Cross-border e-commerce logistics order analysis and management system based on big data

Abstract

The invention relates to the technical field of data processing and logistics management, and discloses a cross-border e-commerce logistics order analysis and management system based on big data, and the system comprises a data obtaining module which is used for calculating and forming real-time state data; the potential energy calculation module is used for quantifying the state data into a regional logistics potential energy value; the risk identification module is used for monitoring the potential energy value change to identify a potential risk area; the risk matching module is used for comparing the current risk feature with a preset historical interrupt event feature fingerprint; and the strategy generation module is used for generating a self-adaptive intervention strategy accordingly. According to the method, the potential energy field model and the fingerprint database are constructed, so that the logistics risk is pre-judged in advance and accurately determined, invalid distribution caused by traffic control can be effectively avoided, the decision-making efficiency is improved, the customer recognition degree is guaranteed by generating an intervention strategy, and the analysis and management level of cross-border e-commerce logistics is systematically improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing and logistics management, and particularly relates to a cross-border e-commerce logistics order analysis management system based on big data. BACKGROUND

[0002] With the rapid development of the cross-border e-commerce industry, the logistics timeliness has become a core index for measuring service quality and market competitiveness. The complex international transportation link makes the logistics network often face sudden traffic control caused by natural disasters, bad weather or major accidents, which constitutes a severe challenge to the cross-border logistics management pursuing efficient transportation. How to ensure that the order can still be stably and efficiently circulated in a complex and changeable environment is a problem to be solved by the industry.

[0003] In practical application, the traditional response to the above challenges mainly relies on post-repair or manual monitoring. For example, when the package actually encounters congestion in the transportation process, the logistics management personnel will temporarily adjust the subsequent route; or, a special team continuously pays attention to various public traffic information and manually intervenes the order delivery in the affected area according to experience. Although some systems can access some third-party traffic data, they usually lack deep intelligent linkage and automatic decision-making ability with the massive logistics order system.

[0004] However, these existing technical means have obvious deficiencies. Essentially, they are passive responses, which cannot effectively predict before congestion or interruption events have a large-scale impact on the logistics network, resulting in delayed response and often causing problems such as increased transportation costs and ineffective delivery. At the same time, manual monitoring is not only inefficient, but also difficult to comprehensively and real-time analyze massive and dynamic logistics data, lacking the ability to quantitatively evaluate and classify potential risks, so as to avoid risks and develop the optimal adaptive intervention strategy.

[0005] Therefore, the present application provides a cross-border e-commerce logistics order analysis management system based on big data to solve the problems of the prior art. SUMMARY

[0006] The purpose of the present application is to provide a cross-border e-commerce logistics order analysis management system based on big data, which solves the problem of passive response, delayed decision-making and difficulty in systematically avoiding operational risks caused by sudden events in the existing cross-border logistics management.

[0007] To achieve the above purpose, the present application is realized by the following technical scheme: The present application provides a cross-border e-commerce logistics order analysis management system based on big data, comprising: a data acquisition module for acquiring state data of a plurality of packages in a logistics network in real time; a fingerprint library storage module, configured to store an interrupt event fingerprint library, wherein at least one interrupt event feature fingerprint corresponding to a historical logistics interrupt event is stored in the interrupt event fingerprint library; a potential energy calculation module, configured to calculate a regional logistics potential energy value of each of a plurality of preset geographical regions based on the state data; a risk identification module, configured to identify a potential risk region based on a change in the regional logistics potential energy value; a risk matching module, configured to extract a current risk feature of the potential risk region, and match the current risk feature with the interrupt event feature fingerprint to obtain a matching result; a strategy generation module, configured to determine a risk level of the potential risk region based on the matching result, and generate an adaptive intervention strategy corresponding to the risk level.

[0008] Preferably, the state data comprises at least one of the following: a node residence time of a package at a logistics node; an interval transition rate of a package between adjacent logistics nodes; a scanning event entropy of the logistics node per unit time.

[0009] Preferably, the interrupt event feature fingerprint comprises at least one quantitative description of the historical logistics interrupt event: a potential energy value change rate, a spatial influence range or a time evolution mode during the historical logistics interrupt event.

