45 results about "Cloud atlas" patented technology

The embodiment of the invention relates to the technical field of data modeling analysis, and discloses a tunnel rock stratum large deformation analysis method and system based on three-dimensional modeling, and the method comprises the steps: collecting initial three-dimensional point cloud data through three-dimensional laser scanning; constructing an initial three-dimensional geologic model containing spatial morphological characteristics and a rock stratum interface geometric topological relation through space coordinate matching and curved surface reconstruction; finite element unstructured mesh generation is carried out on the model, and a rock stratum mechanical analysis model is established in combination with physical and mechanical parameters; inputting construction parameters and process information, activating unit nodes in sequence, and solving a force balance equation to simulate stress distribution; identifying a potential deformation area through iterative calculation, and adjusting boundary conditions to predict a deformation trend; the three-dimensional space distribution cloud atlas, the time evolution curve and the position marks are fused to generate multi-view deformation early warning information, the accuracy and the visualization degree of tunnel rock stratum deformation analysis are improved, and support is provided for construction safety.

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.

The invention relates to a PINN-CNN target impact damage nephogram prediction model establishment method based on MHA enhancement. Comprising the following steps: acquiring data: acquiring a target impact condition data set and a damage cloud picture data set, and dividing the data into a training set, a verification set and a test set after preprocessing; building a network model based on a CNN-MHA neural network algorithm; integrating a PINN physical constraint module into the established network model to obtain a depth prediction network model; updating hyper-parameters of the deep prediction network model to obtain an optimal model; and performing impact damage prediction. According to the method, hierarchical characterization of spatial features of the damage cloud picture is realized through CNN, time sequence correlation and cross-regional coupling effect of damage evolution are captured by means of an MHA mechanism, and generalization ability of a physical constraint enhancement model introduced by PINN is combined, so that the damage cloud picture can be identified in a complex impact condition scene with large data noise or missing data. The high-precision prediction from the current damage state to the future multi-moment damage cloud atlas has important engineering application value.

The invention provides a method for predicting a temperature field in a cold metal transition technology additive manufacturing process based on deep learning. According to the method, the problems of long acquisition period and insufficient quantity of the deep learning data set are solved. The method mainly comprises the following steps of: 1, establishing a finite element model in the CMT electric arc additive manufacturing process, and comparing a result obtained by simulation with experimental data to check the accuracy of the model; 2, creating an APDL command stream template file containing a basic simulation process on the basis of the checked finite element model, wherein key parameters are represented by placeholders; 3, Python and Ansys are subjected to joint simulation, and calculation, extraction and storage of CMT welding temperature cloud atlas data under different technological parameters are achieved; and 4, enhancing existing temperature cloud picture data by using a GAN (Generative Adversarial Network) model, forming a deep learning data set, inputting the deep learning data set into the ResNet model for training, verification and testing, and finally applying the trained model to online prediction of the electric arc additive manufacturing temperature field. The method is suitable for batch calculation of the welding temperature field under any welding method, simulation analysis efficiency is improved, a deep learning data set is expanded, deep learning is fully trained, and the generalization ability of deep learning is improved.

The invention relates to the technical field of Beidou inspection data processing, and discloses an automatic inspection data acquisition and space-time association method under Beidou space-time reference. The method comprises the following steps: acquiring historical working condition data of inspection equipment based on nanosecond-level time synchronization and centimeter-level space positioning information of a Beidou system, and constructing a space-time dynamic prediction model; acquiring an equipment sensor measurement value, a moving track point set and a timestamp sequence in real time, and outputting a theoretical inspection value through a model; performing space-time multi-dimensional comparison on the theoretical value and the actual value, analyzing a time accumulation error, a space deviation degree and data flow form consistency, and generating a difference feature tensor; inputting the tensor into a geocoding topological reasoning network, simulating anomaly diffusion and generating an anomaly probability cloud picture in combination with topographic map node attributes and equipment distribution, and delineating an anomaly range; polling parameters are dynamically configured according to the cloud atlas, enhanced collection is started for a high-probability abnormal area, and data credibility inspection is executed on peripheral equipment.

