Supply chain anomaly cause identification and task derivation method and system

Abstract

The application discloses a supply chain abnormal cause identification and task derivation method and system, receives link details and an unclosed loop link set output by a link tracing end, identifies an abnormal cause type of each unclosed loop link in the link details and the unclosed loop link set, determines a signature of each unclosed loop link according to the abnormal cause type, generates a corresponding candidate treatment item according to the signature of each unclosed loop link and the abnormal cause type to which the unclosed loop link belongs, determines a candidate treatment item that passes verification through multi-layer verification, performs treatment party distribution and business interaction auditing on the candidate treatment item that passes verification, determines an adopted candidate treatment item, performs multi-treatment path selection processing according to the adopted candidate treatment item, and executes adaptive treatment and performs abnormal signature back-checking update. The application realizes continuous evolution of an after-sales reverse supply chain rule library.

Description

Technical Field

[0001] This invention relates to the field of supply chain, and in particular to a method and system for identifying the causes of supply chain anomalies and generating tasks. Background Technology

[0002] The business rules that the material link system in the after-sales supply chain relies on are usually stored in the form of various configuration metadata such as rule path library, warehouse status mapping table, and cycle whitelist. The continuous maintenance of business rules has a decisive impact on the accuracy of traceability.

[0003] In existing technologies, domain-specific decision-making systems based on rule evolution (such as patent CN121883181A) can be used, along with anomaly identification methods from other fields (such as patent CN122046094A), or anomaly handling capabilities related to link / material traceability (such as patent CN107346451A). The above solutions have the following problems: First, they can only categorize incomplete anomalies in a general way and cannot pinpoint the responsible party. The existing solutions simply refer to all incomplete links as "anomalies" or "pending processing," without distinguishing whether the business side, data side, or rule maintenance side should handle them according to their causes, resulting in inefficient operational response. Second, business cycles are easily misjudged as anomalies. Reasonable cycle scenarios such as factory rework due to non-compliance are often simply misreported as anomalies in the existing solutions. Third, it is difficult to distinguish between missing data and incomplete rule coverage. The two are superficially similar (e.g., the link is interrupted at a certain node), but the former is fundamentally caused by omissions in the upstream data collection process, requiring the data team to be notified to supplement the data, while the latter is fundamentally caused by the rule path library not defining the continuation relationship, requiring the rule maintenance side to be notified to expand the rules. The existing solutions lack clear criteria, and the business side has to check each order one by one. Fourth, a closed loop of identification-handling-re-inspection is not formed. The existing solutions lack an automatic verification mechanism, and anomalies may recur without being fundamentally resolved. Summary of the Invention

[0004] Given that the existing solutions for identifying the causes of reverse supply chain anomalies can only broadly categorize anomalies that are not closed-loop and cannot locate the responsible party, business cycles are easily misjudged as anomalies, data gaps and rule coverage are difficult to distinguish, and a closed loop of identification-handling-review is not formed, it is impossible to achieve the continuous evolution of the after-sales reverse supply chain rule base.

[0005] In view of the above problems, the present invention provides a method for identifying the causes of supply chain anomalies and deriving tasks, including: Receive the link details and the set of unclosed links output by the link tracing end, identify the anomaly cause type of each unclosed link in the link details and the set of unclosed links, and determine the signature of each unclosed link based on the anomaly cause type. Based on the signature of each unclosed link and its corresponding anomaly cause type, corresponding candidate disposal items are generated; the candidate disposal items are subjected to multi-level verification to determine the candidate disposal items that pass the verification. The verified candidate disposal items are distributed to the disposal party and reviewed through business interaction to determine the candidate disposal items to be adopted; Based on the adopted candidate disposal items, a multi-disposal path selection process is performed to execute the adaptation disposal; based on the result of the adaptation disposal, an anomaly signature is checked and updated.

[0006] Optionally, the link details and set of unclosed links output by the link tracing end are received, and the anomaly cause type of each unclosed link within the link details and set of unclosed links is identified; based on the anomaly cause type, a signature of each unclosed link is determined, including: For the link details and the set of unclosed links output by the link tracing end, identify the anomaly cause type of each unclosed link in the following order: If the same node in an unclosed loop is accessed more than twice, and the node is not in the preset loop whitelist, then the anomaly cause type is determined to be business loop type. If the end node of the unclosed link has a valid downstream path in the preset rule path library, and the associated field used to match the downstream event in the actual event corresponding to the end node is not empty, but no corresponding downstream event is found, then the anomaly cause type is determined to be data missing. If the end node of the unclosed link does not have any downstream path corresponding to the preset rule path library, the anomaly cause type is determined to be the first rule not covered type. If the downstream material library type corresponding to the actual event of the end node of the unclosed link is not in the preset mapping table, then the anomaly cause type is determined to be the second rule not covered type. If none of the anomaly cause types belong to the business cycle type, data missing type, first rule not covered type, or second rule not covered type, then the anomaly cause type is determined to be an unclassified type. When the anomaly cause type is business cycle type, the hash function processing result of the repeatedly accessed node is used as the signature of the unclosed link; When the anomaly cause type is data missing, the hash function processing result of the two fields, downstream business node ID and downstream spare parts code, corresponding to the legitimate downstream path is used as the signature of the unclosed loop link. When the anomaly cause type is the first rule not covered, the hash function processing result of the end node's encoding is used as the signature of the unclosed link; When the anomaly cause type is the second rule not covered type, the hash function processing result of the downstream material library type encoding is used as the signature of the unclosed loop link.

