A cross-border e-commerce logistics order management system based on big data

Abstract

This invention relates to the field of logistics order management technology and discloses a big data-based cross-border e-commerce logistics order management system. The system includes a data acquisition and processing unit, a potential energy calculation and risk identification unit, an order clustering and route planning unit, a strategy generation and execution unit, and a feedback and model optimization unit, used to acquire the execution result data of the final delivery instruction. This big data-based cross-border e-commerce logistics order management system constructs a dynamic logistics potential energy field in real time through a potential energy calculation module. This potential energy data is used as a dynamic input factor for the fitness function of the genetic algorithm in the order clustering and delivery unit. This allows the optimization of delivery routes to no longer rely solely on historical data or static geographical information, but can perceive and avoid potential risk areas in real time. It achieves dynamic and automated linkage from macro-risk warning to micro-delivery execution, significantly improving the real-time performance and foresight of delivery route planning.

Description

Technical Field

[0001] This invention relates to the field of logistics order management technology, and in particular to a cross-border e-commerce logistics order management system based on big data. Background Technology

[0002] In recent years, with the deepening development of global trade, the cross-border e-commerce industry has maintained a high-speed growth trend. However, cross-border logistics, as the core link of cross-border e-commerce, has long faced challenges such as high costs, unstable delivery times, and difficult risk control due to its long chain, many links, and complex stakeholders. To address these issues, the industry has developed a variety of logistics order management systems, attempting to optimize logistics management efficiency from different perspectives.

[0003] Currently, mainstream cross-border e-commerce logistics management systems can be mainly divided into the following technical directions: One type of system focuses on the static optimization of logistics channels. These systems integrate data from multiple third-party logistics providers, including freight rates and delivery time commitments, and recommend the optimal logistics channel to merchants before shipment based on information such as the package's destination, weight, size, and product type. While this approach can help merchants reduce shipping costs, its decision-making is primarily based on historical or pre-set channel parameters, lacking the ability to perceive and respond to dynamic changes such as real-time road conditions, regional traffic control, and port congestion.

[0004] Another type of system focuses on node tracking and segmented management of the logistics process. These systems typically use the physical movement of containers or parcels as a foundation, dividing cross-border transportation into domestic, international trunk, and overseas segments, and managing the information, cost, and document flows of each segment in a modular fashion. While this approach makes the order process clearer and more traceable, its management is somewhat fragmented, with limited data linkage and collaborative processing capabilities between segments. It also struggles to proactively and promptly intervene in unforeseen risks across segments (such as sudden congestion at the destination port).

[0005] Another type of system focuses on algorithmic optimization of last-mile delivery routes. These systems use clustering algorithms to group orders and employ heuristic algorithms such as genetic algorithms and ant colony algorithms to optimize delivery order and routes. Factors considered typically include delivery distance, time window, and traffic conditions. This type of solution is effective in improving local delivery efficiency, but its optimization scope is mostly limited to the last mile or same-city delivery. It lacks the ability to perceive macro risks in the long chain of cross-border logistics (such as port inspection backlogs and overseas warehouse overload), which may cause the optimized route to still encounter bottlenecks in the upstream links.

[0006] In addition, some systems that introduce the concept of macro-risk perception have emerged in recent years. These systems dynamically monitor the health of logistics operations in various regions by calculating "regional logistics potential" or similar indicators based on logistics status data, and can identify and warn of risks in the early stages. However, the processing strategies of such systems are usually relatively simple, such as simply suspending shipments to risky areas or manually notifying logistics service providers to handle the situation. The handling methods for packages that have already been shipped and are in transit are particularly limited. They cannot automatically generate differentiated and refined intervention plans according to different types and stages of risk, and they have failed to integrate the risk perception results into the downstream delivery route planning algorithm in real time and dynamically.

[0007] In summary, while existing technologies have made progress in certain aspects of cross-border e-commerce logistics management, they generally suffer from the following technical shortcomings: First, there is a disconnect between macro-level risk perception and micro-level delivery optimization; risk warning information is not integrated into route planning decisions in real time, meaning that even optimized solutions may still encounter black swan events. Second, for parcels already shipped and in transit, there is a lack of an adaptive, multi-layered anomaly handling mechanism, making it difficult to achieve a dynamic balance between ensuring delivery success rates and controlling additional costs. Third, the system as a whole has not yet formed a complete closed-loop chain from data collection, risk identification, strategy generation, execution to feedback optimization, making it difficult for the system's decision-making capabilities to continuously evolve with the accumulation of operational data. Summary of the Invention