[0010] Preferably, the potential energy calculation module is specifically configured to: acquire, based on the state data, an average node residence time T d_avg , an average interval transition rate V t_avg , and an average scanning event entropy H s_avg of the region: and calculate a regional logistics potential energy value Φ(g i , t) of an i-th geographical grid g i in the geographical region at a time t by a regional logistics potential energy calculation formula, wherein the regional logistics potential energy calculation formula is: wherein w d , w v , and w h are preset normalization weight coefficients.

[0011] Preferably, the risk identification module is specifically configured to: monitor a change rate of the regional logistics potential energy value with time in real time; The geographic region with the negative change rate and exceeding the preset change rate threshold is identified as the potential risk region.

[0012] Preferably, the risk matching module is specifically used for: adopting a preset similarity algorithm to calculate the similarity Sim(W new , S k ) between the vector Fp(W new ) formed by the current risk feature and the vector Fp(S k ) formed by the interruption event feature fingerprint, and taking the similarity as the matching result; wherein the similarity calculation formula is: wherein Fp(W new )·Fp(S k ) represents the dot product of two vectors; ||Fp(W new )||·||Fp(S k )|| represents the Euclidean norm of two vectors.

[0013] Preferably, the strategy generation module is specifically used for: comparing the matching result with at least one preset similarity threshold to divide the potential risk region into one of a plurality of predefined risk levels; The plurality of risk levels are singularity precursor level, developing congestion level and instantaneous fluctuation level.

[0014] Preferably, the strategy generation module is specifically used for: when the risk level is the singularity precursor level, generating a restrictive strategy of limiting subsequent package delivery to the potential risk region; when the risk level is the developing congestion level, generating a re-routing strategy of re-routing subsequent packages to an alternative logistics node; when the risk level is the instantaneous fluctuation level, continuously monitoring the potential risk region.

[0015] The second aspect of the present application provides a cross-border e-commerce logistics order analysis management method based on big data, which comprises: real-time acquisition of state data of a plurality of packages in a logistics network; storage of an interruption event fingerprint library, wherein the interruption event fingerprint library stores at least one interruption event feature fingerprint corresponding to a historical logistics interruption event; based on the state data, calculation of a regional logistics potential value of each region in a plurality of preset geographic regions; identify a potential risk area based on the change in the regional logistics potential energy value; extract the current risk features of the potential risk area and match the current risk features with the interruption event feature fingerprints to obtain a matching result; based on the matching result, determine the risk level of the potential risk area, and generate an adaptive intervention strategy corresponding to the risk level.

[0016] In summary, the present application includes the following at least one beneficial technical effect: 1. The present application can predict and accurately qualify serious congestion such as traffic control that may cause end delivery failure by calculating the regional logistics potential energy value in real time and actively identifying potential risk areas based on its dynamic changes, combined with matching with historical interruption event feature fingerprints. This enables the system to timely execute restrictive intervention strategies to avoid ineffective dispatch of packages to problem areas from the source, significantly reducing ineffective delivery problems caused by force majeure in the analysis and management of cross-border e-commerce logistics orders.

[0017] 2. The present application realizes fine division of risk levels by extracting current risk features and matching with fingerprint library. For medium risk such as developing congestion, the system can generate a rerouting strategy containing alternative paths and pre-communicate through a customer confirmation mechanism. This transparent decision and interactive management based on big data ensures customer acceptance of route change schemes, effectively avoiding misunderstandings and disputes caused by unilateral route adjustment, significantly reducing customer dispute rate and improving service experience.

[0018] 3. The present application realizes minute-level intelligent response to cross-border e-commerce logistics order risks by constructing a fully automated closed-loop management process from data acquisition, potential energy calculation, risk identification to strategy generation. This big data-based analysis and management method completely changes the traditional lag mode of relying on manual experience judgment and manual processing, significantly improves the response speed and decision efficiency in dealing with emergencies, and selects the optimal alternative scheme through intelligent path planning, effectively optimizing the overall operation cost of the logistics network. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a cross-border e-commerce logistics order analysis and management system architecture based on big data of the present application; Figure 2 is a data acquisition module process schematic diagram of the present application; Figure 3 is a fingerprint library storage module process schematic diagram of the present application; Figure 4 is a potential energy calculation and risk identification process schematic diagram of the present application; Figure 5A schematic diagram of a risk matching and strategy generation process of the present application; Figure 6 A flow chart of a cross-border e-commerce logistics order analysis management method based on big data of the present application.

