59 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 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.

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 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 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.

The invention discloses a geological model attribute value correction method and device, relates to the technical field of geological model local reconstruction, and mainly aims to improve the quality of a geological model. According to the main technical scheme, a target area with the fitting error exceeding a preset threshold value in an initial geologic model is determined through a preset area determining method; according to the method, actually measured geological attribute values of nodes in a target area and a preset range are taken as hard constraints, a preset variation function is combined, multiple groups of candidate attribute value distribution of the target area is generated by adopting a stochastic simulation algorithm, and then a preliminary optimal scheme is screened out according to geological features of the target area. And replacing the geological attribute value of the node without the measured value in the initial model target area with the simulation value corresponding to the preliminary optimization scheme to obtain a preliminary correction geological model. If the preliminary correction geologic model still has a new target area with an error exceeding a threshold value, adjusting a node simulation value according to an error fine adjustment mechanism until the error reaches the standard; and if not, directly setting the preliminary correction model as the optimal geologic model.

This invention relates to a method for collaborative carbon-health accounting throughout the entire lifecycle of power modules, comprising: S1: collecting data from power modules and performing cross-stage data association and alignment based on digital twin identifiers; S2: determining the electricity carbon emission factor; calculating dynamic operating carbon emissions based on the carbon emission factor and real-time power loss; S3: calculating carbon emissions during manufacturing, use, and end-of-life stages, and normalizing them into a product-level full lifecycle carbon footprint value; S4: randomly sampling the remaining lifespan and future green electricity penetration rate using a random simulation method, calculating the probability distribution of net carbon gains from tiered utilization, and outputting retirement decision recommendations; S5: calculating cryptographic hash values, digitally signing key data packets, and generating zero-knowledge proofs. This method achieves seamless data integration across the entire manufacturing-operation-retirement chain, collaborative dynamic accounting of carbon emissions and health status, repeatable calibration of thermal resistance aging parameters, probabilistic quantification of retirement decisions, and reliable anchoring of data sources.

Embodiments include systems and methods for searching, tracing, and tracking Monero transaction chains. An exemplary method includes receiving, at a user interface using a plurality of Monero transaction menus, Monero transaction identification data for searching, tracing, and tracking Monero transaction chains. The method also includes determining, at the server computing device, an input of the Monero transaction identification data and an output of the Monero transaction identification data. The method also includes analyzing, at the server computing device, the input of the Monero transaction identification data determining a ring signature of the input of the Monero transaction identification data, the ring signature may include a plurality of decoys and a spendable and valid input. The method also includes generating, with a processor, a user interface displaying the plurality of decoys and the spendable and valid input.

This invention relates to the field of 3D model processing technology, and more particularly to a method and system for selecting solar array cells based on 3D layout. The method includes the following steps: acquiring 3D data of the solar array, and constructing a 3D substrate model based on the 3D data to obtain a 3D model of the solar array; performing structure-driven deployment strategy simulation on the 3D model of the solar array to obtain fixed simulation data, and performing perturbation-based layout scheme generation on the 3D model of the solar array using the fixed strategy simulation to obtain random simulation data; performing regional structural domain analysis on the fixed simulation data and random simulation data to obtain regional structural domain data, and performing optimal layout sorting on the regional structural domain data to obtain layout sorting data; and exporting the layout sorting data as 2D drawings to obtain solar array cell selection data. This invention can effectively expand the diversity of schemes, improve local adaptability, and achieve high coverage and optimal sorting output while ensuring structural constraints.

The invention discloses a stochastic simulation method and device based on three-dimensional dynamic spherical model data, electronic equipment and a medium. The method can comprise the steps of obtaining a first expression of a simulation result of target data through a local prior distribution function according to the Bayesian theorem; establishing a second expression according to the first expression; and solving the second expression through Gaussian simulation to obtain a final simulation result of the target data. By dynamically acquiring the data of the three-dimensional spherical model and calculating the expectation value and the variance value of the dynamic data, a more accurate simulation result can be obtained.

The invention relates to a pressure simulation sensitivity analysis method, device and system for CO2 storage. The method comprises the following steps: acquiring geological data of a CO2 storage area, and constructing a three-dimensional parameter distribution model by utilizing the geological data; optimizing a data missing region of the three-dimensional parameter distribution model by adopting a random simulation mode to obtain a target data set; inputting the target data set into a preset coupling model to obtain a pressure response variable, a CO2 migration response variable and a CO2 sequestration effect response variable output by the coupling model; based on the target data set, the pressure response variable, the CO2 migration response variable and the CO2 sequestration effect response variable, performing variance analysis to obtain an effect significance level value of each parameter in the target data set, and performing response surface modeling to obtain a contribution degree of each parameter; and carrying out sensitivity analysis based on the effect significance level value and contribution degree of each parameter to obtain a sensitivity analysis result. According to the scheme, the construction efficiency of CO2 sequestration can be improved.

