Cross-border e-commerce visual report generation method and system based on drag-and-drop component

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 system based on a drag-and-drop component, and the method comprises the steps: extracting a dynamic sampling interval to generate a preprocessing image frame sequence, and generating a physical semantic region set in combination with a business data set and a geo-fence index; a target centroid coordinate set is generated according to the preprocessed image frame sequence, and stacking density and motion frequency are generated in combination with the physical semantic region set; analyzing the cross-border trade rule base to generate a to-be-activated component base, and responding to a user dragging instruction to call a semantic mapping function to fuse the stacking density, the motion frequency and the business data set to generate semantic binding composite features; and calculating a dynamic bottleneck index according to the semantic binding composite feature and the motion frequency, and comparing an operation safety threshold to trigger visual report adaptive processing and decision suggestion output. According to the method, physical storage real-phase perception and cross-border trade business constraints are fused, and a drag-and-drop visual report dynamic and bottleneck adaptive diagnosis closed-loop mechanism is constructed.

Description

Technical Field

[0001] This invention relates to the field of cross-border e-commerce data processing, and in particular to a method and system for generating visual reports for cross-border e-commerce based on drag-and-drop components. Background Technology

[0002] With the digital transformation of cross-border trade, the refined scheduling of warehousing resources and the real-time perception of logistics timeliness have become crucial to ensuring efficiency. However, existing report generation methods still face limitations such as the isolation between video perception data and business trade data, the difficulty in representing dynamic bottlenecks in physical space in real time, and the high barrier to entry for non-technical personnel to generate personalized decision-making charts. Therefore, how to achieve deep coupling between physical reality perception characteristics and trade business logic, and how to automate report generation and dynamically diagnose business bottlenecks through intuitive interactive means, has become a pressing technical challenge in the field of cross-border warehousing management.

[0003] Chinese patent application CN118551247B discloses an intelligent management method for cross-border e-commerce logistics data. This method includes: constructing a two-dimensional sample space based on order time and order address from logistics data to obtain logistics data points. During iterative self-organizing clustering of the logistics data points, the representativeness of each category in the initial cluster is first analyzed. Offset logistics data points are then selected based on this representativeness. The direction from the cluster center to the offset logistics data points is the offset direction. The degree of offset is obtained using the data distribution along the offset direction. The cluster center is then corrected using the degree and direction of offset to obtain the logistics data category.

[0004] However, current technology still faces many challenges. In high-efficiency scenarios such as the warehousing and turnover of goods in overseas warehouses for cross-border e-commerce exports, traditional warehouse monitoring systems struggle to effectively combine the physical state of the site with actual trade business needs, failing to promptly identify congestion points in the work area. Video surveillance footage is unstructured information, disconnected from structured data such as customs declaration batches and logistics priorities in the business system. When monitoring high-volume product circulation areas and customs clearance waiting areas, warehouse workers can only rely on static statistical data or fragmented monitoring footage for experience-based judgment. If a large amount of goods concentrates in a certain area within a short period or the handling path becomes excessively busy, causing actual congestion, frontline warehouse workers, lacking intuitive, business-oriented auxiliary tools and facing complex parameter configuration hurdles, struggle to correlate the on-site situation with business needs, thus failing to promptly perceive whether the warehouse area has reached saturation. In this situation, the system cannot link the actual warehouse situation with business rules in real time, and cannot trigger effective risk warnings and adaptive scheduling decisions in a timely manner. This not only leads to the incorrect allocation of cargo handling resources to areas with low timeliness requirements, but may also cause a large number of cross-border goods to suffer serious circulation stagnation and high default losses due to missing the customs declaration window. Summary of the Invention

[0005] To achieve the above objectives, this invention provides a method for generating visual reports for cross-border e-commerce based on drag-and-drop components, the specific technical solution of which is as follows: Extract the dynamic sampling interval of the original video stream to generate a preprocessed image frame sequence, and generate a structured business dataset based on the captured original business data stream. Based on the geofence index associated with the trade batch identifier in the structured business dataset and the preprocessed image frame sequence, generate a set of physical semantic regions. Pose localization is performed on the preprocessed image frame sequence to determine the target centroid coordinate set. Geometric membership verification and spatial collision detection are performed in combination with the physical semantic region set to generate packing density features and motion frequency features. The cross-border trade rules database is parsed to extract business metadata to generate a library of components to be activated. In response to the user's drag-and-drop command sequence, the semantic mapping function is called to perform nonlinear fusion on the stacking density feature, motion frequency feature and structured business dataset to generate semantically bound composite features. The dynamic bottleneck index is calculated based on semantic binding composite features and motion frequency features. Real-time logical comparison is performed between the dynamic bottleneck index and the preset work safety threshold to trigger adaptive pixel processing and decision suggestion output in the visualization report.

[0006] Furthermore, the method for generating the physical semantic region set includes: Extract the pixel change rate between adjacent frames in the acquired raw video stream, calculate the dynamic sampling interval based on the pixel change rate, and use the dynamic sampling interval to perform non-equidistant sampling on the raw video stream to extract the raw image sequence. Based on the raw image sequence, obtain the preprocessed image frame sequence. Retrieve customs batch identifiers and trade status codes from the captured raw business data stream, generate priority weight coefficients based on the time sensitivity of trade patterns, and encapsulate the customs batch identifiers, trade status codes, and priority weight coefficients into a structured business dataset. Extract the initial pixel feature layer from the preprocessed image frame sequence, retrieve the geofence index associated with the trade batch identifier to determine the set of regional boundary coordinates, assign business logic function labels according to priority weight coefficients, calculate the turnover load limit using the set of regional boundary coordinates, and generate a set of physical semantic regions based on the priority weight coefficients, business logic function labels, set of regional boundary coordinates, and turnover load limit.

[0007] Furthermore, the method for extracting the original image sequence includes: The pixel change rate is compared with the preset activity threshold. When the pixel change rate exceeds the activity threshold, it is determined that the current state is a high-frequency business turnover operation, and the sampling frequency is increased by logic. The sampling frequency is increased by reducing the time span between adjacent sampling points to increase the sampling density. The dynamic sampling interval is obtained by performing a division operation with the sampling sensitivity adjustment coefficient as the numerator and the sum of the pixel change rate and the signal zero-point stability factor as the denominator. The sampling sensitivity adjustment coefficient is a weighting factor determined based on the processing limit of the back-end computing power of the report generation system. The signal zero-point stabilization factor is a preset positive real constant that prevents the denominator from being zero under the static condition where the pixel change rate approaches zero. The original video stream is sampled at non-uniform intervals using the dynamic sampling interval to extract the original image sequence.

[0008] Furthermore, the method for generating the priority weight coefficients includes: Regular expressions are used to retrieve and match the package operation priority field and the inspection level field from the original business data stream; The package operation priority field is mapped to a numerical operation gain value, and the inspection level field is mapped to a numerical inspection gain value. The basic timeliness coefficient is determined based on the trade model level preset by the business platform, and when the cross-border e-commerce export overseas warehouse trade model is identified, the basic timeliness coefficient is set to a fixed offset value that is greater than the preset benchmark constant of the ordinary trade model. A weighted summation operation is performed on the operation gain value, inspection gain value, and basic timeliness coefficient to generate a priority weight coefficient for the urgency of trade batch circulation.

[0009] Furthermore, the calculation method for the upper limit of the turnover load includes: Retrieve the initial physical boundary coordinate range corresponding to the business geofence index associated with the trade batch identifier, and calculate the intersection-union ratio between the initial physical boundary coordinate range and each candidate pixel cluster in the initial pixel feature layer; When the cross-union ratio exceeds a preset overlap threshold, the corresponding candidate pixel clusters are initially divided into functional areas such as shelf storage area, operation channel area or customs inspection area; the preset overlap threshold is a geometric calibration constant determined based on the installation pitch angle of the video acquisition terminal and the lens distortion rate. The total number of pixels covered by each functional partition is counted based on the set of regional boundary coordinates to obtain the pixel area. The turnover efficiency constant corresponding to the current trade mode is retrieved from the business database. The turnover efficiency constant is a scalar constant of the cargo processing throughput preset by the trade mode under the unit pixel area. The pixel area and the turnover efficiency constant are multiplied to generate the turnover load limit of the physical load limit value of each functional partition.

[0010] Furthermore, the method for performing the geometric membership check includes: Extract multi-scale feature layers from the preprocessed image frame sequence, extract entity semantic activation features from the multi-scale feature layers, and use a real-time target detection neural network architecture to perform classification and pose localization on the entity semantic activation features to generate a set of target centroid coordinates. The target centroid coordinate set is projected onto the physical semantic region set to determine the spatial attribution relationship. Based on the spatial attribution relationship, the ratio of the total projected area of ​​each functional partition to the total pixel area is calculated to obtain the preliminary packing density value. A preset height compensation coefficient is introduced to perform nonlinear correction and generate packing density features. The method for performing spatial collision detection includes: generating motion displacement vectors based on the target centroid coordinate set between consecutive image frames; performing edge closure operation on the region boundary coordinate set using the convex hull algorithm to determine the channel logical boundary; counting the boundary crossing counts of spatial collision detection behavior between the motion displacement vector and the channel logical boundary; and performing weighted calibration in combination with a preset business activity correction operator to generate motion frequency features.

[0011] Furthermore, the method of introducing a preset height compensation coefficient to perform nonlinear correction includes: based on the business logic function tags of each functional partition, associating the trade mode attribute corresponding to the functional partition, so as to determine the stacking height limit under the trade mode; The preset height compensation coefficient is used to perform nonlinear correction on the initial packing density value to generate packing density characteristics. The preset height compensation coefficient is a calibration constant determined based on the average number of stacked layers and packaging specifications of goods under the trade mode. The method for calculating the boundary crossing count of spatial collision detection behavior between the statistical motion displacement vector and the channel logical boundary includes: identifying boundary crossing events where the motion displacement vector, as a line segment entity, crosses the channel logical boundary, as a polygon boundary entity, within a preset sampling time window; and using an accumulator to count the cumulative number of boundary crossing events within the sampling time window to obtain the boundary crossing count.

