505 results about "Geodat" patented technology

The invention provides an intelligent drilling speed prediction method based on physical feature guidance and multi-source information fusion, and relates to the technical field of intelligent drilling speed prediction, and the method specifically comprises the following steps: collecting multi-source heterogeneous data from a drilling real-time database, a logging system, a logging system and a geological database; constructing a dual-channel deep learning prediction model, wherein the dual-channel deep learning prediction model comprises a dual-channel convolution feature extraction module, a feature fusion module, a time sequence fusion module, a time sequence modeling module and a full connection layer which are connected in sequence; obtaining a predicted drilling speed by using a dual-channel deep learning prediction model; a joint loss function is constructed by considering a data driving error and a physical constraint error, an error is calculated according to the joint loss function, and network parameters are updated through back propagation; carrying out loop iteration training until convergence; and the trained dual-channel deep learning prediction model is used for drilling speed prediction. According to the technical scheme, the problems that in the prior art, a mechanism model is insufficient in precision, and a data driving model is poor in reliability are solved.

The invention belongs to the technical field of tunnel construction geological disaster prevention and control, and discloses a multi-source data fused refined treatment decision-making method for a complex stratum disaster source, which comprises the following steps: collecting and fusing multi-source geological data, and constructing a three-dimensional geological model; generating a disaster source risk dynamic assessment and treatment scheme; based on a fluid-solid coupling similarity theory, verifying the preliminary treatment scheme by adopting a physical model test, and determining an optimal treatment scheme; the optimal treatment scheme is executed, and the treatment process is dynamically regulated and controlled; after treatment, the treatment effect is evaluated through posterior data, and the effect data is fed back to the three-dimensional geologic model and the knowledge base, so that the dynamic updating of the model and the self-learning of the decision-making system are realized. By the adoption of the treatment decision method, the problems that a traditional method depends on experience, information is one-sided, and treatment is extensive are solved, advanced accurate forecasting and refined and personalized treatment of complex stratum disaster sources are achieved, and the safety and efficiency of tunnel construction are remarkably improved.

The invention discloses a geological structure three-dimensional reconstruction and analysis system based on multi-modal data fusion, and relates to the technical field of geological exploration information. The method comprises the following steps: acquiring and normalizing multi-source heterogeneous geological observation data through a preprocessing module, and generating basic data and a data coverage density map; the adaptive fusion module performs analysis to generate a dynamic adaptive weight field, and performs weighted fusion on the data to obtain a primary three-dimensional geological data volume; the construction and optimization module constructs a three-dimensional space residual field in a vector form based on the difference between the fusion body and the original data, and deduces the geometric morphology of the geological interface; the reconstruction module ensures the consistency between model layers through hierarchical bidirectional belief propagation processing; the quantification module calculates an uncertainty index; and the visualization module finally generates a three-dimensional geological scene. According to the method, the problems of conflict resolution and reliability evaluation in multi-source geological data fusion are effectively solved, and the precision of the three-dimensional geological model and the engineering decision support capability are improved.

The invention provides a landslide prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the following steps: obtaining multi-source geological data of a target region, constructing a three-dimensional geological model, and dividing slope units; calculating an initial safety coefficient of each slope unit by adopting a limit equilibrium method and Monte Carlo simulation; acquiring real-time monitoring data, establishing an inverse analysis optimization model, and inverting and calibrating physical and mechanical parameters; updating calibration parameters to the model, coupling rainfall infiltration and underground water seepage simulation, dynamically updating a pore water pressure field, and calculating a real-time dynamic safety coefficient; and according to a comparison result of the dynamic safety coefficient and a preset threshold value, determining the stable state of the slope and sending out corresponding early warning. According to the method, through multi-source data fusion, parameter dynamic inversion calibration and real-time hydrological and mechanical coupling simulation, accurate and dynamic evaluation and graded early warning of slope stability are realized, and the accuracy and timeliness of landslide prediction are remarkably improved.

