65 results about "Spatial aggregation" patented technology

The invention discloses an element quantitative fusion-based urban coastal zone landscape evaluation and prediction method, which comprises the following steps of delimiting a vector boundary of a target urban coastal zone based on land change data, and dividing an area covered by the vector boundary into a plurality of grid units; associating the preprocessed spatial element data to the corresponding grid units, and constructing a structured spatial database; establishing a multi-element quantitative index system, calculating a combined weight, and outputting a comprehensive evaluation score of each grid unit through weighted summation; calculating global and local Moran's I indexes based on the comprehensive evaluation score, and identifying a spatial aggregation region and an abnormal region with statistical significance; obtaining dynamic monitoring data in a coastal zone range of a target urban area, and updating the structured spatial database; and based on the updated structured spatial database, comprehensively evaluating the score and the recognition results of the spatial accumulation area and the abnormal area, and generating an evaluation and prediction result.

The invention discloses an aquatic seedling counting and growth parameter measuring system, which comprises the following steps: acquiring a seedling flow image sequence and generating an image confidence score, selecting kernel function width according to different confidence conditions to construct a density map, and obtaining a density distribution feature vector capable of representing a spatial aggregation state; extracting morphological features of the seedlings under a multi-scale receptive field based on the feature vectors, constructing a multi-scale feature pyramid, calculating a spatial overlapping degree index according to a difference relationship between scales, and identifying a dense region; the fusion weight of the multi-scale features is determined in combination with an overlapping degree index, and fusion features are generated and used for executing counting and growth parameter measurement; and constructing a performance deviation index according to the counting accuracy and the measurement precision, and performing joint updating on the kernel function parameters and the fusion weight when the deviation exceeds a threshold value to form an optimized path of which the parameters can be iteratively updated so as to realize stable counting and accurate measurement under complex density change and overlapping conditions.

The invention discloses a traffic anomaly detection method based on a space-time double-flow network and multi-modal feature fusion, relates to the technical field of intelligent video analysis and traffic behavior recognition, and is used for solving the problem of high false detection rate of existing traffic anomaly detection in a complex environment. A video image sequence is extracted through a fixed and dynamic combined frame sampling strategy, and motion features are constructed. And in combination with the direction consistency coefficient and the perturbation evolution trend, repairing direction perception distortion by using a mirror image mapping mechanism. Target appearance features are extracted through spatial branches, behavior evolution trajectories are extracted through time branches, and meanwhile, a modal weight dynamic adjustment mechanism is introduced to enhance the adaptability to complex scenes. The multi-target interaction relation is quantified based on a graph structure modeling mode, an adjacent structure is reconstructed through a graph self-encoder, and local behavior mutation is recognized. And finally realizing high-confidence anomaly marking by combining state transition analysis and spatial aggregation characteristics. The method has the advantages of high real-time performance, high robustness and wide scene adaptability.

The invention relates to the technical field of medical image processing, and discloses a chronic obstructive pulmonary emphysema distribution quantitative method and system based on multi-scale segmentation, and the method comprises the steps: carrying out the anisotropic diffusion filtering noise reduction of a chest CT image; segmenting a lung field and removing a blood vessel bronchial structure by adopting a region growing algorithm; multi-scale image representation is constructed based on a Gaussian pyramid, an emphysema candidate area is identified in a coarse scale layer, and a boundary is accurately drawn by adopting a self-adaptive threshold value in a fine scale layer; extracting local texture features to distinguish the lobular central emphysema and the total lobular emphysema; dividing severity levels according to spatial aggregation characteristics and density gradient distribution, and calculating an air swelling volume ratio and a distribution heterogeneity index; the three-dimensional pseudo-color volume is used for drawing visualization, a structured quantitative report is generated, accurate segmentation and subtype classification of the emphysema area are achieved, and comprehensive quantitative analysis indexes are provided.

