Cross-border e-commerce inventory intelligent reconciliation method and system

Abstract

This invention relates to the field of inventory management technology and provides a method and system for intelligent inventory reconciliation in cross-border e-commerce. The method includes the following steps: acquiring raw business data from multiple heterogeneous data sources and converting the raw business data into an inventory event stream with standard fields; starting from a baseline inventory, calculating the theoretical inventory at each reconciliation point in chronological order based on the inventory event stream, comparing the theoretical inventory with physical inventory snapshots obtained from various platforms, and identifying inventory discrepancies; extracting related event sets from the inventory event stream based on the inventory discrepancies; performing matching and reasoning on the related event sets based on a pre-built root cause knowledge graph to determine the root causes of inventory discrepancies, assigning an impact quantity and confidence level to each root cause; determining a handling strategy based on the type and attributes of the root causes, and generating a diagnostic report containing the root cause, impact quantity, confidence level, and handling strategy. This invention can accurately determine the root causes of inventory discrepancies, improving the accuracy and efficiency of reconciliation work.

Description

Technical Field

[0001] This invention relates to the field of inventory management technology, specifically to a smart inventory reconciliation method and system for cross-border e-commerce. Background Technology

[0002] In the current booming cross-border e-commerce market, inventory management has become a crucial element in ensuring smooth business operations. Cross-border e-commerce involves multiple sales platforms, warehouses in different regions, and complex logistics and distribution systems. Inventory data is scattered across multiple heterogeneous systems, including e-commerce platform back-ends, Enterprise Resource Planning (ERP) systems, Warehouse Management Systems (WMS), and logistics tracking systems. These systems are isolated from each other, with varying data formats and storage methods, making unified management and effective integration of inventory data difficult. Furthermore, cross-border e-commerce business scenarios are diverse, including order processing, returns, transfers, damaged or discarded goods, and logistics in transit, all of which impact inventory levels. Accurately understanding dynamic inventory changes and promptly identifying and resolving inventory discrepancies are essential for ensuring timely order fulfillment, avoiding stockouts or overstocking, improving customer satisfaction, and optimizing operational costs.

[0003] However, while existing systems or manual comparison methods can easily list discrepancies in inventory quantities, presenting differences from dimensions such as SKU, platform, and warehouse, they cannot automatically and accurately answer why these discrepancies occurred. The investigation requires manual tracking of dozens of possible events across different systems, including orders, returns, transfers, damaged or discarded items, and logistics in transit—a time-consuming, labor-intensive, and extremely inefficient process. Therefore, there is a need for an intelligent inventory reconciliation method and system for cross-border e-commerce, aiming to solve the aforementioned problems. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a smart inventory reconciliation method and system for cross-border e-commerce, so as to solve the problems existing in the above-mentioned background technology.

[0005] This invention is implemented as follows: a smart inventory reconciliation method for cross-border e-commerce, the method comprising the following steps:

[0006] The system obtains raw business data from multiple heterogeneous data sources and transforms the raw business data into an inventory event stream with standard fields, including inventory unit SKU, warehouse identifier, event type, event time, quantity change, and associated document number.

[0007] Starting with the baseline inventory, the theoretical inventory at each reconciliation point is calculated in chronological order according to the inventory event flow. The theoretical inventory is then compared with physical inventory snapshots obtained from each platform to identify inventory discrepancies.

[0008] Extract a set of related events from the inventory event stream based on the aforementioned inventory discrepancies;

[0009] The related event set is matched and reasoned based on a pre-built root cause knowledge graph to determine the root causes of the inventory discrepancies, and each root cause is assigned an impact quantity and confidence level.

[0010] Based on the type and attributes of the root cause, determine the treatment strategy and generate a diagnostic report that includes the root cause, the number of impacts, the confidence level, and the treatment strategy.

[0011] As a further aspect of the present invention, the step of extracting a set of related events from the inventory event stream based on the inventory discrepancy specifically includes:

[0012] Using the inventory unit SKU and warehouse identifier of the inventory discrepancy as anchor points and the reconciliation time point as the time endpoint, a diagnostic time window for backward look-back is determined;

[0013] Within the diagnostic time window, all inventory events that perfectly match the anchor point are retrieved from the inventory event stream to form an initial set of associated events;

[0014] Based on the associated document numbers in the initial set of associated events, cross-warehouse and cross-platform associated event tracing is performed to obtain cross-dimensional associated events;

[0015] All events are obtained by expanding the search based on cross-dimensional related events, deduplicating and merging them, and sorting them in chronological order to obtain a set of related events for root cause diagnosis.