The invention relates to the technical field of suspended sediment dynamic monitoring, in particular to a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method, which comprises the steps of multi-source fusion data acquisition: synchronously acquiring suspended sediment concentration distribution data, three-dimensional ocean current vector data and seabed microtopography slope data of a target area; simulation of plume dynamic diffusion: inputting the acquired data into a microtopography coupled adaptive plume diffusion model to generate a plume diffusion three-dimensional space-time cloud picture in the future 6 hours; dynamic risk partition generation: performing space-time overlay analysis on the plume diffusion three-dimensional space-time cloud picture and a preset ecological sensitive area dynamic electronic fence to obtain an operation risk thermodynamic diagram; and intelligent partition scheduling execution: dynamically dividing a mining prohibited area, a buffer area and a safe area, and sending an inter-partition rotation scheduling instruction to the mining equipment group. According to the method, the spatial resolution and the aging response capability of sediment diffusion behavior prediction are improved, and a basis is provided for environmental impact assessment and ecological protection.

The application provides a wireless charging coil type and mutual inductance identification method, establishes a circular transmitting coil and a circular receiving coil model, a square transmitting coil and a square receiving coil model, a circular transmitting coil and a square receiving coil model, a square transmitting coil and a circular receiving coil model, obtains a magnetic field cloud atlas and mutual inductance values of the receiving coil and the transmitting coil, and constructs a training data set; taking pixel values of the coil magnetic field cloud atlas as input and taking the coil type as output, a coil type identification model is constructed; taking the pixel values of the coil magnetic field cloud atlas as input and taking the transmitting coil and the receiving coil as output, a coil mutual inductance identification model is constructed; a loss function is set, and the coil type identification model and the coil mutual inductance identification model are trained, so as to identify the wireless charging coil type and mutual inductance. The application realizes identification of the wireless charging coil type and mutual inductance, and provides a new idea for wireless power transmission method research and engineering application.

The present application relates to the technical field of suspended sediment dynamic monitoring, and particularly relates to a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method, comprising multi-source fusion data acquisition: synchronously acquiring suspended sediment concentration distribution data, three-dimensional sea current vector data and seabed microtopography slope data of a target area; plume flow dynamic diffusion simulation: inputting the acquired data into a microtopography coupled adaptive plume flow diffusion model to generate a plume flow diffusion three-dimensional space-time cloud atlas for the next 6 hours; dynamic risk zoning generation: performing space-time superposition analysis on the plume flow diffusion three-dimensional space-time cloud atlas and a preset ecological sensitive area dynamic electronic fence to generate an operation risk heat map; intelligent zoning scheduling execution: dynamically dividing a forbidden mining area, a buffer area and a safe area, and sending zoning interval rotation scheduling instructions to a mining equipment group. The present application improves the spatial resolution and time response capability of sediment diffusion behavior prediction, and provides a basis for environmental impact assessment and ecological protection.

The present invention relates to a cloud map processing method, system, and medium for free-fall penetration soil deformation measurement. The cloud map processing method for free-fall penetration soil deformation measurement comprises: reading PMLAB RDIC software to generate soil surface deformation data based on a soil surface image sprayed with artificial speckle; reading Revealer Motion Analysis software to generate penetrometer half-section model motion data based on a motion image of the penetrometer half-section model; determining the position of the penetrometer at each moment and the shape and size of the penetrometer's soil coverage area based on the penetrometer half-section model motion data, performing secondary processing on the soil surface deformation data, and drawing a soil surface deformation cloud map; and integrating the soil surface deformation cloud map into a user graphical interface. The present invention can effectively solve the problem that when the free-fall penetrometer half-section model is penetrated closely against a glass surface, a thin layer of soil obscures the penetrometer outline, resulting in inaccurate DIC analysis results for the soil coverage area.

