2951results about "Stochastic CAD" patented technology

The present invention belongs to the technical field of fracture network optimization in oilfield exploitation. Specifically disclosed are a method and system for optimizing complex fracture networks in shale reservoirs. The method comprises: S1, on the basis of geological-engineering bimodal data fusion, constructing a three-dimensional digital twin of heterogeneous reservoirs; S2, synchronously calculating the interactions among fluid flow, rock fracturing and proppant transport during a hydraulic fracturing process; and S3, automatically and iteratively optimizing a combination of fracturing operation parameters. By means of constructing a closed-loop system consisting of geological modeling, multi-field coupling simulation, intelligent optimization and dynamic correction, efficient design and real-time optimization of complex fracture networks in shale reservoirs are realized.

The invention relates to the technical field of simulation model optimization, and discloses an aero-engine model Bayesian optimization method for quantizing uncertainty, and the method comprises the steps: building a probability mapping relation from a component index to an output response through constructing a Bayesian neural network agent model based on a probability weight coefficient; and by taking the difference between the output response and the corresponding complete machine test data as a multi-objective loss function and taking the minimization of the multi-objective loss function as an optimization objective, optimizing the component indexes by adopting a Bayesian optimization method based on a Gaussian process to obtain an optimal component index combination. Not only is a nonlinear relationship between high-dimensional parameters and simulation-test deviation accurately modeled through a neural network, but also efficient search of a parameter space is realized through a Gaussian process. The technical problems that when a traditional optimization method is used for processing the high-dimensional, strong-nonlinearity and multi-parameter coupling complex optimization problem of the aero-engine, the calculation efficiency is low, local optimum is prone to occurring, and result uncertainty cannot be quantified are solved.

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention relates to the field of power systems and automation thereof. The invention relates to a power distribution network energy storage optimization configuration method considering dynamic uncertainty and multi-target cooperation. The method is characterized by comprising the following steps: 1) constructing a two-stage robust optimization model: constructing the two-stage robust optimization model with a min-max-min structure; in the first stage, the energy storage construction position and capacity are determined with the lowest annual investment cost of energy storage as the target; in the second stage, the system scheduling cost is minimized in the worst new energy output scene; 2) convex relaxation processing of network constraint; 3) implementation of an iterative solution algorithm: based on a KKT principle and a column constraint generation algorithm, decomposing an original problem into a mixed integer linear main problem and a sub-problem; the main problem optimizes an energy storage configuration scheme, and the sub-problems solve a scheduling strategy in the worst wind and light output scene and feed back to the main problem through cut plane constraint; and carrying out iterative calculation until the solutions of the main problem and the sub-problem converge, and obtaining an optimal energy storage configuration scheme. According to the method, more accurate and efficient energy storage planning can be realized.

The invention belongs to the technical field of tunnel and underground engineering intelligent construction and geotechnical engineering informatization, and discloses a high-energy geological environment surrounding rock classification and decision-making method based on digital twinning and multi-source feedback. The problems caused by difficulty in realizing surrounding rock state dynamic sensing, multi-source data fusion classification and construction decision closed-loop linkage in the prior art in a high-energy geological environment are solved. The method comprises the following steps: firstly, constructing a tunnel three-dimensional geology-structure digital twinborn body based on initial survey data; in the construction process, multi-source data such as geology, construction disturbance and surrounding rock response are collected in real time through the Internet of Things technology and mapped to the digital twinborn body, and virtual-real synchronous updating is achieved. And then, constructing a deep learning-parameter inversion hybrid model on the basis of the multi-source fusion data, outputting a dynamic surrounding rock classification index DRCI and key mechanical parameters, inputting the DRCI and the key mechanical parameters into a multi-objective optimization module, and giving a self-adaptive drilling and blasting scheme. And finally, reversely correcting the model through a construction feedback result to realize closed-loop self-learning.

The invention provides a reservoir bank slope deformation body instability volume prediction method, which comprises the following steps: respectively acquiring earth surface displacement and rock mass internal deformation data through a millimeter wave radar and a tilt angle sensor, and after processing through an adaptive noise decomposition algorithm, identifying a key deformation area and generating a data set. And performing space-time alignment on the data by using the engineering coordinate system and the topological relation to generate a fusion matrix. And reconstructing a potential slip crack surface geometric model in combination with slip crack surface features of historical cases, and calculating instability volume probability distribution by adopting Monte Carlo simulation. And finally, inputting the multi-dimensional data into the space-time prediction model, and outputting an instability volume prediction result with probability distribution. According to the invention, the accuracy and reliability of the prediction result can be improved, and scientific basis and technical support are provided for safety monitoring and disaster early warning of the reservoir bank slope.

