Cross-border order full-life-cycle intelligent management and exception handling system

Abstract

The invention discloses a cross-border order full-life-cycle intelligent management and exception handling system, and relates to the technical field of order cycle management and exception handling. The system comprises a dynamic supplier intelligent matching module, a real-time logistics resource scheduling module and an abnormity co-processing module. According to the invention, a dynamic supplier intelligent matching module calculates a supply and demand matching degree based on a multi-modal feature vector and an attention mechanism model; the real-time logistics resource scheduling module plans an optimal path by means of a digital twinborn model and deals with resource conflicts; and the exception co-processing module monitors the performance node, detects the exception, generates an early warning and triggers a co-processing workflow, so that the full-life-cycle intelligent management and exception processing dynamic responsiveness of the cross-border order is improved, and the problem of low full-life-cycle intelligent management and exception processing dynamic responsiveness of the cross-border order in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of order cycle management and exception handling technology, and in particular to an intelligent management and exception handling system for the entire lifecycle of cross-border orders. Background Technology

[0002] In the supplier matching stage of order fulfillment, the feature engineering of existing algorithm models is limited to static, basic feature dimension screening and construction. It only extracts product category tags, historical transaction counts, and pricing information to form feature vectors, failing to incorporate dynamic, core dimensions such as the supplier's core product categories, real-time inventory response speed, quality pass rate, certification status, capacity utilization, and raw material inventory into the feature system. This leads to systematic biases in the matching process at key decision points. Specifically, when analyzing suppliers operating across product categories, the algorithm cannot identify their core capabilities due to missing features. This may mismatch high-requirement apparel orders with suppliers specializing in textiles, triggering inventory quality risks. Furthermore, existing models often employ static rule engines or traditional machine learning algorithms, dynamically adjusting strategies based on real-time order demand, supplier operational status, and market fluctuations, further amplifying the matching accuracy problems caused by insufficient feature engineering.

[0003] Due to the lack of a technical architecture based on real-time data collection and dynamic analysis, the system can only rely on a static resource list in the logistics resource matching process. It cannot obtain dynamic information such as vehicle location, load rate, warehouse status, and route congestion through real-time data links, resulting in low resource visibility. The algorithm may assign orders to service providers without actual transport capacity. During the scheduling phase, the lack of real-time perception and response mechanisms prevents the system from automatically triggering dynamic rescheduling when anomalies occur, forcing it into a lag process of "manual review-rematching," exacerbating logistics delays. Furthermore, the lack of a full-link data perception mechanism in cross-border logistics monitoring only captures the status of terminal nodes such as the origin and destination, creating a monitoring blind spot for key intermediate nodes such as customs declaration, clearance, and transshipment. This prevents timely detection and intervention of anomalies, resulting in low dynamic responsiveness in intelligent management and anomaly handling throughout the entire lifecycle of cross-border orders. Summary of the Invention

[0004] To address the technical problem of low dynamic responsiveness in the intelligent management and anomaly handling of the entire lifecycle of cross-border orders in existing technologies, this invention provides an intelligent management and anomaly handling system for the entire lifecycle of cross-border orders. The technical solution is as follows: On one hand, a cross-border order lifecycle intelligent management and anomaly handling system is provided, including: a dynamic supplier intelligent matching module, a real-time logistics resource scheduling module, and an anomaly collaborative handling module. The dynamic supplier intelligent matching module constructs multimodal feature vectors for orders and suppliers, including static and dynamic feature dimensions, based on received order lifecycle data. It then calculates the matching degree between suppliers and orders using a deep learning model based on an attention mechanism to adjust the matching strategy weights. The real-time logistics resource scheduling module constructs a digital twin model of the logistics network based on real-time collected resource status data. This digital twin model simulates changes in the status of logistics resources and predicts resource conflicts. It dynamically plans the optimal logistics path within the digital twin model and automatically triggers path replanning when resource conflicts are detected. The anomaly collaborative handling module records and monitors the node status throughout the order fulfillment lifecycle. Based on predefined anomaly identification rules and a machine learning anomaly detection model, it performs real-time analysis of the acquired node status data and generates structured early warning events when anomalies are detected. Based on these structured early warning events, it automatically triggers a pre-defined collaborative handling workflow.

[0005] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By constructing multimodal feature vectors of orders and suppliers through a dynamic supplier intelligent matching module, and realizing matching degree calculation and correction based on a deep learning model with attention mechanism, the accuracy and dynamic adaptation capability of cross-border order supplier matching are significantly improved. On the one hand, it breaks through the limitations of existing technologies that rely solely on static basic features. By extracting dimensions such as category codes and qualification requirements from static order requirements, as well as dimensions such as urgency and delivery time limits from dynamic requirements, and combining features such as core business categories and qualification completeness from static supplier attributes, and data such as real-time inventory response speed and capacity utilization rate from dynamic operations, the constructed multimodal feature vector can comprehensively characterize the matching relationship between order requirements and supplier capabilities. On the other hand, the deep learning model with attention mechanism, through dual-layer weight allocation at the modality and feature levels, can automatically focus on core matching dimensions for different scenarios such as urgent orders and special category orders. Then, feature fusion is achieved through matrix dot product operations in the feature interaction layer, and the matching degree is corrected by combining multi-dimensional correction factors. This effectively solves existing technical problems such as inaccurate identification of core advantages of cross-category suppliers, priority matching of the lowest-priced supplier for urgent orders, and matching of unqualified suppliers for special category orders. Ultimately, it achieves dynamic and accurate matching between suppliers and orders, significantly improving the inventory quality pass rate and order delivery on-time rate.

[0006] 2. By constructing a digital twin model of the logistics network through a real-time logistics resource scheduling module, and realizing dynamic planning of the optimal logistics path and automatic replanning under resource conflicts, the system effectively solves the problems of static logistics resource monitoring, insufficient flexibility in path planning, and lack of monitoring of cross-border logistics nodes in existing technologies, and significantly improves the efficiency and reliability of cross-border logistics scheduling. First, the digital twin model can simulate changes in the state of logistics resources and predict resource conflicts in real time, providing accurate real-time data support for route planning. Second, by calculating the urgency coefficient of delivery deadlines and dynamically adjusting the time priority weights, this module achieves multi-objective dynamic planning of the optimal logistics route. It can flexibly balance the optimization objectives of transportation time and cost according to the urgency of order delivery deadlines, meeting the logistics needs of different types of cross-border orders. Finally, when resource conflict events such as node capacity exceeding limits, route blockage, and time window conflicts are detected, this module can immediately perform simulated impact analysis on affected orders, automatically generate resource conflict warning events, and, using the current order status as the initial condition, re-execute the multi-objective route optimization algorithm after excluding conflicting resources to generate alternative optimal routes. This achieves automatic replanning of logistics routes, reduces the probability of logistics delays, and improves the controllability of the entire cross-border logistics chain.

