159 results about "Random simulation" patented technology

The invention provides a mechanical arm visual grabbing method based on Sim2Real migration under the limited space condition, and relates to the technical field of mechanical arm visual grabbing and Sim2Real. The method comprises the following steps: firstly, building a task scene, selecting visual domain randomization parameters of a simulation environment, and carrying out a grabbing simulation experiment in the task scene to determine the randomization parameters so as to form a plurality of different simulation environments; then, training of a first-stage grabbing model is carried out in the generated random simulation environment, so that a mechanical arm grabs all objects; training of a second-stage grabbing model is carried out in the disordered and crowded simulation environment, so that the mechanical arm grabs the target objects from the disordered and crowded simulation environment in sequence; and finally, performing a target grabbing test in a space limited scene by using the grabbing model trained in the second stage. According to the method, the problem of a real gap between a simulation environment and a real world is considered, so that the grabbing model is in contact with various different environmental conditions, and the robustness of the model is further improved.

The invention relates to the technical field of seismology, in particular to a seismic oscillation field stochastic simulation method based on spatial correlation, which is composed of a single-field-point seismic oscillation model and a spatial correlation model, and specifically comprises the following steps: step 1, building a single-field-point seismic oscillation time-frequency power spectrum model; 2, building a prediction equation of time-frequency power spectrum parameters; step 3, converting a time-frequency power spectrum to a time history; 4, building a spatial correlation model of the time-frequency power spectrum parameters; according to the method, the simulation speed of the seismic motion field is greatly improved, meanwhile, spatial correlation of model parameters is introduced, correlation of time-frequency characteristics between field points in space is well represented, and the seismic motion field simulated through the method can conform to physical cognition.

The invention provides a stochastic simulation method and device for an open quantum system, and the method comprises the steps: decomposing the total Hamiltonian of a new system into a linear term combination of a Fouli operator, and constructing a combination of quantum gates through the coefficient of the linear term combination. And then, operating a classical sampling method taking a Fouli operator as a sampling sample, constructing a quantum circuit by using the combination of quantum gates, and finally obtaining a mean value of the target observed quantity in the open quantum system according to an operation result of the quantum circuit. According to the method, a large-scale actual quantum system dynamics problem is simulated by utilizing a shallow quantum circuit, limitation of quantum chip resources is relieved, an error relieving technology is utilized, the circuit depth is reduced, a part of calculation processes are allocated to a classic computer, and processes related to observed quantity are only operated on the quantum computer, so that the calculation efficiency is improved. And the calculation results of the two parts are integrated on a classic computer, so that quantum hardware resources are saved.

The invention provides a poison gas leakage inversion method, device and equipment, a medium and a product. According to one example of the application, the method may include: using a stochastic simulation algorithm to determine a simulation position and simulation intensity where a poison gas concentration error is minimum, the poison gas concentration error being an error between a sampled poison gas concentration of a sampling point and a stochastic simulated poison gas concentration; based on the simulation position and the simulation intensity, constructing a position constraint condition and an intensity constraint condition of the poison gas leakage source; calculating a leakage source position of a poison gas leakage source by adopting a differential evolution algorithm and a position constraint condition; and calculating the leakage source intensity of the poison gas leakage source by adopting a quasi-Newton optimization algorithm and an intensity constraint condition.

The invention discloses a post-pumping air tank robustness collaborative design method considering hydraulic parameter time-varying characteristics, which comprises the following steps: defining a hydraulic parameter uncertainty fluctuation interval of a water delivery system in a full life cycle, and setting a body structure decision variable range of a post-pumping air tank; performing combined sampling in the water conservancy parameter uncertainty fluctuation interval and the body structure decision variable range by using a test design method to generate an initial sample set; performing steady-state and transient-state coupling simulation on the initial sample set, constructing a constant-flow operation condition of the water delivery system by using a hydraulic equation, updating a water pump working point and pipeline pressure distribution, performing transient simulation by using a characteristic line method to obtain a hydraulic response index, and training to obtain a water hammer response agent model; constructing a robustness optimization objective function based on failure probability constraint; and performing global optimization on the target function by using an intelligent optimization algorithm, calling the water hammer response agent model to perform random simulation, evaluating a failure probability, and outputting a target design scheme.

