Cross-border e-commerce supply chain risk monitoring system based on big data

Abstract

The invention relates to the technical field of financial risk monitoring, and discloses a cross-border e-commerce supply chain risk monitoring system based on big data, which comprises a data acquisition module, a data processing and storage module, a demand prediction module, a data processing and storage module, a demand prediction module and a demand management module, the multi-source data analysis module is used for analyzing the multi-source data and outputting a goods demand quantity prediction result in a future preset period, and the storage planning module is used for generating a goods storage scheme according to the demand quantity prediction result; the storage planning module is used for generating a storage scheme, the storage scheme comprises the storage quantity, storage area division and inventory turnover cycle planning of each category of goods, the dynamic adjustment module is used for adjusting and implementing the goods storage scheme generated by the storage planning module, and the risk intelligent analysis engine is used for analyzing the risk. And the analysis module is used for analyzing the processed data based on predefined risk indexes and a machine learning model so as to identify and evaluate supply chain risks.

Description

Technical Field

[0001] This application relates to the technical field of financial risk monitoring, and in particular to a cross-border e-commerce supply chain risk monitoring system based on big data. Background Technology

[0002] With the deep integration of global e-commerce, cross-border e-commerce has become a new form of international trade. However, its supply chain is characterized by extremely long links, numerous participants, complex processes, and a volatile environment, making it more vulnerable to various internal and external risks compared to traditional supply chains. Effective monitoring, accurate early warning, and rapid response to risks across the entire supply chain have become key challenges in ensuring the continuity of cross-border e-commerce business, improving customer satisfaction, and maintaining core competitiveness.

[0003] Traditional supply chain management systems primarily focus on internal process informatization, relying on a single data source and lacking systematic integration of external risk data from suppliers, logistics providers, and the macro environment. Risk perception heavily depends on post-event manual reporting or basic indicator alerts, leading to delayed risk detection and often only responding after problems have already caused substantial impact, missing the optimal intervention window. Most systems can only visualize risks and cannot provide intelligent decision support. After receiving alerts, managers still need to rely on personal experience to make judgments and manually coordinate resources to develop response plans. This process is not only inefficient but also prone to biased decisions in complex, multi-variable decision-making due to incomplete information or time pressure, lacking the ability to quantitatively extrapolate the costs, timeliness, and other dimensions of various contingency plans.

[0004] Therefore, there is an urgent need for a cross-border e-commerce supply chain risk monitoring system that can integrate internal and external big data, achieve intelligent risk prediction and root cause diagnosis, provide data-driven decision support, and has continuous learning capabilities, in order to overcome the shortcomings of existing technologies and improve the overall resilience and intelligent management level of the supply chain. Summary of the Invention

[0005] The purpose of this invention is to provide a cross-border e-commerce supply chain risk monitoring system based on big data, which solves at least one of the above problems.

[0006] This invention provides a big data-based cross-border e-commerce supply chain risk monitoring system, comprising: The data acquisition module is used to acquire multi-source data related to the supplier's goods. The multi-source data includes historical sales data, market demand fluctuation data, customer order reservation data, and seasonal consumption characteristic data. A data processing and storage module is connected to the data acquisition module and is used to clean, transform, and store the acquired data. The demand forecasting module analyzes the multi-source data based on a preset algorithm to output the forecast results of the demand for goods within a preset period in the future. The storage planning module generates a goods storage plan based on the demand forecast results, combined with the storage space capacity, goods storage time requirements, and storage cost data. The storage plan includes the storage quantity of each category of goods, the division of storage areas, and the inventory turnover cycle planning. The dynamic adjustment module is used to adjust and implement the cargo storage plan generated by the storage planning module; The risk intelligence analysis engine, connected to the big data processing and storage module, is used to analyze the processed data based on predefined risk indicators and machine learning models to identify and assess supply chain risks.

[0007] As a further technical solution, the risk intelligent analysis engine includes: The risk prediction unit is used to train machine learning models based on historical data to predict risks such as supplier delivery delays, shipping time delays, inventory shortages, or unsold inventory. The anomaly detection unit is used to monitor logistics trajectories and sensor data in real time through unsupervised learning algorithms, and automatically identify abnormal events that deviate from the normal pattern.