[0016] As a further aspect of the present invention, the step of tracing related events across warehouses and platforms specifically includes:

[0017] For events of type "inter-warehouse transfer out", search the target warehouse within the diagnostic time window to see if there is an inter-warehouse transfer in event with the corresponding transfer order number;

[0018] For events of type "sales outbound" and associated "return creation" events, retrieve the return quality inspection, return shelving, and return scrapping events from the return warehouse.

[0019] For events whose event type involves a tracking number, call the logistics status interface to obtain the latest status event for that tracking number in the diagnostic window, and convert it into a standard logistics status update event to add to the collection.

[0020] As a further aspect of the present invention, the steps of matching and reasoning on the set of related events based on a pre-constructed root cause knowledge graph to determine the root causes leading to the inventory discrepancies, and assigning an impact quantity and confidence level to each root cause, specifically include:

[0021] The set of related events is matched one by one with multiple diagnostic patterns in the root cause knowledge graph; each diagnostic pattern consists of an event type sequence, time constraints, state logic predicates, and a conclusion root cause.

[0022] For each successfully matched diagnostic pattern, the confidence level of the diagnostic pattern is determined based on sequence matching degree, strength of evidence, time decay, and historical accuracy.

[0023] Root causes are determined based on diagnostic patterns. When multiple diagnostic patterns match successfully and the resulting root causes are different, all root causes with confidence levels higher than the confidence threshold are retained. The total number of differences is quantitatively decomposed among root causes with different confidence levels to determine the impact of each root cause.

[0024] As a further aspect of the present invention, the step of constructing a root cause knowledge graph specifically includes:

[0025] Determine the entity types and relation types of the graph. Entity types include operational entities, state entities, and anomaly root cause entities. Relation types include cause, manifestation, precursor, and accompaniment.

[0026] Based on historical inventory discrepancy cases, business expert experience, and platform rule manuals, a general root cause diagnosis model was derived.

[0027] The defined entities, relationships, and diagnostic patterns are stored and linked in a graph database to form an initial root cause knowledge graph.

[0028] As a further aspect of the present invention, the event types include sales outbound, purchase inbound, inter-warehouse transfer out, inter-warehouse transfer in, return creation, return quality inspection, return shelving, return scrapping, inventory adjustment, and logistics status update; the root cause types include logistics in transit type, warehouse operation type, and return processing type, and the root cause attributes include impact quantity and confidence level.

[0029] Another objective of this invention is to provide a smart inventory reconciliation system for cross-border e-commerce, the system comprising:

[0030] The inventory event stream module is used to obtain raw business data from multiple heterogeneous data sources and transform the raw business data into an inventory event stream with standard fields, including inventory unit SKU, warehouse identifier, event type, event time, quantity change and associated document number;

[0031] The inventory discrepancy identification module is used to calculate the theoretical inventory at each reconciliation point in chronological order based on the baseline inventory and the inventory event stream, and compare the theoretical inventory with the physical inventory snapshots obtained from each platform to identify inventory discrepancies.

[0032] The associated event set module is used to extract associated event sets from the inventory event stream based on the inventory difference.

[0033] The inventory discrepancy root cause module is used to perform matching reasoning on the set of related events based on a pre-built root cause knowledge graph, determine the root causes of the inventory discrepancy, and assign an impact quantity and confidence level to each root cause.

[0034] The diagnostic report generation module is used to determine the treatment strategy based on the type and attributes of the root cause, and generate a diagnostic report that includes the root cause, the number of impacts, the confidence level, and the treatment strategy.

[0035] As a further aspect of the present invention, the associated event set module includes:

[0036] The diagnostic time window unit is used to determine a backward diagnostic time window with the inventory unit SKU and warehouse identifier of the inventory difference as anchor points and the reconciliation time point as the time end point.

[0037] An initial associated event unit is used to retrieve all inventory events that completely match the anchor point from the inventory event stream within the diagnostic time window, forming an initial associated event set;

[0038] The cross-dimensional related event unit is used to trace related events across warehouses and platforms based on the related document numbers in the initial related event set, and obtain cross-dimensional related events.

