196 results about "Uncertainty analysis" patented technology

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.

The invention provides a contaminated site remediation technology scoring system which comprises a multi-dimensional evaluation index system construction module, a situational weight dynamic adjustment module, a remediation technology applicability scoring module, an uncertainty analysis and sensitivity test module, a scoring result visualization module, a technical scheme sorting and screening unit and the like. Through four innovations of a dynamic weight adaptation mechanism, a multi-model fusion scoring algorithm, a technology combination scheme evaluation module and uncertainty robust analysis, the problems of single dimension, weight solidification, difficulty in quantification of a synergistic effect and the like in pollution remediation technology evaluation are systematically solved; and an objective, efficient and transparent intelligent decision-making tool is provided for repairing a complex polluted site.

The invention discloses a forest carbon sink acquisition method and system for driving an ecological carbon mode through remote sensing observation, and the method and system are corresponding schemes: in the scheme, a set of high-precision and multi-dimensional satellite-ground collaborative observation data system is formed by combining multi-source observation data, and a solid foundation is laid for subsequent carbon sink inversion; a high-resolution prior carbon concentration field of a research area can be generated by using a machine learning method, and a high-precision posterior carbon concentration field can be obtained; on the basis, through inversion analysis and a correction link, high-precision, wide-coverage and low-uncertainty analysis of forest carbon sequestration in a research area is finally realized, and the problems that an existing carbon sequestration estimation method is insufficient in space coverage, high in model uncertainty, limited in multi-source data fusion and the like are solved.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

The invention provides an uncertainty analysis method and device for a livestock target detection model. The method comprises the following steps: acquiring an RGB image of a livestock target; constructing an uncertainty analysis model of the livestock target detection model, and calling the uncertainty analysis model to detect the livestock target in the RGB image to obtain the category of the livestock target and a detection frame coordinate; and respectively calculating a cognitive uncertainty index and a random uncertainty index of the livestock target detection model according to the category and the coordinates of the detection frame. The method is used for solving the problems that in the prior art, an uncertainty analysis method is not comprehensive enough, uncertainty result attribution is lacked, and the uncertainty evaluation effect is poor.

The invention relates to a Bayesian KAN fused aircraft rapid multidisciplinary uncertainty analysis method, and belongs to the technical field of spacecraft manufacturing and application. According to the method, Bayesian learning is combined with a Colmogorov-Arnodel network, a Bayesian KAN neural network which can cope with high-dimensional input uncertainty, can quantify model cognitive uncertainty and is higher in fitting capacity is provided, a BKAN network agent model is constructed for a subsubject analysis model, time-consuming numerical simulation of each subsubject is replaced, and the calculation amount is reduced; with reference to a basic framework of a global sensitivity equation GSE of a deterministic field coupling multidisciplinary system, input random input and model cognition uncertainty are introduced, and expressions of mean values and covariances of disciplinary coupling variables and system responses are deduced on the GSE framework; a rapid semi-analytical multidisciplinary random and model cognition hybrid uncertainty propagation method is established, efficient multidisciplinary uncertainty analysis is achieved, and the problems that an existing method is poor in precision and large in calculation amount are solved.

The invention discloses a mineral resource metallogenic law simulation and target prediction system based on big data, and relates to the technical field of geological exploration. The multi-source data acquisition and standardization module supports acquisition of five types of data, and the data are preprocessed and then transmitted to the distributed database; mining key elements by using an improved Apriori algorithm, and constructing a correlation graph; building a model based on Unity 3D, and integrating three types of dynamic simulation; performing three-level prediction by using a CNN-LSTM model; three-dimensional rendering and multifunctional display are supported; and automatically updating data, models and rules to form an optimized closed loop. According to the method, multi-source data is integrated to improve ore-forming element recognition comprehensiveness, dynamic simulation fits geological reality, AI prediction is accurate, uncertainty analysis is included, visual interaction is practical, iterative optimization continuously improves efficiency, invalid exploration cost is reduced, and mineral exploration is efficiently guided.

