140 results about "Local space" patented technology

The invention discloses a dynamic inspection method and device based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the technical field of software and platforms or other related technical fields, and the method comprises the steps: carrying out the three-dimensional scene modeling of a to-be-inspected region through the space-time alignment data obtained through preprocessing; acquiring an inspection operation instruction, and performing inspection task distribution of multiple flight constraints on the dynamic environment model obtained by modeling to obtain an optimized task execution scheme; and performing global path planning and local space-time cooperative path optimization on the task execution scheme based on the high-precision three-dimensional scene model obtained by modeling to obtain a target inspection path, thereby remotely controlling the target unmanned aerial vehicle to perform optimized path inspection and anomaly analysis according to the target inspection path, and generating an anomaly inspection report of the target inspection area. According to the invention, the technical problems of poor adaptability and low accuracy of the inspection result of the inspection mode of planning the inspection path based on historical data in the prior art are solved.

The invention belongs to the technical field of computer vision, deep learning and time-space prediction, and discloses an end-to-end time-space prediction method based on improved three-dimensional rotation position coding, which is suitable for various time-space sequence prediction scenes such as weather, traffic flow and the like. According to the invention, through four key improvements, a position coding mechanism is optimized; three-dimensional coding proportions of time, height and width are dynamically adjusted so as to adapt to different scenes; fusing the absolute time and the relative space position, and strengthening local space-time correlation modeling; the position information directly guides attention calculation, and the fusion with an Attention module is deepened; and a rotation matrix cache mechanism is introduced to reduce redundant calculation. Meanwhile, the model is matched with a Patch embedding layer, an adaptive Transform encoder and an MLP de-wharf, a complete link of'feature embedding-position encoding-space-time fusion-prediction output 'is constructed, and the precision, generalization and reasoning efficiency of space-time prediction are effectively improved.

The invention discloses a short-term power load prediction method and system based on a CNN-Transform hybrid model, and the method comprises the steps: carrying out the data collection of historical load data and meteorological data of a power system, carrying out the data preprocessing of the collected data, and carrying out the coding of a periodic time feature, and obtaining periodic time coding information; local space-time features of the load data and the meteorological data are extracted by using a convolutional neural network, and hierarchical expression of the features is realized through a multi-layer convolutional structure during extraction; the local spatiotemporal features and the periodic time coding information are fused to obtain fusion features containing a load sequence, the long-period dependency relationship of the load sequence is modeled through a Transform network, global modeling of the fusion features is achieved through a multi-head self-attention mechanism, and a CNN-Transform hybrid model is obtained; a CNN-Transform hybrid model is used for prediction, and a load prediction result is output; according to the invention, the precision of load prediction and the generalization ability of the model are significantly improved.

The invention relates to the technical field of sea wave height prediction, and discloses a significant wave height prediction method and system based on spatio-temporal evolution multi-scale feature extraction. The method comprises the following steps: performing significant wave height prediction on input significant wave height grid data of a sea area to be predicted by applying a trained hybrid heterogeneous parallel double-convolution dynamic network; the method further comprises the steps of obtaining the effective wave height grid data, inputting the effective wave height grid data into the hybrid heterogeneous parallel double-convolution dynamic network for effective wave height prediction, and then outputting an effective wave height prediction sequence of a future time step. According to the method, multiple features of SWH spatio-temporal dynamic evolution can be extracted, and hierarchical spatio-temporal features from a local scale to a global scale and local spatio-temporal dynamic crossing from a coarse scale to a fine scale can be mined at the same time; by constructing a heterogeneous parallel double-convolution framework, the internal irregularity of the SWH field and the irregular relation between SWH field data are overcome, and meanwhile the capacity of capturing the invariant relation in the SWH field is kept.

The invention provides a factory three-dimensional digital twin model reconstruction and incremental updating method for industrial space intelligence, and the method comprises the steps: obtaining a factory multi-view basic image for a factory panoramic scene, so as to train a bifurcated structure neural implicit field network containing a radiation field branch and an SDF branch, and generating an initial three-dimensional digital twin base; acquiring factory real-time inspection images acquired by the inspection robot under different inspection poses, rendering the initial three-dimensional digital twin base by using a radiation field branch to obtain a virtual reference view, and performing difference analysis on the virtual reference view and the factory real-time inspection images to position a structural change area in a factory; determining an axis alignment bounding box for changing the physical range of the entity in the area, taking the axis alignment bounding box as an effective boundary of the local space patch, and determining matched radiation field network weight and SDF network weight according to the effective boundary; and adjusting the radiation field network weight and the SDF network weight, and updating the radiation field network weight and the SDF network weight to the network in an incremental updating manner to generate a factory three-dimensional digital twinning model based on the implicit three-dimensional geometric field.