[0020] Wherein, 10, data acquisition module; 20, fingerprint library storage module; 30, potential calculation module; 40, risk identification module; 50, risk matching module; 60, strategy generation module. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings Figure 1 -Appendix Figure 6 The present application will be further described in detail.

[0022] Referring to the drawings Figure 1 , Figure 1 A system architecture diagram of a cross-border e-commerce logistics order analysis management system based on big data of the present application. The cross-border e-commerce logistics order analysis management system based on big data provided by the present application can include: a data acquisition module 10, a fingerprint library storage module 20, a potential calculation module 30, a risk identification module 40, a risk matching module 50 and a strategy generation module 60.

[0023] The data acquisition module 10 is used for real-time acquisition of state data of a plurality of packages in a logistics network.

[0024] The fingerprint library storage module 20 is used for storing an interruption event fingerprint library, and the interruption event fingerprint library stores at least one interruption event characteristic fingerprint corresponding to a historical logistics interruption event.

[0025] The potential calculation module 30 is connected with the data acquisition module 10, and is used for calculating a regional logistics potential value of each region in a plurality of preset geographical regions based on the state data.

[0026] The risk identification module 40 is connected with the potential calculation module 30, and is used for identifying a potential risk region based on a change of the regional logistics potential value.

[0027] The risk matching module 50 is connected with the risk identification module 40 and the fingerprint library storage module 20, and is used for extracting a current risk characteristic of the potential risk region, and matching the current risk characteristic with the interruption event characteristic fingerprint to obtain a matching result.

[0028] The strategy generation module 60 is connected with the risk matching module 50, and is used for determining a risk level of the potential risk region based on the matching result, and generating an adaptive intervention strategy corresponding to the risk level.

[0029] The following will be combined with the drawings Figure 1 and Figure 2 , Figure 2Figure 1 is a schematic diagram of the data acquisition module process of the present application. In one embodiment, the specific functions of the data acquisition module 10 are described. The function of the data acquisition module 10 is to provide quantified and structured state data for the subsequent potential calculation module 30. The state data is processed based on raw scan records collected from all over the logistics network. The state data is processed based on raw scan records collected from all over the logistics network. In one specific implementation, to enhance the data dimension to support more accurate strategy generation, the data acquisition module 10 also interfaces with one or more government open data platforms through dedicated lines to obtain supplementary data that affects the logistics state, such as the efficiency of passing through a specific port or the customs inspection cycle data.

[0030] The state data obtained by the data acquisition module 10 includes the node residence time of a package at a logistics node. To calculate the node residence time, the data acquisition module 10 first receives or actively requests the scan records of a specified package at a specific logistics node. The records include the package identifier, the logistics node identifier, and the event type timestamp. The data acquisition module 10 identifies two specific scan records under the same package identifier and the same logistics node identifier: the warehouse-in scan record and the warehouse-out scan record, and extracts their timestamps, respectively, denoted as the warehouse-in scan timestamp t scan_in and the warehouse-out scan timestamp t scan_out . The node residence time T d is calculated by the following formula: T d = t scan_out -t scan_in ; The state data obtained by the data acquisition module 10 also includes the interval transition rate of a package between adjacent logistics nodes. The calculation of the rate involves two logistics nodes that are consecutive on the transportation path of a package. The data acquisition module 10 obtains the warehouse-out scan timestamp t i (n scan_out ) of the starting logistics node n i and the warehouse-in scan timestamp t j (n scan_in ) of the next destination logistics node n j . At the same time, the data acquisition module 10 retrieves the geographical distance Dist(n i , n j ) between the starting logistics node n i and the destination logistics node n j from a pre-set storage unit. The geographical distance is a fixed value determined and stored in advance. The interval transition rate V t is calculated by the following formula: The state data acquired by the data acquisition module 10 further includes the scanning event entropy of the logistics node in a unit time. The entropy value is used to quantify the ordered degree of the operation process in a logistics node or a specified geographical area. First, a set of scanning event types is defined in advance, where each scanning event type e k Corresponding to a specific operation, for example, type e1 is warehouse-in scanning, type e2 is warehouse-out scanning, and type e3 is customs inspection scanning.

