71 results about "Spatial density" patented technology

The invention relates to the technical field of three-dimensional reconstruction, in particular to a lightweight three-dimensional reconstruction method and system based on a three-dimensional Gaussian model, and the method comprises the steps: constructing the three-dimensional Gaussian of a target scene based on a multi-view original image of the target scene through employing a 3DGS algorithm; identifying the minimum resolution of each Gaussian primitive in the three-dimensional Gaussian, setting a spherical region according to the minimum resolution, taking the intersection result of the spherical region and the Gaussian primitive as a region evaluation score, and trimming the redundancy region geometry in the three-dimensional Gaussian in combination with the opacity; and taking the maximum zoom scale of the Gaussian primitive as a spatial density proxy variable, and dynamically distributing spherical harmonic coefficient orders according to a local spatial density proxy variable so as to meet the rendering requirements of different three-dimensional Gaussian geometric regions. On the premise that the image synthesis quality is not lost, the model scale and the transmission overhead can be remarkably compressed, the resource constraint condition of the edge computing equipment can be effectively adapted, and the higher training speed and the better rendering effect are achieved.

The invention discloses a distributed photovoltaic power generation anomaly positioning optimization method, and particularly relates to the technical field of power generation anomaly positioning, and the method comprises the steps: carrying out the space-time alignment of the static information, environmental parameters and dynamic power generation data of multiple stations in a target region, carrying out the spatial density clustering based on the three-dimensional distance of the stations, and dividing spatial sub-clusters; recognizing a space-time hot spot region in combination with a decoupling model of a historical loss rate and irradiance; constructing a space loss gradient field for the hot spot region, and reversely tracing to a normal threshold boundary point to realize pollution propagation path reconstruction; according to the method, wavelet packet decomposition, variational mode decomposition and short-time Fourier transform are adopted for a non-hotspot area to extract multi-scale attenuation and noise features, artifacts are removed through neighborhood slope difference, macroscopic error shielding and microcosmic anomaly positioning of meteorological and component differences are achieved, and the checking accuracy and the operation and maintenance efficiency are improved.

The invention discloses a business district drawing method and device based on geographic space aggregation, and the method comprises the steps: carrying out the spatial density clustering of all network points in a to-be-drawn space, and obtaining a plurality of network point clusters; correspondingly generating a plurality of convex hulls based on the plurality of dot clusters; and performing boundary optimization on the convex hull according to the collected river network data, and determining the optimized convex hull as the business circle in the space to be drawn. According to the method, the problems of fuzzy boundary, data lag, poor strategy adaptability and the like in the traditional technology can be effectively solved, accurate definition of the contour of the business district and standardized output of grade division are realized, and then the efficiency of drawing the business district is improved.

The invention discloses a power distribution equipment point cloud multi-target classification extraction method, system, equipment and medium, and belongs to the technical field of power distribution line three-dimensional visualization, and the method comprises the steps: obtaining three-dimensional laser point cloud data covering a power distribution line region, and carrying out the noise point elimination and spatial density resampling, and obtaining first point cloud data; extracting ground points in the first point cloud data, and constructing a digital elevation model; removing the ground points to obtain second point cloud data containing the power distribution equipment and the environment target; executing clustering processing, and extracting structural feature parameters of each cluster; inputting the extracted structural feature parameters into a pre-trained classification model to complete classification; and correcting a classification result, and outputting a power distribution equipment point cloud classification data set with a category label. The method has the beneficial effects that in the ground point identification and elimination stage, a sliding window area consistent with the distribution line layout characteristics is constructed, an initial ground point set is identified by using a height gradient trend, and a misjudgment point set with obvious terrain disturbance or local ground characteristics is screened out through joint verification of a fitting residual error and a ground structure template, so that the accuracy of the ground point identification and elimination is improved. And the stability of subsequent structure extraction and the precision of ground object separation are enhanced.