[0039] The associated event set unit is used to perform extended retrieval based on cross-dimensional associated events to obtain all events, deduplicate them, merge them, and sort them in chronological order to obtain the associated event set used for root cause diagnosis.

[0040] As a further aspect of the present invention, the cross-dimensional related event unit includes:

[0041] The inter-warehouse transfer-out event subunit is used to search for events of type inter-warehouse transfer-out within the diagnostic time window to see if there is an inter-warehouse transfer-in event with the corresponding transfer order number in the target warehouse.

[0042] The Sales Outbound Event sub-unit is used to retrieve return inspection, return shelving, and return scrapping events from the return warehouse for events of the event type "Sales Outbound" and which have associated return creation events.

[0043] The tracking number event subunit is used to call the logistics status interface for events whose event type involves the tracking number, obtain the latest status event of that tracking number in the diagnostic window, and convert it into a standard logistics status update event to be added to the collection.

[0044] As a further aspect of the present invention, the inventory discrepancy root cause module includes:

[0045] The diagnostic pattern matching unit is used to match the associated event set with multiple diagnostic patterns in the root cause knowledge graph one by one; each diagnostic pattern consists of an event type sequence, time constraints, state logic predicates, and a conclusion root cause;

[0046] The confidence determination unit is used to determine the confidence of each successfully matched diagnostic pattern based on sequence matching degree, evidence strength, time decay and historical accuracy.

[0047] The impact quantity allocation unit is used to determine the root cause based on the diagnostic pattern. When multiple diagnostic patterns are successfully matched and the root causes obtained are different, all root causes with a confidence level higher than the confidence threshold are retained. The total number of differences is quantitatively decomposed among root causes with different confidence levels to determine the impact quantity of each root cause.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] By acquiring raw business data from multiple heterogeneous data sources and transforming it into a standardized inventory event stream, theoretical inventory is calculated chronologically starting from a baseline inventory and compared with a snapshot of physical inventory, automatically identifying inventory discrepancies. Then, based on a pre-built root cause knowledge graph, matching and reasoning are performed on the set of related events to accurately determine the root causes of inventory discrepancies and assign impact quantities and confidence levels, avoiding the tediousness and inefficiency of manual investigation and improving the accuracy and efficiency of reconciliation. Furthermore, it can integrate inventory data scattered across multiple heterogeneous systems, transforming it into a unified inventory event stream, enabling cross-system and cross-time dimension analysis of inventory changes. By calculating theoretical inventory chronologically, the entire process of inventory changes can be clearly traced, identifying complex inventory discrepancies caused by multi-event chain reactions, providing strong support for a comprehensive and in-depth understanding of dynamic inventory changes. Attached Figure Description

[0050] Figure 1 This is a flowchart of a smart inventory reconciliation method for cross-border e-commerce.

[0051] Figure 2 This is a flowchart illustrating the extraction of related event sets in a smart inventory reconciliation method for cross-border e-commerce.

[0052] Figure 3 This is a flowchart for identifying the root causes of inventory discrepancies in a smart inventory reconciliation method for cross-border e-commerce.

[0053] Figure 4 This is a flowchart for constructing a root cause knowledge graph in a cross-border e-commerce inventory intelligent reconciliation method.

[0054] Figure 5 This is a schematic diagram of the structure of a cross-border e-commerce intelligent inventory reconciliation system. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0056] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0057] like Figure 1 As shown in the figure, this embodiment of the invention provides a smart inventory reconciliation method for cross-border e-commerce, the method comprising the following steps:

[0058] S100: Obtain raw business data from multiple heterogeneous data sources and transform the raw business data into an inventory event stream with standard fields, including inventory unit SKU, warehouse identifier, event type, event time, quantity change and associated document number;

[0059] S200, starting from the baseline inventory, calculates the theoretical inventory at each reconciliation point in chronological order according to the inventory event flow, compares the theoretical inventory with the physical inventory snapshots obtained from each platform, and identifies inventory differences.

[0060] S300, extract a set of related events from the inventory event stream based on the inventory difference;

[0061] S400, based on the pre-built root cause knowledge graph, perform matching reasoning on the set of related events to determine the root causes of the inventory difference, and assign an impact quantity and confidence level to each root cause;

[0062] S500 determines the treatment strategy based on the type and attributes of the root cause and generates a diagnostic report that includes the root cause, the number of impacts, the confidence level, and the treatment strategy.