[0008] Given that the existing technologies still have the following problems: First, there is a gap between macro-level risk perception and micro-level delivery optimization, and risk warning information is not integrated into route planning decisions in real time, which means that even optimized solutions may still encounter black swan events; second, for packages already shipped and in transit, there is a lack of an adaptive, multi-level anomaly handling mechanism, making it difficult to achieve a dynamic balance between ensuring delivery success rate and controlling additional costs; third, the system as a whole has not yet formed a closed-loop chain from data collection, risk identification, strategy generation, execution to feedback optimization, which makes it difficult for the system's decision-making capabilities to continuously evolve with the accumulation of operational data, this invention is proposed.

[0009] Therefore, the purpose of this invention is to provide a cross-border e-commerce logistics order management system based on big data. The purpose is to use the potential energy data as a dynamic input factor of the fitness function of the genetic algorithm in the order clustering and delivery unit. This makes the optimization of the delivery route no longer rely solely on historical data or static geographical information, but can perceive and avoid potential risk areas in real time. It realizes dynamic and automated linkage from macro risk warning to micro delivery execution, significantly improving the real-time performance and foresight of delivery route planning.

[0010] To address the aforementioned technical problems, this invention provides the following technical solution: a cross-border e-commerce logistics order management system based on big data, comprising: The data acquisition and processing unit is used to acquire and preprocess logistics status data, geographic information data, real-time traffic data, and basic order data from multiple heterogeneous data sources in real time. The potential energy calculation and risk identification unit is used to calculate the dynamic logistics potential energy value of a preset geographic grid based on the logistics status data, and identify potential risk areas and their risk evolution patterns based on the rate of change of the potential energy value. The order clustering and route planning unit is used to cluster orders based on the basic order data and real-time traffic data, and to optimize the delivery route of each group of orders using a genetic algorithm to generate a preliminary delivery plan. The strategy generation and execution unit is used to adaptively intervene in the relevant orders in the preliminary delivery plan based on the potential risk areas and their risk evolution patterns, and generate the final delivery instructions. The feedback and model optimization unit is used to obtain the execution result data of the final delivery instruction and feed the execution result data back to the potential energy calculation and risk identification unit and the order clustering and route planning unit to dynamically adjust their internal calculation models.

[0011] As a preferred embodiment of the big data-based cross-border e-commerce logistics order management system of the present invention, the potential energy calculation and risk identification unit includes: The potential energy calculation module is used to calculate the regional logistics potential energy value of each geographic grid per unit time based on the logistics status data. The logistics status data includes the node dwell time of packages at logistics nodes, the interval transition rate of packages between adjacent logistics nodes, and the scan event entropy of logistics nodes per unit time. The risk identification module is used to monitor the rate of change of the regional logistics potential energy value of each geographic grid over time in real time, and to identify grids with a negative rate of change and an absolute value exceeding a preset threshold as potential risk areas. The pattern classification module is used to extract the current risk evolution characteristics of the potential risk area and match the current risk evolution characteristics with the historical interruption event feature fingerprints pre-stored in the fingerprint database to determine the risk level and evolution pattern of the potential risk area. The risk evolution pattern includes at least transient fluctuations, developmental congestion, and systemic risk precursors.

[0012] As a preferred embodiment of the big data-based cross-border e-commerce logistics order management system of the present invention, the order clustering and route planning unit includes: The clustering module is used to dynamically cluster orders that are not affected by the potential risk areas by applying the density peak clustering algorithm and combining the order's geographical location, urgency, and customer loyalty. The route optimization module is used to optimize the delivery route of each order using a genetic algorithm. The fitness function of the genetic algorithm comprehensively considers the delivery distance, the time cost based on real-time traffic data, the regional restriction penalty factor, and the regional logistics potential value along the candidate route output by the potential energy calculation module. The fitness function is calculated using the following formula:

[0013] in, For fitness value, This represents the total delivery distance of the candidate routes. The total time cost is calculated based on real-time traffic data. The penalty value for violating the area restrictions. The first mile of the candidate path Regional logistics potential value of a geographic grid This represents the total number of geographic grids traversed by the path. These are the weighting coefficients for each item; The regional logistics potential value As a dynamic factor, the fitness value increases when the candidate path passes through a grid with a low regional logistics potential value, thereby guiding the genetic algorithm to actively avoid potential risk areas when selecting paths.