The invention relates to the technical field of warehousing of bulk grains in a horizontal warehouse, discloses a warehousing method and system for bulk grains in a horizontal warehouse, and aims to solve the problems of uneven accumulation, potential safety hazards and low efficiency in an existing method. A three-dimensional warehouse body coordinate system is constructed through multi-dimensional analysis, and spatial density distribution is simulated; establishing a dynamic accumulation model based on density data to simulate a spatial-temporal trajectory, calculating acting force among particles through finite element analysis to obtain stress distribution, and constructing a stability evaluation system; the impurity rate is corrected through a coupling model by utilizing environment parameters of the internet of things, and a stability evaluation result is dynamically compensated by adopting a multi-factor matrix; a deep reinforcement learning algorithm is applied to optimize a control strategy, an intelligent model is deployed at an edge node, and a joint control automation system realizes closed-loop management. According to the bulk grain warehousing device, the uniformity, stability and efficiency of bulk grain warehousing operation are improved.

The invention discloses a main earthquake group intelligent identification method considering a micro-earthquake contour coefficient and spectrogram clustering. The method comprises the following steps: 1, acquiring micro-earthquake monitoring data; 2, acquiring a spatial similarity matrix, an energy similarity matrix and a time similarity matrix, and weighting to form a comprehensive similarity matrix; 3, forming a feature matrix based on the comprehensive similarity matrix, and mapping data points in the feature matrix into two-dimensional space coordinates; 4, clustering data points in the two-dimensional space coordinates by adopting a DBSCAN algorithm, and obtaining a neighborhood radius and a minimum sample number optimization combination based on a micro-seismic contour coefficient; and 5, under the optimization combination of the neighborhood radius and the minimum sample number, clustering data points in the two-dimensional space coordinates by adopting a DBSCAN algorithm to obtain a plurality of micro-seismic event optimization clusters. And 6, screening out a micro-seismic event cluster with the highest comprehensive feature score as a main seismic group. The method is reasonable in design, and the main earthquake group is selected by fusing evaluation indexes of space density, energy characteristics and time characteristics.

The invention provides an image processing system and method for petroleum pipeline leakage detection, and belongs to the technical field of image processing.The method comprises the steps that a global edge feature map of a petroleum pipeline is extracted from a pipeline monitoring image, and a pipeline structure feature point set of the petroleum pipeline is extracted from a contour curve in the global edge feature map; extracting an oil stain identification mask of the petroleum pipeline according to a gradient modulation coefficient matrix determined by the spatial density distribution of the pipeline structure feature point set; initializing a closed evolution curve surrounding the suspected petroleum leakage area, performing evolution on the closed evolution curve by using an edge constraint function constructed by the pipeline structure feature point set and the oil stain identification mask, and detecting a spatial relationship between the current position of the closed evolution curve and the pipeline structure feature point set; and segmenting a petroleum leakage area of the petroleum pipeline according to the spatial relationship. According to the technical scheme, segmentation of the petroleum leakage boundary can be achieved in the state that the contrast ratio of the weak edge of the oil stain diffusion area and the pipeline metal surface is insufficient.

The application relates to the technical field of machine vision and image processing, and discloses a plastic valve visual defect real-time detection method and system based on deep learning, which comprises the following steps: acquiring a gray image containing a plastic valve, positioning the centroid of a plastic valve main body area by using geometric moments; converting the gray image to a polar coordinate based on the centroid, applying one-dimensional high-pass filtering, and determining a dynamic segmentation threshold to extract abnormal gray pixel points; performing spatial density clustering after reverse mapping to determine a suspected defect area; calculating the Euclidean distance between the centroid of the suspected defect area and the centroid of the plastic valve main body area, synchronously inputting the distance and image features into a double-input convolutional neural network to output a plastic valve defect detection result. The application filters out cloud and fog-shaped diffuse reflection interference, and improves the accuracy of defect detection.

