Cross-border e-commerce multinational data collaborative management method and system

Abstract

The invention discloses a cross-border e-commerce multi-country data collaborative management method and system, and belongs to the field of computer network and data processing, and the method comprises the steps: obtaining local original e-commerce service data by each edge node, and obtaining a standardized feature vector; each edge node uses the standardized feature vector to train a local model, generates a model parameter update package indicating a model optimization direction, executes privacy protection processing including differential privacy noise injection on the model parameter update package, and sends the model parameter update package to a collaborative management center server; the collaborative management center server receives the model parameter updating packages from the multiple edge nodes, calculates an adaptive aggregation weight coefficient, and executes weighted aggregation to update a global collaborative model; and the collaborative management center server predicts the commodity demand quantity of the future time domain based on the updated global collaborative model, and generates a deterministic service scheduling instruction. According to the invention, the total cost of cross-border logistics is reduced, and the inventory turnover rate is improved.

Description

Technical Field

[0001] This invention relates to the field of computer networks and data processing, and in particular to a method and system for collaborative management of multi-country data in cross-border e-commerce. Background Technology

[0002] With the booming development of the digital economy, cross-border e-commerce has become an important form of international trade. Unlike traditional e-commerce operations in single-country markets, cross-border e-commerce faces extremely complex challenges related to the heterogeneity of "people, goods, and places." Specifically, users in different countries have vastly different languages, consumption habits, and currency units. At the same time, increasingly stringent data sovereignty and privacy protection laws in various countries (such as the EU's GDPR and China's PIPL) explicitly prohibit the direct export of raw user data (such as clickstreams and detailed profiles). In existing technologies, traditional centralized machine learning model training methods require all data to be aggregated on a central server, which is clearly impractical under cross-border compliance requirements. Although federated learning technology allows data to be trained collaboratively without leaving the local machine, many unresolved pain points remain in the practical application of cross-border e-commerce. For example, the semantic spaces of data from different countries differ, resulting in extremely poor model performance from direct average aggregation. Furthermore, cross-border network latency (RTT) is huge and unstable, and the mismatch between the amount of data and information in different countries makes the traditional FedAvg algorithm prone to model bias, and training efficiency is limited by lagging data. Moreover, federated learning outputs probabilistic predictions, while supply chain scheduling requires deterministic physical instructions. Most importantly, increasingly stringent compliance requirements have also posed new challenges to the development of cross-border e-commerce. When a country suddenly imposes a new privacy ban, the traditional process of modifying code and redeploying takes too long and is prone to compliance risks.

[0003] Therefore, there is an urgent need for a technical solution that can simultaneously address cross-language semantic alignment, data privacy compliance, heterogeneous network collaboration, and the implementation of deterministic decisions. Summary of the Invention

[0004] One of the objectives of this invention is to provide a method for collaborative management of multi-country data in cross-border e-commerce, in order to solve the problems in existing technologies that cannot effectively overcome the semantic heterogeneity of multi-country data, the low efficiency of cross-border network collaboration, the difficulty in converting prediction results into deterministic physical scheduling instructions, and the lag in response to sudden compliance changes.

[0005] This invention is achieved through the following technical solution: a cross-border e-commerce multi-country data collaborative management method, comprising the following steps: S1, each edge node acquires local raw e-commerce business data, and performs local heterogeneous data standardization and feature extraction based on a pre-set domain knowledge graph and graph neural network to obtain a standardized feature vector; S2, each edge node uses the standardized feature vector to train a local model, generates a model parameter update package indicating the direction of model optimization, and performs privacy protection processing including differential privacy noise injection on the model parameter update package before sending it to the collaborative management center server; S3, the collaborative management center server receives model parameter update packages from multiple edge nodes, constructs an adaptive aggregation weight function based on the data distribution Shannon entropy and network round-trip delay of each edge node, calculates the adaptive aggregation weight coefficient, and performs weighted aggregation to update the global collaborative model; S4, based on the updated global collaborative model, the collaborative management center server predicts the future demand for goods in the time domain, and uses the demand for goods as an input parameter to substitute into a mixed integer linear programming model, solves the globally optimal allocation matrix under the premise of satisfying physical constraints, and generates deterministic business scheduling instructions.

[0006] Furthermore, the edge nodes are deployed in geographical areas with independent data sovereignty.

[0007] Furthermore, S1 performs local heterogeneous data standardization and feature extraction, specifically including: based on the physical space mapping of the domain knowledge graph, using a linear projection layer containing a learnable semantic alignment projection matrix, mapping the original e-commerce business data of different dimensions to a unified dimension latent space, and processing it through a nonlinear activation function; modeling the mapped data as graph structure data containing products and user behavior, generating entity embedding vectors using the neighbor aggregation mechanism of graph neural networks; introducing interactive feedback feature vectors as reference signals, calculating attention weight vectors, and applying the attention weight vectors to the entity embedding vectors through Hadamard product to generate the dynamically corrected standardized feature vectors.

[0008] Furthermore, the interactive feedback feature vector is generated based on the actual click and purchase behavior of local users; the attention weight vector is configured as an adaptive filter to dynamically amplify the key feature dimensions that drive business growth and suppress the noise feature dimensions that have no impact, based on the local market validation results.

[0009] Furthermore, the physical space mapping of the domain knowledge graph can be calculated using the following formula:

[0010] ,in, Representing the country Medium entity node The original attribute vector, its dimensions It is specific to that country; This is the semantic alignment projection matrix, which will... Mapping local data of dimensions to a globally unified one The semantic space has different dimensions for different countries. But output dimension This ensures global consistency. This is a bias vector used to adjust the activation threshold; It is a non-linear activation function used to enhance the expressive power of the model and capture the non-linear features between data; This is the initial feature vector obtained after mapping.

[0011] Furthermore, the attention weights can be calculated using the following formula:

[0012] The standardized feature vector can be calculated using the following formula: ,in, A feature vector representing the real-time interactive feedback of local users; For processing It is a weight transformation matrix used to process the learning of interactive features; This represents a vector concatenation operation; It is an attention vector used to compress the concatenated high-dimensional vector into a scalar score; This constitutes the standard Softmax function form, ensuring attention weights across all feature dimensions. The sum of these values ​​is 1, and the values ​​are between 0 and 1, thus achieving normalization. It represents the Hadamardi (or Hadama) stack.

[0013] Furthermore, in S2, privacy protection processing is performed on the model parameter update package, specifically including: calculating the loss function based on the local dataset and the current global model parameters, and calculating the original gradient vector relative to the model parameters through backpropagation; performing sensitivity truncation on the original gradient vector to obtain the pruned gradient vector; generating a random noise vector with the same dimension as the gradient vector using a Gaussian or Laplacian mechanism based on differential privacy criteria, wherein the random noise vector follows a probability distribution whose variance is proportional to the pruning threshold and the privacy protection strength; and superimposing the random noise vector onto the pruned gradient vector to generate a secure update package.

[0014] Furthermore, after generating the security update package, the method further includes: encapsulating the security update package using an asymmetric encryption algorithm and sending it through an encrypted tunnel constructed using a transport layer security protocol; and uploading the hash value of the security update package to the blockchain consortium chain ledger for use in cross-border data compliance auditing.

[0015] Furthermore, the original gradient vector can be calculated using the following formula:

[0016] ,in, These are the current global model parameters; National nodes Owns a local private dataset; The loss function; Indicates the parameter The operation of finding partial derivatives; The calculated original gradient vector represents the gradient in the current data. Below, parameters How should we adjust it to reduce the error?

[0017] Furthermore, the clipped gradient vector can be calculated using the following formula:

[0018] ,in, The L2 norm of the original gradient vector; The gradient pruning threshold is a hyperparameter that determines the system's tolerance for outlier data and the baseline for privacy protection. It is a scaling factor, when When, the denominator is ,but (Without change), when When, the denominator is ,at this time Its modulus length is compressed to ; This is the clipped gradient vector.

