52 results about "Gaussian mixture distribution" patented technology

The invention relates to the technical field of electric energy metering of a low-voltage power distribution system, and provides an EM algorithm-based district metering device misalignment identification method and system. The method comprises the following steps: establishing a theoretical misalignment value model of each outgoing line in different metering misalignment states, and determining a mapping relation between the different misalignment states and actual electric quantity data; the method comprises the following steps: constructing theoretical deviation values of each outgoing line metering device in different misalignment states on the basis of a transformer area incoming and outgoing line electric energy data and energy conservation relationship, estimating an initial attribution probability of a line state in combination with an index normalization mapping method, and obtaining a posterior attribution probability of each outgoing line line through Gaussian mixture distribution modeling under dynamic prior guidance; and based on the posterior affiliation probability of each outgoing line, constructing an objective function of joint observation value fitting quality, time continuity and misalignment amplitude balance, solving the objective function, and quantifying the electric quantity misalignment proportion of each outgoing line at each moment so as to judge classification and identification of misalignment behaviors.

A method for multimedia data generation by energy guidance of diffusion model is disclosed. The method comprises obtaining a Gaussian mixture distribution of K components for approximating a diffusion posterior, wherein K is an integer greater than 1, and the diffusion posterior is a distribution of multimedia data to be generated given intermediate noisy multimedia data; estimating an intermediate energy for guiding the multimedia data generation, by a plurality of samples drawn from the obtained Gaussian mixture distribution; and generating the multimedia data with a pre-trained noise prediction network and an intermediate energy guidance computed based on the estimated intermediate energy.

The invention discloses a two-stage optimization scheduling method for a mine energy system. The method comprises the following steps: constructing a mine comprehensive energy system model; establishing a distributed photovoltaic output uncertainty model, quantitatively predicting error space-time correlation through a gamma function and a Gaussian mixture distribution model, and decomposing a regional error by using a node weight coefficient; a distributed ADMM coordination algorithm is designed according to a distributed photovoltaic output uncertainty model, spatial-temporal correlation compensation is realized through a local layer error transfer function and coordination layer global variable updating, and a voltage out-of-limit probability constraint is introduced; and designing a day-ahead-day two-stage optimization scheduling framework, taking the minimum sum of the power generation cost of the gas turbine in the mine integrated energy system model and the photovoltaic light abandoning penalty as a day-ahead stage target, and dynamically adjusting the power deviation through virtual energy storage in the day-ahead stage. The method can effectively solve the problem of mine energy system scheduling under distributed photovoltaic access, and is suitable for a mine comprehensive energy scene with high-proportion new energy access.

The invention discloses a redundant space robot inverse kinematics solving method based on a graph neural network, and relates to the technical field of robot motion control and artificial intelligence crossing. A distance geometric graph is constructed to represent the configuration of the mechanical arm, an inverse kinematics problem is converted into a completion problem of a partial graph, joint configuration is sampled, and data pairs of a complete graph and the partial graph are stored as a data set; training based on an isotropic graph neural network and a conditional variation auto-encoder, and training a model based on a loss function of an evidence lower bound; and in the reasoning process, constructing a partial graph, inputting the partial graph into the trained prior network, outputting hidden variables meeting Gaussian mixture distribution, extracting sampling points, and generating a reconstructed complete graph to obtain joint angle information. The mechanical arm structure is represented through the distance geometric diagram, the inverse kinematics problem is converted into the complementation problem of partial diagrams, probability distribution of a solution space is learned through a conditional variation auto-encoder frame, multi-solution generation of different mechanical arm configurations is supported, and the solving precision and efficiency are high.

This invention discloses a collaborative control method for highway merging zones based on deep reinforcement learning. A LiikeSim-Python co-simulation environment is established, and loop detectors are set up in the simulation environment to acquire traffic flow data upstream and downstream of the highway merging zone. An EM algorithm based on Gaussian mixture distribution is used as a traffic state classifier, taking the traffic flow data of the highway merging zone as input to classify the traffic state of the merging zone. A state space, action space, and reward function are designed. Using the state space of the highway merging zone as input and the actions of the variable speed limit agent and the ramp metering agent as output, a multi-agent shared experience network model under time-series characteristics is constructed. An independent experience pool is set up for the variable speed limit agent and the ramp metering agent, and the interaction experience between the agent and the traffic simulation environment is collected at the control cycle frequency. The agent model is trained using sampled data. The trained agent model is used to realize collaborative control of the highway merging zone. This invention can reduce travel delays in highway merging zones.