[0012] Furthermore, the method for generating the semantic binding composite feature includes: The system parses the cross-border trade rules database to extract business metadata, constructs logical boundaries based on the business metadata, performs component construction operations in combination with a preset set of logical templates, and generates a library of components to be activated. Responding to the user's drag command sequence, the system identifies the landing pose and business logic function label of the component library moving to the region boundary coordinate set. It then uses a semantic mapping function to nonlinearly fuse the stacking density features, motion frequency features, and structured business dataset associated with the business logic function label within the region boundary coordinate set to generate semantically bound composite features. The method for performing nonlinear fusion includes: calling a spatiotemporal synchronization verification function to calculate the temporal deviation between the feature capture time of the stacking density feature and the motion frequency feature and the most recent update time of the structured business dataset; When the timing deviation value is determined to be within a preset synchronization window, the semantic mapping function is used to perform nonlinear fusion mapping of the stacking density feature, motion frequency feature and structured business dataset to generate semantically bound composite features.

[0013] Furthermore, the method for triggering the adaptive pixel processing and decision suggestion output of the visualization report includes: Based on the user's drag-and-drop instruction sequence, the stacking density feature is extracted from the semantic binding composite feature. The number of physical entities exceeding the preset trade timeliness standard within the functional area is counted to determine the business violation risk item. The stacking density feature, movement frequency feature and business violation risk item are weighted and summed by the preset weighting coefficient to obtain the dynamic bottleneck index. The dynamic bottleneck index and the preset operation safety threshold are compared in real time to determine whether there is a risk of logistics turnover obstruction. Adaptive pixel processing is performed on the boundary coordinate set of areas with logistics turnover obstruction risk to generate a visual report. Logical backtracking is performed in combination with business logic function tags to generate decision suggestions. The adaptive pixel processing execution method includes: based on the load deviation increment, calling a preset color difference enhancement operator, adjusting the saturation and brightness components of pixels in the HSV color space within the region boundary coordinate set, and mapping the pixel clusters of functional partitions at risk of logistics turnover obstruction to red highlighted pixel markers to identify logistics turnover obstruction points. The execution method of the logical backtracking includes: using the load deviation increment exceeding the preset fluctuation threshold as the trigger index, retrieving the business logic function tags associated with the functional partition defined by the set of regional boundary coordinates in reverse, and performing correlation analysis in combination with the preset big data rule base to identify the causes of logistics turnover obstruction; The method for performing correlation analysis includes calculating the instantaneous fluctuation of stacking density characteristics and movement frequency characteristics, the causal correlation coefficient between them and priority weight coefficients and trade status codes, and determining whether the logistics turnover obstruction risk is caused by business triggering factors under different trade models or by physical stacking density overload. Based on the business triggering factor, a matching job scheduling strategy is retrieved from a preset decision template library, and a job optimization instruction is generated according to the job scheduling strategy.

[0014] A drag-and-drop component-based cross-border e-commerce visual report generation system is used to implement the aforementioned drag-and-drop component-based cross-border e-commerce visual report generation method. The system includes an image acquisition module, a physical feature extraction module, a semantic binding module, and a visual report module. The image acquisition module is used to extract the dynamic sampling interval of the original video stream to generate a preprocessed image frame sequence, and to generate a structured business dataset based on the captured original business data stream. Based on the geofence index associated with the trade batch identifier in the structured business dataset and the preprocessed image frame sequence, a set of physical semantic regions is generated. The physical feature extraction module is used to perform pose localization on the preprocessed image frame sequence to determine the target centroid coordinate set, and to perform geometric membership verification and spatial collision detection in combination with the physical semantic region set to generate packing density features and motion frequency features. The semantic binding module is used to parse the cross-border trade rules database to extract business metadata to generate a component library to be activated, and to respond to the user's drag command sequence by calling the semantic mapping function to perform nonlinear fusion on the stacking density feature, motion frequency feature and structured business dataset to generate semantic binding composite features. The visualization report module is used to calculate the dynamic bottleneck index based on semantic binding composite features and motion frequency features, and to perform real-time logical comparison between the dynamic bottleneck index and the preset work safety threshold to trigger the adaptive pixel processing and decision suggestion output of the visualization report.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention achieves source-level parameterized coupling between preprocessed image sequences generated from dynamic image sampling and structured business datasets that have undergone priority quantization. This enables semantic mapping of pixel features of cross-border warehousing physical space to physical semantic regions with clearly defined trade attributes, thus improving the problem of logical isolation and difficulty in collaborative analysis between unstructured video data and structured business system data in traditional cross-border logistics monitoring.

[0016] This invention performs geometric membership verification and spatial collision detection on the centroid coordinates of the target located in the image sequence and the set of physical semantic regions, and introduces a height compensation mechanism to perform nonlinear correction. This enables the extraction of unstructured pixel features into parameterized indicators that represent physical space saturation and flow heat. This improves the problem in traditional cross-border logistics monitoring where the utilization rate of warehouse space and dynamic bottlenecks cannot be perceived in real time due to the difficulty in quantitatively representing physical reality data.

[0017] This invention empowers the execution logic of the components to be activated by using business accounting operators generated from parsing cross-border trade rules, and calls semantic mapping functions in response to user drag commands to perform spatiotemporal alignment and cross-domain mapping of real-world perception features and structured business data. This transforms discrete physical pose dimensions into semantically bound composite features with business decision-making depth, solving the problem of isolated physical scene features and trade compliance rules in traditional cross-border e-commerce logistics monitoring, and the difficulty for warehouse workers to locate operational bottlenecks in real time.

[0018] This invention generates a dynamic bottleneck index by weighting and synthesizing the stacking density and movement frequency, which reflect the physical reality, and the timeliness risk characteristics, which reflect trade rules. The dynamic bottleneck index is then used to trigger adaptive pixel enhancement processing of the visualization report and logical backtracking of the causes of business anomalies. This enables real-time and accurate location of bottlenecks in warehousing operations and automatic synthesis of scheduling instructions. This solves the problem in traditional reporting systems where monitoring signals are difficult to transform into effective decision-making basis due to the isolation between physical state and business logic. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the principle of the drag-and-drop component-based cross-border e-commerce visual report generation method of the present invention. Figure 2 This is a schematic diagram illustrating the principle of dynamic sampling of original image sequences based on pixel change rate according to the present invention. Figure 3 This is a schematic diagram illustrating the principle of generating physical semantic regions based on business logic constraints according to the present invention; Figure 4 This is a schematic diagram of the cumulative counting principle based on spatial collision detection behavior of the present invention; Figure 5 This is a functional module diagram of the drag-and-drop component-based cross-border e-commerce visual report generation system of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: Please see Figure 1 As shown, this embodiment provides a method for generating visual reports for cross-border e-commerce based on drag-and-drop components, including: Step S1000: Extract the original video stream. Dynamic sampling interval To generate a preprocessed image frame sequence And based on the captured raw business data stream Generate structured business datasets Based on structured business datasets China Trade Batch Marking Associated geofence index and preprocessed image frame sequences Generate a set of physical semantic regions .

[0023] Specifically, this step aims to process the raw video streams that are dynamically flowing within the physical space of the cross-border warehouse. The unstructured pixel information in the image is mapped into a computable preprocessed image frame sequence with spatiotemporal continuity. The original business data stream that is in an asynchronous update state within the business platform Transforming trade pattern information into structured business datasets At the data source, parameterized coupling of physical reality information and business logic information is achieved, generating a set of physical semantic regions with business attribute tags. .

[0024] Further, step S1000 includes: Step S1100: Extract the acquired raw video stream. Pixel change rate between adjacent frames Based on pixel change rate Calculate the dynamic sampling interval and utilize dynamic sampling interval For the raw video stream Non-uniformly spaced sampling is performed to extract the original image sequence, and a preprocessed image frame sequence is obtained based on the original image sequence. .

[0025] Specifically, this step aims to process the raw video streams that are in a dynamic flow at the warehouse site. As a data sensing object, the instantaneous evolution trend of pixel features in adjacent frames within the field of view is mapped to a dynamic sampling interval using sampling feedback closed-loop logic based on pixel evolution gradient. This technology enables intensive capture of action sequences in high-frequency business turnover areas and redundant compression of redundant information in static storage areas. It optimizes the matching of physical dynamic features and computing resources at the data source, providing preprocessed image frame sequences adapted to the business turnover frequency for subsequent steps. .

[0026] In practice, the report generation system receives real-time raw video streams through video acquisition loops deployed at the work site within the physical warehouse area. The original video stream It contains unstructured spatiotemporal pixel information, including cargo loading and unloading processes, pallet stacking status, and the work trajectories of sorting personnel, captured by multiple monitoring terminals.

[0027] This step does not perform a fixed, equally spaced frame extraction operation. Instead, it performs pixel grayscale space mapping based on the image frames corresponding to two adjacent sampling times, and calculates the pixel change rate by extracting the pixel evolution gradient. This is used to quantitatively characterize the instantaneous activity level of goods handling within a storage area or operational aisle. The pixel change rate... The specific calculation logic is as follows: The report generation system retrieves the current acquisition frame and the previous reference frame, and uses the frame difference method to perform grayscale value subtraction on the corresponding spatial coordinates of the pixels in the two frames. By accumulating the sum of the absolute values ​​of the grayscale differences of all pixel coordinates within the field of view, the motion vector value representing the displacement state of the object in the current field of view is extracted. The motion vector value is divided by the total number of pixels in a single frame within the field of view to obtain the pixel change rate, which represents the instantaneous evolution of pixel features. The total pixel count of a single frame image is the product of the horizontal and vertical pixel counts at the current acquisition terminal's output resolution.

[0028] To achieve a sampling feedback closed loop triggered by pixel evolution gradient, the pixel change rate is... The logic compares the pixel change rate with the preset activity threshold. When the activity level exceeds the activity threshold, it is determined that the current operation is in a high-frequency business turnover state, triggering the sampling frequency increase logic, that is, increasing the sampling density by reducing the time span between adjacent sampling points. The activity threshold is an empirical constant set based on the basic operating frequency under the cross-border e-commerce export overseas warehouse model, used to filter pixel disturbances caused by ambient light fluctuations and ensure that the sampling action is triggered by actual goods movement.

