An order management method and system of a cross-border logistics platform

Abstract

This invention relates to the field of order management technology and discloses an order management method and system for a cross-border logistics platform. Upon receiving an instruction to copy an original order, the method first removes all tracking codes and identifiers generated by the platform's internal system from the original order, and then generates internal tracking codes and identifiers for the newly created order that are not internally associated with the original order. This severs the internal association between the old and new orders at the source, effectively avoiding the problem of incorrect inheritance of internal tracking codes due to order copying. Furthermore, this application introduces an identifier verification process. By detecting whether the identifiers carried in external status update information have cross-associations with the internal tracking codes of invalid or failed orders within the platform, and determining whether the master tracking code does not match the master tracking code of the currently active order, the method accurately identifies "ghost order" information contamination.

Description

Technical Field

[0001] This invention relates to the field of order management technology, and in particular to an order management method and system for a cross-border logistics platform. Background Technology

[0002] In modern cross-border logistics platforms, order management is a core component, requiring efficient and accurate handling of the entire process from order creation to receipt. However, when a platform expands its business to new trading partner countries, the existing order management system faces significant challenges due to differences in national regulations, infrastructure, and information systems. For example, the complexity of customs declarations, tax calculations, and logistics node approvals can lead to chaotic data flow within the system and even the emergence of "ghost orders," severely impacting operational efficiency and customer trust.

[0003] Specifically, when a cross-border logistics platform frequently resorts to manual intervention to address the complexities of customs declarations, logistics interfaces, and payment settlements in new trading partners, operators sometimes use the "copy order" function to quickly process orders locked due to declaration failures. This function, not adequately designed for complex failure scenarios, may mistakenly copy the internal tracking code of the original failed order to the new order. When the new order receives a status update, the platform system may incorrectly prioritize or associate it with the legacy tracking code, causing the actual status information of the new order to be incorrectly updated on the already invalidated old order record. This confusion in order data association leads to a serious deviation in the platform's internal understanding of the true status of orders, creating the difficult-to-detect "ghost orders" problem. This fundamental data inconsistency prevents the platform from accurately assessing the overall timeliness and cost of orders, resulting in a significant increase in customer complaint rates and severely impacting the overall efficiency of the cross-border logistics platform's order management methods and user experience.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This invention provides an order management method and system for a cross-border logistics platform, aiming to solve the technical problem that existing cross-border logistics platforms, when handling complex failed order scenarios, suffer from the technical problem of incorrect copying of internal tracking codes due to design flaws in the order copying function, which leads to "ghost order" information pollution and seriously affects the accuracy of order status, operational efficiency and customer trust.

[0006] The technical solution of this application is as follows: Firstly, this application discloses an order management method for a cross-border logistics platform, including: Receives an instruction to copy the original order, which is in a failed, canceled, or abnormally locked state; In response to the instruction, initiate order copying processing, which includes: Remove all tracking codes and identifiers generated by the platform's internal system from the original order, and generate internal tracking codes and identifiers for the newly created order that are not internally associated with the original order; Receive status update information from an external system, which includes the master tracking code and at least one identifier; Initiate the identifier verification process to check whether at least one identifier carried in the status update information is cross-related with the internal tracking code of an invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order. Once ghost order information contamination is detected, status update information will be refused to be written to any order record.

[0007] Through this technical solution, this application can effectively identify and prevent "ghost order" information pollution, solve the problem of internal tracking code confusion caused by order duplication from the source, ensure the accuracy of order status, and thus improve the operational efficiency and customer trust of cross-border logistics platforms.

[0008] Furthermore, in the identifier verification process, it is checked whether at least one identifier carried in the status update information has a cross-association with the internal tracking code of an invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order, including: Parse at least one identifier carried in the state update information, and identify the sub-identifier types and hierarchical relationships contained within at least one identifier; Based on the sub-identifier type, select the corresponding matching strategy and select the corresponding abandoned identifier pool within the platform for cross-association detection; Compare the generation time of the sub-identifier type with the expiration time recorded in the obsolete identifier pool; By combining the detection results of the sub-identifier type and the matching of the main tracking code of the status update information with the main tracking code of the current active order, it can be determined whether there is ghost order information pollution.

[0009] Through this technical solution, this application can more accurately determine the pollution of ghost order information by refining identifier parsing and multi-dimensional cross-correlation detection, avoid misjudgment, and improve the accuracy of verification.

[0010] More specifically, in some implementations, the generation time of the sub-identifier type is compared with the expiration time recorded in the obsolete identifier pool, including: Acquire time zone differences between the platform and the external systems of new trading partners; Based on the time zone differences, the generation time of the sub-identifier type and the expiration time recorded in the discarded identifier pool are uniformly converted into the standard time base. Compare the generation time of the converted sub-identifier type with the expiration time recorded in the converted obsolete identifier pool.

[0011] This technical solution eliminates time errors caused by cross-time zone data transmission by using a unified time reference, ensuring the accuracy of time comparison and further improving the reliability of ghost order detection.

[0012] Building upon the above, this application further proposes selecting a corresponding matching strategy based on the sub-identifier type, including: Parse at least one identifier carried in the state update information and identify the type of sub-identifier contained within at least one identifier; When the identified sub-identifier type is not defined in the preset rule base, the format characteristics of the unknown sub-identifier are compared with the existing sub-identifier types to infer the semantics and purpose of the unknown sub-identifier. A matching strategy is generated based on the context position of the unknown sub-identifier in the state update information and the type of external system to which the state update information belongs. Record the unknown sub-identifier type and its matching strategy in the strategy queue to be reviewed, and trigger a manual alert; After manual review and confirmation, the unknown sub-identifier type and its matching strategy are added to the configurable matching strategy rule base. Based on the historical matching success rate of unknown sub-identifier types and human feedback, adjust the matching confidence threshold and strategy priority of the matching strategy; Adjust the stringency or parameters of the matching strategy based on the historical data quality assessment results of the identified sub-identifier types.

[0013] Through this technical solution, this application can achieve adaptive learning and strategy generation for unknown sub-identifier types, effectively cope with the complex and ever-changing identifier formats of new trading partners, and improve the robustness and scalability of the system.

[0014] Preferably, based on the historical matching success rate of unknown sub-identifier types and human feedback, the matching confidence threshold and strategy priority of the matching strategy are adjusted, including: Identify business scenario specificities for unknown sub-identifier types; Based on the specific business scenario, an initial matching strategy is set for the unknown sub-identifier type. This initial matching strategy includes: assigning a preset conservative matching strategy to the unknown sub-identifier type. This conservative matching strategy has a low matching confidence threshold and a medium strategy priority. Before the amount of matching data for unknown sub-identifiers reaches a preset minimum threshold, the matching results of unknown sub-identifiers are manually sampled and verified. The frequency of this manual sampling and verification is inversely proportional to the sparsity of the matching data. Based on the results of manual sampling and verification, the matching confidence threshold and strategy priority are gradually adjusted; Once the amount of matching data for unknown sub-identifiers reaches the preset minimum threshold, the matching confidence threshold and strategy priority of the matching strategy are dynamically adjusted based on the historical matching success rate of the unknown sub-identifier type and human feedback.

[0015] Through this technical solution, this application can gradually optimize the matching strategy of unknown sub-identifiers while ensuring matching accuracy through a progressive adjustment and manual review mechanism, thereby reducing the risk of misjudgment and improving the intelligence level of the system.

[0016] In one implementation, the matching confidence threshold and strategy priority of the matching strategy are dynamically adjusted based on the historical matching success rate of the unknown sub-identifier type and human feedback, including: Receive status update information and identify the data source system of the status update information; Calculate the current data quality confidence level of the data source system based on the historical data quality assessment results of the data source system; Based on the current data quality confidence level, the historical matching success rate data of unknown sub-identifiers is weighted to obtain the weighted historical matching success rate. Based on the weighted historical matching success rate and human feedback, and according to the current data quality confidence level, the matching confidence threshold and strategy priority of the matching strategy are dynamically adjusted.

[0017] Through this technical solution, this application can introduce the quality confidence of the data source system, weight the adjustment of the matching strategy, make the strategy adjustment more intelligent and accurate, and further improve the system's adaptability to external data quality fluctuations.

[0018] As a technical improvement, based on the historical data quality assessment results of the data source system, the current data quality confidence level of the data source system is calculated, including: Obtain a set of data quality assessment indicators from the data source system within a preset time window. This set of indicators includes data integrity rate, data accuracy rate, and data timeliness rate. The data quality assessment index set is subjected to time decay weighting processing, and data from more recent time points are assigned higher weights based on the interval between the data generation time and the current time. Based on the weighted set of data quality assessment indicators, the current data quality confidence level of the data source system is obtained.

[0019] Through this technical solution, this application can more comprehensively and dynamically evaluate the quality of the data source system by using multi-dimensional indicators and time decay weighting, providing a more reliable basis for subsequent strategy adjustments.

[0020] As a further improvement, the data quality assessment index set is subjected to time decay weighting processing. Based on the interval between the data generation time and the current time, data from more recent time points are assigned higher weights, including: Obtain the original generation timestamp of each data point in the data quality assessment indicator set; Compare the original generation timestamp with the timestamp of the data point entering the platform system; If the data exceeds the preset threshold, the data point will be marked as backtracking or retransmission. For data points marked as backtracking or retransmission data, a time decay weighting function is used to calculate the weight based on the interval between the original generated timestamp and the current time. This time decay weighting function assigns a low initial weight and a faster decay rate to backtracking or retransmission data. For data points not marked as backtracking or retransmission data, the weights are calculated using a time decay weighting function based on the interval between the data generation time and the current time.

[0021] Through this technical solution, this application can distinguish between normal data and retrospective / retransmission data, and adjust the weights accordingly, effectively reducing the negative impact of historical or delayed data on the current data quality assessment, and improving the real-time performance and accuracy of the assessment.

