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CN-122022280-A - Cross-border order full life cycle intelligent management and exception handling system

CN122022280ACN 122022280 ACN122022280 ACN 122022280ACN-122022280-A

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 abnormal collaborative handling module. The intelligent matching module of the dynamic supplier calculates the supply and demand matching degree based on the multi-mode feature vector and the attention mechanism model, the real-time logistics resource scheduling module plans the optimal path by means of the digital twin model and handles resource conflict, the abnormal collaborative handling module monitors the performing node, detects abnormal generation early warning and triggers collaborative handling workflow, the intelligent management of the whole life cycle of the cross-border order and the dynamic responsiveness of abnormal handling are improved, and the problem that the intelligent management of the whole life cycle of the cross-border order and the dynamic responsiveness of the abnormal handling are low in the prior art is solved.

Inventors

  • HUA ZHIBIN

Assignees

  • 企发丝路(北京)科技发展有限公司

Dates

Publication Date
20260512
Application Date
20260109

Claims (10)

  1. 1. The intelligent management and exception handling system for the whole life cycle of the cross-border order is characterized by comprising a dynamic supplier intelligent matching module, a real-time logistics resource scheduling module and an exception collaborative handling module: The dynamic supplier intelligent matching module is used for constructing an order multi-mode feature vector containing static feature dimensions and dynamic feature dimensions and a supplier multi-mode feature vector according to the received order full life cycle data, and calculating the matching degree of the supplier and the order based on a deep learning model of an attention mechanism so as to adjust matching strategy weights; The real-time logistics resource scheduling module is used for constructing a logistics network digital twin model based on the real-time acquired resource state data, wherein the digital twin model is used for simulating state change of logistics resources and predicting resource conflict, dynamically planning an optimal logistics path in the digital twin model, and automatically triggering path re-planning when the resource conflict is monitored; The abnormal collaborative handling module is used for analyzing the acquired node state data in real time based on a predefined abnormal recognition rule and a machine learning abnormal detection model by recording and monitoring the node state of the full life cycle of order execution, generating a structured early warning event when an abnormal event is detected, and automatically triggering a preset collaborative handling workflow according to the structured early warning event.
  2. 2. The cross-border order full lifecycle intelligent management and exception handling system as described in claim 1, wherein the static feature dimension extracts order static demand data, including but not limited to commodity class codes and specification parameter vectors; the dynamic feature dimension extracts order dynamic demand data, wherein the order dynamic demand data comprises but is not limited to emergency degree weight and multi-mode feature vectors of an order formed by feature normalization in a splicing mode; And inputting the constructed order multi-modal feature vector and the supplier multi-modal feature vector into a deep learning model of an attention mechanism, wherein the attention layer of the deep learning model respectively carries out weight distribution on each static feature dimension and each dynamic feature dimension in the order multi-modal feature vector and each static feature dimension and each dynamic feature dimension in the supplier multi-modal feature vector.
  3. 3. The intelligent management and exception handling system for full life cycle of a cross-border order according to claim 1, wherein the deep learning model based on the attention mechanism comprises the following specific steps of: inputting the weighted order multi-modal feature vectors and the supplier multi-modal feature vectors into a feature interaction layer of a deep learning model, and realizing semantic association and feature fusion of the two types of feature vectors through matrix dot product operation to obtain a feature fusion matrix; Inputting the feature fusion matrix into a full-connection layer of the deep learning model, outputting normalized matching degree quantized values, wherein the range of the matching degree quantized values is [0,1], the value of the matching degree quantized values is 1, which indicates that the higher the matching degree between a provider and an order is, the lower the matching degree between the provider and the order is, and correcting the output matching degree quantized values according to the deep learning model.
  4. 4. The intelligent cross-border order full life cycle management and exception handling system as claimed in claim 3, wherein said specific step of correcting the output matching degree quantization value according to the deep learning model comprises: based on the supply chain context information acquired in real time, a multidimensional correction factor set is established, wherein the multidimensional correction factor set comprises a matching degree negative correction factor and an emergency order response factor; The matching degree negative correction factor is used for carrying out negative correction on the matching degree of the high-load suppliers, and the emergency order response factor is used for carrying out upward correction on the matching degree of the suppliers with quick response; And carrying out synthesis processing on the normalized matching degree quantized value, the matching degree negative correction factor and the emergency order response factor to obtain a corrected supplier-order matching quantized value, and carrying out normalization processing on the corrected quantized value to ensure that the finally output supplier-order matching degree value range is [0,1].
  5. 5. The system for intelligent management and exception handling of a full lifecycle of a cross-border order according to claim 2, wherein the specific steps of weight distribution are: generating a modal level weight by calculating the dot product similarity of the query vector and each modal key vector for each modal sub-vector in the multi-modal feature representation set of the provider; Inside each mode, feature level weight distribution is realized, a spliced vector of a query vector and a current mode value vector is taken as input, a feature importance mask vector is output, and each dimension of the feature importance mask vector corresponds to a dynamic weight coefficient of a feature dimension in the mode value vector and is used for carrying out element-by-element weighting on the current mode value vector to obtain feature level weighted mode features; And combining the obtained modal-level weight and feature-level weighted modal characteristics to generate a final weighted context vector, wherein the weighted context vector fuses the overall preference of order demands on different modalities of the provider and the fine granularity attention of different feature dimensions in the same modality.
