139 results about "Self-organizing map" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention provides a deep learning-based rapid ore sorting method under adaptive illumination intensity, and relates to the technical field of ore sorting, the deep learning-based rapid ore sorting method comprises the following steps: obtaining illumination intensity distribution information and ore three-dimensional point cloud data, and calculating ore space orientation parameters based on quaternion attitude estimation; inputting the obtained data into a layered illumination compensation network, and obtaining an illumination normalized enhanced image through adaptive brightness mapping and local light spot correction in combination with reflectivity recovery and non-local mean filtering; inputting the illumination normalized enhanced image and the ore three-dimensional point cloud data into a multi-branch feature extraction network, constructing feature high-order association, and adopting adaptive feature reconstruction and conditional random field optimization features; and inputting the reconstructed features into a classification model based on active learning, optimizing a feature manifold space by using a self-organizing mapping network, carrying out sample labeling in combination with active sampling of adaptive entropy threshold screening and uncertainty perception, and realizing ore classification and sorting through random forest integration.

The invention relates to an integrated management system for diabetic patients, which comprises the following steps of: constructing a high-dimensional metabolic data model, performing multi-modal fusion on genome information, metabolome data and life behavior data, and mining a potential mode of metabolic characteristics of the patients; carrying out patient typing by utilizing a clustering algorithm comprising a self-organizing mapping network, and identifying different metabolism types comprising an insulin resistance type and an insulin secretion insufficiency type; developing a hierarchical management strategy based on a typing result, wherein the hierarchical management strategy comprises diet, exercise and medicine intervention schemes for different types; collecting dynamic quantitative indexes of the metabolic load, and integrating the blood glucose fluctuation amplitude, insulin sensitivity and daily nutrition intake of the patient; constructing a metabolic load prediction model, and combining a time sequence deep learning algorithm with real-time data, including blood glucose, diet and exercise, of a patient to measure a future metabolic load; and establishing a causal relationship model between patient behaviors and metabolic reactions, and analyzing an interaction mode of diet, exercise and metabolic loads by adopting a causal inference technology.

The invention discloses a dynamic DEM spatial interpolation method which comprises the following steps: performing depression filling, flow direction analysis and confluence cumulant calculation on DEM data, and extracting a natural sub-basin unit by adopting a minimum catchment area threshold method; constructing a topographic feature matrix, performing refined second-level classification on the first-level drainage basin through an improved self-organizing mapping network, generating a hydrological response unit through boundary processing, and establishing a hydrological attribute library; fusing multi-source data, supplementing attribute interpolation such as underlying surface and rainfall, and constructing an interpolation auxiliary parameter system; a drainage basin is divided into regular grids as neurons, a dynamic neural network containing dynamic states and static attributes is constructed, and nonlinear mapping of DEM correction parameters is achieved through optimization of a dynamic activation function and a loss function. The method overcomes the defects that a traditional interpolation algorithm does not consider hydrological boundary constraints, a neural network model topological structure is fixed and the like, and the DEM interpolation precision and the hydrological simulation effect of the complex terrain area are improved.

The invention discloses an unmanned aerial vehicle three-dimensional flight path planning method for wireless sensor network data collection and energy supplementation, and belongs to the technical field of Internet of Things. A network is divided into a plurality of clusters through a density peak clustering algorithm, and a dynamic cluster head selection mechanism based on a routing protocol is designed to optimize the positions of cluster heads. The shortest flight path is determined by adopting a self-organizing mapping network introducing a penetration mechanism. Through a depth deterministic strategy gradient algorithm, the flight height of the unmanned aerial vehicle is dynamically adjusted, and meanwhile, the flight route of the unmanned aerial vehicle is planned, so that the unmanned aerial vehicle can efficiently collect data and supplement energy to sensor nodes in a charging range. According to the method, the distance of the unmanned aerial vehicle for charging the sensor and collecting data is obviously optimized, the energy utilization efficiency is improved, the energy supplement and data collection efficiency of the network is improved, and the network survival time is prolonged.