[0022] To enhance functionality, for data points marked as backtracking or retransmission data, a time decay weighting function is used to calculate weights based on the interval between the original generation timestamp and the current time, including: Obtain the business scenario type to which the data points marked as backtracking or retransmission data belong. The business scenario type includes at least one of customs declaration, logistics node, and payment settlement. Based on the business scenario type, the corresponding sensitivity parameters are obtained from the preset business scenario sensitivity configuration. These sensitivity parameters include the data importance level and real-time requirements. Based on the importance level of the data and the real-time requirements, the initial weights and decay rate in the time decay weighting function are dynamically adjusted, and the weights are calculated using the time decay weighting function based on the interval between the original generated timestamp and the current time. When the business scenario type is customs declaration, the initial weight is increased and the decay rate is decreased according to the compliance requirements of customs declaration data. When the business scenario type is logistics node, the initial weight is moderately adjusted according to the timeliness requirements of logistics node data, and the decay rate is also moderately adjusted. When the business scenario is payment settlement, the initial weight is lowered and the decay rate is increased based on the accuracy requirements of the payment settlement data.

[0023] Through this technical solution, this application can finely adjust the weight calculation of backtracking / retransmission data according to the characteristics of different business scenarios, ensuring the rationality and effectiveness of data quality assessment under different business scenarios, and further improving the business adaptability of the system.

[0024] Secondly, this application also discloses an order management system for a cross-border logistics platform, comprising: The response end is used to receive instructions to copy the original order, which is in a failed, canceled, or abnormally locked state; respond to the instructions and start the order copying process, which includes: clearing all tracking codes and identifiers generated by the platform's internal system in the original order, generating internal tracking codes and identifiers for the newly created order that are not internally associated with the original order; and receiving status update information from an external system, which includes the master tracking code and at least one identifier. The detection end is used to initiate the identifier verification process, to detect whether at least one identifier carried in the status update information is cross-related with the internal tracking code of an invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order. The processing end is used to refuse to write status update information to any order record once ghost order information contamination is detected.

[0025] Through this technical solution, this application can achieve automated detection and blocking of ghost order information pollution through systematic module design, providing a stable and reliable order management foundation for cross-border logistics platforms and effectively improving the overall robustness and data consistency of the system.

[0026] Beneficial effects The order management method for a cross-border logistics platform disclosed in this application first removes all tracking codes and identifiers generated by the platform's internal system from the original order after receiving an instruction to copy the original order. Then, it generates internal tracking codes and identifiers for the newly created order that are not internally related to the original order. This severs the internal association between the old and new orders at the source, effectively avoiding the problem of incorrect inheritance of internal tracking codes due to order copying. Building on this, this application further introduces an identifier verification process. By detecting whether the identifiers carried in external status update information have cross-related relationships with the internal tracking codes of cancelled or failed orders within the platform, and determining whether the master tracking code does not match the master tracking code of the currently active order, it accurately identifies "ghost order" information pollution. Once pollution is detected, the system will refuse to write status update information to any order record, thus completely blocking the interference of erroneous information on the order status.

[0027] Compared to existing technologies, the solution proposed in this application effectively addresses the "ghost order" problem caused by manual intervention and deficiencies in order duplication functionality, preventing the incorrect updating of new order status information onto already invalidated order records. By eliminating internal association identifiers and introducing a multi-dimensional verification mechanism, this application fundamentally ensures the accuracy and consistency of order data, enabling the platform to accurately understand the true status of orders. This effectively improves the order management efficiency of cross-border logistics platforms, reduces customer complaint rates, and enhances user experience. Attached Figure Description

[0028] Figure 1 This is a flowchart of an order management method for a cross-border logistics platform provided by an embodiment of the present invention; Figure 2 This is a flowchart of a method for detecting status update information provided in an embodiment of the present invention; Figure 3 This is a flowchart of a method for comparing sub-identifier types provided in an embodiment of the present invention; Figure 4 This is a system architecture diagram of order management for a cross-border logistics platform provided in this application. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Reference Figure 1 , Figure 1 This is a flowchart of an order management method for a cross-border logistics platform provided by an embodiment of the present invention, including the following steps: S1 receives an instruction to copy the original order, which is in a failed, canceled, or abnormally locked state. S2, responding to the instruction, initiates order copying processing. Order copying processing includes: clearing all tracking codes and identifiers generated by the platform's internal system in the original order, and generating internal tracking codes and identifiers for the newly created order that are not internally associated with the original order. S3, receive status update information from an external system, the status update information including the master tracking code and at least one identifier; S4, initiate the identifier verification process to check whether at least one identifier carried in the status update information is cross-related with the internal tracking code of an invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order. S5, once ghost order information contamination is detected, refuses to write status update information to any order record.

[0031] This application effectively solves the "ghost order" problem that may occur when traditional cross-border logistics platforms handle complex order status updates by introducing a mechanism for clearing and regenerating tracking codes and identifiers in order duplication processing, as well as an innovative identifier verification process, thereby significantly improving data accuracy and system stability.

[0032] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0033] "Original orders" refer to order records on cross-border logistics platforms that failed to circulate normally or have been terminated due to various reasons (such as customs declaration failure, user cancellation, system abnormal lock-up, etc.). Although these orders are no longer active, their historical data may still exist in the system.

[0034] "Track codes" and "identifiers" are key pieces of information used to uniquely identify and track the status of an order at each stage of the logistics chain. Track codes are typically the primary identification code for an order, while identifiers can be various auxiliary identification information related to the order, such as package numbers, waybill numbers, customs declaration numbers, etc.

[0035] "Tracking codes and identifiers generated by the platform's internal system" refers to internal identification codes created and assigned by the cross-border logistics platform itself during the order processing process, used for order status management and circulation within the platform.

[0036] "External systems" refer to third-party systems that interact with the cross-border logistics platform, such as customs systems, carrier systems, and payment systems. These systems send order status updates to the platform. "Ghost order information contamination" refers to status updates sent by external systems that carry identifiers associated with the internal tracking codes of cancelled or failed orders within the platform, while their master tracking codes do not match the master tracking codes of currently active orders. This can cause these updates to incorrectly affect non-target orders, resulting in data corruption.

[0037] The order management method for cross-border logistics platforms proposed in this application aims to solve the "ghost order" problem caused by imperfect order duplication and status update mechanisms in existing technologies. The main features of this method will be described in detail below.

[0038] First, regarding the feature "receiving an instruction to copy the original order, where the original order is in a failed, canceled, or abnormally locked state." In practice, when an original order cannot continue its normal flow due to various reasons (such as customs declaration failure, user-initiated cancellation, or system locking of the order due to abnormal circumstances), the platform operator may need to quickly create a new order based on the information of the original order. At this time, the system will receive an instruction from the user to copy the original order. For example, the operator can click the "Copy Order" button through the user interface or send a corresponding copy request through the API interface. Receiving this instruction is the starting point for order copying processing.

[0039] Secondly, regarding the feature of "responding to the instruction and initiating order copying processing, which includes: clearing all tracking codes and identifiers generated by the platform's internal system in the original order, and generating internal tracking codes and identifiers for the newly created order that are not internally associated with the original order," the system initiates the order copying process upon receiving the copy instruction. The key to this process is the thorough clearing of the internal tracking codes and identifiers of the original order, and the generation of entirely new internal tracking codes and identifiers for the newly created order that are not internally associated with the original order. For example, when an original order is locked due to a customs declaration failure, and the operator decides to copy the order for re-declaration, the system, when creating the new order, will first clear all internal identifiers such as waybill numbers and package tracking numbers automatically generated by the platform's internal system in the original order. Subsequently, the system will assign a completely new and independent set of internal waybill numbers and package tracking numbers to this new order, ensuring that the identification and tracking of the new order within the platform are completely independent of the original order, avoiding data association errors caused by confusion of internal identifiers.

[0040] Secondly, regarding the feature of "receiving status update information from external systems, including the master tracking code and at least one identifier," during order processing, cross-border logistics platforms need to interact with multiple external systems to obtain the latest order status. These external systems may include carriers' logistics tracking systems, customs declaration feedback systems, payment institutions' transaction status systems, etc. When an order status changes in an external system, it will send status update information to the platform. For example, when a package is scanned at a logistics node, the carrier's system will send a status update message to the platform, which includes the package's master tracking code (e.g., international waybill number) and possibly sub-identifiers (e.g., internal barcode, destination country customs code, etc.).

[0041] Finally, regarding the feature of "initiating an identifier verification process to detect whether at least one identifier carried in the status update information is cross-correlated with the internal tracking code of an invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order; once ghost order information contamination is detected, the status update information is refused to be written to any order record," this is one of the core innovations of this application, aiming to proactively identify and prevent "ghost order information contamination." Upon receiving status update information from an external system, the platform immediately initiates an identifier verification process. This process performs two key checks: First, it checks whether at least one identifier carried in the status update information is cross-correlated with the internal tracking code of an invalid or failed order within the platform. For example, if the status update information sent by the external system contains a package number that previously belonged to the internal tracking code of a cancelled old order, this check will be triggered. Second, it checks whether the main tracking code of the status update information does not match the main tracking code of the currently active order. For example, if the main tracking code sent by the external system is inconsistent with the main tracking code of any currently active order within the platform, this check will be triggered. The system will only determine that "ghost order information contamination" has been detected when both of these checks meet specific conditions (i.e., there is cross-correlation and the master tracking code does not match). Once such contamination is detected, the system will immediately refuse to write the status update information to any order record, thereby effectively preventing erroneous information from interfering with and damaging the platform's internal order data.

[0042] The order management method for cross-border logistics platforms proposed in this application forms a complete and efficient solution through the synergistic effect of the aforementioned technical features, aiming to solve the "ghost order" problem that may occur when traditional cross-border logistics platforms handle complex order status updates.

[0043] The core innovation of this application lies in the introduction of a mechanism for clearing and regenerating tracking codes and identifiers during order duplication processing, as well as an innovative identifier verification process. Specifically, during order duplication, this application explicitly requires the clearing of all tracking codes and identifiers generated by the platform's internal system from the original order, and the generation of entirely new internal tracking codes and identifiers for the newly created order that are not internally related to the original order. This mechanism severs the potential association between the old and new orders in terms of internal identifiers at the source, effectively avoiding internal data confusion caused by the duplication operation.