[0014] As a preferred embodiment of the big data-based cross-border e-commerce logistics order management system of the present invention, the strategy generation and execution unit includes: The strategy generation module is used to generate differentiated adaptive intervention strategies based on the risk level and evolution pattern. An execution module is used to apply the adaptive intervention strategy to the corresponding orders, including orders that have not yet been dispatched and orders that have been shipped and are in transit.

[0015] As a preferred embodiment of the big data-based cross-border e-commerce logistics order management system of the present invention, the strategy generation module is specifically used for: When the risk level fluctuates instantaneously, a monitoring strategy is generated to conduct high-frequency monitoring of orders in the affected areas. When the risk level is developmental congestion, a rerouting strategy is generated to dynamically reroute undelivered orders, and an instruction to "temporarily postpone delivery - re-deliver at an opportune time" is generated for orders that have been shipped and are in transit. When the risk level is a precursor to systemic risk, a restrictive strategy is generated to "suspend dispatch and transfer to the emergency handling pool" for undelivered orders, and an instruction is generated to "force detour and activate backup logistics channels" for orders that have been shipped and are in transit.

[0016] As a preferred embodiment of the big data-based cross-border e-commerce logistics order management system of the present invention, the dynamic rerouting strategy includes: using the regional logistics potential energy value as a dynamic weight, combined with real-time traffic data, and utilizing an improved Dijkstra algorithm to calculate the globally optimal alternative path for affected orders to avoid the potential risk area.

[0017] As a preferred embodiment of the big data-based cross-border e-commerce logistics order management system of the present invention, the feedback and model optimization unit is specifically used for: Collect and analyze the actual delivery time, actual transportation cost, and customer satisfaction score from the execution result data; Based on the deviation between the actual receipt time and the estimated time, the weighting coefficient of the regional logistics potential energy value calculation in the potential energy calculation module is adjusted. Based on the actual transportation cost and customer satisfaction score, adjust the weight coefficients of delivery distance, time cost, and regional logistics potential value in the fitness function of the genetic algorithm in the route optimization module.

[0018] As a preferred embodiment of the big data-based cross-border e-commerce logistics order management system of the present invention, the data acquisition and processing unit uses a hash algorithm based on a Bloom filter to clean duplicate order data, maps key order information into a bit array, and quickly identifies and removes duplicate order data by judging the bit status in the bit array.

[0019] To achieve the above objectives, the present invention provides the following technical solution: a management method for a cross-border e-commerce logistics order management system based on big data, comprising the following steps: Step 1: Collect multi-source heterogeneous data in real time and perform preprocessing; Step 2: Based on the preprocessed logistics status data, construct a dynamic logistics potential energy field and identify potential risk areas and their evolution patterns; Step 3: Combine basic order data and real-time traffic data to cluster orders and initially optimize delivery routes; Step 4: Based on the identified potential risk areas and their evolution patterns, adaptively intervene in the initially optimized delivery path to generate the final delivery instruction; Step 5: Obtain the delivery execution results and provide feedback to optimize the potential energy field construction model in Step 2 and the path optimization model in Step 3.

[0020] As a preferred embodiment of the present invention, the adaptive intervention in step four further includes: For undelivered orders, if their original delivery route passes through the potential risk area, dynamic rerouting or delivery suspension will be performed according to the risk evolution model. For orders that have already been shipped and are en route, if their planned route is about to enter the potential risk area, dynamic rerouting or delivery postponement will be performed according to the risk evolution model.

[0021] Compared with the prior art, the present invention has at least the following beneficial effects: 1. This invention constructs a dynamic logistics potential energy field in real time through a potential energy calculation module, and uses this potential energy data as a dynamic input factor for the fitness function of the genetic algorithm in the order clustering and delivery unit. This makes the optimization of delivery routes no longer dependent on historical data or static geographical information, but can perceive and avoid potential risk areas in real time, realizing dynamic and automated linkage from macro risk warning to micro delivery execution, which significantly improves the real-time performance and foresight of delivery route planning.

[0022] 2. This invention classifies the evolution patterns of identified risk areas through a risk matching module and then uses a strategy generation module to generate differentiated intervention strategies. Specifically for packages already shipped and in transit, the system can intelligently select different levels of processing options based on the risk level, such as "delay delivery - continuous monitoring," "replanning dynamic routes," or "transferring to the emergency handling pool - triggering high-cost support channels." This overcomes the shortcomings of existing technologies with their single processing method and achieves a better balance between ensuring delivery success rates and controlling additional costs.

