accurate model construction analysis and numerical simulation.

The invention discloses a ceramic matrix composite tenon feature structure uniaxial tensile failure simulation analysis method, and belongs to the field of CMC structure tensile mechanical property and failure mechanism analysis. The method specifically comprises the steps that firstly, a fiber-matrix longitudinal stretching mesoscopic model corresponding to a CMC system is established in ABAQUS, the random failure strength of fibers and matrixes is introduced into a VUSDFLD subprogram, and the bilinear characteristic material constitutive of the CMC in the fiber direction is fitted; secondly, establishing a macroscopic model of a CMC tenon feature structure, and adding a glue layer to simulate an internal interface of the tenon; compiling a VUMAT subprogram to dynamically update a reduction stiffness matrix of the macroscopic model based on the constitutive of the bilinear characteristic material and a self-defined damage state variable; and the strength of a continuous triangular area, where pore defects are easily formed, of the inclined surface of the tenon insertion layer group is weakened. And finally, different failure modes are obtained by changing performance parameters of the tenon interface, and a failure mechanism is analyzed. According to the method, the material constitutive of the microscopic model is transmitted to the macroscopic model, and the structural damage starting and evolutionary process is simulated.

The invention relates to the technical field of intelligent traffic, and particularly discloses a traffic flow prediction system based on trip chain dynamic evolution. The system comprises a trip chain real-time sensing and reconstruction module, a trip decision behavior dynamic deduction module, a group trip demand evolution calculation module and a macroscopic traffic flow dynamic prediction module which are connected in sequence. Through real-time sensing and reconstruction of an individual trip chain, individual behaviors are dynamically deduced, group demands are simulated and aggregated based on an intelligent agent, and a macroscopic model is driven to carry out prediction. The system constructs a complete technical closed loop from microscopic individual behavior real-time perception to macroscopic road network flow dynamic prediction. According to the system, through cooperation of the travel decision behavior dynamic deduction module and the group travel demand evolution calculation module, fine simulation of a group travel behavior emergence effect under an emergency situation is realized.

The application discloses a kind of coupling molecular dynamics first principle's gas cooled reactor cladding surface particle adsorption calculation method, steps are as follows: (1) based on computational fluid dynamics and particle discrete phase model establishes numerical simulation framework, including cladding, break, flow channel modeling, grid drawing, boundary condition setting;(2) according to the first principle of molecular dynamics, the binding surface energy of fission product molecules released into the gas gap and the cladding material molecular base is calculated;(3) establish the particle deposition model including particle's own attribute, effective modulus, relaxation time and surface energy;(4) the binding surface energy is implanted into the particle deposition model, and the particle deposition model is used as the wall boundary, and the gas-solid two-way coupling calculation of particle deposition is carried out.The method solves the problem that traditional macroscopic model depends on empirical parameters, realizes high-precision simulation based on physical mechanism, reveals the mechanism of micro-interface properties on macroscopic deposition behavior, and predicts complex deposition morphology.

The invention discloses a super-large-scale high-performance three-dimensional rock-fill dam multi-scale numerical simulation method, and belongs to the technical field of rock-fill dam numerical calculation, and the method comprises the steps: building a stress-strain coupling relation between a macroscopic model and a mesoscopic model, and obtaining a three-dimensional rock-fill dam multi-scale model; after a load is applied to the macroscopic model, carrying out multi-scale coupling operation on the multi-scale model based on a stress-strain coupling relationship, and in the process of carrying out the multi-scale coupling operation on the multi-scale model, decoupling the time step length of the macroscopic model and the time step length of each RVE model in the mesoscopic model, the time step length of the macroscopic model is greater than that of each RVE model in the mesoscopic model; and updating the macroscopic model according to an operation result of the multi-scale coupling operation to realize three-dimensional rock-fill dam multi-scale numerical simulation. According to the method, the time spent on multi-scale numerical simulation of the rock-fill dam can be remarkably shortened.