The invention discloses a spherical crown dome horizontal circumference drilling and blasting parameter model optimization construction method, and the method comprises the steps: collecting geological, crustal stress, hydrological and structural plane data, building a three-dimensional geological information model, carrying out the working condition partitioning based on a surrounding rock quality evaluation index, and constructing a parameter probability distribution model and an uncertainty boundary of each partition. Therefore, precise space description and parameter variability quantification of the spherical crown dome construction area are realized. The method comprises the following steps: establishing a three-dimensional numerical model of a cavern group, integrating blasting power, seepage and stress multi-physical field effects by adopting a finite element-discrete element coupling method, introducing a surrounding rock deterioration constitutive model considering cumulative damage and unloading aging, calibrating key parameters through an indoor test and field trial explosion, and establishing a three-dimensional model of the cavern group. And the numerical model can truly reflect the mechanical property degradation track of the surrounding rock under repeated blasting disturbance.

The invention provides a structural material corrosion damage cross-scale evaluation method and system, and belongs to the field of service performance cross-scale evaluation. The method comprises the following steps: firstly, collecting corrosion data of related structural materials, establishing a structural material service safety evaluation database based on the corrosion data, and then respectively extracting material scale characteristics and structural scale characteristics of corrosion damage of the structural materials; predicting the service life of the structural material according to the scale characteristics of the material; setting a service safety evaluation index of a material scale and measuring the service safety evaluation index; predicting the service life of the structural material according to the structural scale characteristics; setting a service safety evaluation index of a structure scale and measuring the service safety evaluation index; and carrying out cross-scale correlation on the material scale characteristics and the structure scale characteristics, constructing a transmission model for representing the service performance through material-structure cross-scale correlation, fusing the evaluation indexes of the material scale and the structure scale, generating the evaluation index of the structure material, and evaluating the service performance of the structure material. The accuracy and precision of corrosion damage evaluation are improved.

The invention relates to the technical field of intelligent traffic infrastructure engineering, in particular to a roadbed settlement high polymer grouting repair grouting parameter optimization method, which comprises the following steps of: firstly, constructing a multi-source fusion training data set, and learning a prediction model based on a training machine; establishing a driving mapping relation between the grouting parameters and the road lifting effect and the maximum stress data of the repair area; then, based on the mapping relation, a multi-objective optimization model including pavement settlement repair precision, road stress, economic cost control and environmental friendliness evaluation is constructed; solving the model by adopting a Bayesian optimization algorithm to obtain an optimized grouting parameter combination; a marginal contribution of each parameter to a prediction result is calculated in combination with an SHAP interpretability analysis method, and an engineering decision basis is provided for parameter selection; constructing a transfer learning adaptation framework to adapt to different disease, geology and road types; and finally, establishing a real-time monitoring system, and realizing optimal control by combining sensor data and model feedback.

The invention provides a dam life dynamic probability prediction method and device. According to the dam life dynamic probability prediction method provided by the embodiment of the invention, the dynamic probability prediction of the residual life of the dam can be realized, the early warning timeliness and the scientificity of the maintenance decision are improved, and the full-life-cycle maintenance cost and the accident risk are effectively reduced.

The invention relates to the field of intelligent automatic driving, in particular to a multi-vehicle cooperative controllable confrontation test method based on a diffusion model. Comprising the following steps: S1, a diffusion model training stage: training a diffusion model based on real driving data, and learning vehicle behavior distribution through forward noise addition and reverse denoising to obtain fixed model parameters; s2, scene and multi-vehicle space-time modeling: fusing the road map, the lane topology and the vehicle state information, and constructing a space-time scene representation of multi-vehicle interaction as input of diffusion generation; s3, a diffusion generation mechanism based on adversarial guidance; s4, performing partial diffusion control and diversified confrontation generation; and S5, evaluating the authenticity and controllability of the multi-agent confrontation scene. Compared with the prior art, the method has the advantage that the authenticity, controllability and closed-loop consistency of multi-vehicle cooperative confrontation scene generation are obviously improved.

