Intelligent inventory and logistics scheduling method and system driven by cross-border e-commerce data

Abstract

The invention discloses a cross-border e-commerce data-driven intelligent inventory and logistics scheduling method and system, and belongs to the technical field of cross-border e-commerce systems. In order to solve the problems of high data synchronization delay, complex inventory allocation and difficult logistics path selection in cross-border business, the method is realized through an event-driven data synchronization architecture, distributed differential synchronization, an encryption mechanism, multi-target inventory allocation optimization and a logistics path optimization method. According to the invention, real-time safe synchronization of cross-border orders, inventory and logistics data, automatic multi-warehouse inventory allocation and cross-border logistics path optimization can be realized, and the scheduling efficiency and reliability of the system are improved.

Description

Technical Field

[0001] This invention belongs to the technical field of cross-border e-commerce systems, specifically a data-driven intelligent inventory and logistics scheduling method and system for cross-border e-commerce. Background Technology

[0002] With the expansion of cross-border e-commerce business, although the existing platform has completed the basic framework construction of cross-border mall portal, supplier center, buyer center and operation center, and has the ability to interact with third-party platforms externally, and to deeply integrate with financial system, ERP system and related business system internally, and has established a two-way data channel between transaction system and business management system to realize real-time interaction of business flow and fulfillment data, the following problems still exist in actual operation: First, there are delays and inconsistencies in the synchronization of cross-border order, inventory and logistics data. Especially in environments with multiple warehouses and multiple country nodes, the data update speed is difficult to meet the second-level synchronization requirements, resulting in lag in inventory status, order fulfillment information and logistics tracking information, which in turn affects the real-time nature of inventory allocation and logistics decisions.

[0003] Secondly, existing data synchronization mainly relies on full updates or batch transmissions, which consumes a lot of bandwidth, puts a heavy load on cross-border networks, and is prone to data loss or transmission errors. It also lacks efficient incremental update and data consistency guarantee mechanisms.

[0004] Furthermore, inventory allocation and cross-border logistics route optimization lack systematic multi-objective decision-making capabilities. Existing scheduling schemes mostly rely on manual experience or static rules, and cannot be dynamically optimized based on real-time inventory distribution, node processing capacity, and network conditions, resulting in low inventory resource utilization and suboptimal logistics routes.

[0005] Furthermore, cross-border data faces security risks during transmission and storage, and sensitive data is susceptible to unauthorized access or tampering, lacking a unified encryption protection and auditable mechanism.

[0006] Therefore, existing cross-border e-commerce platforms have systemic deficiencies in data synchronization, inventory allocation, logistics route optimization, and security, making it difficult to achieve efficient, intelligent, and reliable collaborative operation of cross-border business. Summary of the Invention

[0007] The purpose of this invention is to propose a data-driven intelligent inventory and logistics scheduling method and system for cross-border e-commerce, in order to solve the problems of difficulty in real-time synchronization of order, inventory and logistics data in cross-border business systems, and the complexity of cross-warehouse inventory transfer and cross-border logistics route optimization, so as to achieve the goals of real-time, secure and consistent data synchronization, reasonable inventory distribution, optimal cross-border logistics routes, and intelligent and efficient system scheduling.

[0008] To achieve the above objectives, the present invention adopts the following technical solution.

