A cross-border e-commerce information risk analysis method combined with cloud computing

Abstract

The application discloses a cross-border e-commerce information risk analysis method combined with cloud computing and relates to the technical field of e-commerce information security.The application comprises the following steps: accessing multi-source data by using a cloud-edge collaborative architecture, generating a cross-border unique feature code through data fingerprint identification; dynamically time warping transaction behavior to capture time sequence abnormal mode of cross-border transaction; constructing a transaction space-time graph, capturing abnormal space-time mode in cross-border transaction through space-time feature coding; constructing a risk assessment screening model to perform abnormal detection screening; constructing a risk assessment analysis model to score cross-border merchant credit; and establishing a hierarchical response mechanism to automatically generate qualified reports.The application uses a three-level risk perception system of 'cloud-edge-end' through distributed data collection, real-time feature engineering and a self-adaptive deep learning model, proposes a cross-border feature cross-validation algorithm, solves the problem of non-uniform data standards in multiple countries and improves cross-border e-commerce information processing efficiency.

Description

Technical Field

[0001] This invention belongs to the field of e-commerce information security technology, and in particular relates to a method for cross-border e-commerce information risk analysis that combines cloud computing. Background Technology

[0002] With the global cross-border e-commerce transaction volume exceeding $6 trillion (Statista data, 2024), traditional risk control systems face three major technological bottlenecks:

[0003] Data silo problem

[0004] Existing systems often employ independent risk control modules (such as PayPal's fraud detection and DHL's logistics monitoring), resulting in fragmented data across payment, logistics, and customs declaration. A report by the International E-Commerce Association shows that this fragmented analysis leads to a cross-channel fraud detection rate of less than 43%.

[0005] Real-time defects

[0006] Traditional rule-based methods (such as IBM ODM) have an average response latency of 2.3 seconds (MIT test data from 2023), which is insufficient to cope with the high-frequency "flash fraud" attacks in cross-border payments (average time of 1.8 seconds).

[0007] Technology has limitations

[0008] Single cloud computing architectures (such as AWS Fraud Detector) struggle to handle real-time data at the edge, and machine learning models (such as FICO Falcon) lack the ability to cross-validate cross-border features.

[0009] These shortcomings result in huge losses for cross-border e-commerce companies on average each year due to risk control failures, necessitating a technological breakthrough in cross-border e-commerce information risk analysis methods that incorporate cloud computing. Summary of the Invention

[0010] The purpose of this invention is to provide a cross-border e-commerce information risk analysis method that combines cloud computing. Through distributed data collection, real-time feature engineering, and adaptive deep learning models, it solves the problems of existing cross-border e-commerce data processing difficulties, edge-side real-time data, payment fraud, logistics anomalies, and policy compliance issues faced by cross-border e-commerce.

[0011] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0012] This invention relates to a method for analyzing information risks in cross-border e-commerce using cloud computing, comprising the following steps:

[0013] Step S1: Use a cloud-edge collaborative architecture to access multi-source data and generate a unique cross-border feature code through data fingerprint identification; the multi-source data includes order data, payment logs, and customs records;

[0014] Step S2: Perform dynamic time normalization on transaction behavior to capture abnormal time patterns in cross-border transactions;

[0015] Step S3: Construct a transaction spatiotemporal graph and use spatiotemporal feature encoding to capture abnormal spatiotemporal patterns in cross-border transactions;

[0016] Step S4: Construct a risk assessment and screening model to perform anomaly detection and screening;

[0017] Step S5: Construct a risk assessment and analysis model to score the creditworthiness of cross-border merchants;

[0018] Step S6: Establish a tiered response mechanism and automatically generate qualified reports.

[0019] As a preferred technical solution, in step S1, data normalization processing is performed on multi-source data; wherein, the order data extracts the triplet of "user ID + product SKU + timestamp"; the payment log converts SWIFT code to Unified Financial Code; the customs records reconstruct the declaration fields using the UN standard; the normalization processing is achieved by deploying a data cleaning module on IoT devices and using a Bloom Filter to filter duplicate data;

[0020] The specific steps for generating a cross-border unique feature code from the data fingerprint identifier are as follows:

[0021] Step S11: Divide the 24-hour time window using the UTC+8 time zone, and associate each time window with an independent hash seed pool (pre-generate 3 sets of seeds to achieve seamless switching).

[0022] Step S12: Combine server clock noise with cross-border transaction volume fluctuation data;

[0023] Step S13: Iteratively process the entropy source data using the SHA-256 algorithm and output a 256-bit intermediate seed;

[0024] Step S14: A rolling hash function based on a time window is used, which automatically updates the hash seed every 24 hours and performs debiasing by eliminating random number bias through a VonNeumann corrector.

[0025] The formula for the rolling hash function is as follows:

[0026] ;

[0027] In the formula, This represents the output of the dynamic hash function. This indicates that the SHA-3 standard family of 256-bit hash algorithms is used. Indicates input data, This indicates a bitwise XOR operation. This is for performing a modulo operation on the hash result.

[0028] As a preferred technical solution, when generating the data fingerprint, the edge node receives the raw data, divides it into 128KB blocks, adds a timestamp and GPS coordinate metadata to each block, and uses SIMD instructions to process the feature extraction of 16 data blocks in parallel. The transaction amount is discretized into 256 levels plus an exchange rate fluctuation index to obtain payment features, and the logistics information timeliness deviation value is normalized into a [0,1] interval plus a path similarity score to obtain logistics features; wherein...

[0029] ,

[0030] ;

[0031] The feature weight vector dot product is performed using the VPMADDWD instruction, and the final output is a 64-bit intermediate fingerprint (the high 32 bits are the feature summary and the low 32 bits are the check code).

[0032] As a preferred technical solution, after the data fingerprint is generated, it is clustered according to physical location, and adjacent edge node fingerprints are grouped into the same processing group. Geographically proximate edge nodes (such as different IoT devices in the same cross-border warehouse) are mapped to the same geographical block through GeoHash encoding, so that data related to physical location is naturally aggregated, reducing the computational complexity in the cloud. Outdated fingerprints with a time difference of more than 500ms are discarded, the fingerprint cluster center point with a Hamming distance of less than 3 is calculated, and bit-level majority voting is performed on the weighted fingerprints to generate the final fingerprint. Merging fingerprints within the group can avoid data conflicts caused by differences in administrative rules.

[0033] As a preferred technical solution, the specific process for dynamically adjusting the transaction time in step S2 is as follows:

[0034] Step S21: The original transaction flow is grouped by user ID to generate a transaction time series. Z-score standardization is performed to eliminate differences in monetary units. A sliding window mechanism is used to generate sub-sequences, with multiple overlapping sub-sequence windows generated for each user.

[0035] Score standardization is performed to calculate the transaction amount statistics for each user:

[0036] ;

[0037] In the formula, The mean of all data points. Let i be the value of the i-th data point. Standard deviation represents the degree of dispersion of the data.

[0038] Step S22: Construct the cost matrix and calculate the Euclidean distance as the local cost function; the cost matrix is ​​constructed by inputting the standardized transaction sequences X (length m) and Y (length n), and calculating an m×n matrix: each element , As a feature dimension, the holiday data points are multiplied by a weighting factor. (Typically 1.2-1.5 times); Quantify local differences in trading behavior to capture instantaneous anomalies in characteristics such as amount / frequency;

[0039] Step S23: Extract the cumulative cost of the optimal path as a risk indicator, calculate the local cost matrix of transaction sequence A and reference sequence B, and use the improved weighted Euclidean distance from the endpoint. Tracing the minimum cumulative path in reverse, recording all paths traversed by the path. Coordinates; using Sakoe-Chiba bandwidth constraints to limit the maximum offset of the time axis for path search optimization; initializing the cumulative cost matrix C and recursively filling it: ; Apply Sakoe-Chiba band restriction To prevent excessive distortion of the timeline;

[0040] Step S24: Calculate the dynamic volatility of the normalized sequence and generate anomaly pattern template library; sequence alignment is performed nonlinearly on the original sequence according to the DTW optimal path to eliminate time axis distortion, and volatility outliers are identified based on Hampel filter; it can capture hidden patterns of high-risk behaviors such as money laundering (such as drastic fluctuations in amount in a short period of time), and form multi-dimensional cross-validation with accumulated cost (high cost + high volatility = extremely high risk).

[0041] When constructing the abnormal pattern template library, K-means clustering is performed on historical high-risk transaction sequences to extract key features, such as median transaction interval, coefficient of variation of amount, and geographical dispersion. When new fraud patterns emerge, the template library is dynamically updated using online clustering algorithms (such as StreamKM++).