[0023] 3. This invention feeds back the execution results of order delivery (such as actual delivery time and customer feedback) to the data acquisition and processing unit to update the historical database. This updated data optimizes the potential energy calculation model, making its risk identification more accurate; it also optimizes the fitness function weights in the genetic algorithm, making the path planning strategy more aligned with actual operational conditions. This forms a complete closed loop of "data acquisition - risk identification - strategy generation - delivery execution - effect feedback - model optimization," enabling the system to continuously learn and evolve, constantly optimizing the accuracy of logistics decisions. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the framework structure of the cross-border e-commerce logistics order management system based on big data of the present invention; Figure 2 This is a schematic diagram illustrating the management method steps of the cross-border e-commerce logistics order management system based on big data according to the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] Reference Figures 1-2As an embodiment of the present invention, the present invention provides a cross-border e-commerce logistics order management system based on big data, comprising: a data acquisition and processing unit, a potential energy calculation and risk identification unit, an order clustering and path planning unit, a strategy generation and execution unit, and a feedback and model optimization unit.

[0027] The data acquisition and processing unit connects to external e-commerce platforms, logistics carrier systems, government open data platforms, and transportation information service platforms to acquire multi-source heterogeneous data in real time. The input of the potential energy calculation and risk identification unit is connected to the data acquisition and processing unit, while its output is connected to the order clustering and route planning unit and the strategy generation and execution unit, respectively. The input of the order clustering and route planning unit is connected to both the data acquisition and processing unit and the potential energy calculation and risk identification unit, while its output is connected to the strategy generation and execution unit. The output of the strategy generation and execution unit communicates with external execution systems (such as warehouse management systems and driver applications). The input of the feedback and model optimization unit is connected to the execution result feedback interface of the strategy generation and execution unit, while its output is connected to both the potential energy calculation and risk identification unit and the order clustering and route planning unit, forming a closed-loop optimization chain.

[0028] Data acquisition and processing unit

[0029] The data acquisition and processing unit is equipped with a multi-source data interface adapter for real-time acquisition of the following four types of data: First, logistics status data. This unit connects with the systems of major logistics carriers through an application programming interface (API) to obtain scan records of packages at key nodes such as sorting centers, customs ports, and transshipment hubs. This includes information such as package identifier, node identifier, event type (inbound scan, outbound scan, customs inspection, etc.), and timestamp.

[0030] Second, geographic information data. This unit obtains basic geographic information from third-party map service providers and divides the monitoring area into equally sized geographic grids according to latitude and longitude. Each grid has a unique geographic identifier for subsequent refined monitoring and data analysis.

[0031] Third, real-time traffic data. This unit connects to the data stream of traffic information service providers to obtain information such as traffic congestion index and average traffic speed for different road sections at different time periods, providing dynamic basis for route planning.

[0032] Fourth, basic order data. This unit synchronizes order information from the cross-border e-commerce platform, including order number, customer information, product information, shipping address, order time, expected delivery time, and order amount.

[0033] In the data cleaning stage, this embodiment employs a hash algorithm based on a Bloom filter for the identification and removal of duplicate orders. Specifically, key information such as order number, customer name, product SKU, and order time are extracted from each order and combined into a string. This string is then mapped into a bit array using multiple independent hash functions. When a new order arrives, the system maps its key information to the bit array using the same hash functions. If all mapped bits are 1, the order is considered a possible duplicate, and the order amount and delivery address are further compared for confirmation. If not all bits are 1, the order is considered new, and the Bloom filter is updated. This method achieves efficient duplicate order filtering with minimal memory usage, making it suitable for processing massive amounts of cross-border e-commerce order data.

[0034] Potential energy calculation and risk identification unit

[0035] The potential energy calculation and risk identification unit further includes a potential energy calculation module, a risk identification module, and a pattern classification module.

[0036] The potential energy calculation module performs the following operations in each preset calculation cycle (e.g., every 15 minutes): First, for each geographic grid, the real-time status data of all packages within that grid are aggregated, and three core metrics are calculated: average node dwell time, which is the average dwell time of all packages within that grid at each logistics node; average interval transition rate, which is the average transportation speed of packages between adjacent nodes within that grid; and average scan event entropy, which is the entropy value of the distribution of scan events at each logistics node within that grid, and this entropy value is used to reflect the degree of orderliness of the operation process.