[0009] A data-driven intelligent inventory and logistics scheduling method for cross-border e-commerce includes the following steps: 1) Generate synchronous events based on an event-driven architecture. When an order is created, inventory changes, or logistics status is updated, relevant fields are extracted to generate event data, including event identifier, event type, timestamp, differential field, and hash signature; 2) Utilize distributed message queues to transmit event data, and ensure consistent data processing order among cross-border nodes by having multiple consumer groups pull events in parallel. 3) Perform local differential synchronization in the distributed cache by comparing the hash values ​​of the cached data with the corresponding data in the data center, generating an incremental update package and updating the local cache; 4) Perform consistency checks and retransmissions on cross-border synchronized data. If the check fails, the retransmission mechanism is automatically triggered and an exponential backoff strategy is adopted. 5) Securely encrypt the synchronized data. The transport layer uses symmetric key encryption, the session key is exchanged using an asymmetric algorithm, and the storage layer encrypts sensitive data in segments. 6) Execute intelligent inventory transfer based on multi-warehouse inventory information and logistics node information, including collecting inventory quantity, physical attributes and transmission restriction parameters, constructing a multi-objective optimization function and solving the inventory transfer scheme; 7) Perform logistics route optimization by collecting data on link latency, packet loss rate, retransmission rate, and node processing capabilities to construct a route scoring function, and search for and select the optimal route; 8) Based on historical inventory changes, SKU periodic fluctuations and link status, the large model performs demand forecasting, generates future SKU demand, and identifies abnormal orders and abnormal system requests, providing input for inventory scheduling and path selection.

[0010] Furthermore, the differential field in the event data generated in step 1) is used to indicate the specific content of this data change, and the hash signature is calculated by the SM3 algorithm.

[0011] Furthermore, in step 2), the distributed message queue includes Kafka or RocketMQ, and the consumer nodes are divided into different partitions according to merchants or warehouses to ensure the consistency of event order.

[0012] Furthermore, the differential synchronization processing steps in step 3) include: (1) Compare the local cached data with the corresponding data in the data center; (2) Compare the hash values ​​of the corresponding fields; (3) Synchronize the difference fields when the number of difference fields is lower than the preset threshold; otherwise, perform full synchronization. (4) Generate incremental update packages; (5) Update the local cache to complete the synchronization.

[0013] Furthermore, in step 4), consistency verification is achieved by generating an SM3 hash value for the synchronized data, with a maximum of three retransmissions, and the interval between each retransmission is controlled by an exponential backoff strategy.

[0014] Furthermore, the security encryption process in step 5) includes using SM4 symmetric encryption at the transport layer, exchanging session keys via the SM2 asymmetric algorithm, and using segmented encryption and SM4-CBC mode encryption for sensitive fields at the storage layer.

[0015] Furthermore, the encrypted synchronization data output in step 5) is recorded on the blockchain audit chain to achieve tamper-proof storage of key events.

[0016] Furthermore, in step 6), the multi-warehouse inventory information includes the inventory quantity, SKU volume, category and shelf life of each warehouse, and the logistics node information includes node processing capacity, network bandwidth and average node processing latency. The multi-objective optimization function for inventory allocation is formed by weighted summation of node processing latency, network load and node stability indicators, and solved by heuristic search and iterative optimization.

[0017] Furthermore, in step 7), the logistics path optimization includes collecting link round-trip delay, packet loss rate, retransmission rate, CPU utilization and I / O throughput of each node, constructing a path comprehensive scoring function, and using Dijkstra's algorithm or genetic algorithm to search for the path with the lowest score.

[0018] Furthermore, in step 8), demand forecasting includes predicting future SKU demand based on historical inventory change sequences, node access frequency, SKU periodic fluctuations, and link status patterns, and identifying abnormal access frequencies or abnormal request distributions.

[0019] A data-driven intelligent inventory and logistics scheduling system for cross-border e-commerce includes: The event generation module is used to extract relevant fields to generate event data based on the event-driven architecture when order is created, inventory changes or logistics status is updated, including event identifier, event type, timestamp, differential field and hash signature; The message transmission module is used to transmit event data through a distributed message queue and pull events in parallel with a multi-node consumer group to ensure that the data processing order is consistent across cross-border nodes. The differential synchronization module is used to compare the hash values ​​of local data with the corresponding data in the data center in the distributed cache, generate incremental update packages, and update the local cache. The data verification and retransmission module is used to perform consistency verification on cross-border synchronized data and automatically trigger the retransmission mechanism when the verification fails. It adopts an exponential backoff strategy to control the retransmission interval. The data encryption module is used to encrypt synchronized data. The transport layer uses symmetric key encryption, the session key is exchanged through an asymmetric algorithm, and the storage layer performs segmented encryption on sensitive data. The inventory allocation module is used to collect inventory information from multiple warehouses and logistics node information, construct a multi-objective optimization function, and solve for the inventory allocation scheme. The logistics route optimization module is used to collect link round-trip delay, packet loss rate, retransmission rate and node processing capacity, construct a route scoring function and search for and select the optimal route; The demand forecasting module is used to predict future SKU demand based on historical inventory change sequences, SKU periodic fluctuations, and link status patterns using a large model, and to identify abnormal orders and abnormal system requests, providing input for inventory allocation and route optimization.