[0007] 3. Through the abnormal collaborative handling module, real-time monitoring of the status of all nodes in the order fulfillment lifecycle, dual abnormal detection, generation of structured early warning events, and automatic triggering of collaborative handling workflow are realized, thus constructing a complete cross-border order abnormal handling mechanism, which significantly improves the efficiency of abnormal event identification and handling capabilities. First, by monitoring status data across the entire lifecycle of supplier production, domestic warehousing, customs clearance, international transportation, and last-mile delivery, and employing a predefined anomaly detection rule base combined with a machine learning anomaly detection model based on a fusion of Isolation Forest and LSTM, dual anomaly detection is achieved. This not only identifies explicit anomalies such as exceeding thresholds and timing errors, but also implicit anomalies beyond the rule base's coverage, significantly improving the comprehensiveness and accuracy of anomaly identification. Second, by extracting and structurally encapsulating core anomaly information, the generated structured warning events clearly and accurately reflect the anomaly's type, location, severity, and other key information, providing a clear basis for subsequent handling. Finally, based on the anomaly type, occurrence node, and severity level of the warning event, the module automatically matches the corresponding collaborative handling workflow, identifies and associates collaborative handling nodes such as the supplier, logistics service provider, and platform operation, pushes a handling task list, and monitors the task completion status in real time. This achieves collaborative and efficient handling of anomalies. Furthermore, by upgrading the handling process settings, it ensures that serious anomalies are handled promptly and effectively, reducing the impact of anomalies on order fulfillment and improving customer satisfaction and order fulfillment stability. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic diagram of the structure of a cross-border order lifecycle intelligent management and exception handling system provided in this application embodiment; Figure 2 This application provides a flowchart for calculating and correcting the supplier-order matching degree of a cross-border order lifecycle intelligent management and anomaly handling system. Detailed Implementation

[0010] The technical solution provided in this application will now be described with reference to the accompanying drawings.

[0011] To facilitate understanding of the embodiments of this application, the following points will be explained first: First, in this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the preceding and following related objects, but it does not exclude the possibility of indicating an "and" relationship; the specific meaning can be understood in context. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c; a and b; a and c; b and c; or a and b and c. Here, a, b, and c can be single or multiple.

[0012] Second, the use of prefixes such as "first" and "second" in this application is solely for the purpose of distinguishing and describing different things belonging to the same category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no chronological, size, or priority relationship between them.

[0013] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0014] like Figure 1The diagram shown is a structural schematic of a cross-border order full lifecycle intelligent management and exception handling system provided in an embodiment of this application, including: a dynamic supplier intelligent matching module, a real-time logistics resource scheduling module, and an exception collaborative handling module.

[0015] As the first module of this system, the dynamic supplier intelligent matching module is used to construct multimodal feature vectors for orders and suppliers, including static and dynamic feature dimensions, based on the received order lifecycle data. It also calculates the matching degree between suppliers and orders based on a deep learning model with an attention mechanism, so as to adjust the matching strategy weights.

[0016] It should be understood that when constructing the order multimodal feature vector, the static feature dimension extracts the product category code, specification parameter vector, quality standard level, qualification certification requirement identifier, and price threshold from the static demand data of the order; the dynamic feature dimension extracts the urgency weight, delivery time limit quantification value, and batch size coefficient from the dynamic demand data of the order. After feature normalization, these are concatenated to form the order multimodal feature vector. When constructing the supplier multimodal feature vector, the static feature dimension extracts the core business category code, historical transaction qualification rate, average basic price range, and qualification certification completeness identifier from the static attribute data of the supplier; the dynamic feature dimension extracts the real-time inventory response speed quantification value, current inventory quality qualification rate, real-time capacity utilization rate, raw material inventory satisfaction rate, and real-time fulfillment on-time rate from the dynamic operation data of the supplier. After feature normalization, these are concatenated to form the supplier multimodal feature vector.

[0017] It needs to be explained that the static feature dimension extracts static demand data for orders, which includes, but is not limited to, product category codes and specification parameter vectors; the dynamic feature dimension extracts dynamic demand data for orders, which includes, but is not limited to urgency weights and concatenation of normalized features to form a multimodal feature vector for orders; the constructed multimodal feature vectors for orders and suppliers are input into a deep learning model with an attention mechanism, and the attention layer of the deep learning model assigns weights to each static and dynamic feature dimension in the multimodal feature vector for orders, and to each static and dynamic feature dimension in the multimodal feature vector for suppliers.

[0018] In this embodiment, the dynamic supplier intelligent matching process first extracts feature dimensions. Static feature dimensions specifically extract static order demand data, including but not limited to product category codes and specification parameter vectors. This extraction step accurately captures the inherent and stable core demand attributes of the order, avoiding supplier matching deviations caused by missing static demand information and effectively solving the problem of incomplete characterization of basic order demands in existing technologies. Dynamic feature dimensions extract dynamic order demand data, including but not limited to urgency weights. After extraction, the results of both static and dynamic feature dimensions are normalized. Normalization eliminates the dimensional differences between different feature dimensions, ensuring that each feature contributes equally in subsequent calculations. The normalized static and dynamic features are then concatenated to form a multimodal feature vector for the order. This multimodal feature vector construction step breaks through the limitations of existing technologies that rely solely on a single static feature. This approach overcomes limitations by comprehensively characterizing order requirements through a combination of static basic attributes and dynamic changes, providing complete data support for subsequent accurate matching. The completed multimodal feature vectors of orders and suppliers are then input into a deep learning model with an attention mechanism. The model's attention layer first assigns weights to each static and dynamic feature dimension in the order multimodal feature vector, and simultaneously performs weight assignments to each static and dynamic feature dimension in the supplier multimodal feature vector. This weight assignment step enables the model to automatically focus on core matching dimensions. For example, for urgent orders, the weight of urgency in the dynamic features of the order can be increased, while correspondingly increasing the weight of the dimension related to inventory response speed in the dynamic features of the supplier. This effectively solves the problem of inaccurate core requirement matching caused by equalizing feature weights in existing technologies, significantly improving the targeting and effectiveness of feature matching, and laying a core foundation for subsequent accurate calculation of the matching degree between suppliers and orders.

