35 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 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 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.

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 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 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.

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 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 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.

The invention relates to a beach survey line wave height extraction method and system based on single-point laser radar data, and relates to the technical field of ocean observation data processing, and the method comprises the steps: converting the collected original single-point laser radar data into a predetermined format; then, data segmentation and reconstruction are carried out through visual analysis, and the elliptical trajectory of the single-point laser radar is segmented into left and right section space-time distribution data; coordinate system accurate conversion is carried out on the spatial-temporal distribution data, conversion from an instrument coordinate system to a geodetic coordinate system is completed, and three-dimensional data to be converted are obtained. And then intelligent data cleaning is introduced, cleaning is carried out according to data characteristics based on spatial density, abnormity is diagnosed synchronously, and cleaned data is output. And finally, performing wave height extraction by using multiple algorithms, constructing a framework processing data by combining an upper zero crossing method and a spectral analysis method, and outputting a final wave height parameter. According to the invention, high-efficiency and high-precision wave height extraction is realized, and the method can be widely applied to monitoring of beach topography and wave parameters.

This invention relates to the field of foundation pit monitoring technology, and discloses a method and system for foundation pit deformation monitoring. The method includes: performing consistency verification on the original physical quantity monitoring data to obtain a standardized monitoring sequence; acquiring a design parameter set and historical construction condition records, and constructing a dynamic deformation behavior model based on the coupling relationship between the design parameter set and historical construction condition records; inputting the current construction progress into the dynamic deformation behavior model to calculate the expected deformation reference sequence; comparing the standardized monitoring sequence with the expected deformation reference sequence in real time to identify local non-coordinated deformation modes; when a local non-coordinated deformation mode triggers a preset early risk criterion, generating an abnormal structural state warning and a graded response strategy; dynamically adjusting the sampling frequency and spatial density of the distributed monitoring network, and performing rolling optimization on the parameters in the dynamic deformation behavior model; this invention can improve the efficiency of foundation pit deformation monitoring.

The invention discloses an anti-occlusion group re-identification method and system based on multi-granularity cross-modal representation, and the method comprises the steps: constructing a member set for members in images captured by different cameras, and carrying out the group matching; respectively marking a bounding box for each member, calculating the average spatial density of each member, establishing a local candidate subset, identifying sheltered and unsheltered members, and marking the sheltered and unsheltered members; for shielded members, dividing human body parts and key points, calculating the visibility of each part, and identifying shielded parts; a double-layer weighting strategy is designed, local part features of all parts of the members are obtained, the local part features are spliced, and enhanced member features are generated; and establishing an overall loss function to supervise model training, and completing re-identification of the occlusion group in the camera image. According to the method and the corresponding system, the positioning precision of the shielding group and the shielding part is improved, and the calculation overhead is remarkably reduced, so that quick reasoning can be realized under the condition that the precision is not influenced.

The invention relates to the technical field of image processing, in particular to a fisheye lens image pixel-level edge enhancement and denoising method, which comprises the following steps: a data acquisition step: acquiring original fisheye image data and preset fisheye lens optical distortion parameters; a weight generation step: determining the distortion stretch rate of the pixel points according to the optical distortion parameters; generating a spatial density weight map; a threshold determination and denoising step: determining a target denoising threshold according to the spatial density weight map; de-noising the original fisheye image data to generate an intermediate de-noised image; a convolution kernel generation step: generating a self-adaptive curved surface convolution kernel according to the optical distortion parameters; an edge enhancement step of performing edge enhancement processing on the intermediate de-noised image by using an adaptive curved surface convolution kernel; generating an enhanced image; the risk that key semantic features of the edge view field are irreversibly erased is effectively avoided, and it is ensured that high-reliability feature retention is achieved in the extreme view field.

