1867 results about "Macroscopic scale" patented technology

The invention relates to the field of shale oil and gas, and discloses a method for identifying a shale oil and gas reservoir dessert, which comprises the following steps: acquiring a core CT image, three-dimensional seismic data and production dynamic data, and carrying out cross-scale preprocessing; constructing a fractional-order non-local seepage field model to represent nano-to-kilometer-level flow characteristics; predicting seepage field parameters through a Lie group symmetry constrained neural network; establishing a cross-scale coupling model of the quantum adsorption effect and the macroscopic seepage law; dynamically updating model parameters based on real-time monitoring data; and executing multi-target collaborative optimization to generate a sweet spot three-dimensional distribution and development scheme. According to the method, a fractal dimension dynamic constraint cross-scale data fusion technology is adopted, the effect of accurate mapping of nanopore and macroscopic fracture network parameters is achieved, and the problem of misalignment of CT scanning and seismic inversion data space registration is solved through pore communication fractal analysis.

The invention relates to the technical field of oil and gas field development, and discloses an ultra-large type true triaxial hydraulic fracturing fracture evolution path prediction method and system.The prediction method comprises the steps that a coupling geomechanical model integrating microscopic, macroscopic and wellbore flow scales is constructed; initializing the model and performing crack initial expansion simulation; collecting construction data in real time, and dynamically optimizing model parameters through a data assimilation algorithm; performing fracture evolution advanced prediction by using the updated model; and generating an optimization decision based on the prediction result and feeding back to the construction site. According to the method, fusion of a multi-scale physical mechanism and real-time dynamic prediction is realized, and accurate prediction and active control can be performed on crack expansion under the ultra-large true triaxial condition.

A real-time quantitative characterization method, equipment and a medium for rock mass evolution are disclosed. The method includes: the multiphase field detection and monitoring data is fused; a deep learning model driven by physical principles is established based on the physical principles of fluid density; the deep learning model is trained using real-time fused data as input and corresponding evolution distribution images as output; a trained deep learning model is used to obtain an evolution distribution image based on multi-phase field detection and monitoring data of different time periods and types; a mathematical model is used to quantitatively characterize of physical and mechanical parameters in the whole process of progressive failure of the dynamic evolution of the rock mass based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

The invention discloses a rock multi-field coupling test system and a damage evaluation and prediction method thereof, belongs to the technical field of geotechnical engineering and geomechanics experiments, and solves the problem that the rock state under the multi-field coupling condition cannot be monitored in real time in the prior art. The system comprises a multi-physics field coupling loading system, a multi-field data monitoring system and a multi-field data acquisition system. The system can simulate and apply axial pressure, confining pressure, pore pressure and temperature of an in-situ environment to a rock sample, integrates an acoustic monitoring system, a deformation monitoring system and a CT scanning system to realize multi-dimensional real-time monitoring of rock sample damage, overcomes the limitation of a single monitoring method, realizes multi-system electrical connection by taking an upper computer as a data acquisition center, and realizes multi-dimensional monitoring of rock sample damage. And a multi-parameter model is constructed by fusing sound waves, deformation and CT data, so that the whole-process quantitative evaluation from microcrack initiation to macroscopic fracture is realized, and the damage evaluation is comprehensively performed.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention discloses an underground-ground-sky combined rock burst and earth surface deformation early warning method, which comprises the following steps of: S1, arranging an underground monitoring system, and acquiring underground monitoring data; s2, setting a ground monitoring system, and collecting ground monitoring data; s3, setting a sky monitoring system, and collecting sky monitoring data; s4, calibrating the time scale difference of the underground monitoring data, the ground monitoring data and the sky monitoring data through a multi-scale time alignment algorithm; and S5, constructing a multi-stage joint probability assessment framework to carry out mining area rock burst and earth surface deformation risk assessment. According to the method, multi-source monitoring data are integrated through a multi-scale space-time fusion framework, and a full-chain association model of'microcosmic fracture-mesoscopic deformation-macroscopic accumulation 'is constructed. And on the basis of cross-scale time sequence alignment, spatial heterogeneity is optimized and eliminated, a multi-stage joint probability assessment framework quantifies a cross-scale collaborative risk, full-period intelligent management and control of'monitoring-early warning-feedback-optimization 'is realized, and early warning accuracy and timeliness are improved.

