1055 results about "Analogue computation" patented technology

The invention discloses a turbofan engine operation monitoring method and system based on digital twinning, belongs to the technical field of turbofan engine monitoring, and aims to solve the problems that weak fault signals such as early cracks and abrasion are difficult to extract and the prediction precision of a multi-source fault propagation path is low under a strong noise background. An original operation signal is collected through a sensing array, and is processed by an adaptive resonance demodulation chain to generate a demodulation signal. The method comprises the following steps: carrying out time-frequency transformation on a demodulation signal, constructing an initial candidate feature set by combining feature frequency prior matching actual measurement and theoretical feature frequency, and generating an independent feature set by fusing multi-scale decoupling network separation features of digital twin constraints; for independent features, effective causal pairs are screened by adopting a physical coupling relationship combining Granger causal analysis and digital twinborn simulation, a dynamic Bayesian network is constructed to simulate fault propagation, a posterior probability is calculated through digital twinborn verification and Monte Carlo simulation, early warning is triggered, and a maintenance decision is generated. And weak signal extraction and accurate fault prediction under strong noise are realized.

The invention discloses a riverway flood control and disaster reduction risk point AI identification method and device based on a digital twinborn computable engine. The method comprises the following steps: acquiring three-dimensional scene basic data and hydraulic engineering parameters in a drainage basin; acquiring real-time rain condition, water condition, soil moisture content, reservoir condition and dike monitoring data to obtain real-time data; establishing a physical entity and a twin simulation scene according to the three-dimensional scene basic data and hydraulic engineering parameters; performing AI simulation calculation of water regimen according to the real-time data to obtain a simulation result; carrying out twinborn computable simulation according to the physical entity, the twinborn simulation scene and the simulation result to obtain a simulation result; and identifying the risk point of the river channel in the drainage basin according to the simulation result. By implementing the method, the problem that the prediction accuracy is affected due to the fact that the digital twinborn technology mainly lacks intelligent risk identification and multi-factor integration in flood control and disaster reduction of the river channel can be solved.

The invention relates to the technical field of heat exchange modules, and discloses a multi-cavity cooperative control method and device for a micro-channel heat exchange module and electronic equipment, and the method comprises the steps: carrying out the numerical simulation calculation of a plurality of heat exchange cavities of the micro-channel heat exchange module, and obtaining a training data matrix; coefficient calculation and heat flow coupling relation analysis between cavities are carried out, and a heat flow distribution mathematical model is generated; substituting the heat flow distribution data, the temperature distribution data and the flow distribution data into a non-homogeneous heat conduction equation, and constructing a multi-objective optimization function; executing chaotic mapping processing and multi-target collaborative differential evolution to obtain an optimal control scheme set; carrying out Q-learning state value function solving, and generating a cooperative control instruction; and inputting the cooperative control instruction into an execution unit, and performing real-time adjustment and online updating on inlet temperature parameters and flow parameters of each heat exchange cavity to obtain a target heat flow distribution model. Therefore, information interaction and optimized cooperation among the heat exchange cavities are achieved, and the heat exchange efficiency is remarkably improved.

The invention provides a sea level rise influence risk assessment method, medium and system, and belongs to the technical field of risk prediction and assessment, and the method comprises the steps: firstly, analyzing historical sea level data through wavelet decomposition and an ARIMA model, and predicting a future change trend; meanwhile, a digital elevation model is established by using Kriging interpolation, and dynamic change of the terrain is described by combining real-time monitoring data. Secondly, identifying key infrastructures and influence factors by applying an analytic hierarchy process and principal component analysis, and extracting key features through singular value decomposition; and based on a singular value decomposition result, constructing a sensitivity and vulnerability matrix, calculating the exposure degree in combination with Monte Carlo simulation, and comprehensively evaluating the regional vulnerability. Furthermore, asset loss is predicted by applying a random forest algorithm, economic loss is calculated, and an ecological vulnerability index matrix is established to evaluate ecological influence. And finally, realizing quantitative risk assessment by adopting fuzzy comprehensive evaluation of an entropy weight method and a risk prediction model of support vector regression.