The application discloses a power generation abnormality positioning optimization method for distributed photovoltaics, and particularly relates to the technical field of power generation abnormality positioning. The method is characterized in that the static information, environmental parameters and dynamic power generation data of multiple stations in a target area are spatio-temporally aligned, the spatial density is clustered based on the three-dimensional distance of the stations, and the spatial sub-clusters are divided. Then, the decoupling model of historical loss rate and irradiance is combined to identify the spatio-temporal hotspot area. For the hotspot area, a spatial loss gradient field is constructed, and the pollution propagation path is reconstructed by reverse tracing to the normal threshold boundary point. For the non-hotspot area, the multi-scale attenuation and noise characteristics are extracted by wavelet packet decomposition, variational mode decomposition and short-time Fourier transform, and the artifacts are removed by neighborhood slope difference, so that the macroscopic error shielding and microscopic abnormality positioning of meteorological and component differences are realized, and the checking accuracy and operation and maintenance efficiency are improved.

The present invention provides a write head having an asymmetrical side gap (SG) that offers a narrower side gap (SG) width while mitigating any limitations or constraints on write capability that may arise from reducing the side gap SG. [Solution] In the asymmetrical writing head 600, the main pole 602 is positioned offset with respect to the central axis A1 of the writing head such that it is closer to the first SS 606A portion than to the second side shield (SS) 606B portion. The asymmetrical design can be achieved using an ion-beam etching (IBE) or ion-beam deposition (IBD) process.

The present invention is a density detection system using CCTV, comprising: a shooting unit that photographs a set area with a camera; an object recognition unit that recognizes stationary or moving objects in an image captured through the shooting unit and identifies the objects using an object detection algorithm based on deep learning or machine learning; a density analysis unit that determines density using information obtained through the object recognition unit, collects object location data to generate a heatmap, and visually and numerically analyzes spatial density based on the distribution and number of objects within a set area; a storage unit that stores object recognition, density analysis data, and original images obtained through the object recognition unit, and, if necessary, performs statistical analysis or situation reconstruction based on past data; and a control unit that is respectively connected to the shooting unit, the object recognition unit, the density analysis unit, and the storage unit to control the operation of the shooting unit, the object recognition unit, the density analysis unit, and the storage unit.

The invention discloses a reservoir spill detection method fusing instance segmentation and a density clustering algorithm, and the method comprises two stages: a first stage, inputting an image containing a dam spill phenomenon into a trained instance segmentation algorithm, and carrying out the segmentation of three types of targets: a dam, a water surface, and a drop; and in the second stage, the output instance segmentation image obtained in the first stage is input into a proposed adaptive spatial density clustering algorithm based on multi-scale density characteristics for clustering analysis, correction is carried out, isolated noise points are filtered, and an instance segmentation image with a clearer boundary is formed. And finally, aiming at the corrected instance segmentation image, detecting and judging different conditions of reservoir overflow in a three-stage progressive judgment mode by utilizing an instance segmentation-based type and a relationship between a dam and a water body. According to the method, a computer vision technology, deep learning and an image processing technology are organically combined, and automatic high-precision detection and identification of reservoir dam overtopping risks under various conditions are realized.

The invention discloses a scene three-dimensional reconstruction method based on density-gradient dual-drive, which performs multi-round reconstruction to obtain a three-dimensional model, introduces a space density sensing mechanism on the basis of gradient feedback, and effectively solves the problem of scarcity of anchor points in a low-gradient region. Through dynamic density threshold scheduling and anchor point budget control, self-adaptive balance of the number of anchor points is realized; according to the method, the state reconstruction frequency is obviously reduced and the reconstruction stability and the calculation efficiency are improved by performing dynamic cutting and then performing adjustment through an optimizer instead of performing adjustment directly through the optimizer in combination with hash value de-duplication and anchor point growth, so that the state reconstruction frequency is obviously reduced, and the reconstruction stability and the calculation efficiency are improved; a dynamic pre-cutting strategy is adopted, so that the training process is more stable and the occupation of a video memory is controllable; on the whole, the method can keep high geometric continuity and rendering precision under the sparse view angle, the shielding boundary and the complex geometric structure, and double improvement of the three-dimensional reconstruction quality and the calculation efficiency is achieved.

A method for evaluating the flux of near-earth objects colliding with the earth, comprising: dividing the solar system orbital space into a plurality of space units according to a preset heliocentric distance step and a preset yellow latitude step with the heliocenter as the center; calculating the spatial density of near-earth objects in the space unit; establishing a geocentric ecliptic inertial coordinate system, obtaining the relative speed of the near-earth object and the earth in the geocentric ecliptic inertial coordinate system; and calculating the impact flux of the near-earth object relative to the earth based on the spatial density and the relative speed.

