1192 results about "Dynamic simulation model" patented technology

The invention relates to the field of chip verification, and provides a chip verification method, device and equipment, a medium and a chip. The method comprises the following steps: establishing a dynamic simulation verification environment of a to-be-verified design, and generating a plurality of test scenes and corresponding test excitation according to a design specification definition; sending the test excitation to a driver through a sequencer, converting the test excitation into a signal conforming to a to-be-verified design interface protocol by the driver, and transmitting the signal to the to-be-verified design; the monitor collects an input signal and an output signal of a to-be-verified design in real time, converts the input signal and the output signal into transactions, and transmits the transactions to the scoreboard; comparing the reference model with an output signal of the to-be-verified design to generate a dynamic simulation verification result; according to the dynamic simulation result, the state space is reduced, state space traversal is carried out on a design part which is not covered by dynamic simulation in the design to be verified, and a formal verification analysis result is generated; and evaluating the to-be-verified design according to the formal verification analysis result.

The invention relates to a wellbore multiphase flow model numerical solution and gas-liquid distribution state inversion method and system, and belongs to the technical field of petroleum engineering, and the method comprises the steps: 1, constructing and training a physical information neural network for drilling wellbore multiphase flow dynamic simulation and overflow gas distribution state inversion; determining input and output of the physical information neural network; determining a loss function of the physical information neural network; training a physical information neural network; 2, designing an adaptive optimization algorithm, optimizing the final solution precision and convergence speed of the physical information neural network, and obtaining an adaptive physical information neural network; designing an adaptive activation function; designing a self-adaptive sampling mechanism based on residual errors; 3, based on the self-adaptive physical information neural network, numerical solution and gas-liquid distribution state inversion of the shaft multiphase flow model are achieved. According to the method, the problem that a traditional numerical method usually needs high-precision grid division and a large number of computing resources is effectively solved.

The invention relates to the technical field of geological disaster monitoring, and discloses a landslide dynamic simulation monitoring and early warning method based on multi-source sensing fusion, and the method comprises the steps: collecting multi-source data of a landslide body through a plurality of heterogeneous sensors, and enabling the multi-source data to be in space-time alignment after preprocessing; building a multi-parameter fusion model fusing a displacement field, a mechanical field and an environment field based on the preprocessed data, enabling the multi-parameter fusion model to output a deformation rate and a stability coefficient, and dynamically adjusting the weights of the displacement field, the mechanical field and the environment field according to a landslide evolution stage; predicting a future deformation trend of the landslide mass in combination with a geological structure and historical data, comparing a stability coefficient with a dynamic safety threshold to judge a risk level, and generating early warning information; and dynamically correcting a reference weight coefficient in the model based on the deviation between the monitoring data and the model output, so that the model is adaptively optimized. The problems of single monitoring dimension and static model solidification in the prior art are solved, and accurate and adaptive monitoring and early warning of the risk state of the landslide mass are realized.

The invention provides a deep rock mass creep-seepage coupled near-field dynamics simulation method and system, and relates to the technical field of geotechnical engineering.The method comprises the steps that a near-field dynamics numerical calculation model is established based on geological survey data; based on the Burgers creep constitutive model, a near-field dynamic creep-seepage coupling basic motion equation is formed; and carrying out rock mass creep process simulation considering the seepage effect by utilizing the near-field dynamics numerical calculation model and the creep-seepage coupling basic motion equation, and carrying out iterative solution on stress, strain, displacement, water pressure and damage of each time step in the simulation process until iteration stop conditions are met, so as to obtain the rock mass creep process. And finally, obtaining distribution characteristics of a rock mass displacement field, a damage field and a seepage field and a creep characteristic curve, and realizing near-field dynamic simulation of a rock mass creep-seepage coupling process. According to the method, the technical blank of near-field dynamics in the field is filled, and the long-term stability of the engineering rock mass and the disaster evolution process are accurately reproduced and predicted.

A method for predicting in-situ leaching effects of sandstone-type uranium mining deposits after blasting is provided, which comprises: based on a finite element analysis platform, establishing an HJC rock-blasting constitutive model, and carrying out a blasting numerical simulation; calibrating a cloud map of blasting crack damage by using a Kriging interpolation algorithm; establishing a COMSOL multiphysics model; establishing a coupling interface between the COMSOL multiphysics model and PHREEQC based on MATLAB, and optimizing a cycle duration and a time step; performing a geochemical reaction calculation and saving results, running COMSOL files, and inputting the results of geochemical reaction calculation into PHREEQC to obtain a dynamic cyclic iterative simulation; dynamically correcting migration coefficients of uranyl complexes by integrating an LSTM neural network, and stopping the cycle to output multi-scale dynamic simulation results of migration of uranium.

The invention discloses a distribution-micro collaborative operation simulation optimization method, which belongs to the technical field of simulation optimization, and comprises the steps of preprocessing grid-connected point voltage data, tie line power data and communication time delay data, constructing a power distribution network power flow physical network following a Kirchhoff's law, outputting a source load power prediction curve by using a long short-term memory network algorithm, and calculating the distribution-micro collaborative operation according to the source load power prediction curve. And a distribution-micro collaborative simulation optimization model is obtained based on residual error rolling correction tie line impedance parameters, a delay penalty term is set in a target function in combination with the preprocessed communication delay data, a power regulation instruction is obtained by using a particle swarm optimization algorithm, and a dynamic simulation video stream is generated. According to the invention, through rolling correction of the tie line impedance parameters and setting of the delay penalty term positively correlated with the time delay, the problem of control failure caused by physical deviation caused by model parameter solidification and communication time delay accumulation is solved, and the defects of voltage deviation calculation distortion and inaccurate network loss evaluation are eliminated. And the simulation precision and the operation stability of distribution-micro cooperation are improved.

The invention relates to the field of vacuum circuit breakers, in particular to a vacuum circuit breaker opening and closing dynamic characteristic calculation method, device and equipment and a medium. Establishing a three-dimensional geometric model of the pole-mounted vacuum circuit breaker arc extinguish chamber and the moving and static contacts; setting material attributes for each part in the three-dimensional geometric model; constructing a multi-physics field coupling system; the area where the moving contact is located is divided into movable grids, and the grid density of the moving contact and the grid density of the static contact dynamically change along with time; and performing multi-field coupling transient solution according to the three-dimensional geometric model, the multi-physical field coupling system and the movable grid to obtain the dynamic characteristics of the vacuum circuit breaker. The electromagnetic field, the thermal field, the fluid field, the structure field and the circuit are subjected to full coupling analysis, multi-physical field strong coupling dynamic simulation of the whole opening and closing process is achieved, and the dynamic characteristics of the whole opening and closing process can be accurately calculated.

The invention discloses a high-voltage switch cabinet partial discharge fault identification method and a storage medium. The method comprises the following steps: synchronously acquiring acoustic, electric, magnetic and thermal signals through a multi-physical field sensor array; performing preprocessing and feature extraction on the signals; using a cascade deep learning network to fuse multi-modal features to identify fault types and levels; realizing accurate positioning of the discharge source in the digital twinborn model by adopting a dynamic simulation coherent positioning method; and risk assessment and early warning are carried out by integrating diagnosis and positioning results. According to the method, deep learning and physical mechanism simulation are deeply fused through a data and model dual-drive normal form, so that the defects of shallow fusion level, low positioning precision, lack of interpretability and the like in the prior art are overcome, and intelligent, precise and prospective identification and early warning of the partial discharge fault of the high-voltage switch cabinet are realized.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention discloses an electromagnetic transient simulation and mapping method and system oriented to multi-physics field coupling, and belongs to the technical field of substation digital operation and maintenance and electromagnetic simulation. Deploying multiple types of sensors in a transformer substation, preprocessing the acquired multi-physical field data, and fusing with the three-dimensional twinborn model; a coupling algorithm is adopted to carry out multi-physical field coupling analysis, multi-physical quantity display and thermodynamic diagram superposition are realized, and scene evolution change simulation under various typical working conditions is supported; a micro-service architecture is adopted to construct a digital twin system, double closed-loop verification of simulation and actual measurement is performed through actual measurement data and simulation data of a real-model test field, model optimization is completed, and real-time mapping of electromagnetic transient dynamic simulation and a transformer substation physical entity is realized. The method improves the precision of model and entity mapping, meets the requirements of real-time tracking and response of the electromagnetic transient change of the transformer substation, adapts to the digital transformation of different transformer substations, and provides a basis for the operation and maintenance of the transformer substations.