The invention relates to the technical field of circuit design, in particular to a digital circuit simulation design method and system. The method comprises the following steps: identifying a design demand document, performing deep semantic analysis and demand constraint mining, and constructing a demand specification constraint parameter set; performing demand complexity evaluation and multi-level division according to the demand specification constraint parameter set, and constructing a basic circuit architecture design blueprint; circuit routing path planning is carried out according to the basic circuit architecture design blueprint, and a standardized circuit topology network is constructed; performing electric signal propagation simulation on the standardized circuit topology network, and performing global circuit adaptability adjustment to obtain a circuit topology design scheme; and carrying out an extreme condition simulation test based on the circuit topology design scheme, and carrying out comprehensive performance evaluation to obtain a circuit performance evaluation report. By deeply understanding the user demand intention, the circuit layout matched with the user demand is designed, and the overall circuit performance is improved.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

The invention discloses a cloud data center full life cycle monitoring and early warning system based on digital twinning, and the system comprises a multi-source collection and preprocessing module which is used for collecting and preprocessing multi-source data; the twinborn modeling and simulation module is used for constructing a digital twinborn body and completing virtual-real mapping and mechanism simulation; the time sequence modeling module is used for constructing a state space-Kalman enhancement time sequence evolution twinborn model and carrying out continuous time sequence modeling; the virtual-real residual error self-correction closed loop module is used for generating a virtual-real residual error and updating the digital twinborn body and time sequence evolution twinborn model; and the risk and linkage module is used for executing risk identification, grading and life cycle linkage according to the corrected time sequence characteristic representation. According to the invention, a state space-Kalman enhanced time sequence evolution twinborn method is adopted, virtual-real self-correction and full-life-cycle early warning of the cloud data center are realized, and the method has the advantages of high precision, strong robustness and self-optimization.

The invention discloses a coal drop pipe full-life-cycle operation and maintenance decision-making method. The method comprises the steps of S1, coal drop pipe full-life-cycle data collection; s2, constructing and updating a digital twinborn body of the coal drop pipe: S2.1, constructing a multi-dimensional digital twinborn body comprising a geometric model, a physical model, a behavior model and a rule model, and comprehensively describing the characteristics of each aspect of the coal drop pipe; s2.2, dynamically updating the twinborn body, and dynamically updating the twinborn body through a data driving algorithm by using real-time monitoring data; s3, health state evaluation and reliability prediction, including short-term health diagnosis and long-term reliability prediction; s4, carrying out dynamic operation and maintenance decision making based on risks and cost; and S5, performing decision issuing execution and closed-loop feedback. According to the method, the design data, the real-time monitoring data, the environment data and the operation and maintenance historical data are integrated, the coal drop pipe digital twinborn body is constructed and dynamically updated, it is ensured that the model is consistent with the physical entity state, and the comprehensiveness and timeliness of the data are improved.

The invention relates to the technical field of bridge engineering detection and repair, in particular to a bridge crack repair effect prediction method and system based on performance simulation, and the method comprises the following steps: obtaining basic data and environmental load data of a target bridge, and matching at least two candidate repair schemes from a preset repair scheme library; constructing an initial performance model before repair, and calculating an initial bearing capacity loss rate and a crack propagation rate; quantifying the interaction influence of the multi-modal data through a coupling factor matrix, and iteratively calculating the repair condition of a set time period after repair to obtain an initial simulation result; and collecting real-time monitoring data in a set time period in the repairing process and after repairing, and inputting the real-time monitoring data into a preset LSTM adaptive correction model to obtain a corrected simulation result. According to the method, the long-term repair condition after repair can be accurately calculated, the accuracy of a prediction result and the environmental adaptability are remarkably improved, and a scientific basis is provided for long-term service performance pre-judgment.

The invention relates to a DRL-based AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance method in a partially observable environment, which comprises the following steps of: firstly, executing an AUV path planning and obstacle avoidance task under the condition that marine environment information is partially observable; secondly, constructing an underwater environment model and an AUV kinetic model, and constructing a dynamic and static separated dual-channel adaptive sensing model through local environment information acquired by detection equipment; then, path planning is modeled as a Markov decision process, and a multi-objective optimization reward function is designed for collaborative optimization; finally, an improved maximum entropy SAC algorithm is combined with a priority experience playback mechanism to train a strategy network, a gliding mode is dynamically triggered through a physical constraint layer, attitude angle and thrust output is corrected in real time, and the safety boundary condition is monitored in real time. According to the method, the real-time performance of path planning, the dynamic and static obstacle avoidance success rate and the ocean current utilization rate under the complex sea condition can be remarkably improved, and a high-robustness autonomous navigation solution is provided for ocean exploration.