[0042] As a preferred technical solution, in step S3, the transaction spatiotemporal graph includes nodes, edges, and dynamic attributes; the nodes include merchants, users, and goods (including geographical location attributes); the edges represent transaction relationships (timestamped payment flows and logistics paths); the dynamic attributes are price volatility and transaction frequency change gradients; the specific process for capturing abnormal spatiotemporal patterns in cross-border transactions using spatiotemporal feature encoding is as follows:

[0043] Step S31: Transform the coordinates of the logistics nodes to generate a physical grid, and extract the transaction time pattern using periodic decomposition (daily / weekly / seasonal);

[0044] Step S32: Construct the spatiotemporal trajectory coding sequence of the transportation route;

[0045] Step S33: Employ the periodic decomposition and transaction time extraction model;

[0046] Step S34: Use graph attention mechanism to learn the risk propagation relationship between nodes, and use causal dilated convolution to capture long-period anomalies;

[0047] Step S35: Perform multimodal alignment on text (user reviews), images (product images), and structured data (payment records), and achieve feature complementarity through a cross-attention mechanism.

[0048] As a preferred technical solution, in step S34, node features are projected onto the cross-border compliance space through a learnable parameter matrix, and six sets of attention heads are used in parallel to capture risk dimensions such as money laundering patterns, fraudulent order behavior, and abnormal logistics. The outputs of each head are dynamically merged through a gating mechanism, and a directed propagation graph is constructed based on attention weights to identify high-risk subnets. Risk source nodes are located through gradient backpropagation.

[0049] As a preferred technical solution, the specific process for constructing the risk assessment screening model for anomaly detection screening in step S4 is as follows:

[0050] Step S41: Obtain the logistics trajectory dispersion (number of Geohash grid transitions), payment IP address offset speed (km / hour), and multi-account device fingerprint similarity;

[0051] Step S42: Reduce the dimensionality of high-dimensional features using t-SNE while preserving the core density distribution characteristics;

[0052] Step S43: Calculate the LOF value based on the pre-segmented data domain of MiniBatch K-Means clustering;

[0053] The local reachability density is calculated using the following formula:

[0054] ;

[0055] In the formula, For point Locally achievable density, To control the size of the local neighborhood, For point of Nearest neighbor set Indicates belonging to the neighbor set One of the points, For point To the neighbor's point The reachable distance, It is a smoothing factor;

[0056] Dynamic LOF value calculation:

[0057] ;

[0058] In the formula, This is the time decay factor;

[0059] Step S44: Establish a whitelist mechanism to automatically reduce the LOF threshold by 20% for high-frequency cross-border merchants, combine it with isolated forest for secondary verification to improve the recall rate, and design a streaming processing architecture so that only the LOF value of the affected nodes is updated when new data arrives.

[0060] As a preferred technical solution, the specific process for constructing a risk assessment and analysis model and scoring the credit of cross-border merchants in step S5 is as follows:

[0061] Step S51: Adopt a vertical federated learning architecture to obtain transaction flow characteristics provided by financial institutions, user behavior characteristics contributed by e-commerce platforms, and logistics clearance data shared by customs. Feature alignment is achieved through homomorphic encryption technology to ensure that the original data does not leave the local domain.

[0062] Step S52: The merchant credit scoring model adopts the XGBoost algorithm, with each participant training a subtree locally, adding differential privacy noise through gradient aggregation, and dynamically adjusting feature weights.

[0063] Step S53: Evaluate the cross-border sample identification effect through AUC-ROC curve, deploy the federated inference interface, and return credit scores and interpretability reports in real time;

[0064] Step S54: Construct a knowledge graph, use the PageRank algorithm to identify key control nodes and locate the actual controller, use the community detection algorithm to detect abnormal transaction clusters, and trigger an alert when multiple stores under the same IP segment experience a sudden drop in ratings; When constructing the knowledge graph: Entity identification uses merchant nodes (including registered IP and legal person fingerprint), product nodes (HS code), and logistics nodes (warehouse GPS); Relationships are defined as holding relationships (shareholding > 30%), equipment sharing relationships (same MAC address), and closed-loop funding links;

[0065] Step S55: Construct three-layer judgment rules: basic rules (such as daily refund rate > 40%), related rules (multiple stores sharing payment accounts), and deep mode (cross-border fund circulation). Create a dynamic rule engine and calculate the weighted average of the credit score (0-100) and the graph risk score (0-5) output by the federated model.