The invention discloses an ultrasonic image denoising method based on variational mode decomposition and local space sparse fusion, which comprises the following steps of: firstly, adaptively optimizing key parameters of variational mode decomposition by using a grey wolf optimization algorithm to realize stable and efficient decomposition of an ultrasonic image; then, classifying the modal components according to the structural features of the modal components, and implementing differentiated denoising strategies for different types of modals to separate noise and reserve useful information; after modal reconstruction, a sparse expression method based on local space information is further introduced, according to the method, accurate boundary detection is carried out through gradient vector flow and gray scale proportion analysis, self-adaptive partitioning is carried out on an image according to boundary information, and finally sparse reconstruction is carried out through double dictionaries trained for different areas. According to the method, speckle noise in the ultrasonic image can be effectively suppressed, and meanwhile, the capability of keeping the edge and detail information of a tissue structure is remarkably improved, so that the ultrasonic image with higher quality is obtained.

The invention relates to a binding material single particle local spatial evolution analysis method, and belongs to the technical field of civil engineering binding material microstructure characterization. The method comprises the following steps: acquiring back scattering (BSE) and characteristic X-ray energy spectrum (EDS) images of a sample; selecting a target gel particle based on the BSE image and intercepting a local area; identifying the reaction edge of the target gel particle; performing equal-width stepped strip division inwards and outwards by taking the reaction boundary as a starting point, and storing the mask; carrying out image operation on adjacent masks to extract strips, and carrying out statistical analysis on gray feature and element feature changes of each strip; according to the method, surrounding phase interference is avoided through local analysis, whole-process analysis is realized by adopting unified image processing software, a programming basis is not needed, the technical threshold is reduced, the operation process is simplified, the uniformity of strip division and the reliability of an analysis result are ensured, and the method is suitable for large-scale popularization and application. And a simple, convenient and effective technical means is provided for revealing the reaction mechanism and quantitative reaction activity of the cementing material.

The invention discloses a space-time elastic weight consolidated edge federal continuous learning method, which is applied to a system comprising a server and a plurality of edge devices, is used for processing space-time heterogeneity time sequence data, and comprises the following steps: initializing training, broadcasting a previous time sequence global time Fisher diagonal matrix (the first time sequence is not broadcasted, the second time sequence is not broadcasted, and the third time sequence is not broadcasted) by the server; however, the global model needs to be randomly initialized and broadcasted); in the model training stage, based on local data, a global time Fisher diagonal matrix and the like, an edge device updates a local model through a loss function containing a time / space regular term, calculates a local space Fisher diagonal matrix, uploads the local space Fisher diagonal matrix, and then a server weights and aggregates the global model according to the data volume and issues the global model, and circulates until convergence; and in the global time Fisher diagonal matrix calculation stage, the equipment calculates a local time Fisher diagonal matrix based on a convergence model, and uploads and aggregates the local time Fisher diagonal matrix for the next time sequence. Historical data does not need to be stored, original data does not need to be transmitted, storage calculation / communication overhead is reduced, privacy is protected, and the model convergence speed and precision are improved.

The invention discloses a deep learning-based satellite remote sensing collapsible loess foundation evaluation method, which comprises the following steps of: acquiring an optical remote sensing image, a radar remote sensing image and an infrared remote sensing image of a target area, and preprocessing; collapsibility feature weighted data are screened, and feature weights are distributed; feature extraction is carried out through a local space exhibition structure and a multi-layer Transform structure of the collapsibility foundation evaluation network; collapsibility area self-supervised collaborative learning is carried out, and self-supervised training is carried out based on spatial correlation, pseudo labels and spatial consistency regular terms; and carrying out risk grade division on the target area, and outputting a collapsibility risk result of each spatial position. According to the method, multi-mode remote sensing and deep learning are fused, high-precision intelligent partitioning of the collapsible loess foundation is achieved, and the method has the advantages of being self-adaptive, low in manpower and high in spatial resolution.

