118 results about "Random simulation" patented technology

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 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 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 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.

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 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.

The invention discloses an XOR majority logic-based equivalence verification method, which comprises the following steps of: constructing two circuits of which the equivalence is to be verified into a Miter circuit, if main output values of the Miter circuit after structure rewriting are not all zero, ending the method, otherwise, performing equivalence verification on the Miter circuit by using an SAT solver; if the SAT solver obtains a solving result, the method is ended, otherwise, simulation signals of all nodes in the Miter circuit are obtained through random simulation, potential equivalent nodes are classified into an equivalent class and optimized, nodes with potential equivalent relations are randomly obtained from the optimized equivalent class, the equivalent nodes are iteratively merged, and the optimized Miter circuit is obtained. And finally, carrying out final equivalence verification on the optimized Miter circuit by using an SAT solver. The method can improve the efficiency and accuracy of equivalence verification, and is especially suitable for verifying complex multiply-add mixed arithmetic circuits and XOR intensive circuits.

The application discloses a reservoir group scheduling rule extraction method based on runoff space-time two-dimensional random simulation, which comprises the following steps: preferably selecting runoff edge distribution functions of each reservoir and each time period, and preferably selecting runoff Copula joint distribution functions of the same reservoir, different time periods, the same time period and different reservoirs; a plurality of groups of conventional scenario and extreme scenario runoff sequence data are generated through random simulation by using the preferred runoff Copula joint distribution functions and combining with inverse transformation sampling of the Monte Carlo method; a reservoir group optimization scheduling model is established, the plurality of groups of runoff sequence data generated through random simulation are used as model inputs, and the corresponding plurality of groups of reservoir group optimization scheduling results are obtained through model optimization solving; a key decision factor is determined by using a data mining method, and a reservoir group scheduling rule model is established by using a machine learning method; the current and a plurality of time period state quantities of the determined key decision factor are used as the reservoir group scheduling rule model inputs, and reservoir group scheduling operation results are predicted to support decision-making practice.

The invention discloses a missing data filling model establishment method and device, equipment and a storage medium. The missing data filling model establishment method comprises the steps of obtaining historical energy data in an energy station; based on the historical energy data, generating a data missing mask matrix by adopting a stochastic simulation algorithm, and generating and constructing a missing sample data set based on the historical energy data and the data missing mask matrix; carrying out model training on a pre-constructed machine learning model by adopting the missing sample data set until a preset model training ending condition is met; and taking the trained machine learning model as a missing data filling model. Through the scheme, the authenticity of missing data filling is improved.

The invention relates to a stochastic simulation method considering dimensional deviation and concrete material variability, and belongs to the field of concrete structure analysis. According to the method, accurate simulation of the mechanical properties of the concrete structure under the influence of construction errors and material randomness is realized by determining random factors, establishing a random field matrix, carrying out sample transformation, establishing random models in batches, solving in batches and carrying out post-processing. A random simulation result is used as an output parameter, a random sample is used as an input parameter, and a PRRM-EPR algorithm is adopted to establish a machine learning prediction model. Compared with a traditional method, the method has the advantages that the generation quality of the random sample is improved, the calculation cost is reduced, the randomness in actual engineering can be better reflected, and the established prediction model can directly predict the mechanical response (displacement, load and the like) of the component through actually measured random parameters. The method is suitable for design, evaluation and optimization of a concrete structure, and particularly has a remarkable effect on improvement of structural safety.

The application belongs to the field of electric vehicles, and discloses a method and device for predicting the cruising range and electronic equipment, the method comprising: dividing micro segments according to historical vehicle driving data; predicting according to the micro segments by using a Markov Monte Carlo model; determining the corresponding energy consumption prediction value according to the obtained driving state prediction result, and determining the corresponding cruising range prediction value according to the energy consumption prediction value. The application divides the historical vehicle driving data into micro segments to capture the subtle changes in driving characteristics. Then, the random simulation capability of the Markov Monte Carlo model is used to predict the driving state of the vehicle according to these micro segments, and finally the cruising range prediction value is calculated by predicting the driving state. The application can adapt to the changes in driving conditions in real time, provide a cruising range prediction that is more in line with the actual driving situation, make the prediction result more accurate, help optimize the energy management of electric vehicles and improve the overall performance, and at the same time improve the user satisfaction.