This invention belongs to the field of metal smelting technology and relates to a method for predicting alloy element segregation in large-scale titanium alloy VAR ingots. The method includes: acquiring VAR furnace structure data; establishing a macroscopic FE model based on the VAR furnace structure data to calculate the ingot temperature field distribution; acquiring microscopic CA grids and state parameters; establishing a microscopic CA model using the CA grids and state parameters to calculate the microscopic solute concentration distribution; mapping the ingot temperature field distribution to the microscopic CA model for FE-CA cross-scale coupling to generate an FE-CA cross-scale coupled model for simulating the VAR process under different process parameter combinations, and generating an optimal process parameter combination with the objective that the maximum segregation degree of the target element should not be less than a preset threshold. This invention simulates the solidification process under different process conditions to predict the occurrence and development of compositional segregation in large-scale VAR titanium alloy ingots, thereby reducing segregation.

The invention discloses a novel buffer layer material evaluation method for solving the problem of buffer layer ablation, and belongs to the field of electrical engineering. The method comprises the following steps: acquiring a two-dimensional fault slice of a buffer layer material by adopting CT scanning, and reconstructing a microscopic model reflecting a real pore and fiber structure through a volume data method through AI artifact removal, filtering and ternary processing; discretizing the model into a finite element mesh, optimizing the finite element mesh, and embedding the finite element mesh into a cable macroscopic model to construct a macro-micro coupling three-dimensional finite element model; accelerating simulation by adopting local grid refinement, parallel calculation, model order reduction or a reasonable selection solution method, and calculating electric field and current distribution in an electricity-heat-force multi-physics field coupling environment; and by taking the polyester non-woven fiber water-blocking tape as a reference, comparing the ablation resistance of the novel buffer material, and judging whether the ablation resistance of the novel material reaches the standard or not. According to the method, quantitative evaluation from a real microstructure to system performance is realized, and the method has the advantages of high precision, good efficiency and high engineering applicability.

The invention discloses a composite material aero-engine large-size blade multi-scale solidification analysis method, and relates to the field of aero-engine composite material part manufacturing, and the method comprises the steps: importing a pre-configured composite material aero-engine blade model into finite element analysis software, and carrying out mesh generation; giving parameters to the homogenized composite material, performing transient heat transfer analysis, and defining a temperature boundary condition, a third-class heat boundary condition and a heat generation rate of a boundary node to obtain a macroscopic model; performing blade curing degree field solving and thermal coupling analysis on the macroscopic model to obtain a temperature curve, a curing degree curve and residual stress of the macroscopic model, and embedding a representative volume unit in a macroscopic high-stress area; and constructing a representative volume element model and defining a fiber and resin material constitutive model. The method depends on finite element simulation and algorithm optimization, the number of physical experiments is greatly reduced, the research and development cost is reduced, and the development efficiency is improved.

This invention discloses a layered design method for snow surface overlay on ice-structured load-bearing structures, relating to the field of ice and snow engineering technology. The method includes the following steps: S1, determining the design constraints and performance indicators of the snow surface overlay based on target working conditions and environmental data; S2, obtaining the microstructural parameters of the snow material to be laid, including initial porosity, ice crystal size distribution, coordination number, and contact geometry. This invention establishes a cross-scale mechanical performance evolution model to accurately capture the microscopic mechanisms of ice crystal sintering, particle creep, and fatigue damage of snow material under long-term load and temperature, solving the problem of performance prediction deviations caused by the inability of existing macroscopic models to reflect microscopic evolution. By constructing a quantitative mapping relationship between microstructural parameters and macroscopic mechanical properties, combined with thermo-coupling load history simulation, accurate performance evolution data of the overlay can be obtained.

The present invention relates to the technical field of molding of components made from fiber fabric-reinforced resin matrix composite materials, and specifically relates to a simplified optimization method for an efficient molding process of a high-quality component made from a fiber fabric-reinforced resin matrix composite material. In the present invention, a finite element is used to perform a thermo-chemical analysis of a macroscopic model and a thermo-mechanical analysis of a microscopic model of a component, achieving simulation of the temperature gradient, curing degree, and residual stress of the component in a molding process; then, a genetic algorithm is used to simultaneously minimize the temperature gradient, molding time, and residual stress as optimization objectives, with a curing process curve as a parameter variable, to perform multi-objective optimization of molding process parameters, and obtain the efficient molding process of the high-quality component. The method of the present invention can reduce the temperature gradient and residual stress of the component in the molding process, shorten the molding time, and scientifically and effectively reduce the manufacturing cost while improving the quality of the component, and is more effective and simpler.