The application discloses a kind of automatic driving vehicle performance boundary excavation method, system, equipment and medium, wherein the method comprises: decoupling five kinds of elements of people, vehicle, road, environment and task characterized by automatic driving traffic simulation test scene;Five kinds of elements are parameterized expression;Filter out the key parameter set of five kinds of related elements;With the key parameter set filtered out as the decision variable, with the automatic driving simulation platform as the evaluation tool, with the absolute value of the speed difference of the measured automatic driving vehicle and the surrounding dynamic traffic participants and static objects and the minimum distance as the optimization goal, a double-objective random simulation optimization model for automatic driving performance boundary excavation is established;Solve by using double-objective monkey swarm random agent optimization algorithm, obtain the Pareto random optimal key parameter solution set, and then obtain the performance boundary of the measured automatic driving vehicle.The application realizes accurate excavation of the performance boundary of automatic driving car by combining virtual simulation test and agent optimization technology.

The application discloses a method and device for determining a lithologic trap boundary, electronic equipment and a storage medium. The method comprises the following steps: simulating a pseudo-well vertical lithologic combination based on a continuous-time Markov chain model, determining a pseudo-well lithofacies curve of different lithologic combinations according to a simulation result; determining a target elastic parameter through the pseudo-well lithofacies curve of the different lithologic combinations based on elastic parameter Monte Carlo random simulation with lithofacies constraints; determining pseudo-well simulation seismic data of the different lithologic combinations through the target elastic parameter based on a wave equation forward modeling method of a propagation matrix; and determining a lithologic trap boundary according to the pseudo-well simulation seismic data of the different lithologic combinations. The technical scheme of the application avoids the problems of strong subjectivity, large deviation of boundaries delineated by different personnel, and insufficient objective basis in the determination of a traditional lithologic trap boundary, thereby avoiding the situation of deviation of the delineated boundary caused by human factors.

The invention relates to the technical field of seismic data interpretation, and particularly discloses a virtual well construction method and device based on seismic trace matching, and the method comprises the steps: carrying out the statistical analysis of logging curve data, and obtaining a spatial covariance matrix and a parameter covariance matrix; constructing a virtual well based on the spatial covariance matrix and the parameter covariance matrix; performing forward modeling on the virtual well and the known well to obtain a simulation record; and based on the well-side seismic trace and the simulation record, constructing an objective function, and obtaining a virtual well at a target position. The method comprises the following steps: calculating a space covariance matrix and a parameter covariance matrix by using data after logging statistical analysis, carrying out virtual well random simulation by solving a comprehensive covariance matrix of the first two items, carrying out seismic trace matching by using a deep network, obtaining a mapping relation between seismic data and logging parameters, and finally inputting the seismic trace into the deep network, so as to obtain the seismic data and the logging parameters. The virtual well at the target position is obtained, so that the well group formed by the real well and the virtual well is distributed more uniformly, and the modeling stability and the inversion precision are improved.

The embodiment of the invention provides a stochastic simulation method and device for pulse type seismic oscillation, and belongs to the technical field of seismic oscillation simulation. The corresponding method comprises the following steps: determining a pulse parameter and a residual parameter of pulse type seismic oscillation according to a scene parameter of the pulse type seismic oscillation; wherein the scene parameters comprise the moment magnitude, fault distance, site shear wave velocity and directivity effect parameters of the pulse type seismic oscillation; and generating a time history model of the pulse type seismic oscillation according to the pulse parameters and the residual parameters so as to simulate the pulse type seismic oscillation. The method provided by the invention breaks through the limitation of a single pulse function of an existing related method, pulse type seismic oscillation with waveform diversity in a specific seismic scene can be generated, the long-period response spectrum precision is remarkably improved, and high-reliability input data is provided for seismic design of a near-fault engineering structure.