[0029] To ensure the preprocessed image frame sequence To ensure data continuity in the time domain and suppress sampling frequency oscillations caused by instantaneous changes in ambient lighting, this step determines the dynamic sampling interval using nonlinear pulse adjustment logic. The specific execution logic of the nonlinear pulse adjustment logic is as follows: adjust the sampling sensitivity coefficient. As a molecule, the pixel change rate and signal zero-point stability factor The sum is used as the denominator, and the dynamic sampling interval is obtained by performing a division operation. Wherein, the sampling sensitivity adjustment coefficient The weighting factor is determined based on the processing limits of the backend computing power of the report generation system. It is used to set the basic sampling step size under specific computing resources to ensure that the number of sampling frames does not exceed the system's parsing load under high-frequency transfer conditions; the signal zero-point stability factor It is a preset positive real constant that infinitely approaches zero, used when the goods are in a static storage condition, i.e., the pixel change rate. To prevent singular values ​​from appearing in the calculation results when the denominator is zero under static conditions approaching zero.

[0030] Further, please refer to Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the principle of dynamically sampling the original image sequence based on pixel change rate according to the present invention. Figure 2 As shown in the diagram, the top of the image represents the raw video stream acquired through the video capture loop. .like Figure 2 As shown in the middle section, the pixel change rate characterizing the cargo handling state within the field of view is calculated by extracting the pixel evolution gradient between image frames corresponding to two adjacent sampling times. And based on the pixel change rate Determine the dynamic sampling interval The icon with the moving puppet on the left side of the image represents a high-frequency business cycle. The corresponding horizontal line below the sampling points shows a dense pulse distribution, indicating that the dynamic sampling interval has been reduced. The sampling density is increased to achieve dense capture of the action sequence; the icon on the right side of the figure with stationary goods represents the current static storage condition, and the corresponding horizontal line of the sampling points below shows a sparse pulse distribution, indicating that the dynamic sampling interval has been increased. This is used to perform sampling density downsampling, thereby compressing redundant information and optimizing the allocation of computing resources. The bottom of the diagram represents the dynamic sampling interval. The extracted original image sequence with non-uniformly spaced sampling.

[0031] After obtaining the dynamic sampling interval After extracting the original image sequence with non-equidistant sampling, in order to eliminate the interference of light fluctuations in the cross-border warehousing environment and optical distortions of different visual acquisition terminals on the semantic binding accuracy of subsequent physical pixels and trade business logic, this step calls the pixel feature consistency parameter set pre-calibrated based on the acquisition terminal hardware parameters and the reference ambient light intensity to perform preprocessing mapping on each frame of the extracted original image sequence. The specific execution logic of the preprocessing mapping is as follows: First, the Laplacian operator is used to perform edge enhancement and grayscale correction on each sampled image frame. That is, the grayscale gradient evolution distribution of the cargo outline and background area in the field of view is extracted by second-order differential calculation, and the feature contrast of the cargo edge outline is obtained by superimposing the original grayscale values ​​of the sampled image to generate a frame to be corrected with structured edge features. Then, in order to solve the data inaccuracy problem caused by differences in physical installation position and hardware resolution of different visual acquisitions, the spatial transformation matrix in the pixel feature consistency parameter set is retrieved, and the bilinear interpolation algorithm is used to perform spatial size normalization on the frame to be corrected. That is, the frame to be corrected in different physical camera coordinate systems is resampled and mapped to a preset unified standard pixel coordinate system that matches the warehouse electronic map through the spatial transformation matrix, and finally a preprocessed image frame sequence is generated. .

[0032] Step S1200, from the captured raw business data stream Search for customs declaration batch identifiers Trade status codes Priority weight coefficients are generated based on the time sensitivity of trade patterns. and mark the customs batch. Trade status code and priority weight coefficient Encapsulate as a structured business dataset .

[0033] Specifically, this step aims to process the raw business data stream pushed by the business platform interface that is in an asynchronous update state. As a business logic awareness object, it utilizes the near real-time listening capability of the asynchronous observer pattern to monitor the original business data stream. The abstract, unstructured trade customs status description is mapped to a numerical trade status code with logically calculated weights. And calculate priority weight coefficients based on the timeliness constraints of trade models such as cross-border e-commerce export overseas warehouses. This enables the parameterization and quantification of business urgency at the underlying logic level, providing a logically consistent structured business dataset for subsequent physical-business semantic binding. .

[0034] In practice, the report generation system executes the asynchronous observer pattern through a pre-defined asynchronous observer interface to monitor the raw business data stream generated in real time by the business platform. Real-time synchronization. The original business data stream... This includes trade characteristics such as customs declaration numbers, logistics priority instructions, and expected warehouse entry timestamps from various cross-border trade models, including the cross-border e-commerce export overseas warehouse trade model. For the cross-border e-commerce export overseas warehouse model and other parallel trade models, this step uses regular expressions to extract data from the raw business data stream. Search and match trade batch identifiers Core fields include trade customs status description, parcel operation priority, and inspection level.

[0035] Subsequently, to eliminate the logical interference of unstructured text on the subsequent calculation model, this step executes the business semantic numerical conversion logic, the specific execution logic of which is as follows: First, establish a trade status mapping matrix. The trade status mapping matrix The row vectors correspond to various preset trade customs status descriptions, such as "pending inspection" and "released," while the column vectors correspond to preset scalar values. The retrieved and matched trade customs status descriptions are used as indexes to map data from the trade status matrix. The corresponding numerical target value is retrieved from the database, thereby mapping the character-based trade customs status description to a preset, numerical trade status code. .

[0036] Simultaneously, priority quantification calculations are performed based on the time sensitivity of the current trade model to obtain priority weight coefficients that characterize the urgency of circulation within the storage area. In the specific priority quantification calculation process, the report generation system will include the job gain value corresponding to the job priority field. The verification gain value corresponding to the verification level field. and the basic timeliness coefficient of the current trade model Perform a weighted summation operation to obtain the priority weight coefficients. This makes the priority weight coefficient The quantification presents the physical handling priorities resulting from differences in business attributes. Among these, the job gain value... The report generation system is based on the original business data flow. The numerical weight obtained by mapping the parcel operation priority field in the data; the inspection gain value The report generation system is based on the original business data flow. The numerical weight obtained by mapping the inspection level field in the database; the basic timeliness coefficient These parameters are determined based on the trade model level preset by the business platform. For trade batches involving the cross-border e-commerce export overseas warehouse model, the corresponding basic timeliness coefficient is... It is set to a fixed offset value greater than the preset benchmark constant for the ordinary trade mode; the benchmark constant for the ordinary trade mode is a calibration value determined based on the average circulation cycle of general cross-border parcel logistics, and is used to quantitatively characterize the high timeliness technical requirements of cross-border bulk cargo circulation.

[0037] Finally, the resulting feature vectors are encapsulated, and a structured business dataset is constructed by performing a set union operation. The structured business dataset The mathematical expressions for these relationships are as follows: .in, This demonstrates the encapsulation operation performed on the feature vectors of each trade batch; This represents the trade batch index value within the current asynchronous observer mode acquisition window; This represents the total number of trade batches that have been synchronously completed within the current asynchronous observer mode acquisition window. Indicates the first The unique identifier for each trade batch, namely the trade batch identifier, is used to enable data indexing and association across steps; Indicates the first Numerical trade status codes are generated after each trade batch is numerically mapped and used to eliminate semantic ambiguity in the text. Indicates the first The priority weight coefficients for each trade batch are calculated based on trade models such as cross-border e-commerce exports and overseas warehouses, and are used to quantitatively represent the urgency of the flow of goods in the corresponding trade batch.

[0038] For example, in actual warehouse operations, warehouse staff often need to coordinate the handling of cargo areas with different trade weights. For instance, there might be a location A storing 10 ordinary small packages, and a location B storing only 5 packages from a cross-border e-commerce export warehouse model that include popular TikTok products. Traditional static report generation systems often rely solely on the higher physical stacking volume of location A, leading to incorrect operational load predictions and misjudging it as a major logistics bottleneck. This results in a misallocation of decision-making resources to goods with lower timeliness requirements. However, by performing the priority quantification calculation in step S1200, the report generation system incorporates the basic timeliness coefficient unique to the cross-border e-commerce export warehouse model. and the operation gain value that characterizes the operation weight of blockbuster products Real-time weighted summation is performed due to the basic timeliness coefficient under the cross-border e-commerce export overseas warehouse model. It includes a high fixed offset value preset for cross-border bulk cargo, and TikTok hit products are allocated an extremely high operational gain value. It exhibits an overwhelming advantage in quantitative values, making it possible to calculate the priority weight coefficient of cargo location B. In terms of quantitative indicators, it is more than three times that of location A. The aforementioned priority quantification process represents a technological leap from observing physical volume to understanding the essence of the business: even if location B presents a lower visual perception of actual stacking, when the warehouse clerk uses a drag-and-drop command sequence in the visual interface... When binding components to this region's coordinates, the report generation system will use the calculated priority weight coefficients. The indicators automatically trigger risk warning signals. This decision-making empowerment, achieved through parameterization of business urgency, successfully eliminates the problem of fragmented information-assisted decision-making caused by the isolation between physical monitoring and business logic in the background technology.

[0039] Step S1300: Extract the preprocessed image frame sequence Initial pixel feature layer Search for trade batch identifiers Associated geofence index To determine the set of region boundary coordinates Based on priority weight coefficient Assign business logic function tags and using the set of region boundary coordinates Calculate the upper limit of turnover load Based on priority weight coefficient Business logic function tags Set of regional boundary coordinates and the upper limit of turnover load Generate a set of physical semantic regions .

[0040] Specifically, this step aims to process the preprocessed image frame sequence from step S1100. The physical storage space pixels serve as the semantic modeling carrier, utilizing the structured business dataset from step S1200. The trade logic inherent in the text serves as a spatial prior constraint, mapping randomly distributed pixel clusters within the field of view into a set of physical semantic regions with specific operational attributes. Implement the set of region boundary coordinates at the data architecture level. and business logic function tags The strong coupling provides a digital map foundation with deep business awareness for the dynamic binding of subsequent report components.