[0037] Then, the potential energy value of the grid at the current moment is calculated using a regional logistics potential energy calculation method. Specifically, the regional logistics potential energy value of the grid is obtained by weighted summing of the reciprocal of the average node dwell time, the average interval transition rate, and the reciprocal of the average scan event entropy. The weight coefficients for each term are preset normalized weight coefficients, with initial values ​​determined based on historical data training, ranging from 0 to 1, and the sum of the three is 1. In this calculation method, shorter dwell time, faster transition rate, and lower event entropy result in a higher potential energy value, indicating a healthier regional logistics operation.

[0038] The risk identification module monitors the rate of change of potential energy values ​​for each geographic grid in real time. For each grid, the change in its potential energy value between two adjacent monitoring times is calculated and divided by the time interval to obtain the rate of change. When the rate of change is negative and its absolute value exceeds a preset threshold, that is, when the potential energy value decreases at a rate exceeding the preset threshold (e.g., decreasing by 0.5 per hour), the grid is identified as a potential risk area.

[0039] The pattern classification module extracts the current risk evolution characteristics of potential risk areas. Specifically, the module obtains the potential energy value time series for a continuous period (e.g., 24 hours) before the area is identified as a risk, and calculates the following feature parameters: potential energy value decrease rate, duration of continuous decrease, influence range (number of adjacent low potential energy grids), and the lowest potential energy point. These parameters are then combined into a current risk feature vector.

[0040] Subsequently, the pattern classification module retrieves all pre-stored historical disruption event feature fingerprints from the fingerprint database and uses a preset similarity algorithm (such as cosine similarity) to calculate the matching degree between the current risk feature vector and each historical fingerprint. Each historical disruption event feature fingerprint is a quantitative description of a major logistics disruption event that has occurred in history (such as port strikes, extreme weather, and large-scale traffic control), including feature parameters such as the rate of change of potential energy value during the event, the spatial impact range, and the temporal evolution pattern.

[0041] Based on the matching results, the risk is divided into three levels: when the matching degree is higher than the first preset threshold, it is judged as the "systemic risk precursor" level, which corresponds to the evolution pattern of extreme events that have caused large-scale logistics paralysis in the past; when the matching degree is between the second preset threshold and the first preset threshold, it is judged as the "developmental congestion" level, which corresponds to the risk that is spreading but has not yet reached the level of systemic collapse; when the matching degree is lower than the second preset threshold, it is judged as the "transient fluctuation" level, which corresponds to local and short-term abnormal fluctuations.

[0042] Order clustering and route planning unit

[0043] The order clustering and route planning unit further includes a clustering module and a route optimization module.

[0044] The clustering module first obtains a set of orders unaffected by potential risk areas from the data acquisition and processing unit. Then, it uses a density peak clustering algorithm to group the orders. When calculating the distance between orders, a weighted Euclidean distance is used, while order urgency and customer loyalty are incorporated as adjustment factors.

[0045] The quantification rules for order urgency are as follows: delivery times within 24 hours are assigned to the highest level, 24 to 48 hours to the medium level, and over 48 hours to the normal level. Customer loyalty is assessed based on a comprehensive evaluation of historical order volume and spending amount, categorized into three levels: high loyalty, medium loyalty, and low loyalty. The adjusted local density calculation method is as follows: based on the initial local density, an adjustment coefficient determined according to order urgency and customer loyalty is multiplied, giving higher weight to orders with high urgency and high loyalty in the cluster, ensuring that these orders are prioritized.

[0046] The route optimization module uses a genetic algorithm to optimize the delivery route for each order. The core innovation of this module lies in the design of the fitness function, which considers both static factors and dynamic risk factors.

[0047] For each candidate delivery route, the fitness value is calculated by comprehensively considering the following four factors: First, the total delivery distance of the candidate route, which is obtained by summing the distances of each road segment through the route planning algorithm; Second, the total time cost calculated based on real-time traffic data, which is calculated by weighting the current congestion index and expected traffic speed of each road segment; Third, the penalty value for violating regional restrictions, which is given a larger penalty value when the route passes through certain controlled areas; Fourth, the regional logistics potential value of the geographical grid through which the route passes.

[0048] Specifically, the fitness value is a weighted sum of the four factors mentioned above. The fourth factor is calculated by summing the reciprocals of the regional logistics potential values ​​of each geographic grid traversed by the path. This means that when a candidate path passes through areas with low regional logistics potential values ​​(i.e., poor logistics efficiency), the accumulated value increases, thus increasing the overall fitness value of the path. In the iterative optimization of the genetic algorithm, solutions with higher fitness values ​​are less likely to be selected; therefore, the algorithm automatically tends to choose paths that avoid risky areas. The weight coefficients of each factor can be initially set according to business priorities and dynamically adjusted subsequently through feedback optimization units.