This invention discloses a fault diagnosis method for aero-engines based on an adaptive spatiotemporal decoupled graph convolutional network, comprising: acquiring and preprocessing multi-channel sensor signals to construct time-series samples; adaptively constructing an adjacency matrix to form a fault signal graph structure representing the spatial correlation of multi-source signals; inputting the fault signal graph structure into a graph convolutional network to extract spatial features; inputting the extracted spatial feature sequence into a bidirectional gated recurrent unit network to obtain temporal features; introducing a global attention mechanism to the temporal features to assign adaptive weights to features at different time steps; decoupling the spatiotemporal features using a variational autoencoder decoupling layer based on a Gaussian mixture distribution; and outputting the fault category of the aero-engine to complete the fault diagnosis. This invention significantly improves the accuracy, robustness, and engineering applicability of aero-engine fault diagnosis under complex operating conditions by jointly modeling the spatiotemporal characteristics of fault signals and effectively decoupling and fusing features.

The invention discloses a micro-earthquake dry and wet event separation method and device based on a Gaussian mixture model, and the method comprises the steps: S1, building a corresponding Gaussian mixture model based on a micro-earthquake detection result, carrying out the random initialization of parameters of the Gaussian mixture model, and setting the number of mixed components; s2, calculating the posterior probability of each microseismic event point relative to each Gaussian component; s3, updating Gaussian mixture distribution parameters based on the posterior probability of each microseismic event point relative to each Gaussian component; s4, based on the updated Gaussian mixture distribution parameters, calculating a maximum likelihood function and evaluating whether the maximum likelihood function accords with a threshold value, if not, returning to S2, and if yes, executing S5; and S5, outputting the Gaussian mixture model after parameter updating and a dry and wet event classification result. According to the method, effective transformation events corresponding to fracturing construction and fracture activity events caused by ground stress changes can be rapidly distinguished.

The embodiment of the application discloses a kind of reactive voltage control methods and devices of regional distribution network, the method comprises: obtaining each regional node sample corresponding to regional distribution network, each regional node sample includes the voltage and power factor corresponding to each regional node;The voltage and power factor corresponding to each regional node sample are input into Gaussian mixture distribution function, determine the target distribution probability corresponding to regional node sample;Based on target distribution probability and preset interval threshold, each regional node is clustered, to determine the target regional category cluster corresponding to each regional node;According to the electrical parameter characteristics corresponding to each target regional category cluster, determine the target control strategy of regional distribution network.Gaussian mixture distribution function is used for the clustering of regional distribution network VQC control, and the characteristics of each electrical parameter characteristic can be obtained according to the category cluster in the regional VQC control, and a targeted target control strategy can be used.

The invention belongs to the technical field of image classification, and particularly relates to a sample weighted domain adaptive image classification method based on source domain variance and Gaussian mixture distribution modeling. In order to improve the accuracy of image classification, the method comprises the following steps: firstly, carrying out probability modeling on feature spaces of a source domain and a target domain by utilizing a Gaussian mixture model so as to accurately characterize data distribution features; secondly, dynamically evaluating the importance of a source domain sample by introducing the variance of source domain classification loss, and adjusting the weight of the sample according to the importance of the source domain sample so as to realize modeling and control on the variability of the sample in the source domain; and finally, the classification model is effectively optimized through a weighted training strategy, and the feature distribution difference between the source domain and the target domain is reduced, so that the recognition accuracy and the model stability in a cross-domain image classification task are improved.

This invention relates to the field of wind power equipment fault diagnosis technology, and provides a method for extracting fault features from wind turbine gearboxes based on Gaussian mixture modeling. The method includes: modeling a mathematical model of the wind turbine gearbox observation signal by superimposing impact fault feature vectors and multi-source noise vectors, wherein the fault feature vector is the product of a redundant dictionary D and a sparse coefficient vector; modeling the multi-source noise vector as a Gaussian mixture distribution; constructing an objective function within a Bayesian framework to solve for the sparse coefficient vector in the mathematical model using maximum a posteriori probability estimation; simplifying the objective function; and using the EM algorithm and ADMM algorithm in a joint alternating iterative solution to obtain the optimized sparse coefficient vector; and reconstructing the impact fault feature vector in the wind turbine gearbox observation signal. This method improves the accuracy and robustness of extracting fault features from the observation signal of offshore wind turbine gearboxes.

