513 results about "Data space" patented technology

Disclosed is a three-dimensional deformation field modeling method fusing global navigation satellite system (GNSS) / synthetic aperture radar interferometry (InSAR) observation data. The three-dimensional deformation field modeling method includes: five parts of solving an InSAR line-of-sight velocity field, unifying a spatial reference datum of the GNSS data and the InSAR data, verifying precision of a result of the InSAR data, solving a three-dimensional deformation field having high precision based on a least square method, and analyzing and verifying a result. According to the method, the InSAR data is processed according to a high-precision time series method, a long-wavelength error of the InSAR data is corrected on the basis of a difference of two data spatial reference datums, and the accuracy of the result is ensured through verification of internal coincidence precision.

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

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

The invention discloses a power grid load prediction and scheduling optimization system based on artificial intelligence, particularly relates to the technical field of power system automation, and solves the technical problems of low power grid load prediction precision, poor scheduling strategy robustness and insufficient source grid load storage coordination in the prior art. Multi-source heterogeneous data space-time alignment is realized by constructing a data acquisition layer based on edge calculation, a load prediction result is generated by adopting an AI prediction module fused by a graph convolutional network and an attention mechanism, and a source-network-load-storage collaborative scheduling scheme is generated through a multi-target risk hedging optimization algorithm. And closed-loop optimization is realized by using digital twinborn pre-check and incremental learning. And finally, the load prediction accuracy, the scheduling decision reliability and the system adaptive capability in the new energy access environment are improved.

The invention discloses a major hazard source risk dynamic calculation system based on AI large model enabling, and relates to the technical field of risk calculation, and the system comprises a multi-modal data collection and preprocessing module which is used for achieving the synchronous collection of original data through the butt joint of an industrial protocol with a sensor; the heterogeneous data space-time fusion engine module is used for constructing a space-time diagram model and analyzing a nonlinear coupling relationship of multi-source data; the online incremental learning and model fine tuning module is used for triggering dynamic parameter adjustment based on the real-time data flow; a risk conduction probability calculation module; a multi-modal knowledge self-evolution module; and a self-adaptive threshold management and alarm module. According to the method, sliding window dynamic confidence interval calculation is combined with a time-varying confidence coefficient adjustment mechanism, self-adaptive fitting of a threshold value to real environment disturbance is achieved by embedding a periodic correction term, the bidirectional contradiction between detection sensitivity and false alarm suppression is effectively cracked, and a complete technical closed loop from dynamic sensing and cross-domain verification to rapid linkage is formed.

The invention discloses a 5G message video monitoring method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal data set; s2, multi-modal fusion features are generated by using a joint convolutional neural network; s3, generating a target behavior trend curve and a target behavior prediction result by using the 3D convolutional neural network; s4, generating a target threshold curve by using an adaptive algorithm based on variable domain search, and obtaining behavior abnormality warning information; s5, generating comprehensive alarm information by using a method of combining fuzzy logic reasoning and a Bayesian network; s6, cross-camera data space-time correlation reasoning is carried out by using the graph neural network; s7, constructing an event severity curve by using a multivariable regression analysis and analytic hierarchy process, and generating a severity level and an event response measure of the event; and S8, pushing alarm information and event response measures to related personnel or equipment through the 5G message platform. The invention provides an efficient video monitoring method in combination with artificial intelligence and a 5G network.

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention discloses a method for dynamically establishing a knowledge graph based on multi-source heterogeneous data fusion, and belongs to the field of intelligent robot systems. Firstly, multi-source heterogeneous data fusion is carried out, a distributed Kalman filter network is constructed, filters are designed for different sensors, and data space-time alignment is realized; carrying out conflict detection and resolution by adopting an improved D-S evidence theory, and dynamically adjusting a fusion weight through a confidence evaluation model; secondly, constructing a dynamic knowledge graph, and establishing a three-layer graph structure comprising an environment entity, a space-time relationship and probabilistic reasoning; designing an event-triggered incremental updating algorithm, starting local reconstruction when the environmental change degree exceeds a threshold value, and introducing an LSTM memory network to enhance the time sequence reasoning capability; and finally, constructing a reinforcement learning model based on the dynamic knowledge graph, designing a multi-target reward function, and finally training until the model is converged. According to the method, the positioning error is reduced through EKF / UKF combined filtering, the multi-sensor data space alignment error is reduced through a conflict resolution mechanism, and the execution efficiency and the coping capacity of the omni-directional mobile robot in a complex dynamic environment can be improved.