[0007] Optionally, based on the signature of each unclosed link and its corresponding anomaly cause type, corresponding candidate handling items are generated; the candidate handling items are subjected to multi-level verification to determine the candidate handling items that pass the verification, including: When the anomaly cause type is business cycle type, data missing type, first rule not covered type, or second rule not covered type, corresponding candidate disposal items are generated according to the signature of each unclosed link and its anomaly cause type; When the anomaly cause type is an unclassified type, corresponding candidate disposal items are generated manually; The candidate disposal items are sequentially validated for format, conflict, and feasibility to determine the candidate disposal items that pass the validation.

[0008] Optionally, the verified candidate disposal items are distributed to the disposal party and reviewed through business interactions to determine the adopted candidate disposal items, including: The verified candidate disposal items are distributed to the rule maintainer, data team, and business operation team for rule change processing, data supplementation processing, and business process investigation processing, respectively, in order to determine the candidate disposal items to be adopted.

[0009] Optionally, based on the adopted candidate disposal items, a multi-disposal path selection process is performed to execute the adaptation disposal; based on the result of the adaptation disposal, an anomaly signature re-check and update is performed, including: When the adopted candidate disposal item is a configuration change candidate disposal item, gray-scale verification is performed on the adopted candidate disposal item to determine the gray-scale verification result of the candidate disposal item; When the adopted candidate disposal item is a data supplementation candidate disposal item, the adopted candidate disposal item is converted into a data supplementation work order and pushed to the upstream business end for processing; Based on the grayscale verification result or the processing result of the data supplement work order, the signature of the corresponding non-closed loop link is updated with an anomaly identifier.

[0010] As one aspect of the present invention, the present invention also provides a supply chain anomaly identification and task derivation system, including: The anomaly cause identification module is used to receive the link details and the set of unclosed links output by the link tracing end, and identify the anomaly cause type of each unclosed link in the link details and the set of unclosed links. The signature determination module is used to determine the signature of each unclosed link based on the anomaly cause type. The candidate disposal item determination module is used to generate corresponding candidate disposal items based on the signature of each unclosed link and its corresponding anomaly cause type; The verification module is used to perform multi-level verification on the candidate disposal items and determine the candidate disposal items that pass the verification. The review module is used to review the distribution and business interaction of the verified candidate disposal items to the disposal party and determine the candidate disposal items to be adopted. The update module is used to perform multi-processing selection based on the adopted candidate processing items, thereby performing adaptation processing; and to perform anomaly signature re-checking and updating based on the result of the adaptation processing.

[0011] Optionally, the anomaly cause identification module is used to receive the link details and the set of unclosed links output by the link tracing end, and identify the anomaly cause type of each unclosed link in the link details and the set of unclosed links, including: For the link details and the set of unclosed links output by the link tracing end, identify the anomaly cause type of each unclosed link in the following order: If the same node in an unclosed loop is accessed more than twice, and the node is not in the preset loop whitelist, then the anomaly cause type is determined to be business loop type. If the end node of the unclosed link has a valid downstream path in the preset rule path library, and the associated field used to match the downstream event in the actual event corresponding to the end node is not empty, but no corresponding downstream event is found, then the anomaly cause type is determined to be data missing. If the end node of the unclosed link does not have any downstream path corresponding to the preset rule path library, the anomaly cause type is determined to be the first rule not covered type. If the downstream material library type corresponding to the actual event of the end node of the unclosed link is not in the preset mapping table, then the anomaly cause type is determined to be the second rule not covered type. If none of the anomaly cause types belong to the business cycle type, data missing type, first rule not covered type, or second rule not covered type, then the anomaly cause type is determined to be an unclassified type. The signature determination module is used to determine the signature of each unclosed link based on the anomaly cause type, including: When the anomaly cause type is business cycle type, the hash function processing result of the repeatedly accessed node is used as the signature of the unclosed link; When the anomaly cause type is data missing, the hash function processing result of the two fields, downstream business node ID and downstream spare parts code, corresponding to the legitimate downstream path is used as the signature of the unclosed loop link. When the anomaly cause type is the first rule not covered, the hash function processing result of the end node's encoding is used as the signature of the unclosed link; When the anomaly cause type is the second rule not covered type, the hash function processing result of the downstream material library type encoding is used as the signature of the unclosed loop link.