[0066] As a preferred technical solution, in step S6, when establishing the graded response mechanism, the LOF algorithm is used to output risk scores in real time, and three-level warnings are triggered according to the threshold. Among them, a score between 60 and 70 is a blue label, and a notification email is sent to the risk control specialist; a score between 70 and 85 is a yellow label, and the account funds are automatically frozen for 24 hours; a score greater than 85 is a red label, and the transaction is blocked and a judicial investigation process is initiated.

[0067] Based on knowledge graph identification of associated risks, the same device fingerprint will trigger an interception if it registers 5 merchant accounts within 3 hours; the interception strategy is differentiated by jurisdiction, and in the EU, GDPR compliance marks (such as data encryption certification) need to be checked in addition; at the same time, a blockchain evidence storage system is built to record the hash values ​​of fund flow / logistics / information flow, and support one-click generation of evidence chain during judicial evidence collection.

[0068] The automated compliance report generation uses a GDPR compliance engine with a built-in data subject rights enforcement module. It automatically responds to "right to be forgotten" requests, completes full system data erasure within 72 hours, and dynamically generates a Data Protection Impact Assessment (DPIA) report, indicating the legal basis for cross-border transfers (such as standard contractual clauses).

[0069] The present invention has the following beneficial effects:

[0070] This invention proposes a cross-border feature cross-validation algorithm by utilizing a three-level risk perception system of "cloud-edge-device" through distributed data acquisition, real-time feature engineering, and adaptive deep learning models. This algorithm addresses the problem of inconsistent data standards across multiple countries and improves the efficiency of cross-border e-commerce information processing.

[0071] This invention projects node features onto a cross-border compliance space using a learnable parameter matrix, and uses six parallel attention heads to capture money laundering patterns, fraudulent order behavior, and logistics anomaly risk dimensions. The outputs of each head are dynamically merged through a gating mechanism, and a directed propagation graph is constructed based on attention weights to identify high-risk subnets. Risk source nodes are located through gradient backpropagation, and abnormal patterns are quickly identified and risk sources are traced.

[0072] This invention transforms the coordinates of logistics nodes to generate a physical grid, and uses periodic decomposition (daily / weekly / seasonal) to extract transaction time patterns, constructs a spatiotemporal trajectory encoding sequence of transportation paths, uses periodic decomposition to extract transaction time patterns, uses graph attention mechanism to learn the risk propagation relationship between nodes, and uses causal dilatation convolution to capture long-period anomalies, quickly identifying and locating logistics anomalies.

[0073] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0074] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 This is a flowchart of a cross-border e-commerce information risk analysis method that incorporates cloud computing, according to the present invention. Detailed Implementation

[0076] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0078] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0079] Please see Figure 1 As shown, this invention is a method for cross-border e-commerce information risk analysis that combines cloud computing, including the following steps:

[0080] Step S1: Use a cloud-edge collaborative architecture to access multi-source data and generate a unique cross-border feature code through data fingerprint identification; the multi-source data includes order data, payment logs, and customs records;

[0081] Step S2: Perform dynamic time normalization on transaction behavior to capture abnormal time patterns in cross-border transactions;

[0082] Step S3: Construct a transaction spatiotemporal graph and use spatiotemporal feature encoding to capture abnormal spatiotemporal patterns in cross-border transactions;

[0083] Step S4: Construct a risk assessment and screening model to perform anomaly detection and screening;

[0084] Step S5: Construct a risk assessment and analysis model to score the creditworthiness of cross-border merchants;

[0085] Step S6: Establish a tiered response mechanism and automatically generate qualified reports.

[0086] In step S1, data normalization processing is performed on multi-source data; specifically, order data is extracted into a triplet of "user ID + product SKU + timestamp"; payment logs are converted from SWIFT code to Unified Financial Code; customs records are reconstructed using the UN standard for declaration fields; and normalization processing is achieved by deploying a data cleaning module on IoT devices and using a Bloom Filter to filter duplicate data.