A geological disasters monitoring method, device, medium and product are provided, which relates to the technical field of geological monitoring. The method includes determining microscopic deformation parameters of an area to be monitored according to remote sensing observation data corresponding to a slope body of the area to be monitored, determining macroscopic deformation parameters of the area to be monitored according to optical remote sensing data and terrain data of the area to be monitored, and determining the landslide remote sensing geomechanical deformation type in the area to be monitored according to material composition, movement mode, slope structure, the microscopic deformation parameters and the macroscopic deformation parameters of the area to be monitored. The present disclosure improves the accuracy of monitoring geological disasters.

The invention provides a macroscopic finite element simulation analysis method and device for a sealing element considering plastic deformation, and belongs to the field of sealing element design. The method comprises the following steps: acquiring an engineering stress-strain curve of a plastic material in a compression state; converting the engineering stress-strain curve into a real stress-strain curve; determining a yield limit, and taking a strain point corresponding to the yield limit as a demarcation point of elasticity and plastic deformation; an elastic section of a real stress-strain curve is extracted, the elastic modulus is calculated, and the Poisson's ratio is obtained through axial and transverse strain measurement in a uniaxial compression test; a true stress-strain curve plastic section is extracted, and a multi-linear isotropic hardening model is established; constructing a complete elastic-plastic constitutive model covering the elastic deformation area and the plastic deformation area; the stress distribution, the strain response and the contact performance of the plastic sealing element in a compression deformation state are simulated. According to the method and the device provided by the invention, the problem that the result does not conform to the reality due to the fact that only elastic modulus and Poisson's ratio are used in existing sealing element simulation can be solved.

The invention relates to the technical field of polishing, and discloses an intelligent steel rail polishing control method which comprises the following steps: acquiring surface and internal defect data of a steel rail through laser and ultrasonic fusion detection, discretizing the data into multi-dimensional nodes, and constructing a dynamic graph model comprising a microscopic-macroscopic dual-cluster neural network to predict a defect evolution trend; based on fractal dimensions and information entropy, multi-source data are dynamically fused, abnormities are eliminated, chaotic optimization and wear prediction are adopted to generate global optimal polishing parameters, and finally parameter dynamic adjustment is achieved through closed-loop feedback of pulse frequency and control errors. Steel rail defect collaborative perception is achieved through a multi-scale dynamic graph model, fractal-entropy weight dynamic fusion is combined to enhance data reliability, global optimal parameters are generated through chaos optimization and abrasion prediction, accurate grinding is achieved through pulse-error closed-loop control, and the method is simple and convenient to implement. The problems of data fusion failure, defect response lag, parameter local optimization and insufficient control precision in the prior art are solved.

The invention discloses a dam measurement system and method based on multi-modal data processing, and relates to the technical field of dam measurement system data processing, a physical model is adopted to extract dam physical form change features in visual data, dam physical change associated sound features in audio data and micro-displacement features in radar point cloud data; according to the method, a three-dimensional monitoring network from macroscopic deformation to microcosmic displacement is constructed through cooperation of visual, audio and radar three-mode data, a monitoring blind area of a single sensor is broken through, and correlation analysis of dam body surface deformation and internal stress change is achieved. Feature fusion driven by physical constraints is carried out, finite element model constraints are embedded in a feature level fusion stage, radar point cloud displacement vectors need to conform to geological structure direction constraints, audio and voiceprint features need to be in space-time synchronization with visual crack expansion rates, and environmental noise interference is eliminated; the crack propagation early warning time is greatly shortened; the cavitation damage positioning is more accurate, and the abnormal working condition recognition rate is greatly improved.

The invention provides a digital modeling method for enhancing connectivity of a low-porosity rock core, a medium and equipment, and relates to the field of digital modeling of rock cores, and the method comprises the following steps: reconstructing a Gaussian random field based on SAXS data to obtain an initial model of a three-dimensional digital rock core pore structure; extracting pore center points of the initial model, and constructing an initial network connecting all the pore center points; based on a minimum spanning tree algorithm, identifying mutually isolated pore clusters in the initial network, and selecting a most efficient seepage path skeleton connected with the isolated clusters; based on the seepage path skeleton, constructing a throat with fractal characteristics; and according to the total volume of the throat, performing morphological corrosion operation on an original pore area in the initial model, embedding the constructed throat into the corroded model, and then performing controllable morphological expansion operation until the volume variation of the final model is smaller than a preset error threshold. According to the method, the reconstruction precision of the digital rock core in microstructure and macroscopic connectivity is effectively improved.

