2803 results about "Imaging technology" patented technology

The invention provides a soybean planting fertilization management system, and relates to the technical field of management systems.Soil physicochemical properties, a meteorological microenvironment, a plant growth state and agricultural machinery operation data are collected in real time through a field sensor network and an unmanned aerial vehicle multispectral imaging technology, and a farmland dynamic portrait is constructed; the intelligent decision-making unit uses a deep learning model and a GLCM texture analysis technology to dynamically identify plant nutrient deficiency pathological characteristics, determines a nutrient deficiency type in combination with an element concentration threshold and triggers early warning; the scheme decision module dynamically optimizes the nitrogen-phosphorus-potassium ratio through a reinforcement learning algorithm, generates a field-level fertilization prescription map by using Kriging interpolation, recommends the optimal fertilization opportunity and dosage, and the precise execution module is linked with a variable fertilizer applicator to realize integrated precise application of water and fertilizer. The user interaction module supports remote monitoring and intervention of farmers through a GIS visual interface and a mobile terminal APP, can dynamically adapt to environmental changes and crop requirements, and significantly improves the nutrient utilization efficiency.

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The present disclosure provides methods and assay systems for use in spatially encoded biological assays, including assays to determine a spatial pattern of abundance, expression, and / or activity of one or more biological targets across multiple sites in a sample. In particular, the biological targets comprise proteins, and the methods and assay systems do not depend on imaging techniques for the spatial information of the targets. The present disclosure provides methods and assay systems capable of high levels of multiplexing where reagents are provided to a biological sample in order to address tag the sites to which reagents are delivered; instrumentation capable of controlled delivery of reagents; and a decoding scheme providing a readout that is digital in nature.

The invention discloses an automatic identification method and system for a wafer internal defect image of a 3D stacked chip, and belongs to the technical field of semiconductor manufacturing and detection. According to the method, optical, X-ray and ultrasonic image data are synchronously acquired based on a multi-modal imaging technology, imaging parameters are dynamically adjusted to adapt to different wafer levels and material characteristics, multi-modal features are extracted in combination with layered filtering and denoising, multi-resolution registration and a self-supervised deep learning method, and micron-sized defects are positioned by using an attention mechanism. And further constructing a defect-process parameter correlation model through reinforcement learning, generating a closed-loop process optimization instruction, and transmitting the closed-loop process optimization instruction to an execution system. The system comprises a multi-modal imaging module, a noise suppression module, a deep learning analysis module and a process optimization module, and supports edge computing deployment. According to the invention, the internal defect detection efficiency and precision of the multi-layer stacked chip are significantly improved, real-time closed-loop control of detection-analysis is realized, the wafer manufacturing quality risk is reduced, and the method is suitable for intelligent defect detection of an advanced packaging production line.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention provides a power management chip high-low side driving signal time sequence optimization method and system. The method comprises the following steps: acquiring temperature data of a power management chip under a switch switching transient working condition through an infrared thermal imaging technology, and synchronously acquiring three-dimensional temperature field distribution of junction temperatures on the surface and inside a packaging layer; based on the space continuity of a temperature field, extracting the temperature change characteristics of a power device area, converting the temperature gradient into Seebeck voltage by using a thermoelectric material embedded in a hot spot area, and constructing a dynamic thermoelectric feedback network; according to Seebeck voltage data, a coupling relation between hot carrier concentration and driving signal phase deviation is established, and disturbance of hot carrier mobility change on MOSFET switching delay is quantified; the rising / falling edge slope of the grid voltage is adjusted based on the coupling relation, the phase deviation caused by the hot carrier effect is eliminated through dynamic compensation, and the switching performance is optimized. The dynamic response precision of the power management chip is improved.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention discloses a real estate information verification method and system based on an image technology, and relates to the technical field of real estate information verification, and the method comprises the following steps: image collection: employing a collection device with multiple functions, and through the means of adjustable illumination, automatic paper flattening, multi-mode scanning, real-time quality monitoring, etc. Obtaining a high-quality and comprehensive paper house property certificate file image; acquiring a paper house property certificate file image by utilizing acquisition equipment which is provided with an adjustable lighting system and has an automatic paper flattening function; according to the method, comprehensive information is acquired through multi-mode scanning, the risk of information omission is effectively reduced, noise is accurately removed through image preprocessing, the contrast ratio is enhanced, information missing is repaired, a clear image is provided for follow-up processing, multiple kinds of accurate feature analysis are newly added in the feature extraction link, rich and accurate feature vectors are formed, and the image quality is improved. And the capability of judging the authenticity and integrity of the file is greatly enhanced, so that the accuracy and reliability of verification are remarkably improved.