The invention discloses a shear capacity analysis method for a reinforced concrete beam with stirrups based on machine learning, and the method comprises the steps: constructing a training set and a test set based on a shear capacity database of the beam with stirrups; establishing an interpretable prediction model by utilizing machine learning, setting hyper-parameters for the interpretable prediction model, and performing training; obtaining a shear bearing capacity prediction value of the samples in the test set by using the trained prediction model; and quantifying the contribution of each key anti-shearing characteristic to the anti-shearing bearing capacity predicted value, and analyzing and judging the reasonability of the anti-shearing bearing capacity predicted value output by the prediction model according to the anti-shearing theory. According to the method, the problems of high prediction difficulty, high discreteness, lack of reliable physical models, large calculation amount of high-precision numerical simulation and the like in machine learning are solved for the first time; the prediction model can accurately reflect the complex nonlinear relation between the shear bearing capacity and the influence factors, and meanwhile, the model reasonability is verified and improved in combination with a prediction result and priori knowledge, so that the man-machine interaction trust in practical application is improved.

The invention belongs to the technical field of multi-physics field coupling simulation, and particularly relates to a multi-physics field coupling Fluent simulation analysis integrated system, which comprises a data preprocessing module used for carrying out data integration, grid division and boundary condition setting on a multi-physics field coupling problem; the multi-physical field coupling module comprises a fluid-structure coupling sub-module, a thermal-force coupling sub-module, an electromagnetic-fluid coupling sub-module and the like, and interaction simulation among different physical fields is realized respectively; the calculating and solving module is used for calculating and solving a multi-physics field coupling problem based on a Fluent software platform and coordinating the calculating sequence and the time step length of each physics field; and the post-processing and visualization module is used for performing post-processing analysis and visualization display on the simulation result. The method effectively solves the problems that a traditional system is low in data interaction efficiency, insufficient in grid precision and poor in calculation coordination, and has the advantages that simulation precision is improved, resource consumption is reduced, and system expansibility is enhanced.

The invention provides a game model penetration prevention method and device, equipment and a storage medium. The method comprises the steps that a moving direction vector of a hand model is calculated according to current frame position data and previous frame position data of the hand model in a virtual environment; predicting the next frame of position data of the hand model according to the moving direction vector and the current frame of position data, and emitting rays based on the next frame of position data and the moving direction vector; and judging whether the ray has a collision event in the virtual environment or not, and performing position adjustment and model rendering on the hand model when the ray has the collision event. According to the method, through vector calculation and ray detection, the calculation complexity is reduced, and the real-time performance is improved. The introduction of model prediction enables the system to cope with the potential mold-penetrating situation in advance, and further improves the efficiency of mold-penetrating prevention. A direct position adjustment strategy is adopted for collision processing, complex physical simulation calculation is avoided, and therefore the problems that in the prior art, calculation complexity is high, and real-time performance is insufficient are solved.