[0041] In the specific implementation process, the report generation system first retrieves a pre-trained warehousing spatiotemporal semantic enhancement network that has undergone transfer learning for cross-border warehousing specific scenarios. The warehousing spatiotemporal semantic enhancement network adopts a deep residual convolutional neural network (ResNet) with an integrated time-domain attention mechanism on the underlying framework, and its parameter weight set is a preset model parameter that has undergone feature enhancement processing for standardized pallets, customs seals, and automated conveyor belt nodes in the warehousing environment.

[0042] The report generation system utilizes the aforementioned warehouse spatiotemporal semantic enhancement network to process preprocessed image frame sequences. The pixel-by-pixel classification process is performed, and the specific execution logic of the pixel-by-pixel classification process is as follows: A preprocessed image frame sequence is extracted through the convolutional layers of the deep residual convolutional neural network. The high-dimensional spatiotemporal feature vectors in the model are used, and a temporal attention mechanism is employed to enhance the recognition accuracy of cross-border trade logistics nodes. Finally, pixels are classified into different candidate region masks to generate an initial pixel feature layer containing the geometric boundaries of the candidate regions. .

[0043] Subsequently, business logic constraints are introduced for the initial pixel feature layer. Perform spatial geometric corrections to generate a set of physical semantic regions. The specific execution logic of the aforementioned business logic constraints is as follows: First, the report generation system checks the structured business dataset... Trade batch markings Retrieve the associated business geofence index from the pre-set warehouse electronic map database. And obtain the geofence index for this business. The corresponding initial physical boundary coordinate range. Subsequently, the initial physical boundary coordinate range and the initial pixel feature layer are calculated. The cross-over-union ratio (CLOUD) between candidate pixel clusters is used to determine the logical consistency between physical reality and business fences. If the CLOUD exceeds a preset overlap threshold, the corresponding candidate pixel clusters are initially divided into functional areas such as shelf storage area, work channel area, or customs clearance inspection area. The preset overlap threshold is a geometric calibration constant determined based on the installation tilt angle of the video acquisition terminal and the lens distortion rate, used to determine the logical consistency between physical reality and business fences.

[0044] Further, please refer to Figure 3 As shown, Figure 3 This is a schematic diagram illustrating the principle of physical semantic region generation based on business logic constraints according to the present invention. Figure 3 As shown, the top left side of the diagram represents the initial pixel feature layer. The blue area represents candidate pixel clusters identified through pixel-by-pixel classification; the top right side represents the business geofence index retrieved from the pre-set warehouse electronic map database. The corresponding initial physical boundary coordinate range. For example... Figure 3 As shown in the middle, the blue area representing candidate pixel clusters and the light orange area representing geofence boundaries are subjected to an intersection-union (IoU) operation, with the overlapping portion appearing as a dark orange area. The bottom of the diagram uses a diamond-shaped decision box to illustrate the process of determining the logical consistency between physical reality and the business fence. When the overlap of this dark orange area exceeds a preset overlap threshold, the corresponding candidate pixel clusters are initially divided into functional zones such as shelf storage areas, work passage areas, or customs clearance waiting areas. This aims to map the randomly distributed pixel clusters within the field of view into a set of physical semantic regions with specific operational attributes. .

[0045] Furthermore, the Conditional Random Field (CRS) algorithm is invoked to perform pixel-level edge clustering correction. Using the initial physical boundary coordinate range as a constraint potential function, edge smoothing and noise suppression processing are applied to the candidate pixel clusters corresponding to the functional zones, thereby eliminating visual recognition bias and determining the final set of regional boundary coordinates for each functional zone, aligned with the warehouse electronic map coordinate system. During this process, the report generation system relies on structured business datasets. Medium priority weighting coefficient The numerical range is used to assign corresponding business logic function labels to each functional partition. For example, when the priority weight coefficient If the threshold for the cross-border e-commerce export overseas warehouse model is exceeded, the corresponding functional area will be labeled as "overseas warehouse high-frequency operation tag".

[0046] Meanwhile, the report generation system is based on the final set of region boundary coordinates. The total number of pixels covered by each functional area is counted to obtain the pixel area. And retrieve the turnover efficiency constant corresponding to the current trade model from the business database. Wherein, the turnover efficiency constant It is a scalar constant characterizing the pre-defined cargo processing throughput of cross-border e-commerce export overseas warehouses and other trade models per unit pixel area. This is achieved by considering the pixel area of ​​the current functional partition. and turnover efficiency constant Perform multiplication to obtain the corresponding upper limit of turnover load. .

[0047] Ultimately, the report generation system uses a multidimensional heterogeneous graph attention network (HAN) as a segmentation mapping function to complete the physical semantic region set. The generation of the physical semantic region set. The specific generation logic is as follows: The structured business dataset is generated... Priority weight coefficient Business logic function tags Set of regional boundary coordinates and the upper limit of turnover load As input, the attention allocation operator of the multidimensional heterogeneous graph attention network is used to calculate the correlation weights between different feature dimensions, mapping unstructured pixels into structured business units with business attributes, and finally generating a digital warehouse logical layer with both physical and business attributes, i.e., a set of physical semantic regions. .

[0048] Step S2000: Process the preprocessed image frame sequence Perform pose localization to determine the target centroid coordinate set Combined with physical semantic region set Perform geometric membership verification and spatial collision detection to generate packing density features. and motion frequency characteristics .

[0049] Specifically, this step aims to transform the preprocessed image frame sequence from the unstructured state in step S1100 into a physical storage space. As the data extraction object, the instantaneous spatial distribution state of goods within the field of view is mapped to a set of physical semantic regions from step S1300. Packing density characteristics within the coverage area The instantaneous pose evolution trajectory of the cargo is mapped into motion frequency features. At the feature level, parameterized extraction of physical space occupancy information and flow kinetic energy information is achieved.

[0050] Further, step S2000 includes: Step S2100: Extract the preprocessed image frame sequence The system extracts entity semantic activation features from multi-scale feature layers, and uses a real-time object detection neural network architecture to perform classification and pose localization on these features, generating a set of target centroid coordinates. .

[0051] Specifically, this step aims to process the preprocessed image frame sequence from step S1100, which is in a dynamic flow state, within the physical space of the cross-border warehouse. As a data transformation carrier, it maps the physical reality of goods and equipment that are discretely distributed and in a dynamic state of displacement into a set of target centroid coordinates with a unique center point attribute. This enables the conversion of unstructured pixel information into computable spatial pose digital signals at the data sensing source.

[0052] In the specific implementation process, the report generation system first calls the warehouse spatiotemporal semantic enhancement network described in step S1300, and uses its deep residual convolutional neural network convolutional layers to preprocess the image frame sequence. Multi-dimensional convolution operations are performed to extract high-dimensional spatiotemporal feature vectors describing physical reality. To address feature inaccuracies caused by varying cross-border parcel sizes in warehousing environments and geometric distortions resulting from differences in object depth relative to the lens during camera imaging, this step introduces a Feature Pyramid Network (FPN) at the output of the residual stage of the deep residual convolutional neural network to process the preprocessed image frame sequence. Multi-scale feature extraction is performed on the package entities, handling equipment, and sorting personnel in the process, and a multi-scale feature layer containing multi-level resolution information is output.

[0053] Subsequently, the report generation system analyzes the evolution of pixel texture gradients in multi-scale feature layers to capture entity semantic activation features reflecting the outline of logistics entities, thereby eliminating redundant interference from light noise in the complex lighting environment of cross-border warehousing on target identification. It is important to clarify that the pixel texture gradient extracted in this step focuses on the structural edges of the image's internal spatial dimension to identify entity shapes; while the pixel evolution gradient described in step S1100 focuses on the temporal changes between adjacent image frames to adjust the sampling frequency. The report generation system defines the pixel texture gradient evolution extracted in this step as the second pixel gradient and the pixel evolution gradient extracted in step S1100 as the first pixel gradient. By focusing on the orthogonal complementarity of the two in the computational dimension, the accuracy of logistics entity feature extraction in a dynamic flow environment is ensured.

[0054] Based on this, a pre-defined real-time object detection neural network architecture is used to perform classification and pose localization on the extracted entity semantic activation features. This real-time object detection neural network architecture is trained based on Faster R-CNN or YOLOv8 series architectures, and integrates a Softmax classification branch responsible for feature classification and a bounding box regression branch responsible for geometric localization. The specific execution logic of the classification and pose localization is as follows: the entity semantic activation features are input into the classification branch to calculate the probability score of the current target entity belonging to a package, forklift, or warehouse worker; the highest probability score is taken as the business attribute of the current target entity, generating a structured entity category attribute set. The entity category attribute set Each element corresponds to the identity code of an independent physical entity within the field of view; subsequently, based on the entity category attribute set... For each identified target entity, a center point regression calculation is performed on its geometric boundary using the bounding box regression branch. This maps the identified physical entities—such as packages, pallets, or forklifts that actually exist in the field of view—to the unified standard pixel coordinate system preset in step S1100, thereby generating a set of target centroid coordinates. The set of target centroid coordinates It refers to the real-time pixel coordinates of discrete physical entities, i.e., dynamic instantaneous pose information, which is compared with the set of region boundary coordinates generated in step S1300 as the static functional partition boundary. There is a logical relationship of inclusion and being included.

[0055] Step S2200: Set the target centroid coordinates Projected onto the set of physical semantic regions To determine spatial attribution, the ratio of the total projected area to the total pixel area of ​​each functional zone is calculated based on the spatial attribution to obtain a preliminary packing density value. A preset height compensation coefficient is then introduced to perform nonlinear correction, generating packing density features. .

[0056] Specifically, this step aims to set the target centroid coordinates from step S2100 within the physical storage space. Projected onto the set of physical semantic regions defined in step S1300 that have cross-border trade attributes. In this paper, spatial projection mapping logic and quantitative decomposition logic are used to map the object pose signal in a two-dimensional field of view into a packing density feature that quantitatively describes the percentage of storage space resource occupancy. This enables real-time perception of the saturation state of physical space within a digital logical layer.