[0044] Furthermore, the identifier verification process proposed in this application forms a dual verification mechanism by simultaneously detecting whether the identifier carried in the status update information has any cross-correlation with the internal tracking codes of cancelled or failed orders within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order. Once both conditions are met, it is determined to be "ghost order information contamination," and the system will immediately refuse to write the status update information into any order record. This ability to proactively identify and reject contaminated information is not available in existing technologies. Traditional order management methods usually rely on simple tracking code matching, which cannot effectively distinguish the internal correlation between new and old orders, nor can it identify "ghost" updates of cancelled orders by erroneous or outdated data sent from external systems.

[0045] Therefore, the method of this application can effectively prevent "ghost orders" caused by problems such as residual historical order data, delays or erroneous transmission of data from external systems, and significantly improve the accuracy, consistency and reliability of order data on cross-border logistics platforms. This improvement not only optimizes the platform's internal data management and reduces the cost of manual intervention and error handling, but more importantly, it enhances customers' trust in the accuracy of the platform's data, thereby improving the overall user experience and operational efficiency.

[0046] For details, please refer to Figure 2 , Figure 2 Here is a flowchart of a method for detecting status update information provided in an embodiment of the present invention: S41, parse the at least one identifier carried in the state update information, and identify the sub-identifier types and hierarchical relationships contained within the at least one identifier; S42, based on the sub-identifier type, select the corresponding matching strategy and select the corresponding abandoned identifier pool within the platform for cross-association detection; S43, compare the generation time of the sub-identifier type with the expiration time recorded in the discarded identifier pool; S44. Based on the detection results of the sub-identifier type and the matching situation between the master tracking code of the status update information and the master tracking code of the current active order, determine whether there is ghost order information pollution.

[0047] Specifically, when the platform receives status update information from an external system, it first needs to perform deep parsing on at least one identifier carried within it. This parsing process aims to identify the internal structure of these identifiers, including their specific sub-identifier types (e.g., tracking numbers, customs declaration numbers, payment transaction numbers, etc.) and the hierarchical relationships between these sub-identifiers (e.g., a main tracking number may contain multiple sub-parcel tracking numbers). This meticulous parsing lays the foundation for subsequent accurate verification.

[0048] After identifying the sub-identifier type, the system dynamically selects a preset matching strategy based on that type. Different sub-identifier types may have different encoding rules, length limits, or verification algorithms, thus requiring customized matching strategies to improve detection accuracy. Simultaneously, the system selects a corresponding discarded identifier pool from multiple discarded identifier pools maintained internally by the platform, based on the sub-identifier type. For example, if it's a tracking number, it will select the discarded tracking number pool for comparison; if it's a payment transaction number, it will select the discarded payment transaction number pool. This aims to narrow the detection scope and improve detection efficiency.

[0049] In practical applications, to further improve the accuracy of detection, the system also compares the generation time of the sub-identifier with the expiration time recorded in the discarded identifier pool. The purpose of this step is to determine whether the identifier logically falls into the category of expired or invalidated. For example, if an identifier's generation time is much earlier than its recorded expiration time in the discarded identifier pool, its likelihood of being a "ghost order" increases significantly.

[0050] Ultimately, the system will comprehensively evaluate the above detection results. This includes the cross-correlation detection results between the sub-identifier and the discarded identifier pool, the time comparison results, and whether the master tracking code of the status update information matches the master tracking code of the currently active order. Only when all relevant conditions meet the characteristics of a "ghost order," such as a correlation between the sub-identifier and the discarded pool and a mismatch in the master tracking code, will it be ultimately determined that ghost order information pollution exists.

[0051] This application's solution, through refined analysis of identifiers in status update information, accurately identifies the types and hierarchical relationships of their internal sub-identifiers, thus providing a data foundation for subsequent precise matching and detection. Specifically, by deeply analyzing the internal structure and hierarchical relationships of identifiers and selecting customized matching strategies and obsolete identifier pools based on their types, the efficiency and accuracy of cross-correlation detection are greatly improved. Furthermore, the introduction of a time-dimensional comparison enables the system to more effectively identify identifiers that have become invalid or obsolete over time, thereby enhancing its ability to identify potential ghost orders. Finally, by integrating multiple detection results, this application provides a more robust and reliable mechanism for judging ghost order information contamination, effectively preventing erroneous status update information from being written into order records and ensuring the accuracy and consistency of order data on cross-border logistics platforms.

[0052] For details, please refer to Figure 3 , Figure 3 This is a flowchart of a method for comparing sub-identifier types provided in an embodiment of the present invention.

[0053] S431, obtain the time zone difference configuration between the platform and the external systems of new trading partners; S432, Based on the time zone difference configuration, the generation time of the sub-identifier type and the expiration time recorded in the discarded identifier pool are uniformly converted into a standard time base; S433, compare the generation time of the converted sub-identifier type with the expiration time recorded in the converted discarded identifier pool.

[0054] Specifically, "obtaining the time zone difference configuration between the platform and the external systems of new trading partners" refers to the system querying or calculating the difference between its own time zone and the time zone of the external system providing status update information before performing time comparison. This difference configuration can be pre-stored in a database or dynamically obtained through protocol negotiation with the external system. Its purpose is to eliminate time discrepancies caused by different time zones and ensure the accuracy of subsequent time conversions.

[0055] Furthermore, "converting the generation time of the sub-identifier type and the expiration time recorded in the discarded identifier pool to a standard time base according to the time zone difference configuration" means that after acquiring the time zone difference, the system will convert the generation time of the sub-identifier parsed from the status update information and the expiration time of the corresponding identifier retrieved from the platform's internal discarded identifier pool to a common standard time base, such as Coordinated Universal Time (UTC) or Greenwich Mean Time (GMT). This conversion step is crucial to ensuring the comparability of time data from different sources.

[0056] Therefore, "comparing the generation time of the converted sub-identifier type with the expiration time recorded in the converted obsolete identifier pool" means that after all relevant time data has been unified to a standard time base, the system will directly perform a precise time point comparison. In this way, it is possible to accurately determine whether the generation time of the sub-identifier falls within or is earlier than the expiration time of the obsolete identifier, thus providing a reliable time dimension basis for the detection of ghost order information pollution.

[0057] This application's solution addresses the issue of inaccurate timestamp comparisons in cross-border logistics scenarios, where different countries or regions may use different time zones due to the introduction of time zone difference configuration and unified time base conversion. In the identifier verification process, directly comparing timestamps without time zone calibration may incorrectly classify an actually expired identifier as active, or vice versa, thus affecting the accuracy of detecting ghost order information contamination. By first acquiring and applying the time zone difference configuration, it ensures that all time points to be compared are accurately mapped to the same timeline, enabling subsequent comparisons between generation and expiration times to accurately reflect the identifier's lifecycle status and avoiding misjudgments caused by time zone differences.

[0058] This application further proposes an optimization scheme for selecting the corresponding matching strategy based on the sub-identifier type during the identifier verification process, especially for intelligent identification, strategy generation and dynamic adjustment of unknown sub-identifier types, so as to enhance the adaptability and robustness of the system.

[0059] Specifically, the above-mentioned selection of the corresponding matching strategy based on the sub-identifier type includes: Parse the at least one identifier carried in the state update information and identify the sub-identifier types contained within the at least one identifier; When the identified sub-identifier type is a type not defined in the preset rule base, the format characteristics of the unknown sub-identifier are compared with the existing sub-identifier types to infer the semantics and purpose of the unknown sub-identifier. A matching strategy is generated based on the context position of the unknown sub-identifier in the state update information and the type of external system to which the state update information belongs; Record the unknown sub-identifier type and its matching strategy in the policy queue to be reviewed, and trigger a manual alert; After manual review and confirmation, the unknown sub-identifier type and its matching strategy are added to the configurable matching strategy rule base; Based on the historical matching success rate and human feedback of the unknown sub-identifier type, adjust the matching confidence threshold and strategy priority of the matching strategy; The stringency or parameters of the matching strategy are adjusted based on the historical data quality assessment results of the identified sub-identifier types.

[0060] The purpose of parsing the at least one identifier carried in the status update information is to accurately extract key sub-identifiers constituting order tracking and identification from complex external status update information. This can be achieved through predefined parsing rules, regular expression matching, or machine learning-based text analysis techniques to ensure that all potential identifiers are correctly identified. Identifying the types of sub-identifiers contained within the at least one identifier refers to classifying the parsed identifiers and determining their business meaning or technical format, such as waybill numbers, batch numbers, parcel numbers, customs declaration numbers, etc.

[0061] When the identified sub-identifier type is not defined in the preset rule base, the system will not immediately reject the status update information, but will instead initiate an intelligent identification process. Specifically, the format characteristics of the unknown sub-identifier are compared with existing sub-identifier types, such as by comparing string length, character composition rules, and delimiter patterns, to infer the semantics and purpose of the unknown sub-identifier. For example, if the format of an unknown identifier is highly similar to that of a known waybill number, the system can initially infer that it may be a waybill number.

[0062] Furthermore, a matching strategy is generated based on the contextual position of the unknown sub-identifier in the status update information and the external system type to which the status update information belongs. The contextual position can provide clues about the identifier's purpose; for example, if it appears in the "package information" field, it may be related to a package. The external system type helps to limit the matching scope; for example, for an unknown identifier from a specific logistics carrier, the matching strategy should prioritize that carrier's internal identifier pool. The generated matching strategy may include a matching algorithm, matching fields, and an initial matching confidence threshold.

[0063] To ensure the accuracy and security of the new strategy, the unknown sub-identifier type and its matching strategy are recorded in a strategy queue awaiting review, and a manual alert is triggered. This allows human experts to review and verify the newly identified identifier type and the system-generated matching strategy to prevent automation errors. After manual review and confirmation, the unknown sub-identifier type and its matching strategy are added to the configurable matching strategy rule base, thereby expanding the platform's recognition capabilities.