[0008] As a further technical solution, the demand forecasting module incorporates a dynamic multi-algorithm selection mechanism; The multi-algorithm dynamic selection mechanism is based on the characteristics of multi-source data, including data volume, fluctuation frequency, and completeness. The multi-algorithm dynamic selection mechanism includes: When the amount of historical sales data is greater than or equal to a preset fixed value and the fluctuation is stable, the ARIMA time series algorithm is selected; when the market demand fluctuation data contains multiple influencing factors, the LSTM deep learning algorithm is switched to; when the proportion of customer order reservation data is greater than or equal to a preset threshold, the weighted regression algorithm is used. Each quarter, the algorithm parameters are iteratively optimized based on the deviation rate between the forecast results and the actual demand to ensure that the deviation rate of the forecast results for the demand of goods within the preset period is less than or equal to a preset fixed value.

[0009] As a further technical solution, the storage planning module also includes a storage cost optimization submodule; The storage cost optimization submodule calculates the total cost under different storage strategies based on the cargo storage plan, and selects the optimal storage strategy with the lowest total cost through comparative analysis. For perishable goods, the storage cost optimization submodule incorporates loss rate constraints into the cost optimization process, taking into account the timeliness requirements of goods storage, to ensure that the storage plan meets the quality requirements of the goods.

[0010] As a further technical solution, the storage planning module also includes a dynamic calibration function for inventory turnover cycle; The inventory turnover cycle dynamic calibration function is based on the average daily forecast outbound volume of each category of goods in the demand forecast results, combined with the goods storage time requirements, to preliminarily calculate the inventory turnover cycle. If the initially calculated turnover period exceeds the preset percentage of the storage time requirement, the predicted storage quantity of that category of goods will be automatically reduced so that the calibrated turnover period is less than or equal to the preset percentage of the storage time requirement. Meanwhile, for high-turnover goods with a turnover cycle ≤ storage time, a buffer pre-storage amount is added to the storage plan, and an additional safety stock of a fixed amount of the average daily predicted outbound quantity is reserved.

[0011] As a further technical solution, the data acquisition module includes real-time data retransmission and updating; When a data source is temporarily interrupted, the data acquisition module stores the incremental data during the interruption period through a local caching mechanism, and automatically re-transmits it after the data source is restored. The data acquisition module updates all collected data once every fixed preset time threshold to ensure that the demand forecasting module uses the latest data and avoids prediction deviations due to data lag.

[0012] As a further technical solution, the preprocessing process in the data processing and storage module includes: processing missing values ​​and outliers in the data; Unify data from different sources into a standard format; By associating all different data information with the same information subject, a complete data view of the supply chain is formed; The processed data is organized and stored in the database.

[0013] As a further technical solution, the function of the dynamic adjustment module includes: Used for real-time monitoring of the deviation between actual demand and predicted demand; when the deviation between actual outbound goods and predicted demand exceeds a preset threshold, the dynamic adjustment module automatically triggers an adjustment mechanism, which includes a first adjustment mechanism and a second adjustment mechanism. The first adjustment mechanism feeds back deviation data to the demand forecasting module to correct the forecasting algorithm parameters; The second adjustment mechanism sends adjustment instructions to the storage planning module to update the cargo storage plan, including increasing or decreasing the storage quantity of specific categories of goods and adjusting the allocation of storage areas to ensure that the storage quantity matches the actual demand.

[0014] As a further technical solution, the preventive measures also include cross-border payment risk prevention; The system connects to transaction data from cross-border payment platforms in real time and identifies suspicious payment behavior through anomaly detection algorithms; If a risk is detected, the payment and settlement process will be immediately suspended, a verification request will be sent to the user, and the risk control department will conduct a manual review to reduce losses from cross-border payment fraud.