[0012] Optionally, the candidate disposal item determination module is used to generate corresponding candidate disposal items based on the signature of each unclosed link and its corresponding anomaly cause type, including: When the anomaly cause type is business cycle type, data missing type, first rule not covered type, or second rule not covered type, corresponding candidate disposal items are generated according to the signature of each unclosed link and its anomaly cause type; When the anomaly cause type is an unclassified type, corresponding candidate disposal items are generated manually; The verification module is used to perform multi-level verification on the candidate disposal items to determine the candidate disposal items that pass the verification, including: The candidate disposal items are sequentially validated for format, conflict, and feasibility to determine the candidate disposal items that pass the validation.

[0013] Optionally, the review module is used to review the distribution and business interaction of the verified candidate disposal items to the disposal party, and determine the candidate disposal items to be adopted, including: The verified candidate disposal items are distributed to the rule maintainer, data team, and business operation team for rule change processing, data supplementation processing, and business process investigation processing, respectively, in order to determine the candidate disposal items to be adopted.

[0014] Optionally, the update module is configured to perform multi-processing selection based on the adopted candidate disposal items, thereby executing adaptation processing; and to perform anomaly signature re-checking update based on the result of the adaptation processing, including: When the adopted candidate disposal item is a configuration change candidate disposal item, gray-scale verification is performed on the adopted candidate disposal item to determine the gray-scale verification result of the candidate disposal item; When the adopted candidate disposal item is a data supplementation candidate disposal item, the adopted candidate disposal item is converted into a data supplementation work order and pushed to the upstream business end for processing; Based on the grayscale verification result or the processing result of the data supplement work order, the signature of the corresponding non-closed loop link is updated with an anomaly identifier.

[0015] The beneficial effects of the above-mentioned technical solutions provided in the embodiments of the present invention include at least the following: This invention provides a method and system for identifying supply chain anomalies and generating tasks. The system receives link details and a set of unclosed links output from the link tracing terminal, identifies the anomaly cause type of each unclosed link within these categories, determines the signature of each unclosed link based on its cause type, generates corresponding candidate disposal items based on the signature and its anomaly cause type, performs multi-level verification on the candidate disposal items, determines the verified items, distributes the verified items to the disposal party and conducts business interaction review to determine the adopted items, performs multi-path selection processing based on the adopted items, and executes the appropriate disposal. Based on the results of the appropriate disposal, anomaly signatures are checked and updated. By automatically identifying the causes of unclosed links and categorizing disposal tasks into configuration change and data entry types for dispatch and review, the system enables continuous evolution of the after-sales reverse supply chain rule base.

[0016] The supply chain anomaly identification and task derivation method and system of the present invention have the following advantages: First, this invention automatically identifies unclosed links output by the link tracing end into four categories based on their causes: business cycle type, data missing type, first rule not covered type (upstream path), and second rule not covered type (database mapping), thus achieving operational response diversion. A cycle whitelist exemption mechanism is introduced in the determination of business cycle type anomalies to avoid misjudging reasonable business cycles as anomalies. Furthermore, the existence of a legitimate downstream in the rule path database is used as a key criterion to clearly distinguish between data missing type and rule not covered type anomalies. Differentiated handling tasks are derived for unclosed links according to their causes, and a combination of gray-scale activation and automatic rollback mechanisms is used for rule supplementation candidate handling items to avoid unexpected impacts of rule changes on the entire dataset, forming a complete closed loop of "identification-handling-verification-closure". Secondly, this invention automatically determines the causes of unclosed loop links according to four categories and dispatches them to the business party, data party, and rule maintenance party respectively. Compared with the traditional solution, operators can directly locate the handling party based on the cause list, avoiding repeated manual investigation of the root cause of unclosed loop anomalies and significantly improving the operational response efficiency. Third, by introducing a "circular whitelist" exemption mechanism, reasonable business cycles such as factory re-inspection failure and repair failure will no longer be misjudged as abnormal. The whitelist itself is also incorporated into the evolution system. After handling business process investigation tasks, business parties can write back to the whitelist, so that the system can continuously adapt to new reasonable cycle patterns. Fourth, the present invention no longer relies on rule maintainers to manually check each rule for exceptions not covered by the rules. The candidate handling items have business interpretability, which is convenient for business parties to verify. Fifth, this invention employs a mechanism combining gray-scale activation and automatic rollback to ensure the safety of rule changes, effectively avoiding potential accidents caused by drastic fluctuations in statistical indicators due to multiple rule changes, and ensuring that the continuous evolution of the rule path library is carried out under a safety barrier. Compared with the traditional alarm system that "does not verify after work order is dispatched", the return inspection and closure mechanism of this invention forms a complete closed loop of "identification-candidate-review-handling-verification-closure", and the quality of abnormal handling is continuously improved with each of the four steps.