[0020] The present invention has achieved the following beneficial effects.

[0021] 1. This invention generates synchronous events through an event-driven architecture, enabling real-time capture of cross-border order, inventory, and logistics data.

[0022] 2. This invention utilizes a distributed message queue to transmit event data in parallel, ensuring consistent data processing order among multiple nodes and improving the efficiency of cross-border data synchronization.

[0023] 3. This invention employs distributed caching and differential synchronization algorithms to synchronize only changed fields or incremental data, reducing bandwidth consumption and accelerating data updates.

[0024] 4. This invention improves the reliability of data synchronization through consistency verification and automatic retransmission mechanisms.

[0025] 5. This invention employs a combination of symmetric and asymmetric encryption to achieve secure protection for data transmission and storage.

[0026] 6. This invention constructs a multi-objective optimization model based on multi-warehouse inventory information, logistics node processing capabilities, and network bandwidth to achieve intelligent decision-making for inventory allocation.

[0027] 7. This invention constructs a path scoring function by collecting link latency, packet loss rate, retransmission rate, and node processing capabilities to achieve optimized selection of cross-border logistics paths.

[0028] 8. This invention combines historical inventory changes, SKU periodic fluctuations, and link status to perform demand forecasting, supporting the robustness of inventory allocation and route optimization. Detailed Implementation

[0029] To make the various technical features, advantages, or effects of the present invention more apparent and understandable, detailed descriptions are provided below through embodiments.

[0030] This embodiment provides a data-driven intelligent inventory and logistics scheduling method for cross-border e-commerce. It achieves efficient operation of the cross-border business system through an event-driven data synchronization architecture, differential synchronization algorithm, security encryption mechanism, and multi-objective optimization model. The method mainly includes real-time cross-border data synchronization and intelligent logistics scheduling. Real-time cross-border data synchronization ensures real-time, secure, and consistent synchronization of order, inventory, and logistics data between cross-border nodes. The intelligent logistics scheduling component is used to automate inventory allocation, route calculation, and demand forecasting in cross-border multi-warehouse scenarios through system performance indicators. Detailed explanations follow.

[0031] Step 1: Generate synchronous events based on Event-Driven Architecture (EDA) When an order is created, inventory changes, or logistics status is updated, the system automatically extracts fields related to the change to generate event data. Event data includes an event identifier, event type, timestamp, difference field, and hash signature. The difference field indicates the data content of this change, and the hash signature is calculated using the SM3 algorithm based on the event content.

[0032] Step 2: Use a distributed message queue (Kafka / RocketMQ) to transmit event data.

[0033] The generated events are written to a distributed message queue and partitioned into different partitions according to merchants or warehouses. Consumers, consisting of multiple nodes, pull events in parallel. A sequence offset management mechanism ensures the consistency of data processing order across multiple nodes across borders.

[0034] Step 3: Perform local differential synchronization based on the distributed cache (Redis).

[0035] To avoid the high latency caused by cross-border full synchronization, this embodiment adopts a differential synchronization algorithm, the processing flow of which is as follows: (1) Compare the local cached data with the corresponding data in the data center; (2) Compare the hash values ​​of the corresponding fields; (3) When the number of difference fields is lower than the preset threshold, only the difference fields are synchronized; when the difference exceeds the threshold, it is rolled back to full synchronization. (4) Generate incremental update packages based on the differences in content; (5) Update the local cache and complete the synchronization cycle.