[0015] As a further technical solution, the system operation process includes: Step S1: Obtain multi-source data related to supplier goods and process the data. Update all collected data once every fixed preset time threshold. When a data source is temporarily interrupted, the system starts a local caching mechanism to store incremental data during the interruption period and automatically re-upload it after the data source is restored. Step S2: Dynamically select the optimal prediction algorithm based on the characteristics of the multi-source data; Step S3: Based on the demand forecast results, combined with the storage space capacity, storage time requirements and storage cost data, generate an initial storage plan; Step S4: The data processing and storage module acquires the processed data and predicts whether there is any risk. Step S5: Automatically assess the risk level according to preset rules, aggregate events from the same source to avoid duplicate alarms, and generate detailed alarm prompts.

[0016] In summary, this application includes at least one of the following beneficial technical effects: 1. This invention integrates big data from multiple internal and external sources and utilizes machine learning models for risk prediction and anomaly detection, enabling early warnings to be issued before risks occur or in their nascent stages. The system can automatically match contingency plans and perform simulations through algorithms, providing users with data-driven quantitative decision-making suggestions. This transforms the decision-making process from relying on personal experience to scientific, globally optimized intelligent computing, significantly shortening response time and improving decision quality.

[0017] 2. This invention utilizes data fusion technology to integrate previously fragmented data on procurement, inventory, logistics, suppliers, and the macro environment into a unified risk view. When a risk occurs, the system can quickly pinpoint the root cause of a series of apparent problems. Simultaneously, by tracking the effectiveness of decision-making and feeding it back to the model and knowledge base, the system forms a continuous optimization loop, enabling the supply chain system to learn from each disruption and self-improve, fundamentally enhancing its resilience.

[0018] 3. This invention, through early warning and optimal decision-making, can effectively avoid or mitigate direct losses caused by supply chain disruptions, such as inventory backlog, emergency transportation, and order cancellations. It avoids redundant investment and ineffective management, saving management resources. This invention transforms supply chain risk management from a cost center into a strategic asset supporting the company's steady growth and winning market competition. Attached Figure Description

[0019] Figure 1 This is a module diagram of a cross-border e-commerce supply chain risk monitoring system based on big data.

[0020] Figure 2 This is a flowchart of a cross-border e-commerce supply chain risk monitoring system based on big data. Detailed Implementation

[0021] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0022] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] Please refer to Figure 1 As shown, this invention discloses a cross-border e-commerce supply chain risk monitoring system based on big data, comprising: The data acquisition module is used to acquire multi-source data related to the supplier's goods. The multi-source data includes historical sales data, market demand fluctuation data, customer order reservation data, and seasonal consumption characteristic data. A data processing and storage module is connected to the data acquisition module and is used to clean, transform, and store the acquired data. The demand forecasting module analyzes the multi-source data based on a preset algorithm to output the forecast results of the demand for goods within a preset period in the future. The storage planning module generates a goods storage plan based on the demand forecast results, combined with the storage space capacity, goods storage time requirements, and storage cost data. The storage plan includes the storage quantity of each category of goods, the division of storage areas, and the inventory turnover cycle planning. The dynamic adjustment module is used to adjust and implement the cargo storage plan generated by the storage planning module; The risk intelligence analysis engine, connected to the big data processing and storage module, is used to analyze the processed data based on predefined risk indicators and machine learning models to identify and assess supply chain risks.

[0024] In this embodiment, the risk intelligence analysis engine includes: The risk prediction unit is used to train machine learning models based on historical data to predict risks such as supplier delivery delays, shipping time delays, inventory shortages, or unsold inventory. The anomaly detection unit is used to monitor logistics trajectories and sensor data in real time through unsupervised learning algorithms, and automatically identify abnormal events that deviate from the normal pattern.

[0025] In this embodiment, the demand forecasting module has a built-in dynamic selection mechanism for multiple algorithms; The multi-algorithm dynamic selection mechanism is based on the characteristics of multi-source data, including data volume, fluctuation frequency, and completeness. The multi-algorithm dynamic selection mechanism includes: When the amount of historical sales data is greater than or equal to a preset fixed value and the fluctuations are stable, the ARIMA time series algorithm is selected; when the market demand fluctuation data contains multiple influencing factors, the LSTM deep learning algorithm is switched to; when the proportion of customer order reservation data is greater than or equal to 80%, the weighted regression algorithm is used. The algorithm parameters are iteratively optimized quarterly based on the deviation rate between the forecast results and the actual demand to ensure that the deviation rate of the cargo demand forecast results is ≤5% within the preset period.