[0064] Furthermore, to make the matching strategy more adaptable, the matching confidence threshold and strategy priority of the matching strategy are adjusted based on the historical matching success rate and human feedback of the unknown sub-identifier types. For example, if the matching success rate of a certain unknown sub-identifier type remains high after manual review, its matching confidence threshold and strategy priority can be appropriately increased, allowing it to be considered earlier or more strictly in subsequent verifications. Conversely, if the matching success rate is low or there are problems with human feedback, its priority can be reduced or its threshold adjusted. Simultaneously, the strictness or parameters of the matching strategy are adjusted based on the historical data quality assessment results of the identified sub-identifier types. For example, for external systems with poor historical data quality, their identifier matching strategy may be set to a stricter mode to reduce false positives.

[0065] This application's solution effectively solves the verification blind spot problem caused by the inability to recognize new identifiers when facing constantly changing external data formats, by introducing an intelligent identification, strategy generation, and dynamic adjustment mechanism for unknown sub-identifier types. Through the above technical solution, this application can significantly improve the robustness and adaptability of cross-border logistics platform order management methods in identifier verification. The system is no longer limited to a preset identifier rule base but has the ability to identify, learn, and process unknown sub-identifier types, thereby effectively avoiding the problems of ghost order information pollution and omission or erroneous rejection of legitimate updates due to the emergence of new identifier types. This not only improves the accuracy and security of order status updates and reduces the frequency and workload of manual intervention, but also enables the entire order management system to better cope with complex and dynamically changing cross-border logistics business scenarios, ensuring smooth and reliable data flow.

[0066] In some preferred embodiments, a specific example is given below. Suppose a cross-border e-commerce platform partners with a new overseas warehousing service provider, which introduces a new package batch number format in its order status update information, such as "WH-BATCH-XYZ-20231026-001". The platform's existing identifier rule base does not contain this "WH-BATCH" type sub-identifier.

[0067] When the platform receives a status update message containing this unknown sub-identifier: First, the system will parse the status update information, identify the sub-identifier "WH-BATCH-XYZ-20231026-001", and determine that its type is a type not defined in the preset rule base.

[0068] Next, the system will compare the format characteristics of "WH-BATCH-XYZ-20231026-001" (e.g., starting with "WH-BATCH" and containing a date and serial number) with existing sub-identifier types. Through comparison, the system may infer that its semantics are "warehouse batch number," used to identify a batch of packages.

[0069] Then, based on the contextual position of the unknown sub-identifier in the status update information (e.g., appearing in the "Package Information" field) and the type of external system it belongs to (e.g., "Overseas Warehousing Service Provider A"), the system generates an initial matching strategy. This strategy may instruct the system to perform cross-correlation checks on this identifier against the internal batch number pool of "Overseas Warehousing Service Provider A" during verification.

[0070] Subsequently, the "WH-BATCH" unknown sub-identifier type and its generated matching strategy will be recorded in the strategy queue awaiting review, triggering a manual alert. Platform operators or technical experts will be notified to conduct a manual review of the new type and strategy.

[0071] After manual review confirms that the strategy is effective and secure, the "WH-BATCH" unknown sub-identifier type and its matching strategy will be added to the configurable matching strategy rule base, enabling the system to identify and process such batch numbers from the overseas warehousing service provider.

[0072] Subsequently, the system will continuously track the historical matching success rate of the "WH-BATCH" type identifier. If its matching success rate remains high and human feedback is positive, the system will dynamically adjust the matching confidence threshold and strategy priority of the matching strategy based on the historical matching success rate and human feedback of the unknown sub-identifier type. For example, it may increase its priority so that it is given higher priority during verification. Simultaneously, if the historical data quality assessment results of "Overseas Warehousing Service Provider A" show that its data accuracy is high, the system will also adjust the strictness of the matching strategy accordingly, enabling more efficient verification while ensuring accuracy.

[0073] In some of the embodiments described above in this application, although the matching confidence threshold and strategy priority of the matching strategy are adjusted based on the historical matching success rate and manual feedback of the unknown sub-identifier type, in the implementation process, the historical matching success rate data may be insufficient or unstable for newly emerging or sparsely populated unknown sub-identifier types. Directly adjusting based on limited historical data may lead to overly aggressive or conservative adjustments to the matching strategy, thereby affecting the accuracy and stability of ghost order information pollution detection. Therefore, this application further proposes a more robust and adaptive adjustment mechanism for fine-grained management of the matching confidence threshold and strategy priority of the matching strategy at different data maturity stages.

[0074] The above adjustments to the matching confidence threshold and strategy priority based on historical matching success rates and human feedback for unknown sub-identifier types specifically include: Identify the business scenario specificity of the unknown sub-identifier type; Based on the specificity of the business scenario, an initial matching strategy is set for the unknown sub-identifier type. The initial matching strategy includes: assigning a preset conservative matching strategy to the unknown sub-identifier type. The conservative matching strategy has a low matching confidence threshold and a medium strategy priority. Before the amount of matching data for the unknown sub-identifier reaches a preset minimum threshold, the matching results of the unknown sub-identifier are manually sampled and reviewed. The frequency of the manual sampling and review is inversely proportional to the sparsity of the matching data. Based on the results of the manual sampling review, the matching confidence threshold and strategy priority are progressively adjusted. Once the amount of matching data for the unknown sub-identifier reaches a preset minimum threshold, the matching confidence threshold and strategy priority of the matching strategy are dynamically adjusted based on the historical matching success rate of the unknown sub-identifier type and human feedback.

[0075] Specifically, the business scenario specificity of identifying unknown sub-identifier types means that when the system identifies a new or undefined sub-identifier type, it analyzes the business processes, data flows, and potential risk levels that may be associated with it. For example, one sub-identifier may be related to payment transactions, while another may be related to customs declarations. These different business scenarios have different requirements for data accuracy and real-time performance.

[0076] The initial matching strategy, tailored to the specific business scenario, and the allocation of a pre-defined conservative matching strategy aim to provide a safe and cautious initial processing solution for unknown sub-identifier types in the absence of sufficient historical data. A low matching confidence threshold means that the system will adopt a more lenient matching standard when determining whether the unknown sub-identifier is associated with a discarded order, making it more likely to trigger manual review or be marked as potential contamination to avoid missed detections. A medium strategy priority ensures that the verification of the unknown sub-identifier is not completely ignored, nor does it excessively interfere with the normal processing flow of other known identifiers.

[0077] In practical applications, before the amount of matching data for unknown sub-identifiers reaches a preset minimum threshold, the matching results are manually sampled and reviewed. The purpose is to compensate for the initial lack of data through human intervention, ensuring the accuracy of the system during the learning phase. The frequency of manual sampling and review is inversely proportional to the sparsity of the matching data; that is, when the data volume is very small, the proportion of manual review will be higher to ensure that each matching result has undergone rigorous examination; as the data volume increases, the frequency of manual review can be appropriately reduced.

[0078] Furthermore, the gradual adjustment of the matching confidence threshold and strategy priority based on manual sampling and review results means that the system will adjust these parameters gradually and incrementally based on feedback from manual reviews. For example, if manual review finds that the matching accuracy of a certain unknown sub-identifier is very high, its matching confidence threshold can be slightly increased, making it easier to be confirmed as a valid match in subsequent automatic verification. This gradual adjustment helps the system accumulate experience and gradually improve the accuracy of automated processing.

[0079] Once the amount of matching data for unknown sub-identifiers reaches a preset minimum threshold, the system dynamically adjusts the matching confidence threshold and strategy priority of the matching strategy based on its accumulated historical matching success rate and continuous human feedback. This means the system can utilize more abundant data for statistical analysis and combine it with professional human judgment to achieve more accurate and adaptive strategy optimization, thereby improving the efficiency of automated processing while ensuring accuracy.

[0080] This application's solution addresses the issue of inaccuracy or instability that can result from directly adjusting strategies based on limited data when sufficient historical data is lacking. By introducing the identification of business scenario specificities for unknown sub-identifier types and setting an initial conservative matching strategy accordingly, this solution effectively resolves the problem of insufficient accuracy and stability in matching strategy adjustments when dealing with newly emerging or sparsely populated unknown sub-identifier types. Specifically, by identifying business scenario specificities and setting a conservative initial matching strategy, the system can process unknown sub-identifiers in a safer and more cautious manner in the early stages, significantly reducing the risk of misjudgment. Furthermore, the introduced manual sampling review mechanism, especially increasing the review frequency when data is sparse, ensures high-quality feedback during the learning phase, providing a reliable basis for subsequent strategy adjustments. This combination of gradual and dynamic adjustment allows the matching strategy to be continuously optimized with the accumulation of data and feedback from human experience, significantly improving the detection accuracy and system adaptability of cross-border logistics platforms when facing new or complex ghost order information contamination, ensuring the purity of order data and the smoothness of business processes.

[0081] In some preferred embodiments, a specific example is given below. Suppose that when a cross-border logistics platform is processing status update information from a new trading partner country, it identifies a sub-identifier type it has never seen before, such as "Customs Pre-screening Number". Because this is a new type, the system first identifies its business scenario specificity and determines that it is associated with the highly sensitive business scenario of "Customs Declaration". Based on this, the system sets an initial conservative matching strategy for "Customs Pre-screening Number", for example, setting its matching confidence threshold to a low 0.3 and its strategy priority to medium.

[0082] Initially, when the platform receives status updates containing "customs pre-approval numbers" and the amount of matching data has not yet reached the preset minimum threshold (e.g., 1000 entries), the system will conduct frequent manual sampling reviews of these matching results. For example, if only 10 data entries containing this identifier are received per day, the system may manually review 50% of them. Manual reviewers will check whether the system's matching judgments for these "customs pre-approval numbers" are accurate, such as whether it correctly identifies their association with cancelled orders.

[0083] Based on the results of manual sampling and review, the system will make gradual adjustments. If manual review finds a high accuracy rate in matching "Customs Pre-screening Number," the system may gradually increase the matching confidence threshold from 0.3 to 0.4. Conversely, if a high false alarm rate is found, the threshold may be further lowered or the priority adjusted.

[0084] Once the number of matching data entries containing "Customs Pre-screening Number" reaches the minimum threshold of 1000, the system will switch to dynamic adjustment mode. At this point, the system will dynamically adjust the matching confidence threshold and strategy priority based on the historical matching success rate of the "Customs Pre-screening Number" (e.g., by statistically analyzing the accuracy of the past 1000 data entries) and continuous human feedback, combined with the specific characteristics of the business scenario. For example, if the historical success rate remains stable above 95%, the system may raise the matching confidence threshold to 0.7 and increase its strategy priority, allowing it to play a greater role in automatic verification, thereby improving processing efficiency while ensuring accuracy.