[0019] Furthermore, the specific process for calculating the matching degree between suppliers and orders using a deep learning model based on the attention mechanism is as follows: The weighted order multimodal feature vector and supplier multimodal feature vector are input into the feature interaction layer of the deep learning model. The semantic association and feature fusion of the two types of feature vectors are realized through matrix dot product operation to obtain the feature fusion matrix. The feature fusion matrix is ​​input into the fully connected layer of the deep learning model, and the output is a normalized matching metric value. The matching metric value ranges from [0,1]. A matching metric value of 1 indicates a higher degree of matching between the supplier and the order, while a matching metric value of 0 indicates a lower degree of matching between the supplier and the order. The output matching metric value is then corrected based on the deep learning model.

[0020] The specific steps for correcting the output matching metric value based on the deep learning model are as follows: Based on real-time acquired supply chain context information, a multidimensional correction factor set is established, which includes a matching degree negative correction factor and an emergency order response factor. The matching degree negative correction factor is used to negatively correct the matching degree of high-load suppliers, while the emergency order response factor is used to positively correct the matching degree of fast-responding suppliers. The normalized matching quantification value is synthesized with the matching degree negative correction factor and the emergency order response factor to obtain the corrected supplier-order matching quantification value. The corrected quantification value is then normalized to ensure that the final output supplier-order matching degree value ranges from [0,1].

[0021] In this embodiment, the weighted order multimodal feature vector and the supplier multimodal feature vector are input into the feature interaction layer of the deep learning model. Matrix dot product operations are used to achieve deep semantic association and efficient feature fusion between the two types of feature vectors, resulting in a feature fusion matrix that comprehensively represents the correspondence between order requirements and supplier capabilities. This matrix effectively solves the technical problem that a single feature vector cannot reflect the intrinsic relationship between the two, significantly improving the richness and relevance of feature representation. Subsequently, the feature fusion matrix is ​​input into the fully connected layer of the deep learning model, outputting a normalized matching quantification value. This quantization value ranges from [0,1], where a value of 1 indicates the highest matching degree between the supplier and the order, and a value of 0 indicates the highest matching degree. This indicates the lowest matching degree between suppliers and orders. The nonlinear transformation of the fully connected layer further realizes the accurate quantification of the matching degree, providing standardized basic data for subsequent matching decisions. To overcome the technical shortcomings of static matching results not being able to adapt to dynamic changes in the supply chain, this solution further dynamically corrects the output matching quantification value based on a deep learning model. Specifically, based on real-time acquired supply chain context information, a multi-dimensional correction factor set is established, including a negative matching degree correction factor and an emergency order response factor. The negative matching degree correction factor can reasonably negatively correct the matching degree of high-load suppliers, effectively avoiding order delivery delays caused by excessive supplier load and improving the overall operational stability of the supply chain. The emergency order response factor can moderately upward correct the matching degree of fast-responding suppliers, prioritizing the rapid response and efficient processing of emergency orders and meeting the differentiated matching needs of different types of orders. Finally, the normalized matching quantification value is weighted and synthesized with the negative matching degree correction factor and the emergency order response factor to obtain the corrected supplier-order matching quantification value. This corrected quantification value is then normalized again to ensure that the final output supplier-order matching quantification value is accurate. The order matching degree remains strictly within the range of [0,1]. This dynamic correction process not only achieves precise optimization of the matching degree, but also ensures the standardization and consistency of the matching results, ultimately greatly improving the accuracy, dynamic adaptability and overall operational efficiency of supplier-order matching.

[0022] It should be further explained that the specific steps for weight allocation are as follows: For each modality subvector in the supplier's multimodal feature representation set, the cross-attention mechanism generates modality-level weights by calculating the dot product similarity between the query vector and each modality key vector; Within each modality, feature-level weight allocation is achieved through a trainable adaptive feature selection layer. This layer takes the concatenated vector of the order demand feature representation query vector and the current modality value vector as input and outputs a feature importance mask vector. Each dimension of the feature importance mask vector corresponds to the dynamic weight coefficient of a feature dimension in the modality value vector, which is used to perform element-wise weighting on the current modality value vector to obtain feature-level weighted modality features. The obtained modal-level weights and feature-level weighted modal features are combined to generate the final weighted context vector. The weighted context vector integrates the overall preference of order demand for different supplier modalities as well as the fine-grained attention to different feature dimensions within the same modality.

[0023] It should be explained that the dynamic weight allocation mechanism is implemented through trainable parameters in the cross-attention layer and the adaptive feature selection layer. During the model training process, these parameters are optimized end-to-end based on massive historical order fulfillment result data (including but not limited to on-time delivery, quality compliance, and cost control indicators) and corresponding real-time market environment data. This enables the weight allocation strategy to be dynamically adjusted according to different order types (such as urgent orders, large orders, and orders with special qualifications), the real-time status of different suppliers (such as capacity fluctuations and inventory changes), and macroeconomic market factors (such as raw material price fluctuations and the tightness of logistics capacity).

[0024] The final weighted context vector constraint expression is: ; In the formula, C represents the final weighted context vector; a i This represents the relative importance weight of the i-th modality within the current order context; v i This represents the value vector of the i-th mode in the current mode.

[0025] In this embodiment, a two-layer attention weighting mechanism is used to refine the supplier's multimodal feature representation set. Specifically, for each modality sub-vector in the supplier's multimodal feature representation set, a cross-attention mechanism is first used to calculate the dot product similarity between the order demand-driven query vector and each modality key vector, generating modality-level weights that reflect the differences in the importance of orders to different modalities. This process effectively realizes the global association between order demand and different modal features of the supplier, solving the technical problem that traditional fixed weights cannot reflect the order's modality preference, and significantly improving the targeting and rationality of modality-level feature selection. On this basis, to further achieve fine-grained screening of feature dimensions within the same modality, a trainable adaptive feature selection layer is introduced within each modality to complete the dynamic allocation of feature-level weights. This adaptive feature selection layer takes the concatenated vector of the received order demand feature representation query vector and the current modality value vector as input, and outputs a feature importance mask vector that perfectly matches the dimension of the modality value vector through nonlinear transformation. Each feature importance mask vector... One dimension corresponds to the dynamic weight coefficient of a feature dimension in the modality value vector. By using this dynamic weight coefficient to perform element-wise weighting on the current modality value vector, feature-level weighted modality features can be obtained. This operation can accurately capture the differentiated attention of order requirements to different feature dimensions within the same modality, effectively filter redundant features and noise information within the modality, and significantly improve the representation accuracy and effectiveness of single modality features. Finally, the modality-level weights obtained through the cross-attention mechanism are combined with the feature-level weighted modality features processed by the adaptive feature selection layer to generate the final weighted context vector. This weighted context vector not only deeply integrates the overall preference of order requirements for different supplier modalities, but also accurately integrates the fine-grained attention to different feature dimensions within the same modality. It realizes two-layer fine-grained weighting from global modality to local feature, effectively solving the dual technical challenges of modality and feature weight allocation in multimodal feature fusion, significantly improving the relevance, richness, and representational ability of supplier feature representation, and laying a high-quality feature foundation for subsequent accurate matching of suppliers and orders.