[0031] To calculate the scanning event entropy H s , the data acquisition module 10 performs the following operations in a preset unit time window for a specified logistics node: First, all scanning events occurring in the time window are collected, and each event is classified according to the pre-defined event type set to count the total number of events N. Next, for each scanning event type e k , the frequency N k of its occurrence is counted.

[0032] Then, the occurrence probability P(e k ) = N k / N of each scanning event type e k is calculated.

[0033] Finally, the scanning event entropy H s of the node in the time window is calculated by the Shannon entropy formula: H s = -∑ k P(e k )log2P(e k ); Where the summation iterates through all pre-defined scanning event types k.

[0034] Referring to the accompanying Figure 1 and Figure 3 , Figure 3 is a flowchart of the fingerprint warehouse storage module of the present application. This part describes in detail the offline construction process of the interrupt event feature fingerprint stored in the fingerprint warehouse storage module 20. This process is a preprocessing work, and its purpose is to provide a benchmark for comparison for subsequent analysis and management of real-time logistics order flow.

[0035] The construction of the disruption event fingerprint library relies on the analysis of historical data. In one specific application scenario, the system accesses a big data platform that stores a large amount of historical records, which collects all cross-border e-commerce logistics order information in the past period of time. The construction process first identifies specific historical logistics disruption events from these historical data. Such events refer to objective situations that have a significant impact on the timeliness of cross-border logistics, for example: the customs inspection process at a certain international port is unexpectedly and continuously delayed in a certain period; or a core international transit center has a significant decline in processing capacity for consecutive days.

[0036] For each identified historical logistics disruption event, the system retrieves all package status data that passes through the affected geographical area during the event from the big data platform. In order to conduct unified quantitative analysis of the historical event, the system adopts a specific index calculation method, the specific composition of which will be described in detail in the description of the potential energy calculation module 30 later. Through this method, an index value can be calculated that can comprehensively reflect the running state of the region in processing cross-border logistics orders.

[0037] Based on the region running state index value calculated by the unified method during the historical event, the system constructs a disruption event characteristic fingerprint corresponding to the historical event. In one embodiment, the disruption event characteristic fingerprint includes one or more quantitative descriptions of the historical logistics disruption event, which is the basis for subsequent accurate analysis and management, as follows: one quantitative description is the index value change rate. This description is achieved by extracting the time series data of the running state index value of each region in the affected range during the historical event, and calculating the first-order difference of the index value with respect to time, to represent the rate of state deterioration of the region in processing logistics orders. For a region g i At discrete monitoring time points t k and t k-1 , the running state index values I(g i ,t k ) and I(g i ,t k-1 ), the calculation formula of the change rate R I is: The calculation result of this formula quantifies the instantaneous rate of change of the index value with respect to time.

[0038] Another quantitative description is the spatial impact range. This description is achieved by setting a preset index threshold I thresholdAt any time during the event, when the running state indicator value of a region falls below this threshold, the region is counted into the set of affected regions. This description is eventually quantified as the total number of affected regions at the moment when the event impact is the most severe, or the geographical identification of the set of affected regions itself.

[0039] Another quantified description is the time-evolution pattern. This description is achieved by analyzing the curve of the average running state indicator value of the affected regions over time, and extracting a number of structured parameters from this curve. These parameters include: the time point when the indicator value first falls below the indicator threshold, the time point when the indicator value reaches the lowest point, the lowest value of the indicator value, the total length of time when the indicator value stays below the indicator threshold, and the rate of recovery of the indicator value from the lowest point. These parameters together form a feature vector, which is used to complete the analysis of the time-evolution pattern of the event.