The invention discloses an electric energy error automatic calibration method and device, and relates to the technical field of electric energy metering. The method comprises the following steps: summarizing an error source of the electric energy meter; traversing an error source of the electric energy meter, retrieving big data, calling a calibration record, and mining bottom calibration logic; positive and negative polarization segmentation and mapping are carried out on the bottom layer calibration logic, a self-adaptive calibration module is supervised and trained, and the self-adaptive calibration module comprises a positive error calibration branch and a negative error calibration branch of a reverse twin architecture; the method comprises the following steps: receiving an electric data stream of operation of an electric energy meter, introducing an analog calculator to perform electric energy correction, performing branch activation and calibration decision by combining an adaptive calibration module, and generating a cascade calibration strategy; and on the basis of a cascade calibration strategy, electric energy error calibration regulation and control are carried out. The technical problems of inaccurate error calibration and low efficiency caused by the fact that the electric energy meter is difficult to adapt to diversified error sources in the prior art are solved, and the technical effect of accurately calibrating the error of the electric energy meter is achieved by training the self-adaptive calibration module.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention relates to the technical field of oil exploitation, and provides a CFD-based electric submersible pump oil-gas-water three-phase flow adaptability analysis method and system, and the method comprises the steps: constructing a three-dimensional geometric model of an electric submersible pump, carrying out the initial grid division through CFD software, and optimizing the grid density through a self-adaptive grid refinement algorithm; setting oil well boundary conditions, a turbulence model and cavitation model parameters; steady-state and unsteady-state simulation calculation is carried out, and a flow field distribution diagram in the electric submersible pump is obtained; key performance parameters are extracted; simulating flow characteristics of oil-gas-water three-phase flow by adopting a turbulence model optimization algorithm based on multiphase flow coupling and combining a turbulence model, a multiphase flow model, an interphase turbulence correction term and an interphase interaction force model; and carrying out adaptability analysis on the oil-gas-water three-phase flow of the electric submersible pump. All-around technical support can be provided for design, operation and maintenance of the electric submersible pump, and technical progress and development of the oil exploitation industry are powerfully promoted.

The invention discloses a machine learning optimization method of a low-frequency sound absorption structure based on three-dimensional printing, and belongs to the technical field of artificial intelligence acoustics, the low-frequency sound absorption structure is prepared from a photosensitive resin material through photocuring three-dimensional printing, and comprises a side seam, an extension cavity, a circular through hole, a resonance cavity, a first partition plate, a second partition plate and a third partition plate, the method has good expansibility and can be adjusted according to different noise control requirements; through a machine learning optimization method, structure parameters can be quickly adjusted to cope with a noise control task on the premise of ensuring a small size, so that a remarkable sound absorption effect is obtained at a specific low-frequency band; according to the method, a machine learning method based on a multi-layer perceptron and a genetic algorithm is adopted, geometric parameters of the low-frequency sound absorption structure are optimized, simulation calculation is carried out by using a commercial finite element solver COMSOL Multiphysics, and the sound absorption performance of the structure is further verified and optimized.

The invention discloses a foundation pile horizontal deformation reliability calculation method and device and electronic equipment, and belongs to the technical field of pile foundation engineering. Factors influencing horizontal deformation of the foundation pile and a simulation calculation value range of the factors are determined; designing a foundation pile horizontal deformation three-dimensional finite element numerical simulation calculation scheme based on a uniform test design method; calculating a horizontal deformation value of the foundation pile based on three-dimensional finite element numerical simulation; establishing a training sample data set and a test sample data set; establishing a foundation pile horizontal deformation agent model based on a machine learning model; establishing a limit state equation for calculating the reliability of the horizontal deformation of the foundation pile; and calculating a foundation pile horizontal deformation failure probability and a reliability index. The method has the advantages of being good in fault tolerance, high in adaptability and high in fitting and prediction precision, and the technical problem that a high-precision foundation pile deformation agent model is difficult to construct due to too many input variables is solved.

The invention provides a large eddy simulation method and system based on a mechanism-only model. The method comprises the following steps: acquiring a current dictionary database of a target turbulent flow field; the current dictionary database comprises multi-dimensional flow field features in a current time step; performing feature screening on the current dictionary database to obtain key flow field features of the target turbulent flow field; inputting the key flow field features into a mechanism-only model, and reconstructing to obtain the sublattice turbulence viscosity of the target turbulence field in the current time step; wherein the mechanism-only model is obtained by training an initial neural network model based on direct numerical simulation data of a sample turbulence field and a sublattice turbulence viscosity label of the sample turbulence field. According to the method, the simulation precision of small-scale turbulence vortex motion in large vortex simulation is improved, so that the problems of poor performance of a bottom layer model and low simulation calculation precision caused by calculation grid scale limitation and sub-grid stress simplification hypothesis are effectively solved.

