234 results about "Regular grid" patented technology

The invention discloses an underground construction decision-making method based on three-dimensional geological modeling and risk hot area identification, and relates to the field of fusion of artificial intelligence and geological engineering. The method comprises the following steps: firstly, acquiring drilling data, geological radar images and seismic reflecting layer information, constructing a three-dimensional geological voxel model with spatial topology constraints, and accurately describing a geological unit structure by adopting an irregular grid mode; and then, extracting a time sequence characteristic index under construction disturbance, forming a continuous time sequence characteristic vector, inputting the continuous time sequence characteristic vector into a convolutional recurrent neural network model with a space attention aggregation mechanism and a deep memory unit, and predicting a risk heat value of each space position. And on the basis, through heat gradient clustering and neighborhood consistency analysis, a dynamic high-risk hot area is identified, and a risk hot area map is constructed. And finally, in combination with the construction stage, the equipment plan and the sensor feedback information, constructing a multi-target auxiliary decision function, and generating a construction decision result including operation path reconstruction, rhythm adjustment and power limit and control suggestions.

The invention discloses a nuclear radiation index early warning system based on multi-algorithm fusion, and the system comprises an environment data collection module which is used for collecting gamma-ray dose rate data, wind speed data, wind direction data and geographic position information, and constructing a nuclear radiation environment data set; the dose rate prediction module is used for predicting a future change trend of gamma ray dose rate data based on a sparse recurrent neural network model; the anomaly detection module is used for identifying an abnormal mutation point and positioning the position of a high-risk sensor; the diffusion region modeling module is used for calculating a nuclear radiation diffusion influence region; the grid risk evolution module is used for simulating time sequence evolution of a regular grid risk state based on the multi-state cellular automaton; and the fusion judgment module is used for fusing output results and generating a final early warning grade and a spatial risk distribution result. According to the invention, accurate prediction of the nuclear radiation risk and dynamic space early warning are realized, and the early warning accuracy and response efficiency are significantly improved.

The invention relates to the technical field of marine surveying and mapping and submarine topography modeling, and discloses a submarine topography and landform construction method based on multi-source water depth data fusion, which comprises a multi-source data acquisition module, a data preprocessing module, a feature extraction module, a fusion algorithm matching module, a three-dimensional modeling module and an uncertainty evaluation module. A historical submarine topographic map is converted into grid data, namely, submarine topography is discretized into digital representation of regular grid units, water depths of other sources are converted into the grid data, the precision of the water depths of different sources is evaluated by adopting a superposition comparison method, and an error distribution model is established to reasonably correct and fuse the water depth data of various sources. The innovation of the method lies in that topographic feature parameters including the gradient, the curvature matrix and the bionic optimization algorithm are introduced, the defect that a single algorithm is preset in a traditional method is overcome, and the precision of coral reef and fault zone complex areas is improved.

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 relates to the technical field of equipment management and control, in particular to an intelligent management and control method and system for a smart park, and proposes the following scheme: dividing a region into regular grids based on spatial data and equipment data, and generating an inspection path in combination with dynamic weights. And the inspection equipment collects equipment operation data and environmental parameters according to the path, and transmits the inspection data to the processing equipment in real time. And the processing equipment accurately identifies equipment operation abnormity, environment parameter abnormity or security risks through time sequence analysis and an abnormity detection model, and links the management and control equipment to process an abnormal area. The method improves the park inspection efficiency and the exception handling capability, and is suitable for industrial park scenes.

The invention provides a hydropower station reservoir water level prediction analysis method, a hydropower station reservoir water level prediction analysis system and a storage medium, and relates to the technical field of reservoir water level prediction. A unified modeling path from hydrological element collection to water level prediction is realized, a directed weighted graph reflecting a real hydrological conduction path is formed by fusing a digital elevation model and hydrological data, constructing a space node and a water flow connection relation thereof and giving edge weights, so that the water level prediction is not limited to point data analysis any more, and the water level prediction accuracy is improved. According to the method, modeling of collaborative change of hydrological states in a whole influence basin is changed, a unified framework fusing a terrain structure, a hydrological process and machine learning capability is constructed, coupling modeling of spatial distribution characteristics and time dynamic characteristics is achieved, and the precision and reliability of water level prediction are improved.