[0085] In some embodiments of this application, the dynamic adjustment of the matching confidence threshold and strategy priority for matching strategies of unknown sub-identifier types is mainly based on the historical matching success rate and manual feedback of the unknown sub-identifier type. However, in practice, the data quality provided by different external systems may vary significantly. Simply treating the historical matching success rates of all data sources equally, without considering the reliability of their data quality, may lead to inaccurate evaluation of the matching performance of unknown sub-identifiers, thereby affecting the adjustment effect of the matching confidence threshold and strategy priority. For example, an external system with low data quality, even if it reports a high historical matching success rate for a certain unknown sub-identifier, may have a misleading success rate due to the inaccuracy of its data itself. If the above problems are not addressed, the adjustment of the matching strategy may be less robust, and may even introduce new risks of "ghost order information contamination."

[0086] In response, this application proposes a more refined adjustment mechanism, which involves introducing the current data quality confidence level of the data source system to weight the historical matching success rate data of the unknown sub-identifier, thereby achieving dynamic adjustment of the matching confidence threshold and strategy priority.

[0087] The above-mentioned dynamic adjustment of the matching confidence threshold and strategy priority of the matching strategy based on the historical matching success rate of unknown sub-identifier types and human feedback includes: Receive status update information and identify the data source system of the status update information; Calculate the current data quality confidence level of the data source system based on the historical data quality assessment results of the data source system; Based on the current data quality confidence level, the historical matching success rate data of unknown sub-identifiers is weighted to obtain the weighted historical matching success rate. Based on the weighted historical matching success rate and human feedback, and according to the current data quality confidence level, the matching confidence threshold and strategy priority of the matching strategy are dynamically adjusted.

[0088] Specifically, the system for receiving and identifying the data source of status update information refers to the process by which a cross-border logistics platform, upon receiving status update information from an external system that includes the master tracking code and at least one identifier, parses the metadata of this information to determine its original sender. This sender could be a specific carrier system, customs system, payment institution system, or the system of another trading partner.

[0089] The calculation of the current data quality confidence level for a data source system, based on its historical data quality assessment results, can be understood as the platform maintaining a historical performance record of each external data source. These records may include key indicators such as data completeness, accuracy, and timeliness. The current data quality confidence level is a quantitative assessment of the current reliability of the data source, based on these historical assessment results and recent data performance. For example, if an external system's data accuracy has been declining over a period of time, its current data quality confidence level will decrease accordingly.

[0090] In practical applications, the historical matching success rate data of unknown sub-identifiers is weighted according to the current data quality confidence level to obtain a weighted historical matching success rate. This means that not all historical matching success rate data are treated equally. If the current data quality confidence level of a data source system is low, then the historical matching success rate data of unknown sub-identifiers contributed by that system will be given a lower weight in the calculation, and vice versa. For example, if the historical success rate of an unknown sub-identifier is 80% in system A (confidence level 0.9) and 90% in system B (confidence level 0.5), then the weighted success rate will be more biased towards the data from system A to reflect the higher reliability of system A.

[0091] Furthermore, based on the weighted historical matching success rate and human feedback, and according to the current data quality confidence level, the matching confidence threshold and strategy priority of the matching strategy are dynamically adjusted. This means that after comprehensively considering the matching success rate after weighting the data source quality and the feedback from human review, the system will adjust the matching strategy for the unknown sub-identifier in real time or periodically based on this information and the current quality confidence level of the data source. For example, if the weighted success rate is high and the human feedback is positive, and the data source system quality confidence level is also high, the matching confidence threshold can be appropriately lowered (making it easier to match) and the strategy priority increased; conversely, the threshold may be increased and the priority lowered to enhance the strictness of the matching.

[0092] This application's solution effectively addresses the problem of inaccurate strategy adjustments that may result from relying solely on historical matching success rates by introducing the current data quality confidence level of the data source system. Through this technical solution, the application significantly improves the accuracy and adaptability of adjusting matching strategies for unknown sub-identifiers in cross-border logistics platform order management. This solution avoids strategy misjudgments caused by differences in the quality of data source systems, enabling the platform to more intelligently adjust matching strategies when processing data from external systems of varying reliability, effectively reducing the risk of "ghost order information contamination." This not only improves the accuracy of order status updates but also reduces the need for manual intervention, enhancing overall operational efficiency and the automation level of data processing.

[0093] In some preferred embodiments, suppose the cross-border logistics platform receives status update information from two different external systems A and B, which includes an undefined, unknown sub-identifier type "X". The system will first identify that this information originates from system A and system B, respectively.

[0094] The platform calculates the current data quality confidence level of System A and System B based on their historical data quality assessment results. For example, System A has high historical data completeness, accuracy, and timeliness, so its current data quality confidence level is calculated to be 0.9; while System B has low historical data accuracy, so its current data quality confidence level is calculated to be 0.6.

[0095] Next, the system will obtain the historical matching success rate of the unknown sub-identifier type "X" in system A and system B. Assume that the historical matching success rate of "X" is 85% in system A and 95% in system B. These historical matching success rates are then weighted according to their respective data quality confidence levels. For example, the weighted historical matching success rate = (0.9 * 85% + 0.6 * 95%) / (0.9 + 0.6) = (76.5% + 57%) / 1.5 = 133.5% / 1.5 = 89%.

[0096] Finally, the system dynamically adjusts the matching strategy for the unknown sub-identifier type "X" based on this weighted historical matching success rate (89%), previous human feedback (e.g., human review indicating that "X" performs well in specific scenarios), and again in conjunction with the current data quality confidence levels of systems A and B. For example, due to the high weighted success rate and positive human feedback, while considering the overall quality of the data sources, the system may adjust the matching confidence threshold for "X" from an initial conservative value (e.g., 0.7) to 0.8, and change its strategy priority from "medium" to "high" to allow more "X" type identifiers from reliable sources to be effectively matched, while maintaining a certain level of vigilance towards "X" type identifiers from low-confidence sources.

[0097] In some of the embodiments described above in this application, a scheme is proposed to dynamically adjust the matching confidence threshold and strategy priority of the matching strategy based on the historical matching success rate of the data source system and human feedback. However, in its implementation, if the assessment of the current data quality confidence of the data source system is not comprehensive enough or the timeliness of the data is not fully considered, the calculated confidence may not accurately reflect the current true reliability of the data source system. For example, evaluating solely based on historical average data quality indicators without distinguishing the importance of new and old data may make the system slow to react to recent changes in data quality, thereby affecting the accuracy and timeliness of subsequent matching strategy adjustments.

[0098] In response, this application further proposes a method for calculating the current data quality confidence level of the data source system. This method comprehensively considers multi-dimensional data quality indicators and introduces time decay weighting processing to more accurately reflect the real-time reliability of the data source system.

[0099] The above calculation of the current data quality confidence level of the data source system based on the historical data quality assessment results of the data source system includes: Obtain a set of data quality evaluation indicators from the data source system within a preset time window. The set of indicators includes data integrity rate, data accuracy rate, and data timeliness rate. The data quality assessment index set is subjected to time decay weighting processing, and data from more recent time points are assigned higher weights based on the interval between the data generation time and the current time. Based on the weighted set of data quality assessment indicators, the current data quality confidence level of the data source system is obtained.

[0100] Specifically, the data quality assessment indicator set refers to a set of key performance indicators used to quantitatively evaluate the data quality of the data source system. Among them, data integrity rate can be understood as the proportion of non-empty or valid fields in data records, aiming to ensure that key information is not missing; data accuracy rate refers to the degree to which data matches real-world facts, for example, by comparing it with reference data or business rules, aiming to ensure the authenticity and reliability of the data; data timeliness rate refers to the time required for data from generation to being received and processed by the system, aiming to reflect the timeliness and freshness of the data. These indicators together constitute a comprehensive data quality assessment system.

[0101] The time-decay weighting of the data quality assessment index set refers to assigning higher weights to recent data and lower weights to older data when calculating data quality confidence scores. The purpose is to ensure that the data quality confidence scores more accurately reflect the current performance and reliability of the data source system, avoiding excessive influence of historical data on the current assessment. For example, exponential or linear decay functions can be used to implement this weighting, giving the highest weight to data from the most recent day and significantly reducing the weight of data from a month ago.

[0102] In practical applications, the current data quality confidence level of the data source system is obtained by summarizing various data quality assessment indicators (such as weighted completeness, accuracy, and timeliness) after time decay weighting processing. For example, through weighted averaging or a specific algorithm model, a single, representative confidence score is obtained. This score intuitively represents the overall data quality level of the current data source system.

[0103] This application's solution addresses the problem of inaccurate data quality confidence assessment caused by traditional methods that may fail to adequately consider data timeliness or have a single assessment dimension by acquiring a multi-dimensional set of data quality evaluation indicators from the data source system within a preset time window and applying time-decay weighting to these indicators. Through this technical solution, this application provides a more accurate and dynamic method for calculating the data quality confidence of the data source system. This method comprehensively considers multiple dimensions such as data completeness, accuracy, and timeliness, and combines this with time-decay weighting to effectively avoid confidence bias caused by a single data quality evaluation dimension or excessive influence from historical data. As a result, the system can obtain more timely and representative data quality confidence, enabling more accurate judgment of the reliability of external system data in subsequent matching strategy adjustments. This significantly improves the accuracy and response speed of detecting ghost order information pollution, reduces the false positive rate, and ensures the stability and data security of order management on cross-border logistics platforms.

[0104] In some preferred embodiments, suppose a cross-border logistics platform needs to assess the confidence level of its data quality relative to an external customs declaration system. The platform can set a preset time window, such as the last 30 days. Within this time window, the system continuously collects data integrity rate, data accuracy rate, and data timeliness rate from the customs declaration system. Specifically, data integrity rate can be calculated by checking the completion status of required fields in the daily received declaration data; data accuracy rate can be assessed by comparing the consistency between the declared data and the actual customs clearance results; and data timeliness rate can be recorded by recording the average time elapsed from when the customs system generates data to when the platform receives and processes it.

