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CN-122018767-A - Cross-border E-commerce visual report generation method and system based on drag type assembly

CN122018767ACN 122018767 ACN122018767 ACN 122018767ACN-122018767-A

Abstract

The invention relates to the field of cross-border e-commerce data processing and provides a cross-border e-commerce visual report generation method and a cross-border e-commerce visual report generation system based on a drag component, wherein the method comprises the steps of extracting a dynamic sampling interval to generate a preprocessing image frame sequence, and combining a service data set and a geofence index to generate a physical semantic region set; generating a target centroid coordinate set according to a preprocessed image frame sequence, generating stacking density and motion frequency by combining a physical semantic region set, analyzing a cross-border trade rule base to generate a component base to be activated, calling a semantic mapping function to fuse the stacking density, the motion frequency and a service data set to generate a semantic binding composite characteristic in response to a user dragging instruction, solving a dynamic bottleneck index according to the semantic binding composite characteristic and the motion frequency, and triggering visual report self-adaptive processing and decision suggestion output by comparing an operation safety threshold. The invention integrates physical storage real-phase perception and cross-border trade business constraint, and builds a drag-type visual report dynamic and bottleneck self-adaptive diagnosis closed-loop mechanism.

Inventors

  • ZHU QI
  • CHEN XINGWEI
  • FANG JUN
  • YANG XIAO
  • YANG HUCHENG
  • LIAN YIWEI

Assignees

  • 浙江国贸数字科技有限公司

Dates

Publication Date
20260512
Application Date
20260416

Claims (10)