[0026] In this embodiment, the storage planning module further includes a storage cost optimization submodule; The storage cost optimization submodule calculates the total cost under different storage strategies based on the cargo storage plan, and selects the optimal storage strategy with the lowest total cost through comparative analysis. ; in, The total cost of the storage strategy, For fixed costs under the storage strategy, For variable costs under the storage strategy, For storage costs, To reduce losses and costs, This represents the actual cost of loss. For perishable goods, the storage cost optimization submodule incorporates loss rate constraints into the cost optimization process, taking into account the timeliness requirements of goods storage, to ensure that the storage plan meets the quality requirements of the goods. ; in, This is the actual amount of loss. This represents the total value of inventory. Temperature and humidity are influencing factors. As a factor affecting inventory turnover, Costs related to timeliness; ; in, The penalty coefficient per unit time. To provide a safety margin for stored goods; =0.05 / day =20%; In this embodiment, the storage planning module also includes a dynamic calibration function for inventory turnover cycle; The inventory turnover cycle dynamic calibration function is based on the average daily forecast outbound volume of each category of goods in the demand forecast results, combined with the goods storage time requirements, to preliminarily calculate the inventory turnover cycle. If the initially calculated turnover period exceeds the preset percentage of the goods storage time requirement by 35%, the predicted storage quantity of that category of goods will be automatically reduced so that the calibrated turnover period is ≤ 35% of the storage time requirement. Meanwhile, for high-turnover goods with a turnover cycle of ≤35%, a buffer pre-stock quantity is added to the storage plan. The buffer pre-stock quantity is 20%-30% of the total order quantity, and an additional safety stock of a fixed amount of the average daily predicted outbound quantity is reserved.

[0027] In this embodiment, the data acquisition module includes real-time data retransmission and updating; When a data source is temporarily interrupted, the data acquisition module stores the incremental data during the interruption period through a local caching mechanism, and automatically re-transmits it after the data source is restored. The data acquisition module updates all collected data once every fixed preset time threshold to ensure that the demand forecasting module uses the latest data and avoids prediction deviations due to data lag.

[0028] In this embodiment, the preprocessing process in the data processing and storage module includes: processing missing values ​​and outliers in the data; Unify data from different sources into a standard format; By associating all different data information with the same information subject, a complete data view of the supply chain is formed; The processed data is organized and stored in the database.

[0029] In this embodiment, the function of the dynamic adjustment module includes: Used for real-time monitoring of the deviation between actual demand and predicted demand; when the relative deviation between the actual outbound volume and the predicted demand exceeds a preset threshold of 8%, the dynamic adjustment module automatically triggers an adjustment mechanism, which includes a first adjustment mechanism and a second adjustment mechanism. In this embodiment, the method for obtaining the relative deviation between the actual outbound volume and the predicted demand includes: ; in, This represents the relative deviation between the actual amount of goods shipped out and the predicted demand. This represents the actual number of goods shipped out of the warehouse. To predict demand; The first adjustment mechanism feeds back deviation data to the demand forecasting module to correct the forecasting algorithm parameters; The second adjustment mechanism sends adjustment instructions to the storage planning module to update the cargo storage plan, including increasing or decreasing the storage quantity of specific categories of goods and adjusting the allocation of storage areas to ensure that the storage quantity matches the actual demand. The warehouse area is divided into multiple sub-areas, which are listed in order as Area A, Area B, Area C, etc.; at the same time, the sub-areas are divided into fast sorting areas and slow sorting areas. If the actual outbound volume continues to exceed the forecast, it is judged as an increase in demand, and the storage quantity is increased accordingly; If the actual outbound volume is much lower than the forecast, it is judged to be slow-moving or weak consumption. At the same time, the storage quantity should be reduced to avoid inventory backlog. Assess the outbound frequency of each product category and the picking efficiency and warehouse space utilization of its respective sub-area; Based on the latest storage quantity and turnover cycle, the turnover goods are allocated to the fast sorting area; Transfer low-turnover or large items to the slow-speed sorting area; Seasonal goods are pre-positioned in warehouses near the target market.