[0087] The specific steps for generating a cross-border unique feature code from a data fingerprint are as follows:

[0088] Step S11: Divide the 24-hour time window using the UTC+8 time zone, and associate each time window with an independent hash seed pool (pre-generate 3 sets of seeds to achieve seamless switching).

[0089] Step S12: Combine server clock noise with cross-border transaction volume fluctuation data;

[0090] Step S13: Iteratively process the entropy source data using the SHA-256 algorithm and output a 256-bit intermediate seed;

[0091] Step S14: A rolling hash function based on a time window is used, which automatically updates the hash seed every 24 hours and performs debiasing by eliminating random number bias through a VonNeumann corrector.

[0092] The formula for the rolling hash function is as follows:

[0093] ;

[0094] In the formula, This represents the output of the dynamic hash function. This indicates that the SHA-3 standard family of 256-bit hash algorithms is used. Indicates input data, This indicates a bitwise XOR operation. A time-dependent dynamic hash seed. This is for performing a modulo operation on the hash result; When transactions surge abnormally, a backup seed is activated in advance. The rolling hash function achieves dynamic hashing through a time-variable seed, allowing the same input to produce different outputs at different times, thus enhancing collision resistance. SHA3-256 provides basic cryptographic strength, XOR operations achieve seed mixing, and modulo operations control the output range.

[0095] When generating data fingerprints, the edge nodes receive the raw data, divide it into 128KB blocks, and attach a timestamp and GPS coordinate metadata to each block. SIMD instructions are used to process the 16 data blocks in parallel for feature extraction. The transaction amount is discretized into 256 levels plus an exchange rate fluctuation index to obtain payment features. The logistics information timeliness deviation is normalized to a [0,1] interval plus a path similarity score to obtain logistics features.

[0096] ,

[0097] ;

[0098] The feature weight vector dot product is performed using the VPMADDWD instruction, and the final output is a 64-bit intermediate fingerprint (the high 32 bits are the feature summary and the low 32 bits are the check code).

[0099] After data fingerprints are generated, they are clustered based on physical location. Fingerprints of adjacent edge nodes are grouped into the same processing group. Geographically proximate edge nodes (such as different IoT devices in the same cross-border warehouse) are mapped to the same geographical block through GeoHash encoding, which naturally aggregates data related to physical location and reduces the complexity of cloud computing. For example, order payment data and logistics scanning data from the same warehouse are grouped into the same processing group to avoid semantic fragmentation caused by cross-regional data mixing. Outdated fingerprints with a time difference of more than 500ms are discarded. The 500ms threshold is set based on the minimum transmission delay of cross-border optical cables (measured average of 230ms) to effectively block the injection of forged data. The center point of fingerprint clusters with a Hamming distance of less than 3 is calculated. Fingerprint clusters with high similarity are identified by the Hamming distance threshold (<3 bits). Data with small binary differences are grouped into the same semantic group to effectively distinguish between real data duplication (such as order retransmission) and malicious forgery attacks (such as bit flip attacks). Bit-level majority voting is performed on weighted fingerprints to generate the final fingerprint. Merging fingerprints within a group can avoid data conflicts caused by differences in administrative rules.

[0100] Set the base threshold to 3 digits and adjust it dynamically according to the business scenario:

[0101] ;

[0102] In the formula, This represents the maximum allowed number of bits for the Hamming distance, used to determine whether two fingerprints belong to the same cluster; Ensure the threshold is at least 3. To ensure high distinguishability even in large clusters, the current fingerprint cluster size (i.e., the number of fingerprints contained in the cluster) is set. Then, a BLAKE2b-128 checksum is added to the center point of each cluster to prevent man-in-the-middle tampering. Time window verification is implemented to reject cluster data with a delay of more than 5 minutes.

[0103] In step S2, the specific process for dynamically adjusting the time of transactions is as follows:

[0104] Step S21: The original transaction flow is grouped by user ID to generate a transaction time series. Z-score standardization is performed to eliminate differences in monetary units. A sliding window mechanism is used to generate sub-sequences, with multiple overlapping sub-sequence windows generated for each user.

[0105] Score standardization is performed to calculate the transaction amount statistics for each user:

[0106] ;

[0107] In the formula, The mean of all data points. Let i be the value of the i-th data point. Standard deviation represents the degree of dispersion of the data.