  6. 6. The intelligent cross-border order full life cycle management and exception handling system of claim 1, wherein the specific steps of dynamically planning the optimal logistic path are as follows: acquiring the current order delivery time delay and the available transportation time length, comparing the available transportation time length with the standard reference time length of each preset transportation path, calculating a delivery time limit urgency coefficient, inputting the delivery time limit urgency coefficient into a predefined time priority weight mapping table, and outputting time priority weight, wherein the predefined time priority weight mapping table is configured to maintain the current time priority weight if the delivery time limit urgency coefficient is in a preset time limit urgency critical interval, and the preset time limit urgency critical interval represents a closed interval formed by a preset time limit urgency critical lower limit and a preset time limit urgency critical upper limit; if the delivery time limit urgency coefficient is smaller than the preset time limit urgency critical lower limit, setting the current time priority weight as a predefined time priority weight reference value; If the delivery time limit urgency coefficient is greater than the preset time limit urgency threshold upper limit, setting the current time priority weight to a predefined time priority weight threshold upper limit, wherein the time priority weight is increased along with the increase of the delivery time limit urgency coefficient.
  7. 7. The system for intelligent management and exception handling of a full lifecycle of a cross-border order according to claim 1, wherein the specific step of automatically triggering path re-planning when a resource conflict is detected is as follows: The digital twin model receives the real-time data stream of the physical distribution resource state, and identifies resource conflict events including, but not limited to, node capacity overrun conflict, path blocking conflict and time window conflict through a predefined resource conflict detection rule and a machine learning anomaly detection model; When a resource conflict event is monitored, immediately performing simulation influence analysis on the current in-transit order and the order to be executed to obtain delay time; after the re-planning instruction is received, one or more alternative optimal paths are generated by taking the state of the current order as an initial condition and based on the recalculated time priority weight of the current delivery time limit, and a path change notification is sent to a preset operator.
  8. 8. The cross-border order full lifecycle intelligent management and exception handling system, as recited in claim 1, wherein the nodes include, but are not limited to, supplier production nodes, domestic warehousing nodes, customs clearance nodes, international shipping nodes, and terminal distribution nodes; the node state number comprises, but is not limited to, the completion percentage of each process, the expected completion time and the actual completion time, the node state data are respectively input into a predefined abnormality recognition rule base and a machine learning abnormality detection model, and double abnormality detection is performed; The predefined abnormal recognition rule base comprises threshold class rules, time sequence class rules and association class rules of each node, wherein the threshold class rules are upper and lower limit thresholds of key indexes of each node, the time sequence class rules are time sequence dependency relations and longest interval time limits of operation of each node, the association class rules are association check conditions of state data among different nodes, and the machine learning abnormal detection model is used for recognizing hidden abnormal data exceeding the coverage range of the rule base; if the node state data is matched with any rule in the predefined abnormal recognition rule base, judging that an abnormal event is detected.
  9. 9. The system for intelligent management and exception handling of a full lifecycle of a cross-border order according to claim 8, wherein if an exception is detected, a structured early warning event is generated, comprising: extracting abnormal core information from standardized node state data corresponding to the detected abnormal event, wherein the abnormal core information comprises but is not limited to abnormal occurrence node identification, abnormal occurrence time stamp, abnormal type, abnormal influence range, abnormal current severity level and associated order unique identification; The anomaly types comprise stock anomalies, logistics anomalies, customs anomalies and distribution anomalies, and the current severity level of the anomalies comprises general, heavier, serious and particularly serious; the method comprises the steps of carrying out structured packaging on extracted abnormal core information to generate a structured early warning event, wherein the structured early warning event comprises, but is not limited to, an event header and an event main body, the event header comprises an early warning event unique ID, a generation time stamp and a data check code, and the event main body is a key value pair set of the abnormal core information.
  10. 10. The system for intelligent management and exception handling of a whole life cycle of a cross-border order according to claim 1, wherein the specific steps of automatically triggering a preset co-processing workflow according to the structured early warning event are as follows: Matching corresponding target collaborative treatment workflow from a preset collaborative treatment database based on the abnormality type, abnormality occurrence node identification and abnormality severity level in the structured early warning event, wherein the collaborative treatment workflow library comprises treatment flow templates under different abnormal scenes, and the treatment flow templates comprise but are not limited to stock delay treatment flow, transport fault treatment flow, cross-border traffic blocking treatment flow, delivery delay treatment flow and qualification failure treatment flow; after the target collaborative handling workflow is triggered, automatically identifying and associating collaborative handling nodes related to abnormal events, wherein the collaborative handling nodes comprise provider end nodes, logistics service provider end nodes, platform operation end nodes and supervision end nodes, and pushing structural early warning events and corresponding handling task lists to the collaborative handling nodes, wherein the task lists comprise but are not limited to task types and completion time limits; And if the task acceptance fails, automatically triggering an upgrade treatment flow, and pushing an early warning upgrade notification to a higher-level responsibility main body.