The invention belongs to the technical field of radiation protection, and particularly relates to a neutron shielding material optimization method based on machine learning and chaotic mapping NSGA-III, and the method comprises the steps: 1, determining an optimization target, and building a multi-target neutron composite shielding material attribute optimization model; step 2, building a Monte Carlo model to simulate and calculate shielding doses of the composite shielding materials with different attributes to a neutron source, and obtaining a data set with the material component proportion and thickness as characteristic attributes and the shielding doses as labels; and step 3, optimizing initial parameters of the BP neural network by adopting an improved particle swarm optimization algorithm, improving calculation precision and operation efficiency of the neural network, and training a neural network regression model by taking the data set obtained in the step 2 as a sample to complete establishment of a shielding dose prediction model and the like. An improved particle swarm optimization algorithm for improving Levy flight mechanism strategy optimization is designed to optimize initial parameters of the BP neural network, and the precision and operation efficiency of a neural network shielding dose regression prediction model are improved.

The invention provides a method and a system for measuring plasma current in a quasi-ring symmetrical star simulator, and relates to the technical field of star simulators, and the method comprises the steps: determining a sensor design index according to a bootstrap current simulation calculation result, and converting the design index into an actual arrangement scheme according to an installation environment constraint; all the magnet coils are independently excited in a plasma-free state, wall eddy current interference signals generated by the magnet coils are calibrated, and thus wall eddy current calibration voltage signals are output; a sensor in an actual arrangement scheme is used for synchronously collecting a total original induced voltage signal including plasma current and wall eddy current; an integral voltage signal representing the total current is output; and compensating the integral voltage signal by using the wall eddy current calibration voltage signal, and inverting the final measurement value of the plasma current in the quasi-ring symmetric star simulator. The device and the method have the beneficial effects that the interference of a stray field on a measurement result is weakened, and a high-precision diagnostic tool is provided for the plasma physical research of the star simulator.

The invention provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, and the method comprises the steps: constructing an accident chain knowledge graph and a physical trigger graph, and building a causal relationship and physical constraints among equipment, states, events and consequences; gathering and mapping historical records, expert rules and online observation data into map entities and relationships, forming a baseline accident scene and calculating a baseline index; generating candidate paths by utilizing graph reasoning and coupled multi-physics field simulation, and realizing a closed loop of graph reasoning and physical solution through consistency check; performing scoring and disturbance simulation analysis on the candidate paths, and screening robust target paths; and carrying out high-fidelity simulation on the target path, identifying key nodes in combination with sensitivity analysis and a minimum cut-off set, and generating disposal suggestions and action priorities. According to the method, high-credibility prediction of accident evolution and emergency response closed-loop linkage are realized, and the method has high precision, high robustness and engineering implementability.

The invention discloses a continuous-discontinuous numerical simulation method considering a rock mass thermal coupling problem, and the method comprises the steps: dividing a simulation region into a potential damage region and a non-damage region, and carrying out the grid division of the simulation region; then generating a thermal calculation node system and a force calculation node system for analog calculation, wherein a mapping relationship exists between the thermal calculation node system and the force calculation node system; jointing units are arranged on the grid boundary of the potential damage area, and the rock mass fracture process is simulated through damage of the jointing units; the simulation process is decomposed into a plurality of calculation steps, the heat conduction process and the mechanical calculation process in each calculation step are solved in sequence, whether the joint unit is damaged or not is judged according to the deformation and stress state of the joint unit, the mapping relation is updated until the last calculation step is finished, and the thermal fracture process simulation of the rock mass is completed. According to the method, artificial heat transfer parameters can be prevented from being introduced in the heat conduction simulation process, the fracture evolution rule of the local area of the rock mass is researched in a targeted mode, the calculation cost of the model is reduced, and the dynamic fracture process in the rock mass thermal coupling problem is effectively simulated.