[0017] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating the supply chain anomaly identification and task derivation method provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the supply chain anomaly identification and task derivation system provided in an embodiment of the present invention. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0021] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," "far," "near," "front," and "rear," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0022] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] Please see Figure 1 As shown, an embodiment of this application provides a method for identifying the causes of supply chain anomalies and deriving tasks. This method includes: Receive the link details and the set of unclosed links output by the link tracing end, identify the anomaly cause type of each unclosed link in the link details and the set of unclosed links, and determine the signature of each unclosed link based on the anomaly cause type. Based on the signature of each unclosed link and its corresponding anomaly cause type, generate corresponding candidate disposal items; perform multi-level verification on the candidate disposal items to determine the candidate disposal items that pass the verification. The verified candidate disposal items are distributed to the disposal parties and reviewed through business interactions to determine the candidate disposal items to be adopted. Based on the adopted candidate disposal items, multiple disposal path selection is performed to execute the appropriate disposal; based on the result of the appropriate disposal, the abnormal signature is checked and updated.

[0024] The beneficial effects of the above embodiments are that the supply chain anomaly identification and task derivation method automatically identifies the causes of unclosed links and distributes and reviews the handling tasks according to their nature into configuration change type and data supplement type, so as to realize the continuous evolution of the after-sales reverse supply chain rule base.

[0025] In another embodiment, the link details and the set of unclosed links output by the link tracing end are received, and the anomaly cause type of each unclosed link in the link details and the set of unclosed links is identified; based on the anomaly cause type, the signature of each unclosed link is determined, including: For the link details and the set of unclosed links output by the link tracing end, identify the anomaly cause type of each unclosed link in the following order: If the same node in an unclosed loop is accessed more than twice, and the node is not in the preset loop whitelist, then the anomaly cause type is determined to be business loop type. If the end node of the unclosed link has a valid downstream path in the preset rule path library, and the associated field used to match the downstream event in the actual event corresponding to the end node is not empty, but no corresponding downstream event is found, then the anomaly cause type is determined to be data missing. If the end node of the unclosed link does not have any downstream path corresponding to the preset rule path library, the anomaly cause type is determined to be the first rule not covered type. If the downstream material library type corresponding to the actual event of the end node of the unclosed link is not in the preset mapping table, then the anomaly cause type is determined to be the second rule not covered type. If none of the anomaly cause types belong to the business cycle type, data missing type, first rule not covered type, or second rule not covered type, then the anomaly cause type is determined to be an unclassified type. When the anomaly cause type is business cycle type, the hash function processing result of the repeatedly accessed node is used as the signature of the unclosed loop link; When the anomaly cause type is data missing, the hash function processing result of the downstream business node ID and downstream spare parts code fields corresponding to the legitimate downstream path is used as the signature of the unclosed loop link. When the anomaly cause type is the first rule not covered, the hash function processing result of the end node's encoding is used as the signature of the unclosed link; When the anomaly cause type is the second rule not covered, the hash function processing result of the downstream material library type encoding is used as the signature of the unclosed loop link.

[0026] The beneficial effects of the above embodiments, in actual operation, are that the link details and the set of unclosed links output by the link tracing end are received first. The above link details and the set of unclosed links include several unclosed links. For each unclosed link The anomaly cause type is determined by matching the following conditions in sequence. All conditions are mutually exclusive. The first matching condition determines the corresponding anomaly cause type, as detailed below: If the same node is accessed more than twice in an unclosed loop link, and the node is not in the preset loop whitelist. If the anomaly is identified as a business cycle type, such as a pre-defined cycle whitelist, then the cause of the anomaly is determined to be business cycle type. Typical reasonable cycle business operations can be "repair failure ↔ awaiting repair" or "in testing ↔ awaiting IQC testing," etc. These typical reasonable cycle business operations can be added to a preset cycle whitelist maintained by the business party. Later exempted; If the end node of an unclosed loop has a valid downstream path in the preset rule path library, and the associated field used to match the downstream event in the actual event corresponding to the end node is not empty, but no corresponding downstream event is found, then the anomaly cause type is determined to be data missing. Suppose that the event connection matching criterion for the end node of the unclosed loop is that the upstream order number, upstream spare part code, and upstream business node ID are all equal to the downstream field corresponding to the end node. It can be understood that when a valid downstream path exists in the preset rule path library and the field of the valid downstream path is not empty, and the actual event corresponding to the end node of the unclosed loop has no corresponding downstream, it is determined that the downstream event should exist but is actually missing. The reason for the missing is usually the omission in the upstream data collection process. The preset rule path library includes the business process field and business node field corresponding to the logistics business flow. If the terminal node of the unclosed link does not have any downstream path corresponding to the preset rule path library, then the anomaly cause type is determined to be the first rule not covered; when the terminal node of the unclosed link The absence of any downstream path definition in the preset rule path library indicates that the end node... The downstream continuation rules have not yet been defined. At this time, the first rule not covered type corresponds to the rule not covered type in the uplink path scenario. If the downstream material library type corresponding to the actual event of the end node of the unclosed loop is not located in the preset mapping table, the anomaly cause type is determined to be the second rule not covered type; the preset mapping table refers to the warehouse status mapping table, which includes the location of each material library and its status type mapping relationship. The above status types may include, but are not limited to, the business status types of the material library connection, etc.; when the end node of the unclosed loop If the downstream material library type corresponding to the actual event is not in the preset mapping table, it indicates that the mapping from the downstream material library area to the business status has not been defined. At this time, the second rule not covered type corresponds to the rule not covered type in the library area mapping scenario. If none of the anomaly causes fall under the categories of business cycle type, data missing type, first rule not covered type, or second rule not covered type, then the anomaly cause type is determined to be an unclassified type. By setting the unclassified type, it serves as a fallback type for downstream handling.