The invention provides an optical cable fault intelligent analysis and positioning method based on big data and AI, and the method comprises the steps: extracting a spatial density feature value from a latitude and longitude coordinate set, judging the coordinate as a high-density region coordinate if the spatial density feature value exceeds a preset threshold value, and obtaining a preliminary spatial distribution point set; performing grouping processing on the preliminary spatial distribution point set by adopting a clustering algorithm, fusing Euclidean distance measurement in the longitude and latitude coordinate set, determining a cluster of fault points, and obtaining a distribution mode cluster group; and performing prediction processing on a time sequence of the high-incidence area identification set through a time sequence analysis algorithm, fusing trend data of the time evolution sequence segment, determining a prediction value of a future evolution rule, and obtaining a prediction rule value set.

The invention provides a sensor data processing method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. According to the specific implementation scheme, n to-be-processed data sets of a target vehicle are acquired; determining m candidate data sets in the n to-be-processed data sets according to a type screening condition; and determining at least one target data set in the m candidate data sets based on the spatial density coefficient. According to the technical scheme, secondary cleaning of multiple types of sensor data can be realized based on the data type composition of the acquired sensor data and the spatial density distribution of the data acquisition position, so that the problem of large redundant data volume caused by sensor data cleaning only based on a single-type dimension is avoided; and the usability of the processed sensor data in an automatic driving simulation test scene is improved.

This invention discloses a target detection method based on variable threshold and matrix calculation, relating to the fields of computer vision and artificial intelligence application technology; including: Step 1: For detection images containing small targets, preprocess all detection boxes output by the detection model; Step 2: Preset the IoU value as the density threshold. If and only if the IoU between two detection boxes is greater than or equal to the density threshold, it is determined that the two detection boxes constitute a spatial proximity relationship. Based on this proximity relationship, for each detection box, count the total number of neighboring detection boxes that meet the density threshold condition, and define the statistical value as the local spatial density value of the detection box; Step 3: Map the local spatial density value one-to-one to the dynamic confidence threshold corresponding to the detection box, and expand the N-dimensional vector of the dynamic confidence threshold of N detection boxes into a threshold matrix T with dimension N×N; Step 4: Compare the IoU matrix I with the threshold matrix T position by position; Step 5: Compare the summation vector S with the confidence vector C element by element: For each detection box i, only the detection boxes that satisfy the condition S[i]≤C[i] are retained as the output result for subsequent target detection processes.

The application discloses a bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision, and the method comprises the following steps: establishing a common coordinate system, sampling the measurement data in the ground-based interferometric radar coordinate system and the inertial vision technology coordinate system to the common coordinate system respectively to obtain registration data of the ground-based interferometric radar and the inertial vision technology in the common coordinate system, interpolating the registration data in the inertial vision technology to obtain interpolation registration data matched with the spatial density of the ground-based interferometric radar data, respectively calculating the spatial model matrix of the ground-based interferometric radar line-of-sight deformation, the inertial vision technology vertical deformation and the inertial vision technology bridge lateral deformation according to the interpolation registration data and the registration data of the ground-based interferometric radar, calculating the fusion weight matrix of both by using an iterative almost unbiased estimation method, and calculating the multi-dimensional dynamic deformation time sequence of the full span of the bridge by using a least square method according to the fusion weight matrix, so that the bridge deformation monitoring is more accurate.

The invention provides an aggregate grain shape and grading identification method and device based on image processing and a storage medium, and the method comprises the steps: obtaining a dynamic falling image flow of an aggregate grain group, separating a closed contour image of a single aggregate grain from the dynamic falling image flow, carrying out the boundary constraint harmonic field construction to generate harmonic field distribution, and carrying out the stable critical structure extraction based on the harmonic field distribution. The method comprises the following steps: identifying a critical turning point of field intensity value change, encoding the critical turning point as a topology invariant sequence, calling a shape semantic mapper calibrated by concrete interface performance, performing semantic mapping processing on the topology invariant sequence, and outputting quantitative representation of single aggregate particle on the aggregate-slurry interface occlusion capability. Spatial density weighted fusion is carried out according to the quantitative characterization of the whole batch of aggregate particles, and after normalization processing is carried out, a structural efficiency index reflecting the aggregate particle shape functional characteristics is generated through weighted fusion. The method provided by the invention can comprehensively reflect the grain shape functional characteristics of the whole batch of aggregate, and provides a direct effectiveness evaluation basis for optimization of aggregate grading in concrete mix proportion design.