The present application relates to the technical field of artificial intelligence, and more particularly to an AI interaction system for ornithological scientific research, which comprises a collection unit, a trigger determination unit, a path determination unit, a dominant determination unit, a prompt generation unit and an adjustment unit. The present application constructs a multi-level behavior determination process through joint analysis of bird song spectrum, image motion characteristics, flight trajectory and environmental factors, and through statistical analysis of the time distribution and spatial distribution of these behavior indicators within a preset period, and correlation analysis with environmental factors such as temperature, humidity, wind speed and illumination, the influence of environmental changes on bird activity frequency and spatial aggregation degree can be identified, so that the acoustic trigger threshold can be dynamically adjusted, so that the system can continuously maintain the sensitive recognition ability of bird activity with the change of the habitat environment, and then realize high-reliable behavior recognition and interaction prompt for ornithological scientific research.

An exemplary multistart and recombination system and method are disclosed for determining unique solutions to spatial aggregation problems using a multistart phase and a recombination phase algorithm, where the multistart phase generates a diverse pool of random solutions and where the recombination algorithm uses the generated random solution to create new, improved solutions, ensuring the discovery of multiple unique optimal or near-optimal configurations. The spatial aggregation problems may be used to analyze census data. In some embodiments, spatial aggregation problems may be used to perform voter district map analysis to identify a revised voter district map.

The application discloses a next interest point recommendation method based on a multi-active area perception network. The method fully utilizes the spatial aggregation of user behavior trajectories, filters the active area center of the user by analyzing the historical activity trajectory of the user, and divides and aggregates according to the distance to realize the acquisition of long-term stable user preference features in the active area. At the same time, the method is based on a negative sample sampling method of the neighbor area, filters the interest point through the activity area of the user, fully utilizes the geographical location characteristics, and acquires sample information with more rich information. The application is based on historical check-in data and other multi-modal information of the user, constructs a network model through a deep learning method, extracts user preference features to predict the next most possible place visited by the current user, has the characteristics of high accuracy and strong scalability, can timely grasp the user behavior trend, and provides guidance for user behavior trajectory prediction personnel.

The invention discloses an airborne ocean laser radar hierarchy and water bottom synchronous three-dimensional reconstruction method. The method comprises the steps of obtaining a three-dimensional point cloud of laser on a water surface according to airborne laser radar data; gridding is carried out on the three-dimensional point cloud of the laser on the water surface, multi-pulse averaging of spatial aggregation is carried out, and signals after gridding aggregation are obtained; and obtaining a water surface peak position, a level peak position and a water bottom peak position, and finally obtaining water bottom and level three-dimensional point clouds at the same time, thereby realizing high-precision three-dimensional reconstruction. According to the method, the thought of voxelized grids is combined, resolution sacrifice caused by multi-pulse averaging in a large space range is avoided, high-resolution three-dimensional reconstruction with adjustable resolution is achieved, a multi-target distinguishing algorithm architecture is designed, and the effect of multi-target simultaneous detection is achieved; the method has reference significance for shallow sea environment detection and shallow sea carbon cycle analysis, and has wide applicability.

The present application relates to a kind of table area distributed photovoltaic power ultra-short term prediction method and system, belong to distributed photovoltaic power prediction technical field.Based on the condensed hierarchical clustering algorithm, the table area photovoltaic in region is clustered into the homogeneous sub-region set of output characteristics, combined with GraphSAGE graph neural network and Transformer encoder model, the decoupling representation of space-time characteristics in sub-region is realized, based on composite space-time characteristics, finally synchronously output the photovoltaic power prediction result of each table area in sub-region, further based on the shared feature between sub-region, construct suitable migration modeling strategy, realize the fast lightweight modeling of each sub-region prediction model, finally, the spatial aggregation of sub-region prediction power obtains regional total power prediction result.

The application provides a kind of urban dissipative space discrimination method and system based on spatial aggregation characteristics, it is related to spatial quality measure technical field.The urban dissipative space discrimination method based on spatial aggregation characteristics, comprising: obtaining city basic geographic space data and its geographic coordinates, form city space information integrated platform;According to the pre-set city dissipative space determination index library, the basic characteristic element data of city dissipative space is calculated, and the basic characteristic element data of city dissipative space is loaded into city space information integrated platform for spatial positioning calibration;Select appropriate grid scale to divide measure area;Each characteristic element data in grid unit is graded assignment;The order of city space is measured to calculate the aggregation;Establish determination rule to obtain four kinds of dissipative space characteristic element data set.The application improves the accuracy and efficiency of city dissipative space identification, and provides targeted guidance and power point for city renewal design and decision control.