[0030] In this embodiment, the preventive measures also include cross-border payment risk prevention; The system connects to transaction data from cross-border payment platforms in real time and identifies suspicious payment behavior through anomaly detection algorithms; If a risk is detected, the payment and settlement process will be immediately suspended, a verification request will be sent to the user, and the risk control department will conduct a manual review to reduce losses from cross-border payment fraud.

[0031] In this embodiment, please refer to Figure 2 As shown, the system's operation process includes: Step S1: Obtain multi-source data related to supplier goods and process the data. Update all collected data once every fixed preset time threshold. When a data source is temporarily interrupted, the system starts a local caching mechanism to store incremental data during the interruption period and automatically re-upload it after the data source is restored. Step S2: Dynamically select the optimal prediction algorithm based on the characteristics of the multi-source data; Step S3: Based on the demand forecast results, combined with the storage space capacity, storage time requirements and storage cost data, generate an initial storage plan; Step S4: The data processing and storage module acquires the processed data and predicts whether there is any risk. Step S5: Automatically assess the risk level according to preset rules, aggregate events from the same source to avoid duplicate alarms, and generate detailed alarm prompts; The risk status is divided into three core levels: low risk, medium risk, and high risk, represented by green, yellow, and red, respectively. When the system is in a low-risk state, no special intervention measures are required. The system will continue to be monitored routinely, and management can focus on strategic optimization rather than emergency handling. When the system is in a medium-risk state, the system will give a clear warning signal, designate the person in charge to formulate a specific response plan, analyze the root causes of the risk, and provide preventive measures; When the system is in a high-risk state, immediately activate the highest level of emergency response to ensure that losses are minimized.

[0032] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A big data-based cross-border e-commerce supply chain risk monitoring system, characterized in that, The method comprises the following steps: a data acquisition module is used to acquire multi-source data related to the goods of the supplier, including historical sales data, market demand fluctuation data, customer order reservation data, and seasonal consumption characteristic data; a data processing and storage module is connected to the data acquisition module and is used to clean and convert the collected data and store it; a demand prediction module is used to analyze the multi-source data based on a preset algorithm and output a demand prediction result for the goods in a future preset period; a storage planning module is used to generate a goods storage plan based on the demand prediction result, in combination with warehouse space capacity, goods storage shelf life requirements, and warehouse cost data; the storage plan includes the storage quantity of each category of goods, the division of storage areas, and the planning of inventory turnover periods; a dynamic adjustment module is used to adjust and implement the goods storage plan generated by the storage planning module; a risk intelligent analysis engine is connected to the big data processing and storage module and is used to analyze the processed data based on predefined risk indicators and machine learning models to identify and assess supply chain risks. 2.The cross-border e-commerce supply chain risk monitoring system based on big data according to claim 1, wherein, The risk intelligent analysis engine comprises: a risk prediction unit that is used to train a machine learning model based on historical data to predict supplier delivery delays, transportation time overruns, inventory shortages, or risk of unsalable goods; an anomaly detection unit that is used to automatically identify abnormal events that deviate from the normal mode by using an unsupervised learning algorithm to monitor logistics tracks and sensor data in real time. 3.The cross-border e-commerce supply chain risk monitoring system based on big data according to claim 1, wherein, The demand prediction module has a multi-algorithm dynamic selection mechanism; The multi-algorithm dynamic selection mechanism is based on the characteristics of multi-source data, including data size, fluctuation frequency, and completeness; The multi-algorithm dynamic selection mechanism comprises: When the historical sales data volume is greater than or equal to a preset fixed value and the fluctuation is smooth, an ARIMA time series algorithm is selected; When the market demand fluctuation data contains multiple influencing factors, an LSTM deep learning algorithm is switched to; when the proportion of customer order reservation data is greater than or equal to a preset threshold, a weighted regression algorithm is used; The algorithm parameters are iteratively optimized based on the deviation rate of the prediction result and the actual demand quantity every quarter to ensure that the deviation rate of the goods demand prediction result in the preset period is less than or equal to a preset fixed value. 4.The cross-border e-commerce supply chain risk monitoring system based on big data according to claim 1, wherein, The storage planning module further comprises a storage cost optimization sub-module; The storage cost optimization sub-module calculates the total cost under different storage strategies based on the goods storage plan and selects the optimal storage strategy with the lowest total cost through comparative analysis; For perishable goods, the storage cost optimization sub-module adds a loss rate constraint condition in the cost optimization in combination with the goods storage shelf life requirements to ensure that the storage plan meets the goods quality requirements.