The invention relates to the technical field of mining, and discloses a method for determining pre-splitting blasting parameters for mining blasting, which comprises the following steps of: 1, acquiring rock mass structure information of a blasting area through three-dimensional geological radar, laser radar and shallow seismic reflection data, and constructing a structural plane distribution map containing a soft and hard interbed and joint combination mode; 2, initial presplitting blasting parameters are generated based on the structural plane distribution map, wherein the initial presplitting blasting parameters comprise the hole distance, the linear charge density and the charge structure; according to the method, the technical scheme that the structural plane distribution map is constructed through multi-source geological data fusion and digital twinborn simulation dynamic correction is adopted, so that the technical effects that the influence of a complex structure combination on blasting energy transfer is accurately quantified, and the presplitting path control precision is remarkably improved are achieved; compared with the technical scheme depending on a static rock mass model or an empirical formula in the prior art, the defects of presplitting path offset and serious parameter mismatch caused by the fact that modeling is not carried out due to the dynamic effect of the structural plane are overcome.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

A dynamic adaptive learning method for mineral prediction includes: collecting a dataset including geological data and labels of the geological data; extracting features from the geological data, initializing parameters of a training model and optimizing the parameters to obtain training parameters; performing an associative training on the training model based on the training parameters and the labels in a dynamic adaptive learning framework to obtain a mineral prediction model, algorithms of the associative training including a variational expectation algorithm and a variational maximization algorithm, and the variational expectation algorithm including an unsupervised learning mode, a semi-supervised learning mode, and a fully supervised learning mode; and predicting, by using the mineral prediction model, a mineral to obtain a mineral prediction result. The method can break through limitations of the traditional machine learning technology, offering a more efficient, universal, and stable strategy for geophysical data analysis and mineral resource assessment.

The invention discloses an open-pit mine slope risk prediction method and system based on multi-source data, and relates to the technical field of intelligent geological engineering, and the method comprises the steps: collecting geological data, displacement monitoring data and environmental activity data of an open-pit mine slope, carrying out the preprocessing, and constructing a three-dimensional digital twinborn body; carrying out fusion calculation on the preprocessed displacement monitoring data and environmental activity data through a physical information neural network, predicting a displacement value and a stress value of the strip mine slope, and obtaining full-field physical quantity prediction data; according to the strip mine slope slip crack surface damage evolution path, the stability risk levels of the whole strip mine slope and different areas in the strip mine slope are judged, high-risk areas are positioned, and a strip mine slope risk prediction report is generated. According to the method, the damage evolution path of the slip crack surface of the strip mine slope is dynamically deduced, so that space-time continuous modeling of the damage evolution process, early recognition of the slope instability precursor and accurate insight of the risk evolution trend are realized.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention relates to the technical field of geological engineering, and discloses a geological safety risk dynamic assessment method based on multi-source data fusion, which comprises the following specific steps: step 1, collecting and standardizing multi-source geological data; 2, performing semantic fusion and conflict resolution on the geologic features; 3, constructing a dynamic risk assessment model; 4, risk situation real-time updating and early warning are carried out; the satellite remote sensing system in the first step adopts the synthetic aperture radar interference measurement technology, the spatial resolution is better than 3 meters, the revisit period is shorter than 7 days, and the earth surface deformation monitoring precision reaches the millimeter level. Through standardized processing and semantic fusion of the multi-source geological data, the problem of heterogeneous data integration is effectively solved, the data basic quality of risk assessment is improved, a space-time coupling neural network model is adopted, nonlinear features and space-time correlation characteristics of geological risk evolution are accurately captured, and prediction precision and timeliness are improved.

The invention discloses a geological information real-time sensing and decision-making method for intelligent mining of a coal mine, and the method comprises the following steps: S1, deploying geological sensing equipment at a working surface and an adjacent region of the coal mine, and collecting original geological data; s2, performing standardization, alignment and denoising on the original geological data, and constructing a tensor structure; s3, inputting the tensor structure into a tensor manifold learning model, extracting an evolution trend and an abnormal region, and generating a state vector; s4, constructing a multi-objective optimization model according to the state vector, and solving an optimal control parameter by adopting an improved badger optimization algorithm; s5, the control parameters are converted into operation instructions, and the operation strategy of the tunneling equipment is dynamically adjusted; and S6, combining operation feedback and a new data updating model to realize closed-loop coupling control. According to the method, the tensor manifold learning model and the improved badger optimization algorithm are fused, and the geological information real-time sensing and decision-making method for intelligent mining of the coal mine is realized.