[0040] Finally, the results of one or more of the above quantified descriptions computed for a historical logistics disruption event are organized into a structured disruption event feature fingerprint data, and stored in the fingerprint library storage module 20 together with the unique identifier of the historical event.

[0041] Referring to the accompanying drawings Figure 1 and Figure 4 , Figure 4 is a schematic diagram of the potential energy calculation and risk identification process of the present application. This part describes in detail the specific functions and implementation methods of the potential energy calculation module 30 and the risk identification module 40 in the online running phase of the system. This part is the core link of real-time analysis and management after obtaining the basic state data.

[0042] To achieve fine management of the wide-area cross-border logistics network, the system first discretizes the monitored physical geographical space into a plurality of pre-set, continuous geographical regions. In a specific implementation, these regions are uniform-sized geographical grids.

[0043] The potential energy calculation module 30 is connected with the data acquisition module 10, and is used to perform the calculation of the region logistics potential energy value. At any calculation time t, for the i-th geographical grid g i i in the plurality of pre-set geographical regions, the potential energy calculation module 30 first aggregates the state data of all relevant cross-border e-commerce logistics orders within the grid. Specifically, it calculates the average node residence time T d_avg (g i , t), the average interval transition rate V t_avg (g i , t), and the average scanning event entropy H s_avg (g i , t) of the grid at the current time.

[0044] Subsequently, the potential calculation module 30 calculates the regional logistics potential value Φ(g i ,t) of the geographic grid g i at time t by using the regional logistics potential calculation formula. The calculation formula is as follows: where w d , w v , and w h are preset normalization weight coefficients.

[0045] The risk identification module 40, connected to the potential calculation module 30, is configured to receive and analyze the time series data of the regional logistics potential values output by the potential calculation module 30. The function of this module is to identify potential logistics risks based on the dynamic changes of the potential values, which is a key step to achieve the active analysis and management of cross-border logistics orders.

[0046] In a specific implementation, the risk identification module 40 performs real-time monitoring for each geographic grid g i . The module obtains the regional logistics potential value Φ(g k ,t i ) at the current time t k and the regional logistics potential value Φ(g k-1 ,t i ) at the last monitoring time t k-1 , and calculates the rate of change of the potential value over time. The specific calculation method of the rate of change is as follows: where t k -t k-1 is a preset monitoring time interval.

[0047] The risk identification module 40 internally stores a preset rate of change threshold λ rate , which is a positive number and can be configured according to historical data analysis or business operation requirements to filter out normal and minor potential fluctuations.

[0048] After obtaining the rate of change ΔΦ(g i ,t k ), the risk identification module 40 performs a judgment. A geographic grid g i is identified as a potential risk area only when the rate of change of the potential value of the grid satisfies the following two conditions simultaneously: 1. The rate of change is negative, i.e., ΔΦ(g i ,t k ) < 0. This condition indicates that the comprehensive efficiency of logistics order processing in the region is declining.

[0049] 2. The absolute value of the rate of change exceeds a preset rate of change threshold, i.e., |ΔΦ(g i ,t k )|>λ rate This condition indicates that the magnitude of the performance decline is significant, rather than fluctuations within a normal range.

[0050] Combining the above two conditions, the discriminant is ΔΦ(g i ,t k )<-λ rate Any geographical grid g i that satisfies this discriminant will have its identifier output as an identified potential risk area and transmitted to the risk matching module 50 for subsequent processing.

[0051] Referring to the accompanying Figure 1 and Figure 5 , Figure 5 is a schematic diagram of the risk matching and strategy generation process of the present application. This part describes the specific functions and implementation methods of the risk matching module 50 and the strategy generation module 60 in detail. This part is the realization of the present application to perform closed-loop, adaptive analysis and.

[0052] The risk matching module 50 is connected with the risk identification module 40 and the fingerprint library storage module 20. When the module receives the identifier of the potential risk area transmitted by the risk identification module 40, it performs the following operations: First, the risk matching module 50 extracts the current risk features of the potential risk area. The extraction process uses the same technical standards as the fingerprint construction process in the third part. Specifically, the module obtains the sequence of regional logistics potential values of the area within the continuous time period before the area is identified as a risk, and calculates the same quantitative descriptions as the historical fingerprint structure based on this sequence, including the current rate of change of potential value, the spatial influence range and the time evolution pattern. These quantitative descriptions are combined into a current risk feature vector, denoted as Fp(W new ).