[0063] It should be noted that inventory data is scattered across multiple isolated systems. Existing reconciliation methods are often "point-to-point" comparisons, meaning they only perform simple checks on data from a single system or at a specific point in time, lacking the ability to connect data across systems and time dimensions. This makes it impossible to identify complex inventory discrepancies caused by a chain reaction of multiple events. For example, a return of an order may trigger a series of subsequent inventory adjustment operations. These operations are scattered across different systems, and existing reconciliation methods struggle to correlate and analyze these related events, thus failing to accurately determine the true cause of inventory discrepancies. Furthermore, existing reconciliation methods only provide surface information about inventory quantity discrepancies, lacking in-depth analysis of the causes of these discrepancies, and cannot provide valuable support for subsequent inventory management and decision-making. Enterprises find it difficult to develop targeted handling strategies based on reconciliation results to optimize inventory management processes and reduce the recurrence of similar discrepancies. The embodiments of this invention aim to solve the above problems.

[0064] In this embodiment of the invention, raw business data is first obtained from multiple heterogeneous data sources. Specifically, raw business data is periodically or in real-time obtained from e-commerce platform systems, enterprise resource planning (ERP) systems, warehouse management systems (WMS), and logistics tracking systems via application programming interfaces (APIs), electronic data interchange (EDI) files, or direct database connections. Then, the raw business data is transformed into inventory events with standard fields through semantic recognition. The timestamps of all inventory events are unified to the same time zone standard and sorted chronologically to form a continuous event sequence based on SKU and warehouse dimensions. Standard fields include Inventory Unit (SKU), Warehouse Identifier, Event Type, Event Time, Quantity Change, and Associated Document Number. The Inventory Unit (SKU) is a unique, standardized code assigned to distinguish different products, used to accurately identify a specific product category, specification, and version within the system. The Warehouse Identifier is a code that uniquely identifies a physical or logical storage location, including but not limited to: e-commerce platform's own warehouse code, third-party overseas warehouse code, domestic shipping warehouse code, and virtual in-transit inventory identifier. Event types include sales outbound, purchase inbound, inter-warehouse transfer out, inter-warehouse transfer in, return creation, return quality inspection, return shelving, return scrapping, inventory adjustment, and logistics status update. Quantity Change refers to the net change in inventory quantity for a specified SKU under a specified warehouse identifier caused by the event. The Associated Document Number is the unique number of the business document that triggered this event, used for cross-system tracking and association, such as: platform order number, return authorization number, purchase order number, transfer order number, logistics waybill number, and warehouse operation serial number. Next, starting with the baseline inventory (the accurate inventory at a specific historical point in time), the theoretical inventory for each reconciliation point (e.g., midnight each day) is calculated chronologically based on the inventory event stream. This theoretical inventory is then compared with snapshots of physical inventory obtained from various platforms to identify inventory discrepancies. For these discrepancies, a set of related events is extracted from the inventory event stream, and this set is matched and reasoned using a root cause knowledge graph to determine the root cause of the discrepancy. Each root cause is assigned an impact quantity and confidence level. Finally, the type and attributes of the root cause are extracted. The root cause types include logistics in transit, warehouse operations, and returns processing. The root cause attributes include impact quantity and confidence level. These are input into a strategy matching library for matching, outputting a handling strategy and generating a diagnostic report containing the root cause, impact quantity, confidence level, and handling strategy. This strategy matching library needs to be pre-defined, with strategies developed for each type and attribute of root cause. This process helps enterprises develop scientific and reasonable inventory management strategies, optimize business processes, reduce inventory discrepancies, and lower operating costs.

[0065] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of extracting a set of related events from the inventory event stream based on the inventory discrepancy specifically includes:

[0066] S301, using the inventory unit SKU and warehouse identifier of the inventory difference as anchor points and the reconciliation time point as the time endpoint, determine a diagnostic time window for backward look-back;

[0067] S302, within the diagnostic time window, retrieve all inventory events that perfectly match the anchor point from the inventory event stream to form an initial set of associated events;

[0068] S303, based on the associated document number in the initial associated event set, perform cross-warehouse and cross-platform associated event tracing to obtain cross-dimensional associated events;

[0069] S304. Based on cross-dimensional related events, expand the search to obtain all events, remove duplicates, merge them, and sort them in chronological order to obtain a set of related events for root cause diagnosis.