This invention discloses a method for creating fish habitats based on swarming effects, belonging to the fields of fish ecological engineering and water conservancy engineering. Existing technologies for creating fish habitats suffer from incomplete quantification of swarming behavior and a lack of quantitative correlation with engineering design parameters, leading to habitats that are difficult to match the needs of fish swarming. This invention quantifies spatial aggregation, movement synchronization, and turbulent adaptability, calculates a comprehensive swarming index and a group spatial demand coefficient, establishes a direct conversion model between swarming behavior indicators and habitat design parameters, and determines flow field control methods and structural layout methods based on the comprehensive swarming index and the group spatial demand coefficient, thereby creating fish habitats. This invention can comprehensively characterize swarming behavior, scientifically guide engineering design, and improve habitat applicability and utilization.

This application discloses a method for parallel integration of geospatial data based on spatial grid-aware routing. The method includes: acquiring source information of geospatial data files and identifying their format and metadata; logically segmenting the data files into record blocks; calculating the bounding rectangle of the geometric objects of geospatial elements in each record block and determining the covering grid set according to spatial grid partitioning rules, then determining a unique master spatial grid identifier according to master attribution rules; constructing grid clusters based on the master spatial grid identifier, merging adjacent grids into scheduling partitions and routing them to corresponding parallel processing tasks; and having each task stream-read and process elements before batch writing them to the target storage. This invention overcomes the bottleneck of single-file parallel processing, achieves spatial locality-aware routing and dynamic load balancing, improves the spatial aggregation and query performance of target data, reduces the risk of memory overflow, and possesses high-throughput writing and precise breakpoint resumption capabilities.

This invention relates to the field of electronic digital data processing technology and discloses a method for processing tunnel construction data based on real-time monitoring. The method involves acquiring real-time data from displacement, stress, and micro-vibration sensors; constructing a spatial graph adjacency matrix based on coordinates and rock strata topology; and plotting the dynamic features of nodes in the time series graph. The data is then input into a physically constrained spatiotemporal graph convolutional network. A displacement-stress transmission constraint term based on the elasticity equation is introduced as a weight attenuation penalty factor in the graph convolutional propagation layer to restrict cross-node information transmission in accordance with the laws of surrounding rock mechanics. The dynamic features of the time dimension are extracted through a time-gated cyclic unit, and a tensor data stream is output. This invention restricts weight updates within physical boundaries, eliminates non-physical interference features from spatial aggregation, avoids feature confusion during multi-source data fusion, and overcomes the problem of distorted precursor feature extraction due to a lack of physical constraints. Furthermore, improvements are made to the coupling processing of multi-source heterogeneous data under geological topology, taking into account classification numbers.

The present application relates to the technical field of image processing, and particularly relates to an image super-resolution reconstruction method and system based on wavelet transform fusion of space domain and frequency domain, inputting a low-resolution image to be processed into a pre-trained target model, and obtaining a corresponding super-resolution reconstruction image by using the target model, wherein the target model comprises: a convolution layer for extracting shallow features of the low-resolution image, a plurality of space frequency collaborative groups for extracting deep features of the image, a reconstruction output layer for performing super-resolution reconstruction based on the shallow features and the deep features of the image, the plurality of space frequency collaborative groups form a cascaded structure, and each space frequency collaborative group comprises: a residual mixed attention group for spatial context aggregation, and a progressive frequency block for band decomposition and collaborative enhancement of spatial aggregation features based on wavelet transform. The present application decouples and collaboratively enhances the image features in the dual domains by using spatial window attention and wavelet frequency, so as to realize high-quality image super-resolution reconstruction.

The invention relates to the technical field of big data processing, in particular to a real-time analysis and mining method for enterprise operation data, which comprises the following steps of: according to received continuous enterprise operation data flow, dividing a time axis into continuous time windows with equal length, extracting space-time coordinates and member quantity of all logistics orders in each time window, and calculating the time-space coordinates and member quantity of each logistics order; and establishing a time sequence hotspot snapshot set. According to the method, the continuous enterprise operation data flow is divided into the equal-length time windows, the space-time coordinates and the member number of the logistics orders are extracted, the space aggregation features are constructed in each time slice, the distribution state of logistics activities can be dynamically reflected, and meanwhile the distribution state of the logistics activities can be dynamically reflected by combining the space boundary overlapping degree and the member number change rate. According to the method, evolution recognition of the hotspot clusters in the time dimension is achieved, the trend judgment capacity is achieved, the structural similarity of path evolution is calculated and summarized through the editing distance, and a semantic association link between structural behaviors and text information is opened.