[0027] For each unclosed loop link corresponding to the above-mentioned anomaly cause types (business cycle type, data missing type, first rule not covered type, and second rule not covered type), a segmented hash function can be used to determine the corresponding signature according to the anomaly cause type to which each unclosed loop link belongs. Specifically, when the anomaly cause type is business cycle type, the hash function processing result of the encoded data of the repeatedly accessed node is used as the signature of the unclosed loop link. When the anomaly cause type is data missing type, the hash function processing result of the downstream business node ID and downstream spare part code fields corresponding to the legitimate downstream path (for example, intentionally omitting the order number field to achieve aggregation of batch missing data of the same type, making it possible to attribute the cause to a single data acquisition environment for repair) is used as the signature of the unclosed loop link. When the anomaly cause type is first rule not covered type, the hash function processing result of the encoded data of the end node is used as the signature of the unclosed loop link. When the anomaly cause type is second rule not covered type, the hash function processing result of the encoded data of the downstream material library is used as the signature of the unclosed loop link.

[0028] In another embodiment, based on the signature of each unclosed link and its corresponding anomaly cause type, corresponding candidate disposal items are generated; the candidate disposal items are subjected to multi-level verification to determine the candidate disposal items that pass the verification, including: When the anomaly cause type is business cycle type, data missing type, first rule not covered type, or second rule not covered type, generate corresponding candidate disposal items based on the signature of each unclosed link and its anomaly cause type; When the cause of the anomaly is an unclassified type, corresponding candidate treatment items are generated manually. The candidate disposal items are sequentially validated for format, conflict, and feasibility to determine the candidate disposal items that pass the validation.

[0029] The beneficial effects of the above embodiments are as follows: For the signature of each unclosed loop link, corresponding candidate disposal items are generated according to its anomaly cause type. Specifically, for business loop type, a "business process investigation task" is generated. The task load includes a loop node sequence and representative link group samples. If the business party confirms after verification that the business loop is a reasonable loop, the corresponding loop node sequence is added to a preset loop whitelist, thereby generating corresponding candidate disposal items. For data missing type, no rule change suggestion is generated, but a "data supplementation task" is generated. The task load includes the association key of the expected downstream event and representative link group samples, thereby generating corresponding candidate disposal items. For the first rule not covered type, all business events are scanned to determine the uncovered end nodes. For the uncovered end nodes, corresponding candidate disposal item sets are recommended. For each candidate disposal item in the above candidate disposal item set, the frequency of each candidate disposal item is greater than a preset frequency threshold, thereby generating corresponding candidate disposal items. For the second rule not covered type, the characteristics of the sample links not mapped to the database area are analyzed (such as the business node type and location characteristics associated with the sample links), and corresponding candidate disposal items are generated based on the above sample link characteristics. For unclassified types, classification suggestions and candidate disposal items will be provided manually.

[0030] Furthermore, the candidate disposal items are sequentially validated for format, conflict, and feasibility to determine the candidate disposal items that pass the validation. Format validation verifies whether the candidate disposal item conforms to a predefined JSON Schema (e.g., all fields are complete, types are correct). Conflict validation involves conditionally executing the validation based on the currently enabled configuration metadata set, cross-checking it against each metadata set. If a candidate disposal item references disabled metadata, it is directly filtered; otherwise, it is retained. Feasibility validation uses the original metadata and candidate patches as a temporary metadata snapshot, re-executing the tracing only on a subset of the corresponding signed link groups to calculate the feasibility increment. If the feasibility increment is less than a preset increment threshold, the candidate disposal item is filtered; otherwise, it is retained. Through these methods, candidate disposal items that pass format, conflict, and feasibility validations are determined as valid candidate disposal items.

[0031] In another embodiment, the verified candidate disposal items are distributed to the disposal party and reviewed through business interactions to determine the candidate disposal items to be adopted, including: The verified candidate disposal items are distributed to the rule maintainer, data team, and business operation team for rule change processing, data supplementation processing, and business process investigation processing, respectively, in order to determine the candidate disposal items to be adopted.

[0032] The beneficial effects of the above embodiments are that, in actual operation, the verified candidate disposal items are distributed to different disposal parties according to their type. For example, candidate disposal items belonging to the rule change category are distributed to the rule maintenance party, candidate disposal items belonging to the data supplementation category are distributed to the data team, and candidate disposal items belonging to the business process investigation category are distributed to the business operation party. Through different disposal parties, the business interaction review of the verified candidate disposal items is realized, and the candidate disposal items that pass the business interaction review are determined as the adopted candidate disposal items.