The invention discloses a ship navigation trajectory prediction method and system, and the method comprises the steps: obtaining the inertial navigation data of a ship, carrying out the inertial navigation calculation based on the inertial navigation data, and obtaining a first trajectory prediction result; obtaining historical navigation data of the ship, and obtaining a second trajectory prediction result through a trajectory prediction model based on the historical navigation data; carrying out credibility evaluation on the historical navigation data in spatial density and mode similarity dimensions to obtain a credibility index of the historical data; dynamically determining data fusion weights of the first trajectory prediction result and the second trajectory prediction result based on a credibility index evaluation result; and fusing the first trajectory prediction result and the second trajectory prediction result according to the data fusion weight to generate a final ship navigation trajectory prediction result.

The application provides a kind of method and system for intelligent management and control of national space planning based on multi-source data fusion, relating to spatial data processing technical field, the method comprises: obtaining remote sensing image data, geographic information vector data and ecological environment monitoring data of target area;The remote sensing image data and geographic information vector data are parsed, and surface cover elements and planning control boundaries are extracted;The intersection area of the surface cover element and the planning control boundary is calculated, and the spatial overlap area data is generated;The spatial overlap area data is executed kernel density estimation operation, and the element distribution density in continuous spatial domain is solved, to generate spatial density mapping result;The change trajectory of the spatial density mapping result on continuous time sequence is extracted.The application improves the accuracy of national space planning control.

The present invention discloses an exhibition interaction method for large-scale XR spaces based on LBE technology, which relates to the technical field of extended reality. The method comprises the following steps: aligning user location coordinates and environmental three-dimensional point cloud data in time and space using an ICP algorithm, filtering noise using a Gaussian filter, and forming a user location matrix; generating a spatial density distribution map using a PPO algorithm from the user location matrix, and dynamically meshing the spatial density distribution map to generate dynamic mesh layout parameters; inputting the dynamic mesh layout parameters into a chaotic prediction model, simulating user movement patterns using an improved Lorenz equation, extracting obstacle positions using a RANSAC algorithm, and simultaneously performing multi-dimensional feature mapping on user movement patterns and obstacle positions to generate a collision risk heat map. The present invention utilizes a chaotic prediction model and a RANSAC algorithm to improve the accuracy of user movement pattern prediction and the ability to identify obstacles in a changing environment, making the interactive experience smooth and natural while ensuring user safety.

The invention discloses an image semantic self-adaptive compression transmission method and system for a mobile device, and relates to the technical field of image transmission communication.The method comprises the steps that original inertial motion data and interactive operation data of the device are obtained, and an interference image index and an interactive operation fitting broken line are obtained through analysis; when the interference is significant, mapping the interference into a global basic compression ratio, and extracting a target region point from a broken line; calculating a preset radius based on the target area point and the interference degree, delimiting an interactive focus area, and calculating an adjustment coefficient according to an intersection relation of each area; a high-quality first compression parameter for an interactive focus area and a second compression parameter for saving bandwidth for other areas are obtained, and differential coding and transmission are carried out according to the first compression parameter and the second compression parameter; based on spatial density analysis of historical interaction data, a focus area range is dynamically corrected for a subsequent period. According to the method, the self-adaptive clear transmission of the user concerned area and the efficient compression of the background area are realized under the condition of equipment shaking.