The invention relates to the technical field of building carbon emission management, in particular to a building full-life-cycle carbon emission management system and method based on a calculation management system. The system comprises a data management module for acquiring and processing carbon emission data in a full life cycle stage of a building, mapping the processed carbon emission data with a factor database through a uniform interface and then storing the processed carbon emission data in the database; the carbon emission accounting module is used for executing carbon emission accounting and simulation and uncertainty analysis; the intelligent management module is used for carrying out real-time monitoring, carbon emission trend analysis, abnormity early warning and visual display on the carbon emission data; and the optimization emission reduction module operates an optimization algorithm, generates an emission reduction scheme through iterative processing and performs carbon asset management. According to the building full-life-cycle carbon emission management method based on the calculation, management and reduction system, closed-loop cooperation of accurate accounting, intelligent management and emission reduction optimization is achieved.

The invention relates to the technical field of new energy power distribution planning, in particular to a new energy power distribution planning intelligent simulation system oriented to a novel power system. Comprising a multi-source data dynamic modeling unit; the multi-objective optimization decision-making unit is used for realizing collaborative optimization of economy and reliability of the power distribution network and generating a planning scheme of equipment configuration, energy storage layout and network reconstruction by improving a heuristic algorithm; and the uncertainty analysis unit is used for quantifying the influence of the wind and light output fluctuation on the planning scheme and outputting a risk assessment index through a scene generation technology and a probability sampling method. Through multi-modal data fusion, time sequence model construction and a self-adaptive updating mechanism of the multi-source data dynamic modeling unit, the dynamic adaptation capability of the model to new energy power fluctuation and load characteristic change can be improved, and meanwhile, the space-time correlation characteristics between the source and the load are mined, so that the power grid model can be synchronously evolved along with the time sequence data change; and the deviation between the model and the actual operation condition is reduced.

The invention discloses an electric power equipment product carbon footprint uncertainty analysis method and system based on AHP-PD, and belongs to the technical field of product carbon footprint evaluation and uncertainty analysis. The method comprises the following steps: constructing a carbon footprint uncertainty hierarchical structure model comprising a target layer, a criterion layer and an index layer; constructing a judgment matrix based on the hierarchical structure model; calculating a weight set W and a local weight set Wi; synthesizing a global weight set omega according to the weight set W and the local weight set Wi; the uncertainty of the carbon footprint source is quantified; obtaining probability distribution of the total uncertainty of the carbon footprint of the electric power equipment product; and adopting the statistical characteristics of the probability distribution as the uncertainty analysis result of the carbon footprint in the full life cycle of the electric power equipment product. According to the method, the final result is in a probability form (such as a confidence interval), the real situation can be better reflected compared with a single determined value, and risk decision is supported.

The invention discloses an ASIC special password intelligent chip aging prediction method, and relates to the field of chip aging prediction, and the method comprises the steps: carrying out the feature fusion coding through obtaining chip power supply wiring layer data and collecting environment parameters, obtaining an aging sensitivity feature vector, carrying out the fractional order aging dynamics modeling based on the aging sensitivity feature vector, and carrying out the aging prediction. Performing chip aging state prediction based on the chip aging state change equation to obtain an aging track prediction sequence, extracting an aging sensitivity feature vector component and the aging track prediction sequence to perform residual life probability evaluation, and obtaining a residual life cumulative distribution function; on the basis of the residual life cumulative distribution function, uncertainty analysis is carried out on chip aging prediction to obtain a two-factor risk index, a coupling aging rule between hardware structure weakness and environmental stress is grasped, real aging sensitive points of the chip can be accurately reflected, and accurate reference can be provided for preventive maintenance of the chip.

The invention discloses a natural electric field-electric field joint detection and inversion method based on GMM prior, and relates to the technical field of tunnel engineering geological detection, and the method comprises the steps: obtaining the resistivity and natural potential observed in a to-be-inverted region; an equivalent body source of a natural potential is parameterized into a body source vector on a grid unit, weighted L1 sparse regular constraint is introduced into the body source vector, a GMM prior model is used as underground electrical structure prior shared across physics fields, and a multi-task joint inversion objective function of a shared underground electrical structure label is established; and performing inversion iteration by adopting maximum posteriori, updating parameters of the GMM prior model and the underground electrical structure label by adopting an expectation maximization algorithm until an end condition is met, and obtaining a joint inversion imaging result of the tunnel water-rich anomalous body. The advantages of a multi-source electrical method are fused, underground electrical structure priori is introduced, Bayesian uncertainty analysis is combined, and high-resolution and high-reliability recognition of the water-rich anomalous body in front of the tunnel is achieved.