The application relates to a kind of artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to meteorological intervention operation technical field, comprising the following steps: establishing multi-resolution dynamic data grid system, coarse granularity grid layer covers entire operation area, for carrying satellite cloud atlas and the macro data of conventional radar;Fine grid layer, for receiving and mapping the real-time data provided by high-resolution radar;Receive all cloud block real-time data and map to corresponding grid layer, project the data collected at different time points to unified time reference frame to obtain cloud system state view;Using the latest data of conventional radar and the macro trend of satellite cloud atlas, combined with motion vector and confidence weighted, predictive interpolation is carried out to obtain cloud system information;Form a visual cloud system three-dimensional view, identify and track target cloud block with artificial precipitation enhancement potential and generate operation instruction.The application has the effect that target cloud block with operation value can be identified and tracked to generate accurate operation instruction.

The invention relates to the technical field of gas pipeline safety engineering, and discloses a Fluent simulation-based pipeline hydrogen-doped leakage emergency disposal method, which comprises the following steps of S1, constructing a hydrogen-doped gas pipeline leakage diffusion model; s2, setting simulation parameters; s3, leakage diffusion simulation is carried out; s4, establishing a first-aid repair risk evaluation model; and S5, generating an emergency disposal scheme. According to the pipeline hydrogen-doped leakage emergency disposal method based on Fluent simulation, a three-dimensional atmosphere-soil mixed domain geometric model is constructed, a structured and unstructured mixed grid division mode is adopted, grids around leakage holes are encrypted, multiple sets of simulation parameters such as the hydrogen-doped proportion, the pipeline pressure and the environment wind speed are set, unsteady-state solving is carried out through a PISO algorithm, and the pipeline hydrogen-doped leakage emergency disposal method based on Fluent simulation is achieved. The concentration distribution data and the diffusion cloud atlas of the monitoring points are obtained, refined leakage diffusion simulation based on the three-dimensional atmosphere-soil mixed domain is achieved, and accurate data support is provided for judging the leakage influence range and risk degree.

The invention relates to a cloud picture processing method and system for deformation measurement of free fall penetrating into soil and a medium, the cloud picture processing method for deformation measurement of free fall penetrating into soil comprises the following steps: based on a soil surface image sprayed with artificial speckles, reading PMLAB RDIC software to generate soil surface deformation data; on the basis of the motion image of the half-section model of the free falling body penetrometer, RevealerMotionAnalysis software is read to generate motion data of the half-section model of the penetrometer; determining the position of the penetrometer at each moment and the shape and size of a soil coverage area of the penetrometer by combining the motion data of the penetrometer half-section model, performing secondary processing on the soil surface deformation data, and drawing a deformation cloud picture of the soil surface; and integrating the deformation cloud picture of the soil surface into a user graphical interface. The method can effectively solve the problem that when the half-section model of the free falling body penetrometer is tightly attached to the glass surface for penetration, thin-layer soil shields the profile of the penetrometer, and consequently the DIC analysis result of the soil coverage area is inaccurate.

The invention provides a ground-based cloud picture classification method and system based on a fisheye system, and the method comprises the steps: collecting meteorological data of a preset region, generating a ground-based cloud picture through the fisheye system, and constructing a historical ground-based cloud picture data set; a dynamic gating fusion module and a physical guidance attention mechanism are fused, and meteorological data and a historical foundation cloud atlas data set are utilized to construct a multi-modal physical guidance gating fusion model; and based on the multi-modal physical guidance gating fusion model, classifying the to-be-classified foundation cloud atlas to obtain a cloud atlas classification result. According to the invention, through combination of a dynamic gating fusion module (DGF) and a physical attention guidance mechanism (PGAM), collaborative optimization of visual features and meteorological information is realized.