[0019] Furthermore, the random noise vector can be calculated using the following formula:

[0020] ,in, Represents a multivariate Gaussian distribution; For noise multiplier, This is the covariance matrix, which ensures that the noise amplitude is consistent with the previously set clipping threshold. They match; This is the final output gradient vector with differential privacy protection.

[0021] Furthermore, the adaptive aggregation weight function constructed in S3 is based on a multi-factor adaptive weighting system, specifically including: S3.1, performing quality assessment based on Shannon entropy of data distribution. For each edge node, the Shannon entropy of its local data distribution is calculated, and the Shannon entropy is used to quantify the uniformity of the category distribution and the amount of information contained in the node's data; S3.2, performing state awareness based on network latency. The round-trip latency between each edge node and the collaborative management center server is monitored in real time to perceive the current cross-border network congestion status; S3.3, constructing an adaptive aggregation weight function that includes sample size, information entropy, and network latency. The basic weight is set based on the sample size, and the basic weight is non-linearly rewarded using an exponential growth factor based on the Shannon entropy, and non-linearly penalized using an exponential decay factor based on the round-trip latency, thereby generating the final adaptive aggregation weight coefficient.

[0022] Furthermore, in S3.1, the Shannon entropy is used to quantify the uniformity of the category distribution of the node data. Specifically, if the calculated Shannon entropy value is high, it is determined that the data category distribution of the edge node is relatively balanced and contains rich general knowledge. During aggregation, its contribution to the global model is amplified by the exponential growth factor. If the calculated Shannon entropy value is low, it is determined that the data category distribution of the edge node is skewed or homogenized. During aggregation, its weight is limited to prevent the global model from overfitting to data of a specific single pattern.

[0023] Furthermore, the Shannon entropy can be calculated using the following formula:

[0024] ,in, Indicates the first Local dataset for each node Shannon entropy, The total number of categories defined globally (; Indicates at node In the data set, it belongs to the category The probability of the sample; Usually, 2 or Logarithm with base 0.

[0025] Furthermore, in S3.3, the basic weights are non-linearly penalized using an exponential decay factor based on the round-trip latency, including: in response to a surge in the round-trip latency of a specific edge node, the weight ratio of that edge node in the global aggregation is compressed using the exponential decay factor; by reducing the weight of high-latency nodes, the time the collaborative management center server waits for slow nodes to transmit data is reduced, thereby alleviating the system bottleneck effect caused by cross-border network differences and ensuring that the global collaborative response cycle meets the timeliness requirements of high-frequency trading scenarios.

[0026] Furthermore, the adaptive aggregation weight function can be formally expressed as:

[0027] ,in, For nodes The normalized weights during global aggregation satisfy the following conditions: ; For nodes The number of samples, Central server and nodes Network round-trip latency between; Adjusting hyperparameters for information entropy ( ), The larger the value, the more the model favors nodes with a more balanced data distribution; For delay penalty hyperparameters ( ), The larger the value, the more sensitive the model is to network latency, and the greater the penalty for slow nodes.

[0028] Further, weighted aggregation is performed to update the global collaborative model, including: obtaining the model parameter update package uploaded by each edge node after differential privacy processing, and parsing it to obtain a gradient vector containing noise; using the calculated adaptive aggregation weight coefficients, performing a weighted summation operation on the gradient vectors of each edge node to calculate the global average gradient; and using the global average gradient to iteratively update the parameters of the current global collaborative model, so that the updated global collaborative model moves in the direction of reducing the loss function.

[0029] Furthermore, the parameters of the global collaborative model can be iteratively updated using the following formula:

[0030] ,in, These are the global collaborative model parameters for the current time step; The parameters of the updated global collaboration model, The global learning rate is used to control the step size for updating model parameters. The global gradient after weighted aggregation, since Having taken into account both data quality and network state, this aggregated gradient is more representative of the global optimal descent direction than the traditional average gradient, and it converges faster.

[0031] Further, in S4, the commodity demand is input into a pre-set mixed-integer linear programming model, specifically including: constructing a global objective function, which includes: a total cross-border logistics cost item and a stockout penalty cost item, the stockout penalty cost item including a unit stockout penalty coefficient; setting physical constraints, which at least include: an inventory limit constraint that the sum of the transfer quantities of any node does not exceed the current physical inventory of that node, and a discrete constraint that the transfer instruction variable must be a non-negative integer; and using a mathematical programming solver to solve the global objective function to find the transfer combination that minimizes the total cost.

[0032] Furthermore, S4 also includes: the globally optimal allocation matrix obtained by analytical solution; for elements in the matrix where the allocation instruction variable is greater than zero, generating a cross-border allocation order and triggering the logistics interface; for elements where local inventory is insufficient and cannot be satisfied through allocation, generating an emergency purchase order.

[0033] Furthermore, the objective function can be formally expressed as:

[0034] ,in, The total operating cost is the sole objective of optimization. As a core decision variable, it represents the decision-making variables from the national level. Transferred to the state The quantity of goods, which is an integer to be solved for, all Together, they form the allocation matrix we ultimately need. ; From arrive The unit logistics cost parameter matrix; The countries predicted by the federated learning model In time Demand; It is a country Current inventory.

[0035] Furthermore, the physical constraints can be formally expressed as:

[0036] , ,in, Represents a node The total amount of goods shipped to all other countries; For nodes Locally available inventory; Represents a set of integers.

[0037] Furthermore, the collaborative management method also includes: S5 adaptive adjustment steps for sudden compliance changes: When the collaborative management center server detects a compliance change event in a specific region, it converts legal constraints into a compliance policy update patch that can be executed by machines. The compliance policy update patch contains an index or mask vector of restricted features. The compliance policy update patch is pushed to the corresponding edge node through a control plane channel independent of the data transmission channel. The network priority of the control plane channel is higher than that of the data plane channel used to transmit model parameter update packages. The edge node receives the patch and triggers a hot update mechanism to load the compliance policy update patch.

[0038] Furthermore, the hot update mechanism for loading the compliance policy update patch includes: running a daemon process in the inference engine of the edge node to monitor the memory address of the configuration file; using atomic pointer swapping technology to switch the pointer to the old feature mapping table to a feature mapping table with a new compliance policy generated based on the compliance policy update patch; the switching process is performed without terminating the main business process or cutting off existing user connections.

[0039] Furthermore, after the edge node loads the compliance policy update patch, it performs the following operations: establishes a dynamic compliance firewall between steps S1 and S2; performs a bitwise scan of the standardized feature vector according to the policy in the compliance policy update patch; intercepts or sets the feature dimensions that do not conform to the compliance policy to zero, while keeping the tensor shape of the feature vector unchanged, so that the input dimension of subsequent model training remains consistent.

[0040] Another aspect of the present invention provides a cross-border e-commerce multi-country data collaborative management system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the cross-border e-commerce multi-country data collaborative management method as described above.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] 1. This invention maps diverse local raw data into a unified-dimensional latent mathematical space by introducing domain knowledge graph mapping and graph neural network (GNN) embedding at edge nodes, coupled with learnable linear projection layers. This makes commodity entities from different countries comparable, breaks through the semantic heterogeneity barrier of cross-border data, and achieves high-precision global feature alignment. Furthermore, by introducing a dynamic attention mechanism based on interactive feedback, it adaptively amplifies key features and suppresses noisy features, making the generated standardized feature vectors not only a translation of data but also rich in high-value information from local market insights. This significantly improves the generalization ability and accuracy of the global model in predicting markets in various regions.

[0043] 2. This invention constructs a two-factor adaptive weighted system based on Shannon entropy and network round-trip time (RTT). It utilizes Shannon entropy to quantify the information richness of data and assigns higher weights to nodes with evenly distributed data categories through an exponential reward mechanism. This effectively avoids overfitting the global model to markets with large amounts of data but simple patterns (low entropy), thus improving the model's versatility. Furthermore, it uses RTT as a penalty term, automatically reducing the weight of high-latency nodes through an exponential decay function. This eliminates the need for the system to wait indefinitely for network congestion nodes, solving the common problem of lagging behind in distributed systems. This ensures that the system maintains sub-second response speeds and convergence efficiency even under high concurrency scenarios such as large-scale promotions and harsh network environments.

