118 results about "Spatial prediction" patented technology

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

The invention relates to the technical field of civil engineering, and discloses a road subgrade settlement prediction method and system based on multi-source data. The method comprises the following steps: acquiring and correcting a precipitation sequence and roadbed soil moisture distribution data; analyzing rainfall accumulation characteristics and carrying out penetration risk classification, thereby extracting a penetration depth estimation value, and utilizing finite element analysis to simulate a soil body saturation state change trend; quantifying a soil body supporting capacity reduction range, constructing and calibrating a settlement initiation probability calculation model, and obtaining settlement probability distribution; identifying a high-risk evolution area, integrating path weights and accumulated influence factors, and generating a settlement prediction interval subjected to multi-dimensional verification; and fusing the prediction interval and actual roadbed structure data through a geographic information system to generate a comprehensive evaluation report. According to the method, the whole-process accurate evaluation of the roadbed settlement risk from multi-source perception, mechanism simulation to space prediction is realized, and the accuracy and timeliness of the roadbed settlement risk prediction and the pertinence of engineering maintenance are improved.

The invention discloses a soil organic matter digital mapping method and device based on crop rotation historical change, and the method comprises the steps: bringing crop rotation interannual change and crop rotation change frequency into an environment variable set, and carrying out the screening and combination with other environment covariables; the historical crop rotation change and the long-term frequency thereof are brought into the environment variable set, and the environment variable set is enriched, so that the influence of human activities on soil spatial-temporal heterogeneity is considered in soil organic matter prediction, and the precision of model prediction is improved. By utilizing the method, the influence of historical agricultural practice can be reflected, and the spatial heterogeneity of the farmland soil organic matters can be better described, so that the performance of a soil organic matter spatial prediction model is improved.

The invention relates to the technical field of information retrieval, in particular to an academic supply and demand dynamic sensing method and system based on space-time big data, and the method comprises the following steps: obtaining the path length of a source and a target college, screening migration direction stable labels, recognizing a trend consistent region, evaluating the local transfer capacity, and fusing a high-frequency path region. And extracting behavior track data, performing sequence comparison on the associated nodes, and generating a prediction chain space offset mark set. According to the method, space label units with stable migration characteristics are effectively distinguished by processing enrollment information and population migration trends and constructing a path length filtering and direction section repeating mechanism, and supply and demand migration origin areas are accurately locked in combination with change trends reflected by continuous direction consistency; spatial aggregation judgment is enhanced through a statistical means of a neighborhood capacity proportion and path intersection frequency, the sensitivity and dynamic response capability of a spatial prediction structure are improved, and active sensing and intervention prompting of a supply and demand change trend under multiple space-time dimensions are realized.

The invention discloses a soil erosion resistance prediction method and system based on earth surface parameters, and relates to the technical field of soil erosion and water and soil conservation monitoring and prediction, and the method comprises the steps of earth surface parameter collection, parameter preprocessing, erosion response modeling, erosion resistance threshold tensor inversion and erosion resistance prediction. Collecting multi-source earth surface parameter data; secondly, constructing a unified earth surface parameter feature set; obtaining erosion response simulation data by adopting an erosion response modeling method combining physical process simulation and lightweight machine learning residual correction; an explicit Bayesian neural network and a variational reasoning framework are constructed, observation data are combined, and a four-dimensional anti-corrosion threshold tensor is obtained through inversion; introducing a double-branch space-time multi-task fusion prediction model of an anti-erosion threshold tensor, and outputting a prediction value and a prediction grade of the soil anti-erosion capability; according to the scheme, dynamic and spatial prediction of the erosion resistance of the soil can be realized, and a scientific basis is provided for water and soil conservation and ecological environment management.