The invention provides a high and low voltage power distribution cabinet remote operation and maintenance management and control system based on cloud computing, relates to the field of remote operation and maintenance management, and improves the accuracy and operation and maintenance efficiency of power distribution cabinet fault early warning. The method comprises the following steps: firstly, acquiring sensing data through an edge intelligent sensing module, performing multi-dimensional correlation analysis based on a static safety threshold, identifying an abnormal trend with spatial-temporal correlation, and generating a preliminary early warning; then, a cloud platform prediction decision module drives the digital twin model to carry out dynamic simulation, and a predictive maintenance strategy and a dynamic safety threshold are generated in combination with a prediction algorithm of a historical performance decline curve; and finally, the adaptive control execution module optimizes subsequent analysis by using a dynamic threshold value, and triggers a differential control action from monitoring adjustment to emergency isolation according to a strategy type. According to the invention, by constructing the closed-loop intelligent operation and maintenance architecture of cloud-side cooperation, the operation and maintenance mode transformation from passive response to active prediction is realized, and the reliability and safety of the operation of the power distribution system are significantly improved.

The invention discloses a mineral resource metallogenic law simulation and target prediction system based on big data, and relates to the technical field of geological exploration. The multi-source data acquisition and standardization module supports acquisition of five types of data, and the data are preprocessed and then transmitted to the distributed database; mining key elements by using an improved Apriori algorithm, and constructing a correlation graph; building a model based on Unity 3D, and integrating three types of dynamic simulation; performing three-level prediction by using a CNN-LSTM model; three-dimensional rendering and multifunctional display are supported; and automatically updating data, models and rules to form an optimized closed loop. According to the method, multi-source data is integrated to improve ore-forming element recognition comprehensiveness, dynamic simulation fits geological reality, AI prediction is accurate, uncertainty analysis is included, visual interaction is practical, iterative optimization continuously improves efficiency, invalid exploration cost is reduced, and mineral exploration is efficiently guided.

The invention relates to the technical field of computer simulation testing, in particular to a dynamic analogue simulation testing method and system for plate type structure service deformation. The technical problem that the structural damage identification precision is insufficient when fixed signal decomposition parameters are used for processing all detection signals is solved. The method comprises the following steps: acquiring a mesoscopic parameter set and a bimodal ultrasonic guided wave original signal set of a to-be-detected plate type structure; based on the mesoscopic parameter set, determining optimization parameters for decomposing the bimodal ultrasonic guided wave original signal set; decomposing the bimodal ultrasonic guided wave original signal set based on the optimized parameters and a signal decomposition algorithm, and determining a target signal set related to structural damage; and performing simulation comparison on the mesoscopic parameter set and the target signal set, and determining the service damage state of the plate type structure. The method is used for plate type structure damage identification scenes.

The invention provides an ecological system service flow simulation and collaborative tradeoff relation quantification method based on a graph neural network, and relates to the technical field of ecological system service simulation and quantification. According to the method, the Huaihe River basin serves as a research area, firstly, a landscape is abstracted into a graph structure, ecological patches serve as nodes and are endowed with multi-dimensional features, ecological process flow paths serve as edges, and flow attributes are quantified; then, constructing a graph neural network model, and learning inherent rules of generation, flow and consumption of an ecological system service through training; and finally, realizing ecological system service, particularly dynamic simulation of supply service and adjustment service, and quantifying the collaborative tradeoff relation by adopting a multi-index fusion method. According to the method, the nonlinear correlation of the ecological process can be accurately captured, the flow simulation and relation quantification precision is improved, scientific support is provided for ecological planning and management of the Huaihe River basin, and compared with an InVEST model, the simulation error is reduced by 20%-30%, and the collaborative tradeoff recognition accuracy is improved by 15% or above.