[0044] 3. In response to the strict restrictions on data export imposed by laws such as the EU GDPR and China PIPL, this invention adopts a multi-layered privacy protection architecture. All original business data is strictly stored on local edge nodes, and only abstract gradient information is transmitted, which physically eliminates the risk of sensitive data leaving the country. Furthermore, before gradient transmission, sensitivity truncation and differential privacy noise injection are performed. By adding mathematical noise that follows a specific distribution to the gradient, it is mathematically prevented from allowing attackers to reverse engineer any individual user's specific information, thus achieving compliance requirements in terms of security. At the same time, in conjunction with asymmetric encrypted tunnels and blockchain notarization, the confidentiality and non-repudiation of data in the transmission link are further guaranteed.

[0045] 4. Based on global model prediction, this invention introduces a mixed integer linear programming (MILP) model. By incorporating hard constraints from the physical world, such as inventory limits and integer transfers, into the mathematical model, invalid instructions are avoided. By constructing a global objective function that includes explicit logistics costs and implicit stockout penalty costs, the system can automatically find the mathematically optimal balance between high freight costs for delivery and low cost tolerance for stockouts. This makes the final output no longer a reference suggestion, but a deterministic instruction that can be directly issued to WMS (Warehouse Management System) or AGV (Automated Guided Vehicle) for execution, significantly reducing the total cost of cross-border logistics and improving inventory turnover.

[0046] 5. This invention proposes a hot update mechanism that combines control / data plane separation with atomic pointer swapping. Compliance patches are issued through a high-priority control channel to ensure that instructions are not blocked by data floods. By using atomic operations at the operating system level to switch feature mapping tables, new compliance policies can be implemented in real time without interrupting existing user connections or stopping the main business process. By handling non-compliant features by bitmasking rather than deleting dimensions, the stability of the model input dimensions is guaranteed, allowing enterprises to adapt to new regulations without redesigning the neural network structure, greatly reducing operation and maintenance costs and compliance circuit breaker risks. Attached Figure Description

[0047] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 The above is a flowchart of the overall method provided in Embodiment 1 of the present invention.

[0049] Figure 2 This is a schematic diagram of the spatial distribution of heterogeneous raw data provided in Embodiment 1 of the present invention.

[0050] Figure 3 This is a schematic diagram of heterogeneous spatial alignment of the semantic alignment projection matrix provided in Embodiment 1 of the present invention.

[0051] Figure 4 This is a schematic diagram of the feature attention weight distribution provided in Embodiment 1 of the present invention.

[0052] Figure 5 This is a schematic diagram illustrating the noise feature suppression effect provided in Embodiment 1 of the present invention.

[0053] Figure 6 This is a schematic diagram of gradient clipping based on the L2 norm provided in Embodiment 1 of the present invention.

[0054] Figure 7 This is a schematic diagram of gradient clipping and differential privacy noise injection provided in Embodiment 1 of the present invention.

[0055] Figure 8 This is a schematic diagram illustrating the Shannon entropy evaluation of the data distribution of edge nodes provided in Embodiment 1 of the present invention.

[0056] Figure 9 This is a schematic diagram illustrating the data quality quantification of Shannon entropy assessment for different sample data from two different edge nodes, as provided in Embodiment 1 of the present invention.

[0057] Figure 10 This diagram illustrates a comparison of the convergence speeds of ordinary federated learning and adaptive aggregation training learning in Embodiment 1 of the present invention.

[0058] Figure 11 This is a visualization diagram of the gap between node inventory and predicted demand provided in Embodiment 1 of the present invention.

[0059] Figure 12 This is a schematic diagram of the unit logistics cost parameter matrix provided in Embodiment 1 of the present invention.

[0060] Figure 13 This is a schematic diagram illustrating the cost-benefit optimization comparison analysis provided in Embodiment 1 of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0062] Example 1

[0063] This embodiment discloses a method and system for cross-border e-commerce multi-country data collaborative management. Specifically, the cross-border e-commerce multi-country data collaborative management system can be integrated into a distributed network architecture, which includes a collaborative management center server and multiple edge node devices distributed in different countries. The edge node devices can be servers, edge computing gateways, or local private cloud clusters deployed in regional data centers in various countries. The collaborative management center server can be a single headquarters server or a cloud cluster composed of multiple servers. This system processes local sensitive data through edge nodes and only interacts with the central server with anonymized model parameters, thereby realizing the cross-border e-commerce multi-country data collaborative management method of this application.

[0064] Figure 1 The overall method flowchart of this embodiment is shown. As can be seen from the figure, this embodiment includes the following steps:

[0065] Step 1: Obtain the local raw e-commerce business data of each edge node, and perform local heterogeneous data standardization and feature extraction to obtain standardized feature vectors.

[0066] In this embodiment, an edge node refers to a distributed computing unit deployed within a specific sovereign state or geographical region. Edge nodes can manifest as cloud data center (IDC) servers, CDN edge gateways, or privately deployed local clusters rented by cross-border e-commerce companies. Their core characteristic lies in data residency, meaning they are governed by local data security laws (such as the EU GDPR and China PIPL). The physical storage media within the node is strictly restricted to within the national border, and any direct export of raw binary data is blocked by physical or logical firewalls.

[0067] Raw e-commerce business data refers to a set of multimodal physical quantities used to fully describe the e-commerce operation status of the country where the edge node is located. Specifically, this data includes, but is not limited to:

[0068] Structured data includes: Stock Unit (SKU) IDs, prices in local currencies (e.g., Euros, Japanese Yen), specifications in local units of measurement (e.g., size UK8, weight lbs), and timestamped user clickstream logs. Unstructured data includes: Listing text descriptions written in the local language, user reviews, and metadata for product images or videos.

[0069] In real-world cross-border e-commerce scenarios, raw data from different countries exhibit significant semantic heterogeneity. For example, the same clothing item might be labeled 'XL' in China, '42' in Europe, and 'Size 12' in the United States. Similarly, various promotional activities have completely different semantic expressions and user sensitivities across different cultural contexts. This multi-dimensional heterogeneity constitutes a semantic gap, and direct numerical calculations can lead to model non-convergence or distorted predictions. In other words, in cross-border logistics and e-commerce scenarios, data from different countries (nodes) often have completely different dimensional definitions and attribute spaces (e.g., product descriptions are text vectors in country A but structured enumerations in country B), making direct mixing and calculation of this data impractical. Figure 2 A schematic diagram of the spatial distribution of heterogeneous raw data in this embodiment is shown.

[0070] To address the language barrier issue caused by multi-source heterogeneous data, this embodiment constructs a semantic mapping and feature embedding model to perform local heterogeneous data standardization and feature extraction. Specifically, performing local heterogeneous data standardization and feature extraction in this embodiment may include the following steps:

[0071] 1) Based on the physical space mapping of the domain knowledge graph, in this embodiment, in order to smooth out the above heterogeneity, the system pre-sets a domain knowledge graph specific to the country market at each edge node.

[0072] Domain knowledge graphs are structured semantic networks that define the topological relationships between local entities (such as size, color, and brand) and global standard concepts. Based on this, a semantic alignment projection matrix is ​​introduced, which can be understood as a universal converter or mathematical lens in a high-dimensional space. Its design philosophy is that since the physical form of original data from different countries (such as units and language) cannot be changed, linear or nonlinear transformations can be used to force these differently defined local original attribute vectors onto a predefined, dimensionally unified shared semantic space. In this process, the projection matrix learns the mapping rules between local data and global standards (e.g., learning that 1 inch and 2.54 centimeters are equivalent in feature space), thereby aligning features with different physical meanings but the same business meaning.

[0073] Understandably, this step forms the basis for all subsequent collaborative computing, ensuring that even data from different countries and in different formats are mathematically comparable before entering the deep processing layers of the neural network.

[0074] Specifically, for each participating national node... By defining each local node as having its own independent original data space, a specific linear projection layer is deployed at each local node to achieve alignment. At the core of this projection layer is a learnable semantic alignment projection matrix, which acts as a translation dictionary; regardless of the original dimension of the local data, it is mapped to a latent space of the same dimension through multiplication operations on this matrix. Furthermore, to enable the model to handle non-linear relationships (such as complex product attribute interactions), a non-linear activation function is introduced after the linear transformation, achieving not only dimensionality unification but also initial extraction of higher-order features from the original data.

[0075] For example, in this embodiment, the physical space mapping based on the domain knowledge graph can be calculated using the following formula:

[0076]

[0077] in, Representing the country Medium entity node The original attribute vector, its dimensions It is specific to that country (e.g., US nodes are 128-dimensional, European nodes are 256-dimensional). This is the semantic alignment projection matrix, which is the core parameter of this algorithm for solving heterogeneity. This matrix will... Mapping local data of dimensions to a globally unified one The semantic space has different dimensions for different countries. But output dimension This ensures global consistency. This is a bias vector used to adjust the activation threshold; Non-linear activation functions (such as ReLU activation function) are used to enhance the expressive power of the model and capture the non-linear features between data. This is the initial feature vector obtained after mapping, at which point the data from different countries have achieved mathematical isomorphism. Figure 3 A schematic diagram of heterogeneous spatial alignment of the semantic alignment projection matrix in this embodiment is shown.

[0078] 2) Deep Feature Embedding Based on Graph Neural Networks (GNNs): After initial mapping, to capture complex relationships in e-commerce data (such as 'user-click-product-belong-category'), this embodiment uses graph neural networks for feature extraction. By modeling local products and user behavior as graph-structured data, and utilizing the non-linear activation function and neighbor aggregation mechanism of GNNs, standardized entity embedding vectors are generated. This entity embedding vector is a high-dimensional numerical sequence, a mathematical abstraction of the original product or user profile. This vector no longer contains specific language or currency symbols, but rather implies the entity's relative position and potential attributes in the market. Since not all product attributes have the same impact on users' purchasing decisions, and user preferences differ significantly across markets—for example, a product with high cost-performance, suitable for summer, and popular among young people—will generate embedding vectors pointing in similar directions in the vector space, regardless of whether its original description is in English or French. This design effectively improves the model's ability to understand cross-linguistic semantics.

[0079] 3) Dynamic attention correction incorporating user interaction feedback: In this embodiment, to further improve the accuracy of features and address the issue that static features cannot reflect dynamic market changes, an attention mechanism is introduced for dynamic correction during feature extraction. Considering that the key factors (feature weights) determining user purchasing behavior differ significantly across different national markets, for example, in price-sensitive developing country markets, the weight of the price feature dimension should be extremely high; while in quality-focused developed country markets, the weight of material or quality should be even higher.

[0080] Therefore, in this embodiment, an attention weight vector is calculated by introducing an interactive feedback feature vector as a reference signal. This attention weight vector is essentially an adaptive filter that dynamically amplifies key feature dimensions that truly drive business growth and suppresses noisy feature dimensions that have no impact on purchasing decisions by scanning each dimension of the feature vector one by one and considering the actual feedback from local users (whether they clicked or purchased). The attention weight is then applied to the initial feature vector using a Hadamard product (element-wise multiplication) to generate a corrected feature vector. This ensures that the final output data features are not merely a translation of the original information, but also information validated in the local market and rich in business insights. Consequently, the model exhibits stronger robustness and predictive accuracy when facing diverse consumer habits across different countries.

[0081] Specifically, to enable the model to observe and reflect, this embodiment introduces real-time interaction data from local users (such as click-through rate, return rate, and browsing time) as feedback signals. An attention mechanism is constructed by calculating the correlation between the initial feature vector and user feedback features. This mechanism automatically evaluates the importance of each feature dimension to the final business outcome (such as whether a transaction is completed).

[0082] For features that are strongly correlated with high click-through rates or high conversion rates, the model will amplify their values; while for noisy features that are unrelated to user behavior, the model will suppress their values. This mechanism enables the dynamic redistribution of feature weights.

[0083] For example, in this embodiment, the attention weights of the feature dimensions can be calculated using the following formula:

[0084]

[0085] Subsequently, the calculated weights are used to correct and update the features:

[0086]

[0087] in, The feature vector representing the real-time interactive feedback of local users is a key variable for introducing external market signals; and It is a learnable weight transformation matrix, used to process node features and interaction features respectively, mapping them to the same dimension for comparison; This indicates a vector concatenation operation that combines the product's attributes with user interaction feedback for joint analysis; It is an attention vector used to compress the concatenated high-dimensional vector into a scalar score; This constitutes the standard Softmax function form, ensuring attention weights across all feature dimensions. The sum of these values ​​is 1, and the values ​​are between 0 and 1, thus achieving normalization. This represents the Hadamard Product, which is an element-wise multiplication.

[0088] It should be noted that through this step, It's no longer just a static description of product attributes, but incorporates features that adjust based on dynamic market feedback. For example, if a certain color attribute leads to an extremely high return rate in a particular country (included in...), it can be used to refine the product description. (In the middle) The attention mechanism automatically identifies and amplifies the weight of the feature, making the model pay more attention to the color factor in subsequent training, thereby significantly improving the accuracy of prediction. Figure 4This embodiment shows a schematic diagram of the feature attention weight distribution that incorporates user feedback. Figure 5 A schematic diagram illustrating the effect of the attention weighting mechanism in this embodiment on suppressing noise features is shown.

[0089] Through the above steps, the local, messy, heterogeneous, and privacy-involving raw e-commerce business data is transformed into standardized, anonymized mathematical vectors that highly condense market characteristics, laying a solid foundation for subsequent global collaboration without compromising privacy.

[0090] Step 2: Generate a local model parameter update package containing local data distribution characteristics and perform privacy protection processing.

[0091] In this embodiment, the local model parameter update package refers to a set of values ​​calculated by the edge node after training the global initial model using local data, which indicates the direction of model optimization. The update package is usually represented by a high-dimensional gradient vector or weight increment matrix. It does not directly contain any specific product sales or user identity information, but rather records in a highly abstract way the model parameters should be increased or decreased by how much in which dimensions in order to make the global model more accurately fit the local market rules.

[0092] Understandably, although gradient information is already an abstraction of the original data, in high-dimensional space, potential attackers may still be able to recover the original training samples through gradient inversion attacks. Therefore, in order to meet the compliance requirement of data being usable but not visible, the update package must be subjected to deep privacy protection processing.

[0093] Specifically, privacy protection processing in this embodiment may include the following steps:

[0094] 1) Gradient calculation and sensitivity truncation based on local data; First, the normalized feature vector generated in step 1 is input into the local model through edge nodes for forward and backward propagation to calculate the original gradient vector. .

[0095] Specifically, based on the local dataset and the current global model parameters, an optimization direction can be calculated, aiming to find the parameter update direction that minimizes the local prediction error. This applies to each participating country node. By defining a loss function (such as cross-entropy loss or mean squared error), which measures the difference between the model's current predictions and the true labels, and combining this with the backpropagation algorithm, the partial derivative vector of the loss function with respect to the model parameters is calculated, i.e., the original gradient vector. .

[0096] For example, in this embodiment, the original gradient vector can be calculated using the following formula:

[0097]

[0098] in, These are the current global model parameters; National nodes Owns a local private dataset; The loss function; Indicates the parameter The operation of finding partial derivatives; The calculated original gradient vector represents the gradient in the current data. Below, parameters How should we adjust it to reduce the error? It is completely accurate, but this also means it contains too much local data fingerprinting, posing a very high risk of privacy breaches.