The invention discloses a hyperspectral image and LiDAR data collaborative classification method based on double-domain mask and multi-scale local reconstruction, and belongs to the field of remote sensing image classification. According to the method, the problems of scarcity of annotation data and insufficient multi-source feature fusion precision in cross-modal classification of a traditional method are solved. According to the invention, feature learning is carried out through mask random image blocks and channels; a hierarchical multi-scale reconstruction architecture is designed, a lower-layer encoder learns fine-grained features, an upper-layer encoder recovers macroscopic semantic information, and multi-level feature space alignment is realized in combination with deconvolution up-sampling and adaptive pooling. According to the method, a multi-modal feature interaction mechanism and a cross-modal attention module are constructed by fusing the local feature extraction advantages of a convolutional neural network (CNN) and the global modeling capability of Transform, and the complementarity of heterogeneous data is enhanced. According to the method, through multi-level feature dynamic fusion and adaptive weight distribution, the collaborative classification precision of the hyperspectral image and the LiDAR data is improved. The method can be applied to remote sensing image classification.

The invention is suitable for the technical field of electrical equipment corrosion detection, and provides an electrical equipment corrosion detection method based on double-spectrum collaborative analysis and space-time prediction, and the method comprises the following steps: carrying out fusion analysis on a visible light image and an infrared image of acquired electrical equipment, and outputting position information and morphological category of a corrosion region; a binocular vision system and an IMU sensor are combined to calculate three-dimensional space coordinates, main direction angles and environmental parameters of the corrosion area, and pose data are generated; inputting the pose data into a preset corrosion extension path prediction model for processing, and outputting physical characteristic parameters; fusing the dual-frequency RTK GPS positioning data, the IMU attitude data and the physical characteristic parameters to construct a space-time association data set, and inputting the space-time association data set into a preset space-time diagram neural network model to analyze a space-time evolution relationship of diagram structure modeling corrosion nodes; and outputting microscopic and macroscopic prediction results. The identification accuracy of corrosion characteristics and the corrosion detection efficiency can be improved, and the labor cost and the operation risk are reduced.

The invention discloses a metro shield tunnel crack width refined numerical simulation method under surface loading, and the method comprises the following specific steps: describing the overall stress deformation characteristics of a soil body-shield tunnel structure based on a stratum-structure method, and simulating a macroscopic equivalent model by adopting a continuous medium model, and the equivalent stiffness reduction effect caused by the influence of longitudinal seams and circular seams is fully considered, the most unfavorable section position along the shield tunnel is determined, so that the key section is replaced with a refined model considering local hand hole weakening, multi-contact and built-in steel bars, and the shield tunnel crack evolution characteristics are described by adopting a total strain crack model. And obtaining the millimeter-scale crack width of the target tunnel structure based on a load-structure method. According to the analysis method, the millimeter-scale crack width of the shield tunnel under the action of ground surface loading can be accurately obtained, the structure safety state can be rapidly evaluated in combination with current specifications, and an important basis is provided for operation and maintenance of the subway shield tunnel.

The invention discloses an airborne infrared-visible double light combined building outer wall hollowing identification and positioning method. The method comprises the following steps: designing flight parameters of an unmanned aerial vehicle; designing a calibration checkerboard; shooting an infrared thermal image and a visible light image of the checkerboard calibration target, recording the images as dual-light images for calibration, and calibrating and calculating internal and external parameters and a distortion coefficient of a camera; collecting images of all external facades of the target building; carrying out distortion removal processing on the dual-light image to form a dual-light image database for detection; the registration between the dual-light images synchronously shot by the unmanned aerial vehicle is realized; realizing registration between the building facade view and the visible light image; determining a correct hollowing area identification result, and marking the infrared thermal image corresponding to the correct hollowing area identification result as a hollowing infrared thermal image; calculating a pixel coordinate range of the hollowing area; and accurate positioning of the hollowing area is realized. According to the method, the visible light image serves as a medium, registration of the infrared thermal image and the macroscopic 3D model is achieved, the problem that calibration and registration are difficult due to the fact that the imaging quality of the infrared thermal image is low is solved, and high readability and user friendliness of an output result are guaranteed.