The invention discloses a polluted river water nitrogen and phosphorus removal efficiency evaluation system and method based on element analysis, and relates to the technical field of sewage treatment evaluation. An algae growth dynamic model is trained, and the future growth dynamic state of algae is obtained according to future water body data and future environment driving data; the method comprises the following steps: analyzing the interference influence of interference ion concentration on sensor measurement according to future water body data, dynamically simulating the attachment rate and biofilm thickness of algae on the surface of a sensor probe according to future growth of the algae, analyzing the attachment influence of the algae on sensor measurement, and correcting the measurement precision of the sensor according to the interference influence and the attachment influence. According to the corrected sensor data, whether the nitrogen and phosphorus removal efficiency of the polluted river water meets the removal standard or not is evaluated, real-time correction of the sensor data is achieved through interference ion concentration analysis and dynamic simulation of biological adhesion influence, and the accuracy of monitoring data and the scientificity of pollution treatment efficiency evaluation are effectively improved.

The invention provides a rock porosity adaptive modeling and blasting crack expansion calculation method based on key-type near-field dynamics, and the method comprises the steps: constructing a unified near-field dynamics model containing pore adaptive generation and blasting dynamic response; dynamic simulation of the rock blasting process is realized through pore distribution control, blasting load time history application and crack evolution judgment, and crack initiation, expansion and penetration rules can be accurately reproduced while the calculation stability is ensured. By introducing a pore adaptive discrete algorithm, the construction of rock models under different porosity conditions is automatically realized, and the initiation, expansion and penetration processes of cracks in the rock mass are accurately predicted in combination with near-field dynamics mechanical calculation under the action of an explosion load. According to the method, the heterogeneous characteristic of the rock and the discontinuous effect of the blasting load can be considered at the same time under a unified dynamic framework.

The invention relates to the technical field of industrial automation control and dynamic simulation, and discloses a centralized control console and 3D model linkage virtual-real simulation system, which comprises a control signal sampling interface, a load response modulation unit, a dynamic model evolution unit and a virtual-real closed-loop feedback unit, the load response modulation unit performs energy consumption weight accumulation on all the virtual controlled objects in transient action to generate a global time scaling factor so as to correct an inertia time constant of the system in real time; the dynamic model evolution unit introduces a hard limiting boundary by using an amplitude limiting link and calculates a process variable; the virtual-real closed-loop feedback unit extracts a control deviation signal and drives a physical instrument to generate a nonlinear damping action, and by establishing a global load modulation model based on energy constraint, a discrete control system spontaneously emerges a nonlinear hysteresis characteristic conforming to physical energy conservation under the low calculation power condition without complex fluid network solution.

The invention relates to the technical field of three-dimensional simulation, in particular to a tunnel blasting whole process three-dimensional dynamic simulation method and system. The method comprises the following steps: obtaining a tunnel three-dimensional design mathematical model; identifying a tunnel high-risk geometric region in the tunnel three-dimensional design mathematical model; constructing a space blasting constraint field covering the blasting rock mass based on the high-risk geometric region of the tunnel, and reversely planning an ideal blasting drilling path; deducing a blasting energy sequence according to the ideal blasting drilling path, executing rock mass damage dynamic blasting simulation by taking the blasting energy sequence as a boundary condition, and evaluating drilling layout correction parameters for correcting the undercut risk; and applying the drilling layout correction parameters to a preset tunnel blasting scheme to obtain an optimized tunnel blasting scheme. By constructing the space blasting constraint field, accurate dynamic simulation of the blasting process of the non-circular tunnel is achieved, and the problem of misalignment of over-break and under-break prediction of the key position of the complex section is solved.