[0099] Then, to achieve differential privacy, noise must be added to the gradient. However, if the original gradient... The range of values ​​for sensitivity is unbounded, so no matter how much finite noise is added, privacy cannot be mathematically guaranteed (because a single anomalous sample may cause the gradient to become infinite, thus masking the noise); therefore, the maximum range of influence of the gradient must be limited before adding noise.

[0100] In other words, to prevent individual anomalous data (such as a large purchase order or malicious attack data) from having an excessive impact on the model, and to provide mathematical boundary guarantees for subsequent differential privacy mechanisms, gradient pruning is required. The core of gradient pruning lies in setting a preset gradient pruning threshold (C). This threshold physically represents the maximum impact a single data record is allowed to have on the overall model. This can be determined by calculating the L2 norm of the original gradient vector (i.e., the magnitude of the vector, representing the strength of the gradient). If the gradient norm is less than or equal to... If the gradient is within a safe range, it should be preserved; if the norm of the gradient exceeds... This indicates that the gradient has an excessive impact (possibly caused by outliers or specific sensitive data). Therefore, it should be forcibly scaled to make its norm equal to... However, it maintains its direction.

[0101] For example, in this embodiment, the clipped gradient can be calculated using the following formula:

[0102]

[0103] in, This represents the L2 norm (Euclidean norm) of the original gradient vector. The gradient pruning threshold is a hyperparameter that determines the system's tolerance for outlier data and the baseline for privacy protection. It is a scaling factor, when When, the denominator is ,but (Without change), when When, the denominator is ,at this time Its modulus length is compressed to ; This represents the pruned gradient vector. By forcibly pruning, the upper limit of model parameter changes that any single piece of privacy data can cause can be mathematically locked. This is not only a guarantee of model training stability but also a necessary prerequisite for implementing differential privacy. Figure 6 This diagram illustrates the gradient clipping based on the L2 norm in this embodiment.

[0104] 2) Noise injection based on differential privacy; after pruning, the processed gradient vector needs to undergo a core privacy-preserving operation—noise injection—to ensure it meets the requirements. - Differential privacy standard. In this embodiment, noise injection can employ a Gaussian or Laplace mechanism to generate a random noise vector with the same dimension as the gradient vector. This noise vector follows a specific probability distribution, for example, ,in This serves as a noise multiplier. Finally, the random noise vector is injected into the clipped gradient through superposition to generate the final safety update package. .

[0105] Specifically, a Gaussian (normal) distribution with a mean of 0 can be constructed, whose variance is related to the pruning threshold. and privacy protection strength (noise multiplier) The noise vector sampled from the distribution is proportional to the gradient and is superimposed on the clipped gradient to form the final gradient used for transmission.

[0106] For example, in this embodiment, the final transmission gradient can be calculated using the following formula:

[0107]

[0108] in, Represents a multivariate Gaussian distribution; This is the noise multiplier; the larger the value, the more noise is added, and the higher the level of privacy protection. The smaller the value, the lower the convergence accuracy of the model may be. This is the covariance matrix, which ensures that the noise amplitude is consistent with the previously set clipping threshold. They match; This is the final output gradient vector with differential privacy protection.

[0109] It should be noted that only this final gradient vector is ultimately available. It will be sent out of the local node, and because it contains random noise and has been amplitude-limited, the noise multiplier... Size and the system's privacy budget This is directly related to the fact that, while the gradient vector with added noise retains the statistical regularity of the local data on a macroscopic level (and can guide the convergence of the global model), on a microscopic level, due to the masking effect of the noise, potential attackers cannot distinguish whether the minute changes in model parameters originate from the real behavior of a specific user or from random noise injected by the system. This approach strictly satisfies mathematical requirements. Differential privacy fundamentally cuts off the mathematical path of inferring the original data from the model parameters, making it mathematically impossible for potential attackers to obtain the final gradient vector. Inverse solution Furthermore, it is impossible to deduce the result. This achieves the compliance requirement that data does not leave the country and only flows through gradients. Figure 7 A schematic diagram of gradient clipping and differential privacy noise injection in this embodiment is shown.

[0110] 3) Secure encryption encapsulation: After completing the above mathematical transformations, in order to prevent data from being intercepted or tampered with by a man-in-the-middle during cross-border transmission, the edge node can also perform traditional encryption encapsulation on the noisy security update packet.

[0111] Specifically, the update package can be packaged using a high-strength asymmetric encryption algorithm (such as RSA or ECC) and sent to the collaborative management center server through an encrypted tunnel built using TLS (Transport Layer Security).

[0112] Furthermore, in some high-security implementations, a blockchain notarization mechanism can be introduced to upload the hash value of the update package to the consortium blockchain ledger. This step aims to ensure data integrity and non-repudiation. Since blockchain notarization does not store the data itself, but rather its digital fingerprint, regulatory agencies can verify whether the cross-border transfer has been tampered with by comparing the hash value on the chain in the event of a compliance audit, thereby constructing a complete closed loop for cross-border data compliance auditing.

[0113] Through the above steps, the edge node outputs no longer sensitive raw business data, but a model parameter update package that contains local market characteristics and trends (usefulness), is tightly wrapped by mathematical noise (privacy), and is encrypted and authenticated (security), thus adapting to the dual requirements of compliance and intelligence in cross-border e-commerce scenarios.

[0114] Step 3: The collaborative management center server receives the security update package and performs a weighted aggregation based on entropy weight and network latency.

[0115] In this embodiment, the collaborative management center server refers to a logical control unit deployed on cloud infrastructure with high computing power and high bandwidth throughput (e.g., AWS core nodes or Alibaba Cloud headquarters cluster); it is the brain of the entire cross-border e-commerce data collaboration network, responsible for maintaining global model parameters and coordinating the pace of various edge nodes worldwide. Receiving security update packets refers to the central server collecting security update packets in parallel through an encrypted communication channel from... The gradient vectors uploaded by edge nodes in different countries have undergone differential privacy processing. It is understandable that simply receiving and averaging (i.e., the traditional FedAvg algorithm) has serious shortcomings in cross-border scenarios: on the one hand, the development of e-commerce infrastructure in various countries is uneven, resulting in huge differences in network transmission speed; on the other hand, the value of data in different countries is completely different - some countries have a large amount of data but it is singular, while some countries have a small amount of data but it covers all categories.

[0116] Specifically, the traditional FedAvg algorithm cannot handle the data heterogeneity common in cross-border e-commerce, where a node may have a large amount of data but a single category (e.g., all shoes), resulting in far less information content than a node with a small amount of data but a rich variety of categories (covering shoes, clothing, hats, and bags). Furthermore, due to the significant differences in network latency across borders, waiting for the worst-performing node to complete its transmission will severely hinder the training efficiency of the entire system—a phenomenon known as the "fallback problem" caused by system heterogeneity.

[0117] Therefore, in this embodiment, a multi-factor adaptive weighted system can be constructed. This system quantifies data quality by introducing the concept of information entropy and perceives network state by introducing round-trip latency, thereby achieving efficient and high-quality model aggregation. In this embodiment, the multi-factor adaptive weighted system can be constructed through the following steps:

[0118] 1) Quality assessment based on Shannon entropy of data distribution: Before aggregation, the central server first evaluates the information richness of the data uploaded by each edge node. This embodiment introduces Shannon entropy as a metric. In a federated learning scenario, a higher entropy value means that the data category distribution of that node is more uniform, and the more general knowledge the model learns from it; therefore, the contribution of that node should be amplified. Conversely, if the data categories are extremely skewed and the entropy value is low, it indicates that the data may lead to model bias, and its weight should be appropriately limited.

[0119] In cross-border e-commerce, the volume of data is not the same as the amount of information. For example, node A uploaded 100,000 data entries, but they were all sales records for a single product: white cotton socks. Node B, on the other hand, uploaded only 10,000 data entries, but they covered hundreds of categories, including electronics, apparel, and home furnishings, with a more evenly distributed user profile. Clearly, node B is more valuable for training a global model with strong generalization ability. Therefore, the Shannon entropy of the local data distribution can be calculated. To quantify this value, the higher the entropy value, the more balanced the distribution of data categories at that node, the greater the uncertainty, and the richer the information it contains.

