2858 results about "Field data" patented technology

The present invention relates to the technical field of data transmission. Disclosed is an application-driven three-dimensional spatial data transmission method. The method comprises: determining system key performance indicators (KPIs) by means of qualitative and quantitative analysis; constructing an AI-driven adaptive three-dimensional data transmission mechanism, and dynamically adjusting a transmission strategy on the basis of a real-time network state, a device capability, an application scenario and the KPIs; developing an adaptive compression algorithm set oriented to three-dimensional data, so as to meet differentiated compression requirements of different application scenarios; performing loop execution of a test, and adjusting and optimizing the data transmission mechanism and the compression algorithm set on the basis of a test feedback result and the real-time network state; and deploying an optimized transmission method to a production environment, and collecting field data to optimize the system performance and verify the achievement of the KPIs. By constructing a qualitative and quantitative analysis framework based on machine learning, the present invention quantifies differentiated transmission requirements of different application scenarios and formulates transmission strategies meeting the scenario requirements.

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention discloses an industrial data real-time acquisition monitoring system fusing edge computing and 5G, and belongs to the technical field of industrial Internet of Things. The system is composed of a multi-source heterogeneous data acquisition module, an edge computing node cluster, a 5G communication network and a cloud analysis platform, an edge-cloud collaborative architecture is innovatively adopted, a multi-protocol adapter is integrated to realize unified access of heterogeneous data of industrial equipment, a low-delay transmission channel is constructed by using a 5G network slicing technology, and the heterogeneous data of the industrial equipment is transmitted to the cloud analysis platform. And transmitting the preprocessed data to the edge computing node in parallel. And the edge layer realizes dynamic resource scheduling by adopting a containerization technology, and realizes millisecond-level response and local decision feedback. And meanwhile, through an edge-cloud data synchronization mechanism, a distributed time sequence database is constructed, and visual monitoring and deep analysis of multi-dimensional data are supported. The system improves the real-time processing capability of industrial field data and the reliability of the system, and has the technical advantages of low time delay, high concurrency and optimized resource utilization rate.

The invention relates to the technical field of vacuum electric furnace monitoring, and discloses a state monitoring system suitable for a vacuum electric furnace. A multi-source sensor array of the system collects multi-dimensional physical signals such as temperature distribution, pressure change and vacuum degree fluctuation in a furnace in real time; a furnace cavity feature reconstruction module extracts sampling point feature parameters and correlates coordinates to construct a three-dimensional dynamic feature field; the process anomaly analysis module calculates a process deviation degree in combination with a preset reference parameter, and marks an anomaly coordinate area; the state transition evaluation module analyzes an abnormal trend according to historical records and predicts a state transition path and rate; the collaborative regulation and control decision-making module generates a multi-stage vacuum maintenance compensation strategy and a heating power regulation gradient scheme according to the multi-stage vacuum maintenance compensation strategy; and the running log feedback module records a strategy execution process, and associates the three-dimensional feature field data to generate a state tracing log. The system can realize comprehensive monitoring of the state of the vacuum electric furnace, accurate abnormity identification, trend prediction, cooperative regulation and control and state tracing, and helps to improve the operation management level of the vacuum electric furnace.

The invention provides an intelligent event identification method and system based on a high-speed camera, and the method comprises the steps: setting the collection parameters of the high-speed camera, and triggering the camera to collect a target scene video stream. And hardware acceleration decoding processing is carried out on the collected original video data stream, real-time environment illumination information of the environment illumination sensor is obtained, and dynamic brightness equalization processing is executed. And performing motion adaptive denoising processing on the video sequence. Geometric distortion correction is carried out on the image sequence through camera calibration parameters, sub-pixel-level displacement vectors and dense optical flow field data of a moving object are extracted, and multi-scale morphological features are extracted. And the features are fused to generate motion feature data, the data are processed through a spatio-temporal joint event classification model, an event identification result is output, the result is bound with a high-precision timestamp, and event identification information is output to an industrial control system display device in real time. According to the invention, the accuracy and real-time performance of event identification can be improved.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention relates to the field of engineering management, and discloses a wharf steel structure construction progress dynamic management and control and resource allocation system, which comprises a real-time data perception and fusion module for collecting field data and an initial plan and generating a fusion data stream; the dynamic construction digital twinborn construction and evolution module is used for updating the digital twinborn and a dynamic empirical risk layer in real time based on the fusion data flow; the multi-agent self-adaptive decision engine is used for generating a self-adaptive construction scheme based on digital twinborn simulation deduction and feeding back a risk signal; and the man-machine cooperation and closed-loop execution module is used for visualizing the scheme for manual decision making and issuing an instruction to a site. According to the method, the physical field data sensed in real time is fused with the digital model, the dynamic construction digital twin capable of being synchronously updated with the physical construction field state is constructed, and the technical effect that digital information and the physical entity state are kept highly consistent is achieved.