The invention discloses a particle accelerator multi-particle dynamics efficient simulation method, system and device and a medium. The method comprises the steps that a small number of macro particles are sampled from an initial macro particle beam to form a new macro particle beam; performing multi-particle beam flow dynamics simulation on the new macro particle beams by adopting a PIC algorithm, and projecting the new macro particle beams to four two-dimensional phase spaces in a distributed manner to generate low-resolution phase diagrams of the four two-dimensional phase spaces; obtaining beam physical parameters based on the new macro particle beam; and inputting the low-resolution phase diagram and the beam physical parameters into a beam halo super-resolution network, and outputting a high-resolution phase diagram of each phase space to realize multi-particle beam flow dynamics simulation. Therefore, by combining a traditional numerical calculation method and an artificial intelligence technology, the problem that an existing artificial intelligence algorithm is weak in generalization ability in the field of beam dynamics simulation is solved, rapid and high-precision simulation of the large-scale multi-particle beam flow dynamics process is achieved, and the method is applied to the field of particle accelerator beam dynamics simulation.

The invention relates to the technical field of industrial robots, in particular to a dynamic simulation damping parameter solving method based on a gravity compensation mechanism, which comprises the following steps of: acquiring a joint basic parameter set of a to-be-tested industrial robot, and determining a test boundary condition according to the joint basic parameter set, and constructing a test task sequence based on the test boundary conditions. According to the method, the fusion weight of the static stiffness and the dynamic stiffness is intelligently adjusted according to the characteristics of the movement speed, the acceleration or the force control frequency band in a specific application task, and the limitation of a single parameter under complex and changeable working conditions is avoided. According to the parameter fusion mechanism aiming at the specific task characteristics, the generated joint comprehensive rigidity parameters can truly reflect the physical response of the robot in different operation stages, and the prediction error between the physical simulation environment and the actual physical operation is reduced; and the strict requirements of high-precision scenes such as semiconductor packaging and the like on micron-level positioning precision and force control stability are met.

The invention discloses a method and a system for constructing a fiber scale parameterized model of a single-sided weft-knitted fabric, relates to the field of dynamic simulation of weft-knitted fabrics, and solves the problems that an existing knitted fabric simulation system only displays an embroidery pattern in a yarn scale or a fabric scale and lacks a three-dimensional dynamic effect based on a fiber level and the like. The method comprises the steps that the weft-knitted fabric is formed by mutually intermeshing coils, and the number and coordinates of coil control points are set; the simulation of different knitting effects is realized by adjusting the positions of coil control points; based on the single fiber curve model, using a random jitter algorithm to carry out modeling on the fiber bundle, and constructing an untwisted fiber bundle model and a twisted fiber bundle model; a branch model is constructed to simulate the hairiness form of the weft-knitted fabric, and dynamic regulation and control of hairiness characteristics are achieved by adjusting the number of breakpoints, the branch length range and the disturbance amplitude core parameters. Environment parameters are set, an OpenGL illumination function is used for representing the stereoscopic impression of the weft-knitted fabric model, and after the weft-knitted fabric model is rendered, the model is output.

The invention discloses a dynamic simulation test verification method and system for a pumped storage unit monitoring system, and the method comprises the steps: building a high-dimensional system state matrix of a pumped storage unit based on the physical parameters and structural features of a hydraulic, mechanical and electrical system of the unit, carrying out the dimension reduction of the high-dimensional system state matrix, and obtaining a dynamic simulation test result of the pumped storage unit; a dynamic simulation model of unit multi-physics field coupling is obtained; constructing a multi-dimensional parameter space based on the parameter sensitivity coefficient and the working condition boundary condition of the dynamic simulation model, and calculating optimal test point distribution through a high-dimensional point flow diameter algorithm to obtain a test scene library; based on the dynamic simulation model and the test scene library, obtaining a real-time monitoring signal sequence conforming to a monitoring system communication protocol; and based on the real-time monitoring signal sequence, signal acquisition, logic judgment and alarm control functions of the monitoring system are tested through an automatic test script, and a performance evaluation result of the monitoring system is obtained. The test comprehensiveness and effectiveness of the pumped storage unit monitoring system are improved.