[0105] To ensure the real-time nature of the assessment, the platform applies time-decay weighting to the daily or hourly collected metrics data. For example, an exponential decay function can be used, giving today's data a weight of 1, yesterday's data a weight of 0.95, the day before yesterday's data a weight of 0.90, and so on, with older data having lower weights.

[0106] Finally, the weighted average of the data completeness rate, data accuracy rate, and data timeliness rate is calculated to obtain a comprehensive current data quality confidence score. For example, if the weighted completeness rate is 0.98, the accuracy rate is 0.95, and the timeliness rate is 0.90, and the platform assigns weights of 0.4, 0.4, and 0.2 to these three indicators respectively, then the current data quality confidence score might be calculated as (0.98 * 0.4) + (0.95 * 0.4) + (0.90 * 0.2) = 0.392 + 0.380 + 0.180 = 0.952. This confidence score of 0.952 will be used to guide subsequent adjustments to the matching strategy for the status update information of the customs declaration system. For example, if the confidence score falls below a certain threshold, the strictness of the matching strategy may be increased or manual review may be triggered.

[0107] In some embodiments described above, this application proposes a time-decay weighted processing method for the data quality assessment index set. Based on the interval between the data generation time and the current time, data from more recent times is assigned a higher weight to calculate the current data quality confidence level of the data source system. However, in actual cross-border logistics scenarios, data transmission from external systems may experience data backtracking or retransmission due to various reasons (such as network latency, system failures, or data synchronization requirements). If these "backtracked or retransmitted data" are treated the same as normal real-time data and subjected to the same time-decay weighted processing, it may lead to misjudgment of data timeliness, thereby affecting the accuracy of data quality confidence assessment. For example, data with an earlier original generation time but recently retransmitted, if weighted only based on its entry time into the platform system, may be incorrectly assigned a higher timeliness weight, leading to a bias in the overall quality assessment of the data source. This bias may weaken the accuracy of detecting ghost order information pollution. Therefore, this application further proposes an optimized scheme for the time-decay weighted processing of the aforementioned data quality assessment index set.

[0108] Specifically, the above-mentioned set of data quality assessment indicators undergoes time-decay weighting processing. Based on the interval between the data generation time and the current time, data from more recent time points are assigned higher weights, including: Obtain the original generation timestamp of each data point in the data quality assessment index set; Compare the original generation timestamp with the timestamp of the data point entering the platform system; If the data point exceeds a preset threshold, it will be marked as backtracking or retransmission. For data points marked as backtracking or retransmission data, a weight is calculated using a time decay weighting function based on the interval between the original generated timestamp and the current time. The time decay weighting function assigns a low initial weight and a faster decay rate to the backtracking or retransmission data. For data points not marked as backtracking or retransmission data, the weights are calculated using the time decay weighting function based on the interval between the data generation time and the current time.

[0109] The original generation timestamp refers to the actual time when the data was generated in the external system or its original business process, not the time when it was received by the platform system. The timestamp of entering the platform system refers to the time when the platform system first received the data. The preset threshold can be a time length, such as 5 minutes, 10 minutes, or longer, used to determine whether the data is "backtracked or retransmitted data". When the difference between the original generation timestamp and the timestamp of entering the platform system exceeds the preset threshold, it indicates that the data may not have been generated and transmitted in real time, but rather has undergone a certain delay before being backtracked or retransmitted.

[0110] The time decay weighting function can employ exponential decay, linear decay, or other nonlinear decay models, with the aim of assigning lower weights to older data points. For data points marked as backtracking or retransmission, the time decay weighting function is configured to give them low initial weights and faster decay rates. This means that even if their "timestamp of entering the platform system" is relatively recent, their weight calculated based on the "original generation timestamp" will decrease rapidly to reflect the true "freshness" of their data. Conversely, for data points not marked as backtracking or retransmission, a conventional time decay weighting function is used to calculate their weights based on the interval between their data generation time and the current time to reflect their normal timeliness.

[0111] This application's solution, by comparing the "original generation timestamp" of data points with the "timestamp of entering the platform system," can accurately identify "backtracked or retransmitted data" that is not generated and transmitted in real time. Through this technical solution, this application can process data from different sources and with varying timeliness characteristics more precisely. Especially when facing data backtracking or retransmission from external systems, it can effectively avoid distortion in overall data quality assessment caused by biases in judging data timeliness. Therefore, the calculated confidence level of the current data quality of the data source system will be more accurate and reliable, significantly improving the accuracy of subsequent detection of ghost order information contamination, reducing the risk of misjudgment or omission, and further enhancing the robustness and reliability of the cross-border logistics platform's order management method.

[0112] In some preferred embodiments, it is assumed that a cross-border logistics platform receives status update information from an external logistics partner. This information includes a data point A with an original generation timestamp of T1 and a data point B with an original generation timestamp of T2. The platform system receives data point A with a timestamp of T1' and data point B with a timestamp of T2'.

[0113] Specifically, if the difference between the original generation timestamp T1 of data point A and the timestamp T1' of entering the platform system is less than a preset threshold (e.g., 5 minutes), data point A is considered normal data. In this case, its weight will be calculated using a standard time decay weighting function based on the interval between T1 and the current time. However, if the difference between the original generation timestamp T2 of data point B and the timestamp T2' of entering the platform system exceeds a preset threshold (e.g., T2 is 2 hours earlier than T2'), data point B is marked as backtracked or retransmitted data. In this case, even if T2' is relatively new, its weight calculation will be based on the interval between T2 and the current time, using a time decay weighting function specifically designed for backtracked or retransmitted data, which has a low initial weight and a faster decay rate. For example, this function may decay the weight to a very low level in a short time to reflect the fact that it is not real-time data.

[0114] In this way, the platform can accurately distinguish the true timeliness of data point A and data point B, avoiding an inappropriate overestimation of the data quality confidence due to the resending of data B. As a result, the data quality confidence of the data source system calculated in the end is closer to the actual situation, providing a more solid data foundation for the identification of ghost orders.

[0115] In some embodiments described above in this application, when calculating the weights of data points marked as backtracking or retransmission data using a time decay weighting function, a lower initial weight and a faster decay rate are typically assigned. However, in actual cross-border logistics operations, data from different business scenarios have different importance levels and real-time requirements. If a uniform weighting strategy is applied to all backtracking or retransmission data, it may fail to accurately reflect its true data quality and impact on business operations. In particular, in the calculation of data quality confidence, it may lead to insufficient evaluation of critical business data (such as customs declaration data) or overly stringent evaluation of non-critical data (such as certain logistics node data), thereby affecting the accuracy and effectiveness of the overall data quality assessment.

[0116] In response, this application further proposes a method for calculating the weight of data points marked as backtracking or retransmission data using a time decay weighting function based on the interval between the original generated timestamp and the current time. Specifically, this method includes: Obtain the business scenario type to which the data points marked as backtracking or retransmission data belong, wherein the business scenario type includes at least one of customs declaration, logistics node, and payment settlement; Based on the business scenario type, obtain the corresponding sensitivity parameters from the preset business scenario sensitivity configuration. The sensitivity parameters include the data importance level and real-time requirements. Based on the data importance level and real-time requirements, the initial weights and decay rate in the time decay weighting function are dynamically adjusted, and the weights are calculated using the time decay weighting function based on the interval between the original generated timestamp and the current time. When the business scenario type is customs declaration, the initial weight is increased and the decay rate is decreased according to the compliance requirements of customs declaration data. When the business scenario type is a logistics node, the initial weight is moderately adjusted according to the timeliness requirements of the logistics node data, and the decay rate is moderately adjusted as well. When the business scenario type is payment settlement, the initial weight is adjusted downward and the decay rate is adjusted upward according to the accuracy requirements of payment settlement data.

[0117] Specifically, business scenario type refers to the specific business process or field associated with the data point. For example, customs declaration data involves compliance requirements such as import and export customs declaration and taxation; logistics node data involves various stages in the cargo transportation process (such as pickup, warehousing, transportation, and delivery); and payment settlement data is related to financial activities such as fund transfer and expense accounting. These different business scenarios have different emphases on the accuracy, timeliness, and importance of the data.

[0118] The business scenario sensitivity configuration can be understood as a predefined set of rules or parameters used to quantify the criticality of data in different business scenarios. Data importance level measures the impact of data on business operations and decision-making; for example, customs declaration data typically has the highest importance level because it directly affects customs clearance and legal compliance. Real-time requirements refer to the urgency with which data needs to be processed and reflected in the system after its generation; for example, updates to logistics nodes may require high real-time performance to ensure the accuracy of cargo tracking.

[0119] In practical applications, dynamically adjusting the initial weights and decay rate in the time decay weighting function means moving away from a single, fixed weighting pattern and instead adaptively adjusting it based on the business attributes of the data. For example, for customs declaration data, due to its high compliance and importance, even retrospective or retransmitted data requires a high degree of trust. Therefore, its initial weight is increased, and its decay rate is decreased to ensure it maintains a high level of influence in data quality assessment. Conversely, for payment settlement data, accuracy is crucial, but if the data is retrospective or retransmitted, it may indicate a decrease in timeliness. Therefore, the initial weight may be decreased, and the decay rate increased to more quickly reflect the quality decline caused by timeliness.

[0120] This application's solution identifies the business scenario type to which the backtracked or retransmitted data points belong and dynamically adjusts the initial weights and decay rate in the time decay weighting function based on the sensitivity parameters of that business scenario (including data importance level and real-time requirements). This technical solution enables more refined and reasonable weighting of backtracked or retransmitted data according to the characteristics of different business scenarios. This significantly improves the accuracy and reliability of data quality assessment, especially when processing cross-border logistics data with different importance levels and real-time requirements. The solution avoids assessment bias caused by a uniform weighting strategy, ensuring that the quality assessment of key business data (such as customs declaration data) fully reflects its business value, while also promptly identifying and reducing the weight of expired backtracked data with high timeliness requirements. As a result, the platform can obtain more realistic and business-guiding data quality confidence, thereby more effectively identifying and preventing ghost order information pollution and improving the robustness and decision support capabilities of the entire cross-border logistics order management system.