The invention discloses an intelligent anchor position detection algorithm based on multilevel spatial constraints. The intelligent anchor position detection algorithm comprises the following steps: establishing a ship safety distance model based on a Monte Carlo stochastic simulation technology; establishing an anchoring safe water depth detection model based on KD-Tree; and screening anchor location points which simultaneously meet safety spacing and water depth constraints. According to the method, the advantages of global fast search and local fine detection are effectively integrated by constructing a layered progressive space optimization framework. The method comprises the following steps: firstly, establishing a multi-level spatial constraint model for ship anchor position detection on a theoretical level, and defining a collaborative optimization mechanism between different levels; and secondly, organic fusion of spatial index and dynamic filtering is realized in algorithm design, an anchoring position point meeting the safe distance between ships is obtained through an anchoring safety detection model based on plane distance constraint, an anchoring position point meeting the water depth constraint is obtained through water depth detection based on KD-Tree, and compared with an existing method, the calculation efficiency is remarkably improved.

The invention discloses a robot measurement system calibration and uncertainty evaluation method. Firstly, a robot motion error model is established, and an error transmission link is defined; after a theoretical framework is established, N groups of end effector pose data are measured, geometric parameter errors are calculated, and meanwhile, non-geometric parameter errors caused by an approximate external load are experienced. Secondly, estimating discrete probability distribution of related parameters, and obtaining a continuous probability density function and features through approximate fitting; and finally, setting a confidence probability, substituting the confidence probability into the error model to carry out large-scale pseudo-random simulation, obtaining error probability distribution of the end effector, and completing evaluation by combining a measurement mean value and standard uncertainty. According to the method, an uncertainty core source is locked, a parameter influence path is defined, finite sample information is fully utilized, the problems of distribution representation deviation and distortion are avoided, and a precise theoretical basis and reliable data support are provided for uncertainty evaluation of a high-dimensional parameter system.

This application relates to a method, apparatus, electronic device, and storage medium for dynamic process capability assessment. In this disclosure, an access control threshold is first calculated; in the absence of historical data, multi-level random simulations are performed to generate a virtual sample set, and then initial prior distribution parameters are determined; if it is determined that the measured samples to be assessed in the current group pass the access control threshold verification, the prior distribution parameters are recursively updated based on the sample statistical parameters to obtain posterior distribution parameters characterizing the current manufacturing process. Then, a first probability that the process mean is within a preset mean interval, a second probability that the process standard deviation is less than a preset standard deviation limit, and a third probability that the process capability index is greater than or equal to the process capability index target are calculated to obtain the process capability assessment result for the current manufacturing process. This disclosure has at least one advantage: it does not rely on historical data, has a data quality filtering mechanism, and is applicable to small sample scenarios.

The invention provides a strike-slip fault surface fracture dislocation prediction method, and the method comprises the steps: carrying out the grid modeling of a target strike-slip fault, and carrying out the two-dimensional grid division; calculating earthquake occurrence rates of different earthquake magnitudes in the two-dimensional grid according to the relationship between the earthquake magnitudes and the frequency; based on the earthquake occurrence rate, fracture parameters including fracture length, fracture width and average dislocation amount are obtained through a Monte Carlo method; determining an annual dislocation accumulation rate according to the fracture parameters; according to the annual dislocation accumulation rate, earth surface dislocation quantity distribution is obtained through calculation; and based on the surface dislocation quantity distribution, predicting the dislocation quantity generated by the earthquake on the surface through an exponential decay model. According to the method, by fusing the fault slip rate, the magnitude-frequency relation, the fracture parameters and the earth surface attenuation mechanism and introducing the Monte Carlo stochastic simulation and gridding modeling technology, the problems of complex fault geometric structure, earthquake recurrence and earth surface response simulation insufficiency are solved, and the precision and reliability of strike-slip fault earth surface dislocation prediction are improved.