This disclosure provides a method, device, and storage medium for identifying the spatial aggregation features of immune cells in nasal polyps, relating to the field of data processing technology. The implementation scheme is as follows: A nasal polyp pathological slide is segmented into multiple pathological sub-images; positive immune cells are identified in each pathological sub-image to obtain the sub-image coordinates of each positive immune cell in each pathological sub-image; based on the positional information of each pathological sub-image on the nasal polyp pathological slide, the sub-image coordinates of each positive immune cell in each pathological sub-image are mapped onto the nasal polyp pathological slide to obtain the full-map coordinates of each positive immune cell in the nasal polyp pathological slide; based on the full-map coordinates of each positive immune cell in the nasal polyp pathological slide, cluster analysis is performed on the positive immune cells in the nasal polyp pathological slide to obtain the spatial aggregation features of immune cells in the nasal polyp tissue in the nasal polyp pathological slide. This improves the accuracy of the spatial aggregation features of immune cells in nasal polyps.

Direct time-of-flight (dToF) sensor data processing is proposed to address the challenge of efficient scene change detection. In an embodiment, a system comprises a sensor processing circuit and a dToF sensor with a light emitter and photon detector array. The circuit generates compact normalized histograms (CNH) from raw sensor data, performing spatial aggregation across configurable detector zones and temporal accumulation over adjustable periods. Statistical distances between CNH vectors from different time frames are computed using Mahalanobis distance calculations. Scene change indicators are determined by comparing these distances to a threshold derived from a configurable false alarm probability. The approach enables low-power operation, eliminates flicker issues in change detection, and offers flexible trade-offs between spatial-temporal resolution and signal-to-noise ratio. Applications can include presence detection, gesture recognition, and environmental mapping.

The application belongs to the field of house data processing, and is a mass house risk hidden danger visualization evaluation method and system, which comprises the following steps: performing building comprehensive risk evaluation on a single house to calculate a risk hidden danger coefficient of the single house; performing spatial aggregation processing of different degrees on mass houses from multiple administrative region levels to obtain a risk hidden danger distribution situation of the aggregated point houses after aggregation; converting discrete vector points into continuous grid network data for display through kernel density analysis; and performing result analysis and visual rendering display according to the heat space distribution characteristic grid network data. The application calculates heat distribution based on the density weight of the risk hidden danger index, enhances the display effect of the geographical space distribution characteristics of the house risk hidden danger index, and solves the technical problems of lag, full memory occupation and the like during the rendering of the billion-level building surface and attribute data.

The application discloses an electroencephalogram emotion recognition method based on dynamic space-time graph convolution and mixed expert attention, and comprises the following steps: preprocessing a multi-channel electroencephalogram signal to obtain a local stationary time slice; inputting each time slice into a one-dimensional convolution network and a multi-head self-attention module by using a time encoder; introducing a mixed expert dynamic routing and sparse activation mechanism in the multi-head attention to obtain a time feature representation of an emotion-related electroencephalogram pattern; constructing a channel-level node feature matrix based on the time feature, calculating a Pearson correlation coefficient between channels, adopting a neighborhood truncation strategy based on correlation ranking to obtain a sparse functional connection matrix, and superimposing a frontal lobe emotion regulation prior weight to construct a functional connection dynamic graph that changes with time; and adopting a graph convolution network on the dynamic graph to perform spatial aggregation, obtaining a space-time fusion representation, and outputting an emotion category. The method can improve the precision, stability and generalization performance of emotion recognition through collaborative modeling of time and space information.