This invention relates to the field of data representation technology, and discloses a semi-supervised clustering method and its open-ended question answer text encoding method. The semi-supervised clustering method includes: acquiring a dataset to be clustered, its labeled dataset, and its unlabeled dataset; mapping the dataset to be clustered to a spatial density map and / or a topological density map; and using the labeled and unlabeled datasets to cluster the data in the dataset to be clustered into several clusters, wherein each data point in each cluster has a cluster label, which can be an existing label or a new label. This invention uses a semi-supervised clustering method to efficiently and accurately cluster open-ended question answer text data, and can discover new classes with limited prior knowledge. After clustering, keywords can be extracted and encoded from the open-ended question answer text of each class, facilitating a quick and accurate understanding of the situation of the interviewed group and improving the efficiency and quality of diagnosis and treatment.

The invention provides a region division and dynamic node selection method for space-time synchronization of a heterogeneous giant satellite base, and belongs to the technical field of giant satellite base division and node selection. The method comprises the following steps: acquiring state data of all satellites in a constellation; based on the spatial density, performing region division on the constellation in the rolling time domain by using a spatial density partitioning algorithm, and obtaining a region set based on the minimum hop count constraint; for a plurality of region sets, selecting a main node star and a backup main node star by utilizing a main node star efficiency matrix; link establishment observation is carried out on the slave satellites, a centralized processing parameter estimation equation is constructed, and a state optimal solution is calculated; and on the basis of the state optimal solution, mutual link establishment observation is carried out on the main node stars, and a distributed data processing method is utilized to calculate and obtain the state optimal estimated value of the main node stars. The method solves the problems that an existing region division method lacks dynamic adaptability, the main node selection matching degree is low, and the overall efficiency, robustness and dynamic response capacity of a constellation system are poor.

Provided herein are methods for analysis of a data set comprising a plurality of input data points in the form of numbers or multidimensional coordinates. These methods can comprise: (a) processing the plurality of input data points to define a study area; (b) computing a tessellation cell for each of the plurality of input data points; (c) removing any tessellation cells that are unbounded or lie outside of the study area; (d) computing an instantaneous spatial density at each of the plurality of input data points using the tessellation cell; (e) identifying clustered data points as those whose instantaneous spatial density exceeds an analytically defined density threshold; and (f) grouping clustered data points into ensemble point clusters (clustered points with adjacent tessellation cells.

The application discloses a mobile device-oriented image semantic adaptive compression transmission method and system, relates to the technical field of image transmission communication, and obtains original inertia motion data and interactive operation data of a device, analyzes to obtain an interference image index and an interactive operation fitting broken line; when the interference is significant, the interference is mapped into a global basic compression ratio, and a target area point is extracted from the broken line; a preset radius is calculated based on the target area point and the interference degree, and an interactive focus area is demarcated, a calibration coefficient is calculated according to the intersection relationship of each area; a high-quality first compression parameter used for the interactive focus area and a bandwidth-saving second compression parameter used for the remaining area are obtained according to the calibration coefficient, and differential encoding and transmission are performed according to the calibration coefficient; based on spatial density analysis of historical interactive data, the focus area range is dynamically corrected for subsequent periods. The application realizes adaptive clear transmission of a user attention area and efficient compression of a background area under the condition that the device shakes.

The invention discloses an airborne laser radar depth sounding land waveform classification method, and relates to the technical field of marine surveying and mapping and remote sensing information processing, and the method comprises the steps: obtaining airborne laser radar depth sounding (ALB) full waveform data of a target region; the full waveform data at least comprises infrared laser waveform data and waveform data of a deep water channel and a shallow water channel of blue-green laser; based on the amplitude characteristics of the infrared laser waveform data, clustering the collected laser foot points for the first time to obtain an initial classification result containing a water body point set, a land point set and an undetermined point set; fusing the three-dimensional space position information of the laser foot points, and carrying out space density clustering analysis on the to-be-determined point set; by adopting a three-stage progressive strategy of waveform amplitude preliminary screening, spatial relationship correction and multi-feature fusion fine judgment, the classification uncertainty of different layers is solved layer by layer, misclassification caused by noise, special ground features and boundary fuzzy regions is effectively reduced, and the overall classification precision is remarkably improved.