[0033] In another embodiment, based on the adopted candidate disposal items, a multi-disposal path selection process is performed to execute the adaptation disposal; based on the result of the adaptation disposal, an anomaly signature re-check and update is performed, including: When the adopted candidate disposal item is a configuration change candidate disposal item, gray-scale verification is performed on the adopted candidate disposal item to determine the gray-scale verification result of the candidate disposal item; When the adopted candidate disposal item is a data supplementation candidate disposal item, the adopted candidate disposal item will be converted into a data supplementation work order and pushed to the upstream business end for processing; Based on the grayscale verification results or the processing results of the data supplement work order, update the abnormal identification of the signature of the corresponding non-closed loop link.

[0034] The beneficial effects of the above embodiments are that, in actual operation, the adopted candidate disposal items are divided into two disposal paths according to their nature. Specifically, when the adopted candidate disposal item is a configuration change candidate disposal item, a preset proportion of samples are extracted from the abnormal signatures in the historical preset statistical window. The original configuration and candidate patches are used to re-execute the tracing of the corresponding abnormal signature link group subset to obtain the link integrity and abnormal signature reproduction rate, and the gray-scale verification result is determined accordingly. If the gray-scale verification passes, the corresponding configuration takes effect; if the gray-scale verification fails, the original configuration is restored. When the adopted candidate disposal item is a data supplementation candidate disposal item, it is converted into a data supplementation work order and pushed to the upstream business end for processing. The data maintenance team at the upstream business end enters the work order processing result. Finally, based on the gray-scale verification result or the processing result of the data supplementation work order, the corresponding link is traced again and the abnormal identifier of the unclosed link signature is updated, thereby completing the disposal result review.

[0035] Please see Figure 2 As shown, an embodiment of this application provides a supply chain anomaly cause identification and task derivation system. This supply chain anomaly cause identification and task derivation system includes: The anomaly cause identification module is used to receive the link details and the set of unclosed links output by the link tracing end, and identify the anomaly cause type of each unclosed link in the link details and the set of unclosed links; The signature determination module is used to determine the signature of each unclosed link based on the type of anomaly cause. The candidate disposal item determination module is used to generate corresponding candidate disposal items based on the signature of each unclosed link and its corresponding anomaly cause type; The verification module is used to perform multi-level verification on candidate disposal items and determine the candidate disposal items that pass the verification. The review module is used to distribute the verified candidate disposal items to the disposal party and review the business interactions to determine the candidate disposal items to be adopted. The update module is used to perform multi-processing for processing based on the adopted candidate disposal items, thereby executing the adaptation disposal; and to perform anomaly signature re-check and update based on the result of the adaptation disposal.

[0036] The beneficial effects of the above embodiments are that the supply chain anomaly identification and task derivation system automatically identifies the causes of unclosed links and distributes and reviews the handling tasks according to their nature into configuration change type and data supplement type, so as to realize the continuous evolution of the after-sales reverse supply chain rule base.

[0037] In another embodiment, the anomaly cause identification module is used to receive the link details and the set of unclosed links output by the link tracing end, and identify the anomaly cause type of each unclosed link in the link details and the set of unclosed links, including: For the link details and the set of unclosed links output by the link tracing end, identify the anomaly cause type of each unclosed link in the following order: If the same node in an unclosed loop is accessed more than twice, and the node is not in the preset loop whitelist, then the anomaly cause type is determined to be business loop type. If the end node of the unclosed link has a valid downstream path in the preset rule path library, and the associated field used to match the downstream event in the actual event corresponding to the end node is not empty, but no corresponding downstream event is found, then the anomaly cause type is determined to be data missing. If the end node of the unclosed link does not have any downstream path corresponding to the preset rule path library, the anomaly cause type is determined to be the first rule not covered type. If the downstream material library type corresponding to the actual event of the end node of the unclosed link is not in the preset mapping table, then the anomaly cause type is determined to be the second rule not covered type. If none of the anomaly cause types belong to the business cycle type, data missing type, first rule not covered type, or second rule not covered type, then the anomaly cause type is determined to be an unclassified type. The signature determination module is used to determine the signature of each unclosed link based on the anomaly cause type, including: When the anomaly cause type is business cycle type, the hash function processing result of the repeatedly accessed node is used as the signature of the unclosed loop link; When the anomaly cause type is data missing, the hash function processing result of the downstream business node ID and downstream spare parts code fields corresponding to the legitimate downstream path is used as the signature of the unclosed loop link. When the anomaly cause type is the first rule not covered, the hash function processing result of the end node's encoding is used as the signature of the unclosed link; When the anomaly cause type is the second rule not covered, the hash function processing result of the downstream material library type encoding is used as the signature of the unclosed loop link.

[0038] In another embodiment, the candidate disposal item determination module is used to generate corresponding candidate disposal items based on the signature of each unclosed link and its corresponding anomaly cause type, including: When the anomaly cause type is business cycle type, data missing type, first rule not covered type, or second rule not covered type, generate corresponding candidate disposal items based on the signature of each unclosed link and its anomaly cause type; When the cause of the anomaly is an unclassified type, corresponding candidate treatment items are generated manually. The verification module is used to perform multi-level verification on candidate disposal items and determine the candidate disposal items that pass the verification, including: The candidate disposal items are sequentially validated for format, conflict, and feasibility to determine the candidate disposal items that pass the validation.