This invention proposes an intelligent identification method for bauxite outcrops based on UAV multispectral imagery, comprising: extracting the geometric morphology map, topographic relief feature map, spatial aggregation point density map, and spectral absorption feature map of the ore body to be identified from the multispectral imagery acquired by the UAV; extracting the boundary contour parameters from the geometric morphology map and performing vector raster fusion processing with the elevation values ​​and slope angles from the topographic relief feature map in three-dimensional space to obtain the local features and spatial distribution contour features of the ore body to be identified; mapping the local features, spatial distribution contour features, spatial aggregation point density map, and spectral absorption feature map to the same manifold space through differential homeomorphism, marking and obtaining the extension extrema of each distribution direction in the manifold space to calculate the extension measure of each distribution direction, thereby solving the problem that existing identification methods easily misjudge tailings piles as economically valuable ore bodies, resulting in inflated resource estimates and deviations in mining planning.

The application discloses a forest leaf area index inversion acquisition method and system based on data fusion. The acquisition method comprises the following steps: performing cloud removal processing and cutting on remote sensing image data of a research area to obtain a segmented image; based on land cover data, obtaining forest pixels in each segmented image, and performing spatial aggregation matching with resolution to obtain aggregated remote sensing images; extracting a vegetation index from the segmented image; obtaining forest canopy height data of the research area; taking reflectivity values of different bands in the aggregated remote sensing images, the vegetation index, the forest canopy height data and a sun sensor related parameter as inputs, and taking the forest leaf area index as output to construct a forest leaf area index fusion prediction model, and obtaining a prediction result. The LAI distribution result obtained by the application has more spatial details, is more suitable for mountain forest ecosystems with large spatial heterogeneity, and can accurately and rapidly monitor mountain vegetation parameters.

The invention provides a fruit counting method based on guiding perception and sharpening, and the method comprises the steps: obtaining a to-be-processed first image, and randomly selecting a plurality of fruits in the first image; the progressive spatial aggregation encoder performs feature extraction on the first image to obtain overall features and detail features; extracting features of the frame-selected area from the overall features, and processing the features to obtain example features; the self-adaptive similarity perception module processes the overall features and the example features to obtain a similarity graph; and multiplying the detail features by the similarity graph, inputting the multiplied detail features into an interlaced channel reconstruction decoder, generating a density graph, and further obtaining the number of fruits. Focusing a fruit area by enhancing spatial perception through a progressive spatial aggregation encoder; the adaptive similarity sensing module performs robust fruit phenotype recognition by adopting morphological-guided self-adaption; and the interlaced channel reconstruction decoder improves the definition of fruit distribution through a cross arrangement technology, and finally realizes high-precision counting.

The application discloses a NAND Flash screening and grading method and system based on a three-dimensional wide-temperature-capability hotspot diagram, which comprises the following steps: for each coordinate point in the three-dimensional physical coordinate system, based on the corresponding electrical characteristic distribution data, a parameter for representing the distribution width and the variation amount thereof under different temperature conditions are calculated, and whether the coordinate point is a risk point is determined according to a preset threshold value; whether an abnormal aggregation cluster meeting a preset spatial aggregation condition exists is identified; and the chip is eliminated or graded according to a preset rule according to the identification result of the abnormal aggregation cluster. Thus, the electrical characteristic distribution original data of the chip under a wide-temperature range is acquired; on this basis, the change of the electrical characteristic distribution mode of each physical unit point with temperature is analyzed, and the aggregation mode of the change points in the three-dimensional space is identified, so that the local weak area caused by systematic process defects is found, and strict grading and elimination are carried out based on this.

The invention provides a ship exploration behavior determination method and device and a readable storage medium. The ship exploration behavior determination method comprises the following steps: acquiring trajectory point data of a plurality of ships in a ship navigation system; processing the multiple pieces of track point data according to a time sequence, determining multiple pieces of track segment data, and generating one piece of track segment data when adjacent track points are in different grids; according to the multiple pieces of track segment data, a grid data structure is updated, and the grid data structure comprises parallel line pairs; determining a preliminary exploration event according to a preset threshold value and the number of parallel line pairs in the grid data structure; according to the trajectory segment data corresponding to the preliminary exploration event, determining a feature index, and according to the preliminary exploration event, the feature index, a trajectory parallelism requirement and a spatial aggregation degree requirement, determining a target exploration event of the plurality of ships; and according to the target exploration event, exploration behaviors of the plurality of ships are determined. The gridding processing can improve the processing efficiency.