[0026] As the second module of this system, the real-time logistics resource scheduling module is used to build a digital twin model of the logistics network based on real-time collected resource status data. The digital twin model is used to simulate the status changes of logistics resources and predict resource conflicts. The optimal logistics path is dynamically planned in the digital twin model, and the path replanning is automatically triggered when resource conflicts are detected.

[0027] It should be understood that, as Figure 2The diagram shows a flowchart of the supplier-order matching degree calculation and correction process for a cross-border order lifecycle intelligent management and anomaly handling system provided in this application embodiment. The specific process is as follows: First, starting from obtaining the order / supplier multimodal feature vector, the process enters the feature dimension extraction stage, extracting the static features (product category code, specification parameter vector) and dynamic features (urgency weight, normalized concatenated features) of the order respectively; then, through attention mechanism weight allocation, on the one hand, supplier modality-level weights (based on dot product similarity calculation) are generated, and on the other hand, supplier feature-level weights are generated (weighted element-wise through mask vectors), and then these two types of weights are fused into a weighted context vector; then, the feature interaction layer matrix dot product fusion step is entered to generate the order-supplier feature fusion matrix; then, the matching degree dynamic correction is initiated, first constructing a multidimensional correction factor set containing a negative matching degree correction factor and an urgent order response factor, then synthesizing the initial matching degree and the correction factor, completing the correction and then normalizing again (ensuring the range of 0-1), and finally outputting the final supplier-order matching degree. The entire process achieves accurate quantification and dynamic optimization of order-supplier matching through hierarchical feature extraction, dual weight allocation, matrix fusion, and dynamic correction.

[0028] It should be noted that the specific steps of dynamic programming for the optimal logistics route are as follows: Obtain the current order delivery delay and available transportation time, compare the available transportation time with the preset standard benchmark time for each transportation route, calculate the delivery time urgency coefficient, input the delivery time urgency coefficient into a predefined time priority weight mapping table, and output the time priority weight. The predefined time priority weight mapping table is configured such that if the delivery time urgency coefficient is within the preset time urgency critical interval, the current time priority weight is maintained. The preset time urgency critical interval represents the closed interval formed by the preset time urgency critical lower limit and the preset time urgency critical upper limit. If the delivery deadline urgency coefficient is less than the preset deadline urgency threshold, then the current time priority weight is set to the predefined time priority weight benchmark value. If the delivery deadline urgency coefficient is greater than the preset deadline urgency threshold, the current time priority weight is set to the predefined time priority weight threshold value. The time priority weight increases as the delivery deadline urgency coefficient increases, until it becomes the dominant optimization target under extreme urgency conditions.

[0029] In this embodiment, the actual delivery delay and available transportation time of the current order are first obtained as two core time parameters. Then, the available transportation time is compared with the preset standard benchmark time for each transportation path. A delivery time urgency coefficient, which accurately represents the tightness of the order's delivery time, is obtained through quantitative calculation. The introduction of this coefficient effectively realizes the quantitative description of the order's time constraints, solving the technical problems of vague qualitative judgment and strong subjectivity in traditional scheduling, and providing a standardized quantitative basis for subsequent weight configuration. Next, the calculated delivery time urgency coefficient is input into a predefined time priority weight mapping table. The corresponding time priority weight is output through rule matching in this mapping table. The predefined time priority weight mapping table is specifically configured with hierarchical dynamic adjustment rules: if the delivery time urgency coefficient is within a preset time urgency critical interval (i.e., within the closed interval formed by the preset lower and upper limits of the time urgency critical interval), the current time priority weight remains unchanged. This design effectively avoids frequent weight adjustments caused by reasonable fluctuations in the urgency coefficient, improving the stability and robustness of the scheduling system. If the delivery time urgency coefficient is within a preset time urgency critical interval (i.e., within the closed interval formed by the preset lower and upper limits of the time urgency critical interval), the current time priority weight remains unchanged. This design effectively avoids frequent weight adjustments caused by the urgency coefficient fluctuating within a reasonable range, improving the stability and robustness of the scheduling system. If the delivery deadline urgency coefficient is less than the preset lower limit of the deadline urgency, it indicates that the order delivery time is sufficient and there is no significant time pressure. In this case, the current time priority weight is set to the predefined time priority weight benchmark value. This ensures that in scenarios with relaxed time constraints, the scheduling system can prioritize other optimization objectives such as cost and efficiency, achieving a balance in multi-objective scheduling. If the delivery deadline urgency coefficient is greater than the preset upper limit of the deadline urgency, it indicates that the order delivery time is extremely tight and timeliness must be prioritized. In this case, the current time priority weight is set to the predefined upper limit of the time priority weight. Overall, the time priority weight shows a positive increasing trend as the delivery deadline urgency coefficient increases, until in the extreme urgency state, the time priority weight becomes the dominant optimization objective. This hierarchical dynamic adjustment mechanism can accurately adapt to the order scheduling needs of different time urgency levels. It not only solves the problem of resource waste caused by excessive time weight in relaxed scenarios, but also overcomes the risk of delivery delay caused by insufficient time weight in urgent scenarios. Ultimately, it realizes the dynamic, accurate, and adaptive configuration of time priority in order transportation scheduling, significantly improving the adaptability of the scheduling system to different time constraint scenarios and the overall timeliness of order delivery.