[0108] Step S22: Construct the cost matrix and calculate the Euclidean distance as the local cost function; the cost matrix is ​​constructed by inputting the standardized transaction sequences X (length m) and Y (length n), and calculating an m×n matrix: each element , As a feature dimension, the holiday data points are multiplied by a weighting factor. (Typically 1.2-1.5 times); Quantify local differences in trading behavior to capture instantaneous anomalies in characteristics such as amount / frequency;

[0109] Step S23: Extract the cumulative cost of the optimal path as a risk indicator, calculate the local cost matrix of transaction sequence A and reference sequence B, and use the improved weighted Euclidean distance from the endpoint. Tracing the minimum cumulative path in reverse, recording all paths traversed by the path. Coordinates; using Sakoe-Chiba bandwidth constraints to limit the maximum offset of the time axis for path search optimization; initializing the cumulative cost matrix C and recursively filling it: ; Apply Sakoe-Chiba band restriction To prevent excessive distortion of the timeline;

[0110] Step S24: Calculate the dynamic volatility of the normalized sequence and generate anomaly pattern template library; sequence alignment is performed nonlinearly on the original sequence according to the DTW optimal path to eliminate time axis distortion, and volatility outliers are identified based on Hampel filter; it can capture hidden patterns of high-risk behaviors such as money laundering (such as drastic fluctuations in amount in a short period of time), and form multi-dimensional cross-validation with accumulated cost (high cost + high volatility = extremely high risk).

[0111] When constructing the abnormal pattern template library, K-means clustering is performed on historical high-risk transaction sequences to extract key features, such as median transaction interval, coefficient of variation of amount, and geographical dispersion. When new fraud patterns emerge, the template library is dynamically updated using online clustering algorithms (such as StreamKM++).

[0112] In step S34, node features are projected onto the cross-border compliance space using a learnable parameter matrix. Six sets of attention heads are used in parallel to capture risk dimensions such as money laundering patterns, fraudulent order behavior, and abnormal logistics. The outputs of each head are dynamically merged through a gating mechanism, and a directed propagation graph is constructed based on attention weights to identify high-risk subnets. Risk source nodes are located through gradient backpropagation.

[0113] Temporal anomaly detection using causal dilated convolutional CNN:

[0114] The transaction stream is converted into a multi-channel time-series signal: Channel 1: Payment amount sequence (aggregated by hour); Channel 2: IP geographic offset distance sequence; Channel 3: User behavior entropy value sequence;

[0115] In the underlying convolution, a causal convolution with an inflation factor of d=2 is used to capture the 7-day cycle pattern, and the temporal length is kept constant by padding;

[0116] In high-level convolutions, dilated convolutional layers with d=12 are stacked to cover a 90-day cross-border transaction cycle, and residual connections are introduced to avoid gradient vanishing. Anomaly scores are calculated by reconstructing errors through temporal features, and joint decision-making is performed by combining the spatial risk coefficient output by GAT.

[0117] In step S4, the specific process for constructing a risk assessment screening model to perform anomaly detection screening is as follows:

[0118] Step S41: Obtain the logistics trajectory dispersion (number of Geohash grid transitions), payment IP address offset speed (km / hour), and multi-account device fingerprint similarity;

[0119] Step S42: Reduce the dimensionality of high-dimensional features using t-SNE while preserving the core density distribution characteristics;

[0120] Step S43: Calculate the LOF value based on the pre-segmented data domain of MiniBatch K-Means clustering;

[0121] The local reachability density is calculated using the following formula:

[0122] ;

[0123] In the formula, For point Locally achievable density, To control the size of the local neighborhood, For point of Nearest neighbor set Indicates belonging to the neighbor set One of the points, For point To the neighbor's point The reachable distance, It is a smoothing factor;

[0124] Dynamic LOF value calculation:

[0125] ;

[0126] In the formula, This is the time decay factor;

[0127] Step S44: Establish a whitelist mechanism to automatically reduce the LOF threshold by 20% for high-frequency cross-border merchants, combine it with isolated forest for secondary verification to improve the recall rate, and design a streaming processing architecture so that only the LOF value of the affected nodes is updated when new data arrives.

[0128] In step S5, the specific process for constructing a risk assessment and analysis model to score the credit of cross-border merchants is as follows:

[0129] Step S51: Adopt a vertical federated learning architecture to obtain transaction flow characteristics provided by financial institutions, user behavior characteristics contributed by e-commerce platforms, and logistics clearance data shared by customs. Feature alignment is achieved through homomorphic encryption technology to ensure that the original data does not leave the local domain.