The invention provides a multi-modal hydrological data and underlying surface element data processing method and a computer system, and the method comprises the steps: collecting multi-source observation data in a hydrological cycle process through distributed observation nodes, and carrying out the spatial-temporal feature dynamic decoupling operation of the multi-source observation data, separating to obtain a hydrological dynamic feature set with a time sequence characteristic and an underlying surface static feature set with a spatial distribution characteristic; calling a preset underlying surface parameter adjustment model, performing parameter dynamic optimization on the underlying surface static feature set in combination with the hydrological dynamic feature set, and generating a dynamic parameter configuration set containing a geographic response coefficient; hydrological-underlying surface coupling correction processing is carried out based on the dynamic parameter configuration set, and a hydrological variable set is obtained through optimization; and performing spatio-temporal evolution law analysis according to the hydrological variable set, and outputting a hydrological system evolution trend map and a disaster risk early warning index. According to the invention, a more reliable basis can be provided for hydrological system analysis and disaster risk management.

The invention discloses a flood disaster scene-oriented urban power distribution network toughness evaluation method and system, and belongs to the technical field of power distribution network toughness evaluation, and the method comprises the steps: obtaining rainfall data, land utilization data, elevation data and the like, and inputting the data into a waterlogging simulation module; the waterlogging simulation module simulates rainfall runoff production and runoff convergence to generate ponding distribution; the disaster judgment module obtains the fault probability of the power distribution equipment and the line in combination with the power distribution equipment and line disaster fault model, and generates a system disaster scene under the flood disaster; the load reduction optimization module constructs a power distribution system load reduction optimization model, and takes the minimization of the total load reduction amount of the power distribution system as a target; and the system toughness evaluation module measures the overall toughness of the power distribution system in the flood disaster scene. According to the method, the overall toughness level is quantitatively evaluated based on the system load reduction amount, the node load importance degree and the ponding condition are combined to recognize the weak links of the system, reference is provided for differential reinforcement and operation and maintenance strategies, and subsequent waterlogging prevention planning and operation and maintenance management are guided.

The invention relates to the technical field of bridge engineering structure health monitoring, in particular to a service life prediction method and system for a bridge connection part, and the method comprises the steps: collecting the strain of a grouting sleeve, a reinforcement-grouting interface ultrasonic reflection signal and environment temperature and humidity data through a distributed sensor array; a three-dimensional dynamic void rate model is generated through fusion of a spatial topological mapping algorithm, a correlation function of load circulation and void expansion is established, the dynamic void rate is input into a bond strength coupling degradation model, after strength attenuation is quantized in combination with steel bar corrosion data and bond failure early warning is triggered, stress redistribution is calculated through multi-physics coupling simulation, and the dynamic void rate is obtained. Early warning is taken as a starting node, the actual load spectrum and temperature and humidity data are combined, Monte Carlo sampling is adopted to simulate rigidity degradation and output the residual life probability, the system comprises a multi-source collaborative sensing unit, a micro-void evolution analysis unit and a life prediction decision unit, and the safety control precision of bridge connection parts is improved.

The invention relates to the technical field of underground engineering safety monitoring and disaster simulation, in particular to a digital twinborn deduction platform for underground engineering disaster chain evolution simulation, which comprises a multi-disaster coupling numerical simulation module used for acquiring multi-source data including design drawing data, geological data and engineering monitoring data, and based on the multi-source data, establishing an initial stress field model by using a numerical calculation method combining a finite element and a finite difference, and carrying out leakage-settlement-structure failure multi-disaster coupling numerical simulation. According to the method, the partial differential equation for describing the evolution law of the underground engineering is used as a constraint term to be embedded into the deduction model, and online identification and dynamic extrapolation of parameters are executed by fusing real-time monitoring data, so that the accuracy of disaster evolution simulation under complex working conditions is remarkably improved; and the problem that the traditional numerical calculation method is difficult to meet the real-time requirement of digital twinning is solved.