The invention relates to the technical field of agricultural information, discloses a corn precise topdressing prescription map generation method based on unmanned aerial vehicle multispectral remote sensing, and effectively solves the key problems of insufficient monitoring precision, poor environmental adaptability, low decision intelligent level and the like in the traditional fertilization technology. Through the unmanned aerial vehicle multispectral imaging technology, efficient and accurate monitoring of the corn nitrogen nutrition condition is achieved; in combination with a machine learning algorithm, an intelligent fertilization decision model adapted to different environmental conditions is established, and the fertilization precision in a complex farmland scene is remarkably improved; and a multi-source data fusion technology is adopted, and crop growth, soil characteristics and meteorological factors are comprehensively considered, so that the fertilization scheme is more scientific and reasonable. Compared with a traditional method, the method has the advantages that accurate variable fertilization can be realized according to actual needs of crops, the utilization rate of the fertilizer is increased, environmental pollution is reduced, and reliable technical support is provided for sustainable development of modern agriculture.

The invention relates to the technical field of three-dimensional imaging, in particular to a three-dimensional imaging detection method for surface defects of a connector, and provides the following scheme: collecting a point cloud image of the connector; extracting a suspected disturbance region through a morphological operator to generate a target point; constructing an axial symmetry mapping relation of the target points, and generating a symmetric residual distribution map; based on the disturbance space gradient field, eliminating a disturbance convergent point and reserving a disturbance divergent point; and further extracting a normal abrupt change relation and a curvature minimum value region through a defect identification network, and outputting a three-dimensional coordinate set of a defect region. According to the method, the artifact elimination accuracy and the defect identification precision are effectively improved, and the method is suitable for a high-robustness three-dimensional defect detection task under a complex inner wall structure.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention relates to the field of power transmission line forest fire monitoring, and discloses a power transmission line multi-mode fusion intelligent forest fire monitoring method which is used for constructing an all-dimensional power transmission line forest fire prevention monitoring system and remarkably improving the intelligent level of power transmission line forest fire risk prevention and control. Comprising the steps of reasoning and identifying a smoke and fire target for a fused feature set by utilizing an improved YOLOv8 algorithm model, realizing accurate positioning of a fire point by adopting a haze penetration thermal imaging technology and a smoke cooperative monitoring technology in combination with spatial positioning, and establishing a temperature and humidity and forest fire occurrence probability correlation model based on historical data. According to the real-time temperature and humidity parameter dynamic adjustment algorithm model, the confidence threshold and the sensor acquisition frequency are detected, a dynamic threshold control method is adopted, an early warning threshold is adaptively adjusted according to parameters such as environment illumination, a final early warning result is output after cross validation of a multi-modal data fusion strategy, and the safety of the power transmission line is effectively guaranteed.

The invention relates to the technical field of remote sensing imaging, in particular to a marine organism monitoring method and system based on a remote sensing technology, and the method comprises the following steps: constructing an environment layer based on a remote sensing image and sensing data, analyzing the relation between a spectrum and an environment, monitoring changes, recognizing the spatial distribution of species, extracting a migration path, and superposing environment factors to analyze ecological response. And outputting a remote sensing indication species ecological response interaction layer. According to the method, the remote sensing image and environmental data are sorted and denoised, multi-dimensional factors are effectively integrated, data precision is enhanced, noise interference is eliminated, spectral change and water temperature gradient deviation are combined, abnormal pixels are accurately screened, sea area change trend is insight in advance, key wave bands and hot salt structures are extracted, target organisms are accurately positioned, and identification efficiency is improved. Analyzing a space offset track, restoring a migration path, constructing a dynamic evolution view angle, superposing water temperature and algae changes, analyzing time sequence synchronization characteristics, revealing ecological response turning points, and strengthening early warning and dynamic monitoring capabilities.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention discloses a server temperature control method and system based on deep learning, and belongs to the technical field of server temperature control, and the method comprises the steps: firstly, synchronously collecting server hardware temperature, business load and environment parameters, capturing a heat conduction relation through combining a thermal imaging technology, and constructing a dynamic relation network through time-space fusion processing; a heat flow prediction model is constructed based on the network, model parameters are dynamically corrected in combination with thermal response characteristic attenuation coefficients generated by service load characteristic analysis, and temperature distribution changes are predicted; generating a candidate control strategy set, screening an optimal strategy through composite scene simulation, and generating a control instruction; and comparing the actual temperature after the control instruction is executed with the model prediction temperature in real time, switching to a safety control mode when the deviation exceeds a threshold value, and extracting an incremental sample to update model parameters after the deviation is recovered, thereby realizing accurate temperature control.