[0039] In another embodiment, the review module is used to review the distribution and business interaction of the verified candidate disposal items to the disposal party, and to determine the candidate disposal items to be adopted, including: The verified candidate actions are distributed to the rule maintainer, data team, and business operations team for rule change processing, data supplementation processing, and business process review processing, respectively, in order to determine the candidate actions to be adopted.

[0040] In another embodiment, the update module is used to perform multi-processing selection based on the adopted candidate disposal items, thereby performing adaptive disposal; and to perform anomaly signature re-checking update based on the result of the adaptive disposal, including: When the adopted candidate disposal item is a configuration change candidate disposal item, gray-scale verification is performed on the adopted candidate disposal item to determine the gray-scale verification result of the candidate disposal item; When the adopted candidate disposal item is a data supplementation candidate disposal item, the adopted candidate disposal item will be converted into a data supplementation work order and pushed to the upstream business end for processing; Based on the grayscale verification results or the processing results of the data supplement work order, update the abnormal identification of the signature of the corresponding non-closed loop link.

[0041] The supply chain anomaly identification and task derivation system of the present invention corresponds to and has the same operation and effect as the above-mentioned supply chain anomaly identification and task derivation method, and will not be described again here.

[0042] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. This disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims. Thus, if these modifications and variations of the invention fall within the scope of the claims of the invention and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A method for identifying the causes of supply chain anomalies and generating tasks, characterized in that, include: Receive the link details and the set of unclosed links output by the link tracing end, and identify the abnormal cause type of each unclosed link in the link details and the set of unclosed links; Based on the type of anomaly, determine the signature of each unclosed link; Based on the signature of each unclosed link and its corresponding anomaly cause type, corresponding candidate disposal items are generated; the candidate disposal items are subjected to multi-level verification to determine the candidate disposal items that pass the verification. The verified candidate disposal items are distributed to the disposal party and reviewed through business interaction to determine the candidate disposal items to be adopted; Based on the adopted candidate disposal items, a multi-disposal path selection process is performed to execute the adaptation disposal; based on the result of the adaptation disposal, an anomaly signature is checked and updated.

2. The supply chain anomaly identification and task derivation method as described in claim 1, characterized in that: Receive the link details and the set of unclosed links output by the link tracing end, and identify the abnormal cause type of each unclosed link in the link details and the set of unclosed links; Based on the aforementioned anomaly cause type, determine the signature of each unclosed link, including: For the link details and the set of unclosed links output by the link tracing end, identify the anomaly cause type of each unclosed link in the following order: If the same node in an unclosed loop is accessed more than twice, and the node is not in the preset loop whitelist, then the anomaly cause type is determined to be business loop type. If the end node of the unclosed link has a valid downstream path in the preset rule path library, and the associated field used to match the downstream event in the actual event corresponding to the end node is not empty, but no corresponding downstream event is found, then the anomaly cause type is determined to be data missing. If the end node of the unclosed link does not have any downstream path corresponding to the preset rule path library, the anomaly cause type is determined to be the first rule not covered type. If the downstream material library type corresponding to the actual event of the end node of the unclosed link is not in the preset mapping table, then the anomaly cause type is determined to be the second rule not covered type. If none of the anomaly cause types belong to the business cycle type, data missing type, first rule not covered type, or second rule not covered type, then the anomaly cause type is determined to be an unclassified type. When the anomaly cause type is business cycle type, the hash function processing result of the repeatedly accessed node is used as the signature of the unclosed link; When the anomaly cause type is data missing, the hash function processing result of the two fields, downstream business node ID and downstream spare parts code, corresponding to the legitimate downstream path is used as the signature of the unclosed loop link. When the anomaly cause type is the first rule not covered, the hash function processing result of the end node's encoding is used as the signature of the unclosed link; When the anomaly cause type is the second rule not covered type, the hash function processing result of the downstream material library type encoding is used as the signature of the unclosed loop link.

3. The supply chain anomaly identification and task derivation method as described in claim 2, characterized in that: Based on the signature of each unclosed link and its corresponding anomaly cause type, generate corresponding candidate disposal items; The candidate disposal items are subjected to multi-level verification to determine the candidate disposal items that pass the verification, including: When the anomaly cause type is business cycle type, data missing type, first rule not covered type, or second rule not covered type, corresponding candidate disposal items are generated according to the signature of each unclosed link and its anomaly cause type; When the anomaly cause type is an unclassified type, corresponding candidate disposal items are generated manually; The candidate disposal items are sequentially validated for format, conflict, and feasibility to determine the candidate disposal items that pass the validation.

4. The supply chain anomaly identification and task derivation method as described in claim 1, characterized in that: The verified candidate disposal items are distributed to the disposal party and reviewed through business interactions to determine the adopted candidate disposal items, including: The verified candidate disposal items are distributed to the rule maintainer, data team, and business operation team for rule change processing, data supplementation processing, and business process investigation processing, respectively, in order to determine the candidate disposal items to be adopted.