The present application provides a kind of enterprise labor efficiency evaluation model construction method, medium and system, belong to artificial intelligence model technical field, the present application is by gathering staff behavior data to construct high-dimensional sparse feature matrix, after coding by hash embedding layer, gaussian mixture distribution modeling and double network cross screening noise correction are carried out to labeled label, employee collaboration relationship graph is constructed based on training sample set, after hierarchical random neighbor sampling and graph clustering presegmentation, employee performance evaluation vector is obtained by inputting causal inference enhancement double-flow comparative evaluation model, then the efficiency distribution is time series evolution by optimal transport gradient flow continuous flow algorithm, finally, according to the deviation of prediction result and equilibrium threshold, external incentive term is adjusted and intervention strategy is output, the technical problem that employee performance evaluation model cannot accurately estimate causal effect in organization network and realize group efficiency time series evolution prediction is solved.

The invention relates to a nano photonics structure design method and system based on a mixed probability sampling network, and belongs to the technical field of nano photonics structure design, and the method comprises the steps: constructing a data set for training; the data set comprises structure parameters of various nanometer optical structures and corresponding optical property parameters; training the forward pre-training network; predicting one-to-one corresponding optical property parameters by using the structure parameters in the data set for training; training a reverse network; inputting optical property parameters in the data set for training, and outputting a plurality of Gaussian mixture distributions; sampling the Gaussian mixture distribution by using a sampling network to obtain a plurality of candidate structure parameters; and for the candidate structure parameters, utilizing a forward pre-training network to predict corresponding optical property parameters, and selecting the structure parameter with the minimum error with the target optical property parameter as a final nanometer photonics structure. According to the method, accurate and efficient structure reverse design in a complex solution space is realized.

The application discloses an industrial process fault detection method and system based on double subspace division, which firstly performs normality evaluation on process variables, divides a data space into Gaussian subspace and non-Gaussian subspace; a fault detection model based on principal component analysis is established in the Gaussian subspace, and a fault detection model based on support vector data description is established in the non-Gaussian subspace; current test data variables are divided into the Gaussian subspace and the non-Gaussian subspace, and are respectively input into the trained models, so that fault detection statistics corresponding to each subspace are calculated; each subspace statistic is converted into a fault probability by using Bayesian inference and is weightedly fused to construct a comprehensive monitoring statistic, and fault detection is realized. The application effectively solves the problem that a traditional method has poor adaptability to Gaussian and non-Gaussian mixed distribution data, improves the fault detection rate while significantly reducing the false alarm rate, and is suitable for real-time monitoring requirements of complex industrial processes.

The application provides a target detection method, device and equipment based on a Gaussian mixture model, which can be applied to the field of computer vision and the field of digital image processing. The target detection method based on the Gaussian mixture model comprises: processing a target image comprising a target to be detected to obtain a first density map related to the target to be detected, wherein the first density map conforms to a Gaussian mixture distribution, and the first density map serves as a Gaussian mixture model; performing integral processing on the first density map to obtain a first quantity value of the target to be detected; performing local peak value extraction on the first density map to obtain a second quantity value of the target to be detected; obtaining a target mean value and a target variance of the target to be detected based on the first quantity value, the second quantity value and the first density map; and determining position information of the target to be detected according to the target mean value and the target variance.

The application discloses a soft measurement modeling method based on mode perception dynamic variational autoencoding regression, and comprises the following steps: (1) acquiring three-phase flow process data with multiple mode dynamic characteristics; (2) data division and pretreatment operation; (3) establishing a mode perception dynamic variational autoencoding regression model, and realizing the prediction of an online quality variable; and (4) predicting a three-phase flow process pressure variable and performing model performance evaluation. The encoder-decoder network with a convolution-deconvolution structure learns the hidden layer feature representation of dynamic data, the multiple mode characteristics of the data are mined in the hidden space by using a Gaussian mixture distribution, the mapping relationship between the latent variable and the key quality variable is effectively learned, in addition, the reconstruction of dynamic multiple mode data is constrained by using a Wasserstein distance, and finally the purpose of improving the accuracy of key quality inference is achieved.