[0070] In this embodiment of the invention, to obtain the set of related events, an inventory unit SKU and warehouse identifier corresponding to the inventory difference are used as anchor points, and the reconciliation time point is used as the time endpoint to determine a backward diagnostic time window. The length of the diagnostic time window can be dynamically adjusted according to the number of differences. Then, within the diagnostic time window, all inventory events that completely match the anchor points are retrieved from the inventory event stream to form an initial set of related events. Next, the related document numbers in the initial set of related events are extracted, and cross-warehouse and cross-platform related event tracing is performed to obtain cross-dimensional related events, avoiding event omissions. Then, an extended search is performed based on the cross-dimensional related events to obtain all events, using the document number as the keyword during the search. Finally, all events are deduplicated and merged, and strictly sorted according to their chronological order to form the final set of related events containing a complete business context for root cause diagnosis.

[0071] In this embodiment of the invention, when tracing related events across warehouses and platforms, for events of type "inter-warehouse transfer out," the system searches whether there is an inter-warehouse transfer in event with the corresponding transfer order number in the target warehouse within the diagnostic time window, and adds the inter-warehouse transfer in event to the set. For events of type "sales outbound" with associated return creation events, the system searches for return quality inspection, return shelving, and return scrapping events in the return warehouse, and adds the retrieved events to the set. For events involving logistics tracking numbers, the system calls the logistics status interface to obtain the latest status event for that tracking number within the diagnostic window, converts it into a standard logistics status update event, and adds it to the set.

[0072] like Figure 3As shown, in a preferred embodiment of the present invention, the steps of matching and reasoning on the set of related events based on a pre-constructed root cause knowledge graph to determine the root causes leading to the inventory discrepancies, and assigning an impact quantity and confidence level to each root cause, specifically include:

[0073] S401, the set of related events is matched one by one with multiple diagnostic patterns in the root cause knowledge graph; each diagnostic pattern consists of an event type sequence, time constraints, state logic predicates and a conclusion root cause;

[0074] S402, For each successfully matched diagnostic pattern, determine the confidence level of the diagnostic pattern based on sequence matching degree, strength of evidence, time decay, and historical accuracy.

[0075] S403, determine the root cause based on the diagnostic pattern. When multiple diagnostic patterns are successfully matched and the root causes are different, retain all root causes with a confidence level higher than the confidence threshold, quantitatively decompose the total number of differences among root causes with different confidence levels, and determine the impact of each root cause.

[0076] In this embodiment of the invention, a root cause knowledge graph is pre-constructed, containing multiple diagnostic patterns. Each diagnostic pattern consists of an event type sequence, time constraints, state logic predicates, and a conclusion root cause. This embodiment of the invention matches the associated event set with each of the multiple diagnostic patterns in the root cause knowledge graph. The matching process checks whether the associated event set satisfies the event type sequence and constraints defined by the diagnostic pattern, thereby determining the successfully matched diagnostic pattern and identifying the root cause through that pattern. Furthermore, for each successfully matched diagnostic pattern, the confidence level of the diagnostic pattern is determined based on sequence matching degree, evidence strength, time decay, and historical accuracy. Sequence matching degree refers to the degree of agreement between the event sequence and the actual event. Evidence strength refers to whether there is an official, tamper-proof document number (such as a platform transaction ID or warehouse operation serial number) as supporting evidence. Time decay refers to the higher the weight of an event as root cause evidence as its occurrence time is closer to the reconciliation point. Historical accuracy refers to the probability that the diagnostic pattern has been verified as correct in historical feedback. Furthermore, when multiple diagnostic patterns match successfully but yield different root causes, all root causes with confidence levels above a certain threshold are retained. The total number of differences is then quantitatively decomposed across root causes with different confidence levels to determine the impact of each root cause. This quantitative decomposition logic is weighted based on the quantity changes and temporal proximity of the events associated with each pattern. Finally, a root cause list is output, with each item containing: the identified root cause type, the impact assigned to that root cause, the calculated confidence score, and a list of supporting evidence events.

[0077] like Figure 4As shown, in a preferred embodiment of the present invention, the step of constructing a root cause knowledge graph specifically includes:

[0078] S601, Determine the entity type and relation type of the graph. Entity types include operation entities, state entities, and abnormal root cause entities. Relation types include cause, manifestation, predecessor, and accompaniment.