[0036] This algorithm reduces cross-border bandwidth consumption and improves synchronization efficiency.

[0037] Step 4: Implement cross-border data consistency verification and retransmission mechanism.

[0038] SM3 hash values ​​are generated for all cross-border synchronized data. The receiving end verifies the received data, and automatically triggers a retransmission mechanism if the verification fails. Retransmissions are performed a maximum of three times, and an exponential backoff strategy is used to control the request interval.

[0039] Step 5: Use national cryptographic algorithms (SM2 / SM4) to achieve cross-border data security protection.

[0040] The transport layer uses SM4 symmetric key encryption, and the session key is exchanged using the SM2 asymmetric algorithm. The storage layer employs a segmented encryption strategy for sensitive data, with key fields encrypted using SM4-CBC mode.

[0041] Step 6: Record cross-border synchronized logs based on the Blockchain Audit Chain.

[0042] As an optional step, in scenarios requiring high auditability, key events can be written into a chain structure to achieve tamper-proof log recording.

[0043] Step 7: Perform Multi-Warehouse Inventory Intelligent Optimization (MIO).

[0044] (1) Collect real-time inventory from warehouses in multiple countries and the physical attributes of each SKU, including volume, category and shelf life, and call the large model to generate the corresponding demand forecast results.

[0045] (2) Obtain the physical limiting parameters of cross-border transmission, including information such as link bandwidth, processing capacity of each node and the size of data packets that can be carried, and obtain the average processing delay of logistics nodes. These parameters will be used as inputs to the allocation model.

[0046] (3) Introduce allocation decision variables The system constructs a multi-objective optimization function around three performance metrics: node processing latency, network transmission load, and node stability. To quantify node processing latency, the system establishes a node latency expression based on the M / M / 1 queuing model: in, Indicates the first The quantity of inventory transferred from each warehouse; For warehouse The maximum processing rate of the node; The current task arrival rate of the node; This is the conversion coefficient between allocation volume and task arrival rate; Represents a node Processing latency; This indicates the total latency for cross-node processing.

[0047] (4) To reflect the impact of cross-warehouse network load, the system establishes a network load function: in, Indicates the size of the allocated data packet; Indicates the available bandwidth of cross-border network links; For the purpose of adjusting variables; This represents a comprehensive indicator of network load during the allocation process.

[0048] (5) To evaluate the impact of node stability on the overall system, a node stability error function is established: in, For warehouse The stability factor of a node is calculated from statistical indicators such as historical node jitter and packet loss rate; Indicates the amount allocated; This is the node stability error function.

[0049] (6) Combine the three types of objective functions using a weighted summation method to form a comprehensive optimization function: in, Delay function for cross-node processing; For network load functions; The node stability error function; These are the weights of each indicator, used to adjust the system's preference for different performance indicators.

[0050] (7) Under the constraints of inventory, warehouse load and bandwidth, the comprehensive objective function is solved by heuristic search and iterative optimization, and the inventory allocation scheme with the minimum delay, balanced network load and optimal node stability is calculated to realize the intelligent scheduling of multi-warehouse inventory.

[0051] Step 8: Perform logistics route optimization (L-Route).

[0052] (1) Collect cross-border link operation indicators, including link round-trip delay, packet loss rate, retransmission rate, CPU utilization and I / O throughput of each node, to estimate node processing time.

[0053] (2) Construct the link cost function based on the above technical indicators: in, Indicates link The estimated latency is usually the inter-node RTT or the historical average latency; Indicates the packet loss rate of the link; Indicates the link retransmission rate; Indicates the processing time of nodes on the link; These are weighting coefficients used to comprehensively measure the impact of link performance on path selection; For link The overall cost.

[0054] (3) Calculate the path scoring function and sum the costs of all links on the path: in, Represents the set of candidate paths; The overall cost of the path; Representing a path Each link included.

[0055] (4) Based on the scoring function, the system uses heuristic algorithms, Dijkstra's algorithm or genetic algorithm to search for feasible paths and selects the path with the lowest score as the candidate path.