A water-rich tunnel permeability dynamic partitioning method based on multi-source data comprises the following steps: acquiring a permeability coefficient, rock compressive strength, an integrity coefficient, RQD and longitudinal wave velocity data of a tunneling area, and constructing a multi-source feature matrix through space-time alignment and standardization processing; dynamically calculating the weight of each index based on an entropy weight method, and adaptively adjusting the contribution degree of the parameter in combination with the geological type; a standard cloud parameter is generated by using a cloud model, the membership degree of real-time data to each penetration grade is calculated through a forward cloud generator, and a final grading result is comprehensively judged; and mapping a grading result to a three-dimensional BIM model to generate a dynamic penetration cloud picture, and triggering a real-time early warning mechanism. According to the method, through multi-source data fusion and dynamic weight distribution, the static and experience dependence of a traditional grading method is broken through, and the complex stratum permeability evaluation precision is remarkably improved; the level boundary fuzziness is quantified by the cloud model, and the risk of misjudgment of a critical interval is avoided; through deep integration of advanced geological forecast data, permeability space prediction of an unexcavated area is achieved, minute-level early warning and grouting parameter intelligent recommendation are combined, the probability of water inrush accidents is effectively reduced, and an intelligent solution is provided for water-rich tunnel safety construction and resource optimization.

The invention relates to the technical field of bioinformatics, in particular to an esophageal cancer RNA-protein interaction space prediction method and device based on a graph neural network, and can solve the problem that it is difficult to accurately distinguish the difference of cancer tissue and normal tissue on RPI in a traditional method to a certain extent. The method comprises the steps of obtaining multi-stage space transcriptome data related to esophageal cancer, and performing preprocessing to obtain a multi-modal feature tensor and a canceration stage tag; using the multi-modal feature tensor and the canceration stage tag, using RNA and protein as nodes, defining a static edge based on sequence complementarity and structure matching degree, defining a dynamic edge based on spatial co-expression correlation, and constructing a space-time dynamic graph updated along with the canceration stage; the space-time dynamic graph serves as input, interaction existence, site coordinates and binding energy are predicted through space-time double-branch coding and attention mechanism fusion features, and an interaction prediction result is output.

The invention provides an energy monitoring method based on user working condition identification and energy-saving space prediction, and the method comprises the steps: carrying out the dynamic adjustment and collection of multi-source data based on a self-adaptive data collection strategy according to a user load rate, carrying out the data fusion of the collected multi-source data according to the spatial-temporal characteristics, and constructing high-dimensional working condition feature data; based on the high-dimensional working condition feature data, generating a working condition identification result by using a pre-trained light-weight deep learning working condition classification model in combination with an adaptive sliding window mechanism; and constructing a dynamic energy efficiency reference model, calculating optimal energy consumption under the current working condition based on matching of historical data and a real-time working condition identification result, and quantifying a single-point energy-saving space, predicting multi-point collaborative energy-saving potential and performing real-time energy efficiency monitoring intervention based on a preset layered energy-saving potential evaluation framework. According to the invention, dynamically changing user working conditions can be effectively identified, and energy space prediction can be accurately detected and quantified.

A multi-medium three-dimensional modeling and rock-soil mechanical response coupling method comprises the steps that a collapse risk assessment model is established according to time sequence evolution data of deformation accumulation, if the local deformation rate exceeds a preset threshold value, a high-risk area is judged, and if the deformation accumulation amount reaches a critical value, a potential collapse position is determined; constructing a spatial prediction model through the high-risk area and the potential collapse position information, and performing quantitative calculation on collapse probabilities of different areas by adopting a risk assessment algorithm to obtain a spatial distribution prediction result of karst collapse; and updating numerical model parameters by adopting spatio-temporal information output by the early warning mechanism, and optimizing and adjusting the prediction precision through a feedback correction mechanism to obtain a karst collapse prediction technical system adapted to complex geological conditions. According to the method, accurate prediction and early warning of the karst collapse are achieved in a data driving and model coupling mode, technical support is provided for prevention and treatment of the karst collapse under complex geological conditions, and the method has important practical value.

The invention discloses a landslide susceptibility evaluation method fusing Bayesian optimization and a sparse residual network, and relates to the technical field of mountain disaster space prediction. According to the method, a landslide multi-source factor set is constructed, space standardization is completed, feature compression is performed by using Bayesian optimization XGBoost, and primary probability features are output; inputting the primary features into a sparse gating residual network, and extracting high-order nonlinear coupling features through a sparse attention and residual contraction dual mechanism; training a network by adopting cross entropy loss and an adaptive learning rate, and outputting a landslide occurrence probability; and dividing susceptibility grades based on a GIS natural breakpoint method and drawing. The method is different from an existing landslide model in the aspects of primary feature extraction and deep network architecture, landslide susceptibility prediction precision and interpretability are remarkably improved, and a scientific basis is provided for traffic line selection and disaster prevention planning in a cold region.