[0130] Step S52: The merchant credit scoring model adopts the XGBoost algorithm, with each participant training a subtree locally, adding differential privacy noise through gradient aggregation, and dynamically adjusting feature weights.

[0131] Step S53: Evaluate the cross-border sample identification effect through AUC-ROC curve, deploy the federated inference interface, and return credit scores and interpretability reports in real time;

[0132] Step S54: Construct a knowledge graph, use the PageRank algorithm to identify key control nodes and locate the actual controller, use the community detection algorithm to detect abnormal transaction clusters, and trigger an alert when multiple stores under the same IP segment experience a sudden drop in ratings; When constructing the knowledge graph: Entity identification uses merchant nodes (including registered IP and legal person fingerprint), product nodes (HS code), and logistics nodes (warehouse GPS); Relationships are defined as holding relationships (shareholding > 30%), equipment sharing relationships (same MAC address), and closed-loop funding links;

[0133] Step S55: Construct three-layer judgment rules: basic rules (such as daily refund rate > 40%), related rules (multiple stores sharing payment accounts), and deep mode (cross-border fund circulation). Create a dynamic rule engine and calculate the weighted average of the credit score (0-100) and the graph risk score (0-5) output by the federated model.

[0134] In step S6, when establishing the tiered response mechanism, the LOF algorithm is used to output risk scores in real time, and three levels of warnings are triggered according to the threshold. Among them, a score between 60 and 70 is a blue label, and a notification email is sent to the risk control specialist; a score between 70 and 85 is a yellow label, and the account funds are automatically frozen for 24 hours; a score greater than 85 is a red label, and the transaction is blocked and the judicial investigation process is initiated.

[0135] Based on knowledge graph identification of associated risks, the same device fingerprint will trigger an interception if it registers 5 merchant accounts within 3 hours; the interception strategy is differentiated by jurisdiction, and in the EU, GDPR compliance marks (such as data encryption certification) need to be checked in addition; at the same time, a blockchain evidence storage system is built to record the hash values ​​of fund flow / logistics / information flow, and support one-click generation of evidence chain during judicial evidence collection.

[0136] The automated compliance report generation uses a GDPR compliance engine with a built-in data subject rights enforcement module. It automatically responds to "right to be forgotten" requests, completes full system data erasure within 72 hours, and dynamically generates a Data Protection Impact Assessment (DPIA) report, indicating the legal basis for cross-border transfers (such as standard contractual clauses).

[0137] It is worth noting that the various units included in the above system embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0138] Furthermore, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the corresponding program can be stored in a computer-readable storage medium.

[0139] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for analyzing information risk of cross-border e-commerce combined with cloud computing, characterized in that, Comprise the following steps: Step S1: access multi-source data using cloud-edge collaborative architecture, generate cross-border unique feature code through data fingerprint identification; multi-source data includes order data, payment log and customs record; Step S2: dynamically time warping transaction behavior to capture time sequence abnormal pattern of cross-border transaction, the specific process of dynamically time warping transaction behavior in step S2 is as follows: Step S21: group original transaction flow according to user ID, generate transaction time sequence, and generate subsequence using sliding window mechanism; Step S22: construct cost matrix and calculate Euclidean distance as local cost function; Step S23: extract cumulative cost of optimal path as risk indicator; Step S24: calculate dynamic volatility rate of warping sequence to generate abnormal pattern template library; Step S3: construct transaction space-time graph, time-space feature coding captures abnormal space-time pattern in cross-border transaction, transaction space-time graph includes node, edge and dynamic attribute; the node includes merchant, user and commodity; the edge is transaction relationship; the dynamic attribute is price volatility rate and transaction frequency change gradient; the specific process of time-space feature coding capturing abnormal space-time pattern in cross-border transaction is as follows: Step S31: convert the coordinates of logistics nodes to generate physical grid; Step S32: construct space-time trajectory coding sequence of transportation path; Step S33: extract transaction time pattern using period decomposition; Step S34: learn risk propagation relationship between nodes using graph attention mechanism, and capture long-period abnormality using causal inflation convolution; Step S35: multi-modal alignment of text, image and structured data; Step S4: construct risk assessment screening model for abnormal detection and screening; Step S5: construct risk assessment analysis model to score cross-border merchant credit; Step S6: establish hierarchical response mechanism to automatically generate qualified report. 2.The cross-border e-commerce information risk analysis method combined with cloud computing according to claim 1, wherein, In step S1, the multi-source data is normalized; wherein the order data extracts "user ID+ commodity SKU+ timestamp" triplets; the payment log converts SWIFT code into unified financial coding; the customs record reconstructs declaration field using UN standard; the normalization process deploys data cleaning module on IOT device and uses BloomFilter to filter duplicate data; The specific steps of generating cross-border unique feature code through data fingerprint identification are as follows: Step S11: use 24-hour time window, each time window is associated with independent hash seed pool; Step S12: combine server clock noise and cross-border transaction volume fluctuation data; Step S13: iteratively process entropy source data to output 256-bit intermediate seed; Step S14: based on time window rolling hash function, automatically update hash seed every 24 hours. 3.The method of claim 1, wherein, The data fingerprint identification generation, after the edge node receives the original data, is divided into blocks with a size of 128KB, each block is attached with a timestamp and GPS coordinate metadata, the feature extraction of 16 data blocks is processed in parallel by using SIMD instructions, the transaction amount is discretized into 256 levels + exchange rate fluctuation index to obtain payment features, the logistics information time deviation value is normalized to the interval [0, 1] + path similarity score to obtain logistics features, the feature weight vector point multiplication is completed by using the VPMADDWD instruction, and finally a 64-bit intermediate fingerprint is output.