[0120] For example, in this embodiment, the Shannon entropy of the data distribution can be calculated using the following formula:

[0121]

[0122] in, Indicates the first Local dataset for each node Shannon entropy, The total number of categories defined globally (e.g., 100 product categories in total); Indicates at node In the data set, it belongs to the category The probability (frequency) of a sample. Usually, 2 or The logarithm with base 0 is used to measure the uncertainty or information content of the data. If a node has samples from all classes and they are uniformly distributed, the sum of this term is the largest (maximum information content); if a node has samples from only one class, the value is 0 (minimum information content). Figure 8 A schematic diagram is shown illustrating the Shannon entropy assessment of the data distribution at edge nodes; Figure 9 This diagram illustrates the quantification of data quality for Shannon entropy assessment of different sample data from two different edge nodes.

[0123] 2) Construction of Adaptive Aggregated Weight Coefficients: After quantifying data quality, it needs to be combined with the network state to generate the final aggregated weights. In this embodiment, instead of solely relying on the number of samples, a ternary weight function is constructed that incorporates sample size, information entropy, and network latency. The ternary weight function in this embodiment is a weighting mechanism based on a variant of Softmax: for information content, an exponential growth function is used. High-entropy nodes are rewarded non-linearly; while for latency, an exponential decay function is used. A non-linear penalty is applied to high-latency nodes to ensure that the model tends to make more use of nodes with good data quality and fast network speed.

[0124] Specifically, considering the extreme complexity of cross-border network environments, congestion or route jumps in submarine optical cables can cause a surge in communication latency at certain nodes. In a synchronous federated learning framework, the update speed of the global model often depends on the slowest node (i.e., the bottleneck effect). If high-latency nodes are given excessively high weights, the system not only has to spend a significant amount of time waiting for their data transmission, but the data from these nodes may also lose timeliness due to prolonged transmission (e.g., failing to reflect minute-level inventory changes in a timely manner). To mitigate this problem, this embodiment introduces a latency penalty factor in the weight calculation. (in This is a penalty factor, reflecting an efficiency-first scheduling strategy. It's understandable that the delay penalty factor... It is an exponential decay function; the higher the network latency of a node, the lower its weight coefficient in global aggregation. The lower the weight of the network, the more the system implicitly reduces its reliance on regions with poor network conditions. By reducing the weight of slow nodes, the model can complete an aggregation round faster, thereby compressing the global coordination response cycle from minutes to seconds in high-frequency trading scenarios (such as various promotional events). Furthermore, it can also introduce... Item (of which) (As an adjustment coefficient), the weight of high-entropy nodes is amplified by an exponential function, so that when the global model is updated, it pays more attention to the data of countries with complex market environments and rich commodity varieties, thereby avoiding the global model from overfitting to markets with large amounts of data but simple patterns.

[0125] For example, in this embodiment, the adaptive aggregation weight coefficient can be calculated using the following formula:

[0126]

[0127] in, For nodes The normalized weights during global aggregation satisfy the following conditions: ; For nodes The number of samples is retained to ensure the contribution of the basic data scale; Central server and nodes Network round-trip time; Adjusting hyperparameters for information entropy ( ), The larger the value, the more the model favors nodes with a more balanced data distribution; For delay penalty hyperparameters ( ), The larger the value, the more sensitive the model is to network latency, and the greater the penalty for slow nodes. It should be noted that the numerator in the above formula... It is a node The overall score is calculated by the denominator, which is the sum of the overall scores of all participating nodes. This is used for normalization to ensure that the sum of all weights is 1. Through this design, the system can automatically achieve dynamic scheduling based on the survival of the fittest.

[0128] 3) Finally, on the central server side, the calculated adaptive weights are used... For the gradients uploaded by each node (after differential privacy processing) We perform a weighted summation to update the parameters of the global collaborative model.

[0129] Specifically, this is the final step in one iteration of federated learning. The central server uses the aggregated gradient directions to propel the global model towards a position with a smaller loss function. By combining the above two dimensions, the collaborative management central server calculates the final adaptive aggregation weight coefficients. This involves performing a weighted aggregation operation to update the global model parameters. The resulting new global model... The new global model It incorporates the characteristics of heterogeneous data from around the world (especially those from high-value regions) and statistically overcomes the latency bottleneck of cross-border physical networks. Understandably, this updated model is no longer a simple average, but a culmination of wisdom from multiple countries, weighted by value and filtered for efficiency. It can be distributed back to each country to guide specific business operations.

[0130] For example, in this embodiment, the parameter update of the global collaborative model can be calculated using the following formula:

[0131]

[0132] in, For the current time step (the 1st time step) The global collaborative model parameters of the (wheel); For the updated (the first) The parameters of the global collaborative model (round 1) will be distributed back to each node for the next round of training; The global learning rate is used to control the step size for updating model parameters. The global gradient after weighted aggregation, since Having taken into account both data quality and network state, this aggregated gradient is more representative of the global optimal descent direction than the traditional average gradient, and it converges faster. Figure 10 The diagram illustrates a comparison of the convergence speeds of ordinary federated learning in this embodiment and adaptive aggregation training in this application. Through the above steps, while ensuring privacy, the two major challenges of uneven data distribution and extremely unstable network environment commonly found in cross-border scenarios are effectively solved, achieving a dual improvement in model performance and training efficiency.

[0133] Step 4: Based on the updated global collaboration model, generate business scheduling instructions for each country.

[0134] In this embodiment, the business scheduling instruction refers to a control signal with deterministic operational logic generated by the collaborative management center server and distributed to each edge node or logistics execution system. In complex cross-border logistics networks, simply knowing that a certain location is experiencing a shortage is insufficient. Decision-makers often face complex trade-offs: should they transfer goods from the nearest warehouse (lower freight costs but potentially tight inventory), or from a distant warehouse with excess inventory (higher freight costs but can alleviate inventory buildup)? Or, due to excessively high transfer costs, might a comparison reveal that abandoning some demand is more cost-effective than transferring goods?

[0135] To address these issues, this embodiment constructs a Mixed Integer Linear Programming (MILP) system. This system comprehensively considers forecast results, real-time inventory, logistics costs, and stockout penalties to automatically calculate the optimal allocation matrix, thereby outputting specific business scheduling instructions. These business scheduling instructions are no longer probabilistic forecast values, but rather specific commands that can be directly recognized and executed by Enterprise Resource Planning (ERP) systems, Warehouse Management Systems (WMS), or automated logistics robots. For example, the specific instruction could be 'allocate 500 items from the Guangzhou warehouse in China (Node A) to the Frankfurt warehouse in Germany (Node B)' or 'initiate a replenishment program for a specific SKU at each local node'.

[0136] Understandably, the updated global collaborative model in step 3 is essentially a high-precision predictor, outputting a probability estimate of the future (e.g., the demand for a certain down jacket in the German market might be 1000 units in the next 7 days). However, business decisions require definite action plans. Therefore, the core logic of this step is to use the machine learning prediction results as input and, through operations research optimization methods, transform them into a cost-optimal physical scheduling plan.

[0137] Specifically, in this embodiment, the implementation process of the mixed integer linear programming (MILP) system includes the following tightly coupled logical steps:

[0138] 1) Based on a global model, the network-wide demand situation is perceived. First, the collaborative management center server uses the newly updated global collaborative model, inputting current time characteristics, promotional activity parameters, and macroeconomic indicators, to assess the future time period (prediction time domain) of the countries where each edge node is located. The demand for goods within the range is extrapolated to obtain a demand forecast matrix. Thanks to the global collaborative model that incorporates features from multiple countries in step 3, the prediction results at this point not only include local historical trends but also implicitly utilize cross-border correlation features (for example, using leading sales data from the Chinese market to help predict the lagged surge in the European market), thereby significantly improving the accuracy of the prediction. Figure 11 This embodiment illustrates a visualization of the gap between node inventory and predicted demand.