The invention discloses a storm surge artificial intelligence forecasting method based on physical equation constraints, and relates to the technical field of artificial intelligence storm surge forecasting. Comprising the following steps: step 1, collecting multi-source data; step 2, assimilating and preprocessing the data; step 3, realizing a machine learning calculation method of physical loss; step 4, constructing a physical and data co-constrained artificial intelligence model; 5, performing hyper-parameter adjustment by using a random search algorithm, and further optimizing the model; and step 6, carrying out interpretability analysis on the forecasting process and result. According to the invention, three control equations for storm surge calculation are introduced into the machine learning model, so that the dependence on the training data volume is greatly reduced compared with a traditional data-driven intelligent forecasting model; and analyzing the change trend of the physical variables in the forecasting process, carrying out visual display and uncertainty analysis, and solving the problem of uninterpretability of an artificial intelligence forecasting model.

The invention discloses an electric power spot market transaction decision-making method and system based on multi-factor dynamic analysis, and the method comprises the steps: building a power generation power prediction model based on historical power generation data, meteorological data and time information, predicting the power generation power of a future time period based on the power generation power prediction model, and carrying out the uncertainty analysis of the power generation power; establishing a market price prediction model based on the multi-source data, performing prediction based on the market price prediction model to obtain a market price in a future time period, and performing uncertainty analysis of the market price; building a transaction decision optimization model by taking profit maximization as a target and considering power generation cost, market risk and new energy consumption based on the power generation power and the market price of a future time period; and solving the transaction decision optimization model by adopting an improved genetic algorithm to obtain an optimal transaction decision scheme. Scientific, accurate and efficient transaction decision support is provided for the new energy power station in the electric power spot market, and the economic benefits and market competitiveness of the new energy power station are improved.

The invention relates to the technical field of bridge engineering, and particularly discloses a method for predicting earthquake-induced high-low geometric irregularity of a high-speed railway target under a longitudinal earthquake, which comprises the following steps of: establishing an earthquake-induced high-low geometric irregularity random sample library on a high-speed railway bridge; the earthquake-induced high-low geometric irregularity is considered as the product of the shape and the amplitude variable; establishing a shape function expression of the target earthquake-induced high-low geometric irregularity; combining uncertainty analysis to fit a high-speed railway pier top displacement peak value upper bound spectrum; solving an amplitude variable through a structure period and a pier top displacement peak value upper bound spectrum; and constructing a time domain sample of the seismic-induced high-low geometric irregularity of the target based on the shape function and the amplitude variable. According to the method, the high-low geometric irregularity under the action of the longitudinal earthquake can be quickly predicted, the post-earthquake traffic capacity of the high-speed railway bridge can be quickly evaluated, and a basis is provided for the post-earthquake running speed limiting and emergency processing scheme optimization of the high-speed railway.

The invention belongs to ecological environment engineering, and particularly relates to a river and lake wetland zoning and classifying ecological water demand fine accounting method, which comprises the following steps of S1, performing zoning and classifying on a river and lake wetland of a research area; s2, establishing a space-time coupling hydrodynamic model based on a partition classification relationship; s3, constructing a multi-factor ecological response model, and calculating an ecological suitability index of each partition; s4, establishing a multi-objective optimization model, and performing optimization solution on the ecological water demand of the partition by combining ecological benefits, water balance and process smoothness objectives; and S5, performing robust correction on an optimization result by adopting an uncertainty analysis method to obtain a final ecological water demand distribution result. By establishing a river and lake wetland partition classification spatial database based on digital elevation, river network distribution, land utilization and ecological attribute information, fine description and dynamic association of different ecological functional areas are realized, and the effects of ecological process differentiation characterization and remarkable improvement of water demand accounting precision are obtained.