The invention discloses a photovoltaic ultra-short-term power prediction method based on cloud picture multi-feature extraction, and belongs to the field of electric power, and the prediction method comprises the steps: obtaining a change image of a cloud cluster, and obtaining a ground-based cloud picture image sequence; preprocessing all cloud pictures in the ground-based cloud picture image sequence to obtain a preprocessed image sequence; obtaining cloud picture features based on the preprocessed image sequence; and carrying out deep learning on historical photovoltaic output power data and the cloud picture features to obtain a photovoltaic ultra-short-term power prediction model so as to carry out photovoltaic ultra-short-term power prediction. The problem that in the prior art, cloud picture feature extraction is insufficient, and consequently the precision of a prediction result is too low is solved. Therefore, through accurate analysis of the features of the cloud atlas and combination of CNN and Autoformer methods, a photovoltaic ultra-short-term power prediction model is constructed, and then photovoltaic ultra-short-term power prediction is carried out.

The invention provides a deep vertical shaft double-air-duct press-in type air supply air duct position optimization method which comprises the following steps: (1) collecting parameters of a vertical shaft and air ducts, and calculating the blasting smoke throwing distance and the generated CO initial concentration according to the amount of explosive needed by blasting; (2) constructing a physical model matched with actual engineering parameters by adopting numerical simulation software based on the component transportation model, and setting different relative arrangement positions of double air ducts; (3) analyzing the derived simulation data at different moments and the related visual cloud atlas, counting the CO concentration condition in the working area, and fitting a curve of the CO concentration changing along with time; and (4) by taking the CO safe concentration threshold value as a reference, analyzing the ventilation time required by the standard reaching of the CO concentration of the operation area under each arrangement working condition, sorting and drawing a chart. According to the method, the ventilation efficiency change characteristics under different air duct arrangement angles can be effectively determined, and scientific reference is provided for optimal air duct arrangement scheme selection of various deep vertical shaft engineering double-air-duct press-in type air supply systems.

The invention relates to the technical field of structural optimization of turbine blades of aero-engines and gas turbines, in particular to a gas-thermal coupling optimization method and related device for a bending, twisting and tilting structure of a turbine blade, and the method comprises the following steps: parameterizing the bending, twisting and tilting structure of the turbine blade to obtain geometric parameters of the bending, twisting and tilting structure of the turbine blade; a turbine blade objective function is constructed, and a design objective function response value is obtained based on the geometric feature parameters of the turbine blade bending, twisting and tilting structure and the turbine blade objective function; constructing a prediction model based on the design objective function response value, inputting the geometric parameters of the bending, twisting and tilting structure of the turbine blade into the prediction model, and obtaining a cloud picture of the temperature and the total pressure loss of the turbine blade; and based on the cloud atlas of the turbine blade temperature and the total pressure loss, the design objective function response value is optimized, an optimization result is obtained, and the problem that in the prior art, traditional agent model construction cannot meet the simulation requirement of the turbine blade internal flowing and heat exchange process is solved.

The application discloses a gradient lattice structure compression simulation method and system based on layered finite element simulation, and relates to the field of finite element simulation.The technical scheme is as follows: a finite element model is established, and simulation data is collected; a load-displacement curve is constructed according to displacement data and load data; layer densification displacement is calculated according to layer densification strain, and the part of the load-displacement curve before the layer densification displacement is intercepted as an effective curve; the effective curve is synthesized to obtain load data and displacement data of the gradient lattice structure; a stress-strain curve is constructed according to the load data and the displacement data of the gradient lattice structure; a stress value is determined according to a preset strain value and a matching strain layer, and the stress value is converted into a load to call a stress-strain cloud atlas, and a total stress-strain cloud atlas is obtained by pasting stress-strain cloud atlases of all layers.The application can solve the problems of large calculation amount, high time cost and difficulty in convergence in finite element simulation of the gradient lattice structure.