The invention belongs to the technical field of image processing, and relates to an egg surface microcrack detection method and system based on machine vision. The method comprises the following steps: acquiring a transmission image of an egg, performing spatial calibration, determining a geometric center and a long axis scale of an egg region, and establishing a polar coordinate mapping relation of each pixel point; according to the linear distance from each pixel point to the geometric center and the included angle relative to the long axis direction, the geometric sensitivity weight of each pixel point is evaluated, and the optical path distortion degree of the edge high-curvature area is represented; extracting contrast basic energy of the pixel points by using a local space window, and carrying out nonlinear evolution on a local gray gradient by combining with a geometric sensitivity weight to obtain an adaptive contrast energy evolution result; and coupling the local background variance and the geometric sensitivity weight, and calculating the crack identification confidence of each pixel point so as to carry out identification decision of the egg surface microcracks. According to the invention, extremely fine crack signals can be captured, and accurate detection of the microcracks on the surface of the egg is realized.

The invention provides a multi-view three-dimensional virtual-real fusion rendering method and related equipment, and relates to the field of computer vision, a target scene is divided into a space-time flow line or local space-time blocks by constructing a geometric field, an appearance field and a space-time flow field on a four-dimensional time-space domain, fragmentation caused by pure frame-by-frame rendering and scene specific rules is avoided, and the rendering efficiency is improved. The problem of multi-scene splitting is solved, and space-time importance distribution at each moment and under each virtual view angle is calculated through the updated feature vector and the attention weight corresponding to the updated feature vector so as to render a geometric field and an appearance field on a four-dimensional time-space domain. And the rendering picture quality is improved on the premise that the total calculation overhead is not obviously increased.

The invention discloses a new energy output scene generation method and system based on tail risk enhancement, relates to the technical field of power scene generation, and aims to solve the problems of insufficient coverage of extreme scenes, spatial-temporal modeling distortion and lack of physical constraints in the prior art. The method comprises the following steps: constructing an improved generative adversarial network comprising an auto-encoder, a generator and a discriminator, extracting spatial-temporal features by adopting local spatial-temporal diagram convolution, and mapping the spatial-temporal features to a submerged space; a Jensen-Renyi divergence loss function is utilized to enhance the generation capability of a tail risk scene; designing a dual discrimination mechanism with an adversarial discrimination branch and a physical verification branch, measuring and counting distribution differences through a Wasserstein distance, and verifying physical feasibility based on a theoretical power curve of a fan; and finally, a target network is obtained through staged training, and a new energy output sequence data set with statistical completeness, space-time fidelity and engineering feasibility is generated according to real-time meteorological conditions. According to the invention, the coverage capability of the extreme risk scene and the engineering practicability of the generated scene are effectively improved.

This invention relates to a method for extracting guided wavenumbers in variable thickness structures, belonging to the field of nondestructive testing technology. It utilizes an excitation signal to generate guided wave signals on the surface of the object under test. Based on a set window function and a set window length, the two-dimensional wavefield signal obtained by sampling the guided wave signal is divided into multiple local wavefield signals. A short-space Fourier transform is then performed on each local wavefield signal to obtain the amplitude-frequency-wavenumber array corresponding to each local space. A frequency range is then selected by summing the amplitude-frequency-wavenumber arrays of each local space, using the center frequency of the excitation signal as the midpoint. The weighted average wavenumber of each frequency point within the selected frequency range is calculated using the amplitude corresponding to the wavenumber sequence at each frequency point as the weight. The mean of the sum of the weighted average wavenumbers at each frequency point is then used as the wavenumber corresponding to the center position of each local signal space, and these values ​​are sequentially spliced ​​to obtain the wavenumber curve of the guided wave signal. This method solves the problem in existing technologies where the obtained guided wavenumbers cannot accurately reflect the thickness changes of variable thickness structures.

1. The name of the design product: air purification unit (FFU). 2. The use of the design product: air purification for local space in pollution industry. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1.

The embodiment of the invention discloses a traffic network scheduling control method and system based on space-time big data and electronic equipment, and belongs to the technical field of control or regulation systems.The method comprises the steps that a first network state feature tensor representing a local space-time evolution mode is extracted from a space-time grid through a three-dimensional convolutional neural network, and aggregating node information on the dynamic weighting graph by using the graph attention network, generating a second network state feature tensor reflecting the global association and congestion propagation situation of the road network, fusing the two feature tensors, and inputting the fused feature tensors into an attention enhancement sequence model for multi-step prediction to obtain a future multi-period whole network traffic state. And cooperatively inputting the prediction result, the real-time state, the rule and the capacity constraint into the optimization model, generating a comprehensive scheduling instruction set including signal timing, lane control and path induction, and issuing and executing the comprehensive scheduling instruction set. According to the embodiment of the invention, holographic perception, prospective prediction and cooperative regulation and control of the traffic network are realized, and the traffic efficiency and scheduling response capability of the road network are improved.