The embodiment of the invention provides a surfactant synergistic flooding parameter optimization method based on core scale simulation, and the method comprises the steps: selecting a real core sample of a target oil reservoir, carrying out the preprocessing of the real core sample, and constructing a three-dimensional digital core model of the target oil reservoir; selecting a surfactant according to reservoir characteristics of the target oil reservoir; according to the synergistic flooding characteristic and the oil-water two-phase flow characteristic of the surfactant in the pore medium of the target oil reservoir, a surfactant synergistic flooding model under the rock core scale is established; carrying out iterative solution on the synergistic flooding model through parallel calculation to realize dynamic simulation of the synergistic flooding process under the rock core scale, and outputting distribution data of a pressure field, a fluid saturation field and a surfactant concentration field in the three-dimensional digital rock core model; and according to the distribution data, determining a target parameter combination for synergistic flooding of the surfactant. The effect of improving the use degree of crude oil is achieved.

The invention relates to the technical field of mechanical equipment fault diagnosis, and discloses a fault diagnosis method and system for an automatic tensioning control valve of a scraper conveyor. The method comprises the following steps: acquiring pressure and flow data of a main flow channel and an auxiliary flow channel through a sensor array, and extracting frequency domain characteristics to obtain a standardized frequency spectrum; detecting parameter coupling abnormity based on the spectrum cross correlation coefficient and generating an abnormal index; environment interference and medium viscosity data are fused, and non-linear feature distribution and correction deviation representing the mechanical state are obtained through classification and filtering analysis; matching the correction deviation with a preset fault mode, determining a fault type, and generating a fault positioning coordinate in a three-dimensional space by using an optimization algorithm; and finally, performing fluid dynamic simulation through the high-fidelity virtual model to verify the coordinate stability, and outputting accurate early warning when conditions are met. Through cooperation of multi-source information fusion and an intelligent algorithm, the accuracy of early-stage internal fault diagnosis of the complex double-flow-channel valve is improved.

The invention discloses an oil and gas field integrated dynamic simulation construction method and system based on artificial intelligence, and the method comprises the steps: S1, carrying out the space-time alignment and standardized cleaning of a multi-source heterogeneous data signal, and generating a fusion data signal with unified space-time; s2, a physical information embedded deep learning model architecture is constructed, the architecture receives the fusion data signal, and a mixed training target signal fusing data driving and mechanism driving is generated; s3, training the deep learning model by using a fusion data signal of a historical time sequence, and finally obtaining a convergent intelligent agent model; and S4, feeding the current oil and gas reservoir state parameters and the production control parameters into the intelligent agent model as input signals, and directly outputting a dynamic prediction signal. The oil and gas field integrated dynamic simulation construction method and system based on artificial intelligence can solve the problem that traditional oil and gas field numerical simulation calculation is low in efficiency.

The invention relates to the technical field of computer-aided modeling, in particular to a heating and ventilation equipment energy efficiency optimization method and system based on digital twinning, and the method comprises the steps: obtaining a real-time operation data set, and constructing a mechanism model; obtaining a model prediction data set by using the mechanism model, and training a data reconstruction model; inputting the real-time operation data set into the data reconstruction model to obtain a reconstruction error, and taking the reconstruction error as a state deviation data set; dynamically correcting an adjustable model parameter of the mechanism model based on the state deviation data set, and generating a dynamic calibration digital twinborn model; and finally, executing a multi-target collaborative optimization algorithm based on the dynamic calibration digital twin model, and generating an energy efficiency optimization control instruction. According to the method, the state deviation caused by the performance degradation of the physical equipment is quantified by utilizing the data reconstruction model, and the mechanism model is dynamically corrected by taking the state deviation as feedback, so that the problem that a static model is disjointed from a physical entity state is solved, and high-precision dynamic simulation and energy efficiency collaborative optimization are realized.