[0053] Next, the risk matching module 50 retrieves all stored historical disruption event feature fingerprints from the fingerprint library storage module 20, each of which is also in the form of a vector, denoted as Fp(S k ), where k is the index of the historical disruption event.

[0054] Subsequently, the risk matching module 50 uses a preset similarity algorithm to calculate the similarity Sim(W new ,S k ) between the current risk feature vector Fp(W new ) and each historical disruption event feature fingerprint vector Fp(S k ).). In one implementation, the similarity algorithm is cosine similarity, whose calculation formula is: wherein, Fp(W new )·Fp(S k ) represents the dot product of two vectors; ||Fp(W new )||·||Fp(S k )|| represents the Euclidean norm of two vectors The calculation result of this formula is a value between -1 and 1, and the closer the value is to 1, the more similar the current risk is to the historical disruption event. The calculated similarity is transmitted to the strategy generation module 60 as a matching result.

[0055] The strategy generation module 60 is connected with the risk matching module 50, and is used to receive the matching result and perform risk grading and generation of adaptive intervention strategies.

[0056] In determining the risk level, the strategy generation module 60 compares the highest similarity matching result received with at least one preset similarity threshold value stored internally to divide the potential risk area into one of a plurality of predefined risk levels. In one implementation, two similarity thresholds θ high and θ low are preset, and θ high > θ low . The division rule of the risk level is as follows: 1. When the matching result is greater than or equal to θ high , it is determined that the risk level is the quasi-singularity precursor level.

[0057] 2. When the matching result is less than θ high but greater than or equal to θ low , it is determined that the risk level is the developing congestion level.

[0058] 3. When the matching result is less than θ low , it is determined that the risk level is the instantaneous fluctuation level.

[0059] In generating the adaptive intervention strategy, the strategy generation module 60 generates instructions for guiding specific logistics operations according to the risk level determined in the previous step: When the risk level is the quasi-singularity precursor level, a restrictive strategy is generated to limit the subsequent delivery of parcels to the potential risk area. The strategy specifically manifests as the system locating all transportation paths with the nodes upstream as the starting point and the nodes in the risk area as the end point, and issuing instructions to the operating systems of these upstream nodes to suspend the delivery of new cross-border logistics orders to the risk area. This aims to avoid subsequent large-scale ineffective distribution from the source and directly solves the problem of ineffective distribution caused by traffic control.

[0060] When the risk level is the developing congestion level, a re-routing strategy is generated to re-route the subsequent parcels to the alternative logistics node. The specific implementation of this strategy includes: The system first identifies cross-border e-commerce logistics orders that are about to flow in or have been shipped on the way to the risk area. To calculate the optimal alternative path, the system can use not only the internally stored logistics network topology data, but also the real-time information affecting path selection obtained by the data acquisition module 10 through a dedicated data connection with an external open data platform of a government agency, such as current traffic efficiency at a specific port or severe weather warning data.

[0061] Based on the above comprehensive information, the system calculates one or more alternative paths that bypass the risk area.

[0062] For orders that have been shipped on the way, the system automatically triggers the on-the-way re-routing mechanism to generate instructions to transfer the parcels to the alternative path at the next operational node. At the same time, the system updates the estimated delivery time of the order and notifies the customer of the changed path information and the new estimated delivery time through the preset channel.

[0063] For orders that have not yet been shipped from the origin warehouse, the system generates a confirmation request containing comparison information between the original path scheme and the alternative path scheme and sends it to the customer. The customer can reply within a set time window (e.g. 2 hours) through any channel to confirm whether to accept the change. If no customer reply is received within the time window, the system performs the preset default operation, such as automatically suspending the shipment of the order.