The invention discloses a lightweight modulation identification method and system based on sparse graph construction, a medium, equipment and a product in the technical field of wireless communication and artificial intelligence. The method comprises the following steps: carrying out data preprocessing on a received wireless communication signal to obtain a complex field signal; carrying out modulation identification on the complex field signal by utilizing a lightweight modulation identification model; carrying out local feature extraction on the complex field signal by utilizing a feature extraction module to obtain amplitude and phase coupled local space-time features; performing dimension reconstruction on the local spatial-temporal features to obtain reconstructed local spatial-temporal features; according to the reconstructed local spatial-temporal features, performing graph convolution processing and feature enhancement by using a graph convolution network based on a multi-connection strategy to obtain enhanced features; and classifying the enhanced features by using a classifier to obtain a modulation type. The method can be widely applied to real-time signal identification tasks in Internet of Things data transmission nodes, low-power-consumption wireless sensor networks, unmanned systems and edge intelligent gateways.

The invention discloses a multi-modal image resolution fusion enhancement method based on brain inspiration, and belongs to the technical field of computer vision and remote sensing images. According to the method, a global spectrum general picture is provided by a low-resolution image, local space details and coordinate information are supplemented by a high-resolution image to serve as guide signals, the global spectrum general picture and the high-resolution image are aligned and fused under a unified framework, and the local texture definition of a reconstructed image is remarkably improved while the consistency of a global structure and a spectrum is ensured. According to the invention, continuous enhancement of resolution is realized based on an implicit fusion mode guided by coordinates. The model is not limited by a fixed pixel grid and an integer magnification factor any more, any spatial position can be inquired, super-resolution reconstruction of the hyperspectral image is achieved at any magnification, and the flexibility and practicability of hyperspectral data in a multi-scale application scene are greatly improved.

The invention discloses a method for forecasting ionized layer delay by combining a ConvLSTM and ViT fusion model. The method comprises the following steps: acquiring single-moment TEC data provided by an ionized layer empirical model in a target area, and VTEC data and a mask layer acquired by a ground GNSS site in the same period; inputting the acquired data into the trained fusion model, and outputting a TEC forecast result in a future preset time period; wherein the fusion model is combined with a ConvLSTM model and a ViT model. According to the method, local space-time details are captured through ConvLSTM, and the structure of capturing global dependence and multi-input fusion auxiliary information in combination with ViT is adopted, so that the delay information of the ionosphere can be well predicted under the active condition of the ionosphere.

The invention discloses a full-slice image cancer prediction and subtype classification method, system and device, and relates to the technical field of image processing and medical artificial intelligence. Comprising the following steps: preprocessing a full-slice image, cutting the full-slice image into image blocks with position coordinates, and extracting features; reconstructing the feature sequence into a two-dimensional feature map which retains the original spatial topology through a spatial recovery module; scanning and fusing along eight directions including a horizontal direction, a vertical direction and a plurality of diagonal lines by using a hyper-cross scanning module so as to capture multi-direction local space correlation; multi-scale global features are extracted and fused by adopting convolution layers with different expansion rates through a pyramid module; and finally, outputting a prediction result and a subtype label through a customized classifier, and generating a focus attention heat map. Through the architecture of spatial reconstruction-multidirectional scanning-multi-scale fusion, while the linear calculation complexity of O (n) is kept, the small focus recognition capability and classification precision are remarkably improved, and an efficient and reliable technical scheme is provided for digital pathological diagnosis.

The invention relates to the technical field of crossing of biological feature recognition and cross-domain machine learning, in particular to a cross-multi-domain robust electroencephalogram identity authentication method and system, and the method comprises the steps: obtaining a differential entropy feature, namely, a DE feature, of electroencephalogram data of a source domain and a target domain; inputting the DE features into a local space alignment module, respectively extracting attention weighted features and local frequency-space features through an ABP module and an EEGNet frequency-space convolution block, and adding the attention weighted features and the local frequency-space features element by element to obtain local embedded features; calculating correlation alignment loss based on the local embedded features; inputting the local embedded features into a global space alignment module, extracting global correlation features through a Transform module, and splicing the local embedded features and the global correlation features along channel dimensions to obtain global fusion features; mMD loss and domain adversarial loss are calculated for the global fusion features; inputting the global fusion features into a brain grain recognition classifier, and calculating classification loss; according to the method, the electroencephalogram authentication precision in cross-time, cross-emotional-state and cross-paradigm task scenes is effectively improved.