The invention discloses a non-standard goods shelf intelligent design method and system based on a parameterized model, and relates to the technical field of computer aided design, and the method comprises the following steps: collecting parameters, and carrying out abnormal value elimination and dimension unified processing to generate a standardized demand parameter set; defining core structure parameters and establishing a component association rule, and constructing a parameterized basic model through three-dimensional modeling; the storage rack structure is optimized by combining storage mechanics and material mechanics characteristics, stress concentration is reduced, and deformation under rated load is controlled; a simulation environment is built for statics and dynamics simulation, and a simulation test report is output; screening an optimal parameter combination to generate a detailed design scheme; and receiving user feedback, adjusting parameters and material selection in combination with production feasibility and cost budget, and repeating optimization simulation steps to generate a final scheme. Efficient design of the non-standard goods shelf is achieved through parametric modeling and intelligent optimization, the structure is safe and reliable, the material utilization rate is high, and the design quality and efficiency are remarkably improved.

The invention discloses a flood inundation range dynamic simulation method and system based on multi-source data fusion. The method comprises the following steps: S1, collecting multi-source original data of a to-be-simulated target drainage basin; s2, extracting an initial submerging range grid according to the multi-source original data; s3, according to the initial submerging range grid, generating a river channel-beach area coupled section terrain file; s4, obtaining the dynamic water level process of each downstream section according to the section topographic file and the weather forecast rainfall field; and S5, obtaining a dynamic simulation result of the flood inundation range according to the dynamic water level process. According to the invention, through continuous driving of the distributed hydrological model and the one-dimensional hydrodynamic model, full-chain simulation from rainfall forecast to submerging dynamic state is realized, so that the submerging range can be updated in real time along with meteorological conditions and riverway water levels.

The invention discloses a wind field yaw wake flow hybrid modeling method and device fusing simulation and deep learning. The method comprises the following steps: firstly, introducing a yaw-corrected actuating disc model, constructing a volume force source item under the yaw action of each fan, performing steady-state computational fluid mechanics simulation on the yaw wake flow of the wind field by combining a Reynolds average Navier Stokes method and a k-epsilon turbulence model, and constructing a yaw wake flow database of the wind field; and then, training the yaw wake flow database by using a deep neural network, and learning a nonlinear mapping relationship between a wind field input wind speed and a multi-fan yaw angle combination and an incoming flow wind speed of each fan, so that a wind field yaw wake flow data driving model is established, and real-time prediction of wake flow characteristics under non-consistent yaw configuration is realized. Compared with an existing yaw wake flow model, the method has the advantages that the technical difficulty of wake flow modeling of multi-fan non-consistent yaw is effectively solved, the prediction speed is remarkably increased while the modeling precision is improved, and the method has high engineering application value.

The invention belongs to the technical field of mine engineering, and particularly discloses a dynamic simulation method for water inflow attenuation in the construction process of a water cut-off curtain of a strip mine. The method comprises the following steps: acquiring meteorological data according to the position of a strip mine, acquiring a hydrogeological structure in combination with drilling data, acquiring hydrogeological parameters by using a water pumping test, and acquiring a source convergence item according to water pumping and drainage; the method comprises the following steps: constructing a hydrogeological model by adopting MODFLOW-USG, constructing a mine pit drainage mathematical model and a mine pit drainage model, setting source sink items and boundary conditions, and realizing dynamic simulation based on python; a PEST calibration model is adopted, parameters are subjected to standardization processing, a random forest substitution model is constructed, SHAP values of all the parameters are analyzed and calculated and normalized to obtain global importance indexes, and the model is optimized; and evaluating the optimized model by adopting NSE, KGE and RMSEs. The method has the advantages of being high in model precision, clear in parameter physical significance and capable of being corrected with measured data in real time.