[0056] (5) For cases with transit nodes, the system calculates the path scores for paths that pass through transit nodes and paths that do not pass through transit nodes, and selects the path with the lower cost by comparing the results.

[0057] (6) Finally, the system calculates the path by accumulating and comprehensively calculating the latency, packet loss rate, retransmission rate of each link in the path and the processing time of each node. Expected completion time This provides predictable execution times for cross-border logistics tasks. Node processing time is calculated using CPU utilization and I / O throughput statistics to reflect the impact of node processing capacity on path execution; link latency, packet loss rate, and retransmission rate are used to accurately estimate data transmission performance on the link, thus obtaining the overall cost and completion time of the path.

[0058] Step 9: Perform demand forecasting based on the large model (LLM-DP).

[0059] The model predicts future (e.g., 7 days, 14 days, 30 days) SKU demand based on historical inventory change sequences, node access frequency, SKU periodic fluctuations, and link latency patterns.

[0060] The model also identifies abnormal orders and abnormal system requests based on abnormal behavior patterns (including abnormal access frequency, abnormal request distribution, and sudden high concurrency patterns), thereby improving the robustness of the scheduling algorithm under changes in node load.

[0061] Performance testing: The technical effects achieved by implementing the technical solution of this invention in actual cross-border data synchronization and inventory logistics scheduling are as follows: 1. Data synchronization speed is improved by 60%~90%, and latency is reduced from minutes (2~5 minutes) to seconds (1~3 seconds); 2. Differential synchronization reduces bandwidth consumption by 60%~90%; 3. Consistency checks reduce data synchronization error rates by over 95%; 4. SM4 encryption performance is improved by more than 20%, making it suitable for high-frequency transmission; 5. MIO enables a more balanced load on system nodes, improving resource utilization of inventory distribution by 20% to 40%; 6. L-Route reduces link congestion and improves cross-node transmission efficiency, with an average reduction in processing path time of 8% to 15%; 7. LLM-DP reduces demand forecasting error by 25% to 45%, thereby improving overall scheduling accuracy.

[0062] In summary, the technical solution proposed in this invention achieves collaborative optimization of cross-border business in terms of data synchronization, inventory management, and cross-node routing, significantly improving the real-time performance, security, and overall computing efficiency of cross-border e-commerce systems.

[0063] Although the present invention has been disclosed above with reference to embodiments, it is not intended to limit the present invention. Appropriate modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention should be covered within the protection scope of the present invention, which is defined by the claims.

Claims

1. A data-driven intelligent inventory and logistics scheduling method for cross-border e-commerce, characterized in that, Includes the following steps: 1) Generate synchronous events based on an event-driven architecture. When an order is created, inventory changes, or logistics status is updated, relevant fields are extracted to generate event data, including event identifier, event type, timestamp, differential field, and hash signature; 2) Utilize distributed message queues to transmit event data, and ensure consistent data processing order among cross-border nodes by having multiple consumer groups pull events in parallel. 3) Perform local differential synchronization in the distributed cache by comparing the hash values ​​of the cached data with the corresponding data in the data center, generating an incremental update package and updating the local cache; 4) Perform consistency checks and retransmissions on cross-border synchronized data. If the check fails, the retransmission mechanism is automatically triggered and an exponential backoff strategy is adopted. 5) Securely encrypt the synchronized data. The transport layer uses symmetric key encryption, the session key is exchanged using an asymmetric algorithm, and the storage layer encrypts sensitive data in segments. 6) Execute intelligent inventory transfer based on multi-warehouse inventory information and logistics node information, including collecting inventory quantity, physical attributes and transmission restriction parameters, constructing a multi-objective optimization function and solving the inventory transfer scheme; 7) Perform logistics route optimization by collecting data on link latency, packet loss rate, retransmission rate, and node processing capabilities to construct a route scoring function, and search for and select the optimal route; 8) Based on historical inventory changes, SKU periodic fluctuations and link status, the large model performs demand forecasting, generates future SKU demand, and identifies abnormal orders and abnormal system requests, providing input for inventory scheduling and path selection.