[0139] 2) Constructing a Mixed Integer Linear Programming (MILP) Model for Global Inventory Coordination: To transform forecast data into optimal decisions, this embodiment constructs a mathematical programming model with physical constraints. The core of this model lies in constructing a global objective function. By finding the optimal balance between logistics costs and service levels, the objective function can typically be expressed as: .

[0140] Specifically, in operations research, this is usually achieved by minimizing total cost. Here, total cost includes not only visible logistics and transportation costs (freight, tariffs) but also invisible opportunity costs (sales losses and brand damage due to stockouts). In this embodiment, a unit stockout penalty coefficient is introduced ( This is a virtual monetization parameter, representing the potential loss for each missing item. If the target is set too high, the model will allocate resources at all costs (even if shipping costs are high) to meet the demand; if If the threshold is set too low, the model may choose a conservative approach, allowing for stockouts to save on expensive international shipping costs. Through this mechanism, the model can automatically find the optimal balance between ensuring supply and controlling costs.

[0141] For example, in this embodiment, the objective function of the linear programming can be calculated using the following formula:

[0142]

[0143] in, The total operating cost is the sole objective of optimization. As a core decision variable, it represents the decision-making variables from the national level. Transferred to the state The quantity of goods, which is an integer to be solved for, all Together, they form the allocation matrix we ultimately need. ; From arrive The unit logistics cost parameter matrix (including freight, customs duties, insurance, etc.); This item represents the total logistics cost; The countries predicted by the federated learning model in step 3. In time Demand; It is a country Current inventory; Representatives transferred from other countries The country's total inflow; This item is used to calculate the net stockout quantity. It is calculated by subtracting (current inventory + incoming goods) from the predicted demand. If the result is less than 0, it indicates sufficient inventory and the stockout cost is 0. If the result is greater than 0, it indicates a shortage and is multiplied by a penalty factor. This refers to the cost of penalties for stockouts. Figure 12 A schematic diagram of the unit logistics cost parameter matrix of this embodiment is shown.

[0144] It should be noted that a penalty coefficient is introduced. This is to prevent algorithms from sacrificing user experience to save on shipping costs during actual processing. The value is usually set much higher than the logistics cost, thus forcing the model to make a rational trade-off between paying high shipping costs for emergency restocking and bearing the loss of customers due to stockouts—in most cases, the model will choose to restock in order to ensure sales.

[0145] 3) Imposing strict physical and logical constraints: When solving the objective function, strict constraints must be imposed to ensure that the generated instructions are executable in the physical world. First, the model cannot allocate items that are not in inventory; and the sum of the allocations from any country must never exceed the current total physical inventory of that country, which guarantees the physical validity of the scheduling instructions; moreover, goods are discrete physical entities, and the system cannot generate instructions such as 'allocate 0.5 pairs of shoes'. By setting strict physical constraints, the problem is forcibly upgraded from simple linear programming (LP) to mixed integer programming (MILP). Although this increases computational complexity, it ensures the logical consistency between the decision results and real-world warehousing operations.

[0146] Specifically, the quantity of goods dispatched from any node must never exceed the existing inventory in its warehouse, therefore, it is necessary to monitor each supply node. Imposing an inventory cap constraint; simultaneously, since the quantity of goods must be physical, the decision variables... The value must be a non-negative integer (you cannot transfer -5 or 3.5 items). For example, in this embodiment, the constraint can be expressed as follows:

[0147] ,

[0148] ,

[0149] in, Represents a node The total amount of goods shipped to all other countries; For nodes Locally available inventory; Represents a set of integers.

[0150] 4) Optimal solution generation and closed-loop execution: The collaborative management center server uses a high-efficiency solver to calculate the above model and outputs the globally optimal allocation matrix. Subsequently, the system parses the non-zero elements in the matrix into specific service scheduling instructions. For items with insufficient local inventory that cannot be met through transfer, an emergency purchase order is generated.

[0151] Specifically, the execution steps are as follows: 1. Input parameterization: The system will parameterize the federated learning predictions... Real-time inventory acquisition and the pre-set cost and Substituting into the above formula; 2. Solver calculation: A mathematical programming solver (such as the Branch-and-Bound algorithm) can be used to search for a solution that satisfies all constraints, thereby maximizing the objective function. Reaching the minimum value Combination; 3. Matrix generation: The algorithm will eventually output a... matrix Elements in the matrix This is the optimal allocation instruction. For example, if This means 'Instruction: Ship 500 items from Country 1 to Country 3', if This means that no allocation is needed.

[0152] Figure 13This diagram illustrates a cost-benefit optimization comparison analysis of this embodiment. Through this step, a complete closed loop is completed: from data perception at the edge to privacy computing at the transmission layer, and finally to intelligent decision-making at the center. It can be understood that this scheduling instruction based on a global collaborative model, compared with the traditional cross-border e-commerce replenishment strategy where each country operates independently, can significantly reduce the total inventory backlog in the global supply chain, while greatly improving the response speed to explosive demand in local markets (such as a suddenly popular product in a certain country), thus achieving effective cost reduction and efficiency improvement.

[0153] Step 5: This embodiment also includes an adaptive adjustment and hot update strategy for sudden compliance changes.

[0154] In this embodiment, sudden compliance changes mainly refer to the issuance of new temporary data protection injunctions or amendments by the legislative or regulatory authorities of the country where the edge node is located. For example, a country suddenly announces a ban on the use of user facial recognition features or precise geographic coordinates for cross-border model training, requiring immediate effect. Such changes are usually unpredictable and time-sensitive. In traditional software engineering practices, responding to such changes typically requires a lengthy cycle of 'code modification - recompiling - shutdown and release - full restart', which not only leads to business interruption but may also trigger compliance circuit breakers due to an excessively long response window; it may even result in huge compliance fines due to an excessively long response window.

[0155] Therefore, in this embodiment, a hot update mechanism based on the control channel is introduced to circumvent this limitation. The specific steps are as follows:

[0156] 1) Generation and distribution of compliance policy update patches,

[0157] When the collaborative management center server detects the aforementioned compliance changes, it first translates the abstract legal language into a machine-executable compliance policy update patch. This patch is not a large, full firmware package at the physical level, but rather a lightweight, structured rule configuration file (such as JSON, YAML format, or a compiled dynamic link library .so file). Simultaneously, the system maintains a global feature index table. For example, index number 105 corresponds to 'GPS longitude', and index number 106 corresponds to 'GPS latitude'; when the 'prohibit GPS data collection' command is issued, the central server generates an update patch containing {Block_List:[105, 106]} or its corresponding binary mask vector. Understandably, the patch generation process maps legal constraints to mathematical zero-value constraints; its size is typically only a few KB, making it extremely easy to distribute quickly in restricted networks.

[0158] The collaborative management center server pushes the patch to the corresponding edge node in the country in real time through the control channel. This embodiment employs a network architecture that separates the data plane and control plane. The data plane is responsible for transmitting large-volume model gradients (GB-level), which may face network congestion and queuing; the control plane is an independent high-QoS channel (similar to an emergency lane) reserved for operational instructions. Pushing patches through the control channel ensures that even during peak periods when data links are fully loaded, compliance instructions can reach the edge node with millisecond-level low latency, achieving the highest priority response to compliance risks.

[0159] This strategy decouples compliance from code, allowing business logic code to handle common computational processes, while compliance policies exist as independent configuration items. The central server pushes the patch to edge nodes in affected countries in real-time via a dedicated control channel separate from the data transmission channel. This control channel typically has higher network priority (QoS), ensuring compliance instructions are delivered within milliseconds even when data links are congested.

[0160] 2) Dynamic loading based on hot update mechanism: After receiving the patch, the edge node triggers the hot update mechanism to load the patch, and intercepts and filters out feature extraction operations that do not comply with the new compliance policy without stopping business services.

