1204 results about "Geodat" patented technology

The invention discloses a soft soil foundation deformation prediction method and system based on big data, and relates to the technical field of rock and soil monitoring. InSAR satellite data, Beidou GNSS displacement data, optical fiber strain data and meteorological and geological parameters are integrated through a multi-source sensing network. ERA5 reanalysis data is adopted to establish an atmospheric delay compensation function, and dynamic sliding window filtering and strain gradient constraint are combined to realize data space-time alignment and anomaly cleaning. Based on a generalized Kelvin creep constitutive model, environment coupling functions of temperature, humidity and pore water pressure are fused to dynamically correct model parameters, and the creep response characterization capability in a complex environment is enhanced. And performing distributed joint training on the regionalized geological data through a federated learning framework, fusing differential privacy encryption and a node credibility verification mechanism, realizing safety aggregation and migration optimization of cross-regional data, and generating a geological partition adaptive deformation prediction result. The method effectively improves the reliability of soft soil foundation deformation prediction.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention discloses a coal mine hole fracture seepage analysis and prediction system based on a deep learning model, and relates to the technical field of coal mine safety. Comprising a geological data acquisition and three-dimensional model construction module, a data preprocessing and cleaning module, a deep learning feature extraction and training module, a graph neural network optimization and topology modeling module and a seepage prediction and risk assessment module, geological exploration data and hydrogeological information of a coal mine area are obtained, and a three-dimensional geological model containing pores and fractures is constructed. Through the deep learning method combining the convolutional neural network and the graph neural network, the accuracy of coal mine fracture seepage analysis is remarkably improved. The CNN extracts local features, the GNN optimizes a global topological structure, and the seepage prediction precision is improved. The model not only can accurately predict a seepage path, but also can evaluate safety risks, help mine managers to identify hidden dangers in advance, reduce water damage and gas accumulation accidents, and optimize mine safety management.

The invention relates to a knowledge graph construction and intelligent prospecting prediction method based on multi-source heterogeneous geological data. The method comprises the steps that geological data information is acquired and preprocessed, and a geological information database is formed; geologic entities, attributes of the geologic entities and mutual relations of the geologic entities in the geologic information database are recognized through the natural language processing technology, and a geologic knowledge graph is constructed; training and optimizing the prediction model to obtain a metallogenic prediction model; and obtaining a prediction result of the metallogenic potential area by using the metallogenic prediction model, and generating a metallogenic analysis report. According to the method, the geological data quality is improved through data preprocessing, the knowledge graph is constructed to integrate geological knowledge, the deep learning model is utilized to accurately extract and predict the metallogenic characteristics, and finally the metallogenic analysis report is generated, so that the prospecting efficiency is improved, the cost is reduced, and the decision scientificity is enhanced.

The invention relates to the technical field of bridge engineering, in particular to a cable structure bridge design method adopting a BIM (Building Information Modeling) model, which comprises the steps of three-dimensional bridge foundation model construction, cable structure parameterization configuration, nonlinear mechanical simulation, construction error modeling, feedback optimization and the like. Through introduction of structured geological data and NURBS curved surface reconstruction, a geological-structure integrated model is realized. Adopting a genetic algorithm and a particle swarm optimization method to intelligently configure pile foundation and cable parameters; a construction error closed-loop adjustment mechanism is constructed through dynamic simulation and real-time tension feedback control; and finally, parameter correction write-back and BIM delivery model integration is realized. According to the invention, the design precision, the construction stage adaptability and the digital delivery integrity of the cable structure can be improved, and the method is suitable for cable structure bridge engineering with complex geology and high-precision control requirements.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.

The invention discloses a mine safety risk prevention and control method and system based on multi-source data fusion, and particularly relates to the technical field of mine safety monitoring, and the method comprises the steps: obtaining surface topography, an underground geologic structure and real-time monitoring parameters through remote sensing images, drilling data and dynamic monitoring equipment, and constructing a mining area geologic database with consistent time and space; fusing geologic feature data, integrating earth surface and underground feature data by using a three-dimensional geologic modeling technology, and mapping dynamic monitoring parameters into a model to generate a dynamic geologic feature field; performing clustering analysis on the abnormal feature points based on a spatial clustering algorithm, and calibrating a potential safety hazard region; calculating a regional risk comprehensive index by combining the risk values of the dynamic features, the geological features and the spatial features; early warning is triggered in a graded mode according to the comprehensive index, prevention and control measures are put forward, and the evaluation model is dynamically updated; comprehensive support is provided for mine potential safety hazard area monitoring and safety management decision making, and the method is suitable for risk management scenes of complex mining areas.