The invention provides an evaluation method for simulating gas storage capacity of hydrate based on molecular dynamics. The method comprises the following steps: 1, establishing a model to research microcosmic nanopores, 2, extending a porous structure model to a micron scale, and 3, establishing a unified evaluation model for gas storage capacity in a multi-scale pore environment. The invention aims to solve the limitation of the existing experimental method and macroscopic model in gas storage capacity evaluation. By constructing a molecular dynamics simulation system and combining different conditions, a porous structure model is extended to a micron scale, and quantitative analysis is performed on pore network complexity and migration and lattice stability of gas molecules in the hydrate, so that evaluation on the gas storage capacity of the hydrate is realized.

The application discloses a kind of rock-soil body permeability test methods, specifically related to rock-soil body material permeability detection technical field, for solving the problem that the sensitivity of traditional method is insufficient when testing high-performance concrete and bedrock interface due to low permeability and micro-pore structure of material, and the applicability deviation problem of macroscopic model;It is by constructing sealed test environment under keeping in situ stress state, applying multistage incremental fluid pressure step to stimulate interface micro-pore flow response, monitoring transient seepage signal and analyzing the nonlinear growth law of the decay rate required by initial signal amplitude to reach stability, combined with waveform form and the matching identification of preset typical seepage mode and form deviation quantification, finally based on multi-parameter fusion generates the equivalent permeability of interface to realize accurate and reliable permeability evaluation.

The application discloses a metal sealing structure leakage prediction method and system, first, a macroscopic service finite element model is constructed according to the structure and service parameters of the sealing structure, the service deformation behavior is simulated, the model is based on the rotation symmetry characteristic of the sealing structure, the axisymmetric shell model is adopted in the finite element software, the cross section is simplified, the grid is divided and the flange movement is set. Then, a microscopic crystal plastic model with a rough surface is constructed based on the surface roughness, the model construction and grid processing are carried out by considering the material microstructure and characteristics. The deformation amount of the contact position is extracted from the macroscopic model as the boundary condition of the microscopic model to simulate the microscopic deformation. Finally, the distance between the rough surface mean line and the flange surface distance is determined from the microscopic model as the gap height, and the internal and external pressure difference in the service of the sealing structure is combined to calculate the leakage amount of the sealing structure. In this way, the simulation result is more close to the actual situation by combining the macroscopic and microscopic models, and the leakage prediction precision and efficiency of the sealing structure are improved.

The invention discloses a gas turbine blade design method, computer equipment and a computer readable storage medium. The method comprises the steps that a reference blade is selected to establish an initial CMC blade shell model; establishing an equivalent macroscopic model according to different weaving processes and carrying out thermal-mechanical coupling analysis; a final plate CMC blade shell is determined through fiber arrangement optimization; designing a CMC and metal combined structure and carrying out interfacial strength analysis and iteration; the blade performance is verified by combining simulation and a high-temperature test; according to the method, rapid verification and performance optimization of CMC blade design are achieved through fiber arrangement optimization, interface structure iteration and metal additive manufacturing, the using amount of cooling gas can be effectively reduced, the manufacturing period is shortened, the verification cost is reduced, the blade strength and reliability are improved, and the method is suitable for blade structure upgrading of an existing gas turbine unit and application of novel high-temperature materials; a computer instruction is stored on the computer readable storage medium and used for enabling a computer to execute the gas turbine blade design method.

This invention provides a method for generating training simulation scenarios based on road driving tests, relating to the field of human-computer interaction technology. The method includes: extracting desensitized visual trajectory sequences to obtain first data representing the spatiotemporal uncertainty of the local visual field; obtaining second data representing the graph topological impedance of the global road network; performing cross-covariance fusion calculation, converting the first spatiotemporal uncertainty data into pressure source terms and substituting them into a macroscopic model for solution, generating a spatiotemporal covariance tensor; generating a first rendering instruction based on the diagonal high-frequency components of the tensor orthogonal decomposition, triggering the dynamic instantiation of a 3D interactive object; and generating a second inference instruction based on the off-diagonal low-frequency components to update the global traffic flow density and perform closed-loop iteration. This method overcomes the technical limitations of the separation between microscopic visual interaction and macroscopic traffic inference, constructing a highly robust, cross-scale spatiotemporal coupling computation mechanism to achieve an ecological closed-loop evolution of high-fidelity simulation scenarios triggered by vision.