2. The method as described in claim 1, characterized in that, The differential field in the event data generated in step 1) is used to indicate the specific content of this data change, and the hash signature is calculated by the SM3 algorithm; the distributed message queue in step 2) includes Kafka or RocketMQ, and the consumer nodes are divided into different partitions according to merchants or warehouses to ensure the consistency of event order.

3. The method as described in claim 1, characterized in that, Step 3) includes the following differential synchronization processing steps: (1) Compare the local cached data with the corresponding data in the data center; (2) Compare the hash values ​​of the corresponding fields; (3) Synchronize the difference fields when the number of difference fields is lower than the preset threshold; otherwise, perform full synchronization. (4) Generate incremental update packages; (5) Update the local cache to complete the synchronization.

4. The method as described in claim 1, characterized in that, In step 4), consistency verification is achieved by generating an SM3 hash value for the synchronized data. Retransmission is limited to a maximum of three times, with the interval between each retransmission controlled by an exponential backoff strategy.

5. The method as described in claim 1, characterized in that, Step 5) involves secure encryption, which includes SM4 symmetric encryption at the transport layer, exchange of session keys using the SM2 asymmetric algorithm, and segmented encryption and SM4-CBC mode encryption for sensitive fields at the storage layer.

6. The method as described in claim 1, characterized in that, The encrypted synchronization data output in step 5) is based on the blockchain audit chain record to achieve tamper-proof storage of key events.

7. The method as described in claim 1, characterized in that, Step 6) The multi-warehouse inventory information includes the inventory quantity, SKU volume, category and shelf life of each warehouse. The logistics node information includes node processing capacity, network bandwidth and average node processing latency. The multi-objective optimization function of inventory allocation is formed by weighted summation of node processing latency, network load and node stability indicators, and solved by heuristic search and iterative optimization.

8. The method as described in claim 1, characterized in that, Step 7) Logistics path optimization includes collecting link round-trip delay, packet loss rate, retransmission rate, CPU utilization and I / O throughput of each node, constructing a path comprehensive scoring function, and using Dijkstra's algorithm or genetic algorithm to search for the path with the lowest score.

9. The method as described in claim 1, characterized in that, Step 8) Demand forecasting includes predicting future SKU demand based on historical inventory change sequences, node access frequency, SKU periodic fluctuations, and link status patterns, and identifying abnormal access frequencies or abnormal request distributions.

10. A data-driven intelligent inventory and logistics scheduling system for cross-border e-commerce, characterized in that, include: The event generation module is used to extract relevant fields to generate event data based on the event-driven architecture when order is created, inventory changes or logistics status is updated, including event identifier, event type, timestamp, differential field and hash signature; The message transmission module is used to transmit event data through a distributed message queue and pull events in parallel with a multi-node consumer group to ensure that the data processing order is consistent across cross-border nodes. The differential synchronization module is used to compare the hash values ​​of local data with the corresponding data in the data center in the distributed cache, generate incremental update packages, and update the local cache. The data verification and retransmission module is used to perform consistency verification on cross-border synchronized data and automatically trigger the retransmission mechanism when the verification fails. It adopts an exponential backoff strategy to control the retransmission interval. The data encryption module is used to encrypt synchronized data. The transport layer uses symmetric key encryption, the session key is exchanged through an asymmetric algorithm, and the storage layer performs segmented encryption on sensitive data. The inventory allocation module is used to collect inventory information from multiple warehouses and logistics node information, construct a multi-objective optimization function, and solve for the inventory allocation scheme. The logistics route optimization module is used to collect link round-trip delay, packet loss rate, retransmission rate and node processing capacity, construct a route scoring function and search for and select the optimal route; The demand forecasting module is used to predict future SKU demand based on historical inventory change sequences, SKU periodic fluctuations, and link status patterns using a large model, and to identify abnormal orders and abnormal system requests, providing input for inventory allocation and route optimization.

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