Embodiments of the present specification disclose a trajectory point query method and device based on a scan operator and equipment. The scheme comprises: receiving a trajectory point query request specifying a query range; determining a global space-time multi-dimensional body in which existing trajectory points are distributed, the global space-time multi-dimensional body being divided into a plurality of local space-time multi-dimensional bodies in order to index the existing trajectory points; converting the query range into a corresponding scan operator according to the division resolution corresponding to the global space-time multi-dimensional body, the scan operator comprising a scan line or a scan surface, each scan operator being associated with: trajectory point indexes corresponding to grids passed by the scan operator or trajectory point indexes corresponding to local space-time multi-dimensional bodies passed by the scan operator, wherein the grids comprise surfaces of the local space-time multi-dimensional bodies; and screening target trajectory points in the global space-time multi-dimensional body according to the trajectory point indexes associated with the corresponding scan operator, in order to determine a trajectory point query result.

Embodiments of the present application disclose a capacity expansion method, device and equipment of a storage system and a storage medium. The method comprises the following steps: when a target device in a hot cluster included in the storage system needs to be expanded, scanning out a storage account from a local space, and keeping each historical business data of the storage account in the local space; determining metadata for describing a business data attribute of the storage account, and generating target routing information of the storage account; migrating the metadata to a data migration-in device corresponding to the target device, and storing the target routing information to an index cluster; after migrating the metadata and storing the target routing information to the index cluster, marking the storage account with a migration-out mark, so that the data migration-in device provides data read-write services of the storage account instead of the target device based on the metadata and the target routing information in the index cluster. The embodiments of the present application can effectively improve the expansion efficiency and ensure the stability of online services when the storage system is expanded.

The invention provides a time-space geographically weighted regression method and device for counting data, equipment and a storage medium, and relates to the technical field of time-space information data analysis. The method comprises the following steps: acquiring multi-source space-time observation data of a target area; constructing a geographically weighted Poisson regression model or a geographically weighted logistic regression model fused with space-time non-stationarity based on a generalized linear model framework so as to fit a local space-time variation relationship between a numeric type or binary type response variable and an independent variable; solving a regression coefficient of the spatio-temporal change through an iterative algorithm; and finally, model output is converted into early warning information, a dredging scheme or a planning decision. According to the method, the data discrete characteristics and the process spatial-temporal heterogeneity are described in a unified manner, so that the problems of modeling error and insufficient precision caused by neglecting the spatial-temporal coupling effect and distribution mismatch when a traditional model is used for coping with spatial-temporal data with discrete distribution characteristics are solved; the accuracy and decision support capability of dynamic risk early warning and refined resource allocation in the fields of environmental monitoring, traffic safety, land planning and the like are remarkably improved.

The application discloses a three-level coordinated production line body intelligent control method and system, and belongs to the technical field of intelligent manufacturing. The method comprises the following steps: establishing a global task planning layer, performing semantic analysis and logical disintegration at a first preset frequency, and generating an atomic action sequence; establishing a local space perception layer, receiving the atomic action sequence at a second preset frequency, constructing a dynamic local map by using sensor data, determining a workpiece pose by point cloud registration, and mapping the atomic action sequence into a continuous motion trajectory flow; establishing a real-time action execution layer, receiving an instantaneous target pose at a third preset frequency, calculating a theoretical driving torque by combining a Lagrange dynamics equation, switching to an impedance control mode at a contact moment, and driving an execution mechanism to complete work; and establishing an asynchronous coordination layer, realizing three-layer asynchronous communication through an industrial network, and controlling the three-layer frequencies to be distributed in a ladder type in an increasing manner. The application has the effects of improving the flexibility, precision and safety of a production line.

The invention provides a multi-target tracking method and device based on AR nodes, and relates to the technical field of augmented reality, and the method comprises the steps: building a three-dimensional environment structure which is updated along with time through the cooperation of multiple AR nodes, synchronously generating a local space fragment through a visible light image and a depth point cloud, and completing the space registration, forming a trafficability matrix which can be dynamically updated; on the basis, the target is analyzed, a target position is generated, and multi-hypothesis trajectory deduction is executed in combination with a trafficability constraint; constructing a global three-dimensional space model through shared space fragments among nodes and trajectory information, and complementing a target trajectory based on re-identification; and finally, a real-time position and a motion trend are generated according to the complete track and are adjusted and displayed according to the prediction confidence, so that the system realizes high-reliability multi-target continuous tracking in a dynamic cabin section structure. According to the invention, the accuracy of multi-target tracking under the condition of dynamic change of the passable area can be improved.