The present application belongs to the technical field of power system operation, and relates to a double-sided random power grid scheduling method. By analyzing historical data of wind power output, Gaussian mixture distribution fitting is carried out by using software. For determined power system parameters, a double-sided chance constraint random scheduling model is established. Then, the hyperbolic tangent function is used to analytically approximate the cumulative distribution function of random variables in the reserve demand constraint and the line flow constraint, so as to convert the double-sided chance constraint into a deterministic constraint, and convert the original problem into a convex optimization problem which is easy to solve. Finally, the scheduling model is solved to obtain the scheduling strategy. The present application has the advantage that the double-sided chance constraint containing the risk level and the random variable is converted into a solvable deterministic convex constraint by using the hyperbolic tangent function, which effectively improves the solving efficiency of the model and provides a more reasonable scheduling basis for decision makers. The present application can be applied to active power scheduling of a power system containing large-scale renewable energy grid-connected.

The application discloses a photovoltaic power prediction method based on initial value optimization and Gauss mixed error probability distribution compensation, and the method comprises the following steps: for the error of photovoltaic prediction, a Gauss mixed distribution model (GMM) based on initial value optimization is used to fit the distribution rule of the preliminary photovoltaic power prediction error, and then the prediction value of the error is compensated to the result of point prediction to realize interval prediction of photovoltaic output. Since environmental factors such as illumination intensity, temperature and visibility have a great influence on the prediction result of photovoltaic power generation output, a K-means clustering algorithm based on irradiance index (K-means) is used to divide historical photovoltaic output data into multiple different weather scenes, the optimal initial value data set selected by inputting a Whale Optimization Algorithm (WOA) is used, then a Gauss mixed distribution model is used in combination with an expectation maximum algorithm (EM) to obtain optimal parameters of the model, and the probability distribution of photovoltaic prediction error is modeled and analyzed, and under a specified confidence level interval, the model fitting result is used to correct the photovoltaic power point prediction value, so that the final prediction interval is obtained. The scheme provided by the application takes photovoltaic power data of a photovoltaic power station in Jiangsu Province as an example for simulation analysis, and the result shows that the method has better fitness and reliability.

A residual life prediction method based on domain invariance and consistent ordinal number representation learning comprises the following steps: firstly, calculating an MMD distance between different bearing domains in a training set, and minimizing the distance through gradient descent to enable feature distribution of each source domain to be globally aligned, extracting domain-invariant and ordered degeneration features, introducing a Gaussian mixture prior coding-decoding structure, and finally obtaining a residual life prediction result; the features are mapped to a potential space constrained by Gaussian mixture distribution through a variational auto-encoder, and the inadaptability of traditional single Gaussian distribution to nonlinear degradation is reduced; and in combination with MMD alignment, ordinal number loss and local consistency constraints, the dominant situation of a single constraint is avoided, the feature space has domain invariance and continuous orderliness at the same time through joint optimization, and the generalization ability of the model in a cross-domain task is remarkably improved.

The invention discloses an information recommendation method and electronic equipment, which are used for improving the accuracy of information recommendation and improving the recommendation performance. The method comprises the steps of extracting data features of target data, and determining Gaussian mixture distribution according to distribution of the data features; the topological relation between the data features and the Gaussian mixture distribution is utilized, the embedded vector of the data features is enhanced, the enhanced embedded vector of the target data is obtained, and the embedded vector of the data features is used for conducting vector representation on the data features; according to the correlation between the embedded vector of the user and the enhanced embedded vector of the target data, the target data pushed to the user is determined, and the embedded vector of the user is used for carrying out vector representation on user features.

The present application relates to a kind of photovoltaic module unsupervised defect detection methods based on contrast learning, including using normal sample training contrast learning network model;Contrast learning network model contains two encoder networks and a object-oriented symmetric cross attention network;Based on encoder network, the feature distribution of normal sample is obtained, and the defect discrimination model for normal sample feature is constructed using multivariate Gaussian mixture distribution;The image to be tested is input into the encoder network to obtain features, and the detection result of image is obtained based on defect discrimination model.The beneficial effects of the present application are: the present application can effectively detect the tiny and diverse defects of photovoltaic module, only normal photovoltaic image is needed for training, and the problem of sample imbalance can be solved;And in the case of few defect images, fast and accurate detection of unknown defects can be realized, with the advantages of environmental adaptability and strong robustness.