The invention relates to the technical field of ocean engineering digital twinning and three-dimensional visualization, and provides a method and system for generating dredging terrain twinning based on a right-angle triangular mesh, and the method comprises the steps: obtaining regular grid elevation unified to an engineering plane coordinate system or preprocessed sampling height data, determining a covered space range, reading the space range and density parameters, and calculating the total row number and the total column number of the grids based on the space range and the density parameters; generating regularly arranged vertexes in the covered space, and constructing a topological structure of an isosceles right triangle grid; dividing the global grid into a plurality of Chunk blocks according to a fixed size by taking a vertex as a unit, establishing a triangular index mapping relationship between global rows and columns and the Chunk blocks, filling three vertexes of an isosceles right triangle and elevation / height data in each Chunk block in parallel, and generating local VBO, IBO and AABB; and performing batch rendering and coloring according to a height color table on all Chunk blocks to realize visualization of the terrain.

The invention discloses a space field prediction method, device and equipment based on a large model and a weighted loss function, and relates to the field of fluid space field dynamic prediction. The method comprises the following steps: carrying out unstructured grid division on an object model for generating a three-dimensional fluid space field, simulating to generate space field data of an irregular grid at a plurality of historical moments, and mapping the space field data into a regular grid; calculating a time sequence mutual information amount of any two historical moments, and reserving space field data of the historical moments with the time sequence mutual information amount greater than a time sequence dependency degree threshold so as to assist in forming a training data set; determining the weight of each regular grid according to the Euclidean distance between each regular grid and the analysis reference line, and obtaining a weighted loss function; training a hybrid convolutional network according to the training data set and the weighted loss function, and constructing a large space field prediction model; and an iterative prediction mechanism is adopted to obtain space field distribution at future moments. The precision, efficiency and adaptability of dynamic prediction of the fluid space field can be improved.

The invention provides a point cloud downsampling method applied to unmanned aerial vehicle obstacle avoidance, and aims to solve the problems of low mapping efficiency and geometric detail loss caused by large point cloud data volume and non-uniform density in environmental perception. The method comprises the following steps: preprocessing an original point cloud through a regular grid to balance density, and extracting an effective flight area of the unmanned aerial vehicle by adopting bidirectional cloth simulation filtering and combining with a flight height; multi-level geometric features including elevation gradient, curvature and roughness are calculated, and point cloud priority scores are fused and generated based on principal component analysis; voxel grids are divided according to the size of the unmanned aerial vehicle, the sampling rate of each voxel is adaptively set in combination with the priority, farthest point sampling is executed in the feature space to select representative points, and finally a simplified point cloud model is output. The method can be integrated to a mapping module of a multi-aircraft cooperative obstacle avoidance and planning system, is suitable for unmanned aerial vehicle path planning tasks in a complex three-dimensional environment, and has the characteristics of light weight, accuracy, high efficiency and the like.

The invention discloses a low-altitude flight base map construction method for unmanned aerial vehicle path planning, and belongs to the technical field of low-altitude flight base map construction. Screening the building data in the target area, and calculating the actual occupied area of each building after screening; dividing the grid level to which each building belongs according to the actual floor area of each building; performing grid coding on the grid level to which each building belongs; constructing an initial grid; and step-by-step refining is carried out on the initial grid by adopting a method based on building space characteristics to obtain sub-grids, and the building space is represented by utilizing a sub-grid group. According to the method, the low-altitude flight base map of the unmanned aerial vehicle is constructed based on the Beidou grid code, and the method has remarkable advantages in the aspects of data storage and calculation efficiency; through regularized grid division and a refinement strategy, an efficient and lightweight low-altitude flight base map of the unmanned aerial vehicle is constructed, so that the data storage amount of the base map is reduced by more than 95%, and the storage overhead is greatly reduced.

The invention discloses a thick vegetation coverage area point cloud filtering method and system based on implicit neural representation, and belongs to the technical field of point cloud filtering, and the method comprises the steps: obtaining point cloud data, building a self-adaptive irregular grid according to the point cloud data, segmenting the point cloud data based on the self-adaptive irregular grid, and obtaining the point cloud data of a plurality of sub-regions; inputting the point cloud data of each sub-region into an up-sampling framework based on an implicit neural network to obtain global point cloud data; and carrying out filtering processing on the global point cloud data based on a multi-scale feature point cloud filtering algorithm, and outputting a global point cloud ground point set. By setting an adaptive irregular grid, the density of the grid is automatically adjusted, so that the fine segmentation of the point cloud data of the thick vegetation coverage area is realized; an upsampling framework based on an implicit neural network is adopted to solve the sparsity of the point cloud; a multi-scale feature point cloud filtering algorithm is used for filtering global point cloud data, and the filtering effect on ground objects and vegetation at all levels is improved.