[0049] In the genetic operations phase, the path optimization module employs an adaptive adjustment strategy: when population diversity falls below a preset threshold, the crossover probability is increased to enhance global search capabilities; when individual fitness exceeds a preset threshold, the crossover probability is decreased to retain superior individuals. The mutation probability is also dynamically adjusted based on population diversity and individual fitness, achieving a balance between exploration and exploitation in the algorithm.

[0050] Strategy generation and execution unit

[0051] The strategy generation and execution unit receives risk level information from the pattern classification module and preliminary dispatch plan from the path optimization module, and generates differentiated adaptive intervention strategies based on different risk levels.

[0052] When the risk level is "instantaneous fluctuation", the strategy generation module generates a monitoring strategy: for orders that have not yet been dispatched, the original delivery plan remains unchanged, but the instruction execution module increases the data collection frequency for this area (for example, from every 15 minutes to every 5 minutes) to continuously monitor the change in potential energy value, and returns to normal after the fluctuation subsides.

[0053] When the risk level is "developing congestion," the strategy generation module generates a rerouting strategy. For orders that have not yet been dispatched, the execution module calls the path optimization module, treating the risk area as a dynamic obstacle, and recalculates the globally optimal alternative path to avoid that area for the affected orders. When calculating alternative paths, the regional logistics potential value is used as a penalty factor in the path cost, causing the generated path to prioritize areas with higher potential values.

[0054] For orders already shipped and en route, the execution module first identifies packages that are about to enter or are already located at the edge of a risk area and issues a "delivery postponed - delivery at a later date" instruction. Specifically, the system instructs the last-mile delivery vehicle to temporarily store the relevant packages at a temporary transfer point outside the risk area and sets a timed mechanism (e.g., every 30 minutes) to reassess the potential energy recovery status of the risk area. Once the potential energy value returns to a normal level, delivery will be arranged again.

[0055] When the risk level is "early signs of systemic risk," the strategy generation module generates restrictive and strong detour strategies. For orders that have not yet been dispatched, the execution module issues a "suspend dispatch - transfer to emergency processing pool" instruction, temporarily freezing the relevant orders and notifying the merchants via system message or email, providing the merchants with two options: "wait for the risk to be resolved before continuing delivery" or "activate a high-cost backup logistics channel."

[0056] For orders already shipped and in transit, the execution module generates a "forced detour - activate backup logistics channel" instruction. For example, if a trans-Pacific route experiences severe congestion at its destination port and is deemed a systemic risk, the system automatically changes the unloading port of the affected containers to a backup port and connects with a pre-configured land transportation plan to transfer the goods from the backup port to the final destination.

[0057] Feedback and Model Optimization Unit

[0058] The feedback and model optimization unit continuously collects execution result data from the strategy generation and execution unit, mainly including: actual delivery time, actual transportation cost, customer satisfaction score (obtained through evaluation questionnaires sent after delivery is completed), etc.

[0059] Based on this feedback data, the unit performs the following optimization operations: First, optimize the potential energy calculation model. Analyze the deviation between the actual delivery time and the estimated time. If it is found that a certain type of area frequently experiences actual delivery times far lower than the health level represented by the potential energy value, then adjust the weighting coefficients in the potential energy calculation formula. For example, if the impact of delay time at a certain port node on actual delivery time is underestimated, then appropriately increase the weight of this indicator.

[0060] Second, optimize the fitness function weights. Analyze actual transportation costs and customer satisfaction scores, and adjust the weight coefficients of various factors in the fitness function of the route optimization module. For example, if data shows that customers are more sensitive to timeliness than cost, appropriately increase the weight of time cost factors; if frequent detours through risky areas lead to a surge in costs but limited improvement in customer satisfaction, appropriately decrease the weight of risk aversion factors.

[0061] Third, update the historical outage event fingerprint database. The current risk event, its evolutionary characteristics, the intervention strategies adopted, and the final execution results are treated as a complete sample. After manual review, these samples can be selectively added to the fingerprint database as a comparison reference for similar future risk events, thereby continuously enhancing the system's risk identification capabilities.

[0062] The following uses a specific application scenario to illustrate the complete workflow of this system.