[0079] S602, based on historical inventory discrepancy cases, business expert experience, and platform rule manual, yields a general root cause diagnosis model;

[0080] S603 stores and links the defined entities, relationships, and diagnostic patterns in a graph database to form the initial root cause knowledge graph.

[0081] In this embodiment of the invention, to construct a root cause knowledge graph, it is necessary to define entity types and relation types. Entity types include operational entities (such as sales outbound, purchase inbound, and return quality inspection), status entities (such as in-transit inventory, pending returns, and damaged goods), and abnormal root cause entities (such as logistics delays, warehouse misdeliveries, system over-sales, inventory omissions, and theft). Relationship types include cause (indicating that an operation or status may lead to a certain root cause), manifestation (indicating that the root cause manifests as a certain inventory difference), predecessor (indicating a temporal causal relationship between events), and accompaniment (indicating that events often occur simultaneously). Then, based on historical inventory difference cases, business expert experience, and platform rule manuals, a general root cause diagnostic pattern is obtained. Finally, the defined entities, relations, and diagnostic patterns are stored and linked in a graph database to form an initial root cause knowledge graph, where the diagnostic pattern is connected to the relevant entity nodes as a special "meta-rule" node. In addition, after each diagnostic task is completed, regardless of whether a clear conclusion is reached, the set of related events and the finally confirmed root cause are stored as a successful or unsuccessful learning case. The learning case library is analyzed regularly, and new, high-frequency event patterns are discovered through machine learning algorithms (such as sequence pattern mining and association rule learning). These patterns are then suggested as new diagnostic pattern candidates and added to the graph after expert review.

[0082] like Figure 5 As shown in the figure, this embodiment of the invention also provides a cross-border e-commerce inventory intelligent reconciliation system, the system comprising:

[0083] The inventory event stream module 100 is used to obtain raw business data from multiple heterogeneous data sources and transform the raw business data into an inventory event stream with standard fields, including inventory unit SKU, warehouse identifier, event type, event time, quantity change and associated document number;

[0084] The inventory discrepancy identification module 200 is used to calculate the theoretical inventory at each reconciliation point in chronological order based on the baseline inventory and the inventory event stream, and compare the theoretical inventory with the physical inventory snapshots obtained from each platform to identify inventory discrepancies.

[0085] The associated event set module 300 is used to extract an associated event set from the inventory event stream based on the inventory difference.

[0086] The inventory discrepancy root cause module 400 is used to perform matching reasoning on the set of related events based on a pre-built root cause knowledge graph, determine the root cause of the inventory discrepancy, and assign an impact quantity and confidence level to each root cause.

[0087] The diagnostic report generation module 500 is used to determine the treatment strategy based on the type and attributes of the root cause and generate a diagnostic report that includes the root cause, the number of impacts, the confidence level, and the treatment strategy.

[0088] In this embodiment of the invention, the associated event set module 300 includes:

[0089] The diagnostic time window unit is used to determine a backward diagnostic time window with the inventory unit SKU and warehouse identifier of the inventory difference as anchor points and the reconciliation time point as the time end point.

[0090] An initial associated event unit is used to retrieve all inventory events that completely match the anchor point from the inventory event stream within the diagnostic time window, forming an initial associated event set;

[0091] The cross-dimensional related event unit is used to trace related events across warehouses and platforms based on the related document numbers in the initial related event set, and obtain cross-dimensional related events.

[0092] The associated event set unit is used to perform extended retrieval based on cross-dimensional associated events to obtain all events, deduplicate them, merge them, and sort them in chronological order to obtain the associated event set used for root cause diagnosis.

[0093] In this embodiment of the invention, the cross-dimensional related event unit includes:

[0094] The inter-warehouse transfer-out event subunit is used to search for events of type inter-warehouse transfer-out within the diagnostic time window to see if there is an inter-warehouse transfer-in event with the corresponding transfer order number in the target warehouse.

[0095] The Sales Outbound Event sub-unit is used to retrieve return inspection, return shelving, and return scrapping events from the return warehouse for events of the event type "Sales Outbound" and which have associated return creation events.