The invention discloses a bay environment simulation forecasting method and device, relates to the technical field of marine environment forecasting, and aims to solve the problem that in the prior art, forecasting precision and forecasting timeliness cannot be taken into account when environment simulation forecasting is carried out on a bay. The method comprises the following steps: based on coastline data, performing encryption processing on a target sub-region in a target gulf region by adopting a curve orthogonal grid, and generating a variable-resolution refined curve orthogonal grid from an inshore region to an open sea region; based on a refined curve orthogonal grid, according to a model simulation data format requirement, a variable-resolution grid file is manufactured and obtained; determining a model input file based on the water depth topographic data and the variable resolution grid file; and inputting the model input file into a preset three-dimensional oblique pressure ocean numerical model for calculation to obtain simulation forecast data. Therefore, the simulation precision requirements of different bay areas can be met, and the simulation calculation speed and the simulation calculation efficiency of the whole target bay area are ensured.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project digital management method based on a BIM model. The method comprises the following steps: acquiring real-time monitoring data; carrying out high-precision topographic data acquisition on a water conservancy project area, and carrying out data preprocessing to obtain preprocessed data; according to the preprocessed data, hydrological information integration modeling is carried out by utilizing a BIM platform, and a hydrological information integration model is obtained; performing multi-dimensional terrain dynamic updating on the hydrological information integration model by using the real-time monitoring data to obtain digital terrain data; performing flood simulation calculation based on the digital topographic data to obtain a flood simulation calculation result; and performing flood scene simulation according to the flood simulation calculation result to obtain flood simulation data. Through real-time monitoring, accurate prediction and timely early warning of the risk of the water conservancy project, a more effective technical means is provided for flood control and disaster reduction and safety management of the water conservancy project.

The invention provides a three-dimensional modeling optimization method and system for a hull fairing, and relates to the technical field of hull fairing three-dimensional modeling, and the method comprises the steps: according to a preliminary hull fairing model, defining a virtual computational domain including the hull fairing and the surrounding environment thereof; setting boundary conditions, dividing the computational domain into a plurality of grid units, and generating boundary layer grids on the surface of the ship body; numerical simulation is carried out, and solving parameters are set and comprise a time step length, a convergence criterion and a discrete format; the resistance performance, including frictional resistance and differential pressure resistance, of the hull fairing in water is calculated through numerical simulation. According to the invention, the design accuracy is improved.

The invention provides a simulation calculation method of an urban rail transit traction power supply system, which comprises the following steps of: constructing a locomotive load model, establishing a locomotive running state information table with a unified time section, and combining the position of a locomotive with the position of a traction station in a line to obtain topological structure analysis of different time sections; constructing a traction power supply system model, and obtaining rated voltage, line length, line unit impedance and traction position; simulating that a plurality of locomotives start from a vehicle depot and run from the head end of a line to the tail end of the line, obtaining power and position values of the locomotives according to a locomotive load model, substituting the power and position values into a traction power supply system model to complete load flow calculation of a direct-current traction network, and obtaining current and voltage of the locomotives and bus voltage and feeder current values of a traction station; according to the invention, the power of the train in different operation states can be simulated, and the bus voltage and feeder current of each traction station of the traction power supply system can be calculated.

The invention relates to the technical field of neuroscience and medical treatment, in particular to an individual head model dynamic optimization method and system based on transcranial alternating current stimulation. According to the method, the three-dimensional initial individual head model is partitioned, the parameters are set, the real-time electrical stimulation parameters and the real-time electroencephalogram information of the individual head under the stimulation effect are synchronously obtained, and then the parameters of the initial head model are adjusted according to the real-time data; then, according to the obtained data, simulating and calculating electrophysiological change data of the three-dimensional initial individual head model under the action of initial transcranial alternating current stimulation, and based on a focusing index value and a uniformity index value calculated through the electrophysiological change data, optimizing dynamically-adjusted head model parameters, and finally, calculating a focus index value and a uniformity index value of the three-dimensional initial individual head model under the action of initial transcranial alternating current stimulation. The information entropy difference value obtained through calculation of the optimized head model parameters and the real-time electroencephalogram information is compared with a preset difference threshold value, a clear termination condition is provided for optimization of the head model parameters, and therefore dynamic optimization of the individual head model is achieved.