The invention discloses a picture style migration method, and belongs to the technical field of image style migration, the picture style migration method comprises the following steps: S100: inputting a picture; s200, using noise to destroy the input picture, adding Gaussian mixture distribution processing based on diffusion in the destroying process, and using the processed picture as a sample; s300, inputting the sample into the style model, and converting to obtain a new image; and S400, denoising the new image, and outputting the new image to complete image style migration. According to the invention, picture style migration can be effectively, quickly and accurately realized.

The invention discloses an abnormal water quality early warning method, system and device based on constant false alarm and a medium, and the method comprises the steps: obtaining historical data of water quality indexes, and constructing a real sequence of the water quality indexes; determining the number K of Gaussian distributions in the Gaussian mixture distribution of the real sequences through an adaptive method, wherein the probability density function of any real sequence is represented by the sum of K Gaussian distributions; calculating a probability density function of a real sequence in Gaussian mixture distribution through a Neyman-Pearson criterion and a dichotomy to obtain a threshold value of an upper limit and a threshold value of a lower limit of judgment of a predetermined false alarm rate; and performing early warning on the water quality according to standards of an upper limit threshold value and a lower limit threshold value. The probability density function of the water quality index is described through Gaussian mixture distribution. The number K of Gaussian distribution in Gaussian mixture distribution is determined through a self-adaptive method, the accuracy of the K value is measured in combination with the statistical magnitude H and the statistical magnitude U, and the threshold calculation process is simplified through a dichotomy. And the false alarm rate is controlled within an acceptable range.

The invention belongs to the technical field of data amplification, and relates to an electroencephalogram signal amplification method and system based on a Gaussian mixture distribution model and a terminal. The amplification method comprises the following steps: collecting and obtaining an original electroencephalogram signal comprising a lead signal; obtaining a plurality of extreme points of each lead signal, and segmenting the lead signal; constructing a phase deviation Gaussian mixture model of each extreme point for each lead signal, and randomly extracting a phase difference from the phase deviation Gaussian mixture model corresponding to the extreme point; after the phase difference is applied to the corresponding extreme point, a sampling point is adjusted through increasing / undersampling to achieve peak offset, an initial amplification signal corresponding to each lead signal is obtained, smooth filtering is carried out on the initial amplification signal, and a filtered amplification signal is obtained; constructing an amplitude scaling Gaussian mixture model for the original electroencephalogram signals, and sampling amplitude change parameters from the amplitude scaling Gaussian mixture model; and for each lead signal, applying an amplitude variation parameter corresponding to the filtered amplification signal to the filtered amplification signal so as to generate amplitude variation, and finally obtaining the amplification signal.

The invention discloses an ore grinding particle size robust soft measurement method based on double Gaussian mixture distribution. The method comprises the following steps: constructing a random configuration network as a basic prediction model; secondly, explicitly establishing a noise model weighted and mixed by a small variance Gaussian component and a large variance Gaussian component, and respectively fitting conventional measurement noise and process abnormal noise; under a Bayesian framework, an expectation maximization algorithm is adopted to jointly iteratively optimize network output weight and noise model parameters, and adaptive learning of the parameters and intelligent weighting of samples are achieved. According to the method, noise decomposition parameters including the normal working condition weight and the abnormal variance ratio can be output, and interpretable decision support is provided for ore grinding operation. Experiments show that the method effectively improves the prediction precision, robustness and interpretability of the model in the mixed noise environment, and provides reliable technical support for optimization control of the ore grinding process.

The invention discloses a charging pile remote metering method and system based on multi-source data fusion, and belongs to the technical field of safety and credibility verification of charging pile metering data. Collecting charging pile electric energy data, user vehicle BMS data and electric energy data of a power grid power supply side; uploading based on hybrid communication and a time synchronization mechanism to realize uploading of multi-source data carrying a unified time label; constructing a Gaussian mixture model error distribution model to obtain Gaussian mixture distribution; based on a Bayesian formula, calculating a charging data credibility probability under the condition that data of a pile end, a vehicle end and a charging station level power grid end are given; calculating the credible probability of the charging data; and performing data fusion and correction according to the obtained credible probability of the charging data to obtain corrected charging data. The safety of the metering data of the charging pile is improved, and tampering and fraudulent behaviors are prevented.