[0030] It should be further explained that the specific steps for automatically triggering path replanning when resource conflicts are detected are as follows: The digital twin model receives real-time data streams of logistics resource status and identifies resource conflict events through predefined resource conflict detection rules and machine learning anomaly detection models. Resource conflict events include, but are not limited to, node capacity over-limit conflicts (the real-time workload or queuing number of a logistics node (port, airport, warehouse) exceeds a preset percentage of its rated processing capacity threshold), path blockage conflicts (a segment of the planned transportation route is deemed impassable or its efficiency is severely reduced due to weather disasters, traffic control, geopolitical events, or infrastructure failures), and time window conflicts (due to delays in the previous link, goods cannot arrive at the transit node within the planned time window, thus missing the preset connecting transportation). When a resource conflict event is detected, the system immediately performs a simulated impact analysis on current orders in transit and orders awaiting execution to obtain the delay duration. For orders that exceed the preset time threshold, the system automatically generates a resource conflict warning event that includes the conflict type, affected path segments, and estimated impact level, and sends a path replanning instruction. Upon receiving the replanning instruction, the system uses the current order status (including current location and latest timestamp) as the initial condition, recalculates the time priority weight based on the current delivery deadline, and after excluding or avoiding specific resources that have already conflicted, re-executes the multi-objective path optimization algorithm in the updated logistics network digital twin model to generate one or more alternative optimal paths, and sends a path change notification to the preset operators.

[0031] In this embodiment, a full-process resource conflict monitoring and adaptive scheduling system is constructed based on a digital twin model of the logistics network. Specifically, the digital twin model first receives real-time data streams of logistics resource status and innovatively integrates predefined resource conflict detection rules with machine learning anomaly detection models to accurately and comprehensively identify various types of resource conflict events. These resource conflict events include, but are not limited to, node capacity overload conflicts, path blocking conflicts, and time window conflicts. Node capacity overload conflicts specifically refer to a logistics node (port, airport, warehouse) whose real-time workload or queuing number exceeds a preset percentage of its rated processing capacity threshold. Path blocking conflicts specifically refer to a segment of a planned transportation route being deemed impassable or having significantly reduced efficiency due to weather disasters, traffic control, geopolitical events, or infrastructure failures. Time window conflicts specifically refer to goods failing to arrive at the transit node within the planned time window due to delays in the previous stage, thus missing the preset connecting transportation. This dual-mechanism integrated detection method... This system ensures the accuracy and real-time performance of conflict identification through predefined rules, while leveraging machine learning anomaly detection models to enhance the ability to identify unknown and latent conflicts. This effectively solves the technical problems of high false positive and missed detection rates in traditional single detection mechanisms, achieving full coverage and high-precision monitoring of various resource conflicts in the logistics network. When the digital twin model detects any resource conflict event, it immediately performs a full simulation impact analysis on all currently en route orders and orders awaiting execution. The model's precise simulation calculations determine the delay duration of each order affected by the conflict. This simulation analysis process can quickly and quantitatively assess the scope and impact of the conflict, providing a scientific quantitative basis for subsequent scheduling decisions. For orders with delay durations exceeding a preset threshold, the model automatically generates standardized resource conflict warning events containing the conflict type, affected path segments, and estimated impact, and simultaneously sends path replanning instructions. This ensures that severely affected orders are prioritized, effectively preventing further expansion of the conflict's impact.Upon receiving a route replanning instruction, the system uses the current order's actual status (including current location and latest timestamp) as initial conditions. Combined with time priority weights recalculated based on the current delivery deadline, and while actively excluding or reasonably avoiding conflicting specific resources, the system re-executes a multi-objective path optimization algorithm within the real-time updated digital twin model of the logistics network. This rapidly generates one or more alternative optimal routes. The replanning process fully considers the order's real-time status, dynamic time priority, and the latest resource situation of the logistics network, effectively addressing the technical shortcomings of traditional static route planning in adapting to dynamic network changes. This ensures the optimality and feasibility of the replanned routes. Simultaneously, the system automatically sends route change notifications to preset operators, achieving an organic combination of automated scheduling and manual supervision. Ultimately, through this end-to-end digital twin-driven mechanism, the system significantly improves the logistics network's rapid response to resource conflicts, the dynamic adaptive capability of order scheduling, and the overall reliability and efficiency of logistics transportation.

[0032] The third module of this system, the Anomaly Collaborative Handling Module, records and monitors the node status throughout the entire order fulfillment lifecycle. Based on predefined anomaly identification rules and machine learning anomaly detection models, it performs real-time analysis of the acquired node status data and generates structured early warning events when anomalies are detected. Based on these structured early warning events, a pre-defined collaborative handling workflow is automatically triggered. This collaborative handling workflow includes at least one of the following: sending a request for assistance to the responsible party, initiating backup resource scheduling, generating a customer reassurance plan, and initiating an insurance claims process.

[0033] It should be understood that nodes include, but are not limited to, supplier production nodes, domestic warehousing nodes, reporting nodes, international transportation nodes, and last-mile delivery nodes; node status data includes, but is not limited to, the percentage of completion of each process, the estimated completion time, and the actual completion time. Node status data is input into a predefined anomaly identification rule base and a machine learning anomaly detection model to perform dual anomaly detection. The predefined anomaly identification rule base includes threshold rules, time-series rules, and correlation rules for each node. Threshold rules are the upper and lower limits of key indicators for each node (including the threshold for stock preparation delay, transportation timeliness, and customs clearance time). Time-series rules are the time-series dependencies and maximum interval limits of operations at each node. Correlation rules are the correlation verification conditions for status data between different nodes. The machine learning anomaly detection model is a model based on the fusion of isolated forest and LSTM, trained using historical anomaly node status data, used to identify latent anomaly data that exceeds the coverage of the rule base. If the node status data matches any rule in the predefined anomaly identification rule base, an anomaly event is detected.