[0161] Hot update mechanism refers to dynamically replacing logical modules in memory at the operating system level without terminating the currently running main process or severing existing user connections. Specifically, in this embodiment, the inference engine of the edge node runs a daemon process that monitors the memory address of the configuration file. When a patch arrives, the system uses atomic pointer swapping technology to instantly switch the pointer pointing to the old feature mapping table to the new feature mapping table. This process is completed at the CPU instruction level, taking only nanoseconds. For front-end users browsing products, their HTTP request processing threads are not suspended or interrupted, thus achieving a smooth transition with zero business impact.

[0162] 3) Interception and filtering of non-compliant features: After loading the patch, a dynamic compliance firewall is inserted between step 1 (feature extraction) and step 2 (parameter generation) of the edge node. The system will scan the feature vector bit by bit according to the new policy.

[0163] Specifically, assuming the original feature vector is The system uses the mask vector in the patch. (Where, compliance dimension is 1, and violation dimension is 0), and perform the Hadamard product operation:

[0164] This operation forces the prohibited feature dimensions (such as the dimension corresponding to GPS) to zero numerically. It's important to note that this is not merely a simple deletion of data; more importantly, it ensures the invariance of the tensor shape, keeping the model's input dimension D unchanged. Therefore, there's no need to recompile or reconstruct the neural network model structure. Furthermore, this operation allows the system to intercept non-compliant information at the source, meaning that subsequent data input into the model no longer contains sensitive features. Thus, the local model parameter update package (gradient) calculated in step 2 is mathematically completely decoupled from the sensitive feature (i.e., the partial derivative of the gradient with respect to that feature is 0). Understandably, this step builds a final line of defense. This mechanism mathematically cuts off the path for sensitive data to enter the global model, ensuring that even if the original code remains unchanged, the gradient data output overseas strictly complies with the latest regulatory requirements. This ensures both the continuity of the company's global business and absolute respect for the changed regulations.

[0165] 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 description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., 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 cross-border e-commerce multi-country data collaborative management, characterized in that, The collaborative management method comprises: S1, each edge node acquires local original e-commerce business data, and Based on the pre-set domain knowledge graph and the graph neural network, the local heterogeneous data standardization and feature extraction are performed to obtain a standardized feature vector; S2, each edge node trains a local model using the standardized feature vector, generates a model parameter update package indicating the model optimization direction, and after performing privacy protection processing including differential privacy noise injection on the model parameter update package, sends it to the collaborative management center server; S3, the collaborative management center server receives the model parameter update package from multiple edge nodes, constructs an adaptive aggregation weight function based on the data distribution shannon entropy and network round-trip delay of each edge node, calculates the adaptive aggregation weight coefficient, and performs weighted aggregation to update the global collaborative model; S4, the collaborative management center server predicts the commodity demand in the future time domain based on the updated global collaborative model, and substitutes the commodity demand as an input parameter into a mixed integer linear programming model to solve a global optimal allocation matrix under the premise of meeting physical constraints, and generates a deterministic business scheduling instruction. 2.The method of claim 1, wherein, The S1 performs local heterogeneous data standardization and feature extraction, specifically comprising: Based on the physical space mapping of the domain knowledge graph, a linear projection layer containing a learnable semantic alignment projection matrix is used to map original e-commerce business data of different dimensions to a unified dimension latent space, and a non-linear activation function is used for processing; The mapped data is modeled as graph structure data containing goods and user behavior, and entity embedding vectors are generated using the neighbor aggregation mechanism of the graph neural network; An interaction feedback feature vector is introduced as a reference signal to calculate an attention weight vector, and the attention weight vector is applied to the entity embedding vector through Hadamard product to generate a dynamically corrected standardized feature vector. 3.The method of claim 1, wherein, The S2 performs privacy protection processing on the model parameter update package, specifically comprising: According to the local data set and the current global model parameter, the loss function is calculated, and the original gradient vector relative to the model parameter is calculated through back propagation; The original gradient vector is subjected to sensitivity clipping to obtain a clipped gradient vector; Based on the differential privacy standard, a random noise vector with the same dimension as the gradient vector is generated using a Gaussian mechanism or a Laplace mechanism, and the random noise vector follows a probability distribution whose variance is proportional to the clipping threshold and the privacy protection strength; The random noise vector is superimposed on the clipped gradient vector to generate a secure update package.

4. The cross-border e-commerce multi-country data collaboration management method of claim 3, wherein, After generating the secure update package, it further comprises: An asymmetric encryption algorithm is used to package the secure update package, and the encrypted tunnel constructed through the transport layer security protocol is used for transmission; The hash value of the secure update package is uploaded to the blockchain consortium chain ledger for cross-border data compliance audit.

5. The cross-border e-commerce multi-country data collaboration management method of claim 1, wherein, The adaptive aggregation weight function in S3 is constructed based on a multi-factor adaptive weighting system, specifically comprising: S3.1, performing quality evaluation based on data distribution shannon entropy, For each edge node, calculate the Shannon entropy of its local data distribution, and use the Shannon entropy to quantify the uniformity of the category distribution and the amount of information contained in the node's data; S3.2, Perform state awareness based on network latency. Real-time monitoring of round-trip latency between each edge node and the collaborative management center server is used to detect the current state of cross-border network congestion. S3.3 Construct an adaptive aggregation weight function that incorporates sample size, information entropy, and network latency. Based on the sample size, a base weight is set, and the base weight is non-linearly rewarded using an exponential growth factor based on the Shannon entropy, and non-linearly penalized using an exponential decay factor based on the round-trip delay, thereby generating the final adaptive aggregate weight coefficient. 6.The method of claim 5, wherein, The execution of weighted aggregation to update the global collaboration model includes: Obtain the model parameter update packets uploaded by each edge node after differential privacy processing, and parse them to obtain gradient vectors containing noise; Using the calculated adaptive aggregation weight coefficients, a weighted summation operation is performed on the gradient vectors of each edge node to calculate the global average gradient; The parameters of the current global collaborative model are iteratively updated using the global average gradient, so that the updated global collaborative model moves in the direction of reducing the loss function.

7. The cross-border e-commerce multi-country data collaboration management method of claim 1, wherein, In step S4, the commodity demand is input into a preset mixed-integer linear programming model, specifically including: Construct a global objective function, which includes: The total cost of cross-border logistics and the cost of stockout penalty, wherein the cost of stockout penalty includes the unit stockout penalty coefficient; Set physical constraints, which at least include: The constraints include the upper limit of inventory that the sum of the transfers from any node cannot exceed the current physical inventory of that node, and the discrete constraint that the transfer instruction variable must be a non-negative integer. The global objective function is solved using a mathematical programming solver to find the allocation combination that minimizes the total cost. 8.The method of claim 7, wherein, S4 further includes: The globally optimal allocation matrix obtained by analytical solution; For elements in the matrix whose transfer instruction variables are greater than zero, generate a cross-border transfer order and trigger the logistics interface; For elements where local inventory is insufficient and cannot be met through allocation, an emergency purchase order is generated. 9.The method of claim 1, wherein, The collaborative management method also includes: S5's adaptive adjustment steps for sudden compliance changes: When the collaborative management center server detects a compliance change event in a specific region, it transforms the legal constraints into a compliance policy update patch that can be executed by the machine. The compliance policy update patch contains an index or mask vector of restricted features. The compliance policy update patch is pushed to the corresponding edge nodes via a control plane channel independent of the data transmission channel. The network priority of the control plane channel is higher than that of the data plane channel used to transmit model parameter update packets; The edge node receives the patch and triggers a hot update mechanism to load the compliance policy update patch.

10. A cross-border e-commerce multi-country data collaborative management system, characterized in that, The collaborative management system includes: processor; A memory that stores a computer program, which, when executed by a processor, implements the cross-border e-commerce multi-country data collaborative management method as described in any one of claims 1 to 9.

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