5. The cross-border e-commerce supply chain risk monitoring system based on big data according to claim 4, characterized in that, The storage planning module further comprises an inventory turnover period dynamic calibration function; The inventory turnover period dynamic calibration function preliminarily calculates the inventory turnover period based on the daily average predicted outbound quantity of each category of goods in the demand prediction result and in combination with the goods storage shelf life requirements; If the preliminarily calculated turnover period exceeds a preset percentage of the goods storage shelf life requirement, the predicted storage quantity of the category of goods is automatically reduced so that the calibrated turnover period is less than or equal to a preset percentage of the storage shelf life requirement. Meanwhile, for high-turnover goods with a turnover cycle ≤ storage time limit, a buffer pre-storage amount is added in the storage scheme, and a fixed number of safety stocks of daily forecasted outbound quantity is additionally reserved. 6.The cross-border e-commerce supply chain risk monitoring system based on big data according to claim 1, wherein, The data acquisition module includes real-time data supplement and update; When a certain data source is temporarily interrupted, the data acquisition module stores the incremental data during the interruption period through a local caching mechanism, and automatically supplements the transmission after the data source is restored; The data acquisition module updates all collected data once every fixed preset time threshold, ensuring that the demand prediction module uses the latest data and avoiding prediction deviation due to data lag. 7.The cross-border e-commerce supply chain risk monitoring system based on big data according to claim 1, wherein, The data preprocessing process in the data processing and storage module includes processing missing values and abnormal values in the data; Different sources of data are unified into a standard format; All different data information is associated with the same information subject, forming a complete data view of the supply chain; The processed data is sorted and stored in the database. 8.The cross-border e-commerce supply chain risk monitoring system based on big data according to claim 1, wherein, The functions of the dynamic adjustment module include: Real-time monitoring of the deviation between actual demand and predicted demand; when the deviation between actual outbound quantity and predicted demand exceeds the preset threshold, the dynamic adjustment module automatically triggers the adjustment mechanism, which includes a first adjustment mechanism and a second adjustment mechanism; The first adjustment mechanism feeds back the deviation data to the demand prediction module to correct the prediction algorithm parameters. The second adjustment mechanism sends adjustment instructions to the storage planning module to update the goods storage scheme, including increasing or decreasing the storage quantity of specific categories of goods and adjusting the allocation of storage areas, to ensure that the storage quantity matches the actual demand. 9.The cross-border e-commerce supply chain risk monitoring system based on big data according to claim 7, wherein, The preventive measures also include cross-border payment risk prevention; The system real-time interfaces with the transaction data of cross-border payment platforms and identifies suspicious payment behavior through an anomaly detection algorithm; If a risk is detected, the payment settlement process is immediately suspended, a verification request is pushed to the user, and the risk control department is simultaneously notified for manual review, reducing cross-border payment fraud losses. 10.A big data based cross-border e-commerce supply chain risk monitoring system, characterized in that, The system working process includes: Step S1, acquire multi-source data related to supplier goods and process the data, update all collected data once every fixed preset time threshold, and when a certain data source is temporarily interrupted, start the local caching mechanism to store the incremental data during the interruption period, and automatically supplement the transmission after the data source is restored; Step S2, dynamically select the optimal prediction algorithm according to the characteristics of the multi-source data; Step S3, generate an initial storage scheme based on the demand prediction results, combined with warehouse space capacity, storage time limit requirements, and storage cost data; Step S4, the data processing and storage module acquires the processed data and predicts whether there is a risk; Step S5, automatically assess the risk level according to the preset rules, aggregate homogenous events to avoid repeated alarms, and generate detailed alarm prompts.

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