[0121] In some preferred embodiments, it is assumed that the platform receives a data point marked as backtracking data, whose original generation timestamp is much earlier than the current time.

[0122] Specifically, if the business scenario type of the data point is identified as "customs declaration," the system will, based on the preset business scenario sensitivity configuration, determine that the customs declaration data has a high importance level and medium real-time requirements. Based on this, the initial weight in the time decay weighting function will be increased, and the decay rate will be decreased. For example, the initial weight may be adjusted from 0.5 to 0.8, and the decay rate from 0.1 per day to 0.05 per day. This means that even if the data is retrospective, its influence in data quality assessment decreases more slowly, reflecting the long-term effectiveness of its compliance information.

[0123] For example, if another data point marked as retrospective data belongs to a business scenario type of "payment settlement," the system will identify it as having high accuracy and high real-time requirements. In this case, the initial weight in the time decay weighting function may be reduced to 0.4, and the decay rate increased to 0.2 per day. This reflects that once payment data expires or is retrospectively reviewed, its accuracy and timeliness value decreases rapidly, thus losing its influence more quickly in data quality assessment.

[0124] In this way, the system can intelligently adjust the weight of data in data quality assessment based on the actual business attributes of the data, thereby providing a more accurate data quality confidence level that better meets business needs.

[0125] In modern cross-border logistics platforms, order management is a core component, requiring efficient and accurate handling of the entire process from order creation to receipt. However, when a platform expands its business to new trading partner countries, the existing order management system faces significant challenges due to differences in national regulations, infrastructure, and information systems. For example, the complexity of customs declarations, tax calculations, and logistics node approvals can lead to chaotic data flow within the system and even the emergence of "ghost orders," severely impacting operational efficiency and customer trust.

[0126] Specifically, when a cross-border logistics platform frequently resorts to manual intervention to address the complexities of customs declarations, logistics interfaces, and payment settlements in new trading partners, operators sometimes use the "copy order" function to quickly process orders locked due to declaration failures. This function, not adequately designed for complex failure scenarios, may mistakenly copy the internal tracking code of the original failed order to the new order. When the new order receives a status update, the platform system may incorrectly prioritize or associate it with the legacy tracking code, causing the actual status information of the new order to be incorrectly updated on the already invalidated old order record. This confusion in order data association leads to a serious deviation in the platform's internal understanding of the true status of orders, creating the difficult-to-detect "ghost orders" problem. This fundamental data inconsistency prevents the platform from accurately assessing the overall timeliness and cost of orders, resulting in a significant increase in customer complaint rates and severely impacting the overall efficiency of the cross-border logistics platform's order management methods and user experience.

[0127] In this regard, refer to Figure 4 , Figure 4 This application provides a system architecture diagram for order management on a cross-border logistics platform, including: The response end is used to receive an instruction to copy the original order, which is in a failed, canceled, or abnormally locked state; in response to the instruction, it initiates an order copying process, which includes: clearing all tracking codes and identifiers generated by the platform's internal system in the original order, generating internal tracking codes and identifiers for the newly created order that are not internally associated with the original order; and receiving status update information from an external system, which includes the master tracking code and at least one identifier. The detection end is used to initiate the identifier verification process, detect whether the at least one identifier carried in the status update information is cross-associated with the internal tracking code of the invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order. The processing end is used to refuse to write the status update information to any order record once ghost order information contamination is detected.

[0128] This application proposes an order management system for a cross-border logistics platform. By modularizing key functions of the order management method into a response end, a detection end, and a processing end, it effectively prevents and handles the problem of "ghost orders" throughout the entire lifecycle of cross-border logistics orders. The system uses the response end to receive instructions and process order replication, ensuring the independence of the internal identifier of new orders; the detection end performs strict identifier verification on external status update information to identify potential ghost order information contamination; and the processing end promptly rejects the writing of contaminated information. Therefore, at the system level, it guarantees the accuracy and consistency of order data, significantly improving the operational efficiency and data reliability of the cross-border logistics platform.

[0129] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0130] "Original orders" refer to order records on cross-border logistics platforms that failed to circulate normally or have been terminated due to various reasons (such as customs declaration failure, user cancellation, system abnormal lock-up, etc.). Although these orders are no longer active, their historical data may still exist in the system.

[0131] "Track codes" and "identifiers" are key pieces of information used to uniquely identify and track the status of an order at each stage of the logistics chain. Track codes are typically the primary identification code for an order, while identifiers can be various auxiliary identification information related to the order, such as package numbers, waybill numbers, customs declaration numbers, etc.

[0132] "Tracking codes and identifiers generated by the platform's internal system" refers to internal identification codes created and assigned by the cross-border logistics platform itself during the order processing process, used for order status management and circulation within the platform.

[0133] "External systems" refer to third-party systems that interact with the cross-border logistics platform, such as customs systems, carrier systems, and payment systems. These systems send order status updates to the platform. "Ghost order information contamination" refers to status updates sent by external systems that carry identifiers associated with the internal tracking codes of cancelled or failed orders within the platform, while their master tracking codes do not match the master tracking codes of currently active orders. This can cause these updates to incorrectly affect non-target orders, resulting in data corruption.

[0134] The order management system for the cross-border logistics platform proposed in this application aims to solve the "ghost order" problem caused by imperfect order duplication and status update mechanisms in existing technologies. The main components of this system will be described in detail below.

[0135] First, regarding the feature of "the response end, used to receive instructions to copy the original order, which is in a failed, canceled, or abnormally locked state; responding to the instructions, initiating order copying processing, which includes: clearing all tracking codes and identifiers generated by the platform's internal system in the original order, generating internal tracking codes and identifiers for the newly created order that are not internally associated with the original order; receiving status update information from an external system, which includes the master tracking code and at least one identifier," the response end can be implemented as a service module, which internally includes an instruction receiving submodule, an order copying processing submodule, and an external information receiving submodule. The instruction receiving submodule can be an API interface used to receive copying instructions from the front-end interface or upstream business systems. After receiving the instruction, the order copying processing submodule is responsible for clearing all tracking codes and identifiers generated by the platform's internal system in the original order, and generating entirely new internal tracking codes and identifiers for the newly created order that are not internally associated with the original order. For example, this submodule can call database operations to clear fields such as the internal waybill number and package tracking number of the old order, and assign a new unique identifier to the new order. The external information receiving submodule can be a message queue consumer or an HTTP / HTTPS interface, used to receive status update information from external systems (such as carrier systems or customs systems). As a preferred implementation, the response end can be configured as a standalone service within a microservice architecture to ensure high availability and scalability.

[0136] Secondly, regarding the feature "the detection end, used to initiate the identifier verification process, detects whether at least one identifier carried in the status update information is cross-correlated with the internal tracking codes of cancelled or failed orders within the platform, and whether the main tracking code of the status update information does not match the main tracking code of currently active orders," the detection end can be implemented as an independent verification service or a data processing engine. This detection end is configured to include an identifier parsing module and a cross-correlation detection module. The identifier parsing module is responsible for extracting the main tracking code and all sub-identifiers from the received status update information. The cross-correlation detection module is responsible for querying the platform's internal order database, comparing whether these identifiers are associated with the internal tracking codes of cancelled or failed orders, and simultaneously checking the matching status code with the main tracking code of currently active orders. For example, this module can maintain an index of internal tracking codes for cancelled / failed orders to facilitate rapid cross-correlation queries. As a specific implementation, the detection end can employ streaming processing technology to perform real-time verification of the incoming status update information, minimizing the risk of data contamination.

[0137] Furthermore, regarding the feature of "the processing end, used to refuse to write the status update information to any order record once ghost order information contamination is detected," the processing end can be designed as a decision execution module or a data write control service. When the detection end identifies ghost order information contamination, the processing end will receive a corresponding alarm or instruction. The core function of the processing end is to prevent the contaminated status update information from entering the platform's order database. This can be achieved in various ways, such as directly discarding the update information or isolating it to an abnormal data queue for manual review, without allowing it to affect any active order records. As a preferred implementation, the processing end can be configured to trigger an alarm notification while refusing to write, so that the system administrator can intervene in a timely manner to handle the abnormal situation and trace the source of contamination.

[0138] The order management system for the cross-border logistics platform proposed in this application forms a complete and efficient solution through the synergistic effect of the aforementioned components, aiming to solve the "ghost order" problem that may occur when traditional cross-border logistics platforms handle complex order status updates.

[0139] Specifically, when the system receives an instruction to copy the original order, it will initiate order copying even if the original order is in a failed, canceled, or abnormally locked state. A key step in this process is clearing all tracking codes and identifiers generated by the platform's internal systems from the original order. This ensures that the newly created order receives internal tracking codes and identifiers that are not internally associated with the original order. For example, when an order locked due to a failed customs declaration is copied, its old internal tracking number and parcel tracking number will be cleared, and the new order will receive a completely new and independent set of internal identifiers. This mechanism severs the association between the old and new orders in terms of internal identifiers at the source, avoiding potential data confusion caused by the copying operation.

[0140] Subsequently, when the system receives status update information from external systems, this information typically includes the master tracking code and at least one identifier. To ensure the accuracy and validity of this external update information, the detection end initiates an identifier verification process. The core of this process is to detect whether at least one identifier carried in the status update information has any cross-correlation with the internal tracking codes of cancelled or failed orders within the platform, and whether the master tracking code of the status update information does not match the master tracking code of currently active orders. For example, if the update information sent by the external system contains a package number that previously belonged to the internal tracking code of a cancelled old order, and the master tracking code of this update information does not match the master tracking code of any currently active order, then the system will identify potential "ghost order information contamination."

[0141] Once such ghost order information contamination is detected, the processing end will immediately refuse to write the status update information to any order record. This rejection mechanism is the last line of defense against data contamination, ensuring that even if an external system sends incorrect or outdated data, this data will not interfere with the platform's active order records. In this way, the system of this application can effectively avoid the "ghost order" phenomenon caused by problems such as residual historical order data, delays or incorrect transmission of data from external systems, thereby ensuring the accuracy, consistency and reliability of order data on the cross-border logistics platform.