The invention discloses a storage tank measurement data processing method, computer equipment and a storage medium, and relates to the technical field of measurement modeling, and the method comprises the steps: collecting multi-physics field operation parameter data, carrying out the filtering, interpolation and abnormity elimination, and obtaining a multi-dimensional original data set; establishing a digital twin model based on the data set and calculating an initial parameter set; collecting real-time observation data, and executing residual optimization inversion to obtain a correction parameter set; performing uncertainty analysis and confidence weighted fusion by using the correction parameter set to obtain a fusion measurement parameter set; constructing a multi-objective cost function and updating the weight by adopting an exponential gradient evolution algorithm to obtain feed-forward control intensity; and in combination with the correction parameter set and the feedforward control intensity, carrying out cross-storage-tank robust aggregation and gating judgment to obtain new digital twinborn model parameters. According to the method, self-adaptive fusion and model closed-loop evolution of multi-source metering information are realized through confidence weighted fusion and an exponential gradient evolution weight optimization mechanism and by executing cross-storage-tank robust aggregation.

The invention discloses a power grid state prediction method under high wind power permeability based on multi-source data fusion, and the method comprises the steps: firstly analyzing the correlation between variables through feature engineering and a Pearson's correlation coefficient, and expanding a data set through a sliding time window to mine time domain information; establishing a graph convolution network model of an adaptive adjacency matrix, dynamically capturing the relationship between fans, and extracting spatial features through multilayer convolution to realize information transmission; and carrying out probability density analysis on the wind power prediction error by adopting a kernel density estimation method, determining an error interval boundary value, and realizing interval prediction by combining a deterministic prediction result. The system based on the method comprises a data preprocessing module, a model building module and an uncertainty analysis module which are matched with a processor and a memory to operate. According to the scheme, the accuracy and stability of power grid state prediction in a high wind power permeability scene can be effectively improved, the certainty and the interval prediction result are considered, and a reliable basis is provided for power grid dispatching and optimized operation.

The invention discloses a signal point table automatic verification method and system based on SCD analysis, and mainly relates to the technical field of data processing. Comprising the following steps: acquiring and analyzing a substation SCD configuration file, and constructing a plurality of virtual IED analog terminals; a plurality of virtual IED simulation terminals are connected to a transformer substation system for three-remote signal simulation interaction, and IED signal information is obtained; obtaining picture signal information of a master station; performing dual uncertainty analysis on the master station picture signal information to obtain a master station picture signal confidence analysis result; and performing signal comparison verification based on the master station picture signal confidence analysis result, the master station picture signal information and the IED signal information to obtain a point table verification result. The method has the beneficial effects that the technical problems of low substation information acceptance efficiency and low signal point table verification reliability in the prior art are solved, and the technical effect of improving the verification reliability is achieved.

The invention discloses a zero-carbon park carbon accounting method and system based on Transform, and the method comprises the steps: collecting multi-source heterogeneous carbon data of a zero-carbon park, completing the preprocessing, and generating a fusion input tensor and a corresponding data quality identifier; inputting the fusion input tensor into a multi-scale time sequence fusion coding model, and generating a time sequence feature intermediate representation through multi-branch coding processing adaptive to different data sources and sampling granularities; emission source contribution degree decomposition information is obtained through a category-constrained carbon flow tracking attention module, uncertainty analysis is completed to obtain a target carbon accounting result and confidence interval data, and a compliant carbon integral scheme and an auditable evidence packet are generated through a carbon integral generation optimization module; therefore, the problems of difficulty in aligning multi-source heterogeneous data of the zero-carbon park, insufficient consideration of period and mutation features and poor traceability of accounting results are effectively solved, the precision and compliance of carbon accounting are greatly improved, and a complete closed loop from carbon accounting to incentive management is realized.

The invention relates to the technical field of low-carbon park carbon reduction potential estimation, and discloses a low-carbon park flexible resource aggregation carbon reduction potential evaluation method, system and device and a medium. Multi-stage preprocessing and analysis are performed on operation data and carbon emission data of flexible resources in the low-carbon park, and the polymerization carbon reduction potential of the low-carbon park can be accurately evaluated. In the data processing process, each preprocessing stage has unique mechanisms and logics, such as spatial-temporal feature extraction, resource clustering and aggregation parameter calculation in the second preprocessing, and carbon footprint dynamic prediction, time-varying capability evaluation and fuzzy weight self-adaption in the third preprocessing. And index calculation, uncertainty analysis, report generation and the like in the fourth preprocessing are carried out.