The invention relates to the technical field of three-dimensional observation, in particular to a geological deformation three-dimensional observation system and method, and the method comprises the steps: multi-dimensional data collection: synchronously obtaining data, and collecting ground temperature gradient field data and underground water flow velocity field data of a monitoring region; data space-time calibration processing: performing space-time reference unification processing on the acquired data to generate a multi-source displacement data set after space-time calibration; cold door parameter extraction: generating an environment coupling parameter matrix based on ground temperature gradient field data and underground water flow velocity field data in multi-dimensional data acquisition; modeling a three-dimensional deformation field: generating an initial three-dimensional deformation field model; dynamic model correction: performing iterative correction on the initial three-dimensional deformation field model to generate an optimized three-dimensional deformation field model; and visual early warning output: generating an early warning signal with a risk level identifier. According to the method, a multi-source heterogeneous data fusion system is constructed, and a cold geological parameter dynamic compensation mechanism is combined, so that high-precision three-dimensional deformation field modeling and early warning are realized.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention relates to the technical field of motor fault diagnosis, in particular to a motor fault diagnosis algorithm based on multi-sensor fusion, and the algorithm comprises the steps: injecting a step excitation signal into a motor, synchronously collecting the original response waveforms of vibration and current sensors, and calculating the inherent response delay. Establishing a mapping relation library of delay values and current sensor filtering parameters, calling the delay values in real time according to the filtering parameters, performing reverse time offset compensation on a current harmonic signal time sequence, performing time alignment on the two types of data, finally performing cross-domain coupling analysis on the aligned data, extracting vibration pulse peak frequency and current harmonic fluctuation quantity, and determining the vibration pulse peak frequency and the current harmonic fluctuation quantity. Early faults are judged by combining the bearing outer ring fault characteristic frequency band and the load rate dynamic threshold value, graded alarm is generated by tracking characteristics, the problem of fault false judgment and missed judgment caused by sensor data space-time dislocation is solved, and the early fault diagnosis accuracy of the motor is improved.

The invention discloses an ultra-short-term wind power combined prediction method based on TCN-SENet-Transformer, and the method comprises the steps: firstly adjusting the channel weight through SENet, and extracting the spatial features of data through TCN; meanwhile, adopting Transform to mine the time sequence characteristics of the data, and capturing the context characteristic relation; and finally, data space and time sequence features are fully fused by using CA. According to the method, the characteristic that the TCN and the Transform can process all data of a sequence in a multi-thread mode is fully utilized, meanwhile, the TCN is used for analyzing spatial characteristics, the Transform is used for extracting time sequence characteristics, and dual parallelism is achieved. According to the method, the prediction precision is effectively improved, and the technical problems of low prediction precision, complex process calculation and low prediction efficiency in the prior art are solved.

The invention provides a multi-band radar data space downscaling method based on data fusion. The method comprises the following steps: step 1, acquiring S-band and X-band observation data from a weather radar; step 2, carrying out data preprocessing on the obtained observation data; step 3, constructing a spatial downscaling model; 4, performing training verification and testing on the spatial downscaling model through the observation data after data preprocessing; step 5, according to the trained spatial downscaling model, performing spatial downscaling of the S-band observation data; and step 6, judging whether the generated data is close to a true value or not. By means of the deep learning neural network, the model can extract and fuse the characteristics of the S-band observation data and the X-band observation data, the original low-resolution S-band data is converted into high-resolution data, the requirement for high resolution of radar data in practical application is met, and dependence on hardware facilities is reduced.

The invention belongs to the technical field of optical detection, and particularly relates to a global power wave aberration prediction-based Ritchey-Common detection method and system, and the method comprises the steps: firstly building a three-dimensional optical model through the parameters of a standard spherical mirror, a to-be-detected plane mirror and an interferometer, and combining a preset Ritchey angle and an out-of-focus / astigmatism coefficient, injecting vibration and temperature noise by using Monte Carlo simulation to generate an enhanced training data space; secondly, constructing a phase error adjustment model based on an adversarial network, and decoupling a mapping relation between an adjustment error and wave aberration distortion through a multi-scale attention mechanism and a radial basis function network to realize adaptive correction of sub-aperture phase data; and finally, splicing sub-aperture phases by using a graph neural network and fusing multi-angle full-aperture measurement data to accurately reconstruct the surface shape of the plane mirror to be measured. According to the invention, through confrontation training and dynamic error compensation of physical constraints, the detection efficiency and reliability of the large-aperture optical element are significantly improved.