The invention discloses a prospecting target area prediction method and system based on geological three-dimensional modeling, and relates to the technical field of mineral resource exploration, and the method comprises the steps: obtaining geological, geophysical, geochemical and remote sensing data of a target area, carrying out the preprocessing, generating a modeling data set, and carrying out the geological modeling; constructing a lithology classification model by combining the three-dimensional geologic model with deep learning, and extracting shallow lithology features to predict the deep lithology of the target area; screening metallogenic factors according to the prediction result of the deep lithology of the target area, the deep geophysical data and the three-dimensional geologic model, and generating a metallogenic prediction factor set; and establishing a prospecting prediction model based on the graph convolutional network, importing the metallogenic prediction factor set to obtain a metallogenic distribution prediction result, and carrying out three-dimensional prospecting target area delineation. According to the method, deep lithologic stratum prediction is carried out through multi-source geological data and geological three-dimensional modeling, and target region delineation is carried out by using the graph convolutional neural network, so that the efficiency and precision of prospecting target region prediction are improved, and the exploration cost is reduced.

The invention provides an innovative deep concealed ore deposit detection method, which comprehensively utilizes geology, geophysics, geochemistry and remote sensing technologies, and realizes effective integration of multi-source geological data by developing an advanced data fusion algorithm. According to the method, data collected by geological survey, wide-area electromagnetic method exploration, hydrocarbon-mercury superposition halo analysis and aviation hyperspectral remote sensing are subjected to standardized preprocessing, so that the consistency and accuracy of the data are ensured; and then, deep analysis is carried out on the fused data by utilizing machine learning and statistical analysis technologies so as to identify and predict mineralization modes and abnormal regions. Finally, a three-dimensional geologic model is constructed, the form and distribution of an ore deposit are visually displayed, and a scientific basis is provided for geological exploration. The method is characterized in that a multi-source data fusion technology and mineralization mode identification automation and precision are realized, so that the efficiency and the accuracy of deep hidden ore deposit detection are remarkably improved.

The invention relates to the technical field of Otwei interactive maps, and discloses a geological data interaction method and system based on an Otwei interactive map. The method comprises the following steps: carrying out feature recognition and structural analysis on original geological data to obtain a standardized data packet, and establishing a geographic space reference conversion index table; performing feature extraction and classification on geological elements in the standardized data packet to obtain structured geological data; importing the structured geological data into an Ovoucher interactive map, and carrying out local registration through a mesh generation technology to obtain a visual geological element map layer; geological element drawing and attribute input are carried out, and an edited geological data set is obtained; and carrying out structure recombination and reverse coordinate conversion to obtain a standard format data file adaptive to the target geological information system. According to the method, accurate conversion of multi-source geological data between different coordinate systems and measuring scales is realized, and the problem of spatial dislocation during integration of different-source geological data in a traditional method is effectively solved.

The invention relates to the technical field of hydrogeological monitoring, in particular to a hydrogeological dynamic monitoring and analysis system and method based on big data. The method specifically comprises the steps that hydrogeological data are collected in real time, edge calculation is combined for data denoising, space-time calibration and error correction, variational mode decomposition, geologic model constraint and wavelet transform are adopted for eliminating noise and drift errors, storage and transmission efficiency are optimized through incremental coding and Huffman coding, and quadratic form calibration codes are generated to ensure data reliability; a hydrological evolution trend is predicted by fusing a space-time deep learning model and a self-attention mechanism, and adaptive anomaly detection is realized by combining DBSCAN clustering and prediction error analysis; and optimizing a water resource allocation strategy through reinforcement learning, hierarchically evaluating a risk level and generating a management scheme, and finally visually displaying monitoring data, an abnormal score and a decision result. According to the method, multi-source data fusion, space-time modeling and intelligent decision making are integrated, and hydrological monitoring precision and response efficiency are improved.

The invention relates to the field of geological exploration, in particular to an intelligent prospecting prediction method and system for an ore deposit three-dimensional geological model, a terminal and a medium. The method comprises the steps of obtaining multi-source geological data of a target area to construct an ore deposit three-dimensional geological model, inputting the ore deposit three-dimensional geological model into a trained intelligent prospecting prediction model for ore-forming potential analysis, and optimizing the model or generating an intelligent prospecting prediction scheme according to a predicted resource quantity confidence degree condition; when the model is constructed, three-dimensional inversion calculation, element anomaly field construction and the like are carried out, the model can be optimized through transfer learning, exploration data can be accessed in real time to realize dynamic updating, and a multi-target optimization model is established to output an exploration scheme; the invention also relates to a corresponding system, a terminal and a storage medium. The method achieves the technical effects of improving the accuracy and efficiency of prospecting prediction, dynamically optimizing the model according to the actual situation, reasonably planning the exploration scheme, and reducing the exploration cost and risk.

The invention relates to the technical field of tunnel engineering, and discloses a tunnel multi-field coupling nonlinear deformation analysis method and system.The method comprises the steps that geological environment information of a tunnel area is collected, a multi-source physical field boundary condition model is built based on collected data, and a heat-seepage-stress-time four-field coupling control model is built based on the collected data; specifying a nonlinear response model for the geological medium to truly reflect the stress-strain behavior of the geotechnical material; solving a coupling equation set, and optimizing the four-field coupling control model; introducing measured data to correct the model; the system comprises a geological data acquisition module, a boundary condition modeling module, a coupling model establishment module, a numerical solution module, a measured data correction module and a visual output and control interface module. According to the method, high-fidelity prediction and evolution analysis of the deformation behavior of the tunnel under the complex geological condition are realized based on comprehensive acquisition of the geological environment, multi-field boundary modeling, control equation construction and numerical calculation.

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 discloses a coal mine pressure risk assessment method based on complex system dynamics, belongs to the technical field of mine pressure risk assessment, is used for mine pressure risk assessment, and constructs a complex system dynamics model, combines various sensor data, and carries out real-time monitoring and risk assessment of mine pressure changes. The method comprises the steps of geological data acquisition, sensor data real-time monitoring, data preprocessing, mine pressure prediction model construction, model training and optimization, real-time risk assessment and the like, data of a stress sensor, a displacement sensor and an acoustic emission sensor are comprehensively utilized, and data preprocessing is performed by adopting a Kalman filtering algorithm; and optimizing the mine pressure prediction model by using a graph convolutional network and an attention mechanism, and performing real-time risk assessment through an adaptive graph neural network. According to the method, the adaptive graph neural network is adopted for real-time risk assessment, potential risks in mine pressure changes can be found in time, risk scores are generated, and scientific decision support is provided for coal mine managers.

The invention discloses a municipal intelligent construction integrated collaborative design method, and relates to the technical field of municipal intelligent construction, and the method comprises the following steps: obtaining initial geological survey data of a municipal project, and monitoring the dynamic change of geological data in a construction process in real time through sensor equipment; through bidirectional dynamic linkage of the BIM model and geological data and the geological change prediction model, a municipal project can master geological changes in real time, design and construction schemes are automatically adjusted, it is ensured that the design is matched with the actual geological condition, and hidden dangers such as subgrade settlement and pipeline dislocation are effectively prevented. In combination with machine learning, an optimization algorithm and an intelligent decision-making system, a construction party can generate adjustment suggestions in real time, the construction sequence, material selection and equipment use strategies are flexibly optimized, the construction efficiency is improved, and the cost is reduced. Multi-dimensional evaluation ensures that the construction decision is scientific and reasonable, risks caused by geological changes are reduced, and an efficient solution is provided for digital and intelligent transformation of municipal engineering.

The invention discloses a mine geological environment characteristic monitoring and recovery treatment method, and relates to the field of geological environment monitoring, and the method comprises the steps: collecting mine geological data; adopting a pre-trained LSTM neural network model to predict membership information of geological disaster evolution in a period of time in the future; adjusting a state transition probability matrix in the Markov chain model by using the membership information; predicting the evolution state of the geological disaster in a future period of time by using the adjusted state transition probability matrix; the method comprises the following steps: mapping mine geological data into nodes of a Bayesian network, and constructing a directed acyclic graph containing environmental factors; based on a Bayesian network inference algorithm, calculating the occurrence probability of geological disasters under different environmental factor combinations; fusing the predicted evolution state of the geological disaster in a future period of time with the geological disaster occurrence probability deduced by the Bayesian network to obtain a geological disaster prediction evaluation result; in view of low mine geological disaster time sequence evaluation precision in the prior art, the evaluation precision is improved.

The embodiment of the invention provides a BIM-GIS three-dimensional twin geological modeling method and system based on cognitive enhancement. The method is applied to the technical field of geological modeling, and comprises the following steps: acquiring different data sources to carry out geological data acquisition, and carrying out preprocessing to obtain preprocessed geological data; converting the geologic body into a voxel three-dimensional model according to the preprocessed geologic data; performing multi-level optimization processing on the voxel three-dimensional model, constructing an optimized model, and performing three-dimensional display; and performing mineral resource space distribution prediction according to the voxel three-dimensional model, predicting the position and the number of mineral resources in combination with historical exploration data and a convolutional neural network, and performing underground water flow simulation analysis in combination with an underground water flow model. In this way, by combining the BIM and GIS technologies, the three-dimensional geological data can be efficiently and accurately managed and displayed, and the method can be widely applied to the fields of geological exploration, mining development, urban construction and the like.

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 relates to the technical field of layered surrounding rock inversion analysis, and discloses a layered surrounding rock three-dimensional crustal stress field inversion intelligent analysis system and method, and the system comprises a dimensional geological value module, an intelligent inversion analysis module and an inversion result verification module. The method comprises the steps of collecting geological data in target layered surrounding rock; carrying out normalization processing on the geological data, and carrying out sensitivity analysis; constructing a three-dimensional geologic model, and performing numerical simulation; a crustal stress field inversion intelligent analysis model is generated based on the three-dimensional geologic model; training the crustal stress field inversion intelligent analysis model by using the training sample; inputting the real-time geological data into the crustal stress field inversion intelligent analysis model to obtain a crustal stress inversion analysis result; carrying out visual output on the ground stress analysis result; and adjusting the crustal stress field inversion intelligent analysis model according to the actually measured data. Scientific ground stress field analysis basis can be provided for complex projects such as tunnel engineering, underground engineering and hydropower stations.

The invention relates to the field of safety risk early warning, in particular to a BIM (Building Information Modeling)-based open-air goaf charging safety risk early warning method and a BIM-based open-air goaf charging safety risk early warning system. A three-dimensional BIM geologic model containing rock mass hardness data, joint density and fracture distribution data is constructed through unmanned aerial vehicle scanning and rock core sampling; determining a minimum effective explosive loading amount meeting a preset crushing lumpiness mean value in the three-dimensional BIM geological model, performing dynamic simulation through the three-dimensional BIM geological model to obtain a simulation result, and outputting a final explosive loading amount when the simulation result meets a preset safety constraint condition; and generating charge parameters according to the final charge amount, binding the charge parameters with space coordinates of the three-dimensional BIM geologic model, and outputting an early warning area through a visual interface. By determining the optimal explosive loading amount and optimizing the blast hole layout and the detonating sequence, the explosive loading safety risk is effectively reduced while the blasting effect is guaranteed.

The invention relates to the technical field of coal mine exploration, in particular to a coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, which comprises the steps of S1, integrating multi-source geological data; s2, data processing and feature enhancement; s3, machine learning modeling; s4, target spot optimization and dynamic verification; and S5, geological modeling and risk grading. According to the coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, a depth domain joint data set is constructed, multi-dimensional features such as seismic amplitude, lithology coding, fracture index and fault distance field are integrated, and multi-source data fusion and physical constraint machine learning are realized through a feature enhancement technology; locking a high-uncertainty region based on the prediction variance, and dynamically updating a target spot through a Gaussian process proxy model to realize targeted drilling and dynamic closed-loop optimization; a multi-attribute risk fusion model is constructed, a four-level risk map is output through the risk factors, grouting resources are guided to be preferentially put into a high-risk area, and risk grading early warning processing is achieved.

The deep water port steel pipe pile foundation construction method comprises the steps that three-dimensional geological modeling and pile position positioning are conducted, specifically, a construction area is scanned through an unmanned aerial vehicle carrying a multispectral radar, geological data are obtained, a dynamic three-dimensional geological model is constructed based on a random forest algorithm, and the pile position layout and the pile sinking path of steel pipe piles are planned; multi-parameter data real-time collection and transmission: collecting perpendicularity, pile body stress and hammering vibration data in real time, uploading encrypted data to a cloud platform through a 5G / Beidou dual channel, and storing the encrypted data; the cloud platform calculates the optimal hammering energy, the vibration frequency and the hammer stopping standard by using a reinforcement learning algorithm in combination with the dynamic three-dimensional geological model and the real-time data; and pile sinking is conducted, specifically, the posture of a pile body is monitored through a laser radar and an inertial measurement unit, a PID control algorithm is adopted to drive a walking type pile driver hydraulic system, and pile sinking is conducted. According to the method, the scientificity of pile position layout and the reasonability of a pile sinking path are improved; the pile sinking efficiency is improved; the energy consumption is reduced; the pile foundation stability is guaranteed.