[0057] In the specific implementation process, the report generation system first executes the spatial projection mapping logic, the specific execution logic of which is as follows: retrieve the target centroid coordinate set. It is then projected onto the physical semantic region set in the unified standard pixel coordinate system preset in S1100. In the middle. By performing pixel-level geometric membership verification, that is, by determining whether the centroid coordinates of each physical entity are located inside the polygonal boundary of the functional area, the spatial belonging relationship between each physical entity and functional areas such as shelf storage area, operation channel area or customs inspection area is determined, thereby completing the logical placement from discrete object pose signal to specific business operation unit.

[0058] Subsequently, the report generation system executes the quantitative calculation logic for space occupancy. The specific execution logic is as follows: It retrieves the bounding box of each physical entity synchronously output by the real-time object detection neural network architecture in step S2100, and calculates the area of ​​the spatial intersection region between the bounding box and its corresponding functional partition based on the spatial affiliation relationship. This area represents the effective pixel occupancy of a single physical entity in the functional partition. Next, by summing the areas of the spatial intersection regions of all physical entities falling within the functional partition, it obtains the total projected area of ​​entities within that functional partition. Then, it performs a ratio calculation between the total projected area of ​​entities and the total pixel area of ​​their respective functional partitions to obtain a preliminary packing density value representing the physical space saturation.

[0059] To eliminate the information loss in the vertical dimension of the two-dimensional visual monitoring terminal, this step introduces a height compensation mechanism for the stacking characteristics of cross-border trade goods. The specific execution logic of the height compensation mechanism is as follows: The report generation system assigns business logic function labels to this functional area according to step S1300. The system automatically associates the trade mode attributes corresponding to the functional areas, such as the standard stacking height limit in the cross-border e-commerce export overseas warehouse model, and calls a preset height compensation coefficient to perform non-linear correction on the initial stacking density value, ultimately generating the stacking density feature. The preset height compensation coefficient is a calibration constant determined based on the average number of stacked layers and packaging specifications of goods under different trade models in a cross-border warehousing environment. It is used to correct for the lack of vertical space occupancy due to a two-dimensional perspective, ensuring the stacking density characteristics. It can truly reflect the three-dimensional saturation state of physical space; the packing density feature The values ​​are distributed in the interval It is used to quantitatively describe the physical congestion intensity of functional zones.

[0060] Step S2300: Based on the target centroid coordinate set between consecutive image frames Generate motion displacement vector Using the convex hull algorithm to analyze the set of region boundary coordinates Perform edge closure operations to determine channel logic boundaries. Statistical motion displacement vector and channel logical boundaries The boundary crossing counts of spatial collision detection events are weighted and calibrated using a preset business activity correction operator to generate motion frequency features. .

[0061] Specifically, this step aims to set the target centroid coordinates from the temporal displacement state obtained in step S2100 within the public warehouse field of view. As the original trajectory data source, the vectorization reconstruction logic maps the discrete spatial pose signal into a motion displacement vector representing the actual flow of logistics. By the motion displacement vector With the physical semantic region set from step S1300 Topology-generated channel logical boundaries By performing spatial collision detection and implementing non-linear weighting of physical displacement frequency and business urgency at the data processing level, motion frequency characteristics that quantitatively describe the busyness of logistics flow lines are generated. This enables real-time perception of dynamic logistics loads within a digital layer.

[0062] In the specific implementation process, the report generation system first executes the vectorization reconstruction logic of the dynamic trajectory. The specific execution logic is as follows: for the set of target centroid coordinates distributed between consecutive image frames... Execute time-series tracing, invoking the Kalman filter algorithm (an existing state estimation technique) and the nearest neighbor data association algorithm (an existing data association technique) to analyze the target centroid coordinate set. Perform trajectory matching, calculate the pose offset of the object between adjacent sampling times, and thus generate a motion displacement vector representing the flow status of goods or handling equipment. Wherein, the motion displacement vector Used to describe the dynamic displacement characteristics of physical entities.

[0063] Subsequently, the spatial collision detection logic based on business boundaries is executed. The specific execution logic is as follows: First, the report generation system retrieves the set of physical semantic regions generated in step S1300. Retrieve and extract business logic function tags. The pixel clusters of the functional partitions are designated as "operation channels" or "inspection zones," and the set of the region boundary coordinates corresponding to the pixel clusters of the functional partitions are also defined. Perform edge closure operations to generate channel logical boundaries. The edge closure operation employs the Convex Hull Algorithm (CH) to perform geometric topology reconstruction. Its specific execution logic is as follows: based on the set of region boundary coordinates... The discrete pixel distribution is extracted using the convex hull algorithm, which extracts the vertex coordinate sequence located on the outermost side of the functional partition. This vertex coordinate sequence defines the polygonal closed contour of the corresponding functional partition, i.e., the channel logical boundary, within a unified standard pixel coordinate system. The channel logical boundary Used as a geometric reference for performing spatial collision detection to determine whether the physical entity enters or leaves a specific business area.

[0064] Next, the report generation system performs a quantitative calculation of the movement heat map. The specific execution logic is as follows: First, it counts the movement displacement vectors within a preset sampling time window. and channel logical boundaries The cumulative count of spatial collision detection actions. The spatial collision detection action refers to identifying the motion displacement vector of a line segment entity by performing a topological intersection determination between the line segment and the polygon boundary. The channel logical boundary crosses the polygonal boundary entity. Next, after identifying the event, the number of events occurring within a unit sampling time window is accumulated using an accumulator to obtain the boundary crossing count value.

[0065] Further, please refer to Figure 4 As shown, Figure 4 This is a schematic diagram illustrating the cumulative counting principle based on spatial collision detection behavior of the present invention. Figure 4 As shown in the figure, the purple polygon represents the coordinate set of the region boundary using the convex hull algorithm. Channel logical boundaries generated after performing edge closure operations The physical entities distributed within the field of view in the figure include forklifts, goods, and forklifts carrying goods. Their pose offsets are reconstructed as black straight arrows representing the dynamic displacement characteristics of the physical entities, i.e., motion displacement vectors. When the black straight arrow and the edge of the purple polygon intersect topologically, the motion displacement vector of the line segment entity is identified. The channel logical boundary crosses the polygonal boundary entity. At this time, a red explosion icon is generated in the image, indicating that a spatial collision detection action has occurred and the boundary crossing count has been accumulated. The right-hand display screen associates the spatial collision event with a blue dashed line.

[0066] Subsequently, a preset business activity correction operator is introduced to perform a nonlinear weighted mapping on the boundary crossing count value, ultimately generating motion frequency features. Among them, the motion frequency feature Used to quantitatively describe the operational flow intensity of physical entities at specific logistics nodes; the preset sampling time window determines the time step of the flow heat statistics; the preset business activity correction operator is a weight calibration constant determined by the different trade mode attributes carried by the current functional partition, used to convert the frequency of physical displacement into a pressure index with business response weight, characterizing the urgency weight of the current trade mode.

[0067] Step S3000: Parse the cross-border trade rules database. Extract business metadata To generate a library of components to be activated Responding to user drag-and-drop command sequences Call the semantic mapping function Packing density characteristics Motion frequency characteristics and structured business datasets Perform nonlinear fusion to generate semantically bound composite features .

[0068] Specifically, this step aims to incorporate the packing density characteristics from step S2200. and motion frequency characteristics from step S2300 As a real data source, it utilizes user drag-and-drop command sequences containing spatial pose information. As a spatiotemporal correlation guide, the physical spatial pressure indicators hidden behind pixel features are combined with the structured business dataset from step S1200. Perform cross-domain semantic alignment to achieve a non-linear fusion mapping of physical turnover units and trade compliance rules at the data level, generating semantically bound composite features with business-aware depth. .

[0069] Further, step S3000 includes: Step S3100: Parse the cross-border trade rules database. Extract business metadata Based on business metadata Constructing logical boundaries Combined with a pre-set set of logic templates Perform component building operations to generate a library of components to be activated. .

[0070] Specifically, this step aims to integrate the cross-border trade rules database. Business metadata that is discretely distributed and in a static storage state As a logic generation carrier, it utilizes business metadata The driven attribute mapping mechanism maps the potential, hard-to-perceive customs compliance constraints and timeliness red lines under the current trade model into a component library to be activated. Business accounting operators with independent accounting capabilities At the data architecture level, modular encapsulation and logic pre-setting of business rules are implemented.

[0071] In the specific implementation process, the report generation system first executes the predefined processing logic of the component logic. The specific execution logic is as follows: First, the report generation system calls the preset rule parsing operator stored in the cross-border trade rule database. The original information in the data is analyzed. For the various trade models and business rules involved in the public warehousing area, including but not limited to the export overseas warehouse rules under the cross-border e-commerce export overseas warehouse model and the cross-border direct mail rules under the direct mail export model, the data, including customs declaration timeliness thresholds, is extracted. Trade mode priority constraints and the red line for position utilization rate Business metadata including The business metadata, in particular... It is a digital feature package generated after parameterizing abstract trade rules; the customs declaration timeliness threshold This indicates the maximum allowable time increment for a specific batch of goods to remain in the customs inspection area, used to trigger timeout alarms; the trade mode priority constraint item This represents the basic operational weights determined according to different trade patterns, which serve as benchmark parameters to adjust the movement frequency characteristics from step S2300. The display priority needs to be different from the priority weight coefficient calculated based on real-time job attributes in step S1200. The aforementioned warehouse utilization rate warning line Characterizing the physical load limit that a specific functional area can bear, used to determine the packing density characteristics from step S2200. Is it in an overload state?

[0072] Subsequently, the report generation system will use the aforementioned business metadata. Mapped to the logical boundaries of digital report generation The specific execution logic of the mapping is to use business metadata. Using the parameter values ​​as a benchmark, a mathematical space representing the legal domain of business states, i.e., the logical boundary, is constructed. The logical boundary The threshold values ​​of the judgment criteria preset by the report generation system during data visualization rendering are used to define the numerical boundary between normal operating conditions and abnormal bottleneck conditions. This logical boundary... The component library to be activated has been determined. The subsequent visualization components in the process use logical criteria to determine the physical reality data received, whereby the physical reality data is the packing density feature from step S2200. and motion frequency characteristics from step S2300 .