4. The cross-border e-commerce information risk analysis method combined with cloud computing according to claim 3, characterized in that, After the data fingerprint identification is generated, clustering is performed according to the physical location, the fingerprints of adjacent edge nodes are classified into the same processing group, and the outdated fingerprints with a time difference exceeding 500ms are discarded, the fingerprint cluster center points with a Hamming distance less than 3 are calculated, the weighted fingerprints are subjected to bit-level majority voting, and finally the final fingerprint is generated.

5. The method of claim 1, wherein the method further comprises: In the step S34, the node features are projected to the cross-border compliance space through a learnable parameter matrix, 6 groups of attention heads are used to capture money laundering patterns, single behavior and logistics abnormal risk dimensions in parallel, the outputs of each head are dynamically fused through a gating mechanism, and a directed propagation graph is constructed based on the attention weights to identify high-risk subnets and locate the risk source nodes through gradient back propagation.

6. The method of claim 1, wherein the method further comprises: In the step S4, the specific process of constructing a risk assessment screening model for abnormal detection screening is as follows: Step S41: obtain the logistics track dispersion, payment IP address offset speed and multi-account device fingerprint similarity; Step S42: reduce the dimensionality of high-dimensional features by t-SNE, while retaining the core density distribution characteristics; Step S43: calculate the LOF value based on MiniBatch K-Means clustering to pre-segment the data domain; Step S44: establish a whitelist mechanism and combine an isolation forest for secondary verification.

7. The method of claim 1, wherein the method further comprises: In the step S5, the specific process of constructing a risk assessment analysis model for scoring cross-border merchant credit is as follows: Step S51: use a vertical federated learning architecture to realize feature alignment through homomorphic encryption technology; Step S52: the merchant credit scoring model uses the XGBoost algorithm, each participant locally trains a sub-tree, adds differential privacy noise in a gradient aggregation manner, and dynamically adjusts the feature weights; Step S53: evaluate the cross-border sample recognition effect through the AUC-ROC curve, deploy a federated inference interface, and return the credit score and interpretability report in real time; Step S54: construct a knowledge graph, identify key control nodes using the PageRank algorithm, locate the ultimate beneficial owner, and detect abnormal transaction clusters using community discovery algorithms; Step S55: construct three layers of judgment rules, make a dynamic rule engine, and calculate the weighted score of the federated model output credit score and graph risk score. 8.The method of claim 1, wherein, In the step S6, when establishing a hierarchical response mechanism, the LOF algorithm is used to output a risk score in real time, and a three-level early warning is triggered according to the threshold; wherein, the score between 60 and 70 is blue, a notification email is sent to the risk control officer; the score between 70 and 85 is yellow, and the account funds are automatically frozen for 24 hours; and the score greater than 85 is red, the transaction is blocked, and the judicial cooperation process is started.

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