The invention relates to a random biochemical reaction kinetic parameter deduction method based on a probability generation function and a neural network, and the method comprises the following steps: simulating a specific biochemical reaction through employing a random simulation algorithm, obtaining the probability distribution of a target molecule, calculating the probability generation function, and carrying out the tensor decomposition; constructing a proper neural network, and entering a parameter inference process; determining the number of reaction parameters to be deduced, inputting an initial parameter, and generating a function value as neural network input; carrying out integration on each probability generation function value by utilizing Gaussian quadrature, and updating parameters; and repeating parameter updating iteration to obtain an optimal parameter. By adopting the random biochemical reaction kinetic parameter inference method and device based on the probability generation function and the neural network, the processor and the computer readable storage medium thereof, efficient identification of chemical main equation parameters is realized under the condition of not explicitly solving an analytical solution, and calculation precision and efficiency are both considered; and the method has good numerical stability and generalization performance.

The invention provides a photovoltaic module environmental performance prediction system based on prospective LCA and dynamic scene deduction, and the system comprises a static list database which is used for storing and managing life cycle list data of a manufacturing stage of an evaluated photovoltaic module; the dynamic parameter model library is used for storing a plurality of parameterized prediction models for describing future evolution trends of the key background parameters; the Monte Carlo-LCA coupling deduction engine is used for receiving the initial data of the static list database and the models in the dynamic parameter model library, and calculating the environmental performance of the whole life cycle of the product through large-scale stochastic simulation and time sequence deduction; the result aggregation and visualization module is used for collecting a large number of simulation results output by the deduction engine, calculating expected values, medians, standard deviations and confidence intervals of the results, and generating a probability density function or an environmental performance year-by-year evolution curve cluster; and presenting a visual prediction result containing the uncertainty information to the user through a graphical user interface.

The invention belongs to the field of random hydrology, and particularly discloses a runoff stochastic simulation method and system based on adaptive bandwidth kernel density estimation and a Copula function. The method comprises the following steps: firstly, fitting historical runoff sequence data by adopting adaptive bandwidth kernel density estimation to obtain a probability density function of flow in any time period; obtaining an edge distribution function of the traffic in any time period according to the cumulative probability density of the traffic in any time period; then constructing a conditional distribution function of the flow in the next time period under the flow condition of the current time period by using a Copula function; and finally, generating runoff flow simulation values by sequential simulation in a period-by-period manner by utilizing a conditional dependency relationship of the flows in adjacent periods in the conditional distribution function. Compared with an existing runoff stochastic simulation method based on a Copula function, the runoff stochastic simulation method is high in runoff simulation precision and stable.

The invention discloses a substation grounding grid corrosion detection and residual life evaluation method and system. The method comprises the following steps: acquiring multi-modal corrosion characteristic data of a grounding grid, wherein the multi-modal corrosion characteristic data comprises electromagnetic induction eddy current distribution, conductor surface microwave reflection characteristics and soil chemical parameters; generating a comprehensive corrosion index sequence based on a nonlinear adaptive weighted fusion algorithm, and decomposing the comprehensive corrosion index sequence into a trend term, a periodic term and a residual term through variational mode decomposition; predicting corrosion depth distribution and corrosion rate by using a bidirectional long-short-term memory network model of an attention mechanism; calculating the residual conductor section and the dynamic current-carrying capacity by combining a material mechanical property degradation model; and evaluating the residual life probability distribution of the grounding grid under different operation conditions by adopting a Monte Carlo stochastic simulation method. According to the invention, non-contact comprehensive sensing of the corrosion state of the grounding grid and scientific evaluation of the residual life are realized, and accurate decision support is provided for state maintenance.

A method comprises simulating rays deterministically, monitoring the intensity of the rays as they undergo ray splitting, dynamically switching from deterministic simulation to stochastic simulation when the ray intensity declines to a specified threshold, continuing the stochastic simulation to model interference effects up to the specified threshold, and outputting a simulation result that provides an enhanced representation of light propagation within the waveguide that is based on both the deterministic simulation and the stochastic simulation. Systems and methods are disclosed.

The invention discloses an insurance risk capital assessment method, which is used for solving the problem that the existing assessment method cannot realize accurate and dynamic capital assessment. The method comprises the following steps: determining loss simulation data corresponding to each service combination through a random simulation method according to obtained historical underwriting claim settlement data; determining maximum loss data in a preset regression period according to the loss simulation data, and determining first risk data according to the maximum loss data; determining maximum reserve fund data in a preset regression period through a stochastic simulation method according to the acquired claim settlement progress data, and determining second risk data according to the maximum reserve fund data; determining an association parameter between the first risk data and the second risk data; and determining risk capital assessment data according to the first risk data, the second risk data and the associated parameters.