The invention relates to the technical field of reservoir dam safety evaluation, in particular to a reservoir dam flood safety risk evaluation method, a computer storage medium and equipment. The reservoir dam flood safety risk assessment method comprises the following steps: deducing estimated values of hydrological statistical parameters of a sample series; randomly simulating a longer series of flood peak flow long series samples, and obtaining a flood hydrograph corresponding to the simulated flood peak flow series; flood regulation calculation is carried out according to a set scheduling scheme by combining the reservoir capacity curve and discharge curve data of the reservoir, and a flood regulation highest water level value is obtained; and deducing the conditional probability that the reservoir level exceeds different characteristic water levels due to the flood based on the obtained flood regulation highest water level value, and evaluating the flood safety risk of the reservoir dam according to the conditional probability. According to the method, simulation and calculation results can be visually presented, the calculation process is direct and clear, and the method has important practical significance and good application prospects in reservoir operation dam safety risk assessment.

The invention relates to the technical field of distributed energy management control under the condition of large-scale access of a micro-grid group, in particular to a micro-grid group optimization configuration method, device and equipment for supporting reactive compensation of a distribution network and a medium, and the method comprises the steps: building a chance constraint reactive power optimization model by considering the random characteristics of grid connection of a large-scale micro-grid group; the optimal installation capacity of the reactive power compensation device is solved through a random simulation particle swarm algorithm; based on the optimal installation capacity, probability distribution of system node voltage and line power flow is calculated by applying a semi-invariant probabilistic power flow method, and confidence level constraint is set for a result of the probability distribution to ensure that a planning result meets a predetermined confidence interval requirement, so that the equivalent annual cost generated by system operation is reduced, and the planning efficiency is improved. The overall economic benefit of the system is improved, and the probabilistic load flow calculation method based on the semi-invariant method converts complex convolution operation into simple arithmetic operation between semi-invariants, so that the complexity of the calculation process is reduced.

The invention relates to the technical field of three-dimensional model processing, in particular to a solar wing battery selecting and matching method and system based on three-dimensional layout. The method comprises the following steps: acquiring three-dimensional data of a solar wing, and constructing a three-dimensional substrate model according to the three-dimensional data of the solar wing to obtain a three-dimensional model of the solar wing; performing structure-driven spreading strategy simulation on the three-dimensional model of the solar wing to obtain fixed simulation data, and performing fixed strategy simulation on the three-dimensional model of the solar wing to generate a disturbance type layout scheme to obtain random simulation data; performing regional structure domain analysis on the fixed simulation data and the random simulation data to obtain regional structure domain data, and performing optimal layout sorting on the regional structure domain data to obtain layout sorting data; and performing two-dimensional drawing export on the layout sorting data to obtain solar wing battery matching data. According to the method, the diversity of the scheme can be effectively expanded, the local adaptability is improved, and the high coverage rate and the optimal sorting output are realized under the condition of ensuring the structural constraint.

The invention discloses a market-oriented hydropower station income risk assessment method, which comprises the following steps: S1, acquiring real-time hydrological data of a hydropower station drainage basin, constructing a hydroenergy income dynamic assessment model, establishing a dynamic mapping relationship between a hydroenergy value and a power generation income, and calculating the hydroenergy values in the current and future time periods; s2, identifying multi-dimensional risk factors influencing the income of the hydropower station; s3, determining the probability distribution of each risk factor, establishing a multi-source risk factor coupling model through a Copula function, and quantifying the comprehensive influence of the interaction between the risk factors on the income of the hydropower station; s4, performing random simulation on the power generation income of the hydropower station in a period of time in the future to generate a large number of income simulation samples; and S5, according to the income simulation sample, calculating a risk assessment index of the hydropower station income, and completing hydropower station income risk assessment of the hydropower station value. Dynamic quantitative evaluation, multi-dimensional risk interaction analysis and full-scene risk prediction of the income of the hydropower station are realized, and the timeliness and accuracy of income evaluation are improved.

The invention discloses a reliability evaluation method and system for a flexible interconnected power distribution network containing a three-terminal SOP, and the method comprises the steps: determining a topological structure of an MMC converter, constructing a sub-module reliability model based on the topological structure, and constructing a bridge arm reliability model considering the correlation between redundant configuration and sub-modules through the sub-module reliability model; constructing a reliability model of the MMC according to the topological structure of the MMC converter and the bridge arm reliability model; analyzing various running states of the three-terminal SOP, and constructing an SOP reliability model through the various running states and the reliability model of the MMC; and based on the SOP reliability model, through random simulation of an operation state and load transfer judgment, reliability evaluation of the flexible interconnected power distribution network is carried out, and the influence of SOP access on the reliability of the flexible interconnected power distribution network is analyzed. According to the method, the reliability change after the multi-terminal SOP is accessed to the power distribution network is quantitatively analyzed, events of different sequential relationships between fault occurrence, recovery and the like in the power distribution network system are considered, and the reliability evaluation level of the system can be improved.