The invention discloses a classification aggregation identification method based on adjustable load response performance, and the method comprises the steps: constructing index order-based classification according to a integrating degree index and an access standard, and dividing adjustable load resources managed by a virtual power plant into a main energy load resource set, a frequency modulation load resource set, a standby load resource set and a comprehensive load resource set; and aggregating the classified resources by using a self-organizing mapping (SOM) clustering method based on feature vectors, and performing external characteristic identification analysis on an aggregate based on maximum flexibility. According to the method, the adaptation degree of the adjustable load resources and the power transaction products is fully considered, corresponding integrating degree indexes are set, classification is carried out in a bit order sorting mode, and the adjustable load resources in different categories are aggregated by adopting a self-organizing mapping neural network (SOM) clustering method based on feature vectors, so that the power transaction products are classified. And finally, evaluating the external characteristics of the polymer by adopting an external characteristic identification mode with maximized flexibility.

The invention relates to the technical field of optical fiber sensing, in particular to an optical fiber temperature measurement method and system, and the method comprises the steps: injecting a multi-scale dynamic modulation coding laser signal into a to-be-measured optical fiber and a reference optical fiber respectively, collecting two paths of optical signals, and generating frequency domain data containing modulation coding characteristics through fast Fourier transform; constructing a virtual reference channel, fusing pre-stored historical data through digital matched filtering and self-organizing mapping, and realizing stable output of a reference signal; adaptive calibration is performed on data to be measured by using extended Kalman filtering in combination with fuzzy logic, and calibrated phase data accurately reflecting temperature change is output; and finally, outputting high-precision temperature distribution data based on the temperature inversion model and multi-dimensional correlation analysis. The temperature measurement precision and the system robustness are remarkably improved, and the method is suitable for the fields of industrial and environmental monitoring and the like.

The invention relates to a user power consumption mode identification method and device considering multi-factor association and a medium. The method comprises the steps of obtaining historical load data and historical meteorological data and performing preprocessing; processing the historical load data and the historical meteorological data under different time resolutions, and constructing a load data set and a meteorological data set under different resolutions; constructing a load curve of each day based on the load data set, performing coarse clustering on the load curves by using a self-organizing mapping neural network, extracting a power utilization mode of the user, and identifying whether the user has an energy storage device or not; calculating the relevance between the meteorological factors and the load through correlation analysis, and selecting key factors; and based on the selected key factors, performing fine-grained analysis on the power consumption mode of the user by adopting a mode of combining a time convolutional network and a pyramid attention mechanism, and accurately identifying the power consumption mode. Compared with the prior art, the method provided by the invention can realize the identification of the power consumption mode of the user with coarse and fine granularity under the multi-resolution condition and multi-factor association.

The invention provides a medical image deep learning auxiliary diagnosis system. The system comprises an image preprocessing module; the feature extraction module is connected with the image preprocessing module; the target detection / segmentation module is connected with the feature extraction module; the classification diagnosis module is connected with the feature extraction module and the target detection / segmentation module; and the result fusion module is connected with the target detection / segmentation module and the classification diagnosis module. According to the invention, a deep learning algorithm, self-organizing mapping, a mixed density network and a clustering algorithm are deeply fused in a feature extraction module, and feature extraction and analysis are carried out on an image from multiple angles. The algorithms cooperate and complement each other, and the limitation of a single algorithm is effectively avoided, so that the system can extract lesion features more comprehensively and accurately, and the disease diagnosis accuracy is improved.