The invention discloses a gravity inversion method and system based on cluster number self-optimization and sub-domain adaptive regularization, a storage medium and equipment, belongs to the technical field of gravity inversion, and aims to solve the problems that regularization parameters are difficult to adaptively adjust, the clustering number depends on manual setting, a partition structure is not matched with geologic features and the like in gravity inversion. The optimal clustering cluster number is automatically determined by calculating the marginal benefit decreasing rule of the intra-cluster quadratic sum error; and constructing a global reference regularization factor based on the spatial density characteristics of each sub-region, and introducing a normalized spatial density exponential attenuation strategy to realize sub-region differential dynamic updating of the regularization factor. According to the method, automatic determination of the cluster number in gravity inversion is realized, dependence on manual setting and fixed prior is avoided, stability, spatial resolution and imaging precision of gravity inversion are improved, spatial adaptive adjustment of regularization constraint parameters is realized, and the method is suitable for mineral resource exploration and geological structure identification under complex geological conditions.

The invention provides an identification and extraction method and system for a project record proof, and the identification and extraction method is applied to the identification and extraction system which comprises a pre-identification unit, a text splicing unit, a text correction unit and an information extraction unit, and specifically comprises the steps: carrying out the pre-identification of a project record proof file, obtaining a plurality of text pairs and position information of the text pairs; based on spatial density clustering, the text pairs are clustered according to the position information of the text pairs, the text pairs are spliced according to a clustering result, and a text region is obtained; fuzzy characters in the text area are recognized and corrected; and performing information extraction on the corrected text region to obtain project record proof information. According to the method, the adaptability of recognition of various character forms is improved through spatial position analysis and processing, the complexity of a file structure is reduced, the accuracy of character recognition is further improved through correction of fuzzy characters, and the accuracy of information extraction of project filing proof files is guaranteed.

The present application relates to the technical field of flat warehouse bulk grain warehousing technology, and discloses a flat warehouse bulk grain warehousing method and system, aiming to solve the problems of uneven stacking, safety hazards and low efficiency existing in the prior art, and the scheme comprises the following steps: obtaining flow parameters containing an initial impurity rate through a distributed sensor, constructing a three-dimensional warehouse coordinate system through multi-dimensional analysis and simulating spatial density distribution; establishing a dynamic stacking model based on density data to simulate space-time trajectories, calculating the inter-particle force through finite element analysis to obtain stress distribution, and constructing a stability evaluation system; using Internet of Things environmental parameters, correcting the impurity rate through a coupling model, and dynamically compensating the stability evaluation results by using a multi-factor matrix; applying a deep reinforcement learning algorithm to optimize the control strategy, deploying the intelligent model on an edge node, and realizing closed-loop management through joint control of an automatic system. The present application improves the uniformity, stability and efficiency of bulk grain warehousing operations.

This invention discloses a reservoir overflow detection method that integrates instance segmentation and density clustering algorithms. The method comprises two stages: First, an image containing dam overflow phenomena is input into a trained instance segmentation algorithm to segment three types of targets: dam, water surface, and waterfall. Second, the output instance segmentation image from the first stage is input into a proposed adaptive spatial density clustering algorithm based on multi-scale density features for cluster analysis, correction, and filtering of isolated noise points to form a more clearly defined instance segmentation image. Finally, based on the corrected instance segmentation image, different reservoir overflow situations are detected and judged using a three-level progressive discrimination method, based on the instance segmentation type and the relationship between the dam and the water body. This invention organically combines computer vision technology, deep learning, and image processing technology to achieve automated, high-precision detection and identification of reservoir overflow risks under various conditions.