[0034] In this embodiment, first, a monitoring node system covering the entire supply chain process is defined. The nodes include, but are not limited to, supplier production nodes, domestic warehousing nodes, customs declaration nodes, international transportation nodes, and terminal distribution nodes. At the same time, multi-dimensional core status data is defined for each node. The node status data includes, but is not limited to, the percentage of completion of each process, the estimated completion time, and the actual completion time. Through the collection of status data for all nodes and multiple dimensions, the problem of monitoring blind spots caused by incomplete node coverage and single status data in traditional monitoring is effectively solved, providing a comprehensive and sufficient data basis for subsequent anomaly detection. Subsequently, the status data of each node collected is respectively input into a predefined anomaly recognition rule library and a machine learning anomaly detection model to execute a dual anomaly detection mechanism. This dual detection design can achieve full-coverage recognition of rule-based explicit anomalies and model-based implicit anomalies, effectively improving the comprehensiveness and accuracy of anomaly detection. Among them, the predefined anomaly recognition rule library contains three types of standardized rules for each node, namely threshold rules, time-series rules, and association rules. Threshold rules are the clear upper and lower limit thresholds for key indicators of each node, specifically including the stock preparation delay threshold, transportation timeliness threshold, and customs clearance duration threshold, which can quickly identify explicit anomalies where the indicators exceed the limits. Time-series rules are the inherent time-series dependencies and the longest interval time limits for operations of each node, which can effectively detect process anomalies such as chaotic process connections and timeout intervals. Association rules are the association verification conditions for status data between different nodes, which can achieve cross-node linked anomaly recognition. The three types of rules complement each other to construct a complete explicit anomaly recognition system, ensuring the standardization and efficiency of anomaly detection. The machine learning anomaly detection model is an innovative model based on the fusion of Isolation Forest and LSTM. This model is trained through a large amount of historical anomaly node status data. Among them, Isolation Forest can quickly identify outlier anomalies in high-dimensional status data, and LSTM can accurately capture trend anomalies in time-series status data. The fusion of the two effectively improves the anomaly recognition ability of the model and is specifically used to identify implicit anomaly data beyond the coverage of the rule library, solving the technical problem of insufficient recognition ability of traditional rule library detection for unknown and implicit anomalies. In the process of dual anomaly detection, if the node status data matches any one of the rules in the predefined anomaly recognition rule library, it is directly determined that an anomaly event has been detected, ensuring the quick recognition and timely response of explicit anomalies. The machine learning anomaly detection model can simultaneously identify implicit anomalies not covered by the rule library. Finally, through this dual anomaly detection mechanism, a comprehensive, accurate, and efficient recognition of status anomalies for all nodes in the supply chain is achieved, significantly improving the reliability and sensitivity of supply chain anomaly monitoring, and providing timely and accurate decision-making basis for subsequent anomaly handling and scheduling optimization.

[0035] It should be further noted that when an anomaly event is detected, a structured warning event is generated, specifically including: Extract core information about the anomaly from the standardized node status data corresponding to the detected anomaly events. The core information about the anomaly includes, but is not limited to, the node identifier where the anomaly occurred, the timestamp of the anomaly occurrence, the anomaly type, the scope of the anomaly's impact, the current severity level of the anomaly, and the unique identifier of the associated order. Anomaly types include inventory preparation anomalies, logistics anomalies, customs clearance anomalies, and delivery anomalies. The current severity level of the anomaly is classified as general, relatively serious, serious, and extremely serious. The extracted core anomaly information is encapsulated in a structured manner to generate a structured early warning event. The structured early warning event includes, but is not limited to, an event header and an event body. The event header includes a unique ID of the early warning event, a generation timestamp, and a data verification code. The event body is a set of key-value pairs containing the core anomaly information.

[0036] In this embodiment, after completing dual anomaly detection across all nodes, the core anomaly information that comprehensively characterizes the nature and impact of the anomaly is first accurately extracted from the standardized node status data corresponding to the detected anomaly events. This core anomaly information includes, but is not limited to, the anomaly occurrence node identifier, anomaly occurrence timestamp, anomaly type, anomaly impact scope, current anomaly severity level, and unique identifier of the associated order. The anomaly types are explicitly categorized into four main types: inventory preparation anomaly, logistics anomaly, customs clearance anomaly, and delivery anomaly. This facilitates the implementation of differentiated handling strategies for different types of anomalies. The current anomaly severity level is further subdivided into four levels: general, moderate, severe, and extremely severe, allowing for precise quantification of the anomaly. The extraction process effectively filters redundant information from the original state data by determining the urgency and impact of the event, achieving highly condensed and accurate characterization of the anomaly. This solves the technical problems of traditional warning information being disorganized and lacking key information, laying a high-quality data foundation for subsequent structured encapsulation. Subsequently, the extracted core anomaly information is structured and encapsulated according to a unified preset format to generate standardized structured warning events. These structured warning events include, but are not limited to, an event header with global identification and verification functions, and an event body carrying core information. The event header contains a unique warning event ID, a generation timestamp, and a data verification code. It enables globally unique identification and full lifecycle tracking of each early warning event. The generated timestamp accurately records the occurrence time of the early warning event, providing a basis for timely management of anomaly handling. The data verification code effectively ensures the integrity and accuracy of the early warning event during transmission and storage, preventing data tampering or loss. The event subject is a set of key-value pairs containing the core information of the anomaly. The key-value pair storage format not only ensures clear correspondence and rapid parsing of the core information of the anomaly, but also improves the scalability and compatibility of the early warning event, facilitating information interaction and sharing between different systems. This structured encapsulation process effectively realizes the standardization and normalization of anomaly early warning information, significantly improving the readability, parsability, and transmissibility of the early warning information. It provides a unified and reliable data carrier for the automated execution of subsequent anomaly handling processes, cross-system information interaction, and statistical analysis of historical anomaly data, ensuring efficient connection and accurate decision-making throughout the entire process of anomaly detection and handling.

[0037] It should be further explained that the specific steps for automatically triggering the preset collaborative handling workflow based on structured early warning events are as follows: Based on the anomaly type, anomaly occurrence node identifier, and anomaly severity level in the structured early warning event, the corresponding target collaborative handling workflow is matched from the preset collaborative handling database. The collaborative handling workflow database contains handling process templates for different anomaly scenarios, including but not limited to the handling process for delayed inventory preparation, transportation failure, cross-border customs clearance obstruction, delivery delay, and qualification expiration. Once the target collaborative handling workflow is triggered, the system automatically identifies and associates the collaborative handling nodes involved in the abnormal event. These nodes include supplier-side nodes, logistics service provider-side nodes, platform operation-side nodes, and regulatory-side nodes. The system pushes structured early warning events and corresponding handling task lists to each collaborative handling node. The task lists include, but are not limited to, task types and completion deadlines. The system monitors the task completion status of each collaborative handling node in real time. When all handling tasks are completed and pass acceptance, an abnormal handling closed-loop record is formed. If the task fails acceptance, the system automatically triggers the escalation handling process and pushes an early warning escalation notification to the higher-level responsible entity.