The invention discloses an aircraft vortex recognition training method and device based on self-supervised Swin Transformer and transfer learning, equipment and a medium, and relates to the technical field of fluid simulation big data intelligent analysis, and the method comprises the steps: obtaining the irregular grid velocity field data of a target aircraft; converting the irregular grid velocity field data into a Cartesian grid velocity field; performing self-supervised learning on a Cartesian grid velocity field through a multi-head attention mechanism of a vortex recognition model, and extracting hierarchical features to obtain multi-scale flow features; performing fine adjustment on an output structure of the vortex recognition model, performing transposition convolution on the multi-scale flow features to obtain a predicted value of a vortex region, and performing voxel-level supervised training to obtain a trained vortex recognition model; the vortex recognition model is a model obtained on the basis of a self-supervised Swin Transform pre-training strategy and transfer learning fine adjustment supervised training. And accurate identification of the three-dimensional vortex of the aircraft flow field is realized.

The invention belongs to the technical field of physical ocean and remote sensing application, and particularly relates to a sub-mesoscale signal extraction method based on Ku / Ka dual-frequency SAR (Synthetic Aperture Radar) height measurement, which comprises the following steps of: acquiring a Ku / Ka dual-frequency sea surface height observation original data set and IMU (Inertial Measurement Unit) attitude and trajectory data; registering the average sea surface and tide height to a space-time sampling position of Ku / Ka synchronous observation and deducting from the original height to obtain an initial sea surface height anomaly sequence of the two channels, and processing the initial sea surface height anomaly data of the two channels to obtain a fused height anomaly sequence; performing attitude coupling mode extraction on the fused height anomaly sequence to obtain a height anomaly sequence after platform error elimination; meanwhile, smoothing processing is carried out to obtain a smoothed sequence; and interpolating the smoothed sequence into a two-dimensional regular grid, performing small-scale secondary smoothing denoising on grid data, interpolating a mesoscale background field at the same time into the same grid, and deducting the mesoscale background field to obtain a sea surface height anomaly data system with sub-mesoscale as a main part.

The invention discloses a salinization remote sensing inversion method fusing crop information and data preprocessing. A land utilization data set is subjected to splicing, cutting and other processing to obtain a high-confidence-coefficient cultivated land range; point distribution sampling is carried out according to a regular grid method, and soil conductivity is measured; preprocessing the remote sensing image; dividing crop growth stages and constructing a multi-temporal spectral feature set; constructing an NDVI time sequence data set and extracting phenological characteristics; extracting planting structure information based on the multi-temporal spectrum and phenological characteristics; a planting structure is converted into crop type data through one-hot coding, and the crop type data is fused with multi-temporal spectrum and phenological characteristics to construct a crop information data set; the method comprises the following steps: constructing a salinity inversion model through data preprocessing and combining with ground actually measured salinity data, and verifying the salinity inversion model; evaluating the cross-year generalization ability of the model; and finally, the inversion result is visualized to generate a salinization map. According to the invention, the system integrates the multi-dimensional information of crops and optimizes the data preprocessing process, and significantly improves the salinization monitoring precision and the cross-space-time adaptability.

The invention provides a slope stability prediction method and device under a rainfall condition, and relates to the technical field of slope stability prediction, and the method comprises the steps: employing a regular grid to divide a slope, and constructing a spatial-temporal feature data set containing geological, hydrological and topographic parameters; calculating a regional cooperation index based on the multi-dimensional feature vector, and dividing the slope into a plurality of communities and isolated risk areas; calculating the dynamic safety coefficient of each community by taking the mass center sub-region as a representative, and dividing the stability level according to a threshold value; and establishing a three-dimensional digital twinborn model to realize risk visualization, triggering early warning for a continuous three-period safety coefficient decreasing area, and displaying an evolution track. According to the method, accurate monitoring and early warning of slope stability are realized through multi-source data fusion and dynamic partition evaluation.