This invention discloses a three-dimensional measurement method for tomato internode length. Addressing the geometric errors easily caused by greenhouse shading, stem bending, and direct two-dimensional pixel ranging, the method acquires a single-frame color image and depth data of the plant. The color image is input into a feature-enhanced multi-task perception model, which outputs an internode instance segmentation mask and two-dimensional coordinates of key points at both ends. Candidate three-dimensional point clouds are extracted based on pixel correspondence, and the three-dimensional coordinates of the internode endpoints are generated from the neighborhood depth of the key points. The candidate point clouds are subjected to voxel downsampling, statistical outlier filtering, depth bandpass filtering, and spatial density clustering. The target main stem point cloud is obtained by combining spatial distance and depth consistency evaluation. Principal component analysis is used to estimate the principal axis, and axial slicing is performed. The centerline is extracted and fitted using a cubic B-spline curve. The three-dimensional coordinates of the endpoints are projected onto the fitted curve, and the outer endpoints of the curve are linearly extrapolated and compensated along the corresponding tangential direction. The arc length of the spatial curve is calculated to obtain the three-dimensional measurement result of the internode length.

The invention belongs to the technical field of stockyard management, and particularly discloses a stockyard stockpile inventory method and system, a terminal and a medium, and the stockyard stockpile inventory method comprises the steps: collecting original spatial data; performing point cloud registration, filtering and noise reduction processing on the original data, and constructing a three-dimensional model of the stockpile; and based on the three-dimensional model, calculating the volume of the stockpile by adopting an integral algorithm, and estimating the weight of the stockpile in combination with a material density parameter. A preset flight task is executed through the unmanned aerial vehicle to automatically collect three-dimensional data of a stockpiling area, manual measurement and climbing operation procedures are omitted, the inventory cycle is greatly shortened, and the overall inventory efficiency of a stockyard is remarkably improved; an air scanning mode is adopted to replace manual short-distance measurement, so that operators are prevented from approaching high heap or dangerous areas, and the operation risk is effectively reduced; meanwhile, full coverage of the material pile is achieved through path planning and multi-view scanning, the space density and uniformity of point cloud data are improved, and then the volume calculation accuracy is improved.

The present application relates to the technical field of image processing, in particular to a fisheye lens image pixel-level edge enhancement and denoising method, comprising: a data acquisition step: acquiring original fisheye image data and preset fisheye lens optical distortion parameters; a weight generation step: determining the distortion stretching rate of a pixel point according to the optical distortion parameters; and generating a spatial density weight map; a threshold determination and denoising step: determining a target denoising threshold according to the spatial density weight map; denoising the original fisheye image data to generate an intermediate denoising image; a convolution kernel generation step: generating an adaptive curved surface convolution kernel according to the optical distortion parameters; an edge enhancement step: using the adaptive curved surface convolution kernel to perform edge enhancement processing on the intermediate denoising image; and generating an enhanced image; the present application effectively avoids the risk of irreversible erasure of key semantic features in the edge field, and ensures high-reliability feature preservation in the extreme field.

The application provides an aggregate grain shape and grading identification method and device based on image processing and a storage medium, a dynamic falling image stream of an aggregate particle group is acquired, a closed contour image of a single aggregate particle is separated from the image stream, a boundary constraint harmonic field is constructed to generate a harmonic field distribution, a stable critical structure is extracted based on the harmonic field distribution, a critical turning point of a field strength value change is identified, the critical turning point is coded into a topological invariant sequence, a shape semantic mapper calibrated by a concrete interface performance is called, semantic mapping processing is performed on the topological invariant sequence, a quantitative representation of a single aggregate particle to an aggregate-paste interface occlusion ability is output, spatial density weighted fusion is performed according to the quantitative representations of the whole batch of aggregate particles, after normalization processing, a structure performance index reflecting the functional characteristics of the aggregate grain shape is generated through weighted fusion. The application can comprehensively reflect the functional characteristics of the whole batch of aggregate, and provides a direct performance evaluation basis for the optimization of the aggregate grading in the concrete mix design.