This invention relates to a method for reducing the order of large-scale spatial truss models considering convex set uncertainties. This method provides a more efficient and accurate control approach for truss structures, applicable to structural control problems of large-scale spatial trusses, and belongs to the fields of truss structure modeling and analysis, and truss structure control system design. By considering the correlation between uncertain parameters, this invention derives the propagation law of uncertain parameters, providing a solution framework for the application of convex set uncertainty in the control of large-scale spatial truss structures. Traditional probabilistic methods are limited by their high dependence on experimental data, restricting their application in large-scale spatial trusses. Convex set uncertainty analysis, as an emerging non-probabilistic method, is more suitable for solving uncertain large-scale spatial truss structure control problems with limited data.

The invention relates to the technical field of power system auxiliary services, in particular to a multi-agent participated user group dynamic combination and economic optimization method in power auxiliary services, which comprises the following steps of: constructing a multi-agent collaborative model of'upper-layer scheduling-middle-layer aggregation-lower-layer execution ', formulating an incentive price through a Stackelberg game between upper-layer ISO (International Standard Organization) and LAs (Local Assisted Services), and establishing a multi-agent collaborative model of'upper-layer scheduling-middle-layer aggregation-lower-layer execution'; the evolutionary game between the lower-layer LAs and the user realizes user strategy interaction; introducing maximum profit power reduction and power transfer potential indexes, and performing user group preliminary screening in combination with a multi-energy profit map; and outputting an optimal user group combination scheme through iterative solution and joint uncertainty analysis. The invention further provides an alternative scheme, and dynamic combination optimization is realized by utilizing the user potential portrait and a reinforcement learning mechanism. The method can effectively improve the economy, flexibility and robustness of the power auxiliary service, and gives consideration to the comfort of the user and the adjustment demands of the system.

The application belongs to the technical field of material performance prediction and uncertainty analysis, and proposes a beryllium aluminum alloy performance uncertainty quantification method based on Bayesian ensemble learning, which is innovative in constructing and training multiple independent performance prediction models to form the basis of ensemble learning. After the training of each model is completed, a performance prediction value can be output for a new input sample. After obtaining the prediction outputs of multiple independent models, a Bayesian fusion method is used to comprehensively process the prediction results on the probability level to obtain the fused performance prediction distribution; and the complete results of the beryllium aluminum alloy performance prediction are output in a clear, intuitive and convenient engineering application format. The application has the advantages that the robustness and generalization ability of the prediction results are improved, a decision basis is provided for material performance evaluation, and good adaptability is achieved for the case of limited data quantity, and important innovations are achieved in the aspects of material performance uncertainty quantification theory and engineering application.

The invention discloses a wind resource assessment method based on multi-source data fusion, a medium and a system. The method comprises the following steps: S1, acquiring project basic information and multi-source data of a wind power project; the project basic information comprises a project name, a geographic position and topographic information; the multi-source data comprises anemometer tower data, mesoscale data and CFD data; s2, preprocessing the anemometer tower data to generate corrected anemometer data; s3, performing multi-source data fusion on the corrected wind measurement data, mesoscale data and CFD data, and performing wind resource parameter analysis to obtain a machine site wind resource evaluation result; s4, performing reduction and uncertainty analysis based on the wind resource evaluation result of the machine site, and calculating the generating capacity; and S5, generating a wind resource assessment report according to the power generation amount calculation result and the project basic information. According to the method, the wind resource state of each machine site in the wind field can be evaluated more accurately.

The invention discloses a coal-based solid waste road base full life cycle dynamic carbon accounting method and system, and relates to the field of carbon accounting, and the method comprises the steps: dividing solid waste types according to component characteristics, building an identification system, and collecting the dynamic parameters of each stage through a monitoring device to form a basic database; building a staged quantitative model based on the data, and correcting an accounting result in combination with a scene-carbon emission correction matrix; predicting a carbon emission curve of 5-10 years of an operation period by using an LSTM neural network, and establishing a carbon balance equation of the waste stage recycled aggregate; and finally, analyzing parameter uncertainty through Monte Carlo simulation, dynamically updating a database and outputting a carbon emission confidence interval. The coal-based solid waste road base carbon accounting method has the advantages that the coal-based solid waste road base carbon accounting is accurately, dynamically and comprehensively completed by refining the full life cycle stage and combining the staged quantitative model, scene correction, LSTM prediction, regenerated carbon balance calculation and Monte Carlo uncertainty analysis, and a basis is provided for long-term planning.