The invention discloses a steep cliff three-dimensional modeling method and system based on structural plane constraint, and relates to the technical field of three-dimensional geographic information system.The steep cliff three-dimensional modeling method comprises the steps that steep cliff area geological data including steep cliff multi-view images and steep cliff surface dense point cloud data are collected, and a geological prior knowledge base is constructed; an image recognition technology is used for processing a steep cliff multi-view image, point cloud geometric analysis is combined, and the structural plane spatial orientation including a rock stratum interface and a joint plane is recognized. According to the method, geological priori knowledge is fused, the geometric distortion problem of a traditional digital elevation model in a cliff area is effectively solved, the spatial orientation of a rock stratum interface and a joint surface is extracted by using an image recognition technology, and the spatial orientation is used as a hard constraint condition to construct a control triangulation network framework; the problems of forced smoothing errors of a regular grid DEM and a suspended surface of a triangular irregular network are avoided, and the rock stratum structure, the crack distribution and the dangerous rock body form of the cliff can be precisely restored through the self-adaptive triangulation algorithm.

The invention discloses a meteorological element spatial interpolation method based on terrain coupling and a hybrid model. The meteorological element spatial interpolation method comprises the following steps: acquiring meteorological station observation data and digital elevation model (DEM) data of a research area; converting the longitude and latitude coordinates of the meteorological stations into a projection coordinate system which is the same as the DEM data, and extracting DEM values corresponding to the positions of the stations; the longitude, latitude and DEM value of a meteorological station are used as input characteristics, and a thin-plate spline function is adopted to carry out preliminary interpolation; constructing input features of the machine learning model; an XGBoost algorithm is adopted to train a machine learning model; creating a regular grid according to the target resolution, and performing interpolation prediction on grid points; and performing Gaussian filtering smoothing processing on the prediction result to obtain a final interpolation result. According to the method, terrain information is integrated, adaptive parameter optimization for different meteorological elements is realized, calculation efficiency and interpolation precision are balanced, nonlinear characteristics and spatial heterogeneity of meteorological data are processed, and spatial continuity and precision of an interpolation result are improved.

The invention discloses an infrared bidirectional thermal effect simulation method based on radiation and GPU acceleration, and belongs to the field of thermal radiation simulation and parallel computing. Dispersing the complex geometry into patch units, constructing an inter-patch radiation energy balance equation based on a radiance theory, and iteratively solving the final temperature of the patches through a radiance method; a three-level parallel strategy of a task level, a data level and an instruction level is designed, data intensive tasks such as shape factor calculation and radiance iteration are migrated to a GPU to be executed, data storage is optimized in combination with a structure array (SoA) layout and a sparse matrix compression technology, and efficient data sharing of a CPU end and a GPU end is achieved through a CUDA zero copy technology. According to the method, the dependence of a traditional method on regular grids is broken through, the calculation efficiency and precision of million-level surface patch heat radiation transmission in a complex scene are remarkably improved, and an efficient tool is provided for heat radiation indirect transmission calculation and a global illumination model in the field of three-dimensional scene infrared simulation.

The invention provides a temperature distribution inversion method based on a multi-vertex grid and a high-robustness error functional, and the method comprises the steps: 1, obtaining a temperature gradient change rule of a measured structure under a typical thermal load condition, and determining a three-dimensional multi-vertex grid division scheme and a temperature sensor layout form of the measured structure; step 2, establishing an m vertex grid basis function model, and constructing a conversion matrix between a regular grid and an irregular grid; 3, establishing a high-robustness error functional between the theoretical temperature of the centroid position of each surface of the m-vertex grid and the actually measured temperature of the sensor; 4, a temperature matrix equation containing a temperature coefficient matrix and a temperature amplitude matrix is constructed, temperature vectors of all grid vertexes are solved, the m-vertex grid basis function model is combined, three-dimensional temperature distribution in the structure space is inverted, and according to the method, only few sensors need to be adopted, the global temperature response of the structure can be obtained, and the inversion precision is high; the method is simple, convenient and high in real-time performance.

The invention relates to the technical field of image processing, and discloses a point cloud connectivity segmentation method and system based on voxel hash and a union-check set, a computer readable storage medium and a computer program product. The method comprises the following steps: calculating effective points of a point cloud to determine a bounding box, and voxelizing a three-dimensional space where the point cloud is located according to the bounding box and a predetermined voxel grid size to generate a regular grid; calculating a grid index of the voxel corresponding to each effective point; constructing Hash mapping from voxels to effective points through double-pass scanning; determining adjacent edges of the point cloud by using an Euclidean distance judgment method based on the Hash mapping, and constructing an adjacent edge set; and performing merging operation and path compression on the adjacent edges in the adjacent edge set by using a union check set structure to obtain a plurality of connected regions of the point cloud. According to the method, rapid spatial positioning is realized through the regular voxel grids, rapid communication and combination are realized by using the union-check set structure, and the segmentation efficiency of point clouds of million level and above scales can be remarkably improved.