The invention provides an intelligent control method for heat treatment furnace temperature, and relates to the technical field of metallurgical heat treatment control. The method comprises the following steps: establishing a blank temperature state space prediction model based on adaptive parameter estimation for predicting the time sequence change of the blank temperature; an optimization objective function is constructed, and an optimal furnace temperature setting sequence is calculated in a prediction time domain; establishing a system parameter adaptive adjustment mechanism, and updating system parameters in real time by adopting a recursive least square method; and valve control is executed based on self-adaptive PID control, and a gain compensation strategy is introduced to deal with equipment aging. According to the method, the furnace temperature can be accurately controlled under different working conditions and different equipment aging degrees without depending on the experience of operators, and the product quality stability is effectively improved.

Systems and methods for providing a neural network system for time series forecasting are described. A time series dataset that includes datapoints at a plurality of timestamps in an observed space is received. The neural network system is trained using the time series dataset. The training the neural network includes: generating, using an encoder of the neural network system, one or more estimated latent variables of a latent space for the time series dataset; generating, using an auxiliary predictor of the neural network system, a first latent-space prediction result based on the one or more estimated latent variables; transforming, using a decoder of the neural network system, the first latent-space prediction result to a first observed-space prediction result; and updating parameters of the neural network system based on a loss based on the first observed-space prediction result.

The invention provides a geomagnetic daily variation prediction method and system based on sub-band Kriging interpolation, and relates to the technical field of geomagnetic daily variation data processing, and the method comprises the steps: obtaining a measurement geomagnetic daily variation signal in a target region; based on the measured geomagnetic daily variation signal, determining a plurality of frequency data corresponding to the measured geomagnetic daily variation signal by adopting wavelet decomposition; on the basis of the multiple pieces of frequency data, adopting a Kriging interpolation prediction model to predict interpolation data corresponding to each piece of frequency data; based on the plurality of interpolation data, the geomagnetic daily variation prediction value of the geomagnetic daily variation observation station to be solved in the target area is obtained through linear superposition reconstruction, and the precision and reliability of geomagnetic daily variation space prediction are effectively improved.

The invention relates to the technical field of smart tourism management, and discloses a dynamic management system based on smart scenic spot resources. The system establishes a resource real-time state portrait through a state portrait module, and a load analysis module extracts a periodic load waveform of each resource point according to a historical operation log and calculates a current theoretical load phase. The offset calculation module compares a real-time state with a standard state under a theoretical phase to generate a real-time offset vector, and a global offset field is constructed through the global fusion module. And the phase calibration module reversely corrects a theoretical load phase according to the space gradient and intensity distribution of the biased field to obtain a predicted load phase after feedback calibration. And the load prediction module extracts a load prediction value sequence of a plurality of time slices in the future. According to the method, through periodic phase modeling and full-field dynamic calibration, finer time sequence description and more collaborative space prediction of scenic spot resource states are realized, and the perspectiveness and adaptability of management are improved.

The application discloses a magnetic resonance signal denoising method combining a shaping Prony algorithm and a spatial prediction filter. Three-dimensional coordinate axis parameters are determined, and a spatial adaptive prediction filter structure is constructed. After data preparation is performed on received noisy signal data of multiple measuring points, a three-dimensional data body is established, Prony decomposition is performed on signal data in the three-dimensional data body by using a Prony algorithm, a shaping regularization method is introduced to solve a least square value, Prony transformation values of the noisy signal of the measuring points and spatial prediction filter coefficient values corresponding to the measuring points are obtained. A pure signal of a target measuring point is predicted and approximated by using Prony components of adjacent measuring points and the spatial prediction filter coefficient values, and suppression of random noise of the noisy signal of the target measuring point is realized. By using the method, coil laying work is reduced, detection efficiency is improved, random noise can be further effectively suppressed, complex effective signals are protected, and the signal-to-noise ratio of the magnetic resonance signal is improved.