[0063] A cross-border e-commerce seller plans to ship 500 parcels from region a (location A) to region b (location B) and surrounding areas. The system operation process is as follows: Step 1: Data Acquisition and Processing

[0064] The data acquisition and processing unit acquires the scanning records of each package in real time, and simultaneously accesses the real-time traffic efficiency data of port area B and local traffic congestion data, and divides area B into a 1-kilometer by 1-kilometer geographical grid.

[0065] Step Two: Risk Identification

[0066] The potential energy calculation module calculated that the potential energy values ​​of multiple grids around Long Beach Port in Zone B continued to decrease over 4 hours, with the rate of decrease exceeding a preset threshold. The risk identification module marked these grids as potential risk areas. The pattern classification module extracted risk features and matched them with a fingerprint database, finding that the evolution pattern was highly similar to historical "port workers' temporary strike" events, with a matching degree exceeding the first preset threshold, and was judged to be at the "developmental congestion" level.

[0067] Step 3: Path Planning

[0068] The order clustering and route planning unit clustered 500 orders into 20 delivery groups. When calculating the delivery route for each delivery group, the route optimization module used a risk avoidance factor in the fitness function to actively avoid low-potential areas around Long Beach Port. In the initial plan, all routes detour to alternative ports or via inland transshipment.

[0069] Step 4: Strategy Generation and Execution

[0070] Upon receiving the "developing congestion" assessment result, the strategy generation and execution unit implemented a rerouting strategy for the 300 parcels that had not yet been shipped from Zone A. Containers originally planned for sea transport to Long Beach were diverted to Oakland Port and then connected to land transport to Zone B. For the 200 parcels already at sea, a "delayed delivery - rescheduled" instruction was issued, instructing the destination port agent to temporarily store the containers in the off-site yard until congestion subsided before pickup and delivery.

[0071] Step 5: Feedback and Optimization

[0072] After delivery, the feedback and model optimization unit found that the actual delivery time was generally 3 to 5 days later than normal, but this was a significant improvement compared to the potential delay of more than 10 days if relying entirely on the original solution. The average customer satisfaction score was 4.2 out of 5. Based on this feedback, the system appropriately reduced the initial weight of risk aversion factors in the fitness function to avoid unnecessary cost increases due to excessive risk aversion in the future.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A cross-border e-commerce logistics order management system based on big data, characterized in that, include: The data acquisition and processing unit is used to acquire and preprocess logistics status data, geographic information data, real-time traffic data, and basic order data from multiple heterogeneous data sources in real time. The potential energy calculation and risk identification unit is used to calculate the dynamic logistics potential energy value of a preset geographic grid based on the logistics status data, and identify potential risk areas and their risk evolution patterns based on the rate of change of the potential energy value. The order clustering and route planning unit is used to cluster orders based on the basic order data and real-time traffic data, and to optimize the delivery route of each group of orders using a genetic algorithm to generate a preliminary delivery plan. The strategy generation and execution unit is used to adaptively intervene in the relevant orders in the preliminary delivery plan based on the potential risk areas and their risk evolution patterns, and generate the final delivery instructions. as well as The feedback and model optimization unit is used to obtain the execution result data of the final delivery instruction and feed the execution result data back to the potential energy calculation and risk identification unit and the order clustering and route planning unit to dynamically adjust their internal calculation models.

2. The cross-border e-commerce logistics order management system based on big data as described in claim 1, characterized in that: The potential energy calculation and risk identification unit includes: The potential energy calculation module is used to calculate the regional logistics potential energy value of each geographic grid per unit time based on the logistics status data. The logistics status data includes the node dwell time of packages at logistics nodes, the interval transition rate of packages between adjacent logistics nodes, and the scan event entropy of logistics nodes per unit time. The risk identification module is used to monitor the rate of change of the regional logistics potential energy value of each geographic grid over time in real time, and to identify grids with a negative rate of change and an absolute value exceeding a preset threshold as potential risk areas. The pattern classification module is used to extract the current risk evolution characteristics of the potential risk area and match the current risk evolution characteristics with the historical interruption event feature fingerprints pre-stored in the fingerprint database to determine the risk level and evolution pattern of the potential risk area. The risk evolution pattern includes at least transient fluctuations, developmental congestion, and systemic risk precursors.