  1. 1. The cross-border e-commerce visual report generation method based on the drag type component is characterized by comprising the following steps of: Extracting dynamic sampling intervals of an original video stream to generate a preprocessing image frame sequence, generating a structured service data set according to the captured original service data stream, and generating a physical semantic region set according to a geofence index associated with trade batch identification in the structured service data set and the preprocessing image frame sequence; Performing pose positioning on the preprocessed image frame sequence to determine a target centroid coordinate set, and performing geometric membership verification and space collision detection by combining a physical semantic region set to generate a stacking density feature and a motion frequency feature; Analyzing the cross-border trade rule database, extracting service metadata to generate a component library to be activated, calling a semantic mapping function in response to a user dragging instruction sequence, and performing nonlinear fusion on the stacking density characteristic, the motion frequency characteristic and the structured service data set to generate a semantic binding composite characteristic; According to the semantic binding composite characteristic and the motion frequency characteristic, a dynamic bottleneck index is solved, and real-time logic comparison is performed on the dynamic bottleneck index and a preset operation safety threshold value so as to trigger self-adaptive pixel processing of a visual report and output of decision advice.
  2. 2. The drag-component-based cross-border e-commerce visualization report generation method of claim 1, wherein the physical semantic region set generation method comprises the following steps: Extracting the pixel change rate between adjacent frames in the acquired original video stream, calculating a dynamic sampling interval according to the pixel change rate, and carrying out non-equidistant sampling on the original video stream by utilizing the dynamic sampling interval to extract an original image sequence, and obtaining a preprocessed image frame sequence based on the original image sequence; Retrieving the customs clearance batch identification and the trade status code from the captured original business data stream, generating a priority weight coefficient according to the time sensitivity of the trade mode, and packaging the customs clearance batch identification, the trade status code and the priority weight coefficient into a structured business data set; Extracting an initial pixel characteristic layer in a preprocessing image frame sequence, searching geofence indexes associated with trade batch identification to determine a regional boundary coordinate set, distributing service logic function labels according to priority weight coefficients, calculating a turnover load upper limit by utilizing the regional boundary coordinate set, and generating a physical semantic regional set based on the priority weight coefficients, the service logic function labels, the regional boundary coordinate set and the turnover load upper limit.
  3. 3. The drag-component-based cross-border e-commerce visual report generation method of claim 2, wherein the extraction method of the original image sequence comprises the following steps: Performing logic comparison on the pixel change rate and a preset liveness judgment threshold value, judging that the current high-frequency service turnover operation state exists when the pixel change rate exceeds the liveness judgment threshold value, and triggering an up-regulation logic of sampling frequency, wherein the up-regulation logic of the sampling frequency increases the sampling density by reducing the time span of adjacent sampling points; Taking the sampling sensitivity adjustment coefficient as a molecule, and taking the sum of the pixel change rate and the signal zero stabilization factor as a denominator to execute division operation to obtain a dynamic sampling interval; the sampling sensitivity adjustment coefficient is a weight factor determined according to the processing limit of the calculation force at the back end of the report generating system, and the signal zero point stabilization factor is a preset positive number real constant for preventing the denominator from being zero under the standing working condition that the pixel change rate approaches zero; and performing non-equidistant sampling on the original video stream by utilizing the dynamic sampling interval to extract an original image sequence.
  4. 4. The drag-component-based cross-border e-commerce visualization report generation method of claim 2, wherein the priority weight coefficient generation method comprises the following steps: Retrieving and matching a package operation priority field and a grade field to be inspected from an original service data stream by using a regular expression; mapping the wrapping operation priority field into a numerical operation gain value, and mapping the grade field to be inspected into a numerical inspection gain value; Determining a basic aging coefficient according to a trade mode level preset by a service platform, and setting the basic aging coefficient to be a fixed offset value larger than a preset standard constant of a common trade mode when a cross-border e-commerce export oversea warehouse trade mode is identified; and performing weighted summation operation on the operation gain value, the checking gain value and the basic aging coefficient to generate a priority weight coefficient of the urgency degree of the trade batch flow.
  5. 5. The drag-component-based cross-border e-commerce visual report generation method according to claim 2, wherein the calculation method of the turnover load upper limit comprises the following steps: Searching an initial physical boundary coordinate range corresponding to a business geofence index associated with trade batch identification, and calculating the intersection ratio between the initial physical boundary coordinate range and each candidate pixel cluster in the initial pixel characteristic layer; When the intersection ratio exceeds a preset overlapping degree threshold, the corresponding candidate pixel cluster is preliminarily divided into functional partitions such as a shelf storage area, an operation channel area or a customs clearance area, and the like, wherein the preset overlapping degree threshold is a geometric calibration constant determined based on an installation pitch angle and a lens distortion rate of a video acquisition terminal; Counting the total number of pixel points covered by each functional partition based on the regional boundary coordinate set to obtain a pixel area, and calling a turnover efficiency constant corresponding to the current trade mode from a service database, wherein the turnover efficiency constant is a scalar constant of cargo handling flux preset by the trade mode under the unit pixel area; and multiplying the pixel area and the turnover efficiency constant to generate a turnover load upper limit of the physical load limit value of each functional partition.
  6. 6. The drag-component-based cross-border e-commerce visual report generation method of claim 1, wherein the execution method of the geometric membership verification comprises the following steps: Extracting a multi-scale feature image layer of the preprocessed image frame sequence, extracting entity semantic activation features in the multi-scale feature image layer, and performing classification and pose positioning on the entity semantic activation features by utilizing a real-time target detection neural network architecture to generate a target centroid coordinate set; Projecting the target centroid coordinate set to the physical semantic region set to determine a space attribution relation, calculating the ratio of the total projected area of the entity to the total pixel area of each functional partition according to the space attribution relation to obtain a preliminary stacking density value, introducing a preset height compensation coefficient to execute nonlinear correction, and generating stacking density characteristics; Generating a motion displacement vector according to a target centroid coordinate set between continuous image frames, executing edge closing operation on a region boundary coordinate set by utilizing a convex hull algorithm to determine a channel logic boundary, counting boundary crossing count values of spatial collision detection behaviors of the motion displacement vector and the channel logic boundary, and carrying out weighted calibration by combining a preset service activity correction operator to generate a motion frequency characteristic.
  7. 7. The method for generating the cross-border e-commerce visualized report based on the drag-type component as claimed in claim 6, wherein the method for introducing the preset height compensation coefficient to perform the nonlinear correction comprises associating trade mode attributes corresponding to the functional partitions according to business logic function labels of the functional partitions to determine a stacking height limit in the trade modes; Invoking a preset height compensation coefficient to perform nonlinear correction on the preliminary bulk density value to generate a bulk density characteristic, wherein the preset height compensation coefficient is a calibration constant determined based on the average stacking number of cargoes and the packing specification in the trade mode; The method for counting the boundary crossing count value of the space collision detection behavior of the motion displacement vector and the channel logic boundary comprises the steps of identifying boundary crossing events of the motion displacement vector crossing the channel logic boundary as a polygon boundary entity in a preset sampling time window, and counting the accumulated times of the boundary crossing events in the sampling time window through an accumulator to obtain the boundary crossing count value.
  8. 8. The drag-component-based cross-border e-commerce visualization report generation method of claim 1, wherein the generation method of the semantic bound composite feature comprises the following steps: Analyzing the cross-border trade rule database, extracting service metadata, constructing a logic boundary according to the service metadata, executing component construction operation by combining a preset logic template set, and generating a component library to be activated; Responding to a user dragging instruction sequence, identifying a drop point pose and a business logic function label which are moved from a component library to be activated to a region boundary coordinate set, and carrying out nonlinear fusion on bulk density features, motion frequency features and structured business data sets associated with the business logic function label in the region boundary coordinate set by utilizing a semantic mapping function to generate a semantic binding composite feature; the nonlinear fusion method comprises the steps of calling a space-time synchronization check function to calculate a time sequence deviation value between a characteristic capturing moment of a stacking density characteristic and a motion frequency characteristic and a latest updating moment of a structured service data set; and when the time sequence deviation value is judged to be in a preset synchronous window, carrying out nonlinear fusion mapping on the stacking density characteristic, the motion frequency characteristic and the structured service data set by utilizing a semantic mapping function to generate a semantic binding composite characteristic.
  9. 9. The method for generating a cross-border e-commerce visual report based on a drag-type component according to claim 1, wherein the method for triggering the adaptive pixel processing of the visual report and the outputting of the decision suggestion comprises the following steps: Extracting stacking density features from the semantic binding composite features according to a user dragging instruction sequence, counting the number of out-of-date physical entities exceeding a preset trade aging standard in a functional partition to determine business violation risk items, and carrying out weighted summation on the stacking density features, the motion frequency features and the business violation risk items by combining preset weighting coefficients to obtain a dynamic bottleneck index; Performing real-time logic comparison on the dynamic bottleneck index and a preset operation safety threshold to judge whether a logistics turnover obstruction risk exists, performing self-adaptive pixel processing on a region boundary coordinate set with the logistics turnover obstruction risk to generate a visual report, and performing logic backtracking in combination with a business logic function label to generate a decision suggestion; Invoking a preset color difference strengthening operator according to a load deviation increment, adjusting saturation components and brightness components of pixel points in an HSV color space in a boundary coordinate set of a region, and mapping pixel clusters of a functional partition at the risk of logistic turnover obstruction into red highlighting pixel identifications so as to identify the logistic turnover obstruction points; The execution method of the logic backtracking comprises the steps of taking a load deviation increment exceeding a preset fluctuation threshold as a trigger index, reversely searching service logic function labels associated with functional partitions defined by a regional boundary coordinate set, and carrying out association analysis by combining a preset big data rule base to identify the cause of logistic turnover obstruction; The method for executing the association analysis comprises the steps of calculating the instantaneous fluctuation quantity of the bulk density characteristic and the motion frequency characteristic, and the causal correlation coefficient between the priority weight coefficient and the trade status code, and judging whether the logistic turnover obstruction risk is caused by service triggering factors under different trade modes or caused by physical bulk density overload; and according to the service trigger factor, searching a matched job scheduling strategy from a preset decision template library, and generating a job optimization instruction according to the job scheduling strategy.
  10. 10. The drag-component-based cross-border e-commerce visual report generation system is used for realizing the drag-component-based cross-border e-commerce visual report generation method as claimed in any one of claims 1 to 9, and is characterized in that the system comprises an image acquisition module, a physical feature extraction module, a semantic binding module and a visual report module: The image acquisition module is used for extracting the dynamic sampling interval of an original video stream to generate a preprocessed image frame sequence, generating a structured service data set according to the captured original service data stream, and generating a physical semantic region set according to a geofence index and the preprocessed image frame sequence which are associated with trade batch identifiers in the structured service data set; the physical feature extraction module is used for carrying out pose positioning on the preprocessed image frame sequence to determine a target centroid coordinate set, and carrying out geometric membership verification and space collision detection by combining a physical semantic region set to generate a stacking density feature and a motion frequency feature; The semantic binding module is used for analyzing the cross-border trade rule database, extracting service metadata to generate a component library to be activated, calling a semantic mapping function to perform nonlinear fusion on the stacking density characteristic, the motion frequency characteristic and the structured service data set in response to a user dragging instruction sequence, and generating a semantic binding composite characteristic; The visual report module is used for resolving a dynamic bottleneck index according to the semantic binding composite characteristic and the motion frequency characteristic, and executing real-time logic comparison on the dynamic bottleneck index and a preset operation safety threshold value so as to trigger self-adaptive pixel processing of the visual report and output of decision suggestions.