The invention discloses a fish habitat building method based on a group swimming effect, and belongs to the field of fish ecological engineering and hydraulic engineering. In the prior art, the fish habitat building has the problems that the quantification of the fish habitat is not comprehensive, and the association with the quantification of engineering design parameters is lacked, so that the habitat is difficult to match the fish swarm swimming demand. According to the method, the space aggregation degree, the motion synchronism and the turbulence adaptability are quantified, the comprehensive cluster index and the group space demand coefficient are calculated, a direct conversion model of group travel as an index and habitat design parameters is established, and the flow field regulation and control mode and the structural layout mode are determined based on the comprehensive cluster index and the group space demand coefficient. Therefore, a fish habitat is built. According to the method, group travel behaviors can be comprehensively described, engineering design is scientifically guided, and the applicability and utilization rate of the habitat are improved.

This invention belongs to the field of tunnel engineering, specifically relating to a data-mechanism-model-driven intelligent analysis method and system for rock mass strength parameters. It includes: generating a simulation dataset adapted to the current engineering rock mass strength and geological conditions through high-fidelity drilling numerical simulation, and training a multilayer perceptron as a physical information proxy model to achieve physical mapping of the rock drilling process. Secondly, the three-dimensional parameters are processed by three-dimensional spatial aggregation and segmentation. Combining graph neural network analysis and transformer model information extraction, a hybrid loss function for multi-dimensional data is defined. This hybrid loss function, together with the physical loss function, forms an optimized loss function with physical information and drilling data. An automatic optimization process for the loss function is then performed to achieve rock mass strength prediction. This invention can effectively analyze rock mass strength parameters.

The application provides a fruit counting method based on guided perception and sharpening, which comprises the following steps: obtaining a first image to be processed, and randomly framing several fruits in the first image; performing feature extraction on the first image by using a progressive spatial aggregation encoder to obtain overall features and detail features; extracting features of the framed regions from the overall features, and performing processing to obtain example features; performing processing on the overall features and the example features by using an adaptive similarity perception module to obtain a similarity map; multiplying the detail features and the similarity map, and inputting the result into an interleaved channel reconstruction decoder to generate a density map, and then obtaining the number of fruits. The progressive spatial aggregation encoder focuses on the fruit area by enhancing spatial perception; the adaptive similarity perception module uses adaptive morphology guidance to robustly identify fruit phenotypes; and the interleaved channel reconstruction decoder improves the clarity of fruit distribution by using cross arrangement technology, so that high-precision counting is finally achieved.

The invention discloses a CLAHE-unsupervised learning InSAR geological disaster hidden danger intelligent identification method and system, and the method comprises the steps: obtaining multi-temporal SAR data of a research region, and carrying out the processing of MT-InSAR to generate an average deformation rate field; noise suppression and weak signal enhancement are carried out through non-local mean filtering and wavelet domain multi-scale processing; a contrast-limited adaptive histogram equalization algorithm is adopted to improve the local deformation contrast, and an enhanced deformation intensity graph is generated; constructing a feature space in combination with the enhanced strength graph and an original deformation value, and preliminarily identifying a hidden danger region by using an unsupervised clustering algorithm and space-deformation double-constraint region growth; optimizing a boundary topological relation through triangulation and a minimum convex hull algorithm; and finally, calculating a comprehensive quality index based on geometric consistency, signal intensity and spatial aggregation degree, realizing risk grading, and outputting a vector boundary, a deformation parameter and a grade result.

The invention relates to the technical field of mountain torrent disaster loss evaluation, and discloses a method and system for evaluating the influence of climate change on mountain torrent disaster economic loss, and the method comprises the following steps: carrying out the data preparation and standardization processing of multi-source heterogeneous data related to rainfall and mountain torrent disaster economic loss; the method comprises the following steps: calculating annual total precipitation, maximum daily precipitation and extreme precipitation frequency characteristic indexes from standardized precipitation data, then identifying time sequence trends of different precipitation variables based on Theil-Sen trend estimation and a Mann-Kendall test method, and analyzing spatial autocorrelation characteristics through global and local Moran index calculation methods; identifying spatial aggregation and regional difference of rainfall change; county is taken as a spatial basic unit, and year is taken as a time basic unit to construct a causal model. According to the method, the real causal influence of rainfall change on the economic loss of the mountain torrent disaster can be extracted from the long-time-sequence observation data, and quantitative identification of climate driving-disaster intensity-economic loss is realized.