[0064] When the risk level is the transient fluctuation level, the potential risk area is continuously monitored. This strategy is manifested in that the system does not generate external intervention instructions, but issues internal instructions to the data acquisition module 10 and the potential calculation module 30 to increase the frequency of data acquisition and potential calculation for the potential risk area for more intensive monitoring until the potential of the area returns to normal or the risk level changes.

[0065] Refer to the accompanying Figure 6 , Figure 6 A flowchart of a cross-border e-commerce logistics order analysis and management method based on big data is provided. The present application provides a cross-border e-commerce logistics order analysis and management method based on big data. The specific implementation steps of the method are as follows: S1, real-time acquisition of state data of a plurality of parcels in the logistics network: This is the data foundation layer of the method. The system continuously interacts with one or more upstream data sources, including but not limited to the internal tracking systems of logistics carriers, third-party data aggregation platforms, and open data platforms of government agencies. Through these connections, the system continuously collects various types of raw scanning records of in-transit packages, such as timestamps and geographic location information of events such as package entry, exit, inspection, etc. at key nodes such as sorting centers, customs ports, transfer hubs, etc. These raw data are cleaned, parsed and processed into structured state data available for subsequent steps in real time.

[0066] S2, store the interrupt event fingerprint library: This is the empirical knowledge layer of the method, which is usually completed as an offline preprocessing work. The system identifies and separates typical interrupt events that have had a significant negative impact on cross-border logistics timeliness in history (such as persistent congestion at a specific port, air route interruption due to major weather events, etc.) by mining historical big data. For each identified historical interrupt event, the system uses a unified quantitative model to extract its core features from multiple dimensions (such as state deterioration rate, spatial influence range, time evolution pattern, etc.) and solidifies it into a structured data entity, namely the interrupt event feature fingerprint. All these feature fingerprints together form an interrupt event fingerprint library, providing a reference benchmark for subsequent real-time risk comparison.

[0067] S3, based on the state data, calculate the regional logistics potential value of each region in the pre-set multiple geographic regions: This is the real-time state evaluation layer of the method. The system first discretizes the monitored wide-area physical space into a series of continuous geographic grids. In each calculation period, the system aggregates all packages falling into each geographic grid and calculates the macroscopic operation indicators of the grid at the current time based on the real-time state data obtained in step S1, such as average node residence time, average interval transition rate, and scanning event entropy. Subsequently, the system fuses these multi-dimensional operation indicators into a single quantitative value (regional logistics potential value) through a pre-set comprehensive potential calculation formula, which directly reflects the current comprehensive efficiency of the geographic region in processing cross-border logistics orders.

[0068] S4, based on the change of the regional logistics potential value, identify potential risk areas: This is the risk discovery layer of the method. Instead of looking at the absolute value of the potential energy at a certain moment, the system focuses on its dynamic trend. The system monitors the time series of the potential energy value of each geographical region and calculates its rate of change over time. When the system detects that the potential energy value of a region not only shows a downward trend (i.e., the rate of change is negative), but also that the magnitude of its decline (i.e., the absolute value of the rate of change) exceeds a threshold set to filter out minor fluctuations, the system marks the region as a potential risk region and triggers the subsequent analysis process.

[0069] S5, extract the current risk features of the potential risk region and match them with the disruption event feature fingerprints to obtain a matching result: This is the risk qualification and tracing layer of the method. Once a potential risk region is identified, the system immediately focuses on it and extracts its dynamic features before and after the risk occurs using the same technical standards as in step S2 to construct the historical fingerprints, thereby generating a current risk feature vector that can describe this real-time risk. Then, the system performs a similarity calculation between this current risk feature vector and all historical disruption event feature fingerprints stored in the fingerprint library. This process aims to find the historical event that is most similar in evolution pattern to the current risk and output a quantitative matching result, usually the highest similarity score.

[0070] S6, determine the risk level of the potential risk region based on the matching result and generate an adaptive intervention strategy corresponding to the risk level: This is the decision and execution layer of the method. The system divides the current potential risk region into a predefined risk level based on the matching result (i.e., the similarity score) obtained in step S5. For example, a very high similarity may correspond to a class singularity precursor level, a moderate similarity to a developing congestion level, and a low similarity to a transient fluctuation level. For each determined risk level, the system automatically generates and triggers a differentiated adaptive intervention strategy corresponding to it, forming a complete closed-loop management. These strategies include but are not limited to: restrictive strategies for high-risk levels (such as suspending shipments), rerouting strategies for medium-risk levels (such as automatically planning alternative routes and notifying customers), and continuous monitoring strategies for low-risk levels (such as increasing data collection frequency).