The embodiment of the invention provides a landslide susceptibility prediction method, system and device and a computer readable storage medium. The method comprises the steps of obtaining a to-be-divided topographic image; wherein the topographic image is a slope direction and mountain shadow map extracted based on the digital topographic image; performing region division on the topographic image through a multi-scale algorithm to obtain slope units; wherein the multi-scale algorithm is used for carrying out slope unit division on the terrain; based on the slope unit, environment factor extraction is carried out, and multiple evaluation factors are determined; performing landslide susceptibility prediction on the slope unit based on the plurality of evaluation factors through a landslide susceptibility prediction model to obtain a landslide susceptibility prediction result corresponding to the slope unit; wherein the landslide susceptibility prediction model comprises a self-organizing mapping neural network module, an information amount module and a support vector machine module; the landslide susceptibility prediction model is a model which is trained by adopting historical landslide data and is used for carrying out landslide prediction on the slope unit. According to the scheme, the accuracy of landslide susceptibility prediction can be improved.

The invention relates to the technical field of land resource management, in particular to a land stock index management method based on data analysis. The method comprises the following steps: establishing a cross-data-source data sharing mechanism through a block chain technology, and obtaining multi-source land data; performing natural resource, planning and building information fusion on the multi-source land data based on a knowledge graph to obtain a geographic space semantic network; performing multi-scale space-time clustering on the geographic space semantic network through a self-organizing mapping algorithm, and performing key feature capture of land utilization change based on an attention mechanism to obtain land utilization change feature data; and performing land utilization mode identification according to the land utilization change characteristic data to obtain a land utilization dynamic characteristic spectrum. Through a multi-agent system and a hierarchical reinforcement learning algorithm, a complex land utilization decision process can be simulated, and an optimal decision scheme is realized.

The invention discloses a deep learning-based drainage basin abnormal rainfall event identification monitoring and weather behavior analysis method and system, and the method comprises the steps: obtaining the summer rainfall original data of a drainage basin, and carrying out the preprocessing; the abnormal rainfall event is divided into different subspace modes through a self-organizing mapping machine learning method; qualitatively analyzing a space-time evolution rule and an interaction mechanism of water vapor conveying characteristics and atmospheric circulation configuration in abnormal rainfall according to each subspace mode; evaluating the influence of different meteorological factors on extreme weather conditions through a Bayesian method, and quantitatively exploring abnormal rainfall event change factors; the system comprises a data preprocessing module, a self-organizing mapping mode classification module, a water vapor conveying and atmospheric circulation influence mechanism module and a Bayesian method meteorological factor influence analysis module. According to the method, accurate identification and monitoring of the drainage basin abnormal rainfall event are realized, the internal characteristics of rainfall data can be effectively learned, and the identification capability of the abnormal rainfall mode is enhanced.

The invention provides a coastal erosion rate prediction method, and belongs to the technical field of coastal erosion, and the method comprises the steps: building a three-dimensional seabed grid model, solving a hydrodynamic field through depth average simplification and GPU parallel calculation, and calculating sediment flux distribution based on shear stress discrimination and a high-order windward format. Outputting an erosion mode category and a local erosion strength coefficient by using a coastline erosion feature recognition model comprising a Josephh ring screening layer and a self-organizing mapping projection layer, starting adaptive grid encryption when the local erosion strength exceeds a threshold value, and calling a corresponding parameter set according to an erosion mode to calculate a seabed elevation change and a coastline erosion rate; and a wave energy spectrum reconstruction algorithm is adopted to generate future wave sequence cycle prediction, so that the technical problem that calculation precision and calculation efficiency are difficult to consider in coast erosion rate prediction under a complex wave power condition is solved.

The invention provides a power grid constant value detection method and system, equipment and a storage medium, and belongs to the technical field of power grid detection, and the method comprises the steps: obtaining to-be-detected test constant value data; determining a target neuron matched with the test constant value data from the self-organizing mapping model, and determining a test error between the test constant value data and a weight vector of the target neuron; based on the test error and an error threshold value, determining whether the test constant value data is abnormal or not; wherein the error threshold value is determined by the following steps: acquiring each historical constant value data of the power grid; determining first neurons respectively matched with the historical constant value data from a self-organizing mapping model; and determining an error threshold value based on the weight vector corresponding to each first neuron and the historical constant value data corresponding to each first neuron. According to the power grid constant value detection method and system, the equipment and the storage medium provided by the invention, the recognition precision of the abnormal constant value can be improved.