Description

Cross-border order full life cycle intelligent management and exception handling system Technical Field The invention relates to the technical field of order cycle management and exception handling, in particular to a cross-border order full life cycle intelligent management and exception handling system. Background In the matching link of the suppliers of order performance, because the feature engineering of the existing algorithm model is only limited to static and basic feature dimension screening and construction, only commodity class labels, historical transaction times and quotation information are extracted to form feature vectors, and the dynamic and core dimensions of the core dominant class, the real-time stock response speed, the quality qualification rate, the qualification authentication state, the productivity utilization rate, the raw material stock and the like of the suppliers cannot be brought into a feature system, so that systematic deviation occurs in key decision points of the matching process. Specifically, when analyzing a cross-product business provider, the algorithm cannot identify the core capacity due to the lack of features, and the high-requirement clothing and apparel order can be mismatched with the provider with the long-standing knitted fabric, so that the risk of stock quality is caused, meanwhile, the existing model mostly adopts a static rule engine or a traditional machine learning algorithm, and dynamic strategy adjustment is carried out according to real-time order requirements, provider operation states and market fluctuation, so that the problem of matching precision caused by insufficient feature engineering is further amplified. Because the technical architecture based on real-time data acquisition and dynamic analysis is not constructed, the system can only rely on a static resource list in the logistics resource matching process, and cannot acquire dynamic information such as vehicle position, load rate, bin position state, line congestion and the like through a real-time data link, so that the resource visualization degree is low, and an algorithm can distribute orders to service providers without actual transport capacity. In the scheduling stage, due to lack of a real-time sensing and response mechanism, the system cannot automatically trigger dynamic rescheduling when an abnormality occurs, and only can fall into a hysteresis flow of 'manual audit-re-matching', so that logistics delay is aggravated. In addition, the cross-border logistics monitoring can only capture the states of terminal nodes such as origin, destination and the like due to the lack of a full-link data sensing mechanism, and forms a monitoring blind area for key intermediate nodes such as customs clearance, transit and the like, so that the abnormality cannot be found and intervened in time, and the problem of low dynamic responsiveness of the cross-border order full-life-cycle intelligent management and the abnormality processing exists. Disclosure of Invention In order to solve the technical problems of low dynamic responsiveness of cross-border order full life cycle intelligent management and exception handling in the prior art, the embodiment of the invention provides a cross-border order full life cycle intelligent management and exception handling system. The technical scheme is as follows: The system comprises a dynamic supplier intelligent matching module, a real-time logistics resource scheduling module and an abnormal collaborative handling module, wherein the dynamic supplier intelligent matching module is used for constructing an order multi-mode feature vector containing static feature dimensions and dynamic feature dimensions and a supplier multi-mode feature vector according to received order full life cycle data, calculating matching degree of suppliers and orders based on a deep learning model of an attention mechanism to adjust matching strategy weights, the real-time logistics resource scheduling module is used for constructing a logistics network digital twin model based on real-time acquired resource state data, the digital twin model is used for simulating state changes of logistics resources and predicting resource conflicts, dynamically planning an optimal logistics path in the digital twin model and automatically triggering path re-planning when the resource conflicts are detected, and the abnormal collaborative handling module is used for analyzing the acquired node state data in real time by recording and monitoring node states of the order full life cycle based on predefined abnormal recognition rules and machine learning abnormal detection models, generating structural early warning when abnormal events are detected, and automatically triggering preset collaborative work flows according to the structural early warning. One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantag