Data statistical processing method and system for cross-border e-commerce

Abstract

The present application belongs to the technical field of cross-border e-commerce data processing, and particularly relates to a cross-border e-commerce data statistical processing method and system. The method comprises: heterogeneous data acquisition and multi-modal feature decoupling, SKU homology determination with adaptive weight and global normalization, tree topology hierarchical cost accounting unit construction, asynchronous cost fine allocation and profit back propagation reconstruction, double exchange rate conversion and drilling traceability report output, and synchronous full-link data notarization; the system comprises a data acquisition and processing module, a SKU normalization module, a cost container construction module, a cost allocation and profit reconstruction module, an exchange rate conversion report module, and a data full-link traceability module. The present application realizes cross-platform SKU unified identification, track segment level cost accounting, profit dynamic reconstruction and compliance audit, effectively improving the accuracy, traceability and compliance of cross-border e-commerce data statistics.

Description

Technical Field

[0001] This invention belongs to the field of cross-border e-commerce data processing technology, specifically relating to data statistical processing methods and systems for cross-border e-commerce. Background Technology

[0002] With the rapid development of the cross-border e-commerce industry, sellers often sell on multiple platforms simultaneously. Existing data analysis techniques typically use ETL tools to aggregate data from various platforms into a data warehouse and generate static reports.

[0003] However, the descriptions of the same product vary greatly across different countries and platforms. Existing technologies mainly rely on manually creating keyword mapping tables or simple regular expression matching, which is costly to maintain and has low accuracy. This results in the inability to accurately identify the same product during statistics, leading to a disconnect between inventory and sales statistics. Secondly, due to the unique nature of cross-border e-commerce, its long cross-border chain means that many costs occur asynchronously. Existing cross-border e-commerce data processing systems typically record costs in the database when an order is generated. If subsequent exchange rate fluctuations or logistics price adjustments occur, the system cannot automatically backtrack and correct the profit data of historical orders, resulting in financial statistics being in a state of "virtual rather than real".

[0004] Therefore, there is an urgent need for a data statistical processing method and system that has semantic intelligent understanding capabilities and supports dynamic cost reconstruction. Summary of the Invention

[0005] In view of the above-mentioned shortcomings in the existing technology, the present invention provides a data statistical processing method and system for cross-border e-commerce to solve the problems in the background technology.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A data statistical processing method for cross-border e-commerce includes the following steps: S1 receives raw heterogeneous data from multiple cross-border e-commerce platforms, parses it using preset standardized parsing rules to generate a standardized dataset, and performs multimodal feature decoupling on the product data to extract text attribute features, visual fingerprint features, and regional specification features respectively. S2, perform topological clustering based on the visual fingerprint features to obtain a candidate product set, and project the regional specification features to a unified standard space using a regional mapping knowledge base; introduce an adaptive weight adjustment mechanism based on visual feature information entropy, calculate multimodal weighted confidence based on text attribute features, and establish a dynamic mapping index between the original SKU of each platform and the global SKU identifier based on the judgment result; S3, based on the event-driven key nodes of logistics, divides the logistics trajectory into multiple spatiotemporal trajectory segments, constructs a hierarchical cost container pool containing trajectory segment-level cost accounting units, order-level cost accounting units, and SKU-level cost accounting units, establishes the ownership and reference relationship of each level of cost accounting unit with a tree-like topology structure, and binds the trajectory segment identifier, global SKU identifier, and order identifier through a distributed inverted index association table; S4 listens for asynchronous cost settlement events, locates the associated trajectory segment-level cost accounting unit, and writes the settlement value according to the billing dimension; triggers a back propagation mechanism based on graph database topology, using the trajectory segment-level cost accounting unit as the state change source, and drives the order-level cost accounting unit and SKU-level cost accounting unit to perform cascading updates of data status; S5 calls the exchange gain and loss stripping logic based on the dual exchange rates of the transaction date and settlement date to convert foreign currency values, generates multi-dimensional cube model data based on the hierarchical cost container pool, and outputs statistical reports that support drill-down traceability.

[0007] Optionally, in step S1, the multimodal feature decoupling specifically includes: Natural language processing techniques are used to segment and filter stop words in product texts, and key semantic entities are extracted using the BERT-Base model to form text attribute features; The U-Net semantic segmentation model is used to remove the background from the main product image, and the deep spatial fingerprint is extracted as a visual fingerprint feature by the ResNet50 convolutional neural network. The measurement parameters are extracted from the product specification description using rule matching technology and transformed into standardized regional specification features.

[0008] Optionally, in step S2, the adaptive weight adjustment mechanism specifically includes: The visual fingerprint feature vector is normalized to obtain a probability distribution, and the information entropy of this distribution is calculated. ; Preset information entropy threshold ,when At the same time, the weight coefficient of visual fingerprint feature similarity in the weighted confidence is reduced, and the weight coefficient of regionalized specification feature mapping matching degree is increased accordingly.

[0009] Optionally, in step S2, the projection process of the regionalized specification features is as follows: It calls upon the regionalized mapping knowledge base, which has built-in multi-regional measurement conversion rules and conflict resolution rules; When multiple standards are detected that the same regional specification feature corresponds to the same standard, the conflict resolution rule is triggered. If the text attribute feature is ambiguous, the key dimension sub-vector of the corresponding pattern outline in the visual fingerprint feature is extracted, and the cosine similarity is calculated with the pre-stored visual feature sub-vector of the typical product corresponding to the candidate standard in the knowledge base. The one with the highest similarity is used as the mapping benchmark.

[0010] Optionally, in step S3, the hierarchical cost container pool is initialized synchronously with order generation, the trajectory segment-level cost accounting unit is used as a leaf node, the order-level cost accounting unit is used as a parent node, and the SKU-level cost accounting unit is used as an aggregation node; The container pool supports dynamic expansion based on concurrent order flows. When the concurrent order flow or storage utilization exceeds a threshold, new storage nodes are added through container orchestration, and the storage is sharded according to the order creation time and logistics route.

[0011] Optionally, in step S4, the backpropagation mechanism based on the graph database topology specifically includes: The cost accounting units at the trajectory segment level, order level, and SKU level are mapped to graph database nodes respectively, and the tree-like topological association of "trajectory segment → order → SKU" is constructed with the belonging relationship between containers as directed edges. An incremental calculation strategy is adopted, which only propagates the cost bias value of the bottom trajectory segment level cost accounting unit upward along the directed edge, and updates the cumulative data of the affected upper-level order level cost accounting unit and SKU level cost accounting unit. The trajectory segment-level cost accounting unit is loaded with an adaptive estimated cost model. After receiving the settlement value, the container state switches from the estimated state to the actual settlement state and retains a snapshot comparing the estimated cost and the actual settlement cost.

[0012] Optionally, in step S4, the asynchronous cost settlement event monitoring and cost allocation process includes: A Pub / Sub architecture is used to subscribe to asynchronous cost settlement events, and redundant settlement events are filtered out by a built-in idempotency discriminator. For mixed settlement data containing both fixed and floating costs, differentiated cost allocation is performed. Specifically, the fixed costs are equally allocated among the associated trajectory segment-level cost accounting units; the floating costs are weighted and allocated according to the proportion of chargeable weight or volumetric weight of the orders bound to each trajectory segment-level cost accounting unit.

[0013] Optionally, in step S5, the processing flow for the exchange gain / loss separation logic based on the dual exchange rates of the transaction date and settlement date is as follows: Obtain the exchange rate for the corresponding foreign currency on the transaction date and the settlement date; The exchange gain or loss is calculated using the aforementioned exchange gain / loss stripping logic, according to the formula: Exchange gain / loss = (Settlement date exchange rate - Transaction date exchange rate) × Original amount of foreign currency cost; If multiple foreign currency expenses are settled together, first calculate the total exchange gain or loss, and then break down the total exchange gain or loss to the corresponding order and associated trajectory segment-level cost accounting unit according to the proportion of the original amount of each foreign currency expense.

[0014] Optionally, it also includes a data end-to-end traceability step, specifically: Based on an immutable time-series database, the entire cross-border e-commerce data processing trajectory is recorded and traceability records are generated. Each traceability record carries an SHA-256 hash fingerprint, which is generated by concatenating the timestamp, record type, unique identifier of the associated subject, core data value, and operation node identifier.

[0015] This invention also discloses a data statistical processing system for cross-border e-commerce, used to implement the above-mentioned data statistical processing method, including: The data acquisition and processing module is used to receive raw heterogeneous data from multiple cross-border e-commerce platforms, perform standardized parsing to generate standardized datasets, and decouple multimodal features from product data to extract text attribute features, visual fingerprint features, and regional specification features. The SKU normalization module has a pre-built regional mapping knowledge base, which is used to obtain a set of candidate products through topological clustering of visual fingerprint features, complete the unified mapping of regional specification features, calculate the weighted confidence and determine the homology of products by combining the adaptive weight adjustment mechanism of visual feature information entropy, and establish and dynamically maintain the mapping index between the original SKUs of each platform and the global SKU identifier. The cost container construction module is used to divide the logistics trajectory into spatiotemporal trajectory segments based on key logistics node events and assign unique identifiers. It constructs hierarchical cost container pools at the trajectory segment level, order level, and SKU level and completes the binding of each level of cost accounting unit with the corresponding identifier. It loads an adaptive estimated cost model for the trajectory segment level cost accounting unit and marks the estimated state. The cost allocation and profit reconstruction module is used to listen to asynchronous cost settlement events and filter redundant events. It allocates costs to the trajectory segment-level cost accounting units in a differentiated manner according to billing weight or volume weight, triggers the back propagation mechanism based on graph database topology to drive the cost cascade update of each level of cost accounting unit, recalculates the order and SKU profit and retains the relevant correction data. The exchange rate conversion report module is used to perform exchange gains and losses stripping based on the dual exchange rates of the transaction date and the settlement date, generate multi-dimensional cube model data based on the hierarchical cost container pool, and output statistical reports that support drill-down traceability, multi-dimensional filtering queries, and export. The data end-to-end traceability module is used to record the entire data processing trajectory based on an immutable time-series database, generate SHA-256 hash fingerprints for the traceability records, and realize end-to-end traceability and closed-loop evidence storage from the collection of raw heterogeneous data to the output of reports.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. By decoupling and extracting multimodal features from text attributes, visual fingerprints, and regional specifications, combined with a regional mapping knowledge base with built-in multi-region rules and conflict deconstruction logic, and with adaptive weighted confidence calculation based on visual feature information entropy, the system achieves cross-platform matching and dynamic mapping maintenance of original SKUs and global SKUs.

[0017] 2. By driving key logistics events, the logistics trajectory is segmented into spatiotemporal trajectory segments and a cost container pool with a "trajectory segment-order-SKU" hierarchy is constructed. Combined with the asynchronous cost event listening and differentiated cost allocation logic of the Pub / Sub architecture, and the incremental backpropagation mechanism of the graph database topology, the cost can be accurately collected from the trajectory segment level to the order and SKU level. It can automatically backtrack and correct the profit data of historical orders and global SKUs.

[0018] 3. Adopting a dual exchange rate model for both the transaction day and the settlement day, the financial gains and losses and business gross profit generated by exchange rate fluctuations are accurately separated by the exchange rate loss stripping operator, avoiding profit distortion caused by a single exchange rate conversion; at the same time, relying on an immutable time-series database and a full-link traceability mechanism based on SHA-256 hash fingerprint, the data from collection, processing to report output can be verified and stored at all nodes. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the data statistical processing method for cross-border e-commerce according to the present invention. Figure 2 This is a schematic diagram of the data statistical processing system for cross-border e-commerce of the present invention; Figure 3 This is a graph showing the contribution rate analysis of the multimodal features of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of this application 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: like Figure 1-2 As shown, this invention discloses a data statistics and processing method and system for cross-border e-commerce, which is applicable to the full-link data statistics, cost accounting and profit reconstruction scenarios of multiple cross-border e-commerce platforms. It can effectively solve the problems of the separation of inventory and sales statistics and the inability of the system to automatically backtrack and correct the profit data of historical orders in the existing cross-border e-commerce data statistics.

[0023] In this embodiment, a data statistical processing method for cross-border e-commerce is as follows: Figure 1 As shown, it includes 5 steps.

[0024] Step S1 is responsible for data collection and feature extraction. Specifically, it involves receiving raw order and product data from multiple cross-border e-commerce platforms through a pre-defined multi-platform standard API interface. Due to differences in the underlying architecture of each platform, the received data exhibits high heterogeneity, including non-standard field formats, discrete data dimensions, and conflicting units of measurement.

[0025] To eliminate heterogeneous biases in the received data, a standardized parsing process based on mapping operators is employed. A pre-defined cross-platform order field mapping table is used as the core basis to establish a one-to-one correspondence between heterogeneous fields across platforms and pre-defined standard fields. A built-in unit conversion matrix automatically unifies numerical values ​​of different units, such as imperial and metric units. To address data missingness, a differentiated completion method is used. Combined with statistical characteristics of similar orders, regression analysis is employed to complete non-core attributes. Core information such as logistics trajectories is asynchronously aligned with a third-party trajectory database using the waybill number. Simultaneously, by setting dynamic and reasonable threshold ranges for core data, closed-loop logical verification rules are established for order status, logistics trajectory, and transaction amount. Outlier data exceeding the threshold range or exhibiting logical mismatches are identified and removed. Order identifiers, end-to-end logistics trajectory information, original currency amounts, and timestamps are extracted from the processed data to generate a standardized dataset for cross-border e-commerce.

[0026] After completing the standardized parsing, multimodal feature decoupling extraction is performed simultaneously on the product data to eliminate the semantic gap in product descriptions across platforms: Natural Language Processing (NLP) technology is used to perform semantic entity recognition on product titles and descriptions. First, the jieba word segmentation tool is used to adapt to the mixed Chinese and English text in cross-border scenarios, filtering out meaningless stop words such as "free shipping", "new products" and "limited-time discount", and converting the text into a standardized token sequence. Then, a BERT-Base pre-trained model finely tuned based on a cross-border product text dataset is used to accurately identify key semantic entities such as brand, material, and core parameters, and extract standardized text attribute features. The U-Net semantic segmentation model is used to perform background removal processing on the main product image, segmenting the product body from the background (such as the white background and promotional watermarks commonly found on cross-border platforms). Then, Gaussian filtering algorithm is used for noise reduction to eliminate image noise interference. Subsequently, ResNet50 convolutional neural network (CNN) is used as the image feature encoding operator. The weights of the first 10 layers of the model are frozen, and only the last 5 layers are fine-tuned to adapt to the feature distribution of the cross-border product main image. A 2048-dimensional deep spatial fingerprint is output through a fully connected layer to generate visual fingerprint features. In addition, through rule matching and keyword automatic indexing technology, the measurement units and size parameters adapted to each site are extracted from the product specification description and transformed into a standardized dimensional vector set.

[0027] The independent extraction of the three types of features enables feature decoupling of commodity data, effectively avoiding the problem of low recognition rate of single-dimensional features, and providing multi-dimensional technical support for cross-platform homology determination based on weighted confidence in subsequent steps.

[0028] As a specific implementation, combining the extraction logic of the three types of features with the characteristics of cross-border e-commerce scenarios, the contribution weight of each feature was quantitatively set: the contribution rate of text attribute features was 20%, visual fingerprint features were 65%, and regional specification features were 15%, with a cumulative contribution rate of 100%, as shown in the following figure. Figure 3 As shown.

[0029] The rationale behind this weighting stems from the extraction logic and practical application scenarios of the three types of features: Visual fingerprint features are 2048-dimensional deep spatial fingerprints extracted through the ResNet50 convolutional neural network, containing core identifying information such as the main outline of the product, brand logo, and pattern details. They are crucial for determining product identity and therefore receive the highest contribution rate. Text attribute features are key semantic entities such as brand and material extracted through the BERT-Base model, used to assist in verifying the consistency of core product attributes; therefore, a 20% contribution rate is assigned. Regional specification features are standardized metric parameters extracted through rule matching, primarily used to resolve cross-platform specification terminology conflicts, serving as a fallback verification dimension; therefore, a 15% contribution rate is assigned. This quantified weighting provides a baseline for the adaptive weighting adjustment mechanism in step S2: By default, the system allocates initial weights according to the aforementioned ratios of 20%, 65%, and 15%. When the information entropy of the visual fingerprint features exceeds a threshold (i.e., the background of the product's main image is complex, and the visual feature's recognizability decreases), the visual feature weights are dynamically lowered, while the text and specification feature weights are increased, ensuring accuracy through multimodal complementarity.

[0030] After completing step S1, based on the extracted text attribute features, visual fingerprint features, and the aforementioned quantization weight benchmark, step S2, SKU normalization, is performed. Specifically: First, for SKU data, the system initiates a visual topology-based pre-screening mechanism, employing the K-means clustering algorithm. Using the 2048-dimensional visual fingerprint feature vector output by the ResNet50 model in step S1 as input, cosine similarity is used to measure the visual homology between products. To overcome misjudgments caused by background and watermark interference in the main image of cross-border platforms, the system presets a similarity threshold. (e.g., 0.85), only those that meet the requirements will be considered. Products that are grouped into the same homogeneous candidate cluster are calculated using the following formula: in, Let be the visual fingerprint feature similarity between the i-th item and the j-th item. , These are the visual fingerprint feature vectors of the two products, , These are the magnitudes of the corresponding feature vectors. Through this pre-screening operation, the system reduces the computational complexity from... By reducing the number of comparisons to the level of intra-cluster comparisons, processing efficiency is greatly improved, and the waste of resources from full comparisons is avoided.

[0031] Subsequently, the system invokes a regionalized mapping knowledge base to perform standard spatial projection on the regionalized specification features within the candidate clusters. This knowledge base not only includes multi-country / regional unit of measurement conversion rules, core category size comparison tables, and synonym mapping relationships for cross-border industry terms, but also incorporates conflict resolution rules to resolve "many-to-one" specification mapping conflicts in cross-border scenarios (e.g., "US Size 8" simultaneously corresponds to "EU Size 38" and "UK Size 6"). When multiple standards are detected as corresponding to the same regionalized specification feature, the system performs secondary verification by combining text attribute features and visual fingerprint features: if the text attribute feature clearly points to a certain standard, the text mapping result is prioritized; if the text feature is ambiguous, the system uses details of the visual fingerprint feature (e.g., clothing pattern outline) to assist in the determination—specifically, from the 2048-dimensional visual fingerprint features output by the ResNet50 model in step S1, key dimension sub-vectors (corresponding to pattern outline, shoulder width, and other specification-related features) labeled by sample training are extracted, and cosine similarity is calculated with the pre-stored visual feature sub-vectors corresponding to each candidate standard in the knowledge base. The standard with higher similarity is taken as the mapping result to ensure the uniqueness and accuracy of specification mapping. Meanwhile, the knowledge base has a built-in cross-border terminology alignment matrix trained by deep learning, which can automatically map regional terms such as “UKSize” and “EUSize” to a unified standard space, eliminating technical judgment barriers caused by different regional standards.

[0032] After completing the specification mapping, the system uses a multimodal fusion decision model to calculate the weighted confidence level. To address the differences in feature weights across different product categories (e.g., clothing has a high visual weight, while electronic components have a high specification weight), this embodiment introduces an adaptive weight adjustment operator. The weighted confidence calculation formula is as follows: in, This represents the weighted confidence level among candidate products, with a value range of [0, 1]. , , These are the weight coefficients for visual fingerprint feature similarity, regionalized specification feature mapping matching degree, and text attribute feature key entity overlap degree, respectively, and satisfying the following conditions: The weighting coefficients can be customized and adjusted according to the product category characteristics and operational needs of cross-border e-commerce. The matching degree of the regional specification feature mapping between the i-th product and the j-th product is 0 or 1 (1 for a complete match and 0 for a partial match). The overlap of key entities in the text attribute features of the i-th product and the j-th product is obtained by calculating the overlap ratio of key entities (brand, material, core parameters) in the text attribute features of the two products, and the value range is [0, 1].

[0033] Specifically, the system dynamically fine-tunes the weight coefficients by detecting the information entropy of the visual feature vector: here, the visual feature vector is the 2048-dimensional floating-point array output by the ResNet50 model in step S1. The system first performs L1 normalization on this vector, converting the absolute values ​​of each dimension's elements into proportions to obtain the corresponding probability distribution. (in, , (where k is the element of the feature vector), and then the information entropy is calculated based on this distribution. The higher the information entropy value, the more dispersed the elements of the visual feature vector. In the actual scenario of cross-border e-commerce product main images, this corresponds to a more complex background (such as multiple promotional watermarks or contextualized backgrounds). The system presets an information entropy threshold. ,when When the image background of the visual fingerprint is deemed complex, α is automatically decreased by 0.1 (e.g., adjusted from the initial value of 0.5 to 0.4), while β is correspondingly increased by 0.1 (e.g., adjusted from 0.3 to 0.4), and γ remains unchanged to ensure... The value of γ is always 1; however, when the key entity of the text attribute feature is ambiguous (such as the title not clearly indicating the brand), the system automatically reduces the value of γ and increases α accordingly, thereby balancing the weight ratio of each feature and ensuring the stability of the judgment result in a complex cross-border environment.

[0034] Finally, based on weighted confidence levels Whether the adaptive threshold (e.g., 0.75) is reached, the system assigns a globally unique identifier (GlobalSKU) to products determined to be from the same source and constructs a bidirectional dynamic mapping index. Simultaneously, the system sets a confidence fallback threshold (e.g., 0.6): when the weighted confidence score (Conf) is in the range [0.6, 0.75), the system marks the SKU as "awaiting manual review" and automatically highlights and aggregates conflicting specification features, visual fingerprint details (e.g., differences in pattern corresponding to multiple specifications), and textual ambiguities (e.g., unclear regional terminology), pushing these to the review interface on the operations side; when Conf is below 0.6, the system directly marks the SKU as "features needing improvement," simultaneously triggering a product information completion task (e.g., calling a third-party product database to supplement regional specification correspondences). After completion, feature extraction and confidence score calculation are re-executed; if the target is still not met, manual review is pushed to the system.

[0035] After the operations staff completes the review, the system records the manual judgment results and automatically updates the conflict rule base and visual feature label base of the regional mapping knowledge base, realizing the self-iteration of the mechanism. The index maintains real-time performance through a feature change monitoring operator: once the image or description of an original SKU on a platform changes, the operator will immediately trigger a local recalculation task, only performing feature extraction and weighted confidence recalculation on the changed SKU, and correcting the mapping relationship through an incremental update mechanism, thereby avoiding the waste of computing resources caused by full recalculation and ensuring that the correspondence between the global SKU identifier and the original SKUs of each platform is always accurate.

[0036] In this embodiment, after completing the SKU normalization operation in step S2, based on the dynamic mapping relationship between the global SKU and the original SKU of each platform established in step S2, a unified identification basis is provided for cost aggregation and accounting across orders and platforms. On this basis, the cost container construction operation in step S3 is performed, as follows: First, based on the end-to-end logistics trajectory information extracted in step S1, the system activates an event-driven trajectory segmentation operator. By identifying the timestamps and status tags of logistics nodes (such as system-triggered events for pickup, customs clearance, and delivery), the complete logistics trajectory is segmented into four spatiotemporal trajectory segments: first-leg pickup, trunk transportation, customs clearance, and last-leg delivery. Each trajectory segment is assigned a unique trajectory segment identifier (including features such as logistics route code and node timestamp). This segmentation method, triggered by events rather than by manual rules, can adapt to the node differences of different cross-border logistics service providers, refining the cost accounting granularity from the order level to the trajectory segment level, providing a technical foundation for subsequent binding and allocation of logistics costs.

[0037] Subsequently, the system constructs a dynamically expandable hierarchical cost container pool. This container pool is initialized synchronously with order generation and adopts a tree-like topology storage structure, containing trajectory segment-level cost accounting units (leaf nodes), order-level cost accounting units (parent nodes), and SKU-level cost accounting units (aggregation nodes). In this invention, the "trajectory segment-level / order-level / SKU-level cost accounting unit" is a data structure object used to collect cost data, status information (estimated state / actual state), and associated identifiers (trajectory segment ID / order ID / global SKU identifier) ​​of the corresponding level. Its storage relies on distributed storage nodes. The K8s container orchestration is only used to add storage / computing nodes when the system dynamically expands, carrying the cluster storage of the cost accounting units, rather than starting an independent virtualization runtime environment for a single cost accounting unit. The trajectory segment-level cost accounting unit serves as the smallest accounting unit, binding its corresponding trajectory segment identifier and global SKU identifier. The order-level cost accounting unit references real-time data from all its subordinate trajectory segment-level cost accounting units via logical pointers. The SKU-level cost accounting unit utilizes the global SKU index to achieve cross-order cost aggregation based on the same source. The container pool supports dynamic expansion based on concurrent order flows, with trigger conditions set to "concurrent order flow ≥ 5000 orders / second" or "container pool storage utilization ≥ 85%". During expansion, new storage nodes are added through K8s container orchestration, and new cost containers are sharded according to "order creation time + logistics route" to avoid single-node performance bottlenecks. Through a hierarchical referencing mechanism, automated collection and penetration management of costs across different business dimensions are achieved.

[0038] After the container pool is built, the system performs multi-identifier inverted index binding. A distributed inverted index association table is built based on Elasticsearch. Core fields include "identifier type (track segment / order / global SKU)," "container unique ID," "list of associated identifiers (e.g., tracking number bound to a track segment, original multi-platform code bound to a SKU)," and "update timestamp." These fields decouple each container from the heterogeneous identifier set. The index table supports bidirectional traceability: forward, a specific identifier can quickly locate the corresponding container; backward, a container can deduce all associated identifiers. It also includes a built-in incremental synchronization trigger, with trigger conditions set to "underlying container cost change ≥ 0.01 yuan" or "container status change." After triggering, only the affected index entries are updated (e.g., only the associated cost snapshot identifier of a track segment-level cost accounting unit is modified), rather than the entire index is rebuilt, ensuring short data verification response latency under high concurrency.

[0039] Simultaneously, an adaptive estimated cost model is loaded for the trajectory segment-level cost accounting unit. This model is generated based on historical cost distribution functions and fits cost distribution patterns using Gaussian distribution functions. It also introduces a time-delay compensation operator to automatically extract scenario-specific characteristic factors such as holidays, tariff fluctuations, and fuel surcharges for dynamic optimization. For example, when the target route detects entering a peak season, the model automatically adjusts the correction coefficient k for the trunk transportation segment. After loading the model, the trajectory segment-level cost accounting unit is marked as "estimated state" and configured with a state transition listener. Once the asynchronous actual settlement data arrives, the listener will drive the container to seamlessly switch from the "estimated state" to the "actual settlement state," retaining a snapshot comparing the estimated and actual settlement data to provide accurate technical evidence for subsequent audit tracing.

[0040] In this embodiment, after completing the cost container construction operation in step S3, the cost allocation and profit reconstruction operation in step S4 is executed.

[0041] First, the system launches a distributed asynchronous cost event monitoring engine. This engine adopts a Pub / Sub publish-subscribe architecture to dynamically subscribe to non-real-time settlement cost events such as customs duty invoices, logistics fuel surcharge adjustment orders, and overseas warehouse service fee invoices. The engine has a built-in idempotency discriminator that accurately captures valid settlement events and filters redundant noise by comparing the hash fingerprints of event metadata. Using the distributed inverted index association table built in step S3 (used for fast query and location of containers and events), the captured events are accurately located to the target trajectory segment-level cost accounting unit. The graph database topology structure built synchronously in step S3 is used for subsequent hierarchical backpropagation of cost data. The two constitute a hybrid storage support for the container pool, ensuring the real-time performance of cost capture and the ability to intercept mismatches.

[0042] Upon detecting a valid asynchronous cost settlement event, the system triggers the batch cost fine-grained allocation operator. For mixed settlement scenarios involving both fixed and floating costs, the operator executes differentiated allocation logic: fixed costs (such as customs clearance service fees and warehousing basic fees) are allocated equally based on the number of associated trajectory segment-level cost accounting units; floating costs (such as fuel surcharges and trunk transportation fees) are weighted and allocated according to the proportion of chargeable weight or volumetric weight of the orders bound to each trajectory segment-level cost accounting unit.

[0043] The formulas for calculating the percentage and amount of floating costs are as follows: in, The billing weight / volume weight ratio of the i-th trajectory segment-level cost accounting unit. The billing weight / volume weight of the order is bound to this container. This represents the total number of trajectory segment-level cost accounting units associated with this batch; This represents the floating cost amount to be allocated to the i-th trajectory segment-level cost accounting unit. This represents the total amount of floating costs for this batch.

[0044] The calculation results serve as a state change signal, driving the trajectory segment-level cost accounting unit to perform an atomic update, atomically switching the container from the "estimated state" to the "actual state", and recording the bias value between the actual cost and the estimated cost.

[0045] Subsequently, the system automatically triggers a backpropagation indexing mechanism based on tree topology—here, "backpropagation" refers to the hierarchical transmission and updating of cost data from trajectory segment-level cost accounting units to order and SKU-level cost accounting units, with its data structure support coming from the graph database used when constructing the hierarchical cost container pool in step S3: The cost accounting units at the trajectory segment level, order level, and SKU level are used as nodes in the graph database. With "attribution relationship" as directed edges, a tree-like topological association of "trajectory segment → order → SKU" is constructed (for example, the edge of a certain trajectory segment cost accounting unit points to the corresponding order container, and the edge of the order container then points to the corresponding SKU container).

[0046] Based on this topology, the system performs cascading updates upwards in the order of "trajectory segment-level cost accounting unit → order-level cost accounting unit → SKU-level cost accounting unit": The cost bias value (actual cost - estimated cost) of the current trajectory segment-level cost accounting unit is passed to the associated order-level cost accounting unit and its cumulative cost is updated; then the order-level cost accounting unit passes the cost change to the associated SKU-level cost accounting unit and incrementally updates its global cost (without full recalculation).

[0047] This mechanism employs an incremental computation and lazy recalculation strategy: only container nodes affected by cost changes are updated, while unrelated nodes retain their original data, thus avoiding the performance bottleneck of full queries in traditional relational databases and improving response efficiency under massive data volumes.

[0048] The formula for reconstructing the actual profit of the order-level cost accounting unit is: in, For the actual profit of the order, Let n be the actual sales amount of the order, and n be the number of trajectory segment-level cost accounting units bound to the order. The actual cost of the trajectory segment-level cost accounting unit. For other non-logistics cost items of this order, Other non-logistics actual costs.

[0049] The SKU-level cost accounting unit aggregates incremental data from all associated order-level cost accounting units through a global SKU index, enabling dynamic calibration of cross-platform profits.

[0050] During the process of reconstructing the entire profit chain, the system activates a comprehensive data correction and traceability mechanism. All cost correction timestamps, trigger event numbers, and backpropagation link records are stored in an immutable time-series manner, forming a closed-loop audit link with the estimated-actual cost comparison snapshot retained in step S3. This not only provides complete evidence for financial verification and data anomaly investigation, but also effectively solves the problems of incomplete asynchronous cost traceability and missing audit links in existing technologies, thereby improving the compliance and traceability of cross-border e-commerce cost accounting.

[0051] In this embodiment, after completing the end-to-end profit reconstruction in step S4, based on the hierarchical cost container pool constructed in step S3 and the reconstructed end-to-end profit data in step S4, the exchange rate conversion and report output operations in step S5 are performed. This achieves accurate foreign currency cost conversion and compliant report output, taking into account the characteristics of multi-currency settlement in cross-border e-commerce. The specific technical implementation is as follows: First, the system activates the dual-exchange-rate mode foreign currency cost conversion engine. This engine connects to the official exchange rate interface of the central bank or mainstream foreign exchange trading platforms to obtain the exchange rate on the transaction date (order payment date). Exchange rate on the settlement date (the date the cost is actually received). Real-time official data. For single foreign currency costs. The engine introduces an exchange rate loss stripping operator to decouple the gains and losses caused by exchange rate fluctuations from business costs. The calculation formula is as follows: in, The exchange rate on the settlement date. The exchange rate on the trading day. This is the original amount of the foreign currency cost.

[0052] For complex scenarios involving the consolidated settlement of multiple foreign currency fees, the system triggers an exchange gain / loss splitting operator based on the percentage of the amount. First, the total exchange gain / loss for the consolidated settlement is calculated. Then, the fee is split into corresponding orders based on the original amount of each foreign currency fee. The splitting formula is as follows: in, The exchange gain or loss to be borne by the order corresponding to the i-th foreign currency fee. Let be the original amount of the i-th foreign currency expense, and n be the number of foreign currency expenses settled in a consolidated manner.

[0053] This dual-currency separation mechanism separates "gross profit" from "financial profit and loss" at the underlying level, effectively solving the common problem of profit distortion in cross-border accounting.

[0054] Subsequently, based on the full-scale traceable data in the hierarchical cost container pool constructed in step S3, the system drives an OLAP analysis engine optimized for cross-border heterogeneous data to generate multi-dimensional statistical reports. This engine pre-builds a multi-dimensional cube model with global SKU identifiers, order identifiers, and spatiotemporal trajectory segment identifiers as core dimensions, and maps it to the full-link profit data reconstructed in step S4. To address the pain point that existing cross-border e-commerce reports can only display macro-level data and cannot penetrate to trace the source, this engine incorporates a drill-down traceability mechanism: users can drill down layer by layer from the macro-level profit aggregation chart at the global SKU dimension to the cost allocation details of the corresponding order, ultimately penetrating to the estimated-actual cost comparison snapshot of a specific spatiotemporal trajectory segment. This achieves seamless full-link traceability of statistical data from macro-level aggregation to micro-level atomization, effectively improving the verifiability and audit compliance of report data.

[0055] Throughout the entire lifecycle from steps S1 to S5, the system synchronously runs a full-link data traceability and evidence preservation mechanism. This mechanism uses an immutable time-series database as the underlying storage, and records feature extraction logs, SKU determination records, container status changes, profit backpropagation links, and exchange rate conversion operators linearly along a unified timeline. Each traceability record carries an SHA-256 hash fingerprint, generated according to the following rules: a fixed combination of fields including "data processing timestamp + record type (e.g., 'feature extraction', 'container status change') + unique identifier of the associated entity (e.g., order ID, GlobalSKU, container ID) + core data value (e.g., feature extraction result summary, cost change amount, exchange rate value) + operation node identifier," which are concatenated into a string in the above order, converted into a byte stream using UTF-8 encoding, and then calculated using the SHA-256 hash algorithm to obtain a unique fingerprint, ensuring that every processing node from the original order collection to the final report output has a verifiable chain of evidence. This closed-loop evidence storage system not only meets the financial compliance audit requirements of cross-border trade, but also effectively alleviates the industry pain point of untraceable statistical results in a distributed heterogeneous data environment, and improves the compliance and verifiability of cross-border e-commerce data statistics.

[0056] In this embodiment, as Figure 2 As shown, a data statistical processing system for cross-border e-commerce is also disclosed. This system is used to implement the aforementioned data statistical processing method for cross-border e-commerce. It includes a data acquisition and processing module, an SKU normalization module, a cost container construction module, a cost allocation and profit reconstruction module, an exchange rate conversion report module, and a data full-link traceability module. Each module is an independent and collaborative functional module, which can be implemented by software programs, hardware circuits, or a combination of software and hardware. An orderly data communication connection is established between each module to ensure the efficient and orderly flow of data between modules.

[0057] The data acquisition and processing module is used to perform all the operations in step S1 above, realizing the reception of raw data from multiple platforms, standardized parsing, and decoupled extraction of product features.

[0058] The data acquisition and processing module is used to receive raw heterogeneous data from multiple cross-border e-commerce platforms, perform standardized parsing to generate standardized datasets, and decouple multimodal features of product data to extract text attribute features, visual fingerprint features, and regional specification features.

[0059] The SKU normalization module has a pre-built regional mapping knowledge base, which is used to obtain a set of candidate products through topological clustering of visual fingerprint features, complete the unified mapping of regional specification features, calculate the weighted confidence and determine the homology of products by combining the adaptive weight adjustment mechanism of visual feature information entropy, and establish and dynamically maintain the mapping index between the original SKUs of each platform and the global SKU identifier.

[0060] The cost container construction module is used to divide the logistics trajectory into spatiotemporal trajectory segments based on key logistics node events and assign unique identifiers. It constructs hierarchical cost container pools at the trajectory segment level, order level, and SKU level, and completes the binding of each level of cost accounting unit with the corresponding identifier. It loads an adaptive estimated cost model for the trajectory segment level cost accounting unit and marks the estimated state.

[0061] The cost allocation and profit reconstruction module is used to listen for asynchronous cost settlement events and filter redundant events. It allocates costs to the trajectory segment-level cost accounting units in a differentiated manner according to billing weight or volume weight, triggers the back propagation mechanism based on graph database topology to drive the cost cascading update of each level of cost accounting unit, recalculates the order and SKU profit and retains the relevant correction data.

[0062] The exchange rate conversion report module is used to perform exchange gains and losses stripping based on the dual exchange rates of the transaction date and the settlement date, generate multi-dimensional cube model data based on the hierarchical cost container pool, and output statistical reports that support drill-down traceability, multi-dimensional filtering queries, and export.

[0063] The data end-to-end traceability module is used to record the entire data processing trajectory based on an immutable time-series database, generate SHA-256 hash fingerprints for the traceability records, and realize end-to-end traceability and closed-loop evidence storage from the collection of raw heterogeneous data to the output of reports.

[0064] The functions of each module are clearly defined, realizing the decoupling and collaboration of each link in data processing. The method and solution of this application are productized and modularized, reducing the implementation difficulty of the technical solution and improving the system's deployability, maintainability and operational efficiency. It can be directly deployed on the data analysis platform of cross-border e-commerce enterprises to realize the full-process automated execution of cross-border e-commerce data statistical processing.

[0065] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data statistical processing method for cross-border e-commerce, characterized in that, Includes the following steps: S1 receives raw heterogeneous data from multiple cross-border e-commerce platforms and generates a standardized dataset through preset standardized parsing rules. Multimodal feature decoupling is performed on the product data to extract text attribute features, visual fingerprint features, and regional specification features respectively; S2, perform topological clustering based on the visual fingerprint features to obtain a candidate product set, and project the regional specification features to a unified standard space using a regional mapping knowledge base; introduce an adaptive weight adjustment mechanism based on visual feature information entropy, calculate multimodal weighted confidence based on text attribute features, and establish a dynamic mapping index between the original SKU of each platform and the global SKU identifier based on the judgment result; S3, based on the event-driven key nodes of logistics, divides the logistics trajectory into multiple spatiotemporal trajectory segments, constructs a hierarchical cost container pool containing trajectory segment-level cost accounting units, order-level cost accounting units, and SKU-level cost accounting units, establishes the ownership and reference relationship of each level of cost accounting unit with a tree-like topology structure, and binds the trajectory segment identifier, global SKU identifier, and order identifier through a distributed inverted index association table; S4 listens for asynchronous cost settlement events, locates the associated trajectory segment-level cost accounting unit, and writes the settlement value according to the billing dimension; triggers a back propagation mechanism based on graph database topology, using the trajectory segment-level cost accounting unit as the state change source, and drives the order-level cost accounting unit and SKU-level cost accounting unit to perform cascading updates of data status; S5 calls the exchange gain and loss stripping logic based on the dual exchange rates of the transaction date and settlement date to convert foreign currency values, generates multi-dimensional cube model data based on the hierarchical cost container pool, and outputs statistical reports that support drill-down traceability.

2. The data statistical processing method for cross-border e-commerce according to claim 1, characterized in that: In step S1, the multimodal feature decoupling specifically includes: Natural language processing techniques are used to segment and filter stop words in product texts, and key semantic entities are extracted using the BERT-Base model to form text attribute features; The U-Net semantic segmentation model is used to remove the background from the main product image, and the deep spatial fingerprint is extracted as a visual fingerprint feature by the ResNet50 convolutional neural network. The measurement parameters are extracted from the product specification description using rule matching technology and transformed into standardized regional specification features.

3. The data statistical processing method for cross-border e-commerce according to claim 1, characterized in that: In step S2, the adaptive weight adjustment mechanism specifically includes: The visual fingerprint feature vector is normalized to obtain a probability distribution, and the information entropy of this distribution is calculated. ; Preset information entropy threshold ,when At the same time, the weight coefficient of visual fingerprint feature similarity in the weighted confidence is reduced, and the weight coefficient of regionalized specification feature mapping matching degree is increased accordingly.

4. The data statistical processing method for cross-border e-commerce according to claim 1, characterized in that: In step S2, the projection process of the regionalized specification features is as follows: It calls upon the regionalized mapping knowledge base, which has built-in multi-regional measurement conversion rules and conflict resolution rules; When multiple standards are detected that the same regional specification feature corresponds to the same standard, the conflict resolution rule is triggered. If the text attribute feature is ambiguous, the key dimension sub-vector of the corresponding pattern outline in the visual fingerprint feature is extracted, and the cosine similarity is calculated with the pre-stored visual feature sub-vector of the typical product corresponding to the candidate standard in the knowledge base. The one with the highest similarity is used as the mapping benchmark.

5. The data statistical processing method for cross-border e-commerce according to claim 1, characterized in that: In step S3, the hierarchical cost container pool is initialized synchronously with order generation, the trajectory segment-level cost accounting unit is used as a leaf node, the order-level cost accounting unit is used as a parent node, and the SKU-level cost accounting unit is used as an aggregation node. The container pool supports dynamic expansion based on concurrent order flows. When the concurrent order flow or storage utilization exceeds a threshold, new storage nodes are added through container orchestration, and the storage is sharded according to the order creation time and logistics route.

6. The data statistical processing method for cross-border e-commerce according to claim 1, characterized in that: In step S4, the backpropagation mechanism based on the graph database topology is specifically as follows: The cost accounting units at the trajectory segment level, order level, and SKU level are mapped to graph database nodes respectively, and the tree-like topological association of "trajectory segment → order → SKU" is constructed with the belonging relationship between containers as directed edges. An incremental calculation strategy is adopted, which only propagates the cost bias value of the bottom trajectory segment level cost accounting unit upward along the directed edge, and updates the cumulative data of the affected upper-level order level cost accounting unit and SKU level cost accounting unit. The trajectory segment-level cost accounting unit is loaded with an adaptive estimated cost model. After receiving the settlement value, the container state switches from the estimated state to the actual settlement state and retains a snapshot comparing the estimated cost and the actual settlement cost.

7. The data statistical processing method for cross-border e-commerce according to claim 1, characterized in that: In step S4, the asynchronous cost settlement event monitoring and cost allocation process includes: A Pub / Sub architecture is used to subscribe to asynchronous cost settlement events, and redundant settlement events are filtered out by a built-in idempotency discriminator. For mixed settlement data containing both fixed and floating costs, differentiated cost allocation is performed. Specifically, the fixed costs are equally allocated among the associated trajectory segment-level cost accounting units; the floating costs are weighted and allocated according to the proportion of chargeable weight or volumetric weight of the orders bound to each trajectory segment-level cost accounting unit.

8. The data statistical processing method for cross-border e-commerce according to claim 1, characterized in that: In step S5, the processing flow of the exchange gain / loss separation logic based on the dual exchange rates of the transaction date and settlement date is as follows: Obtain the exchange rate for the corresponding foreign currency on the transaction date and the settlement date; The exchange gain or loss is calculated using the aforementioned exchange gain / loss stripping logic, according to the formula: Exchange gain / loss = (Settlement date exchange rate - Transaction date exchange rate) × Original amount of foreign currency cost; If multiple foreign currency expenses are settled together, first calculate the total exchange gain or loss, and then break down the total exchange gain or loss to the corresponding order and associated trajectory segment-level cost accounting unit according to the proportion of the original amount of each foreign currency expense.

9. The data statistical processing method for cross-border e-commerce according to claim 1, characterized in that: It also includes a full-chain data traceability process, specifically: Based on an immutable time-series database, the entire cross-border e-commerce data processing trajectory is recorded and traceability records are generated. Each traceability record carries an SHA-256 hash fingerprint, which is generated by concatenating the timestamp, record type, unique identifier of the associated subject, core data value, and operation node identifier.

10. A data statistical processing system for cross-border e-commerce, used to implement the data statistical processing method according to any one of claims 1-9, characterized in that, include: The data acquisition and processing module is used to receive raw heterogeneous data from multiple cross-border e-commerce platforms, perform standardized parsing to generate standardized datasets, and decouple multimodal features from product data to extract text attribute features, visual fingerprint features, and regional specification features. The SKU normalization module has a pre-built regional mapping knowledge base, which is used to obtain a set of candidate products through topological clustering of visual fingerprint features, complete the unified mapping of regional specification features, calculate the weighted confidence and determine the homology of products by combining the adaptive weight adjustment mechanism of visual feature information entropy, and establish and dynamically maintain the mapping index between the original SKUs of each platform and the global SKU identifier. The cost container construction module is used to divide the logistics trajectory into spatiotemporal trajectory segments based on key logistics node events and assign unique identifiers. It constructs hierarchical cost container pools at the trajectory segment level, order level, and SKU level and completes the binding of each level of cost accounting unit with the corresponding identifier. It loads an adaptive estimated cost model for the trajectory segment level cost accounting unit and marks the estimated state. The cost allocation and profit reconstruction module is used to listen to asynchronous cost settlement events and filter redundant events. It allocates costs to the trajectory segment-level cost accounting units in a differentiated manner according to billing weight or volume weight, triggers the back propagation mechanism based on graph database topology to drive the cost cascade update of each level of cost accounting unit, recalculates the order and SKU profit and retains the relevant correction data. The exchange rate conversion report module is used to perform exchange gains and losses stripping based on the dual exchange rates of the transaction date and the settlement date, generate multi-dimensional cube model data based on the hierarchical cost container pool, and output statistical reports that support drill-down traceability, multi-dimensional filtering queries, and export. The data end-to-end traceability module is used to record the entire data processing trajectory based on an immutable time-series database, generate SHA-256 hash fingerprints for the traceability records, and realize end-to-end traceability and closed-loop evidence storage from the collection of raw heterogeneous data to the output of reports.

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