The invention discloses a shale gas well dessert prediction method and device based on integrated learning. The method comprises the steps of obtaining main control factors and feature data of a shale gas well to be predicted, and inputting the main control factors and the feature data into a trained dessert prediction model to obtain dessert prediction data; the model training process comprises the following steps: determining main control factors influencing the dessert according to the correlation between each parameter in the prediction data of the sampled shale gas well in the target area and the dessert; the prediction data comprises first data and second data, the first data comprises geological data, perforation data and oil and gas production data, and the second data comprises logging data and fracturing construction data; performing feature extraction on each parameter in the second data to obtain feature data; constructing a training data set according to the main control factors and the characteristic data of the shale gas well; and training the dessert prediction model based on the training data set. The accuracy of a prediction result can be improved, and fracturing design is effectively guided.

The invention relates to the technical field of mineral exploration, and discloses a method for estimating the mineralization probability of residual slope product type anatase based on multi-source parameters, and the method comprises the steps: determining a geographic region, collecting geological data, sampling a geological sample, and collecting the characteristics of the sample; extracting the first characteristic index set to judge the region classification of the geographic region, wherein the region classification comprises a conventional exploration region, a main mineralization mode region and a secondary mineralization mode region; extracting a second characteristic index set from the geological data to calculate a standard mineralization probability; calculating a weathering correction coefficient and a maturity correction coefficient; and correcting the standard mineralization probability to generate a mineralization probability, and judging whether the geographic region is a high-potential target region or not according to the mineralization probability. According to the technical scheme, the metallogenic probability judgment standard can be determined and corrected under different conditions, and judgment of a high-potential target region is achieved.

The invention discloses a tungsten deposit deep edge metallogenic prediction method based on machine learning, and particularly relates to the technical field of geological prediction and data processing. The method comprises the following steps: acquiring multi-source geological data, constructing a multi-dimensional space grid, and dividing candidate prediction units; quartz vein density, alteration indexes, geochemical combination anomaly weights and high-frequency magnetic anomaly waveform features are extracted, and an ore control feature tensor matrix is constructed; extracting a spatial dependency relationship among the candidate units based on a graph neural network, constructing a metallogenic potential prediction model, and outputting a metallogenic probability graph and a metallogenic hot spot graph; model parameters and feature weights are dynamically optimized through spatial superposition analysis and an abnormal response recognition mechanism; according to the method, the precision and efficiency of deep edge tungsten ore prospecting can be remarkably improved, and good self-adaptability and popularization and application value are achieved.

The invention relates to the technical field of underground space development, and provides a coastal bedrock type city underground space development geological suitability evaluation method comprising the following steps: analyzing key geological problems of land area and sea area underground space development, and extracting related geological factors; basic geological data are collected and classified, a geological suitability evaluation index system is constructed, and geological evaluation units are divided; subjective and objective weights are determined based on an analytic hierarchy process and an entropy weight method, and an optimal comprehensive weight is obtained through game theory combination weighting; modeling based on a grey correlation analysis method, and calculating and grading suitability scores of the evaluation units; and integrating evaluation results, and generating an underground space development geological suitability partition map of land-sea overall planning. According to the scheme, the underground space development pattern is optimized, comprehensive utilization of land and ocean resources is promoted, and safe, efficient and sustainable development and utilization of the coastal city underground space are powerfully supported.