Claims

1. A cross-border e-commerce logistics order analysis and management system based on big data, characterized in that, include: The data acquisition module is used to acquire real-time status data of multiple packages in the logistics network; A fingerprint database storage module is used to store an interruption event fingerprint database, wherein the interruption event fingerprint database stores at least one interruption event feature fingerprint corresponding to a historical logistics interruption event; The potential energy calculation module is used to calculate the regional logistics potential energy value of each region in multiple preset geographical regions based on the state data. The risk identification module is used to identify potential risk areas based on changes in the regional logistics potential energy value. The risk matching module is used to extract the current risk features of the potential risk area and match the current risk features with the interruption event feature fingerprint to obtain a matching result; The strategy generation module is used to determine the risk level of the potential risk area based on the matching results, and generate an adaptive intervention strategy corresponding to the risk level.

2. The cross-border e-commerce logistics order analysis and management system based on big data according to claim 1, characterized in that, The status data includes at least one of the following: The time a package remains at a logistics node; The rate of transition between adjacent logistics nodes for a package; Entropy of scanning events at a logistics node per unit time.

3. The cross-border e-commerce logistics order analysis and management system based on big data according to claim 1, characterized in that, The interruption event feature fingerprint includes at least one quantitative description of the historical logistics interruption event: The rate of change of potential energy, spatial impact range, or temporal evolution pattern during the historical logistics disruption event.

4. The cross-border e-commerce logistics order analysis and management system based on big data according to claim 1, characterized in that, The potential energy calculation module is specifically used for: Based on the state data, the average node dwell time T in the region is obtained. d_avg Average interval transition rate V t_avg and the average scan event entropy H s_avg : And the regional logistics potential energy calculation formula is used to calculate the i-th geographic grid g in the geographic region at time t. i Regional logistics potential energy value Φ(g) i The formula for calculating the potential energy of regional logistics is as follows: Among them, w d w v w h These are the preset normalized weighting coefficients.

5. The cross-border e-commerce logistics order analysis and management system based on big data according to claim 1, characterized in that, The risk identification module is specifically used for: Real-time monitoring of the rate of change of the regional logistics potential energy value over time; Geographical areas with negative change rates exceeding a preset change rate threshold are identified as potential risk areas.

6. The cross-border e-commerce logistics order analysis and management system based on big data according to claim 1, characterized in that, The risk matching module is specifically used for: Using a preset similarity algorithm, the vector Fp(W) formed by the current risk features is calculated. new The vector Fp(S) formed by the interruption event feature fingerprint and the interruption event feature fingerprint k Similarity between Sim(W) new ,S k The similarity is used as the matching result; wherein the similarity calculation formula is: Among them, Fp(W new )·Fp(S k ) represents the dot product of two vectors; ||Fp(W) new )||·||Fp(S k )|| represent the Euclidean norms of the two vectors, respectively.

7. The cross-border e-commerce logistics order analysis and management system based on big data according to claim 6, characterized in that, The strategy generation module is specifically used to determine the risk level as follows: The matching result is compared with at least one preset similarity threshold to classify the potential risk area into one of a number of predefined risk levels; The risk levels mentioned are: singularity precursor level, developmental congestion level, and transient fluctuation level.

8. The cross-border e-commerce logistics order analysis and management system based on big data according to claim 7, characterized in that, The strategy generation module is specifically used to generate the adaptive intervention strategy as follows: When the risk level is a precursor to a singularity, a restrictive strategy is generated to limit the delivery of subsequent packages to the potential risk area. When the risk level is the developmental congestion level, a rerouting strategy is generated to reroute subsequent packages to alternative logistics nodes; When the risk level is a transient fluctuation level, the potential risk area is continuously monitored.

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