The invention relates to the technical field of metal spectrum analysis, in particular to a method for determining the element content of a metal sample by using a direct-reading spectrometer. The method comprises the following steps: acquiring a spectrogram of a to-be-analyzed metal sample; further carrying out iterative decomposition on each spectrum peak to obtain a decomposition peak; further inputting the spectrogram and all decomposition peaks of the spectrogram into a spectrum recognition model obtained through self-organizing mapping network training, and recognizing corresponding element categories; and finally, substituting the spectral intensity of each decomposition peak into a pre-established quantitative correction curve of the corresponding element, and calculating to obtain the content of each element in the metal sample without depending on a priori standard spectrogram or a preset overlapping peak number, so that accurate element quantitative determination under a complex overlapping spectrum peak is realized, and the determination precision and adaptability are improved.

The invention discloses a tree line discharge fault identification method based on big data fault traveling wave current characteristics, and the method comprises the steps: S1, collecting fault traveling wave current waveform data and environment data, and enabling the environment data to comprise wind speed, humidity and temperature data; s2, performing adaptive discrete wavelet transform on the fault traveling wave current waveform to obtain a fault wavelet coefficient, obtaining an enhanced fault wavelet coefficient through an improved dynamic attention network based on the fault wavelet coefficient, and obtaining the optimal frequency band energy of the enhanced fault wavelet coefficient; splicing the enhanced fault wavelet coefficient, the optimal frequency band energy and the environment data into a comprehensive feature vector; s3, introducing a self-organizing mapping network based on the comprehensive feature vector to construct an optimized state space, collecting traveling wave current waveform data in real time, and obtaining waveform time sequence similarity between the traveling wave current waveform data and the optimized state space by using an improved dynamic time warping algorithm; when real-time data is input, subspace matching of corresponding environmental conditions can be quickly positioned.

The invention belongs to the field of control, relates to a resin production waste gas recovery control method and system, and aims to solve the technical problems that in the prior art, working condition division cannot be matched with the real state of a multivariable system, and PID parameters are balanced among a plurality of control targets. The neurons are divided into stable working condition neurons, transition working condition neurons and disturbance working condition neurons; s2, obtaining a Pareto optimal solution set composed of a PID parameter group, wherein the Pareto optimal solution set comprises an ITAE optimal solution, a control output change rate optimal solution and an equilibrium solution; s3, determining an optimal matching unit of the potential feature vector in the self-organizing mapping network; s4, when the optimal matching unit is a stable working condition neuron and the absolute value of the current control error is greater than a high-order threshold value, selecting an ITAE optimal solution; when the absolute value of the current control error is smaller than a low-level threshold value, the optimal solution of the control output change rate is selected; and a balance solution is selected between the two. Accurate identification and division of the operation state of the resin production waste gas recovery process are realized.

The invention discloses an intelligent segmentation and feature analysis method for a medical image, and relates to the technical field of image analysis, and the method comprises the steps: reading a multi-modal medical image, and carrying out the preprocessing and size standardization; performing medical image segmentation based on a U-Net structure, performing edge detection by adopting a Sobel operator, and optimizing the edge of the segmented image in combination with morphological operation to obtain an edge-optimized focus segmentation map; feature point matching is carried out on the multi-modal focus segmentation image based on self-organizing mapping, and non-rigid registration is carried out through TPS transformation based on matched feature points; carrying out foreground region enhancement operation on the registered image, introducing a channel attention module based on a DenseNet-201 model to carry out feature extraction on the registered image, and optimizing features by using a whale optimization algorithm to generate an optimal feature subset; and carrying out illness state classification on the feature subsets by an adaptive neural fuzzy inference system optimized based on a genetic algorithm.