The invention discloses a turbulent boundary layer wall surface pressure pulsation prediction method based on deep learning, and belongs to the field of noise prediction, and the method comprises the steps: training a neural network based on unsteady and steady flow simulation data of wall surface grid points to obtain a turbulent boundary layer wall surface pressure pulsation prediction model; a large batch of samples are obtained through one-time simulation calculation, the cost of sample obtaining can be obviously reduced, a large amount of time is saved, and therefore the prediction precision and timeliness of the deep learning model are guaranteed; the nonlinear relation between the turbulent boundary layer parameters and the wall surface pressure pulsation frequency spectrum is established through a deep learning method, a clear mathematical formula is not needed, and meanwhile the difficulty of mathematical requirements is reduced; compared with a traditional prediction method, the method provided by the invention is higher in applicability, wider in application field and higher in generalization ability; in addition, through simulation verification, the method has the advantages of being accurate in result, small in error, efficient in calculation and the like.

The invention relates to the technical field of image processing, and provides an optimization method and device for a spectrum processing chip, electronic equipment and a storage medium, and the method comprises the steps: constructing a target function for designing modulation units, and enabling the target function to represent a mapping relation between the structure parameters of the micro-nano structure of each modulation unit and the demands of a target application scene; performing optimization solution on the target function based on the natural spectral image sample set in the target application scene, and determining structure parameters of the micro-nano structure of the modulation unit; and constructing the modulation unit according to the determined structural parameters of the micro-nano structure of the modulation unit, and generating the spectrum processing chip according to the constructed modulation unit and the image sensor. According to the invention, the spectrum processing chip is constructed, so that the plurality of modulation units and the image sensor in the chip jointly carry out optical simulation calculation of the inner product of the feature vectors on the incident natural spectrum image, visual light information of a spatial domain and a frequency domain can be directly detected, acquired and perceived in real time, and subsequent analysis of the feature information is efficient and rapid.

The invention relates to an aerodynamic force-thermal-structure multi-field coupling intelligent prediction method for a high-speed aircraft, and belongs to the technical field of high-speed aircraft design. According to the flight path of the aircraft, sample parameters used for calculating aerodynamic force and aerodynamic heat states are extracted; modeling the aircraft, and carrying out analog calculation to obtain an aerodynamic force and aerodynamic heat data set; calculating an aircraft temperature field along the trajectory; training the aerodynamic force data set to obtain an aerodynamic force prediction model; calculating structural stress displacement field data of the aircraft model; extracting concerned aircraft feature point corresponding data from the aerodynamic data set, the aircraft temperature field and the structural stress displacement field data of the aircraft model to obtain a feature point data set; training the feature point data set to obtain an accurate prediction result; according to the method, after artificial intelligence algorithm offline learning modeling is carried out on the aerodynamic force-heat-structure coupling data of analog simulation, the flight path is input, and the data, changing along the flight path, of the feature point data of the output aircraft is rapidly predicted.

The invention relates to the field of multiphase numerical simulation, and discloses a numerical simulation method for solid and liquid phases in a rotating flow channel based on multiple reference systems, which comprises the following steps of: firstly, acquiring a rotating flow field generated by a multiple reference system method, and extracting rotating domain information as well as particle information and fluid computational domain grid information in a current time step; secondly, particles in a rotation domain and particles outside the rotation domain are determined according to the rotation domain information and the particle information; in combination with the particle information and the fluid calculation domain grid information, the final flow field speed and the reconstructed porosity of the particles in the rotation domain and the particles outside the rotation domain are calculated respectively, and then the drag force is calculated according to the final flow field speed, the reconstructed porosity and the particle information; meanwhile, according to the particle information and the rotation domain information, the Korotkoff force and the centrifugal force of the particles in the rotation domain are calculated. Finally, drag force, Korotkoff force and centrifugal force are calculated through iteration, particle information and grid information of the next time step are obtained, and therefore numerical simulation calculation of particle motion in the rotating flow channel under the fixed grid is achieved.