The invention discloses a mining area information three-dimensional perspective model construction method and device, equipment, equipment and a medium, and relates to the technical field of modeling. The method comprises the steps of performing data fusion and voxel filling on multi-source mining area geological data, constructing an initial model according to the data fusion and voxel filling, and performing topological structure optimization; performing surface topology and texture mapping on the model based on a mining area remote sensing image to obtain mining area ground feature distribution transmission data and superposing the data into the model; performing density distribution and density mutation recognition on the model according to the gravity exploration data of the mining area to obtain density distribution transmission data of the mining area, and superposing the density distribution transmission data into the model; resource prediction is carried out based on mining area ground feature / density distribution transmission data; threshold screening is carried out on the topological optimization model based on the prediction result, and then the topological optimization model is superposed into the model; performing deep source focusing processing on the mining area resource area data to obtain deep source focusing area data, and superposing the deep source focusing area data into the model; and carrying out resource visualization on the model to obtain a mining area information three-dimensional perspective model. According to the invention, multi-dimensional fine expression of mining area information can be realized.

The invention provides a construction tunnel surrounding rock stability evaluation method based on digital twinning and a related device. The method comprises the following steps: constructing a tunnel digital twinning model of a tunnel construction section; geological data and real-time progress position data of a target mileage segment in the construction process are obtained; mapping the geological data to a digital twinborn model, and updating soil body unit parameters; according to the real-time progress position data, synchronously updating activation states of a soil body unit and a supporting structure unit in the model by adopting a state change method; and extracting a surrounding rock stability index based on the updated digital twinborn model, evaluating the stability of the current construction tunnel surrounding rock in combination with a safety threshold to obtain a construction tunnel surrounding rock stability evaluation result, and constructing a tunnel construction process dynamic mapping-oriented digital twinborn model by utilizing the description capability of a finite element method on structural mechanical behaviors. Geological conditions and construction progress information which are obtained in real time on site are used as input, and the construction period structure safety risk of the drilling and blasting method tunnel is evaluated and predicted.

The invention relates to the technical field of geological data analysis, discloses a bank slope deep unloading developmental zone identification and evaluation method and system based on a sliding window, and aims at solving the problems that an existing method is low in efficiency and high in subjectivity, and evaluation results are discrete and cannot be quantized. Based on set window parameters, space convolution calculation is carried out through a sliding window, and a continuous feature signal sequence is generated; segmenting the sequence by using a preset background noise threshold value, and identifying candidate sections; matching and calibrating the boundary of the candidate section and the position of a known geological structural plane, and delineating a depth-keeping unloading development zone; the comprehensive development strength index of each deep unloading development zone is calculated, and quantitative evaluation is achieved. According to the method, automatic and continuous space recognition and quantitative representation of the deep unloading development zone are achieved, efficiency and accuracy are improved, and the method is particularly suitable for safe site selection and construction of major projects.

The invention discloses a geological risk identification method based on multi-source data fusion, belongs to the technical field of geological risk monitoring and analysis, and can solve the problems that the existing address risk identification mode is single, the identification precision is poor, and the identification efficiency is low due to the lack of dynamic monitoring means. The method comprises the following steps: S1, collecting geological data of a target area, and constructing a three-dimensional geological risk dynamic model according to the geological data; s2, determining risk index calculation data of the target area according to debris flow source data and rainfall data of the target area and the three-dimensional geological risk dynamic model; s3, determining a risk evaluation coefficient of the target area according to the risk index calculation data and risk index reference data of the target area, and generating a dynamic risk partition map according to the risk evaluation coefficient; and S4, generating early warning information of the target area according to the dynamic risk partition map, and performing geological risk identification according to the early warning information. The method is used for geological risk identification.

The invention discloses a fracture prediction method based on well-to-seismic joint inversion and heterogeneous mechanical modeling, and the method comprises the following steps: collecting and preprocessing basic geological data of a target block; building a high-resolution reservoir rock physical model; establishing a three-dimensional geologic model of the heterogeneous mechanical body; carrying out fracture sensitivity prediction and spatial distribution modeling; and crack network three-dimensional visualization and development decision support. According to the method, well-to-seismic joint inversion, heterogeneous geomechanical modeling and artificial intelligence prediction technologies are deeply fused, and a systematic, intelligent and refined crack prediction solution is formed. Compared with a traditional method, the method has remarkable advantages in the aspects of crack space identification precision, multi-field coupling analysis capability and intelligent level, and provides scientific basis and technical support for efficient development of oil and gas reservoirs.