The embodiment of the invention provides a multi-crop yield prediction method and device based on remote sensing collaborative inversion, and is applied to the technical field of remote sensing images. The method comprises the following steps: performing time sequence remote sensing data reconstruction processing on acquired multi-source remote sensing image data to generate a time sequence remote sensing data set; generating crop area identification data according to the time sequence remote sensing data set based on a preset multi-crop area identification model; generating agricultural condition multi-parameter inversion data in the to-be-processed region according to the time sequence remote sensing data set and the crop area identification data based on a preset multi-agricultural condition multi-parameter inversion model; and based on a preset multi-crop yield prediction model, generating crop product prediction data according to the agricultural condition multi-parameter inversion data. In conclusion, a multi-stage linkage full-process model of'time sequence remote sensing data reconstruction + crop area identification + agricultural condition parameter inversion + crop yield prediction 'can be constructed, and a high-precision, high-timeliness and automatic integrated yield prediction solution is formed.

The invention discloses an internet-of-things senile disease nursing system supporting equipment state monitoring, and relates to the technical field of internet-of-things senile nursing. An acquisition module realizes multi-index monitoring by using a flexible sensor and a non-invasive technology; the edge computing unit fuses the neuromorphic architecture and federal learning; the equipment monitoring module predicts faults in combination with acoustic emission and thermal imaging technologies; the health assessment engine carries out disease early warning through causal reasoning and a knowledge graph; the early warning system realizes hierarchical response and multi-terminal linkage; and the block chain platform ensures data security and sharing. Through multi-module collaborative innovation, precise collection of elderly health data and real-time monitoring of equipment states are realized, diseases and equipment faults are early warned in advance, an intelligent evaluation engine can predict complications, multi-modal early warning improves response efficiency, a block chain technology guarantees data security, medical resource sharing is supported, and the system is suitable for popularization and application. And the safety, timeliness and intelligent degree of senile disease nursing are comprehensively improved.

The invention discloses a laser energy threshold millisecond-level dynamic matching method for multi-metal fuse manufacturing, and the method comprises the steps: pre-storing a multi-metal thermophysical parameter model, calibrating a thermal signal in real time through Kalman filtering, and carrying out the online correction of a melting threshold key parameter during the switching of a material; millisecond-level thermal feedback closed-loop control is achieved through the high-frame-rate thermal imaging technology, and the laser power accurately follows the corrected melting threshold value; meanwhile, a three-dimensional imaging and deep learning segmentation algorithm is adopted, geometric information of a welding wire and a molten pool is obtained, the position and the diameter of a light spot are adjusted in real time through an adjustable optical system after the offset is calculated, and millimeter-level space registration closed-loop control is achieved; the two closed-loop systems cooperatively operate according to respective control periods, and precise dynamic matching of laser heat input in a time domain and a space domain is achieved. The problem that the adaptability of a traditional process is insufficient during multi-metal switching and complex geometric change is solved, and the forming quality, the process stability and the efficiency of manufacturing are remarkably improved.

The invention provides a medical image classification method based on semi-supervised dynamic fusion matching, and belongs to the technical field of images, and the method comprises the steps: generating a pseudo label based on a medical image through calculating the correlation of samples under a weak enhancement view and a strong enhancement view, and carrying out the calibration and fusion processing, and generating a fused class pseudo label; dynamically calculating a threshold value at each time step, and based on dynamic adjustment of the threshold value, optimizing selection and category learning of fused category pseudo labels through a consistency loss function; for long-tail distributed medical image data, category balance loss is introduced, and medical images are classified by dynamically adjusting decision boundaries of each category and taking a total loss function as a target. According to the method, the problems of data scarcity, category imbalance and false label optimization in medical image classification are solved.