[0073] Finally, the report generation system is based on the aforementioned business metadata. And the logical boundaries that serve as the basis for logical judgment. With the preset logic template set Perform component building operations to generate a library of components to be activated. The component library to be activated The specific generation logic is as follows: First, the operator is encapsulated using logic. Perform a mapping transformation from static trade rules to the underlying executable code of components, transferring business metadata. As an attribute parameter for logical operations, and to define logical boundaries. The threshold constraint serves as a basis for determining business status, and the logical template set provides visual representation and data interaction interfaces. Perform association mapping to complete the logical empowerment of visual components and generate a library of components to be activated. The logical encapsulation operator is described above. Used to perform the digital transformation from unstructured trade rules to dynamic execution logic at the computational level; the preset logic template set The visual presentation effects of the visualization components and the communication interface with the backend business system are defined.

[0074] Through the above process, this step preemptively locks the complex cross-border trade compliance logic into the component attributes. This is done based on logical boundaries. The constrained component construction method ensures that when the cargo handler performs drag-and-drop actions, the report generation system can automatically activate the time-lapse countdown or load conflict detection logic for the customs clearance inspection area according to the preset logical criteria, thus solving the technical bottleneck that non-technical personnel cannot generate personalized decision charts through complex parameter configuration.

[0075] The component library to be activated Each visualization component within it encapsulates a specific business accounting operator. The business accounting operator This defines the specific data transformations and logical judgments performed by the component in the interactive active state. The specific execution logic is as follows: The report generation system transforms business metadata... Compared with the increment of time limit that characterizes the time limit constraint of a specific trade batch The input is mapped from a preset logic processing function, which serves as the core of the logic. The time constraint increment is mentioned here. Characterized by the cross-border trade rules database The time-series weights defined by the rule features are used to define the extreme values ​​of allowable time deviations in the physical flow of urgent customs declarations under different trade models; the preset logic processing function is a multi-dimensional parameter coupling operator used to map static trade rule boundaries and dynamic time constraint parameters into executable decision signals.

[0076] This is based on business metadata The attribute-driven component construction approach pre-emptively incorporates complex cross-border trade compliance logic into component attributes, ensuring that when warehouse staff perform drag-and-drop actions, the report generation system can accurately calculate the business operations based on these attributes. It automatically triggers real-time countdown timers, workload conflict detection, or compliance self-audit logic for customs clearance inspection areas or popular product circulation areas, thereby completing the atomic-level binding of abstract trade rules to digital business boundaries and visual report components in the underlying data architecture.

[0077] Step S3200, responding to the user's drag-and-drop command sequence Identify from the library of components to be activated Move to the region boundary coordinate set Landing position and business logic function tags Using semantic mapping functions Set of region boundary coordinates Packing density characteristics within Motion frequency characteristics and business logic function tags Related structured business datasets Perform nonlinear fusion to generate semantically bound composite features .

[0078] Specifically, this step aims to utilize the user-inputted drag-and-drop command sequence. As a spatiotemporal positioning index, the component library to be activated from step S3100 Business accounting operators in Oriented mapping to the set of boundary coordinates of the region to which the pose of a specific landing point belongs in the digital mapping base map of the warehouse scene. Within its defined functional zones, semantic mapping functions are invoked. The packing density features from step S2200 Motion frequency characteristics from step S2300 and the structured business dataset from step S1200 Atomic coupling of the performance features generates semantically bound composite features. This enables cross-domain mapping from the feature space of discrete physical scenes to the semantic space of business logic decisions, providing dynamic related data support for the automatic rendering and generation of subsequent reports.

[0079] In the specific implementation process, the report generation system executes the interactive-driven cross-domain data association and calculation logic. The specific execution logic is as follows: First, the report generation system captures the user drag-and-drop instruction sequence input by the stock clerk in real time through the graphical interaction interface. Identify specific visual components from the component library to be activated. Move to the set of boundary coordinates of the area determined in step S1300 on the base map of the warehouse real-world digital mapping. The landing point pose within the range. Based on the region boundary range to which the landing point pose belongs, the report generation system retrieves the business logic function tags determined in step S1300. For example, for the high-frequency operation tags or customs clearance inspection tags defined in the cross-border e-commerce export overseas warehouse model, the business logic dimension that the current component needs to be bound to is determined, and the logical addressing from physical space coordinates to business function attributes is completed.

[0080] Subsequently, the report generation system executes the spatiotemporal alignment calculation logic between physical features and business data. The specific execution logic is as follows: the report generation system calls the semantic mapping function. Set the boundary coordinates of the region The set of physical entity features within the coverage area, i.e., the current stacking density features. With motion frequency characteristics , and the business logic function tags Structured business datasets associated within the associated functional partitions The corresponding customs declaration priority weight coefficient Nonlinear fusion mapping is performed. To ensure the real-time performance and accuracy of data binding, the report generation system performs time axis alignment calculations using a spatiotemporal synchronization verification function. This spatiotemporal synchronization verification function calculates the packing density characteristics. With motion frequency characteristics Feature capture moment and structured business datasets Latest update time The timing deviation between the two is used to determine whether they are within a preset synchronization window. If the spatiotemporal synchronization constraints are met, the coordinates of the landing area boundary will be used. The instantaneous physical load and business urgency within the system are mapped to a unified semantic coordinate system to generate the final semantically bound composite features. .

[0081] The semantic binding composite feature The quantization generation process is achieved through a semantic mapping function. The specific execution logic for solving the input parameters is as follows: through the semantic mapping function. Responding to user drag-and-drop command sequences The component library to be activated Business accounting operators in As an independent variable, it is combined with the packing density feature of the input. and motion frequency characteristics and structured business datasets Performing cross-domain association resolution ultimately maps to semantically bound composite features that represent business bottlenecks. The semantic mapping function is described above. A nonlinear mapping operator representing interaction-driven behavior is used to perform feature space transformation from discrete physical motion dimensions to static business logic dimensions, converting physical dynamic feedback and static business rules into semantic vectors with decision depth; the semantic binding composite features It is a real-time data index used in the subsequent report component rendering process to record the coupling strength between physical state and business rules.

[0082] Through the above process, the report generation system achieves deep alignment between user interaction intent and underlying heterogeneous data. This is achieved through semantic binding of composite features. The real-time calculation and report generation system can transform fragmented monitoring signals into decision-making basis with business depth, eliminating management blind spots caused by the disconnect between physical scenarios and trade logic in cross-border e-commerce.

[0083] Step S4000: Based on semantic binding composite features and motion frequency features Calculate the dynamic bottleneck index For dynamic bottleneck index and preset work safety thresholds Perform real-time logical comparisons to trigger visual reports. Adaptive pixel processing and decision recommendations The output.

[0084] Specifically, this step aims to semantically bind composite features from step S3200. and the motion frequency characteristics of the material turnover kinetic energy from step S2300 As a heterogeneous data processing object, discrete warehouse reality perception data is mapped into a dynamic bottleneck index with decision-oriented characteristics. This triggers a visual report. and decision-making recommendations The collaborative output enables real-time location of physical bottlenecks and in-depth diagnosis of business bottlenecks within the digital layer.

[0085] Further, step S4000 includes: Step S4100, based on the user's drag-and-drop instruction sequence From semantic binding composite features Extracting bulk density features The statistical function area exceeds the preset trade timeliness standard Number of overdue physical entities To identify business violation risk items, and combine them with preset weighting coefficients for packing density characteristics. Motion frequency characteristics The dynamic bottleneck index is obtained by weighting and summing the risk factors related to business violations. .

[0086] Specifically, this step aims to semantically bind composite features from step S3200. and the motion frequency characteristics of the material turnover kinetic energy from step S2300 As a heterogeneous data processing object, discrete real-world perception data of the warehousing environment is mapped into a dynamic bottleneck index with decision-oriented capabilities. At the algorithm level, the physical space saturation and cross-border trade timeliness risk are orthogonally integrated to provide a quantitative feedback benchmark for subsequent steps.

[0087] In the specific implementation process, firstly, the report generation system executes heterogeneous data decoupling and feature extraction logic. The specific execution logic is as follows: First, it retrieves semantically bound composite features. And through the set of region boundary coordinates The index parsing logic extracts the stacking density features of the current functional partition. and associated priority weight coefficients At the same time, the report generation system simultaneously acquires the movement frequency characteristics that reflect the activity level of physical circulation. The index parsing logic is that the report generation system uses a sequence of user drag-and-drop commands. The landing point pose is the offset, derived from the encapsulated semantically bound composite features. The system locates and reads the semantically aligned feature values ​​within the specific geofenced area. This is different from directly calling the original packing density features in step S2200. From semantic binding composite features Extracted bulk density features It possesses higher spatiotemporal consistency, as it not only includes pixel occupancy information but also carries composite features bound by semantics. The defined business area affiliation attribute.

[0088] Subsequently, the report generation system constructs a dynamic bottleneck evaluation function to perform quantitative synthesis of feature dimensions. The specific execution logic of the dynamic bottleneck evaluation function is as follows: First, a coefficient matrix is ​​introduced based on the historical peak operation data of the public storage area for dynamic optimization. The coefficient matrix is ​​then used to evaluate the packing density feature. Motion frequency characteristics Furthermore, a nonlinear weighted mapping operation is performed to calculate the dynamic bottleneck index, which is obtained by addressing the business violation risks caused by lag in the flow of goods. The historical peak throughput is a benchmark for the maximum throughput of the public warehousing area within a trade cycle. The dynamically optimized coefficient matrix is ​​a set of weighted parameter vectors adaptively adjusted according to the current trade pattern, used to perform feature fusion on the aforementioned spatial, kinetic, and timeliness characteristics through nonlinear summation. The business violation risk is based on business metadata. China's pre-set trade timeliness standards The real-time judgment is executed, and the specific execution logic is as follows: count the number of trade timeliness standards exceeding the current functional area. For example, the number of physical entities that have been in storage for more than 24 hours and have not undergone key circulation stages such as customs declaration and inspection, i.e., the number of overdue physical entities. And calculate its relationship with the total number of physical entities within that functional zone. The numerical ratio is used to characterize the operational risks on the business side. The preset trade timeliness standard... It is a comprehensive load pressure value used to quantitatively describe the storage operation zone.

[0089] The dynamic bottleneck index The comprehensive load pressure value used to describe the warehouse operation zone is generated using the following logic: The dynamic bottleneck index... Defined as a weighted summation across three dimensions, utilizing a preset first weighting coefficient and packing density features. The spatial pressure term obtained by multiplication, using a preset second weighting coefficient and motion frequency characteristics The kinetic pressure term obtained by multiplying the two factors, and the timeliness risk term obtained by multiplying the two factors using a preset third weighting coefficient and a business violation risk term, are summed to generate the final dynamic bottleneck index. The aforementioned business violation risk item is the number of overdue physical entities. and the total number of physical entities The ratio. The preset first weighting coefficient, second weighting coefficient, and third weighting coefficient are all dynamically allocated by the report generation system based on the contribution of each feature dimension to bottleneck formation during peak historical operating periods, by retrieving historical load feature vector mappings of public warehousing areas for specific trade models. For example, in the cross-border e-commerce export overseas warehouse model, the report generation system increases the third weighting coefficient to enhance the sensitivity to customs clearance timeliness risks, thereby correcting the deviation caused by abnormalities in a single physical indicator in the overall judgment.

[0090] Ultimately, the report generation system utilizes a dynamic bottleneck index. The quantitative values ​​accurately identify logistics bottlenecks caused by physical accumulation or business delays, enabling data dimensionality enhancement from a single physical state representation to a multi-dimensional semantic mapping of business bottlenecks in the digital logical layer.

[0091] Step S4200, dynamic bottleneck index and preset work safety thresholds Real-time logical comparisons are performed to determine whether there is a risk of logistics bottlenecks, and the boundary coordinates of areas with potential logistics bottlenecks are analyzed. Perform adaptive pixel processing to generate visual reports. Combined with business logic function tags Perform logical backtracking to generate decision recommendations .

[0092] Specifically, this step aims to utilize the dynamic bottleneck index from step S4100. As a logical trigger source, it interacts with a preset work safety threshold. Execution logic comparison identifies warehouse anomalies, mapping discrete, dynamically operating logistics bottlenecks in the physical world into visual reports. In the middle, and for the business logic function tags from step S3200 Perform causal backtracking to map the logical flow dimensions represented by business triggering factors into specific decision recommendations. This enables self-diagnosis and feedback on the timeliness requirements of cross-border trade at the terminal level, providing real-time decision-making data input for cargo handling operations.

[0093] In the specific implementation process, firstly, the report generation system executes index-driven adaptive rendering logic, the specific execution logic of which is as follows: the report generation system dynamically loads the bottleneck index. Compared with the preset work safety threshold Perform real-time logical comparison. The preset job safety threshold is mentioned above. This is used to define the standard operating capacity limit for a specific functional area under the trade model. Its value is determined by the report generation system based on the customs declaration timeliness threshold from step S3100. and the operational throughput constant that reflects the physical limit capacity of warehouse hardware facilities Joint calibration is performed to define the mathematical boundaries between normal operation and load default states. The operation throughput constant is... It is a preset physical load boundary constant based on the rated speed of the sorting conveyor belt and the total number of shelf locations within the public storage area. The real-time logic comparison process satisfies the following judgment logic: Execute dynamic bottleneck index. and the operation safety threshold Subtraction operation to obtain load deviation increment If the load deviates from the increment If the value is greater than zero, it indicates that there is a risk of logistical bottlenecks in the current functional area.

[0094] If a risk of logistics bottlenecks is identified, the report generation system will adjust the report based on the load deviation increment. The quantized values, for the corresponding set of region boundary coordinates The defined functional area of ​​the work is subjected to adaptive pixel processing. The specific execution logic of the adaptive pixel processing is as follows: the report generation system calls the preset color difference enhancement operator to process the set of boundary coordinates of the area. The pixels within the range undergo saturation gain processing. The preset color difference enhancement operator is a data transformation logic based on digital image enhancement technology, and its specific execution logic is as follows: based on the load deviation increment... By adjusting the saturation and brightness components in the HSV color space, the pixel clusters in functional areas at risk of logistical bottlenecks are visually enhanced and mapped to red highlighted pixel markers, thereby improving the visualization of reports. The system generates red risk warning signals, enabling precise identification of logistics bottlenecks at the pixel level and generating visual reports. .

[0095] Subsequently, the report generation system, in conjunction with a big data analytics engine, targets the dynamic bottleneck index. The abnormal causes, namely the risk of logistics bottlenecks, are traced back through the execution logic. The specific execution logic of the logic traceback is as follows: the report generation system uses the dynamic bottleneck index... Abnormal fluctuations, i.e., load deviation from increment Exceeding a preset fluctuation threshold serves as a trigger index, inverting the search based on the set of boundary coordinates of the region. The defined functional partitions are associated with business logic function tags. By performing correlation analysis using a big data rule base built upon cross-border trade compliance standards and historical anomaly conditions, the causes of logistics bottlenecks can be identified. The preset fluctuation threshold is based on the load deviation increment under historical operating conditions in the defined operational functional area. The sensitivity judgment boundary is dynamically set based on the standard deviation. The big data rule base is a mapping matrix storing historical bottleneck conditions and business logic conflict characteristics. The specific execution logic of the correlation analysis is as follows: the report generation system calculates the packing density characteristics of the physical dimension. and motion frequency characteristics The instantaneous fluctuation amount, and the priority weighting coefficient of the business dimension. and trade status codes The causal correlation coefficient between them is used to determine whether the logistics bottleneck risk is caused by business triggering factors under different trade models or by physical overload. Here, the business triggering factors refer to logical bottlenecks induced by a surge in business attributes, such as batch backlogs of export packages in the cross-border e-commerce export overseas warehouse model due to delayed execution of customs declaration instructions, or flow conflicts caused by a surge in the instantaneous turnover frequency of TikTok best-selling goods.

[0096] If specific business triggers are identified, the report generation system automatically generates targeted decision recommendations. The specific generation logic is as follows: The report generation system retrieves a matching job scheduling strategy from a preset decision template library based on the business triggering factor. The preset decision template library is a feature library storing preset response mechanisms and automated scheduling algorithms for different heterogeneous feature conflict causes. Based on the job scheduling strategy, a job optimization instruction is generated. This instruction includes: adjusting the storage location of specific high-volume goods to a low-energy interference zone, i.e., a functional area where the physical flow kinetic energy frequency is below a preset silence threshold and will not cause cross-interference with the main operation path; or prioritizing the customs declaration of batch goods with specific trade patterns. The physical flow kinetic energy frequency is a motion frequency characteristic. The quantized value is used to reflect the displacement frequency of the entity per unit time; the preset silent threshold is the kinetic energy limit value of the functional area in a non-circulation interference state, and its value is calibrated according to the basic vibration frequency of the sorting line.

[0097] Finally, the report generation system encapsulates the job optimization instructions into decision suggestions. and embed it into the visualization report. Within the corresponding interaction level, collaborative feedback output of the report is completed. This is achieved through visual reports. Simultaneously, pixel-level alerts and expert-level decision-making suggestions are presented to guide non-technical warehouse workers based on the visualized reports. Implement rapid decision-making and response mechanisms to ensure that the physical space utilization of public storage areas is highly aligned with the timeliness requirements of cross-border trade.

[0098] Example 2: This embodiment, based on Embodiment 1, provides a drag-and-drop component-based visual report generation system for cross-border e-commerce, such as... Figure 5 As shown, the system includes an image acquisition module, a physical feature extraction module, a semantic binding module, and a visualization report module: The image acquisition module is used to extract the original video stream. Dynamic sampling interval To generate a preprocessed image frame sequence And based on the captured raw business data stream Generate structured business datasets Based on structured business datasets China Trade Batch Marking Associated geofence index and preprocessed image frame sequences Generate a set of physical semantic regions .

[0099] The physical feature extraction module is used for preprocessing image frame sequences. Perform pose localization to determine the target centroid coordinate set Combined with physical semantic region set Perform geometric membership verification and spatial collision detection to generate packing density features. and motion frequency characteristics .

[0100] The semantic binding module is used to parse the cross-border trade rules database. Extract business metadata To generate a library of components to be activated Responding to user drag-and-drop command sequences Call the semantic mapping function Packing density characteristics Motion frequency characteristics and structured business datasets Perform nonlinear fusion to generate semantically bound composite features .

[0101] The visualization report module is used to bind semantic composite features and motion frequency features. Calculate the dynamic bottleneck index For dynamic bottleneck index and preset work safety thresholds Perform real-time logical comparisons to trigger visual reports. Adaptive pixel processing and decision recommendations The output.

[0102] The parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0103] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for generating visual reports for cross-border e-commerce based on drag-and-drop components, characterized in that: include: Extract the dynamic sampling interval of the original video stream to generate a preprocessed image frame sequence, and generate a structured business dataset based on the captured original business data stream. Based on the geofence index associated with the trade batch identifier in the structured business dataset and the preprocessed image frame sequence, generate a set of physical semantic regions. Pose localization is performed on the preprocessed image frame sequence to determine the target centroid coordinate set. Geometric membership verification and spatial collision detection are performed in combination with the physical semantic region set to generate packing density features and motion frequency features. The cross-border trade rules database is parsed to extract business metadata to generate a library of components to be activated. In response to the user's drag-and-drop command sequence, the semantic mapping function is called to perform nonlinear fusion on the stacking density feature, motion frequency feature and structured business dataset to generate semantically bound composite features. The dynamic bottleneck index is calculated based on semantic binding composite features and motion frequency features. Real-time logical comparison is performed between the dynamic bottleneck index and the preset work safety threshold to trigger adaptive pixel processing and decision suggestion output in the visualization report.