The invention relates to the field of multi-energy coupling complementary scheduling, and discloses a watershed water-wind-light dynamic adaptability aggregation decomposition scheduling rule compilation method, which comprises the following steps: S1, constructing a multivariable joint stochastic simulation model, and jointly reconstructing a water-wind-light non-consistent combination scene; the method comprises the following steps: S1, solving an optimal scheduling trajectory by adopting a successive approximation dynamic programming method, S3, based on an aggregation and decomposition theory, respectively selecting appropriate independent variable factors and scheduling decision factors for an aggregation system (an integrated system) and a decomposition system (a water-wind-light-bundled subsystem in the integrated system), and S4, based on a water-wind-light scene under a non-consistency condition, carrying out scheduling on the water-wind-light-bundled subsystem in the integrated system. S5, taking the watershed water-wind-light non-consistency scene combination as the input of the model, and applying the EnKF-KNN process in the S4 on the basis of the static scheduling function type determined in the step S3, taking a static scheduling function parameter as an initial value, and applying a data assimilation and machine learning coupling model.

The invention relates to a hybrid flow shop scheduling robust optimization method and device based on a hybrid genetic algorithm, which can realize the improvement of the robustness of a hybrid flow shop scheduling scheme, emphasizes the mean value performance while paying attention to the worst situation, and has relatively strong capabilities of global search and jumping out of a locally optimal solution. The method comprises the following steps: determining a hybrid flow shop scheduling problem mathematical model based on robust optimization; constructing a first optimization algorithm based on a genetic algorithm according to the constraint of the hybrid flow shop scheduling problem; constructing a second optimization algorithm based on a simulated annealing algorithm framework; constructing a scheduling scheme evaluation method based on the stochastic simulation algorithm and considering the worst situation and the average performance; and fusing the first optimization algorithm and the second optimization algorithm to obtain a target optimization algorithm, and performing scheduling scheme optimization based on the target optimization algorithm and the scheduling scheme evaluation method to obtain an optimal scheduling scheme.

The invention discloses a water quality index interval prediction method in a non-consistency scene, and the method comprises the following steps: obtaining a runoff time sequence and a water quality time sequence in a specified time period, and executing the non-consistency comprehensive judgment; adopting a plurality of probability distribution models to respectively fit the runoff time sequence and the water quality time sequence, and determining an optimal probability distribution model; constructing an AR model, realizing stochastic simulation of runoff, and outputting a simulated runoff sequence; adjusting the simulated runoff sequence to obtain a non-consistent scene; constructing a two-dimensional joint Copula function model, inputting a future inconsistent scene into the two-dimensional joint Copula function model, and outputting a water quality prediction sequence set; and extracting a residual error set between the water quality point simulation value and the measured value in the actual measurement period from the water quality simulation sequence set, extracting a residual error sample set, and realizing water quality index interval prediction according to the residual error sample set. According to the technical scheme, the point prediction and the water quality interval prediction of the water quality data under the non-uniform background can be obtained.

The invention discloses a new energy fan maintenance operation safety monitoring method combined with big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting new energy maintenance data, carrying out the time synchronization, format standardization and space alignment processing, and forming a unified comprehensive data set; inputting the comprehensive data set into a pre-trained high-fidelity digital twinborn model, mapping risk factors in the comprehensive data set into a three-dimensional space of the digital twinborn model through a built-in risk quantification algorithm, and outputting a dynamic risk field; and associating the risk points and the intensity values in the dynamic risk field as nodes and attributes to nodes and attributes corresponding to a predefined causal knowledge graph, deducing a potential risk evolution path and occurrence probability, and generating a risk deduction conclusion report. According to the method, path exploration and stochastic simulation are carried out through the causal knowledge graph in combination with Monte Carlo tree search, and probabilistic deduction of the potential risk evolution path in the fan maintenance operation is realized.

The invention discloses a three-dimensional geological modeling method based on sequential Gaussian simulation, and particularly relates to the technical field of geological modeling, in particular to a three-dimensional geological modeling method based on sequential Gaussian simulation. The method comprises the steps of obtaining and preprocessing geological data, constructing a three-dimensional grid model, analyzing a space structure by adopting an improved variation function integrated with anisotropic parameters, carrying out random simulation by utilizing a sequential Gaussian simulation algorithm and introducing a dynamic conditional mechanism, and meanwhile, integrating multi-source soft data as collaborative variables to apply geological constraints. And finally, post-processing and uncertainty quantification are carried out on a simulation result. According to the method, the description precision of the model on a complex geological structure, the efficiency of simulation calculation and the reliability of multi-source data collaborative application are effectively improved.