[0096] The tracking number event subunit is used to call the logistics status interface for events whose event type involves the tracking number, obtain the latest status event of that tracking number in the diagnostic window, and convert it into a standard logistics status update event to be added to the collection.

[0097] In this embodiment of the invention, the inventory discrepancy root cause module 400 includes:

[0098] The diagnostic pattern matching unit is used to match the associated event set with multiple diagnostic patterns in the root cause knowledge graph one by one; each diagnostic pattern consists of an event type sequence, time constraints, state logic predicates, and a conclusion root cause;

[0099] The confidence determination unit is used to determine the confidence of each successfully matched diagnostic pattern based on sequence matching degree, evidence strength, time decay and historical accuracy.

[0100] The impact quantity allocation unit is used to determine the root cause based on the diagnostic pattern. When multiple diagnostic patterns are successfully matched and the root causes obtained are different, all root causes with a confidence level higher than the confidence threshold are retained. The total number of differences is quantitatively decomposed among root causes with different confidence levels to determine the impact quantity of each root cause.

[0101] The above description only details the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0102] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0103] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0104] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A smart inventory reconciliation method for cross-border e-commerce, characterized in that, The method includes the following steps: The system obtains raw business data from multiple heterogeneous data sources and transforms the raw business data into an inventory event stream with standard fields, including inventory unit SKU, warehouse identifier, event type, event time, quantity change, and associated document number. Starting with the baseline inventory, the theoretical inventory at each reconciliation point is calculated in chronological order according to the inventory event flow. The theoretical inventory is then compared with physical inventory snapshots obtained from each platform to identify inventory discrepancies. Extract a set of related events from the inventory event stream based on the aforementioned inventory discrepancies; The related event set is matched and reasoned based on a pre-built root cause knowledge graph to determine the root causes of the inventory discrepancies, and each root cause is assigned an impact quantity and confidence level. Based on the type and attributes of the root cause, determine the treatment strategy and generate a diagnostic report that includes the root cause, the number of impacts, the confidence level, and the treatment strategy.

2. The intelligent inventory reconciliation method for cross-border e-commerce according to claim 1, characterized in that, The steps for extracting a set of related events from the inventory event stream based on the inventory discrepancies specifically include: Using the inventory unit SKU and warehouse identifier of the inventory discrepancy as anchor points and the reconciliation time point as the time endpoint, a diagnostic time window for backward look-back is determined; Within the diagnostic time window, all inventory events that perfectly match the anchor point are retrieved from the inventory event stream to form an initial set of associated events; Based on the associated document numbers in the initial set of associated events, cross-warehouse and cross-platform associated event tracing is performed to obtain cross-dimensional associated events; All events are obtained by expanding the search based on cross-dimensional related events, deduplicating and merging them, and sorting them in chronological order to obtain a set of related events for root cause diagnosis.

3. The intelligent inventory reconciliation method for cross-border e-commerce according to claim 2, characterized in that, The steps for tracing related events across warehouses and platforms specifically include: For events of type "inter-warehouse transfer out", search the target warehouse within the diagnostic time window to see if there is an inter-warehouse transfer in event with the corresponding transfer order number; For events of type "sales outbound" and associated "return creation" events, retrieve the return quality inspection, return shelving, and return scrapping events from the return warehouse. For events whose event type involves a tracking number, call the logistics status interface to obtain the latest status event for that tracking number in the diagnostic window, and convert it into a standard logistics status update event to add to the collection.

4. The intelligent inventory reconciliation method for cross-border e-commerce according to claim 1, characterized in that, The steps of matching and reasoning on the set of related events based on a pre-constructed root cause knowledge graph to determine the root causes of the inventory discrepancies, and assigning an impact quantity and confidence level to each root cause, specifically include: The set of related events is matched one by one with multiple diagnostic patterns in the root cause knowledge graph; each diagnostic pattern consists of an event type sequence, time constraints, state logic predicates, and a conclusion root cause. For each successfully matched diagnostic pattern, the confidence level of the diagnostic pattern is determined based on sequence matching degree, strength of evidence, time decay, and historical accuracy. Root causes are determined based on diagnostic patterns. When multiple diagnostic patterns match successfully and the resulting root causes are different, all root causes with confidence levels higher than the confidence threshold are retained. The total number of differences is quantitatively decomposed among root causes with different confidence levels to determine the impact of each root cause.