The invention relates to a double-color magnetic nanoparticle imaging method based on a single-harmonic system matrix, which comprises the following steps: respectively moving two different types of magnetic nanoparticle samples to a central zero magnetic field point, applying a scanning magnetic field to enable the zero magnetic field point to scan the whole imaging field of view, applying an excitation magnetic field to magnetize magnetic nanoparticles, and applying an excitation magnetic field to magnetize the magnetic nanoparticles; acquiring magnetization frequency spectrum signals of the two magnetic nanoparticle samples; the method comprises the following steps: constructing single harmonic system matrixes A1 and A2 based on single harmonic of magnetization spectrum signals of two kinds of magnetic nanoparticle samples; measuring a to-be-measured object formed by the two kinds of magnetic nanoparticles to obtain a measurement magnetization signal U; transversely splicing the single harmonic system matrixes A1 and A2 of the two different types of magnetic nanoparticles to obtain a new single harmonic system matrix A; the concentration distributions c1 and c2 of two different types of magnetic nanoparticles are obtained by combining a new single-harmonic system matrix A with a measurement magnetization signal U, and the concentration distributions of the two different types of magnetic nanoparticles are respectively sent into different color channels to be displayed in different colors. The method has important significance in promoting the application of the magnetic nanoparticle imaging technology in clinical medicine.

The invention provides an airfield pavement detection method based on an earthquake background noise imaging technology, and relates to the technical field of airfield pavement detection. Comprising the following steps: acquiring seismic background noise data of an airport pavement to be measured; preprocessing the seismic background noise data to obtain preprocessed noise data; carrying out cross-correlation calculation on the preprocessed noise data and carrying out empirical Green function extraction; according to the empirical Green function, a surface wave group velocity frequency dispersion curve is extracted by adopting spatial autocorrelation and multiple filtering methods; inverting shear wave velocity distribution of each layer of the pavement based on the surface wave group velocity dispersion curve, and constructing a three-dimensional velocity model; determining a shear wave velocity distribution diagram according to the three-dimensional velocity model; and determining a detection result of the airfield pavement to be detected according to the transverse wave velocity distribution diagram. According to the invention, the problems of low detection precision and poor real-time performance of a common detection technology in the prior art are solved.

The invention belongs to the technical field of three-dimensional imaging, and particularly relates to a single-photon point cloud three-dimensional imaging method based on cross-modal feature fusion, and the method specifically comprises a point cloud data preprocessing algorithm which is used for converting single-photon laser radar echo data into a three-dimensional point cloud form, main body target extraction in the complex scene point cloud is realized, and calculation denoising processing is completed; a cross-modal feature fusion point cloud reconstruction network is designed, two-dimensional features are introduced multiple times in the reconstruction process, interaction with three-dimensional point cloud features is established, and incomplete point clouds and real complete point clouds are assisted to construct a mapping relation; a KNN segmentation coordinate filling algorithm is designed, point cloud vacant coordinates are calculated and complemented until the resolution of the point cloud vacant coordinates is matched with a two-dimensional infrared image, and two-dimensional image and three-dimensional point cloud pixel-level fusion imaging is achieved.

The invention belongs to the field of CT (Computed Tomography) tomography reconstruction technology and artificial intelligence, and discloses a finite angle CT reconstruction method based on combination of a three-dimensional conditional diffusion model and synchronous iteration. The CT is an imaging technology which utilizes X-rays to irradiate a target from different angles and acquire projection, and obtains an internal three-dimensional structure through reconstruction. Different from traditional CT depending on nearly full-angle scanning, the method only collects limited-angle projection, and achieves fault reconstruction under the limited-angle condition through a three-dimensional condition diffusion model of space domain-frequency domain two-way decoding and by means of structural generality priori of a workpiece. Meanwhile, projection and fault data consistency correction is carried out in combination with a synchronous iteration reconstruction technology, the advantages of an iteration method in the aspect of physical mechanism characterization are exerted, interpretable physical constraints are provided for a deep learning network, and therefore the reliability and precision of a reconstruction result are improved.

The invention relates to the technical field of oral cavity imaging, in particular to an oral cavity nursing monitoring imaging system and method.The system image structure decomposition module, the local contour recognition module, the image change detection module, the abnormal area filling module and the color gamut dynamic regulation and control module are included. Functional areas are clearly divided, the area recognition precision of images is improved, particularly, in tooth boundary recognition, contrastive analysis of brightness and color differences is utilized, the recognizability of tooth contours and continuous frame tracking of tooth structure changes are remarkably enhanced, the dynamic deformation path of the tooth structure changes is displayed, the monitoring capacity of tiny changes is improved, and meanwhile, the recognition accuracy of the tooth contours is improved. The automatic completion technology is used for repairing the fracture boundary in the image and restoring the boundary continuity, and the color channel is adjusted to ensure the overall visual consistency of the image and avoid visual discordance, so that the image quality and the analysis accuracy are improved.