[0038] In this embodiment, after generating a structured early warning event, the system first intelligently matches the corresponding target collaborative handling workflow from a pre-set collaborative handling database based on three core dimensions: the accurately labeled anomaly type, the anomaly occurrence node identifier, and the anomaly severity level. This collaborative handling workflow database pre-constructs standardized handling process templates covering different anomaly scenarios, including but not limited to those for handling delayed inventory preparation, transportation failures, cross-border customs clearance disruptions, delivery delays, and qualification failures. This multi-dimensional accurate matching mechanism effectively solves the technical problems of strong subjectivity in process selection and inconsistent handling strategies in traditional anomaly handling, enabling rapid location and standardized invocation of anomaly handling processes, significantly improving the efficiency and targeting of process initiation. After the target collaborative handling workflow is triggered, the system automatically identifies and accurately associates all collaborative handling nodes involved in the anomaly event. These nodes cover the responsible entities across the entire supply chain, including supplier nodes, logistics service provider nodes, platform operation nodes, and regulatory nodes. Simultaneously, the system pushes the structured early warning event and corresponding personalized handling task lists to each collaborative handling node. These task lists include, but are not limited to, clearly defined task types and strict completion deadlines. This automated process... The point-to-point association and task push process effectively breaks down information barriers between nodes in the traditional handling model, enabling precise allocation and efficient issuance of handling tasks. This ensures that all responsible parties can promptly obtain complete anomaly information and clear handling requirements, significantly improving the linkage and execution efficiency of collaborative handling. Subsequently, the system will monitor the task completion status of each collaborative handling node in real time and throughout the entire process, establishing a closed-loop handling process management mechanism. When all handling tasks are completed as required and pass acceptance, a complete anomaly handling closed-loop record is automatically formed, providing a complete and reliable basis for subsequent anomaly review, process optimization, and accountability. If a task fails acceptance, an escalation process is automatically triggered immediately, and an escalation warning notification is sent to a higher-level responsible party. This dynamic monitoring and escalation mechanism effectively avoids situations where task execution is inadequate or abnormal issues remain unresolved, ensuring the quality and effectiveness of abnormal handling. At the same time, the escalation process design enables hierarchical handling of complex abnormal events, further improving the reliability and comprehensiveness of abnormal handling. Ultimately, through this standardized collaborative handling mechanism across the entire process, the efficiency, collaboration level, and closed-loop management capabilities for handling abnormal events in the supply chain are significantly improved, effectively ensuring the stable operation of the entire supply chain.

[0039] The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Furthermore, certain method or process blocks may be omitted in some embodiments. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, the described blocks or states may be performed in an order different from the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The exemplary systems and components described herein may be configured differently from those described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

[0040] The various operations of the example methods described herein can be performed at least in part by an algorithm. This algorithm can be contained in program code or instructions stored in memory (e.g., the aforementioned non-transitory computer-readable storage medium). Such an algorithm may include a machine learning algorithm. In some embodiments, the machine learning algorithm may not be explicitly programmed into the computer to perform the function, but can learn from training data to create a predictive model that performs the function.

[0041] The various operations of the example methods described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation that operates to perform one or more of the operations or functions described herein.

[0042] Similarly, the methods described herein can be implemented at least in part by a processor, where one or more specific processors are examples of hardware. For example, at least some operations of a method can be performed by one or more processors or an engine implemented by a processor. Furthermore, one or more processors can also be operated to support the performance of related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations can be performed by a set of computers (as an example of a machine including processors), where these operations are accessible via a network (e.g., the Internet) and via one or more suitable interfaces (e.g., application programming interfaces (APIs)).

[0043] The performance of certain operations can be distributed across processors, residing not only within a single machine but also deployed across multiple machines. In some example embodiments, the processor or processor-implemented engine may reside in a single geographic location (e.g., within a home environment, office environment, or server cluster). In other example embodiments, the processor or processor-implemented engine may be distributed across multiple geographic locations.

[0044] In this specification, multiple instances may implement components, operations, or structures described as single instances. Although individual operations of one or more methods are shown and described as separate operations, one or more of the separate operations may be performed simultaneously and do not need to be performed in the order shown. Structures and functions presented as separate components in the example configuration may be implemented as composite structures or components. Similarly, structures and functions presented as single components may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of this document.

[0045] While an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes can be made to these embodiments without departing from the broader scope of embodiments of this disclosure. Such embodiments of the subject matter are referred to herein, individually or collectively, by the term "invention," and are used for convenience only and are not intended to limit the scope of this application to any single disclosure or concept, should more than one disclosure or concept be disclosed in fact.

[0046] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. A cross-border order full lifecycle intelligent management and exception handling system, characterized in that, It includes a dynamic supplier intelligent matching module, a real-time logistics resource scheduling module, and an anomaly collaborative handling module: The dynamic supplier intelligent matching module is used to construct multimodal feature vectors for orders and suppliers that include static and dynamic feature dimensions based on the received order lifecycle data, and to calculate the matching degree between suppliers and orders based on a deep learning model with an attention mechanism, so as to adjust the matching strategy weights. The real-time logistics resource scheduling module is used to construct a digital twin model of the logistics network based on real-time collected resource status data. The digital twin model is used to simulate changes in the status of logistics resources and predict resource conflicts. The optimal logistics path is dynamically planned in the digital twin model, and path replanning is automatically triggered when resource conflicts are detected. The anomaly collaborative handling module is used to record and monitor the node status of the entire lifecycle of order fulfillment, analyze the acquired node status data in real time based on predefined anomaly identification rules and machine learning anomaly detection models, generate structured early warning events when an anomaly event is detected, and automatically trigger a preset collaborative handling workflow based on the structured early warning event.

2. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 1, characterized in that, The static feature dimension extracts static order demand data, which includes, but is not limited to, product category codes and specification parameter vectors. The dynamic feature dimension extracts dynamic order demand data, which includes, but is not limited to, urgency weights and multimodal feature vectors formed by concatenating them after feature normalization. The completed order multimodal feature vector and supplier multimodal feature vector are input into the deep learning model with attention mechanism. The attention layer of the deep learning model assigns weights to each static feature dimension and dynamic feature dimension in the order multimodal feature vector and each static feature dimension and dynamic feature dimension in the supplier multimodal feature vector.

3. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 1, characterized in that, The specific process by which the attention-based deep learning model calculates the matching degree between suppliers and orders is as follows: The weighted order multimodal feature vector and supplier multimodal feature vector are input into the feature interaction layer of the deep learning model. The semantic association and feature fusion of the two types of feature vectors are realized through matrix dot product operation to obtain the feature fusion matrix. The feature fusion matrix is ​​input into the fully connected layer of the deep learning model, and the output is a normalized matching metric value. The matching metric value ranges from [0,1]. A matching metric value of 1 indicates a higher degree of matching between the supplier and the order, and a matching metric value of 0 indicates a lower degree of matching between the supplier and the order. The output matching metric value is then corrected based on the deep learning model.

4. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 3, characterized in that, The specific steps for correcting the output matching metric value based on the deep learning model are as follows: Based on real-time acquired supply chain context information, a multidimensional correction factor set is established, which includes a matching degree negative correction factor and an emergency order response factor. The matching degree negative correction factor is used to negatively correct the matching degree of high-load suppliers, and the emergency order response factor is used to positively correct the matching degree of fast-responding suppliers. The normalized matching quantification value is synthesized with the matching degree negative correction factor and the emergency order response factor to obtain the corrected supplier-order matching quantification value. The corrected quantification value is then normalized to ensure that the final output supplier-order matching degree value ranges from [0,1].

5. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 2, characterized in that, The specific steps for weight allocation are as follows: For each modal subvector in the supplier multimodal feature representation set, modal-level weights are generated by calculating the dot product similarity between the query vector and each modal key vector; Within each modality, feature-level weight allocation is implemented. The concatenation vector of the query vector representing the order demand feature received by this layer and the current modality value vector is used as input, and a feature importance mask vector is output. Each dimension of the feature importance mask vector corresponds to the dynamic weight coefficient of a feature dimension in the modality value vector, which is used to perform element-wise weighting on the current modality value vector to obtain feature-level weighted modality features. The obtained modal-level weights and feature-level weighted modal features are combined to generate a final weighted context vector. The weighted context vector integrates the overall preference of order demand for different supplier modalities and the fine-grained attention to different feature dimensions within the same modality.

6. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 1, characterized in that, The specific steps of the dynamic programming optimal logistics path are as follows: The system obtains the current order delivery delay and available transportation time, compares the available transportation time with the preset standard benchmark time for each transportation route, calculates the delivery time urgency coefficient, inputs the delivery time urgency coefficient into a predefined time priority weight mapping table, and outputs the time priority weight. The predefined time priority weight mapping table is configured such that if the delivery time urgency coefficient is within a preset time urgency critical interval, the current time priority weight is maintained. The preset time urgency critical interval represents the closed interval formed by the preset lower limit and the preset upper limit of time urgency. If the delivery deadline urgency coefficient is less than the preset deadline urgency threshold, then the current time priority weight is set to the predefined time priority weight benchmark value. If the delivery deadline urgency coefficient is greater than the preset deadline urgency threshold, the current time priority weight is set to the predefined time priority weight threshold value, and the time priority weight increases as the delivery deadline urgency coefficient increases.

7. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 1, characterized in that, The specific steps for automatically triggering path replanning when resource conflicts are detected are as follows: The digital twin model receives real-time data streams of logistics resource status and identifies resource conflict events through predefined resource conflict detection rules and machine learning anomaly detection models. The resource conflict events include, but are not limited to, node capacity over-limit conflicts, path blocking conflicts, and time window conflicts. When a resource conflict event is detected, a simulated impact analysis is immediately performed on current orders in transit and orders awaiting execution to determine the delay duration. For orders that exceed a preset time threshold, a resource conflict warning event will be automatically generated and a path replanning instruction will be sent. Upon receiving the replanning instruction, the system generates one or more alternative optimal paths based on the current order status as the initial condition and the time priority weight recalculated according to the current delivery deadline, and sends a path change notification to the preset operator.

8. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 1, characterized in that, The nodes include, but are not limited to, supplier production nodes, domestic warehousing nodes, reporting nodes, international transportation nodes, and last-mile delivery nodes; The node status number includes, but is not limited to, the percentage of completion of each process, the estimated completion time, and the actual completion time. The node status data is input into a predefined anomaly identification rule base and a machine learning anomaly detection model to perform dual anomaly detection. The predefined anomaly identification rule base includes threshold rules, time-series rules, and correlation rules for each node. Threshold rules are the upper and lower limits of key indicators for each node. Time-series rules are the time-series dependencies and longest interval limits of operations for each node. Correlation rules are the correlation verification conditions for state data between different nodes. The machine learning anomaly detection model is used to identify hidden anomaly data that exceeds the coverage of the rule base. If the node status data matches any rule in the predefined anomaly detection rule base, then an anomaly event is detected.

9. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 8, characterized in that, If an abnormal event is detected, a structured early warning event is generated, specifically including: Extract core information about the anomaly from the standardized node status data corresponding to the detected anomaly events. This core information includes, but is not limited to, the anomaly occurrence node identifier, the anomaly occurrence timestamp, the anomaly type, the anomaly impact range, the current severity level of the anomaly, and the unique identifier of the associated order. The types of anomalies include inventory preparation anomalies, logistics anomalies, customs clearance anomalies, and delivery anomalies. The current severity levels of the anomalies include general, relatively severe, severe, and extremely severe. The extracted core anomaly information is encapsulated in a structured manner to generate a structured early warning event. The structured early warning event includes, but is not limited to, an event header and an event body. The event header includes a unique ID of the early warning event, a generation timestamp, and a data verification code. The event body is a set of key-value pairs containing the core anomaly information.

10. The intelligent management and exception handling system for the entire lifecycle of cross-border orders as described in claim 1, characterized in that, The specific steps for automatically triggering a preset collaborative handling workflow based on the structured early warning event are as follows: Based on the anomaly type, anomaly occurrence node identifier, and anomaly severity level in the structured early warning event, the corresponding target collaborative handling workflow is matched from the preset collaborative handling database. The collaborative handling workflow database contains handling process templates for different anomaly scenarios, including but not limited to the handling process for delayed inventory preparation, transportation failure, cross-border customs clearance obstruction, delivery delay, and qualification failure. Once the target collaborative handling workflow is triggered, the collaborative handling nodes involved in the abnormal event are automatically identified and associated. The collaborative handling nodes include supplier-side nodes, logistics service provider-side nodes, platform operation-side nodes, and regulatory-side nodes. The structured early warning event and the corresponding handling task list are pushed to each collaborative handling node. The task list includes, but is not limited to, task type and completion deadline. The system monitors the task completion status of each collaborative handling node in real time. When all handling tasks are completed and pass acceptance, an abnormal handling closed-loop record is formed. If the task fails acceptance, the system automatically triggers the escalation handling process and pushes an early warning escalation notification to the higher-level responsible entity.

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