The invention discloses a method and a system for predicting the breakage rate of a pressurized water reactor nuclear fuel rod, which are particularly suitable for a pressurized water reactor AFA3G nuclear fuel rod and are used for predicting the breakage rate of the pressurized water reactor AFA3G nuclear fuel rod by establishing a data-driven nuclear fuel rod fretting wear model and combining random uncertainty analysis. The method specifically comprises the following steps: firstly, constructing a flow-induced vibration-fretting wear coupling analysis finite element model of the nuclear fuel rod, and calculating the wear depth of the nuclear fuel rod along with time change; fitting a nuclear fuel rod wear depth curve according to the operation condition of the nuclear fuel rod by combining the geometric dimension and the material attribute of the nuclear fuel rod, and establishing a data-driven nuclear fuel rod wear mathematical model; further, establishing an uncertainty analysis model of the nuclear fuel rod, and calculating the failure probability of the nuclear fuel rod by using Monte Carlo simulation; according to the method, finite element analysis and uncertainty analysis are combined, so that the damage rate of the pressurized water reactor AFA3G nuclear fuel rod is predicted.

The application relates to the technical field of power system dispatching, and discloses an intra-day forward-looking dispatching fast solving method considering large-scale new energy cluster power generation fluctuation, which comprises the following steps: S1: new energy cluster space-time fluctuation scene generation based on neuron cellular automata, S2: power grid dynamic security domain definition and simplification based on physical information neural network, S3: dispatching fast optimization solving based on model prediction path integral, and S4: dispatching scheme dynamic elasticity and stability evaluation based on the theory of Koopman operator. The new energy cluster space-time fluctuation scene generation method based on neuron cellular automata can effectively generate a space-time scene reflecting the large-scale new energy cluster power generation fluctuation, support uncertainty analysis, has the advantages of high calculation efficiency and scene authenticity, and solves the problem that the traditional statistical model ignores space-time coupling, thereby causing inaccurate scene generation.

The application discloses a time-frequency electromagnetic exploration method and system for improving exploration depth and resolution, and relates to the technical field of exploration. The method comprises the following steps: arranging a multi-directional excitation source and a mobile remote end array observation system, and sparsely collecting time-frequency electromagnetic signals; reconstructing data and reducing noise based on compression sensing; constructing a time-frequency dual-domain joint inversion target function, introducing cross gradient constraints and physical constraints, and realizing intelligent collaborative inversion through depth reinforcement learning; outputting a high-precision three-dimensional geological model through Bayesian uncertainty analysis and multi-source data fusion verification; and the system comprises excitation, observation, data processing, intelligent inversion and interpretation visualization modules. The scheme improves exploration depth and resolution, improves data acquisition and inversion efficiency, and guarantees the accuracy of geological interpretation.

The invention provides a wave-rider aircraft aerodynamic data uncertainty analysis method and device, and the method comprises the steps: obtaining the numerical calculation aerodynamic data of a wave-rider aircraft; factors influencing the uncertainty of the numerical calculation pneumatic data are determined; carrying out uncertainty analysis on the basis of pneumatic data, calculating model uncertainty by adopting a range method, designing an orthogonal test according to factors, calculating numerical value and input parameter uncertainty, and carrying out significance analysis on factor influence and interaction according to the orthogonal test; and calculating the total uncertainty according to the model uncertainty, the numerical value and the input parameter uncertainty, and verifying the reliability of the total uncertainty by adopting new sample data. According to the method, the uncertainty of the pneumatic data is comprehensively and effectively obtained, meanwhile, the influence factors of the uncertainty of the numerical calculation pneumatic data and the significance of the interaction are visually obtained, and support is provided for the reliability of the numerical calculation pneumatic data.