Description

Cross-border E-commerce visual report generation method and system based on drag type assembly Technical Field The invention relates to the field of cross-border e-commerce data processing, in particular to a cross-border e-commerce visual report generation method and system based on a drag component. Background Along with the digital transformation of cross-border trade, the refined scheduling of storage resources and the real-time perception of logistics aging become the key of ensuring the efficiency, but the conventional report generation method still faces the limitations brought by the fact that video perception data and business trade data are isolated from each other, the dynamic bottleneck of a physical space is difficult to characterize in real time, and the threshold of a personalized decision chart generated by non-technicians is high. Therefore, how to realize the deep coupling of physical and real-phase perception characteristics and trade business logic, and realize the automatic generation of reports and the dynamic diagnosis of business bottlenecks through visual interaction means, has become a technical problem to be solved in the field of cross-border warehouse management. The Chinese patent application with the bulletin number of CN118551247B provides a cross-border electronic commerce logistics data intelligent management method, which comprises the steps of constructing a two-dimensional sample space based on order time and order address of logistics data, and further obtaining logistics data points. In the iterative self-organizing clustering process of the logistics data points, firstly analyzing the class representative degree of each logistics data point in the initial cluster, and screening the offset logistics data points according to the class representative degree. The direction in which the cluster center points point to the offset stream data points is the offset direction. And obtaining the offset degree by utilizing the data distribution in the offset direction, and correcting the clustering center point by the offset degree and the offset direction to obtain the logistics data category. However, the current technology still faces many challenges. In the export overseas warehouse goods warehousing and turnover scene of high aging requirements such as the cross-border electronic commerce export overseas warehouse trade mode, the traditional warehousing monitoring system is difficult to effectively combine the physical state of the site with the actual trade business requirements, and the congestion point of the operation area cannot be found in time. The picture generated by video monitoring is unstructured information and is not communicated with structured data such as customs batch, logistics priority and the like in a business system. The tally staff can only rely on static statistics data or fragmentation monitoring pictures to carry out empirical judgment in the explosive circulation area and the customs clearance area. Once a certain area is concentrated in a large quantity of cargoes in a short time or the carrying path is too busy, and when actual congestion is formed, first-line cargo crews face complex parameter configuration thresholds due to lack of visual and business guiding auxiliary tools, and are difficult to correspond to field conditions and business requirements, and whether a warehouse area reaches a saturated state or not cannot be timely perceived. Under the condition, the system cannot correlate the actual condition of the warehouse with the business rule in real time, and cannot trigger effective risk early warning and adaptive scheduling decision in time, so that the tally resources are wrongly distributed to the low-timeliness requirement area, and serious circulation stagnation and high-amount default loss are more likely to be caused by missing the customs clearance window period of large-batch cross-border cargoes. Disclosure of Invention In order to achieve the above purpose, the invention provides a cross-border electronic commerce visual report generation method based on a drag component, which comprises the following specific technical scheme: Extracting dynamic sampling intervals of an original video stream to generate a preprocessing image frame sequence, generating a structured service data set according to the captured original service data stream, and generating a physical semantic region set according to a geofence index associated with trade batch identification in the structured service data set and the preprocessing image frame sequence; Performing pose positioning on the preprocessed image frame sequence to determine a target centroid coordinate set, and performing geometric membership verification and space collision detection by combining a physical semantic region set to generate a stacking density feature and a motion frequency feature; Analyzing the cross-border trade rule database, extracting service metadata to generate a c