The invention belongs to the technical field of image detection, and discloses a finite element cloud picture crack fitting method based on direction priori and dark quantile ROI, which comprises the following steps: calculating a preliminary direction angle of a crack according to a reference point coordinate as the direction priori; taking the direction prior as a central axis direction and one of the reference points as a benchmark, constructing a strip-type ROI, extracting brightness values of pixels in the strip-type ROI, and screening the pixels with the brightness values lower than or equal to a threshold value as a preliminary crack candidate point set; calculating the local gradient direction of each pixel point in the candidate point set, screening out the pixel points of which the deviation between the local gradient direction and the direction prior is smaller than a preset direction tolerance, and forming a direction consistency set; and carrying out total least square fitting on the coordinates of the pixel points in the direction consistency set to obtain a main direction straight line and fitting parameters of the crack, and superposing the main direction straight line on the original finite element cloud picture to generate a crack fitting result. According to the method, robust and high-precision extraction of the crack direction in the complex finite element cloud picture is realized.

This invention discloses a low-altitude unmanned aerial vehicle (UAV) meteorological support system, relating to the field of meteorological monitoring technology. It constructs a control command set for several UAVs deployed within a UAV platform to control their low-altitude operations and acquire real-time flight parameters during these operations. This data is used to monitor the actual flight status of the UAVs and determine whether to participate in flight control. The system uses meteorological cloud images captured by the UAVs at different time intervals and locations to overlay and process these images into a meteorological cloud image set for the monitored location area on each UAV's local terminal. A data exchange network is constructed to conduct bidirectional data interaction with each UAV. The meteorological cloud image sets stored in each UAV are imported into the data exchange network, and all the meteorological cloud image sets are parsed to determine the actual meteorological conditions in different locations. Finally, support measures are implemented for locations where the actual meteorological conditions do not meet the standards.

The application discloses a kind of welding molten pool image prediction weld appearance deep learning method, it is related to welding technical field, including the following steps: first, by the two CCD cameras of upper and lower arrangement from different angles to the molten pool picture of the same position at the same time when welding is collected, molten pool picture is carried out cutting, filtering and other image processing, after using target paper calibrates camera, by three-dimensional reconstruction, obtain the three-dimensional cloud atlas of molten pool, the three-dimensional curved surface relationship z=f (x, y) of molten pool surface is fitted, using pixel instead of the horizontal and vertical coordinates of molten pool, the size of gray scale value indicates the fluctuation of molten pool surface, it is converted from three-dimensional image into two-dimensional image containing three-dimensional information, using optimized loss function to improve convolutional neural network calculation, extract and learn the molten pool information in two-dimensional molten pool image, the appearance of weld is predicted.The application solves the problem of insufficient accuracy caused by using only welding parameters to predict the appearance of weld.

The application discloses a mechanism decoupling transfer learning driven composite bolt connection failure efficient prediction method, relates to the technical field of composite material failure research, and comprises the following steps: a high-throughput simulation database is established, unstructured grids are converted into structured tensors by using a physical coordinate mapping strategy, and a generative network containing coordinate injection and gradient consistency constraints is constructed, so that the second-level high-precision prediction of a composite material connection damage cloud atlas under any geometric parameters is realized. The method is used to effectively fill the blank of the prior art, and provides a new failure prediction mode for composite material bolts with different layer designs, width-diameter ratios and end-diameter ratios.

The present application relates to the technical field of photovoltaic system, solve the technical problem that the prior art cannot accurately predict irradiance and temperature, the prediction process is easy to appear gradient disappearance or gradient explosion, especially relates to a kind of photovoltaic power prediction method, system and computer equipment, the method includes the following steps: S1, obtain the cloud atlas data set from the weather database in the place of photovoltaic equipment;S2, select several zenith cloud atlas from cloud atlas data set and obtain ground cloud atlas set consisting of several ground cloud atlas by preprocessing;The present application obtains the moving curve of cloud layer in the sky by image analysis to zenith cloud atlas, and establishes prediction model according to moving curve, can avoid the problem of gradient disappearance or gradient explosion of neural network model, also carries out real-time cloud layer detection to zenith cloud atlas, so that the prediction result is more accurate, improves the accuracy of irradiance, makes the prediction process more smooth, improves work efficiency.

The invention relates to a construction method and system for a large-section underground excavation tunnel, and belongs to the technical field of underground excavation tunnel construction. The construction method comprises the following steps that a three-dimensional model is established, and the influence of tunnel construction on surrounding stratum disturbance is analyzed in a numerical simulation mode; determining construction parameters including an excavation sequence based on a numerical simulation result; the pilot tunnel is excavated according to the prepared excavation sequence, a supporting structure comprising a primary support and a temporary support is arranged, and arrangement of the temporary support can be optimized in a targeted mode according to a cloud picture of the stress change process of the supporting structure in the prepared excavation sequence; and the stress state of the supporting structure is collected in real time, and the monitoring data and the predicted value in the stress change cloud picture are compared and analyzed so as to adjust the supporting scheme. The construction system comprises a simulation unit, a supporting structure, a monitoring unit and a central control unit, and the construction system can execute the construction method to assist in completing large-section underground excavation tunnel construction.

The invention provides a manufacturing method of a cloud chart of number density, size and component surface distribution of inclusions in steel. The method comprises the following steps: automatically analyzing inclusions of a metallographic sample to collect inclusion data, carrying out grid division on an analysis range, and carrying out inclusion distribution information drawing on the analyzed inclusion data according to a grid position assignment mode, so as to obtain a surface distribution cloud chart of the inclusions in the steel under the large-area multi-view-field condition. According to the method, the characterization effect of large-area multi-view-field inclusion distribution can better reflect the distribution rule of the inclusions, the method is also effective for the distribution of the inclusions with high density and large particle size change, and the method is suitable for the distribution of the inclusions with various collection areas.

The invention provides a numerical simulation method for a spreading form of jet printing micro-droplets based on a finite element method. The numerical simulation method is used for simulating dynamic spreading behaviors of the micro-droplets on cylindrical curved surfaces with different curvature radiuses. The method comprises the following steps: constructing a three-dimensional computational domain containing micro-droplets and a cylindrical curved surface substrate by performing certain model hypothesis, discretizing the three-dimensional computational domain by adopting a structured hexahedral mesh, and accurately simulating the dynamic spreading process of the micro-droplets by combining a CLSVOF interface tracking technology and a continuous surface tension model; and the post-processing module outputs key morphological parameters such as spreading diameter changing along with time, and supports synchronous visualization of a three-dimensional phase interface evolution process and multi-physical field cloud pictures such as a pressure field and a temperature field. The method supports various materials, can adapt to various working condition parameters such as variable temperature fields, different curvature radiuses and surface wettability, can quickly predict the influence of process parameters on the forming form, remarkably reduces the experiment trial and error cost, improves the jet printing precision and yield, and is suitable for the high-end manufacturing fields of flexible electronics, curved surface circuits, biological scaffolds, micro-optical devices and the like.

The invention discloses a bridge cluster traffic flow load cloud picture deduction method based on wide-area road network intermodulation data, and the method comprises the steps: integrating the section traffic parameters of each intermodulation station in a road network, and building a new intermodulation parameter system for bridge mechanical analysis; generating a load cloud picture reflecting the distribution of new parameters in the whole road network by using a kernel density analysis algorithm of a geographic information system (GIS) based on the geographic position information of each station; and constructing a multi-feature agent model based on the new intermodulation parameter system, inputting latitude and longitude coordinates of any target bridge in the road network into the multi-feature agent model, deducing load parameters of the position of the bridge by the model, and further recovering a full-bridge-floor traffic flow load time history of the target bridge through a traffic flow simulation technology. The method has the advantages that large-scale conversion from cross-modulation data to bridge mechanical analysis parameters is realized, dynamic load input is provided for a bridge cluster, and a maintenance mode is promoted to jump from'single bridge supervision 'to'bridge network intelligent management'.

The invention belongs to the related technical field of engineering matching construction, and discloses a remote control method for high-quality wet spraying operation of a wet spraying machine, which comprises the following steps: starting a positioning detection module, and performing position positioning of the wet spraying machine; starting a 3D imaging module, scanning feature points on the rock surface and the wet spraying machine body, generating a real three-dimensional point cloud model, and determining absolute coordinates of the boom and the rock surface; a joint detection module is started, and initial state parameters of joints are collected; calculating the current position of the nozzle; an over-excavation and under-excavation cloud picture is obtained; the rotating angle and the telescopic distance of the joint are adjusted; and along with the movement of the wet spraying machine, the optical fiber of the remote transmission module is operated through the unwinding disc. Through the system, high-precision positioning of the positions of the wet spraying machine and the arm support can be achieved, dynamic and real-time adjustment can be carried out on various spraying parameters, controllability of operation quality is remarkably improved, and meanwhile the system has the advantages of being convenient to control, high in automation degree, low in remote control delay, stable, good in environmental adaptability and the like.

The invention discloses an inter-axis self-locking nut closing-in process variable parameter influence analysis method based on finite elements. Comprising the steps that S1, a finite element model containing the shaft self-locking nut and the closing-in device is constructed; s2, configuring a material constitutive parameter, a grid parameter, a contact parameter and a boundary parameter; s3, carrying out univariate simulation by taking the key process parameters as variables, and analyzing the forming performance by combining point tracking and cloud atlas; s4, outputting a process optimization scheme; through finite element model construction, multi-dimensional parameter configuration and univariate simulation analysis, the influence rule of key process parameters on equivalent stress, damage distribution and forming load is accurately obtained, the optimal process parameters giving consideration to the forming quality and the production efficiency are determined, and a reliable theoretical basis is provided for actual production process adjustment.

The invention discloses a static pressure rotary table temperature field prediction and cloud picture visualization method, which comprises the following steps: constructing a multivariable time sequence based on rotary table operation conditions and environment variables, and carrying out joint prediction on a plurality of temperature measurement points by using a multi-output BiLSTM agent model to obtain predicted temperature vectors of discrete measurement points; taking the predicted temperature as a boundary condition of a voxel thermal field, and solving a pseudo-physical thermal diffusion equation in a three-dimensional voxel space to obtain a continuous temperature field; constructing a local correction model based on linear regression at a sensor position, and spreading a correction value in space through inverse distance weighting to realize unification of data consistency and spatial continuity; and a real-time temperature cloud picture is generated at the GPU end through Gaussian weight voxel weighted rendering based on Shader. According to the method, data-driven prediction and physical consistency constraint can be organically fused, and real-time visualization and thermal state evaluation of the internal temperature field of the hydrostatic pressure turntable can be realized in an industrial field.

The invention discloses a sensitivity measurement and calibration compensation method based on a multi-modal full-focusing acoustic response cloud atlas, and the method comprises the steps: constructing an opposed array multi-modal full-focusing welding line detection coordinate system model, calculating each single-modal acoustic response diagram in an acoustic response cloud atlas simulation mode, and carrying out the normalization of each single-modal acoustic response diagram; the multiple of each modal compensation is calculated and recorded, and the same acoustic response amplitude is achieved by detecting the defects of the same equivalent in each single modal; then, in the imaging area, theoretical fusion processing is carried out on each normalized single-mode acoustic response diagram, and multi-mode full-focusing acoustic response cloud diagram sound field distribution measurement is achieved; and calculating a coefficient compensation matrix corresponding to the multi-mode full-focusing acoustic response cloud picture to realize calibration of the multi-mode full-focusing acoustic response cloud picture. And finally, applying the coefficient compensation matrix obtained by theoretical calculation to the multi-modal full-focusing imaging formed by fusion after actual detection, and performing sensitivity compensation on the detection result of the multi-modal full-focusing imaging.