The invention discloses a three-dimensional space field prediction method and device based on a large model and data compression, equipment and a medium, and relates to the field of space field prediction. The method comprises the following steps: simulating and generating a dynamic three-dimensional space field data set of each irregular grid under different boundary conditions, mapping the irregular grids into regular grids, and compressing dynamic three-dimensional space field data by using a downsampling method; a training set is constructed according to the three-dimensional space field compression data, the boundary conditions and the three-dimensional space field compression data at the next historical moment, so that a large model pre-training network is trained, the network combines a 3D convolutional neural network and an auto-encoder structure, and a three-dimensional space field prediction large model is obtained; and predicting three-dimensional space field distribution at future moments by using the model. According to the method, the three-dimensional space field can be accurately and efficiently predicted.

The invention relates to the technical field of photogrammetry, in particular to an earthwork measuring and calculating system based on an unmanned aerial vehicle surveying and mapping technology, which comprises a BIM (Building Information Modeling) route partitioning module, a point cloud quality evaluation module, a digital earth surface model generation module and an earth volume measuring and calculating module. According to the method, by analyzing geometric parameters such as gradient and curvature of a designed curved surface, intelligently partitioning and optimizing an unmanned aerial vehicle surveying and mapping route, and executing independent density clustering on point cloud data streams according to route partitions, dynamic evaluation and marking of spatial data quality are realized, and vegetation point clouds are removed in combination with image color information; the method comprises the following steps: correcting data by using an abnormal mark of quality evaluation, generating an accurate digital earth surface model, finally carrying out elevation deviation comparison on the accurate digital earth surface model and a designed curved surface, finely calculating the earth volume through a regular grid, and carrying out statistical analysis by using a root mean square error, thereby improving the accuracy and reliability of a measurement result.

The embodiment of the specification discloses a self-adaptive variable grid forward modeling method for middle-deep dry hot rock mass. p , a transverse wave velocity V s and a medium density p model, pre-setting forward modeling parameters; performing self-adaptive variable grid processing on the velocity model, determining a mapping relationship between coordinate systems, and determining a wave equation under variable grid based on the mapping relationship; performing self-adaptive variable grid processing on the velocity model, determining a mapping relationship between coordinate systems, obtaining the underground medium longitudinal wave velocity, transverse wave velocity and density after variable grid, determining a wave equation under variable grid based on the mapping relationship, and generating final regular grid horizontal direction longitudinal wave field, horizontal direction transverse wave field, vertical direction longitudinal wave field and vertical direction transverse wave field information by using the mapping relationship reversely according to the wave field information in each direction at a specific moment after the variable grid, so that the memory occupation is small and the calculation speed is faster.

The invention discloses an irregular grid seismic data reconstruction method based on deep learning, and belongs to the technical field of compressed sensing irregular grid seismic data reconstruction. In order to improve the quality of seismic data reconstruction, the method comprises the steps that a seismic data training set is generated, and each group of data in the seismic data training set comprises complete seismic data and two interpolation matrixes; an irregular convex set projection convolutional neural network is constructed, and the irregular convex set projection convolutional neural network is obtained by unfolding a loop structure of an irregular convex set projection method into a cascade structure and replacing original sparse transformation with a convolutional layer and an activation function; training the irregular convex set projection convolutional neural network by using the seismic data training set to obtain a trained irregular convex set projection convolutional neural network; and inputting acquired missing irregular grid seismic data and two interpolation matrixes calculated according to the irregular grid information into the trained irregular convex set projection convolutional neural network, and outputting reconstructed seismic data.

The invention relates to the technical field of wafer testing, in particular to a wafer testing result visualization method and system. Through spatial discretization processing, continuous physical coordinates of test points are mapped to a fixed N * N regular grid, and mass and disordered coordinate points are classified into limited and ordered grid units through the conversion, so that the complexity of data positioning is reduced from O (n) to O (1). When rendering is carried out, a system can directly access grid data at any position through simple coordinate transformation without traversing all test points based on a data structure established on the basis of the regular grid. More importantly, the rendering mode is dynamically selected and drawn, so that the rendering calculation amount of the system does not depend on the total amount of test points any more, but depends on the number of grids in the current viewport. And the processing object is fundamentally optimized from a'test point 'to a'viewport inner grid', so that the data processing and rendering efficiency is greatly improved.