The invention relates to the technical field of mining exploration, and discloses a surface mine mining system and method based on a three-dimensional model, and the system comprises a three-dimensional modeling module which is used for fusing earth surface and deep geological data to construct a dynamic digital twinborn body, receiving environment dynamic sensing data, and calculating rock mass stress and a water flow coupling field in real time; the mining planning module is used for generating a mining step design and equipment scheduling instruction based on the lithologic parameters and stress distribution of the dynamic digital twin; the equipment control module is used for converting the equipment dispatching instruction into centimeter-level action control signals of a mine truck and an electric shovel through a low-delay communication chain; and the monitoring feedback module is used for collecting mining surface deformation and muck pile characteristic data. Through the dynamic digital twinborn body fusing earth surface centimeter-level scanning and deep quantum detection and stress and water flow coupling field real-time calculation, synchronous visualization of the rock mass mechanical state and hydrological response is achieved, full-time-domain geological perspective capacity is provided for mining decision, and mining efficiency is improved.

The invention belongs to the technical field of geological analysis, and discloses a ground subsidence analysis method and device, and the method comprises the steps: matching a sensor layout strategy corresponding to a foundation subsidence risk and meteorological data of a monitoring region; acquiring land subsidence data of the monitoring area according to a sensor; performing time-frequency analysis on the land subsidence data to obtain a geoacoustic spectrogram corresponding to the land subsidence data; inputting the geoacoustic spectrogram into a preset neural network, and outputting a settlement semantic vector corresponding to the geoacoustic spectrogram; acquiring and analyzing historical land subsidence data, and constructing a subsidence rule corresponding to the historical land subsidence data; and inputting the settlement semantic vector and the settlement rule into a preset space-time analysis model, and predicting to obtain a future settlement trend of the monitored area. By using the method disclosed by the invention, the problem that all risks in a certain area cannot be checked when geological data is monitored based on local monitoring points can be solved, and the problem that the geological risk analysis efficiency is relatively low can be solved.

The invention discloses a three-dimensional geologic modeling method based on deep learning, which belongs to the technical field of geologic model modeling, and comprises the following steps: acquiring multi-source geologic data and carrying out fusion processing, generating a preliminary three-dimensional geologic attribute model through multi-scale feature extraction, synchronously generating a theoretical three-dimensional geologic model through physical simulation and carrying out comparison, generating three-dimensional difference volume data, performing weight distribution on the three-dimensional difference volume data and quality indexes of multi-source geological data extracted from the three-dimensional grid data, generating dynamic weight distribution, and performing weighted fusion on the preliminary three-dimensional geological attribute model and the theoretical three-dimensional geological model to generate an optimized three-dimensional geological model; and based on the three-dimensional difference volume data, performing iterative optimization by utilizing the optimized three-dimensional geologic model to obtain a final three-dimensional geologic model. Through data driving and physical simulation parallel modeling, dynamic weighted fusion and iterative feedback optimization, a final three-dimensional geologic model with high data matching degree and geologic rationality can be generated.

The invention relates to the technical field of channel monitoring and underwater topography analysis, in particular to a multi-beam sounding data driven underwater topography profile extraction method and device and a medium, and the method comprises the steps: obtaining channel region multi-beam sounding original data, carrying out the standardized preprocessing, and outputting a sounding data set; performing spatial interpolation and vectorization processing on the sounding data set to generate channel geological data, and issuing the channel geological data as a similar three-dimensional channel bottom graph through a geographic information service platform; generating a profile path, and generating interpolation points according to a preset interval; constructing a spatial index structure based on the sounding data set, and establishing an adjacent sounding point retrieval channel for each interpolation point; and in allusion to the same profile path, acquiring multiple periods of sounding data, calculating water depth difference, and dynamically and visually displaying an abnormal movement phenomenon by using a broken line graph. According to the method, efficient extraction, dynamic comparison and automatic early warning of the multi-period sounding data can be realized, and the analysis efficiency and accuracy of the channel topographic change are remarkably improved.