The invention belongs to the related technical field of electric power scene simulation, and discloses a water, wind and light scene stochastic simulation method and system considering space-time correlation, and the method comprises the steps: collecting historical data of water, wind and light resources; determining a scene simulation sequence and fitting edge distribution of each variable in any time period; for the first resource, constructing first condition distribution of the t time period variable when the t-1 time period variable is known through a two-dimensional Copula function; according to the first condition distribution, acquiring an analog value of the t time period variable; for any one of the second resource and the third resource, constructing second condition distribution of the t time period variable of the any resource through a three-dimensional Copula function when the t time period variable of the previous resource and the t-1 time period variable of the any resource are known; and according to the second condition distribution, acquiring an analog value of the variable in the t time period of the any resource. According to the method, correlation characteristics in different resource spaces are integrated, and multiple uncertainties of water, wind and light resources can be more comprehensively simulated and analyzed.

The invention provides a design method and system for a heterogeneous integrated circuit of a microwave system, and relates to the technical field of integrated circuits, and the method comprises the steps: building a multi-layer structure model of the heterogeneous integrated circuit of the microwave system, and the multi-layer structure model comprises a substrate layer, an active device layer, a passive device layer and an interconnection layer; based on the established multilayer structure model, constructing an electromagnetic simulation model of a heterogeneous integrated circuit of the microwave system; performing full-wave electromagnetic simulation on the whole heterogeneous integrated circuit by using a finite element method to obtain an electromagnetic coupling effect and a parasitic effect between devices; based on the optimized electromagnetic simulation model, performing performance evaluation and optimization on the heterogeneous integrated circuit of the microwave system; carrying out large-scale random simulation by utilizing a Monte Carlo method, and evaluating the performance stability of the microwave system under different working conditions; and dynamically adjusting key parameters of the heterogeneous integrated circuit by adopting a self-adaptive algorithm according to a simulation result.

The invention discloses a three-dimensional reservoir modeling method cooperatively driven by geostatistics and deep learning, and the method comprises the steps: generating a plurality of three-dimensional reservoir structure simulation results with equal probabilities according to the logging data and seismic data of a known reservoir through employing a multi-point geostatistics random simulation method; selecting a three-dimensional reservoir structure with good global connectivity from the simulation result to construct a reservoir structure mode library; constructing a generative adversarial network model, training the generative adversarial network model by using the reservoir structure pattern library, and constructing a three-dimensional reservoir structure model of a target reservoir by using the trained generative adversarial network model; the problems that the global connectivity of a simulation result of multi-point geostatistics is poor and a deep learning modeling method is difficult to train and optimize are solved.

Certain aspects of the present disclosure are directed towards a method for circuit testing. The method generally includes: determining a probability distribution indicating prior failure probabilities associated with a circuit design; determining a first likelihood associated with occurrence of at least one failure for the circuit design; determining a quantity of test instances to be performed using simulation to detect the at least one failure based on the probability distribution and the first likelihood; and outputting the quantity of test instances.

The invention discloses an auxiliary decision-making method, device and equipment for power transaction, a storage medium and a product, and relates to the technical field of power spot transaction. The method comprises the following steps: carrying out random simulation on predicted source data of power transaction in a preset time period based on a Monte Carlo model to obtain transaction strategy probability models under different transaction strategies; randomly generating a group of initial transaction strategies as an initial population, and inputting the transaction strategy probability model and the prediction source data into a preset deep learning model to obtain prediction transaction data of each individual in the initial population; the initial population is selected, crossed and varied based on the predicted transaction data of each individual, an optimal transaction strategy is obtained, and the optimal transaction strategy is used for aiding decision-making of the power transaction, so that the transaction prediction accuracy can be improved, an aided decision-making method with reference value is provided for a trader, and the trader is helped to make a transaction strategy in advance.