The invention discloses a universal laser path optimization method and module for metal 3D printing, and the method comprises the following steps: S1, carrying out the slicing and initial path planning of a three-dimensional model, and generating printing data containing vector coordinates, a scanning time sequence, laser power and a scanning speed; s2, the printing data and the equipment parameters are imported into a path heat balance optimization module, and a high forming risk area is evaluated based on a temperature field distribution model; s3, laser path secondary optimization is conducted on the high forming risk area through a preset optimization function, and a new laser scanning path vector data set is generated; s4, importing the optimized path vector data set into printer control software for processing; the laser parameters and the scanning path are dynamically adjusted, so that the local temperature gradient is reduced, the thermal stress concentration is reduced, and the defects of warping, collapse and the like are overcome; the optimization function based on the mathematical model can adapt to various complex structures and is suitable for efficient printing of macroscopic-mesoscopic integrated parts, and the production efficiency is improved.

The invention relates to a solidified soil proportion and strength prediction method and system based on SEM image analysis, and belongs to the technical field of solidified soil strength testing. Comprising the following steps: S1, measuring physical indexes of disturbed soil; s2, multi-working-condition sample preparation and maintenance; s3, fractal dimension calculation of the SEM image is carried out; s4, unconfined compressive strength testing; s5, establishing a microstructure inversion model and a macroscopic strength prediction model; and S6, proportion optimization and strength prediction: utilizing the microstructure inversion model and the macroscopic strength prediction model to carry out solidified soil proportion optimization or strength prediction. According to the method, the quantitative relation between the microstructure (fractal dimension) and the macroscopic strength is established through SEM image analysis, the defects in the prior art are overcome, and a brand new technical path is provided for resource utilization of disturbed soil.

The invention provides a concrete interface morphology feature extraction and reconstruction method. The method mainly comprises the following steps: (1) obtaining a representative profile curve of a concrete interface morphology; (2) decomposing the obtained representative profile curve into three components, namely a macroscopic shape profile, a waviness profile and a roughness profile; (3) carrying out statistical analysis on three components of a macroscopic shape contour, a waviness contour and a roughness contour obtained by decomposition; and (4) reconstructing the concrete rough interface morphology based on the statistical law of the three components of the macroscopic shape contour, the waviness contour and the roughness contour. The method disclosed by the invention is reasonable in conception, not only breaks through the limitation of single representation by a'roughness' index traditionally, but also fully excavates the multi-scale information of the surface appearance of the concrete, and provides a basis for the reconstruction of interface characteristics and the analysis of the adhesive property.

The invention provides a method and a device for evaluating and measuring microscopic mechanical properties of TC4 alloy, relates to the technical field of microscopic mechanical properties, and realizes accurate evaluation of material properties through combination of multi-scale characterization and numerical simulation. Firstly, a standard sample is prepared through selective laser melting, and residual stress field distribution is obtained through X-ray diffraction; dividing a stress field into sub-regions, and establishing a quantitative relationship between microscopic structure parameters and residual stress by combining SEM and EBSD analysis; the method comprises the following steps: acquiring local mechanical properties through a nanoindentation test, and constructing a stress-performance correlation model; and finally, establishing a finite element model, transmitting stress and tissue parameters by adopting trilinear interpolation and an inverse distance weighting method, verifying data reliability through a stress difference criterion, and calculating mechanical properties of each unit. According to the method, multi-scale correlation from microstructure characteristics to macroscopic mechanical properties is innovatively realized, so that the mechanical property distribution of the material is accurately evaluated.

The invention discloses a vacuum sealing leakage rate prediction method and device, equipment and a medium. The method comprises the following steps: constructing a three-dimensional microstructure model of a flange sealing surface according to a processing mode of the flange sealing surface; based on the three-dimensional microstructure model, establishing leakage micro-channels between the sealed contact interfaces in different contact states, analyzing channel characteristics, and determining flow characteristics of leakage media in the leakage micro-channels in different contact states; constructing a first leakage rate prediction model represented by the microscopic characteristic parameters according to the working environment parameters and the physical attributes and flow characteristics of the leakage medium; and constructing a mapping relation between the macroscopic design parameters and the microscopic characteristic parameters through characteristic multi-scale coupling, and substituting the mapping relation into the first leakage rate prediction model to obtain a second leakage rate prediction model driven by the macroscopic design parameters so as to predict the vacuum sealing leakage rate. The method has the advantages of high precision, strong adaptability and good engineering practicability.

The invention relates to the technical field of concrete curing, in particular to a concrete crack risk prediction and curing system based on thermo-acoustic coupling indexes. According to the scheme, a temperature field and an acoustic emission signal are synchronously acquired through the multi-modal data acquisition module; a thermal diffusion shear index and an acoustic emission energy density are calculated by a multi-modal data fusion module, and are fused into a unified thermo-acoustic coupling factor to quantify a cross-scale risk; the structural constraint compensation module corrects the factor by using pre-stored constraint information to generate a final risk index; the self-adaptive maintenance decision-making module maps a dynamic maintenance strategy according to the indexes; and finally, the maintenance robot executes precise maintenance operation. The technical problems that macroscopic thermal stress and microcosmic damage signals cannot be evaluated in a unified mode, different-source data are difficult to fuse, and high-constraint area risk identification is inaccurate are solved, and continuous prediction and self-adaptive cooperative control of cracks from a potential stage to an initiation stage are achieved. And the accuracy and the intelligent level of early-age crack prevention and control of the concrete are remarkably improved.

The invention relates to the field of digital core modeling, in particular to a stomatal basalt three-dimensional digital core reconstruction method, which comprises the following steps: extracting target characteristic parameters from CT (Computed Tomography) scanning gray volume data of a stomatal basalt sample, the target characteristic parameters comprising porosity, cluster number, cluster size statistics, spatial uniformity index and simplified compactness; generating an initial three-dimensional digital core model according to the target characteristic parameters; carrying out iterative optimization on the initial three-dimensional digital core model by utilizing a self-adaptive Markov chain-Monte Carlo algorithm so as to enable cluster features of the optimized three-dimensional digital core model to approach target feature parameters; and performing curvature smoothing post-processing on the optimized three-dimensional digital core model to obtain the pore basalt three-dimensional digital core model. The model not only is highly matched with real pore basalt in the aspect of macroscopic statistical characteristics, but also shows natural and smooth curved surface characteristics in the aspect of microscopic pore boundary morphology, and provides a reliable digital basis for subsequent rock physical property analysis.

The invention discloses an urban ventilation corridor construction method based on local climate region classification and remote sensing inversion, and belongs to the technical field of urban climate regulation and space optimization. According to the method, a multi-scale urban thermal environment analysis framework is constructed, a coupling mechanism of an urban form and a thermal effect is systematically identified from macroscopic, mesoscopic and microscopic levels, and fine modeling and regulation and control path identification of the thermal environment are realized. Meanwhile, in combination with machine learning and interpretability analysis methods, scientificity and transparency of thermal environment driving factor identification are enhanced. Through combination of remote sensing inversion, morphological factor construction and three-dimensional analogue simulation, a complete technical path of cold source identification, resistance modeling, path optimization and microclimate response evaluation is provided, and the aeration-cooling potential identification capability of blue-green infrastructures is effectively improved. The method is suitable for wind gallery planning and climate adaptation type update design of various urban spaces, and has wide popularization and application prospects.

The invention discloses a distributed photovoltaic power generation anomaly positioning optimization method, and particularly relates to the technical field of power generation anomaly positioning, and the method comprises the steps: carrying out the space-time alignment of the static information, environmental parameters and dynamic power generation data of multiple stations in a target region, carrying out the spatial density clustering based on the three-dimensional distance of the stations, and dividing spatial sub-clusters; recognizing a space-time hot spot region in combination with a decoupling model of a historical loss rate and irradiance; constructing a space loss gradient field for the hot spot region, and reversely tracing to a normal threshold boundary point to realize pollution propagation path reconstruction; according to the method, wavelet packet decomposition, variational mode decomposition and short-time Fourier transform are adopted for a non-hotspot area to extract multi-scale attenuation and noise features, artifacts are removed through neighborhood slope difference, macroscopic error shielding and microcosmic anomaly positioning of meteorological and component differences are achieved, and the checking accuracy and the operation and maintenance efficiency are improved.

The invention provides a blood cell image recognition method based on a multi-scale Vision Transform model, and the method comprises the steps: constructing a multi-scale feature extraction architecture, combining the global modeling capability of a Transform network, carrying out the parallel processing of Patches of different sizes, and fusing the multi-scale information, thereby achieving the recognition of a blood cell image. The cells in the blood smear can be comprehensively perceived from microscopic details to macroscopic contexts, so that not only is the adaptability of the model to cell size difference and morphological diversity enhanced, but also the recognition stability under the conditions of cell overlapping and dyeing difference in a complex visual field is improved; the accuracy and calculation efficiency of cell classification and counting are effectively improved, and full-process automation from visual field quality evaluation to automatic cell recognition and counting is achieved.