The invention relates to the technical field of slope reinforcement, and discloses a slope anti-slide pile reinforcement method based on intelligent monitoring, which comprises the following steps: firstly, acquiring slope data of various monitoring points such as displacement, stress, pore water pressure and the like, extracting characteristic parameters, matching to obtain parameter pairs, screening the parameter pairs through bidirectional verification and consistency verification, and then, carrying out slope anti-slide pile reinforcement. And constructing a reinforcement model by taking the parameter pairs as reference points, and generating a standard reinforcement scheme by adopting layered parameter packaging. And meanwhile, a reinforcement path is generated according to side slope geological data, grid areas are divided by constructing a two-dimensional geological distribution diagram, core reinforcement areas are classified, priorities are marked, an initial reinforcement path is iteratively optimized by utilizing a fitness evaluation system in combination with a genetic algorithm, and an optimal reinforcement path is obtained. And finally, on-site verification is conducted on the standard reinforcement scheme, and parameters such as the pile body distance and length are adjusted according to the mean square error of verification data and original data. According to the method, the reinforcement precision and efficiency are improved, and the reliability and adaptability of the scheme are ensured.

The invention discloses a multi-source three-dimensional geological data fusion and management method and related equipment, and relates to the technical field of geological information, and the method comprises the steps: obtaining standardized geological data; fusing the standardized geological data based on a Bayesian fusion framework; constructing a multi-scale three-dimensional geologic model by adopting a potential field interpolation method based on probability constraints provided by posterior probability distribution; performing uncertainty quantification on the multi-scale three-dimensional geologic model by adopting a Monte Carlo simulation method; and storing the multi-scale three-dimensional geologic model and the uncertainty quantification result to a data management platform conforming to an OGC standard, and providing visualization, query and access of a geologic map layer through a Web service interface. According to the method, the sharing efficiency, the reuse value and the application convenience of the geological result data are greatly improved, and the intelligent level and the decision support capability of geological data analysis are enhanced on the whole.

The invention discloses a geological data classification method and device based on deep learning, and relates to the technical field of data classification. The method comprises the following steps: constructing a geological data classification model by using a deep learning algorithm based on a double-layer coevolution framework; the method comprises the following steps: collecting multi-source geological data, and preprocessing the multi-source geological data to obtain preprocessed standard geological data; and inputting the preprocessed standard geological data into the geological data classification model to generate a classification result. The problems that in the prior art, multi-source heterogeneous data fusion is difficult, the model optimization efficiency is low, and the model generalization ability is insufficient are solved.

The invention belongs to the field of transformer substation design, and provides a transformer substation building parameterization intelligent design method and system, and the method comprises the steps: constructing a parameterization component library, building a component model, and configuring adjustable parameters and constraint rules of components; obtaining a station area general map and geological data of a target transformer substation, and generating a three-dimensional model containing terrain and a foundation; corresponding building components are selected from the parameterized component library, a building contour is arranged, and a preliminary model is formed; a design rule is called, corresponding parameters of the preliminary model are adjusted in a parameterized mode under the design rule, a structure calculation model is generated, and structure finite element calculation analysis is conducted; whether conflicts exist in the adjusted model is determined, if yes, conflict positions are marked, and an optimization scheme is produced; and if not, generating a drawing of the adjusted model, calculating an analysis result according to the structural finite element, and outputting a material list and an engineering quantity. The problem of multi-specialty collision can be solved, and the design efficiency and the standardization level are improved.

The invention relates to the technical field of geological monitoring, and particularly discloses an InSAR and geological model linked disaster risk assessment system, which comprises a data input and management module used for performing automatic acquisition and standardized processing on multi-source geological data and providing basic data for InSAR processing; the integrated InSAR data processing module is used for automatic and batch processing of SAR data and providing observation basis for geologic model calibration; the model coupling and inversion analysis core module is used for deep linkage of InSAR observation deformation and a geologic model, optimizing model parameters through inversion, generating a high-confidence geologic model and providing core support for risk assessment; the risk evaluation and prediction module is used for realizing disaster stability evaluation, short-term or long-term prediction and extreme scene simulation by the calibrated geologic model, and outputting a risk level and early warning information; and the result visualization and output module is used for realizing multi-form visualization and standardized output of the risk assessment result.