The invention discloses an automatic driving vehicle performance boundary mining method, system and device and a medium. The method comprises the following steps: decoupling five elements of people, vehicles, roads, environments and tasks representing an automatic driving traffic simulation test scene; performing parameterized expression on the five types of elements; screening out a key parameter set of five types of related elements; taking the screened key parameter set as a decision variable, taking an automatic driving simulation platform as an evaluation tool, and taking the minimum speed difference absolute value and distance between a tested automatic driving vehicle and surrounding dynamic traffic participants and static objects as optimization targets, and establishing a double-target random simulation optimization model for automatic driving performance boundary mining; and solving by adopting a double-target monkey group random agent optimization algorithm to obtain a Pareto random optimal key parameter solution set so as to obtain the performance boundary of the tested automatic driving vehicle. According to the method, the performance boundary of the automatic driving automobile is accurately mined by combining the virtual simulation test and the proxy optimization technology.

The application designs an intelligent soft beam shape reverse design method embedded with an agent model. The finite element method is used to model and calculate the corresponding actuated shape of the unit, and the overall soft beam structure with different shapes is formed by combination. The actuated deformation shape of several intelligent soft beams is calculated by random simulation, and the database of corresponding voltage parameters and shape coordinates is established. The voltage signal applied to each unit of the intelligent soft beam is taken as the input, and the shape coordinates of the beam are taken as the output. The spatial sequence data agent model based on the long short-term memory neural network is trained to establish the implicit relationship between the given voltage signal and the overall shape. The generalization ability of the long short-term memory neural network agent model is tested. The application can be applied to the rapid and targeted reverse design of the shape of the intelligent soft robot, and can save the time cost and computing resources of the design.

The invention relates to a single-layer abrasive particle diamond grinding wheel surface topography random generation method, which realizes the digital generation of the grinding wheel surface topography by establishing an abrasive particle size distribution, space distribution and exposure height three-dimensional parameter model based on the idea of combining statistical modeling and random simulation. By introducing truncated normal distribution to form an abrasive particle size distribution function and applying a three-dimensional random bias vector, random features of the size and position of the abrasive particles on the surface of the diamond grinding wheel are truly represented, the authenticity and applicability of the model are remarkably improved, the accuracy of the characterization modeling technology is improved, and the method is suitable for large-scale popularization and application. And the technical defect of machining damage caused by random distribution of the abrasive particles can be overcome.

The invention relates to the technical field of quantitative characterization, in particular to a delta front edge interlayer three-dimensional quantitative characterization method, which comprises the following steps of: obtaining single sand body plane distribution characteristics by adopting a seismic wave impedance inversion technology; taking a single sand body plane distribution characteristic as a plane trend constraint, and establishing a sand shale lithofacies three-dimensional model in Petrel by adopting a sequential indication simulation algorithm; carrying out three-dimensional configuration modeling on the underwater diversion river channel to obtain a configuration model; and in the configuration model, setting a long variable range in the river channel direction, setting a short variable range in the direction perpendicular to the river channel, and carrying out stochastic simulation to obtain an interlayer three-dimensional distribution model. According to the method, the problem that an interlayer model established by an existing method is large in uncertainty is solved, a unique hierarchical step-by-step constraint modeling method is established, the three-dimensional distribution rule of the interlayer in the layer is accurately clarified, a reliable basis is provided for simulation and prediction of remaining oil of a high-water-content oil field, and the method is suitable for popularization and application. And the method is a necessary precondition for later-stage potential tapping and efficiency improvement of an oil field, development cost reduction and benefit development realization.