The invention provides a carbonate cave type reservoir prediction method and system based on data mining, and relates to the technical field of data mining, and the method comprises the steps: carrying out the feature extraction of historical logging data, and screening out key logging parameters and a seismic attribute data set; processing the logging data based on a data mining method to obtain a characteristic mode of reservoir development; in combination with the seismic attributes and the real-time data of the current well drilling, optimizing a reservoir mode by adopting a self-organizing mapping neural network and a Bayesian reasoning method, and constructing an optimized reservoir distribution prediction model; historical oil and gas yield data is utilized, a reservoir prediction rule is constructed through a grey correlation analysis and decision tree method, a high-quality reservoir area is identified, and probability prediction is carried out; and performing spatial interpolation and visualization processing by adopting a Kriging interpolation and connectivity analysis method to generate a connectivity distribution map of the reservoir and a probability density map of the oil and gas enrichment area, and effectively improving the prediction precision of the carbonate cave type reservoir.

The invention discloses a harmonic current signal-based high-voltage cable defect identification method and system. The method comprises the steps of obtaining harmonic current monitoring data of a high-voltage cable metal sheath; analyzing the harmonic current monitoring data by using a principal component analysis method to obtain a principal component characteristic quantity; determining a defect identification initial model based on a self-organizing mapping algorithm, and training and optimizing the defect identification initial model based on the principal component characteristic quantity to obtain a defect identification optimal model; and inputting harmonic data of a to-be-detected high-voltage cable into the defect identification optimal model so as to identify the defect type of the to-be-detected high-voltage cable. Amplitude-frequency characteristics of harmonic current signals are extracted through principal component analysis, abnormal harmonic data generated by different defects are classified and distinguished through a self-organizing mapping algorithm, and therefore defect identification based on high-voltage cable line harmonic signal abnormity is achieved. And reliable technical support can be provided for cable defect identification in a power system in practical application.

The invention discloses an industrial park weather typing method and device integrating a self-organizing mapping neural network and a k-means algorithm, and the method comprises the steps: firstly, constructing an initial data set through air quality monitoring data and ERA5 meteorological reanalysis data, converting the data into a numerical value type, and carrying out the standardization, so as to guarantee that the feature data has the distribution with the mean value being 0 and the standard deviation being 1; thirdly, initializing a self-organizing mapping (SOM) model, and performing dimension reduction on the data by setting parameters such as grid size, sigma and learning rate so as to obtain two-dimensional feature representation; then, initializing a k-means clustering model, setting a clustering number and a random seed, and performing clustering analysis on the feature data after dimension reduction by using a fitpredict () method so as to obtain a clustering label of each sample; in the evaluation and visualization stage, evaluation indexes such as SSE, MSE, RMSE, a contour coefficient and a Clinski-Harabasz index are calculated so as to evaluate the clustering effect and the separation degree. And finally, adding a clustering label into the original data, and visually analyzing the distribution of each feature under different clusters by using a violin chart, thereby providing a visual basis for further analysis. According to the method, through integration of the SOM and the k-means model, the precision of ozone concentration weather typing of the small-scale industrial park is remarkably improved, and the high-concentration ozone weather is divided more finely and more accurately.

The invention discloses an electric pile state identification and classification method, equipment and a storage medium, and relates to the field of battery data calibration. The method comprises the following steps: according to the similarity between each type of galvanic pile parameters and galvanic pile voltage, determining the correlation between each type of galvanic pile parameters and galvanic pile voltage; selecting galvanic pile parameters with large correlation as training data; training the training data through a self-organizing mapping neural network to obtain an optimal clustering model for clustering the pile parameters; clustering the training data through the optimal clustering model to obtain a plurality of clusters of clustering data; generating a pseudo pile state label for each type of clustering data and then forming a sample set; and training the sample set through a one-dimensional convolutional neural network to obtain a pile state classification model for pile state classification. According to the method, the workload of manual labeling is reduced, and the state of the galvanic pile can be effectively identified and classified.