The invention discloses a video subtitle extraction method. The problems that in the prior art, the recognition noise is large, a subtitle area is difficult to accurately position, the subtitle cross-frame stability is poor, structured output is lacked, and the adaptability of a clustering algorithm is insufficient are solved. The method comprises the following steps: performing frame sampling on input video data to form a video frame sequence; carrying out OCR (Optical Character Recognition) on the video frame sequence to obtain an initial recognition result; multi-dimensional filtering processing is executed on the initial recognition result to remove non-subtitle text interference, and the multi-dimensional filtering processing at least comprises cleaning based on text content and area preliminary screening based on a global position rule; performing clustering analysis based on spatial density on the text subjected to the multi-dimensional filtering processing so as to identify a subtitle text cluster from the preliminary screening region; and performing time sequence processing on the recognized subtitle text cluster, and outputting structured subtitle data.

This invention discloses an adaptive tolerance query method for electronic nautical charts. The method includes the following steps: parsing the geometric type code of the chart element to be queried; selecting the corresponding basic tolerance value based on the geometric type; establishing a mapping model between the geometric type and the tolerance value; constructing a spatial neighborhood of the query point; counting the number of elements within the neighborhood and calculating the local data density; applying a density adjustment function to adaptively adjust the basic tolerance value; constructing a query range rectangle using the adaptive tolerance value; calling the spatial index interface to perform a range query operation to obtain a set of candidate chart elements; and performing geometric precision detection on the candidate chart element set to determine the target element matching the query point. This invention solves the problem of balancing query accuracy and efficiency caused by fixed tolerance in electronic nautical chart queries by integrating geometric type features, spatial density features, and projection transformation features into a three-dimensional adaptive tolerance mechanism.

The parameters of an optical code are optimized to achieve improved signal robustness, reliability, capacity and / or visual quality. An optimization program can determine spatial density, dot distance, dot size and signal component priority to optimize robustness. An optical code generator employs these parameters to produce an optical code at the desired spatial density and robustness. The optical code is merged into a host image, such as imagery, text and graphics of a package or label, or it may be printed by itself, e.g., on an otherwise blank label or carton. A great number of other features and arrangements are also detailed.

The invention provides a thread pitch diameter calculation method and equipment based on spatial point cloud, and belongs to the technical field of precision measurement. The method comprises the following steps: acquiring three-dimensional point cloud data of a measured thread through a laser scanner; preprocessing the point cloud data, including removing noise points based on spatial density filtering, and clustering and segmenting thread flank point clouds by using a DBSCAN algorithm based on a standard slope reference value; a random sampling consistency algorithm is used for fitting thread flank straight lines, intersection points of adjacent straight lines are calculated, and the position of a middle diameter point is determined by optimizing an objective function; performing coordinate system conversion on the intermediate diameter point cloud by using specified system parameters; and finally, all the pitch diameter points are fitted into an ideal cylindrical surface in a machine tool coordinate system by adopting a Levenberg-Marquardt algorithm, and the diameter of the cylinder obtained by fitting is the pitch diameter value of the thread. According to the invention, non-contact, automatic, high-precision and high-robustness measurement of the pitch diameter of the internal thread is realized.

The invention provides a multi-dimensional data matching method and system for scheduling storage resources of cross-border electric merchants, and relates to the technical field of data processing, and the method comprises the steps: dividing a reference decision hyperplane based on the spatial density of a projection point set, and generating a feature response partition set; mapping the discrete feature distribution configuration to a feature response partition set, calculating a statistical moment of an original feature vector in the feature response partition set, extracting a shape invariant moment feature of the original feature vector, and generating a resource matching correction parameter; calibrating the initial multi-dimensional matching demand feature vector to generate an optimized matching demand feature vector; calculating the optimized matching demand feature vector and the multi-dimensional data resource pool to generate a storage and logistics linkage scheduling plan; and converting the warehousing and logistics linkage scheduling plan into an operation instruction sequence, and issuing the operation instruction sequence to a corresponding warehousing and logistics execution unit to start a resource allocation operation. According to the invention, the accuracy and efficiency of the distribution of the storage resources of the cross-border e-commerce merchants are improved.