The invention provides a lithium battery residual life prediction method and system based on data space position analysis, and the method comprises the steps: building a battery residual life space prediction model based on a convex polytope mechanism according to the basic measurement physical quantity of a lithium battery and the health condition parameter of the battery; determining the shortest length of the online trend as a spatial trend constraint according to the battery health condition parameter of the lithium battery; extracting spatial trend characteristics based on the characteristic parameters of the online operation data of the lithium battery to be detected, and determining a spatial trend straight line by combining spatial trend constraints; and determining a residual service life prediction value corresponding to the space trend characteristics of the lithium battery to be detected by integrating the battery residual service life space prediction model. By adopting the scheme, the defects of complicated operation and insufficient prediction result precision in the prior art can be overcome, and a battery residual life prediction result with high physical significance and high interpretability can be efficiently obtained.

An apparatus includes a decoder including a spatial prediction engine, a temporal prediction engine, a reconstruction engine, and a decoded picture buffer. The apparatus also includes a controller configured to cause the decoder to reconstruct a base resolution version of the block of the frame in a base resolution mode using at least one of the spatial prediction engine or the temporal prediction engine and the reconstruction engine. The controller is further configured to cause the decoder to generate an enhanced resolution version of the block in an enhanced resolution mode using at least one of the spatial prediction engine or the temporal prediction engine and the reconstruction engine.

The application discloses a kind of fusions of multi-source data and its spatial autocorrelation soil property spatial prediction method, the environmental variable of target area is collected;The environmental variable of the target area is respectively input into the linear relationship model of pre-trained soil property data and environmental variable and the nonlinear relationship model of pre-trained soil property data and environmental variable, respectively linear model soil property data prediction value and nonlinear model soil property data prediction value are obtained;Linear model soil property data prediction value and nonlinear model soil property data prediction value are fused using multi-scale geographic normalization weighted fusion model, and the final prediction value of the soil property data of the target area is obtained.The advantages are: the prediction result accuracy is greatly improved compared with the original fused data, and is better than conventional linear and nonlinear fusion method;The spatial autocorrelation of soil property is considered, and the application can maintain good prediction performance in various complex environments.

This invention discloses an interpretable bidding space prediction method based on multimodal data analysis, comprising the following steps: S1, acquiring multi-source time series data and processing it to form standardized input data; S2, constructing training samples and generating lower bound labels, upper bound labels, and boundary information; S3, dividing the data into external driving forces, system endogenous inertia, and market operating state feature groups; S4, inputting an improved boundary-aware temporal Kolmogorov-Arnold network model to obtain boundary response temporal mapping features and boundary response intensity; S5, fusing and generating bidding space fusion features; S6, generating a prediction lower bound and a prediction upper bound, and updating the model parameters; S7, outputting the bidding space prediction interval and multidimensional interpretation. This invention uses an improved boundary-aware temporal Kolmogorov-Arnold network model to achieve interpretable interval prediction of the bidding space.

The application discloses a terrain self-adaptive rainstorm waterlogging spatio-temporal joint prediction method and system, and belongs to the field of alarm responding to disaster events. In order to overcome the defects of the existing method, such as insufficient terrain adaptability of fixed feature weight, and prediction fragmentation caused by independent operation of the spatio-temporal model, the technical scheme of the application is as follows: on the one hand, a terrain self-adaptive dynamic feature weight mechanism is constructed, an adjustment coefficient is generated by quantifying the terrain undulation, and the feature factor weight of the geographical spatio-temporal weighted regression model is dynamically adjusted to adapt to the complex underlying surface; on the other hand, a spatio-temporal joint weighted fusion framework is proposed, a random forest spatial model and a PredFormer time series model are combined, the spatial prediction result is taken as the initial condition of the time series, and the time series collaborative prediction of the flooded range and the water depth is realized. The application is suitable for rainstorm waterlogging prediction of different terrains in cities, and has the advantages of strong terrain adaptability, high prediction accuracy and stability, and unified spatio-temporal rapid response.

The invention discloses a multi-source multi-scale intelligent fusion scenic spot passenger flow prediction method, device and equipment and a storage medium, and relates to the technical field of data processing. According to the scheme, the method comprises the steps that feature fusion and multi-scale time modeling module processing are conducted on multi-source data of stations in a scenic spot, prediction of passenger flow data of all the stations is achieved through a regression decoder, multi-source deep fusion is conducted on the multi-source heterogeneous data with the stations in the scenic spot as the minimum prediction unit, and a space fine-grained result is output. The problems of insufficient multi-source heterogeneous data fusion capability and rough spatial prediction granularity in the prior art are solved.

The invention relates to the technical field of safety risk assessment, in particular to a historical building safety system risk level assessment system and method, and aims to solve the technical defects that a historical building risk assessment method in the prior art is static, one-sided and lack of predictability. According to the system and the method, multi-modal sensing data of a historical building is acquired, time-space reference alignment is performed, a multi-dimensional physical degradation index is quantitatively extracted, dynamic weighted fusion calculation is performed to generate a structure health degradation feature vector, and a nonlinear state space prediction model is utilized to deduce a future evolution trajectory of the model and perform risk grade division. Through adoption of the technical scheme, comprehensive and refined description of historical building states can be realized, evaluation is upgraded from static diagnosis to dynamic early warning, and the perspectiveness and decision support capability of evaluation are improved.

The invention relates to a biological disaster prediction method and system, and the method comprises the following steps: S100, integrating multi-source remote sensing and environment data and a field investigation sample, and constructing an initial data set; s200, based on the initial data set, a space prediction model set and a time sequence prediction model are trained, and the space prediction model set comprises at least two anomaly detection models; s300, selecting the anomaly detection model with the optimal performance in the spatial prediction model set; and S400, outputting a prediction result based on the anomaly detection model with the optimal performance and the time sequence prediction model with the optimal performance. Through the arrangement, the timeliness, precision and adaptability of locust disaster prediction are remarkably improved, and the locust disaster prediction system is widely applied to agricultural disaster prevention, grassland ecological monitoring and disaster early warning platform construction.

The application discloses a kind of hierarchical light field image encoding methods, comprising: enhancement layer coding network utilizes the enhancement layer coding strategy based on domain-specific reference to carry out interlayer prediction and residual compression to spatial feature, and angle feature is generated at decoding end, and the light field image is encoded;Interlayer spatial prediction module is constructed, the co-location and local self-similarity within reference spatial feature are modeled simultaneously, to implicitly reconstruct high-frequency details in high-resolution light field representation, predict spatial latent representation;Spatial-angle context-aware reconstruction module is constructed, long-distance and short-distance spatial-angle correlation is explored in long-range and short-range context-aware branch respectively to model light field image geometry and preserve local details, and the light field image of enhancement layer is reconstructed.The application encodes light field image of different spatial resolution in an end-to-end manner, and improves the encoding performance of spatial hierarchical light field image.

This application discloses a gesture recognition method, system, device, and computer-readable storage medium, relating to the field of artificial intelligence technology. The method involves training a gesture recognition model using a first training view and a second training view. The gesture recognition model is built based on a spiking neural network model. The method extracts a first spatial feature sequence and a first spatial prediction vector from the first training view generated by the gesture recognition model. It also extracts a second spatial feature sequence and a second spatial prediction vector from the second training view generated by the gesture recognition model. Based on the differences between the first spatial feature sequence and the second spatial prediction vector, and the differences between the second spatial feature sequence and the first spatial prediction vector, a temporal contrast loss value is generated. The gesture recognition model is adjusted based on the temporal contrast loss value to obtain a trained gesture recognition model. A gesture view is then acquired. Finally, the gesture recognition model is applied to recognize the gesture view to obtain the gesture recognition result. This improves the accuracy of gesture recognition.

The invention relates to the technical field of risk assessment, in particular to a method for providing risk assessment level map information, which comprises the following steps of: acquiring real-time data from a plurality of sources, and performing unified standardization processing on the real-time data to form a fusible data set; analyzing historical event data based on the fusible data set, and generating a historical event mode and a corresponding initialization parameter; and performing uncertainty fusion on the fusible data set in combination with a historical event mode, allocating various data weights, and generating a regional risk level with a confidence index. According to the method, a historical event association graph is constructed, time and space association rules between events are mined, and a historical event mode is used for initializing event propagation model parameters, so that prospective space prediction of potential risk events between regions is realized; therefore, the system can predict the propagation trend of the event between the areas and the potential high-risk area.

This invention relates to a method and system for analyzing the meteorological suitability of tobacco cultivation, specifically a method for evaluating and spatially predicting the climate suitability of tobacco cultivation based on multi-source data fusion. It addresses the problems of insufficient spatial resolution of meteorological data, static evaluation models, inadequate multi-source data fusion, separation of disaster early warning and suitability evaluation, and lack of intelligent service capabilities in existing technologies. This invention collects and fuses multi-source data, filters key meteorological elements, constructs fuzzy membership functions and spatial distribution models, calculates grid meteorological element values, and calculates a comprehensive suitability index. Based on this, it classifies the data into levels and generates a climate suitability zoning map. This invention is applicable to fields such as climate suitability evaluation for tobacco cultivation, crop planting zoning, and agricultural meteorological disaster risk assessment.

The invention provides a traffic flow prediction method and system, and a terminal, and the method comprises the steps: obtaining a traffic network in a target region, and constructing an adjacent matrix and a distance matrix; extracting a plurality of historical traffic flow sequences from the historical traffic flow collected by each node device, and converting the historical traffic flow sequences to obtain corresponding feature embedding; obtaining a corresponding time embedding vector from a preset time embedding vector table, and splicing the time embedding vector with the feature embedding to obtain space-time embedding; performing expansion causal convolution and gating processing on the space-time embedding to obtain gating characteristics; dividing a neighbor group and a far neighbor group according to the distance threshold and the distance matrix; and respectively obtaining a first gating feature and a second gating feature, processing the first gating feature and the second gating feature by adopting a corresponding self-attention mechanism to obtain a first attention feature and a second attention feature, splicing the first attention feature and the second attention feature to obtain a spatial prediction feature, and further predicting the traffic flow corresponding to a future time period in the target area. According to the method, the long-range and burst flow prediction precision is improved through grouping attention fusion of far and near spatial-temporal features.

The application provides a stratum absorption compensation method and system based on a seismic signal space, and the compensation method comprises the following steps: acquiring a seismic signal; determining a coherent direction of the seismic signal by using a dip angle scanning method, and designing a spatial prediction error filter with signal identification capability; constructing a non-steady filter representing an absorption effect of the seismic signal; establishing a target functional for non-steady inversion of the seismic signal after absorption compensation, and introducing spatial coherence of the seismic signal into a regularization condition of an inversion system; and performing inversion on the target functional by using a conjugate gradient method to obtain the seismic record after absorption compensation. The method is simple to operate, stable to run, and obvious in effect, effectively suppresses the influence of noise interference on absorption compensation, and improves the compensation precision of the seismic signal.

The invention discloses a roadway rockburst risk level total-space prediction method and system, and relates to the technical field of rockburst disaster risk prediction. The method comprises the steps that rock mass mechanical parameters collected through a plurality of discrete monitoring points in a roadway target area are obtained; performing spatial interpolation processing on the rock mass mechanical parameters, and constructing a continuous parameter field covering the target area; inputting the continuous parameter field into a pre-trained rockburst prediction model; the rockburst prediction model performs feature extraction on the continuous parameter field through a Transform module, inputs the extracted features into an Adaboost module for integrated classification, and outputs a rockburst risk level prediction result; and based on the rockburst risk level prediction result, generating a rockburst risk level three-dimensional visualization graph of the target area. According to the method, the full-space continuous prediction and three-dimensional visual expression of the rockburst risk can be realized, the fragmentation limitation of traditional local monitoring is overcome, and the accuracy and decision-making efficiency of disaster early warning are remarkably improved.