This invention belongs to the field of data amplification technology, and relates to a method, system, and terminal for amplifying electroencephalogram (EEG) signals based on a Gaussian mixture model. The amplification method includes: acquiring and obtaining raw EEG signals including lead signals; obtaining multiple extreme points for each lead signal and segmenting the lead signal; constructing a phase-shifted Gaussian mixture model for each extreme point for each lead signal, and randomly sampling the phase difference from the phase-shifted Gaussian mixture model corresponding to the extreme point; applying the phase difference to the corresponding extreme point, adjusting the sampling point using upsampling / undersampling to achieve peak shift, obtaining the initial amplified signal corresponding to each lead signal, and smoothing and filtering it to obtain the filtered amplified signal; constructing an amplitude-scaling Gaussian mixture model for the raw EEG signal, and sampling amplitude variation parameters from it; applying the corresponding amplitude variation parameters to the filtered amplified signal for each lead signal to generate amplitude variation, ultimately obtaining the amplified signal.

The invention discloses a horizontal and vertical seismic oscillation combined selection method based on a multivariable return period, and the method comprises the steps: determining the annual exceeding probability of a site, namely a target return period, and selecting a condition strength parameter and a target horizontal and vertical strength parameter; according to the basic information of the site, obtaining a random seismic directory of the site through Open Quake, and determining the occurrence rate of an earthquake year; constructing multivariate Gaussian mixture distribution based on a site random seismic directory; randomly extracting a large number of simulation spectrums from the multivariate Gaussian mixture distribution; calculating a multivariable recurrence period of each simulation spectrum, and selecting the simulation spectrum with the recurrence period within + / -5% of the target recurrence period as a target spectrum; after the target spectrum distribution is known, a seismic oscillation record set meeting the error requirement is selected from the target database; different from a single-variable return period based on a single-dimensional strength parameter, a horizontal and vertical seismic oscillation selection method based on a multivariable return period can select a group of horizontal and vertical seismic oscillation records with danger consistency.

This invention proposes a federated learning method robust to mixed noise, comprising: sending local metric model parameters updated by the client based on local training data and a subjective logistic loss function to the server; obtaining global metric model parameters calculated by the server based on the local metric model parameters updated by the client and other clients, and the corresponding sample data volume; the client calculating the subjective logistic loss and local Gaussian mixture distribution for each training sample based on the global metric model parameters and local training data, and sending them to the server; obtaining interval thresholds calculated by the server based on multiple local Gaussian mixture distributions, and performing mixed noise identification on the training data based on the interval thresholds and local Gaussian mixture distributions, thereby filtering and correcting the identified open set noise and closed set noise respectively. This invention also proposes a federated learning system robust to mixed noise, and a data processing device for federated learning.

This invention discloses a few-sample image generation method for image generation in scenarios with limited data. The few-sample image generation method provided by this invention includes: randomly sampling from a dynamic Gaussian mixture distribution to obtain a dynamic Gaussian mixture latent code; inputting the dynamic Gaussian mixture latent code into a generator network, enhancing the intermediate features of the generator network through a hybrid attention mechanism, wherein the intermediate features are obtained by mapping the dynamic Gaussian mixture latent code by the generator network, and inputting the enhanced intermediate features into the generator network to obtain a generated image set; inputting the generated image set and a real image set into a discriminator network to obtain image discrimination results for the generated image set and the real image set; updating the generator network and the discriminator network according to the image discrimination results and the objective optimization functions of the generator network and the discriminator network to obtain updated generator networks and discriminator networks.

The application discloses a dam progress simulation parameter dynamic updating method based on incremental learning, mainly comprising the following steps: acquiring construction sensing data flow; adopting an isolated forest algorithm to detect abnormal values of the construction sensing data flow; adopting a cubic spline interpolation method to interpolate and fill in missing values in the construction sensing data flow; using Gaussian mixture distribution to model simulation parameter distribution of the time series data column after interpolation and filling; using an online kernel density estimation method to continuously learn new input construction sensing data flow data after Gaussian mixture distribution modeling, and update new Gaussian mixture distribution, and output dynamic updated simulation parameters; inputting the dynamic updated simulation parameters into a rock-fill dam construction simulation model, wherein the rock-fill dam construction simulation model changes with the dynamic change of the construction simulation parameters, and outputs dynamic changes conforming to actual situations, thereby supporting high-quality intelligent construction in engineering sites and providing decision support for dam progress management.