The utility model discloses a micro-fluidic chip imaging positioning device, and belongs to the technical field of micro-fluidic chip positioning. According to the technical scheme, the system comprises a microcosmic illumination module, a microcosmic imaging module, a chip moving and macroscopic illumination module, a macroscopic imaging module and an optical path multiplexing module; the microscopic illumination module comprises a fly's-eye lens assembly, the fly's-eye lens assembly comprises two fly's-eye lenses, the optical axes of the two fly's-eye lenses are parallel to each other, and the focus of each lens array element in one fly's-eye lens coincides with the center of the lens array element at the corresponding position of the other fly's-eye lens. The micro-fluidic chip clamp is applied to the aspect of micro-fluidic chip imaging positioning, overcomes the defects caused by an existing micro-fluidic chip clamp, solves the technical problems of universality, automation and high precision of micro-fluidic chip positioning, and has the characteristics of rapidness, automation, high precision and universality.

The invention discloses a die temperature field coordinated regulation and control method based on digital twinning, and relates to the technical field of die temperature control. The method comprises the following steps: constructing a mold temperature field digital twin model covering macroscopic, mesoscopic and microscopic scales; a distributed optical fiber temperature sensor is deployed to realize continuous temperature field sensing; physical-digital real-time synchronous mapping is established through ensemble Kalman filtering; performing model prediction control based on the digital twinborn body; three-dimensional visualization and multi-modal man-machine interaction of the temperature field are realized by adopting an augmented reality technology; and reinforcement learning is used to enable the digital twinborn body to have an autonomous optimization capability. Through deep fusion of a physical space and a digital space, precise sensing, real-time mapping and intelligent control of a mold temperature field are realized, and the temperature control precision and the self-adaptive capability of the system are remarkably improved.

The invention provides a deep energy reservoir multi-scale heat-fluid-solid coupling simulation method and system, and belongs to the field of deep energy reservoir numerical simulation. Acquiring spatial distribution characteristics and physical property parameters of different lithologic reservoir media under a micro-scale, and constructing a digital structure model of the reservoir media; a micro-observation action mechanism and a flow mechanism are embedded in the model to obtain a micro-observation numerical model; establishing a mapping relation by solving model structure parameters and physical property parameters of the microscopic numerical model under different thermal-fluid-solid coupling conditions; and on the basis of the mapping relationship, in combination with the macrogeological and engineering data of the target reservoir, establishing a macroscale heat-fluid-solid multi-field coupling control equation and solving, and outputting the multi-physics field response of the target reservoir under the given working condition. According to the method, fine simulation of physical property parameter dynamic transmission and a multi-field coupling evolution process from a micro view to a macro view is realized, and the accuracy and reliability of deep complex reservoir engineering behavior prediction are remarkably improved.

The invention relates to the technical field of artificial intelligence, and discloses an event prediction method and system based on multi-modal fusion, and the method comprises the steps: constructing a knowledge graph encoder, and converting domain expert knowledge into learnable vector representation; constructing a multi-granularity feature extraction network, and extracting features from different microcosmic, mesoscopic and macroscopic scales; realizing a knowledge-guided attention mechanism, and dynamically adjusting feature scale importance; constructing a prototype learning module, and establishing prototype representation of the abnormal category; a knowledge migration mechanism is constructed, and the generalization ability of the model to novel anomalies is enhanced; and multi-granularity abnormal event detection and early warning are realized, and a detection result and interpretable analysis are output. According to the method, through combination of knowledge guidance and multi-scale feature learning, efficient identification and early warning of social abnormal events under the condition of data scarcity are realized, and the method is suitable for the fields of public place safety monitoring, urban traffic safety management, large-scale activity safety guarantee and the like.

The invention provides a physiological mechanism driving and data fusion method and system for sitting posture human body modeling, and relates to the technical field of digital twinning and biomechanics crossing. The method comprises the following steps: mapping human body macroscopic parameters into biomechanical attribute parameters; generating a basic three-dimensional human body grid based on the human body macroscopic parameters; generating a deformed three-dimensional human body grid in combination with the basic three-dimensional human body grid and the human body macroscopic parameters; solving an optimal attitude based on the vehicle geometric parameters, the task scene identifier and the biomechanical attribute parameters; finally, the deformed three-dimensional human body grid and the optimal posture are integrated, the high-fidelity sitting posture digital twinborn body is generated, end-to-end automatic modeling from macroscopic parameters to the digital twinborn body is achieved, and the core problems that the form and the posture are not matched, crowd coverage is insufficient and task adaptability is poor are solved.