The invention belongs to the technical field of remote sensing monitoring, and particularly relates to a sequential SAR crop classification method based on lightweight linear attention. The method comprises the following steps of: firstly, efficiently extracting spatial characteristics of an image of each time step in a time sequence image by utilizing a weight-shared U-Net encoder by utilizing spatial and temporal characteristics of the time sequence SAR image; lLTAEBlock is used for extracting spatiotemporal features from time sequence advanced spatial features, FAVOR + is used for achieving linear calculation complexity, a shared query matrix serves as a learnable parameter, the calculation amount and the parameter amount are reduced, global information interaction in the time dimension is achieved, and modeling is conducted on the complex phenological relation of crops; and the decoder retains the deep spatial-temporal features and fuses the time sequence shallow spatial features to finally obtain a prediction result with the same spatial scale as the input data. And under the constraint of the loss function, adjusting through an optimizer to obtain a crop classification model with the best fitting. The method is especially suitable for crop classification of long-time-sequence SAR images, reduces the need of a memory, and improves the crop classification efficiency.

The invention relates to a runoff hydropower station generating capacity prediction method based on depth data fusion, and the method comprises the following steps: S1, carrying out data layer fusion, integrating multi-source hydro meteorological data, obtaining multi-source data from a meteorological station, a hydrological station, a satellite remote sensing platform and a numerical weather forecasting system, carrying out the cleaning, interpolation and normalization processing of the data, and carrying out the prediction of the generating capacity of a runoff hydropower station; performing data space-time alignment; s2, carrying out feature layer fusion, firstly inputting a multi-source data set of a unified space-time reference, carrying out feature extraction, carrying out feature fusion based on a self-attention mechanism, embedding a water balance equation in a deep learning model, and obtaining a comprehensive feature vector; and S3, performing prediction, inputting the comprehensive feature vector in the step S2, and performing power generation prediction by using a deep neural network.

The invention relates to the technical field of broadband spectrums, and discloses a broadband spectrum monitoring system based on dynamic channel allocation, which comprises a data acquisition unit, a data identification unit, a region division unit, a data alignment unit and a channel allocation unit.The system dynamically adjusts the coverage area through beacon nodes, accurately calculates the coverage radius in combination with the urban environment, and improves the coverage efficiency. Meanwhile, a channel allocation strategy is optimized by a game theory, the change of a frequency spectrum environment is responded, and frequency spectrum state parameters are constructed by fusing multi-dimensional characteristics, so that communication smoothness is guaranteed, blind areas of monitoring nodes are eliminated, data space-time alignment is realized, the monitoring sensitivity and the resource utilization rate are effectively improved, and stable frequency spectrum and reliable communication under large-scale activities in cities are ensured; and the dynamic and reasonable distribution of the channels is ensured.

The invention discloses a geological survey whole-process data aggregation system for ancient marine environment evolution, and relates to the technical field of marine geological survey. The data management platform is in communication connection with a data acquisition module, a data preprocessing module, a data integration module, a data space-time interpolation module, a data quality evaluation module, an evolution analysis module and a result display module, and all the modules are in electric signal connection. By integrating deep-sea sediment, seabed rock, marine organism fossil and ocean surface environment parameter multi-source data, the data integration and processing capacity is improved, the evolution of the ancient ocean environment is simulated and predicted by utilizing the constructed random forest model, a comprehensive and deep analysis result is provided, and the analysis efficiency is improved. Not only is the evolution rule and trend of the ancient marine environment disclosed, but also a scientific basis can be provided for future environmental protection and resource management.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

The invention relates to the technical field of safety monitoring of constructional engineering, in particular to an intelligent construction safety monitoring system and method based on multi-modal data fusion, and the system comprises a plurality of modules: a 5GMEC-based heterogeneous data space-time alignment module which completes coordinate system conversion between laser point cloud and a BIM model by using an improved Fast-ICP algorithm; the feature level fusion network comprises a geometric feature branch (improved unscented Kalman filter processing point cloud normal vector) and a time sequence feature branch (LSTM processing stress sensor data); the NeRFXL fusion engine is used for fusing the point cloud and the video data by adopting a multi-scale neural radiation field; the multi-task anomaly detector is used for constructing a hierarchical Transform architecture and introducing a modal gating mechanism; and the dynamic knowledge graph engine constructs a knowledge graph updating module based on a graph neural network, and all the modules are in communication connection with one another and work cooperatively, so that functions of data processing, fusion, anomaly detection, knowledge updating and the like are realized.