The invention relates to the technical field of slope protection, in particular to a slope stability assessment method and system applied to mine ecological restoration, and the method comprises the steps: collecting basic geological data and dynamic monitoring data of a slope in an assessment region based on a set data collection frequency; performing space-time division according to a preset time window and a space grid to obtain a plurality of space-time window units, and calculating to obtain a multi-dimensional stability evaluation index according to the basic geological data and the dynamic monitoring data corresponding to each space-time window unit; based on the multi-dimensional stability evaluation indexes corresponding to different space-time window units, constructing a space-time serialized slope stability characteristic matrix; inputting the dynamic stability data into a preset multi-dimensional stability evaluation model to obtain a dynamic stability index; correcting the dynamic stability index by considering the obtained real-time environmental load interference data to obtain a corrected stability index; and a preset slope stability threshold value is utilized to perform early warning judgment.

The invention discloses an earth geology physical three-dimensional modeling method based on multi-data fusion, which comprises the following steps: A, data acquisition: acquiring geological data through geological exploration and measurement technologies, comprising geological profile, underground water level, lithology, structure, surface digital elevation model (DEM) data, remote sensing image data, surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data. According to the invention, data of geological profile, underground water level, lithology, structure, earth surface digital elevation model (DEM) data, remote sensing image data, earth surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data are collected. And the data can be effectively integrated.

The invention relates to the technical field of oil field geological prediction, in particular to an oil field geological parameter intelligent prediction system based on machine learning, which acquires static geological data and environmental data in real time through a data acquisition module, and performs noise reduction, normalization and cleaning on the acquired data through a data preprocessing module, so as to obtain an oil field geological parameter prediction result. The feature extraction module adopts an XGBoost model and a long-short-term memory network model to process static features and time sequence data respectively. And the model optimization and self-adaption module dynamically adjusts model parameters according to real-time feedback data by using a reinforcement learning algorithm so as to ensure the accuracy and adaptability of a prediction result. The visualization module provides visual display of a prediction trend, a geological data prediction value and a risk indicator, and helps an operator to make a decision based on data driving. According to the method, the prediction precision of the oil field geological data can be effectively improved, the adaptability to environmental changes is enhanced, and a scientific basis is provided for oil field management and production optimization.

The invention discloses a method for predicting shield tunneling parameters under a composite stratum, which comprises the following steps of: collecting and sorting shield tunneling historical data under the composite stratum, including geological parameters and construction parameters, and performing abnormal value elimination, missing value filling and data standardization; the geological parameters and the geometric parameters are combined according to the tunneling ring number, and a quantitative geological matrix of each ring is generated; the geometric parameters represent the position of the soil layer relative to the tunnel excavation face by adopting 1-5 codes, and input dimension unification is ensured through zero filling alignment; a CNN-Attention-SVR model is constructed, and the CNN-Attention-SVR model is trained; new geological data are input into the trained model, tunneling parameters are predicted, and construction adjustment is guided; reliable technical support and decision basis are provided for tunnel construction under the composite stratum condition, and therefore the overall safety and economical efficiency of shield construction are improved.

The invention discloses a construction environment sudden change risk field prediction method fusing geological and meteorological data, and belongs to the technical field of construction engineering risk prediction, and the method comprises the steps: obtaining geological data, meteorological data and construction progress data of a construction area, carrying out the structural processing of the multi-modal heterogeneous data, and carrying out the construction engineering risk prediction. And generating graph data containing the multi-modal features. According to the method, a space-time adaptive graph neural network prediction model based on an attention mechanism is adopted, weights of different features can be dynamically calculated and allocated according to real-time environment and construction data, and a space-time propagation relationship of risks among geographic nodes is captured by using a message passing mechanism of a graph. According to the method, data of different modals can be deeply fused, accurate prediction of the construction environment sudden change risk is realized, corresponding early warning is triggered according to the risk level, the accuracy and timeliness of risk prediction are improved, and scientific support is provided for intelligent decision making of constructional engineering.

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 relates to the technical field of safety evaluation, in particular to an ultra-deep soft soil foundation pit safety evaluation method and system, and the method comprises the following steps: obtaining the geological data of an ultra-deep soft soil foundation pit, recording the soil type, soil density, underground water level and temperature data, carrying out the data arrangement and formatting, and generating a geological data record sheet. According to the method, the detailed geological data of the ultra-deep soft soil foundation pit are acquired and arranged, the accuracy and the applicability of the basic data are enhanced, so that subsequent analysis is carried out according to clear actual conditions, and the timeliness and the accuracy of risk prediction are ensured by directly associating prediction of mechanical behaviors with risk assessment; the risk assessment model can be dynamically adjusted, the effectiveness and accuracy of early warning are improved, the risk dependency relationship model and the monitoring scheme are integrated, theoretical and actual operations are effectively linked, the systematicness and adaptability of construction safety management are effectively improved, and the accident prevention capability is remarkably improved.