The invention relates to the technical field of three-dimensional modeling, in particular to a numerical analysis-based complex-form karst cave distribution simulation method, and solves the technical problem of inaccurate model construction caused by elimination of irregular boundary forms in a karst cave during dimensionality reduction of karst cave point cloud data in the prior art. The method comprises the following steps: acquiring point cloud data of a karst cave; dividing the point cloud data into a plurality of local space regions, and determining a complexity coefficient based on shape features and edge features of the point cloud data in each local space region; the complexity coefficient is used for representing the form complexity of the local space region; performing adaptive dimension reduction processing on each local space region according to the complexity coefficient to obtain point cloud data of each local space region after dimension reduction; and constructing a three-dimensional distribution model of the karst cave based on the point cloud data after dimension reduction of the plurality of local space regions.

The invention provides an urban mobile laser scanning point cloud semantic segmentation method based on double-domain attention, and the method comprises the following steps: firstly, constructing a local space attention block, extracting local semantic information through a space self-attention mechanism, and enhancing the perception capability of local features; secondly, designing a global channel attention block, focusing on relevance modeling between feature channels to capture global context information, and improving the expression ability of the model to a complex scene; further, an information flow control module is introduced, an information transmission path is regulated and controlled through a gating mechanism, key details are reserved, and redundant information interference is suppressed; and finally, a complete semantic segmentation network is constructed based on the modules, and efficient semantic analysis of the point cloud data is realized. Compared with the existing point cloud segmentation method, the method provided by the invention has higher semantic recognition precision under the urban scene mobile laser scanning point cloud, the extracted urban street structure is more complete and clearer, and the method shows stronger adaptability and robustness to a complex point cloud environment.

The application provides a video rain removal method and system based on a space-time difference state space model, and belongs to the technical field of deep learning. The application solves the problem of how to ensure accurate rain removal while retaining the background texture to the greatest extent and breaking the trade-off between "rain removal completeness" and "background fidelity". The method comprises the following steps: S1: extracting shallow space-time features from an input video; S2: constructing anchor points and reference features by using multi-plane scanning, generating a gating map by calculating geometric consistency, and removing rain information in the features by using a difference mechanism; S3: during training, capturing local space-time details by using orthogonal plane convolution, during inference, enhancing the video background texture by using a single-stream convolution after reparameterization, and outputting a rain-removed video. The system comprises a 3D convolution layer module, a consistency-guided difference gating module, and a reparameterized three-plane feedforward network.

The application provides a multi-view three-dimensional virtual-real fusion rendering method and related equipment, and relates to the field of computer vision. By constructing a geometric field, an appearance field and a space-time flow field on a four-dimensional space-time domain, a target scene is divided into a space-time flow line or a local space-time block, fragmentation caused by pure frame-by-frame rendering and scene-specific rules is avoided, a multi-scene fragmentation problem is solved, a space-time importance distribution under each time and each virtual view is calculated by using an updated feature vector and an attention weight corresponding to the updated feature vector to render the geometric field and the appearance field on the four-dimensional space-time domain, and the rendering picture quality is improved without significantly increasing the overall computing overhead.

The application belongs to the technical field of seamless steel pipe surface treatment, and discloses a seamless steel pipe surface decontamination device and a use method thereof, which comprises a treatment box, a gap is formed in the side surface of the treatment box, and a movable baffle is movably sleeved in the inner wall of the treatment box. The application realizes local cleaning of the seamless steel pipe in the local space at the bottom, and realizes comprehensive decontamination and cleaning in the rotating process by cooperating with the self-rotation effect. In the actual decontamination and cleaning process, the active adjustment of the cleaning angle of the seamless steel pipe is reduced, the operator can clearly monitor the decontamination and cleaning environment on one side, the decontamination and cleaning of the large-volume seamless steel pipe is realized, the convenience of decontamination operation is greatly improved, the decontamination efficiency is improved, the actual decontamination situation of the seamless steel pipe with different pollution conditions can be observed, the decontamination and cleaning process can be timely controlled, the decontamination and cleaning is not incomplete in the fixed time, the decontamination time is not too long, and resources are not wasted, and the use effect is good.