The invention discloses a rocket projectile launching end point intensity influence factor analysis method, system and device and a medium. The method comprises the following steps: establishing a six-degree-of-freedom rigid in-vitro ballistic equation of the variable mass rocket projectile in a matrix form; the method comprises the following steps: generating a parameter sample based on a Monte Carlo random simulation principle by comprehensively considering three types of influence parameters of rocket projectile processing errors, initial disturbance and meteorological conditions, performing external trajectory equation numerical integration by combining a fourth-order Runge-Kutta method, and calculating a rocket projectile launching end point coordinate; three uncertainty sensitivity analysis methods are adopted, the influence degree of various parameters on the emission end point is quantified, results of the three methods are compared, and the influence degree of each factor is determined. According to the method, the influence degree of each factor on the rocket projectile launching terminal point is compared and analyzed by using multiple uncertainty sensitivity analysis methods, and the accuracy is relatively high.

This invention provides a communication countermeasure simulation method based on random simulation, belonging to the field of electronic countermeasure simulation technology. The method first establishes a probability distribution model of reconnaissance information deviation for key parameters of the communication target; then, based on the probability distribution model of each parameter deviation, independent random sampling is performed to generate instantaneous values ​​of frequency deviation, positioning deviation, and modulation pattern recognition results at each moment, and the guidance parameters of the jammer are determined; next, the instantaneous interference-to-signal ratio (ISR) at the target receiver at the current moment is calculated; finally, the ISR calculated at the communication receiver is compared with a preset communication performance degradation ISR threshold to determine whether the jamming is successful. The model of this invention has clear physical meaning and can accurately quantify the impact of reconnaissance information deviation and propagation loss on the jamming effect, significantly improving the credibility and practicality of the simulation evaluation.

The invention provides a textile composite material impact energy absorption simulation method, system and device and a medium, and the method comprises the steps: carrying out the connected domain analysis of a tomography image, and determining the equivalent diameter distribution characteristics of pore defects in a textile composite material; performing stochastic simulation based on the equivalent diameter distribution characteristics in combination with a mesoscopic geometric model of the textile composite material to obtain stochastic pore space distribution of the textile composite material in the resin enrichment area; geometric difference is carried out on the random pore space distribution and the mesoscopic geometric model, and then a mesoscopic finite element model containing pore defects is constructed; and endowing the mesoscopic finite element model with material attributes and carrying out explicit kinetic analysis on impact load, and further determining the impact energy absorption performance of the textile composite material prepared in the batch according to an analysis result. By adopting the scheme of the invention, systematic deviation of impact energy absorption performance prediction caused by model idealization can be reduced.

The application discloses an escort method of an unmanned underwater vehicle and related products. The UUV simulation intelligent agent can first acquire scene state real-time data and a task target, and acquire multiple groups of original simulation data from a target simulation environment based on the task target. Multiple groups of random simulation data are randomly generated based on the multiple groups of original simulation data, and escort strategies are generated based on the multiple groups of random simulation data, the multiple groups of original simulation data and the scene state real-time data, to obtain multiple intelligent escort strategies. Multiple virtual scenes are generated according to state data corresponding to the multiple intelligent escort strategies. Finally, a strategy scene mapping relationship is obtained by matching and associating the multiple intelligent escort strategies and the virtual scenes corresponding to the multiple intelligent escort strategies, and the strategy scene mapping relationship is distributed to each UUV, so that each UUV performs intelligent escort in combination with the strategy scene mapping relationship. The application improves the adaptability and robustness of the UUV to a complex unknown environment.

The invention discloses a method for assessing orchard soil heavy metal pollution risk through multi-source data fusion, and relates to the technical field of soil heavy metal pollution risk assessment, and the method comprises the following steps: constructing a double-layer dynamic adaptive grid to divide an orchard, and setting subsample points based on a fruit tree water drip; carrying out pXRF in-situ detection; iCP-MS ectopic detection is carried out; fusion conditions are judged through correlation, when the fusion conditions are met, a common collaborative Kriging method and a sequential Gaussian stochastic simulation network are adopted for collaborative simulation, and a heavy metal prediction content distribution map and a prediction error variance map are drawn. According to the method, through collaborative simulation in combination with double-layer dynamic division, the fruit tree water drip, the judgment fusion condition, the common collaborative Kriging method and the sequential Gaussian stochastic simulation network, the sample point coverage is improved, low cost and high precision are both considered, and the risk assessment reliability is improved.

The present invention belongs to the technical field related to power scenario simulation, and discloses a stochastic simulation method and system for water-wind-solar scenarios considering spatio-temporal correlation. The method includes: collecting historical data of three types of water-wind-solar resources; determining the scenario simulation order and fitting the marginal distribution of each variable at any time period; for the first resource, constructing the first conditional distribution of the variable at the t time period of -1 t according to the first conditional distribution; obtaining the simulation value of the variable at the t time period; for any one of the second resource and the third resource, constructing the second conditional distribution of the variable at the t time period of the previous resource and the variable at the t time period of -1 of this any resource; obtaining the simulation value of the variable at the t time period of this any resource according to the second conditional distribution; obtaining the simulation value of the variable at the t time period. The present invention incorporates the correlation characteristics of different resources in space, and can more comprehensively simulate and analyze the multiple uncertainties of water-wind-solar resources.