[0142] The order management system for cross-border logistics platforms proposed in this application demonstrates significant technological contributions and innovation in solving the "ghost order" problem. Traditional cross-border logistics platforms often lack a systematic mechanism to effectively identify and prevent "ghost orders," particularly when handling order duplication and external status updates. Existing systems typically focus only on matching the master tracking code, ignoring the potential association between auxiliary identifiers and historical abandoned orders, and failing to provide a proactive mechanism to reject contaminated information at the system architecture level. This results in order statuses within the platform being easily contaminated by erroneous information once data confusion occurs, severely impacting data accuracy and operational efficiency.

[0143] In contrast, the core innovation of this application lies in the introduction of a systematic architecture encompassing the response, detection, and processing ends. Specifically, when copying an order, the response end explicitly requires the removal of all tracking codes and identifiers generated by the platform's internal systems from the original order, and the generation of entirely new internal tracking codes and identifiers for the newly created order that are unrelated to the original order. This mechanism severs any potential association between the old and new orders in terms of internal identifiers at the source, effectively preventing internal data confusion caused by the copying operation.

[0144] Furthermore, the detection system employs a dual verification mechanism by simultaneously checking for cross-correlation between the identifier carried in the status update information and the internal tracking codes of cancelled or failed orders within the platform, as well as whether the main tracking code of the status update information does not match the main tracking code of currently active orders. Once both conditions are met, it is determined to be "ghost order information contamination," and the processing end will immediately refuse to write this status update information into any order record. This proactive ability to identify and reject contaminated information is not available in existing technologies. Traditional order management systems typically rely on simple tracking code matching, which cannot effectively distinguish the internal relationships between new and old orders, nor can it identify "ghost" updates of cancelled orders by erroneous or outdated data sent from external systems.

[0145] Therefore, the system of this application can effectively prevent "ghost orders" caused by problems such as residual historical order data, delays or erroneous transmission of data from external systems, and significantly improve the accuracy, consistency and reliability of order data on cross-border logistics platforms. This improvement not only optimizes the platform's internal data management and reduces the cost of manual intervention and error handling, but more importantly, it enhances customers' trust in the accuracy of the platform's data, thereby improving the overall user experience and operational efficiency.

[0146] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An order management method for a cross-border logistics platform, characterized in that, include: Receive an instruction to copy the original order, which is in a failed, canceled, or abnormally locked state; In response to the instruction, an order copying process is initiated, which includes: clearing all tracking codes and identifiers generated by the platform's internal system in the original order, and generating internal tracking codes and identifiers for the newly created order that are not internally associated with the original order; Receive status update information from an external system, the status update information including a master tracking code and at least one identifier; Initiate an identifier verification process to detect whether the at least one identifier carried in the status update information is cross-related with the internal tracking code of an invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order. Once ghost order information contamination is detected, the status update information will be refused to be written to any order record.

2. The order management method for a cross-border logistics platform according to claim 1, characterized in that, In the identifier verification process, detecting whether the at least one identifier carried in the status update information has a cross-association with the internal tracking code of an invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order, includes: Parse the at least one identifier carried in the state update information to identify the sub-identifier types and hierarchical relationships contained within the at least one identifier; Based on the sub-identifier type, select the corresponding matching strategy and select the corresponding abandoned identifier pool within the platform for cross-association detection; Compare the generation time of the sub-identifier type with the expiration time recorded in the discarded identifier pool; Based on the combined detection results of the sub-identifier types and the matching of the master tracking code of the status update information with the master tracking code of the currently active order, it is determined whether there is ghost order information contamination.

3. The order management method for a cross-border logistics platform according to claim 2, characterized in that, The comparison of the generation time of the sub-identifier type with the expiration time recorded in the obsolete identifier pool includes: Acquire time zone differences between the platform and the external systems of new trading partners; Based on the time zone difference configuration, the generation time of the sub-identifier type and the expiration time recorded in the discarded identifier pool are uniformly converted into a standard time base. Compare the generation time of the converted sub-identifier type with the expiration time recorded in the converted obsolete identifier pool.

4. The order management method for a cross-border logistics platform according to claim 2, characterized in that, The step of selecting the corresponding matching strategy based on the sub-identifier type includes: Parse the at least one identifier carried in the state update information and identify the sub-identifier types contained within the at least one identifier; When the identified sub-identifier type is a type not defined in the preset rule base, the format characteristics of the unknown sub-identifier are compared with the existing sub-identifier types to infer the semantics and purpose of the unknown sub-identifier. A matching strategy is generated based on the contextual position of the unknown sub-identifier in the state update information and the external system type to which the state update information belongs; Record the unknown sub-identifier type and its matching strategy in the policy queue to be reviewed, and trigger a manual alert; After manual review and confirmation, the unknown sub-identifier type and its matching strategy are added to the configurable matching strategy rule base. Based on the historical matching success rate and human feedback of the unknown sub-identifier type, adjust the matching confidence threshold and strategy priority of the matching strategy; The stringency or parameters of the matching strategy are adjusted based on the historical data quality assessment results of the identified sub-identifier types.

5. The order management method for a cross-border logistics platform according to claim 4, characterized in that, The step of adjusting the matching confidence threshold and strategy priority of the matching strategy based on the historical matching success rate and human feedback of the unknown sub-identifier type includes: Identify the business scenario specificity of the unknown sub-identifier type; Based on the specificity of the business scenario, an initial matching strategy is set for the unknown sub-identifier type. The initial matching strategy includes: assigning a preset conservative matching strategy to the unknown sub-identifier type. The conservative matching strategy has a low matching confidence threshold and a medium strategy priority. Before the amount of matching data for the unknown sub-identifier reaches a preset minimum threshold, the matching results of the unknown sub-identifier are manually sampled and reviewed. The frequency of the manual sampling and review is inversely proportional to the sparsity of the matching data. Based on the results of the manual sampling review, the matching confidence threshold and strategy priority are progressively adjusted. Once the amount of matching data for the unknown sub-identifier reaches a preset minimum threshold, the matching confidence threshold and strategy priority of the matching strategy are dynamically adjusted based on the historical matching success rate of the unknown sub-identifier type and human feedback.

6. The order management method for a cross-border logistics platform according to claim 5, characterized in that, The step of dynamically adjusting the matching confidence threshold and strategy priority of the matching strategy based on the historical matching success rate and human feedback of the unknown sub-identifier type includes: Receive the status update information and identify the data source system of the status update information; Calculate the current data quality confidence level of the data source system based on the historical data quality assessment results of the data source system; Based on the current data quality confidence level, the historical matching success rate data of the unknown sub-identifier is weighted to obtain the weighted historical matching success rate. Based on the weighted historical matching success rate and the human feedback, and based on the current data quality confidence level, the matching confidence threshold and the strategy priority of the matching strategy are dynamically adjusted.

7. The order management method for a cross-border logistics platform according to claim 6, characterized in that, The step of calculating the current data quality confidence level of the data source system based on the historical data quality assessment results of the data source system includes: Obtain a set of data quality evaluation indicators from the data source system within a preset time window. The set of indicators includes data integrity rate, data accuracy rate, and data timeliness rate. The data quality assessment index set is subjected to time decay weighting processing, and data from more recent time points are assigned higher weights based on the interval between the data generation time and the current time. Based on the weighted set of data quality assessment indicators, the current data quality confidence level of the data source system is obtained.

8. The order management method for a cross-border logistics platform according to claim 7, characterized in that, The process of applying time decay weighting to the set of data quality assessment indicators, assigning higher weights to data from more recent time points based on the interval between the data generation time and the current time, includes: Obtain the original generation timestamp of each data point in the data quality assessment index set; Compare the original generation timestamp with the timestamp of the data point entering the platform system; If the data point exceeds a preset threshold, it will be marked as backtracking or retransmission. For data points marked as backtracking or retransmission data, a weight is calculated using a time decay weighting function based on the interval between the original generated timestamp and the current time. The time decay weighting function assigns a low initial weight and a faster decay rate to the backtracking or retransmission data. For data points not marked as backtracking or retransmission data, the weights are calculated using the time decay weighting function based on the interval between the data generation time and the current time.

9. The order management method for a cross-border logistics platform according to claim 8, characterized in that, For data points marked as backtracking or retransmission data, a weight is calculated using a time decay weighting function based on the interval between the original generated timestamp and the current time, including: Obtain the business scenario type to which the data points marked as backtracking or retransmission data belong, wherein the business scenario type includes at least one of customs declaration, logistics node, and payment settlement; Based on the business scenario type, obtain the corresponding sensitivity parameters from the preset business scenario sensitivity configuration. The sensitivity parameters include the data importance level and real-time requirements. Based on the data importance level and real-time requirements, the initial weights and decay rate in the time decay weighting function are dynamically adjusted, and the weights are calculated using the time decay weighting function based on the interval between the original generated timestamp and the current time. When the business scenario type is customs declaration, the initial weight is increased and the decay rate is decreased according to the compliance requirements of customs declaration data. When the business scenario type is a logistics node, the initial weight is moderately adjusted according to the timeliness requirements of the logistics node data, and the decay rate is moderately adjusted as well. When the business scenario type is payment settlement, the initial weight is adjusted downward and the decay rate is adjusted upward according to the accuracy requirements of payment settlement data.

10. An order management system for a cross-border logistics platform, characterized in that, include: The response end is used to receive instructions to copy the original order, which is in a failed, canceled, or abnormally locked state; In response to the instruction, an order replication process is initiated, which includes: clearing all tracking codes and identifiers generated by the platform's internal system from the original order; generating internal tracking codes and identifiers for the newly created order that are not internally associated with the original order; and receiving status update information from an external system, which includes the master tracking code and at least one identifier. The detection end is used to initiate the identifier verification process, detect whether the at least one identifier carried in the status update information is cross-associated with the internal tracking code of the invalid or failed order within the platform, and whether the main tracking code of the status update information does not match the main tracking code of the currently active order. The processing end is used to refuse to write the status update information to any order record once ghost order information contamination is detected.

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