The invention discloses a sliding bearing friction wear prediction method based on fluid mechanics, and relates to the technical field of mechanical state monitoring, and the method comprises the steps: collecting single-point temperature, local pressure, vibration time domain signals and a bearing pedestal inclination angle through a sensor, and generating sensor data; performing field reconstruction based on a fluid mechanics conservation equation on the sensor data to obtain multi-field data; performing spatial alignment on the multi-field data, inputting the multi-field data into a long short-term memory (LSTM) network, and generating wear state characteristics; training a sparse correlation vector machine regression model RVM by using the historical wear data, establishing a nonlinear mapping relationship between the wear state characteristics and the wear depth, taking the wear state characteristics as the input of the vector machine regression model RVM, and outputting the predicted wear depth; sensor data and the predicted wear depth are fused in real time through Kalman filtering, and when prediction deviation exceeds a covariance threshold value, vector machine regression model RVM parameters are updated.

The invention provides a compressor flow field reduced-order modeling method and device based on POD and deep learning, and relates to the technical field of compressor flow field modeling, and the method comprises the steps: obtaining three-dimensional transient flow field data of a compressor flow field region under a plurality of working condition parameters, and constructing a flow field data set containing a plurality of time snapshots based on the three-dimensional transient flow field data; obtaining target flow field snapshot data of the compressor based on the flow field data set; performing intrinsic orthogonal decomposition on the target flow field snapshot data, and obtaining a dimension-reduced time coefficient matrix based on the decomposed data; and constructing a training set based on the working condition parameters and the dimensionality-reduced time coefficient matrix, and training a pre-constructed neural network prediction model by using the training set to obtain a target prediction model. According to the compressor flow field reduced-order modeling method, the target prediction model integrated with the flow physical law can be trained and constructed, and the prediction task under the new working condition can be completed by adopting the target prediction model.

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

The invention provides a fiber-optic gyroscope drift compensation method and system fused with LMS self-adaptive filtering, and relates to the technical field of LMS self-adaptive filtering, the method comprises the following steps: synchronously collecting multi-point temperature data, temperature change rate, axial and radial gradient information and original output signals of a gyroscope shell, and calculating the multi-point temperature data, the temperature change rate, the axial and radial gradient information and the original output signals of the gyroscope shell; the original signal is separated into a temperature drift component and a non-temperature noise component through variational mode decomposition, and noise is filtered out; establishing a nonlinear mapping relation by combining the temperature field data and the drift component to generate a drift predicted value, and inputting the predicted value and the residual error of the actual drift component into an LMS adaptive filter for dynamic error compensation; and finally, superposing the compensation result and the predicted value, and outputting a corrected gyro signal. According to the invention, the drift compensation precision and stability of the fiber-optic gyroscope in a complex variable-temperature environment are improved.

The invention relates to the technical field of automobile chassis simulation, and discloses an automobile chassis mechanism simulation optimization method based on digital twinning. The method comprises the following steps: firstly, acquiring a real-time operation data set of an automobile chassis mechanism, including multi-axis acceleration time sequence information, a suspension displacement monitoring sequence and the like, and synchronously transmitting the real-time operation data set to a cloud digital twin platform; constructing a hierarchical digital twin model comprising a suspension subsystem node, a steering mechanism node and a connecting component spatial constraint relation in the platform according to a chassis topological structure; performing multi-physics field feature extraction on the real-time operation data set to generate a chassis multi-physics field data set; executing multi-scale joint simulation calculation based on the model and the data set, and outputting a chassis dynamic response prediction result including fatigue damage distribution of the key connecting piece and the like; and finally, generating an optimization parameter adjustment instruction set according to the result, combining and feeding back to a physical chassis execution mechanism. According to the method, the real-time performance and precision of chassis simulation optimization are realized, and the chassis performance is improved.

The invention discloses a space-time large model intelligent decision support system, and belongs to the technical field of artificial intelligence and engineering. Aiming at the problems of insufficient speciality, lack of space-time processing capability and low decision-making reliability of a general large model in the engineering field, a system comprising a data processing module, a space-time large model module, an intelligent decision-making module, an interaction and integration module and a feedback learning module is constructed; the method comprises the following steps: formulating a multi-source heterogeneous data processing standard and specification of discrete storage in the engineering field, constructing a structured and specialized field data set and a knowledge graph, training a large space-time model with space-time processing capability by adopting technologies such as multi-modal alignment, and verifying and enhancing the reliability of a decision in combination with the knowledge graph; meanwhile, by means of a user feedback continuous optimization method and the like, an intelligent decision support system and application of space-time vector data processing, semantic alignment and illustrative and graphical aspects are achieved, and the accuracy, reliability and intelligent level of decision support in the engineering fields of mines, water conservancy, cities, traffic, factories, buildings and the like can be remarkably improved.

The invention relates to the technical field of BIM, in particular to a BIM-based construction progress intelligent management and control method and system, and the method comprises the steps: constructing a BIM digital twin cloud model, fusing a panoramic image and LiDAR point cloud data, dynamically selecting a data source update model through an algorithm, extracting engineering topology, building a construction network diagram, and generating a state evolution trajectory in combination with environment and resource data. Predicting and visualizing milestone time, comparing field data with a BIM model to generate progress deviation information, performing stability analysis, triggering resource allocation when a threshold value is exceeded, synchronizing a material supplier and a field manager, dynamically adjusting personnel, equipment and materials, dynamically adjusting a milestone plan based on multiple data, and presenting and guiding allocation through the BIM model. The construction progress is ensured to be consistent with the plan, and the complex interaction relationship and dynamic characteristics in the construction process are captured through nonlinear dynamic system modeling in combination with LiDAR point cloud and other high-precision data.

The invention discloses a dynamic error real-time compensation method and system for a heavy-load vertical machining center, and belongs to the technical field of high-end numerical control equipment, precision manufacturing and intelligent control. Inputting the state vector into a dynamic error model to solve a three-dimensional space dynamic error vector; processing the error vector to generate a real-time compensation instruction; a compensation instruction is injected into the numerical control system to correct the machining track online; and obtaining a real error to update the dynamic error model on line. According to the method, the technology of combining multi-physics field data fusion and a neural network agent model is adopted, frequency decoupling and dual-channel compensation injection are performed on errors, and an online model self-optimization feedback closed loop is established, so that real-time and high-precision compensation on multi-source coupling dynamic errors such as thermal-induced and force-induced multi-source coupling dynamic errors can be realized; and the limit machining precision and stability under the heavy-load machining condition are remarkably improved.

The invention relates to the technical field of high-pressure resin transfer molding (HP-RTM), in particular to an intelligent forming system and equipment based on thermal-mechanical coupling carbon fiber composites.The intelligent forming method comprises the steps that real-time temperature field and strain field data are collected through a partition temperature control mold, thermal-mechanical coupling state parameters are obtained through signal filtering and abnormal value removing processing, and the thermal-mechanical coupling state parameters are obtained through a thermal-mechanical coupling module; an ultrasonic probe array is combined to detect internal defect distribution to evaluate a forming quality index, process parameters are dynamically compensated according to the quality index, and a deep learning model is constructed to optimize a forming process. According to the invention, real-time monitoring and closed-loop control of multi-physics field coupling can be realized, the defect rate is obviously reduced, the debugging period is shortened, the production efficiency is improved, and an intelligent solution is provided for carbon fiber composite material forming.

The invention provides an anti-condensation temperature and humidity regulation and control system and method for remote deep learning prediction aided decision making, and the method is characterized in that a field controller is connected with a temperature and humidity collection device, obtains the environment temperature and humidity, calculates the dew point temperature, obtains the surface temperature of a protected part, and starts and stops a heating and / or dehumidification device by comparing the difference between the two values with a preset threshold value; the temperature and humidity acquisition device acquires field environment temperature and humidity data for the field controller to calculate the dew point temperature, and the temperature and humidity acquisition device is also used for acquiring the surface temperature of the protected part; the heating device is used for raising environment temperature; the dehumidification device is used for reducing the environment humidity; the communication module is used for data transmission between the field controller and the remote prediction terminal, sending field data and receiving prediction information; the remote prediction terminal obtains field data through the communication module; an anti-condensation system structure combining local autonomous control and remote prediction assistance is established, and redundancy fault-tolerant capability is achieved; even if the remote prediction function fails, the field control can still operate independently and reliably.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention provides a north pole atmosphere coupling forecasting method capable of automatically fusing sea ice concentration and thickness, which belongs to the technical field of meteorology, and comprises the following steps: firstly, acquiring GFS background field data and correcting by applying an ice-sea heat exchange correction equation; and then sea ice concentration and thickness information is extracted, and physical consistency verification is carried out based on a thermodynamic equilibrium equation. Performing multi-element fusion through an Arctic ice sea assimilation model, calculating the sea ice surface temperature by applying a polar region sea ice heat flux equation, and establishing a sea ice concentration thickness boundary layer interaction equation set to describe the modulation effect of sea ice on a boundary layer; the method comprises the core steps of performing deep fusion on various sea ice features by applying a feature fusion model, adaptively adjusting the weight based on an ice-gas interface balance index, generating an optimized mode initial field and boundary conditions, and finally setting a parameter scheme to execute an integral program to obtain a forecasting result. The technical problem of low forecasting accuracy caused by insufficient physical consistency of sea ice information is solved.

The invention relates to the technical field of workpiece airtightness detection, in particular to a workpiece airtightness detection method and system based on multi-source data fusion, and the method comprises the steps: collecting the pressure fluctuation, temperature field distribution and vibration spectrum data of a workpiece through a sensor array, and executing a stepped pressure excitation process; the method comprises the following steps: dynamically configuring an inflation rate according to the heat-conducting property of a material, reversely compensating pressure based on temperature field data in a pressure maintaining stage to eliminate thermal deformation interference, and extracting a pressure attenuation rate and nonlinear characteristics, an area proportion of an abnormal low-temperature region and space aggregation of an energy abrupt change point in a vibration spectrum from compensated data; according to the method, multi-physical field features are fused, the detection precision and reliability are improved, and the method is suitable for the airtightness detection requirement in the high-precision manufacturing field.

The invention discloses a flow field measurement method based on an event camera, and the method comprises the steps: generating a PIV data set, each time sequence sample sequence comprising a plurality of frames of continuous particle images, a corresponding velocity vector field, and particle event data at all moments; establishing a flow field data acquisition device based on an event camera and a high-speed camera, acquiring real event data and real image data which are synchronous in time so as to adjust parameters of an event simulator, and verifying and updating particle event data in the PIV data set according to the adjusted event simulator so as to obtain a flow field data acquisition result; obtaining the updated PIV data set as a training data set; building an event camera optical flow method model, and training by adopting the training data set; and on the basis of the trained event camera optical flow method model, event sequences in two adjacent time periods are used as inputs to calculate a velocity vector field corresponding to a middle moment. According to the invention, the flow field velocity field at the required moment can be obtained based on the event data within a period of time.

The invention discloses a three-dimensional unstructured grid adaptive refinement method and system based on machine learning, and belongs to the field of machine learning, partial differential equation solving and computational fluid mechanics simulation. Residual errors of a fluid control equation are used as a novel error indicator and a refinement criterion, imprecise flow field data and the fluid control equation are obtained by fusing coarse grids of simulation flow through a physical information neural network, and the total residual errors of the equations are conveniently calculated by using automatic differentiation after training is completed. Therefore, the coarse grid units with relatively high residual errors can be adaptively marked and refined. By matching an h-refinement scheme, Delaunay tetrahedron subdivision is executed after vertexes are strategically inserted to maintain the quality of the refined grid. According to the method, any numerical solver can be flexibly matched to carry out grid adaptive refinement, so that various typical flow problems can be solved with high precision. The method achieves better balance between calculation precision and the number of grids, and has the advantages of being simple, convenient, high in compatibility and universality and the like.

The invention provides an ocean multi-source data fusion method based on an adaptive Fourier nerve process, and relates to the technical field of data fusion, and the method specifically comprises the following steps: carrying out the preprocessing of ocean variable field data; and adaptively generating a set background field for any time point. And organizing the background field, the truth value and the observation value into a standardized data set for adaptive Fourier neural process model training. An AFNP model is constructed, the AFNP model comprises a background field encoder, an observation field encoder, a neural Fourier operator, an adaptive alignment fusion module and a decoder, and finally the decoder outputs a prediction result of a real field. Supervised end-to-end training is carried out on the AFNP model, a nonlinear relation between a background field and an observation field is learned, and a real ocean state field is reconstructed. According to the technical scheme, the problems that in the prior art, dependence on linear hypothesis and Gaussian errors is caused, a deep learning model cannot be physically explained, and the boundary adaptability of a spectral domain method is poor are solved.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention relates to a saline intrusion measurement method, system and equipment based on dynamic ship motion conditions and a medium, and aims to solve the technical problem that the salinity inversion precision is reduced due to spatial-temporal dislocation between particle image velocity measurement flow field data and plane laser-induced fluorescence concentration field data caused by ship six-degree-of-freedom motion. According to the method, future poses are predicted by collecting ship motion parameters and environment disturbance parameters, and space-time compensation parameters are generated in combination with a static calibration matrix; performing three-dimensional coordinate transformation and time synchronization on synchronously acquired original flow field and concentration field data by applying compensation parameters, and eliminating space-time deviation caused by ship movement; a three-dimensional salinity distribution field is calculated based on registration data fusion flow field diffusion characteristics and concentration field distribution; and meanwhile, the pose of the measuring equipment is dynamically adjusted through the closed-loop controller so as to optimize the data acquisition quality. According to the method, ship motion interference is effectively inhibited, and accurate characterization of saline invasion frontal surface spatial characteristics is guaranteed.

The invention discloses an ocean three-dimensional temperature and salt field prediction method based on remote sensing data driving, and the method comprises the steps: firstly constructing a satellite remote sensing sea surface temperature data set, a sea surface height anomaly data set and an ocean three-dimensional reanalysis data set which are needed by an experiment; then, on the basis of a convolutional neural network and a Transform module, a basic framework of the model is constructed according to the temporal and spatial variation characteristics of the remote sensing data and the temporal and spatial variation characteristics of the three-dimensional temperature and salt field; the model adopts an end-to-end design mode, and adapts to planar spatial-temporal variation of historical remote sensing data through a two-dimensional convolutional coding structure; the three-dimensional spatial-temporal change of a future three-dimensional temperature and salt field is concerned through a three-dimensional convolution decoding structure; an attention mechanism and a feature evolution module are designed according to the spatial-temporal features of the two kinds of data, and the change relation of the remote sensing data and the temperature and salt field data under different spatial-temporal dimensions is learned. And finally, inputting a mode of combining satellite remote sensing temperature of historical n days and sea surface height anomaly into the trained model to obtain three-dimensional temperature and salt field data of future n days.

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The invention provides a global magnetic disturbance compensation method in aviation magnetic survey. The method comprises the following steps: S1, performing high-altitude calibration flight through a motion platform, and acquiring total magnetic field data based on detection data of an optical pump magnetometer; s2, obtaining geomagnetic background gradient information and obtaining geomagnetic field data based on the geomagnetic background gradient information; s3, obtaining motion platform magnetic interference data according to the total magnetic field data and the geomagnetic field data; s4, motion platform attitude information, motion platform current information and fluxgate magnetometer detection data are acquired; s5, obtaining a motion platform magnetic interference coefficient according to the motion platform magnetic interference data, the motion platform attitude information, the motion platform current information and the fluxgate magnetometer detection data; and S6, according to the geomagnetic background gradient information and the motion platform magnetic interference coefficient, performing data compensation on the total magnetic field data in real time during actual aviation magnetic measurement.