The present invention discloses a method for predicting and simulating the genotypes of advanced generations of hybrid combinations of self-pollinated crops, comprising the following steps: constructing a parental genotype data set and screening effective marker loci; calculating the linkage disequilibrium coefficient between marker loci, constructing a genomic linkage map, and determining the recombination rate between loci; using a random simulation algorithm to construct a gamete formation model and generate the genotypes of F1 generation individuals; based on the genotypes of the F1 generation, randomly simulating the selfing process to generate genotype data from F2 to Fn generations; establishing a mapping relationship between genotypes and phenotypes using a machine learning model to predict the phenotypic traits of advanced-generation selfing lines; the method of the present invention takes into account chromosomal exchange and linkage disequilibrium, can accurately simulate the gene recombination and segregation phenomena of the hybrid offspring of self-pollinated crops, and combines the gradient boosting decision tree model to achieve high-precision phenotypic prediction, providing an efficient auxiliary tool for the breeding of self-pollinated crops, which can significantly shorten the breeding cycle and improve the breeding efficiency.

The invention discloses a real-time optimal distribution method for flood control reserved storage capacity of a reservoir group considering random incoming water. The method comprises the following steps: generating a reservoir optimal scheduling first sample set oriented to random simulated flood based on a reservoir flood control rule optimal scheduling model; defining and extracting flood control dispatching benefit curve clusters around the first dispatching sample set, and correcting the flood control dispatching benefit curve clusters to obtain a second dispatching sample set oriented to random simulation flood; identifying a key flood characteristic factor set, a downstream peak clipping amount, an upstream reservoir input capacity and other characteristics in the second scheduling sample set, fitting a mapping relation among the sample characteristics by adopting a machine learning method to obtain a flood control benefit function, and taking the flood control benefit function as a constraint condition of a real-time optimization distribution model; and meanwhile, constructing a reservoir group reserved flood control capacity real-time optimal distribution model by taking the maximum residual flood control capacity of the reservoir group as a target, designing a model traversal solving strategy, obtaining a reservoir capacity optimal distribution scheme of the reservoir group according to different peak clipping flow / water level targets, and calculating reliability verification evaluation of the scheme.

The invention discloses a combination equivalence verification method based on intelligent engine selection, and the method comprises the steps: constructing Mitter circuit random simulation, extracting embedded vectors, splicing the embedded vectors into an image, inputting the image into a trained AlexNet network, carrying out the prediction to obtain an optimal scanning engine, and carrying out the equivalence verification of a candidate equivalent node pair, and after all candidate equivalent node pairs are verified and merged, performing final equivalence verification by using an SAT solver. According to the method, efficient and self-adaptive optimal scanning engine selection can be realized, when a high-XOR density circuit is processed, remarkable advantages are shown, the processing speed is greatly improved, and the verification efficiency and accuracy of equivalence verification of the two circuits can be remarkably improved. According to the method, a new research thought is provided for the design of the combination equivalence verification tool, the solving time can be remarkably shortened, the stability and adaptability of solving are improved, and the method has important practical significance in application of the logic synthesis field and development of the equivalence verification tool.

The invention provides a fault control fracture-cavity modeling method utilizing geological trend factor constraints, and relates to the technical field of oil-gas exploration, and the method comprises the steps: calculating seismic coherent body attributes based on post-stack seismic data, obtaining the overall space distribution characteristics of geological structure anomalies and the like, and depicting a strike-slip fracture zone boundary model U1 (x, y, z); calculating the spatial development intensity I1 (x, y, z) of the crack and the hole body based on the distance influence factors from the fracture zone; the longitudinal development strength I2 (x, y, z) of the crack and the hole body is restrained by using the buried depth trend of the stratum, and the comprehensive spatial distribution trend strength Itotal of the crack and the hole body is constructed. The space trend constraint and the stochastic simulation technology are combined, and a space distribution model of the cracks and the hole bodies is established. The model established by the method is more in line with actual space distribution characteristics of cracks and holes in a work area. The method is applied to carrying out numerical simulation research, and is helpful for finding more effective means for processing the fault control fracture-cavity body.