2. The method for generating visual reports for cross-border e-commerce based on drag-and-drop components according to claim 1, characterized in that, The method for generating the physical semantic region set includes: Extract the pixel change rate between adjacent frames in the acquired raw video stream, calculate the dynamic sampling interval based on the pixel change rate, and use the dynamic sampling interval to perform non-equidistant sampling on the raw video stream to extract the raw image sequence. Based on the raw image sequence, obtain the preprocessed image frame sequence. Retrieve customs batch identifiers and trade status codes from the captured raw business data stream, generate priority weight coefficients based on the time sensitivity of trade patterns, and encapsulate the customs batch identifiers, trade status codes, and priority weight coefficients into a structured business dataset. Extract the initial pixel feature layer from the preprocessed image frame sequence, retrieve the geofence index associated with the trade batch identifier to determine the set of regional boundary coordinates, assign business logic function labels according to priority weight coefficients, calculate the turnover load limit using the set of regional boundary coordinates, and generate a set of physical semantic regions based on the priority weight coefficients, business logic function labels, set of regional boundary coordinates, and turnover load limit.

3. The method for generating visual reports for cross-border e-commerce based on drag-and-drop components according to claim 2, characterized in that, The method for extracting the original image sequence includes: The pixel change rate is compared with the preset activity threshold. When the pixel change rate exceeds the activity threshold, it is determined that the current state is a high-frequency business turnover operation, and the sampling frequency is increased by logic. The sampling frequency is increased by reducing the time span between adjacent sampling points to increase the sampling density. The dynamic sampling interval is obtained by performing a division operation with the sampling sensitivity adjustment coefficient as the numerator and the sum of the pixel change rate and the signal zero-point stabilization factor as the denominator. The sampling sensitivity adjustment coefficient is a weighting factor determined based on the processing limit of the back-end computing power of the report generation system. The signal zero-point stabilization factor is a preset positive real constant that prevents the denominator from being zero under the static condition where the pixel change rate approaches zero. The original video stream is sampled at non-uniform intervals using the dynamic sampling interval to extract the original image sequence.

4. The method for generating visual reports for cross-border e-commerce based on drag-and-drop components according to claim 2, characterized in that, The method for generating the priority weight coefficients includes: Regular expressions are used to retrieve and match the package operation priority field and the inspection level field from the original business data stream; The package operation priority field is mapped to a numerical operation gain value, and the inspection level field is mapped to a numerical inspection gain value. The basic timeliness coefficient is determined based on the trade model level preset by the business platform, and when the cross-border e-commerce export overseas warehouse trade model is identified, the basic timeliness coefficient is set to a fixed offset value that is greater than the preset benchmark constant of the ordinary trade model. A weighted summation operation is performed on the operation gain value, inspection gain value, and basic timeliness coefficient to generate a priority weight coefficient for the urgency of trade batch circulation.

5. The method for generating visual reports for cross-border e-commerce based on drag-and-drop components according to claim 2, characterized in that, The calculation method for the upper limit of turnover load includes: Retrieve the initial physical boundary coordinate range corresponding to the business geofence index associated with the trade batch identifier, and calculate the intersection-union ratio between the initial physical boundary coordinate range and each candidate pixel cluster in the initial pixel feature layer; When the cross-union ratio exceeds a preset overlap threshold, the corresponding candidate pixel clusters are initially divided into functional areas such as shelf storage area, operation channel area or customs clearance inspection area; the preset overlap threshold is a geometric calibration constant determined based on the installation pitch angle of the video acquisition terminal and the lens distortion rate. The total number of pixels covered by each functional partition is counted based on the set of regional boundary coordinates to obtain the pixel area. The turnover efficiency constant corresponding to the current trade mode is retrieved from the business database. The turnover efficiency constant is a scalar constant of the cargo processing throughput preset by the trade mode under the unit pixel area. The pixel area and the turnover efficiency constant are multiplied to generate the turnover load limit of the physical load limit value of each functional partition.

6. The method for generating visual reports for cross-border e-commerce based on drag-and-drop components according to claim 1, characterized in that, The execution method for the geometric membership check includes: Extract multi-scale feature layers from the preprocessed image frame sequence, extract entity semantic activation features from the multi-scale feature layers, and use a real-time target detection neural network architecture to perform classification and pose localization on the entity semantic activation features to generate a set of target centroid coordinates. The target centroid coordinate set is projected onto the physical semantic region set to determine the spatial attribution relationship. Based on the spatial attribution relationship, the ratio of the total projected area of ​​each functional partition to the total pixel area is calculated to obtain the preliminary packing density value. A preset height compensation coefficient is introduced to perform nonlinear correction and generate packing density features. The method for performing spatial collision detection includes: generating motion displacement vectors based on the target centroid coordinate set between consecutive image frames; performing edge closure operation on the region boundary coordinate set using the convex hull algorithm to determine the channel logical boundary; counting the boundary crossing counts of spatial collision detection behavior between the motion displacement vector and the channel logical boundary; and performing weighted calibration in combination with a preset business activity correction operator to generate motion frequency features.

7. The method for generating visual reports for cross-border e-commerce based on drag-and-drop components according to claim 6, characterized in that, The method of introducing a preset height compensation coefficient to perform nonlinear correction includes: based on the business logic function tags of each functional partition, associating the trade mode attribute corresponding to the functional partition, so as to determine the stacking height limit under the trade mode; The preset height compensation coefficient is used to perform nonlinear correction on the initial packing density value to generate packing density characteristics. The preset height compensation coefficient is a calibration constant determined based on the average number of stacked layers and packaging specifications of goods under the trade mode. The method for calculating the boundary crossing count of spatial collision detection behavior between the statistical motion displacement vector and the channel logical boundary includes: identifying boundary crossing events where the motion displacement vector, as a line segment entity, crosses the channel logical boundary, as a polygon boundary entity, within a preset sampling time window; and using an accumulator to count the cumulative number of boundary crossing events within the sampling time window to obtain the boundary crossing count.

8. The method for generating visual reports for cross-border e-commerce based on drag-and-drop components according to claim 1, characterized in that, The method for generating the semantic binding composite feature includes: The system parses the cross-border trade rules database to extract business metadata, constructs logical boundaries based on the business metadata, performs component construction operations in combination with a preset set of logical templates, and generates a library of components to be activated. Responding to the user's drag command sequence, the system identifies the landing pose and business logic function label of the component library moving to the region boundary coordinate set. It then uses a semantic mapping function to nonlinearly fuse the stacking density features, motion frequency features, and structured business dataset associated with the business logic function label within the region boundary coordinate set to generate semantically bound composite features. The method for performing nonlinear fusion includes: calling a spatiotemporal synchronization verification function to calculate the temporal deviation between the feature capture time of the stacking density feature and the motion frequency feature and the most recent update time of the structured business dataset; When the timing deviation value is determined to be within a preset synchronization window, the semantic mapping function is used to perform nonlinear fusion mapping of the stacking density feature, motion frequency feature and structured business dataset to generate semantically bound composite features.

9. The method for generating visual reports for cross-border e-commerce based on drag-and-drop components according to claim 1, characterized in that, The methods for triggering the adaptive pixel processing and decision suggestion output of the visualization report include: Based on the user's drag-and-drop instruction sequence, the stacking density feature is extracted from the semantic binding composite feature. The number of physical entities exceeding the preset trade timeliness standard within the functional area is counted to determine the business violation risk item. The stacking density feature, movement frequency feature and business violation risk item are weighted and summed by the preset weighting coefficient to obtain the dynamic bottleneck index. The dynamic bottleneck index and the preset operation safety threshold are compared in real time to determine whether there is a risk of logistics turnover obstruction. Adaptive pixel processing is performed on the boundary coordinate set of areas with logistics turnover obstruction risk to generate a visual report. Logical backtracking is performed in combination with business logic function tags to generate decision suggestions. The adaptive pixel processing execution method includes: based on the load deviation increment, calling a preset color difference enhancement operator, adjusting the saturation and brightness components of pixels in the HSV color space within the region boundary coordinate set, and mapping the pixel clusters of functional partitions at risk of logistics turnover obstruction to red highlighted pixel markers to identify logistics turnover obstruction points. The execution method of the logical backtracking includes: using the load deviation increment exceeding the preset fluctuation threshold as the trigger index, retrieving the business logic function tags associated with the functional partition defined by the set of regional boundary coordinates in reverse, and performing correlation analysis in combination with the preset big data rule base to identify the causes of logistics turnover obstruction; The method for performing correlation analysis includes calculating the instantaneous fluctuation of stacking density characteristics and movement frequency characteristics, the causal correlation coefficient between them and priority weight coefficients and trade status codes, and determining whether the logistics turnover obstruction risk is caused by business triggering factors under different trade models or by physical stacking density overload. Based on the business triggering factor, a matching job scheduling strategy is retrieved from a preset decision template library, and a job optimization instruction is generated according to the job scheduling strategy.

10. A drag-and-drop component-based cross-border e-commerce visual report generation system, used to implement the drag-and-drop component-based cross-border e-commerce visual report generation method according to any one of claims 1-9, characterized in that, The system includes an image acquisition module, a physical feature extraction module, a semantic binding module, and a visualization report module. The image acquisition module is used to extract the dynamic sampling interval of the original video stream to generate a preprocessed image frame sequence, and to generate a structured business dataset based on the captured original business data stream. Based on the geofence index associated with the trade batch identifier in the structured business dataset and the preprocessed image frame sequence, a set of physical semantic regions is generated. The physical feature extraction module is used to perform pose localization on the preprocessed image frame sequence to determine the target centroid coordinate set, and to perform geometric membership verification and spatial collision detection in combination with the physical semantic region set to generate packing density features and motion frequency features. The semantic binding module is used to parse the cross-border trade rules database to extract business metadata to generate a component library to be activated, and to respond to the user's drag command sequence by calling the semantic mapping function to perform nonlinear fusion on the stacking density feature, motion frequency feature and structured business dataset to generate semantic binding composite features. The visualization report module is used to calculate the dynamic bottleneck index based on semantic binding composite features and motion frequency features, and to perform real-time logical comparison between the dynamic bottleneck index and the preset work safety threshold to trigger the adaptive pixel processing and decision suggestion output of the visualization report.

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