3. The cross-border e-commerce logistics order management system based on big data according to claim 2, characterized in that: The order clustering and route planning unit includes: The clustering module is used to dynamically cluster orders that are not affected by the potential risk areas by applying the density peak clustering algorithm and combining the order's geographical location, urgency, and customer loyalty. The route optimization module is used to optimize the delivery route of each order using a genetic algorithm. The fitness function of the genetic algorithm comprehensively considers the delivery distance, the time cost based on real-time traffic data, the regional restriction penalty factor, and the regional logistics potential value along the candidate route output by the potential energy calculation module. The fitness function is calculated using the following formula: in, For fitness value, This represents the total delivery distance of the candidate routes. The total time cost is calculated based on real-time traffic data. The penalty value for violating the area restrictions. The first mile of the candidate path Regional logistics potential value of a geographic grid This represents the total number of geographic grids traversed by the path. These are the weighting coefficients for each item; The regional logistics potential value As a dynamic factor, the fitness value increases when the candidate path passes through a grid with a low regional logistics potential value, thereby guiding the genetic algorithm to actively avoid potential risk areas when selecting paths.

4. The cross-border e-commerce logistics order management system based on big data according to claim 3, characterized in that: The strategy generation and execution unit includes: The strategy generation module is used to generate differentiated adaptive intervention strategies based on the risk level and evolution pattern. An execution module is used to apply the adaptive intervention strategy to the corresponding orders, including orders that have not yet been dispatched and orders that have been shipped and are in transit.

5. The cross-border e-commerce logistics order management system based on big data according to claim 4, characterized in that: The strategy generation module is specifically used for: When the risk level fluctuates instantaneously, a monitoring strategy is generated to conduct high-frequency monitoring of orders in the affected areas. When the risk level is developmental congestion, a rerouting strategy is generated to dynamically reroute undelivered orders, and an instruction to "temporarily postpone delivery - resend at a later time" is generated for orders that have been shipped and are in transit. When the risk level is a precursor to systemic risk, a restrictive strategy is generated to "suspend dispatch and transfer to the emergency processing pool" for undelivered orders, and an instruction is generated to "force detour and activate backup logistics channels" for orders that have been shipped and are in transit.

6. The cross-border e-commerce logistics order management system based on big data according to claim 5, characterized in that: The dynamic rerouting strategy includes: using the regional logistics potential energy value as a dynamic weight, combined with real-time traffic data, and utilizing an improved Dijkstra algorithm to calculate the globally optimal alternative path to avoid the potential risk area for affected orders.

7. The cross-border e-commerce logistics order management system based on big data according to claim 1, characterized in that: The feedback and model optimization unit is specifically used for: Collect and analyze the actual delivery time, actual transportation cost, and customer satisfaction score from the execution result data; Based on the deviation between the actual receipt time and the estimated time, the weighting coefficient of the regional logistics potential energy value calculation in the potential energy calculation module is adjusted. Based on the actual transportation cost and customer satisfaction score, adjust the weight coefficients of delivery distance, time cost, and regional logistics potential value in the fitness function of the genetic algorithm in the route optimization module.

8. The cross-border e-commerce logistics order management system based on big data according to claim 1, characterized in that: When cleaning duplicate order data, the data acquisition and processing unit uses a hash algorithm based on a Bloom filter to map key order information into a bit array. Duplicate order data is quickly identified and removed by judging the bit status in the bit array.

9. A method for managing cross-border e-commerce logistics orders based on big data, applied to the cross-border e-commerce logistics order management system based on big data as described in any one of claims 1-8, characterized in that: Includes the following steps: Step 1: Collect multi-source heterogeneous data in real time and perform preprocessing; Step 2: Based on the preprocessed logistics status data, construct a dynamic logistics potential energy field and identify potential risk areas and their evolution patterns; Step 3: Combine basic order data and real-time traffic data to cluster orders and initially optimize delivery routes; Step 4: Based on the identified potential risk areas and their evolution patterns, adaptively intervene in the initially optimized delivery path to generate the final delivery instruction; Step 5: Obtain the delivery execution results and provide feedback to optimize the potential energy field construction model in Step 2 and the path optimization model in Step 3.

10. The cross-border e-commerce logistics order management system based on big data according to claim 9, characterized in that: The adaptive intervention described in step four further includes: For undelivered orders, if their original delivery route passes through the potential risk area, dynamic rerouting or delivery suspension will be performed according to the risk evolution model. For orders that have already been shipped and are en route, if their planned route is about to enter the potential risk area, dynamic rerouting or delivery postponement will be performed according to the risk evolution model.

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