The invention provides a microscopic image distortion correction and reconstruction method, system and device, and a storage medium, a preset multi-shape calibration glass slide comprising a regular grid glass slide, a concentric circle glass slide, an irregular curve glass slide and a multilayer depth glass slide; the method comprises the following steps: acquiring images of a multi-shape calibration glass slide under different visual fields, different multiplying powers and different focal planes through a microscope, quantifying geometric distortion and fuzzy characteristics of the images by combining edge detection, contour analysis and fuzzy measurement methods, and generating a distortion feature vector; constructing a point spread function model based on the distortion feature vector, and optimizing the point spread function model by using a convolutional neural network; and applying the optimized point spread function model to carry out deconvolution processing and geometric correction on the microscopic image, and finally obtaining a reconstructed clear image. According to the invention, geometric distortion and edge blur can be effectively corrected, and details and definition of the microscopic image can be recovered. The method has a wide application prospect in the fields of scientific research, clinical diagnosis and industrial detection.

The embodiment of the invention provides a rail transit 3D data defect detection method and system based on spatial indexing, and the method comprises the steps: collecting rail 3D original data, carrying out the preprocessing, and unifying the data into a unified rail coordinate system; dividing the preprocessed track 3D data into a plurality of regular grid units as space units according to spatial positions, extracting multi-dimensional features of data in each space unit, establishing a composite index for each space unit, and storing the composite index in a space-time database; after the acquired 3D data of the current track are subjected to identical processing, spatial position areas corresponding to the 3D data of the current track and historical data are quickly matched through a composite index, and a difference value of multi-dimensional features is calculated; dynamically adjusting the defect judgment threshold of each dimension according to the statistical distribution of the difference values, judging the defect type and severity of the region meeting the threshold, and generating a detection report. The problems that traditional 3D data processing is low in efficiency and long in comparison time consumption are solved.

The invention discloses a lithology identification method and system based on TLS, and belongs to the technical field of lithology intelligent identification, and the method comprises the steps: S1, collecting all rock data of a target region, and carrying out the preprocessing of the rock data, and obtaining outcrop point cloud data; s2, constructing a lithology identification unit, and dividing the outcrop point cloud data into point cloud blocks of regular grids according to a preset grid; s3, extracting features with lithologic sensitivity from the point cloud blocks, wherein the features comprise geometric features and spectral features; and S4, inputting the geometric features and the spectral features into a trained random model forest model, and fitting the geometric features and the spectral features by adopting a random forest to obtain lithology categories of various rocks in the target area. According to the method, automatic classification of outcrop lithology is realized, powerful support is provided for geological exploration and lithology analysis, understanding of the geological field is introduced into effective exploration of the artificial intelligence field, and interdisciplinary fusion development is promoted.

The invention provides a geological disaster risk assessment data processing method and system, and relates to the technical field of geological disasters, and the method comprises the steps: obtaining original observation data of an assessment region, and generating a plurality of error measurement indexes according to the original observation data; mapping the original observation data and the error measurement index to a regular grid to obtain a grid field, and constructing a geographic analysis block; constructing disaster-causing state variables based on the geographic analysis blocks, executing spatial adjacency consistency check, and if adjacent geographic analysis blocks are located in the same influence area and the KL divergence of the corresponding disaster-causing state variables is smaller than a probability target value, merging the adjacent geographic analysis blocks into a comprehensive risk block; elevation deviation caused by terrain shielding of a digital elevation model, spatial interpolation uncertainty caused by sparse rainfall stations and distortion caused by atmosphere and decoherence interference on InSAR deformation are converted into quantifiable error measurement indexes, and the accuracy of risk assessment is improved.

A rock burst risk dynamic assessment method based on a Bayesian method comprises the steps that a stope working face is divided into M * M regular grids, and each grid serves as an independent assessment unit; collecting geological and mining data, selecting a plurality of influence factors based on historical data and expert knowledge, and quantifying the plurality of influence factors by adopting a static evaluation model; determining weights of a plurality of influence factors by using an analytic hierarchy process, and calculating an initial risk score of each evaluation unit by using a comprehensive index method; normalizing the initial dangerousness score into prior distribution by adopting an S-type function, and constructing an initial dangerousness prior probability field; a power law attenuation model is introduced to calculate influence weights among the evaluation units, and an influence weight matrix is constructed; real-time monitoring data are obtained through a micro-seismic monitoring system, the risk probability of each evaluation unit is updated in real time based on the Bayesian theorem, and dynamic evaluation and evaluation result output are achieved. According to the method, the accuracy and the real-time performance of rock burst risk assessment can be effectively improved.