The invention discloses a transmission tower vulnerability analysis method and system based on seismic simulation, and belongs to the technical field of electric power facility disaster prevention, and the method comprises the steps: correcting bedrock seismic oscillation parameters based on equivalent shear wave velocity, and generating a high-precision earth surface PGA distribution diagram; automatically positioning a key node layer through eigenvalue decomposition, reducing the order of the three-dimensional model of the power transmission tower into a two-dimensional series multi-degree-of-freedom model, and outputting a layer mass vector, a condensation stiffness matrix and rod piece bending stiffness; performing time-history analysis by using the corrected PGA-driven reduced-order model to generate an earthquake damage probability; the multi-source remote sensing data and the PGA are fused, the landslide space probability is output through a deep learning model, and the impact strength and the landslide kinetic energy are calculated to judge the landslide damage probability; and based on the dual-threshold condition triggering probability union set, calculating a comprehensive damage probability, and generating a three-dimensional vulnerable curved surface. According to the method, the problems of high missing report rate and low calculation efficiency of single disaster assessment are solved, and minute-level accurate early warning of the composite disaster risk of the power transmission tower is realized.

The invention relates to the technical field of environmental monitoring and atmospheric pollution early warning, in particular to a Yangtze River Delta composite extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and pollutant concentration data of a Yangtze River Delta region to form an original data set; and S2, carrying out missing value interpolation and abnormal value elimination on the meteorological data and the pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal characteristic matrix. According to the method, by collecting Yangtze Delta high-resolution weather and pollutant data, performing data cleaning, bimodal feature coding and joint representation modeling, predicting the ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional ozone early warning mostly depends on a single-modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-modal space-time dependent capture, the problem of early warning information lag is caused.

The invention discloses a novel organic pollutant traceability model construction method based on multi-environment medium data, and aims to solve the problem of deformation compensation of source fingerprints in chemical conversion, interphase distribution and medium migration processes. According to the method, a constrained cost matrix is generated by constructing a micro-deformable kernel, hybrid expert gating weighting is carried out, capacity and rate constraints are injected, a shared potential source fingerprint is solved and introduced by adopting staged cascaded entropy regular optimal transmission joint, reverse optimal transmission and two-way cyclic consistency reconstruction and alignment are combined, and the optimal transmission performance is improved. The technical effects of obtaining the compensated source fingerprint and the source contribution rate and improving the cross-medium traceability accuracy and interpretability are achieved.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

Disclosed in the present invention are an optimization-based regulation and control method and apparatus for distributed energy storage, a device, and a storage medium. The method comprises: on the basis of current weather parameters, constructing an extreme weather model; determining a plurality of faulted and out-of-service elements in which faults will occur in a power distribution network; then on the basis of the faulted and out-of-service elements, system parameters of a distributed energy storage system, and an operation parameter of the power distribution network, constructing a dual-objective optimization model taking load loss minimization and power failure time minimization as objectives; and finally, solving for the dual-objective optimization model to generate an optimal regulation and control scheme applied to the distributed energy storage system. Therefore, when a fault has occurred in the power distribution network, the distributed energy storage system is regulated and controlled by means of the optimal regulation and control scheme, such that power supply of the power distribution network is restored as soon as possible in extreme typhoon weather, and the transient power deficit of the power distribution network is made up promptly. Therefore, the present invention can effectively guarantee the safe operation of power distribution networks under the condition of extreme typhoon weather.

The invention discloses an aircraft aerodynamic parameter prediction method based on multi-view airfoil profile concept correlation learning, and belongs to the technical field of aerodynamics, deep learning and computer vision crossing, and the method comprises the steps: converting a three-dimensional model into a multi-view image sequence through a hybrid projection strategy combining orthogonality and surround view; utilizing an optimal transmission theory to map visual features to a learnable airfoil profile concept space to realize semantization and discretization of the features; in combination with flow field working condition coding, aerodynamic parameters are regressed under the constraint of aerodynamic physical laws; and finally, by displaying the matching relationship between the input sample and the airfoil profile concept, the inherent interpretability based on the instance is provided.

The invention provides a geological disaster deduction method and system based on a digital twinborn simulation platform, relates to the technical field of geological disaster deduction, and improves the accuracy and robustness of data fusion by adopting a dynamic weight fusion algorithm and combining information entropy and reliability score; constructing a digital twin model based on a multi-source data inversion and three-dimensional geological modeling technology, updating geological parameters in real time, and reflecting the state change of a geological body; a multi-level stepped early warning model is introduced, and the timeliness and foresight of early warning are enhanced by fusing a physical model and deep learning prediction; and in combination with the graded disaster deduction model, the precision is dynamically selected, and the risk thermodynamic diagram is generated, so that the spatial fineness and decision support capability of geological disaster risk identification are improved. Multi-dimensional organic collaboration is realized, the overall accuracy and response speed of the system are enhanced, and efficient and accurate disaster risk management requirements in a complex geological environment are met.