5. The intelligent inventory reconciliation method for cross-border e-commerce according to claim 1, characterized in that, The steps for constructing a root cause knowledge graph include: Determine the entity types and relation types of the graph. Entity types include operational entities, state entities, and anomaly root cause entities. Relation types include cause, manifestation, precursor, and accompaniment. Based on historical inventory discrepancy cases, business expert experience, and platform rule manuals, a general root cause diagnosis model was derived. The defined entities, relationships, and diagnostic patterns are stored and linked in a graph database to form an initial root cause knowledge graph.

6. The intelligent inventory reconciliation method for cross-border e-commerce according to claim 1, characterized in that, The event types include sales outbound, purchase inbound, inter-warehouse transfer out, inter-warehouse transfer in, return creation, return quality inspection, return shelving, return scrapping, inventory adjustment, and logistics status update; the root cause types include logistics in transit type, warehouse operation type, and return processing type, and the root cause attributes include impact quantity and confidence level.

7. A cross-border e-commerce inventory intelligent reconciliation system, characterized in that, The system includes: The inventory event stream module is used to obtain raw business data from multiple heterogeneous data sources and transform the raw business data into an inventory event stream with standard fields, including inventory unit SKU, warehouse identifier, event type, event time, quantity change and associated document number; The inventory discrepancy identification module is used to calculate the theoretical inventory at each reconciliation point in chronological order based on the baseline inventory and the inventory event stream, and compare the theoretical inventory with the physical inventory snapshots obtained from each platform to identify inventory discrepancies. The associated event set module is used to extract associated event sets from the inventory event stream based on the inventory difference. The inventory discrepancy root cause module is used to perform matching reasoning on the set of related events based on a pre-built root cause knowledge graph, determine the root causes of the inventory discrepancy, and assign an impact quantity and confidence level to each root cause. The diagnostic report generation module is used to determine the treatment strategy based on the type and attributes of the root cause, and generate a diagnostic report that includes the root cause, the number of impacts, the confidence level, and the treatment strategy.

8. The intelligent inventory reconciliation system for cross-border e-commerce according to claim 7, characterized in that, The associated event set module includes: The diagnostic time window unit is used to determine a backward diagnostic time window with the inventory unit SKU and warehouse identifier of the inventory difference as anchor points and the reconciliation time point as the time end point. An initial associated event unit is used to retrieve all inventory events that completely match the anchor point from the inventory event stream within the diagnostic time window, forming an initial associated event set; The cross-dimensional related event unit is used to trace related events across warehouses and platforms based on the related document numbers in the initial related event set, and obtain cross-dimensional related events. The associated event set unit is used to perform extended retrieval based on cross-dimensional associated events to obtain all events, deduplicate them, merge them, and sort them in chronological order to obtain the associated event set used for root cause diagnosis.

9. The intelligent inventory reconciliation system for cross-border e-commerce according to claim 8, characterized in that, The cross-dimensional related event unit includes: The inter-warehouse transfer-out event subunit is used to search for events of type inter-warehouse transfer-out within the diagnostic time window to see if there is an inter-warehouse transfer-in event with the corresponding transfer order number in the target warehouse. The Sales Outbound Event sub-unit is used to retrieve return inspection, return shelving, and return scrapping events from the return warehouse for events of the event type "Sales Outbound" and which have associated return creation events. The tracking number event subunit is used to call the logistics status interface for events whose event type involves the tracking number, obtain the latest status event of that tracking number in the diagnostic window, and convert it into a standard logistics status update event to be added to the collection.

10. The intelligent inventory reconciliation system for cross-border e-commerce according to claim 7, characterized in that, The inventory discrepancy root cause module includes: The diagnostic pattern matching unit is used to match the associated event set with multiple diagnostic patterns in the root cause knowledge graph one by one; each diagnostic pattern consists of an event type sequence, time constraints, state logic predicates, and a conclusion root cause; The confidence determination unit is used to determine the confidence of each successfully matched diagnostic pattern based on sequence matching degree, evidence strength, time decay and historical accuracy. The impact quantity allocation unit is used to determine the root cause based on the diagnostic pattern. When multiple diagnostic patterns are successfully matched and the root causes obtained are different, all root causes with a confidence level higher than the confidence threshold are retained. The total number of differences is quantitatively decomposed among root causes with different confidence levels to determine the impact quantity of each root cause.

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