The invention relates to the technical field of data, and discloses a logging data intelligent processing method. The method comprises the following steps: acquiring data through a multi-source logging device and preprocessing the data to form a result set; identifying and classifying stratum features according to the result set; analyzing oil and gas parameters based on the stratigraphic map and the result set; identifying risks by using the oil-gas evaluation table and the result set and establishing an early warning mechanism; the reservoir physical property is evaluated according to the oil and gas evaluation table and the early warning mechanism; and integrating the stratigraphic map, the oil and gas evaluation table and the reservoir report to reconstruct the geological structure prediction enrichment area. According to the method, full-process intelligent processing from acquisition and preprocessing of multi-source heterogeneous logging data to stratum identification, oil and gas evaluation, drilling risk early warning, reservoir assessment and oil and gas reservoir description is realized, and the problems of data space-time mismatching, parameter isolated analysis, high evaluation subjectivity, early warning lag and the like in a traditional method are solved; and the efficiency, accuracy and systematicness of logging data processing are improved.

A civil aircraft component fault diagnosis method based on conditional diffusion in a variable working condition scene comprises the steps that limited high-confidence-coefficient fault samples and label-free fault data under different working conditions are collected, after hidden space feature reconstruction is carried out, a denoising diffusion probability model is adopted to carry out conditional generation augmentation on target domain high-confidence-coefficient fault samples of a target domain, and the target domain high-confidence-coefficient fault samples of the target domain are obtained; and through joint optimization of an objective function, a mapping relation between a generation feature and a diagnosis decision is restrained while fault sample condition distribution is deduced, and collaborative improvement of generation quality and model generalization ability is realized. According to the method, the fault sample generation and fault cross-domain decision-making process is jointly optimized through the conditional diffusion-based fault diagnosis algorithm (CDFD), so that the fault diagnosis classifier can fully sense noise hidden space and data space characteristics of a source domain and a target domain, and the robustness is effectively improved.

The invention provides a space-time intelligent ocean satellite internet of things perception information screening method and device, and belongs to the technical field of data processing. Marine environment data space-time prediction is carried out based on a block time sequence converter, marine hot spot area mining is carried out based on a density clustering algorithm of mean shift, and marine environment data comprises sensing data of sea surface temperature, salinity, wave height and the like; marine environment risk assessment is carried out based on a BP neural network, information screening feedback control is carried out based on multi-agent reinforcement learning, the efficient information processing requirement of a complex marine environment detection scene is met, and the low-redundancy and high-precision data acquisition and transmission adaptive feedback control effect is achieved. The method can be directly applied to an ocean observation and detection facility networking system in the ocean Internet of Things, can provide powerful support in the aspects of military application and civil application, and has wide and important application prospects and value.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention discloses an oil reservoir end-to-end dynamic modeling method based on a diffusion model, which belongs to the field of petroleum engineering and comprises the following steps: establishing a sample data set; constructing a time sequence feature extraction model based on an Informer model; constructing a basic process architecture of the diffusion model; constructing a de-noising network based on Unet, and packaging the time sequence feature extraction model based on Informer, the basic process architecture of the diffusion model and the de-noising network to obtain an inversion prediction model capable of realizing end-to-end dynamic modeling of the oil reservoir; for observation production data containing noise, a data space inversion method is used for generating multiple pieces of prediction data of future flow, the prediction data serve as input of an inversion prediction model, and dynamic adjustment of a real oil reservoir model is achieved. According to the invention, the matching capability of the oil reservoir model to actual development dynamics can be obviously improved.

The invention relates to the technical field of data processing, in particular to a high-noise data intelligent cleaning and credibility evaluation method based on uncertainty quantization, which comprises the following steps: constructing a noise manifold model of a high-dimensional data space through a Riemannian geometry method, and extracting data intrinsic structure features and noise distribution trajectory tensor; according to the noise distribution trajectory tensor, constructing an anisotropic diffusion equation, and generating confidence distribution with data manifold constraint; the geometric invariance of a data set is kept while noise stripping is carried out in a data manifold tangent space, and noise stripping and structure keeping are achieved; calculating an intrinsic credibility index and generating a credibility evaluation map coupled with the data manifold structure; according to the method, the intrinsic characteristics of the data manifold are kept in the cleaning process, the deformation degree of the quantized data structure is calculated through the geodesic deviation, and it is ensured that the intrinsic geometrical relationship of the data is not affected by noise stripping.

The invention relates to the technical field of spatial transcriptome data clustering analysis, and discloses a spatial transcriptome data clustering method based on progressive learning and multi-modal fusion. According to the method, a high-order structure relationship among sample points is captured by using a multilayer graph convolutional network, and various noises of gene expression data are removed to the greatest extent through comparative learning and a complementary mask mechanism; meanwhile, gene expression data, spatial position information and histological image information are ingeniously fused to the maximum degree through two times of cross attention, and then fusion feature latent representation Latent is obtained. Compared with a mainstream spatial transcriptomics clustering analysis method, the method has the advantages that clustering based on the fusion feature latent representation Latent shows higher capability and excellent clustering performance, and has remarkable robustness.

The invention relates to the field of building construction safety, in particular to a risk prediction method and system for building construction. Aiming at the defects of multi-source data isolated analysis, dynamic risk response lagging, insufficient prediction precision and the like in the prior art, a unified analysis base is formed by constructing a space-time fusion data space and integrating multi-dimensional dynamic data such as structure micro-deformation monitoring, environmental parameters, three-dimensional live-action scanning, personnel positioning, a building information model and the like; based on a deep neural network architecture, designing a multi-modal feature extraction mechanism to quantify the coupling risk, and generating a partition risk probability distribution diagram; and in combination with a construction stage characteristic matching security policy library, implementing a three-level early warning mechanism and an automatic avoidance instruction. A closed-loop optimization mechanism is introduced, model parameters and decision threshold values are dynamically adjusted through actual accident feedback, and continuous evolution of a prediction system is achieved. According to the method, the active prevention and control capacity of compound accidents such as collapse and high-altitude falling is remarkably improved, and a self-adaptive intelligent protection system is constructed for a construction site.

The invention discloses a building full life cycle-oriented BIM data asset precipitation and operation management system. The system comprises six modules including a multi-source data conflict resolution module, a Beidou grid coding association module, a space-time fusion index module, an AEM asset integration module, a full cycle BIM processing module and a data asset operation module. According to the system, a building multi-source data conflict resolution and lightweight algorithm, a Beidou grid coding space-time asset association model and a data space-time fusion and spatial index model are utilized, and an Adobe Explosion Manager Assets platform is combined, so that BIM data of the whole life cycle of a building is processed. Through module collaboration, multi-source data conflicts are eliminated, data space-time association and fusion are achieved, the data are integrated to a professional platform, and a full-cycle data association graph is formed and operated. According to the system, the problems of low data integration efficiency, insufficient time-space association and the like in the prior art are solved, effective precipitation and efficient operation of BIM data assets are realized, and support is provided for refined management of buildings.

The invention belongs to the technical field of visual processing, and particularly relates to a time sequence NDVI crop distribution extraction method based on a mask auto-encoder, which mainly comprises four steps. Firstly, time sequence NDVI data are prepared, a multi-temporal remote sensing image in a complete growth cycle of target crops in a target area is obtained and processed, and the recognition precision is improved by using NDVI feature changes in the growth stage of the crops. And then performing time sequence transformation on the ViT model, dividing data space dimensions, completing Patch flattening and embedding, enabling the Patch to be adaptive to time sequence data, and simultaneously performing image processing advantages. Then, model training is carried out, a mask auto-encoder is pre-trained in a self-supervised mode through a large amount of unlabeled data, and then supervised fine tuning is carried out through a small amount of labeled data; and finally, processing new data by using the fine-tuned model, obtaining pixel-level crop category prediction, and generating a complete distribution map. According to the method, by means of time sequence data and model transformation, crop types and growth stage differences are effectively distinguished, and accurate extraction is achieved.