The invention discloses a navigation body water entry load and motion trail prediction and optimization method based on artificial intelligence, and relates to the field of cross-medium water entry and exit of navigation bodies. The problems that an existing real-scale navigation body cross-medium water entry test is huge in cost, a small-scale mechanical test and other methods are adopted for the cross-medium water entry problem, but large-scale development is difficult due to high cost, and the numerical simulation calculation efficiency is low are solved. The invention discloses a deep learning-based slamming load and motion track prediction method when a navigation body enters water at a high speed, and the method comprises the steps: building a navigation body water entry slamming acceleration and motion attitude database through finite element, meshless and other numerical calculation results or existing test results; and predicting the underwater slamming load characteristics and motion trails of the navigation body under different section shape parameters and underwater parameters, and on the basis, introducing a genetic algorithm to perform reverse optimization design on the shape parameters and underwater parameters of the navigation body by combining the established model, so as to obtain the underwater slamming load characteristics and motion trails of the navigation body. And optimal shape parameters and water entry parameters with strong ballistic stability and small slamming acceleration can be quickly given.

The invention discloses a low-sample neural network structure reliability evaluation system and evaluation method, and relates to the technical field of engineering structure safety monitoring, and the evaluation system comprises a cloud server which is used for constructing a recurrent neural network model containing a static variable embedding mechanism, completing model training and converting a model format; the edge calculation terminal is used for receiving and preprocessing real-time data of the sensor, executing multi-step prediction to output a future time period response sequence, and calculating a future failure probability through virtual Monte Carlo simulation; the sensor assembly is used for collecting structure state time sequence data; and the communication module is used for realizing data interaction and alarm signal transmission operation. According to the method, collaborative modeling of time-varying and static uncertainty is realized by adopting a static variable embedded recurrent neural network model, failure probability distribution is generated at an edge computing terminal in combination with a virtual Monte Carlo technology, failure risk prediction in a future time period is supported, and real-time and accurate reliability early warning can be realized in a resource limited scene.

Provided in the present invention is a high-throughput material simulation calculation optimization method based on time prediction, relating to the field of materials science. The method comprises: first constructing a prediction model of task configurations and corresponding time predictions, and using the prediction model to generate the execution time of all of the tasks in a high-throughput material simulation calculation under different conditions; then generating an optimal scheduling plan for each model in the high-throughput material simulation calculation by means of directed graphs; and, according to the optimal scheduling plan for each model, sequentially executing all of the tasks until all of the tasks are completed. Further, a high-throughput computing simulation optimization apparatus based on a time prediction and a storage medium are provided.

The invention provides a numerical simulation and stability analysis method and system based on monitoring data, and belongs to the technical field of monitoring and early warning. The problems that in the slope monitoring technology, data collecting and processing efficiency is low, calculation model precision and adaptability are poor, dynamic monitoring of slope stability lacks pertinence, the system integration degree is low, cost is high, and application is complex are solved. The method comprises the steps that slope data are collected in real time through a multifunctional sensor; transmitting the acquired data to a data acquisition system for real-time data monitoring and recording; a two-dimensional numerical model of the side slope is constructed through finite element analysis software, and the side slope is analyzed through the two-dimensional numerical model; and comparing and analyzing real-time monitoring data in the data acquisition system and a numerical simulation calculation result, analyzing the change trend of the side slope, identifying a potential instability area, updating the two-dimensional numerical model in real time by using an automatic algorithm, completing real-time dynamic evaluation of the side slope, and early warning potential side slope instability risks in advance.