The invention provides a three-dimensional interface inversion method fusing geological constraints and a level set function, and relates to the technical field of geophysical inversion and imaging, and the method comprises the steps: obtaining geological prior information, carrying out the parametric modeling of an underground structure of a research region through a Gaussian random field, and generating a model parameter vector; and simulating the propagation process of seismic waves in a seismic wave speed model by using a forward modeling calculation method, carrying out iterative optimization on model parameter vectors based on an EKI set Kalman inversion method, recovering an underground three-dimensional structure in a research area, and carrying out interpretation and verification in combination with drilling data to obtain structural characteristics and resource distribution of the research area. According to the method, unified parametric modeling is performed on speed disturbance and interface forms through a Gaussian random field, and a level set function is introduced to represent discontinuous layering, fault cutting, inclusion embedding and other complex geological interfaces, so that collaborative inversion of a speed field and a structure boundary under the same optimization framework is realized; and geometric expressive power and geological consistency of the model are improved.

The invention discloses a deep learning-based coal mine foreign matter visual detection system, which is characterized in that a coal mine image is acquired through a multispectral imaging technology, a static compensation image is obtained through registration, enhancement and motion compensation processing, multidimensional features are extracted, dimensionality reduction is carried out by utilizing principal component analysis to generate an optimized feature set, an improved YOLOv12 model is trained, and the visual detection of foreign matters in a coal mine is realized. A lightweight foreign matter detection model is obtained through parameter simplification and network optimization, and foreign matter real-time recognition and detection report generation are achieved. A time sequence analysis model is constructed by associating historical detection data, and a detection model is iteratively updated in combination with an incremental learning mechanism. Multispectral imaging and deep learning are combined, the foreign matter detection precision of the complex underground environment is improved, industrial site deployment is adapted through model lightweight design, the learning ability of the system for the foreign matter distribution rule is enhanced through a time sequence analysis mechanism, and efficient detection and early warning of the coal mine foreign matter are achieved.

The invention relates to the technical field of computer tomography, in particular to a geometric calibration method and system for an equiangular fan-beam industrial CT (computed tomography) system, and the method comprises the following steps: building a geometric model of the equiangular fan-beam industrial CT system comprising turntable center offset, detector lateral offset and radiation source radial offset; projection data are obtained through two times of scanning before and after displacement of the rotary table; performing interpolation processing on the projection data, and calculating a projection address of a ray penetrating through the rotation center of the rotary table; and calibrating geometric parameters of the geometric model based on a projection address of a ray passing through the rotation center of the rotary table and the basic parameters of the equiangular fan-beam industrial CT system. The method is used for correcting the geometric artifacts in the CT reconstructed image and improving the quality of the reconstructed image.

The invention discloses a mechanical arm regulation and control multi-sensor array underground structure detection system and detection method based on reinforcement learning, a vibration source module provides a sweep frequency vibration signal, a multi-sensor array collects a reflected signal, a data processing module receives and processes the reflected signal, and the data processing module receives and processes the reflected signal; the imaging module generates a three-dimensional underground structure image through a wave velocity inversion and reverse time migration imaging technology, the intelligent optimization and adaptive adjustment module adaptively adjusts vibration source signal parameters and sensor array layout through a reinforcement learning algorithm, and the display terminal displays detection data and imaging results in real time and provides parameter optimization suggestions. According to the invention, real-time and automatic adjustment of vibration source signals and sensor layout is realized through an intelligent optimization and adaptive adjustment module constructed through a reinforcement learning algorithm, and a high-resolution underground structure three-dimensional image is generated accordingly. And an efficient and accurate underground structure identification and positioning scheme can be provided.

The invention relates to the technical field of remote sensing images, in particular to a method, device and equipment for generating an orthoimage based on point cloud and a storage medium. Comprising the following steps: S1, acquiring point cloud data; s2, carrying out grid processing on the point cloud data; s3, sorting single grid units comprising a plurality of points according to elevation values, retaining top point clouds, determining pixel color values of the grid units, generating a three-channel image with a plurality of pixels, and constructing a preliminary ortho-image map; s4, traversing the three-channel image to identify null value pixel points, extracting coordinate values of a plurality of non-null value pixel points in the neighborhood, and calculating a coordinate azimuth angle difference value; and S5, according to the coordinate azimuth angle difference value, judging whether the null value pixel point is a hole pixel point located in the image, when the hole pixel point exists, carrying out image repairing, and outputting a repaired orthoimage. According to the method, the high-fidelity orthoimage map can be quickly generated, and the processing efficiency is remarkably improved while the authenticity and the integrity of the ground feature color are ensured.