This invention provides a method and system for analyzing the mechanism of roadway floor heave, belonging to the technical field of roadway floor heave mechanism analysis. The invention first deploys multiple monitoring points within the roadway to collect real-time monitoring data, including pressure, vibration, temperature, and humidity data. Based on the monitoring data, the discrete element method and the finite element method are applied to construct microscopic and macroscopic models of the roadway. A state assessment is performed on historical monitoring data and corresponding contact forces and total stresses using an expert scoring method, constructing a risk prediction model based on the random forest algorithm. The model predicts the current state of each monitoring point to identify those with anomalies. The floor heave risk index at the abnormal monitoring points is analyzed to determine whether an early warning should be issued. This method effectively integrates multi-source data and model analysis, providing a scientific basis for roadway safety. Through this method, early warning of roadway floor heave risk can be achieved, helping to reduce safety hazards.

This invention discloses a multi-scale wear analysis method based on fractal principles and molecular dynamics. The method establishes a rough surface model for the required surface roughness using fractal principles, performs contact simulation to obtain the contact pressure distribution, and calculates the microscopic wear force load based on the contact pressure distribution. It then performs molecular dynamics wear simulation under the corresponding load to analyze the microscopic wear mechanism and calculate the microscopic tangential force. Based on the microscopic shear stress, it calculates the shear work and the actual interfacial shear strength of the material, and calculates the macroscopic wear volume based on the shear work of all contact nodes under the macroscopic slip distance. This invention establishes a macroscopic model based on fractal principles, solving the problem that wear analysis does not consider the actual morphology under different surface roughnesses. Furthermore, this invention can realize multi-scale wear calculation and analysis from macroscopic contact to microscopic wear and back to macroscopic wear, solving the problem that wear analysis is mostly performed at the same scale and does not reflect multi-scale characteristics.

The invention relates to the technical field of brake disc thermal fatigue damage early warning, and particularly discloses a brake disc thermal fatigue damage real-time early warning method fusing digital twinning and cross-scale simulation, which comprises the following steps: acquiring the surface temperature, the brake specific pressure and the rotating speed of a brake disc, and combining thermal physical parameters of a brake disc material and a friction plate material to obtain a brake disc thermal fatigue damage early warning result; constructing a real-time macroscopic model of the brake disc and the friction plate; taking a friction area load output by the macroscopic model as a boundary condition, adopting a molecular dynamics model to simulate an atomic-scale behavior of a friction area material, and capturing microscopic damage data; constructing a cross-scale simulation model of the brake disc; based on a cross-scale simulation model of the brake disc, all friction area high-stress nodes and real-time microscopic damage parameters of the friction area high-stress nodes at the current moment are recognized; obtaining a damage evolution analysis result based on the currently obtained real-time microscopic damage parameters of the high-stress nodes of all the friction areas; when the damage evolution analysis result triggers an early warning threshold value, a corresponding early warning signal is sent out; and the accuracy of early warning is obviously improved.

The invention belongs to the technical field of metal smelting, and relates to an alloy element segregation prediction method for a large-specification titanium alloy VAR ingot, which comprises the following steps: acquiring VAR furnace structure data, and establishing a macroscopic FE model based on the VAR furnace structure data for calculating ingot blank temperature field distribution; a microscopic CA grid and state parameters are obtained, and a microscopic CA model is established through the CA grid and the state parameters and used for calculating microscopic solute concentration distribution; and the ingot blank temperature field distribution is mapped to the microscopic CA model for FE-CA cross-scale coupling, an FE-CA cross-scale coupling model is generated and used for simulating the VAR process under different technological parameter combinations, and the optimal technological parameter combination is generated with the target that the maximum segregation degree of a target element needs to be not smaller than a preset threshold value. According to the method, the solidification process under different process condition parameters is simulated, and the generation and development of component segregation of the large-specification VAR titanium alloy cast ingot are predicted, so that the generation of segregation is reduced.

This invention discloses a cluster-CPU-GPU heterogeneous parallel method for solving the structural response of large-scale anisotropic materials, belonging to the field of high-performance computing simulation technology. First, macroscopic and microscopic structures are established. The macroscopic model is divided into finite element meshes, and the computational domain is divided using a domain decomposition algorithm. OpenMP is used to accelerate the calculation of the stiffness matrix and displacement of macroscopic model elements. Next, the model is reconstructed at Gaussian points of the macroscopic model, and MPI technology is used for block division. Each process uses multi-threading to calculate the displacement of the microscopic model, and OpenMP is used to accelerate the calculation of the stiffness matrix of the microscopic model elements, achieving cluster MPI parallel computing. In post-processing, the multi-core advantage of the GPU is used to calculate the stress and strain of the macroscopic model for visualization, achieving cross-scale heterogeneous parallel simulation. This method, combined with relevant algorithms, can efficiently utilize resources to solve simulation problems and has broad application prospects.