This application belongs to the field of control and relates to a control method and system for recovering waste gas from resin production. It addresses the technical problems in existing technologies, such as the inability of operating condition division to match the true state of a multivariable system and the need for PID parameters to balance multiple control objectives. The control method includes the following steps: S1, dividing neurons into stable operating condition neurons, transitional operating condition neurons, and disturbance operating condition neurons; S2, obtaining a Pareto optimal solution set composed of PID parameter groups, including the ITAE optimal solution, the control output rate of change optimal solution, and the equilibrium solution; S3, determining the best matching unit for the latent eigenvectors in a self-organizing map network; S4, when the best matching unit is a stable operating condition neuron, selecting the ITAE optimal solution when the absolute value of the current control error is greater than a high threshold; selecting the control output rate of change optimal solution when the absolute value of the current control error is less than a low threshold; and selecting the equilibrium solution when it is between the two. This achieves accurate identification and division of the operating state of the resin production waste gas recovery process.

The invention provides a variable damping hydro-pneumatic suspension system based on a self-organizing mapping algorithm, and the system comprises a combined suspension system intelligent control module which comprises a hydraulic cylinder, a proportional valve, a PID controller, an intelligent sensor and an energy accumulator; a damping-adjustable double-axle communicated type hydro-pneumatic suspension module comprises four hydraulic cylinder differential connection structures of a front axle and a rear axle. And the self-adaptive adjustment control module is configured to optimize PID parameters through a self-organizing mapping algorithm and a self-adaptive genetic algorithm so as to adjust the flow of a proportional valve, so that the suspension damping is dynamically controlled. Through an energy accumulator, a proportional valve and a self-organizing mapping algorithm, damping coefficient dynamic adjustment and multi-objective optimization are achieved. According to the system, the smoothness, the stability and the road surface protection performance are remarkably improved while the bearing capacity is guaranteed, and the system is suitable for different heavy-load transportation scenes.

A map generation apparatus acquires a plurality of pieces of material specification information indicating material specification and physical property information indicating a physical property value of each of a plurality of physical properties for a product that can be generated with the material specification indicated by the corresponding material specification information, clusters the plurality of pieces of physical property information, extracts some pieces of physical property information from each cluster, generates, by using the extracted physical property information, a self-organizing map in which a physical property vector indicating a value related to a physical property value of each of a plurality of types of physical properties of the product is assigned to each node on a map space, assigns the material specification information corresponding to the physical property information extracted from the cluster to any one node of the self-organizing map.

A voice generation method and apparatus, a product, a device, and a medium, which are applied to the technical field of voice generation. The method comprises: using a quantizer to discretize a voice feature vector of an original voice signal, obtaining a discrete symbol representation corresponding to the original voice signal (S11); extracting a text feature corresponding to a target text (S12); and inputting the text feature and the discrete symbol representation into a voice generation model, so that the voice generation model uses the text feature as a condition, and generates a target voice on the basis of the discrete symbol representation (S13). The quantizer is obtained by training a self-organizing map network by means of voice signal training samples. The method may restore an original voice feature more accurately, and improve the quality of the generated voice.

This invention discloses a random flood forecasting method based on machine learning and cloud models. The steps are as follows: using typical historical floods as basic data for model calibration and testing, selecting classification indicators that meet the conditions, and classifying historical floods based on a self-organizing map neural network (SOM); using the maximum information coefficient method (MIC) to screen the impact factors of classified floods, and establishing a random flood forecasting model based on different machine learning methods; for different types of floods, solving the fusion weights of different forecasting models based on the cloud model, and weighting the simulation results of each model to obtain the model integrated forecast result; analyzing and calculating the relative forecast error, and establishing the joint distribution function of the relative forecast errors at adjacent moments based on the Copula method; finally, obtaining real-time online flood information to achieve random flood forecasting. This invention can improve the accuracy of flood forecasting and provide a new approach to hydrological prediction and forecasting.