The invention discloses an agricultural film residual pollution monitoring method based on satellite-ground remote sensing data, and relates to the technical field of remote sensing monitoring and image processing, in particular to an agricultural film residual pollution monitoring method. The objective of the invention is to solve the problem that the existing satellite and unmanned aerial vehicle monitoring system cannot evolve automatically during operation and is always difficult to find an optimal balance point between the coverage range and the monitoring precision, so that the monitoring accuracy of agricultural film residue pollution is low. The method comprises the following steps: 1, collecting ground feature samples to construct a spectrum library, and determining a characteristic wave band for distinguishing agricultural film residues; 2, fusing satellite images and regional statistical data, and carrying out macroscopic inversion to delimit a high-risk key monitoring region; 3, planning a route according to the key area, and controlling the unmanned aerial vehicle to obtain hyperspectral image data; 4, analyzing the hyperspectral data by taking a laboratory spectrum library as a benchmark, and calculating the residual abundance of the agricultural film; and 5, performing scale conversion on the microcosmic data to serve as a true value, and feeding back and correcting satellite macroscopic model parameters so as to realize dynamic optimization.

This invention relates to a reactor temperature control system, comprising a macroscopic model prediction and control unit, a local temperature field analysis and dynamic compensation unit, a multi-source data fusion sensing unit, an adaptive safety decision-making unit, and a control command fusion execution unit. This invention enables simultaneous and precise tracking of the overall reactor temperature and high spatial homogenization of the internal temperature field, while embedding active safety protection throughout the entire process, thereby significantly improving the product quality, consistency, and operational safety of complex chemical reactions.

This invention relates to the field of situational awareness technology, and in particular to a multi-scale digital twin early warning system. The system collects target early warning data through a data acquisition module, performs hierarchical denoising and feature extraction through a data processing module, dynamically feeds micro-evolutionary information to a macro-model through a multi-scale modeling module, constructs a quantitative risk index through a risk assessment module, and integrates and dynamically visualizes multi-source data through a situational awareness module. This transforms complex equipment status information into intuitive situational awareness and presents risk levels in real time, significantly improving the efficiency of early warning.

This invention discloses a method and system for stability analysis of tunnel excavation faces in goaf areas, belonging to the fields of geotechnical engineering and tunnel construction safety. The method first generates a macroscopic stratigraphic model of the goaf area containing microscopic features based on geological survey data, and extracts representative microscopic units from it. Then, numerical limit analysis is performed on the representative microscopic units to obtain equivalent macroscopic strength parameters and their statistical characteristics. Next, a random field is generated based on the statistical characteristics and discretized to a macroscopic grid to construct a heterogeneous macroscopic model. Finally, macroscopic limit analysis is performed on the heterogeneous macroscopic model to obtain the stability evaluation results of the excavation face. This invention realizes the cross-scale transfer of mechanical parameters from microscopic to macroscopic levels, fully considers the spatial random variability of rock mass parameters, and can efficiently and reliably quantify the stability state of the tunnel excavation face, providing a scientific basis for the safe construction of tunnels traversing goaf areas.

The application relates to the technical field of intelligent transportation, and particularly discloses a traffic flow prediction system based on travel chain dynamic evolution. The system comprises travel chain real-time sensing and reconstruction modules, travel decision behavior dynamic deduction modules, group travel demand evolution calculation modules and macroscopic traffic flow dynamic prediction modules which are connected in sequence. Individual travel chains are sensed and reconstructed in real time, individual behaviors are dynamically deduced, group demands are aggregated based on agent simulation, and a macroscopic model is driven to perform prediction. The system builds a complete technical closed loop from real-time sensing of micro individual behaviors to dynamic prediction of macro road network flow. Through cooperation of the travel decision behavior dynamic deduction modules and the group travel demand evolution calculation modules, the system realizes fine simulation of group travel behavior emergent effect under a sudden situation.