5. The supply chain anomaly identification and task derivation method as described in claim 1, characterized in that: Based on the adopted candidate disposal items, a multi-disposal path selection process is performed to execute the adaptation disposal. Based on the results of the adaptation process, an anomaly signature check and update is performed, including: When the adopted candidate disposal item is a configuration change candidate disposal item, gray-scale verification is performed on the adopted candidate disposal item to determine the gray-scale verification result of the candidate disposal item; When the adopted candidate disposal item is a data supplementation candidate disposal item, the adopted candidate disposal item is converted into a data supplementation work order and pushed to the upstream business end for processing; Based on the grayscale verification result or the processing result of the data supplement work order, the signature of the corresponding non-closed loop link is updated with an anomaly identifier.

6. A supply chain anomaly identification and task derivation system, characterized in that, include: The anomaly cause identification module is used to receive the link details and the set of unclosed links output by the link tracing end, and identify the anomaly cause type of each unclosed link in the link details and the set of unclosed links. The signature determination module is used to determine the signature of each unclosed link based on the anomaly cause type. The candidate disposal item determination module is used to generate corresponding candidate disposal items based on the signature of each unclosed link and its corresponding anomaly cause type; The verification module is used to perform multi-level verification on the candidate disposal items and determine the candidate disposal items that pass the verification. The review module is used to review the distribution and business interaction of the verified candidate disposal items to the disposal party and determine the candidate disposal items to be adopted. The update module is used to perform multi-processing selection based on the adopted candidate processing items, thereby performing adaptation processing; and to perform anomaly signature re-checking and updating based on the result of the adaptation processing.

7. The supply chain anomaly identification and task derivation system as described in claim 6, characterized in that: The anomaly cause identification module is used to receive the link details and the set of unclosed links output by the link tracing end, and identify the anomaly cause type of each unclosed link in the link details and the set of unclosed links, including: For the link details and the set of unclosed links output by the link tracing end, identify the anomaly cause type of each unclosed link in the following order: If the same node in an unclosed loop is accessed more than twice, and the node is not in the preset loop whitelist, then the anomaly cause type is determined to be business loop type. If the end node of the unclosed link has a valid downstream path in the preset rule path library, and the associated field used to match the downstream event in the actual event corresponding to the end node is not empty, but no corresponding downstream event is found, then the anomaly cause type is determined to be data missing. If the end node of the unclosed link does not have any downstream path corresponding to the preset rule path library, the anomaly cause type is determined to be the first rule not covered type. If the downstream material library type corresponding to the actual event of the end node of the unclosed link is not in the preset mapping table, then the anomaly cause type is determined to be the second rule not covered type. If none of the anomaly cause types belong to the business cycle type, data missing type, first rule not covered type, or second rule not covered type, then the anomaly cause type is determined to be an unclassified type. The signature determination module is used to determine the signature of each unclosed link based on the anomaly cause type, including: When the anomaly cause type is business cycle type, the hash function processing result of the repeatedly accessed node is used as the signature of the unclosed link; When the anomaly cause type is data missing, the hash function processing result of the two fields, downstream business node ID and downstream spare parts code, corresponding to the legitimate downstream path is used as the signature of the unclosed loop link. When the anomaly cause type is the first rule not covered, the hash function processing result of the end node's encoding is used as the signature of the unclosed link; When the anomaly cause type is the second rule not covered type, the hash function processing result of the downstream material library type encoding is used as the signature of the unclosed loop link.

8. The supply chain anomaly identification and task derivation system as described in claim 7, characterized in that: The candidate disposal item determination module is used to generate corresponding candidate disposal items based on the signature of each unclosed link and its corresponding anomaly cause type, including: When the anomaly cause type is business cycle type, data missing type, first rule not covered type, or second rule not covered type, corresponding candidate disposal items are generated according to the signature of each unclosed link and its anomaly cause type; When the anomaly cause type is an unclassified type, corresponding candidate disposal items are generated manually; The verification module is used to perform multi-level verification on the candidate disposal items to determine the candidate disposal items that pass the verification, including: The candidate disposal items are sequentially validated for format, conflict, and feasibility to determine the candidate disposal items that pass the validation.

9. The supply chain anomaly identification and task derivation system as described in claim 8, characterized in that: The review module is used to review the distribution and business interaction of the verified candidate disposal items to the disposal party, and to determine the candidate disposal items to be adopted, including: The verified candidate disposal items are distributed to the rule maintainer, data team, and business operation team for rule change processing, data supplementation processing, and business process investigation processing, respectively, in order to determine the candidate disposal items to be adopted.

10. The supply chain anomaly identification and task derivation system as described in claim 6, characterized in that: The update module is used to perform multi-processing selection based on the adopted candidate processing items, thereby performing adaptive processing. Based on the results of the adaptation process, an anomaly signature check and update is performed, including: When the adopted candidate disposal item is a configuration change candidate disposal item, gray-scale verification is performed on the adopted candidate disposal item to determine the gray-scale verification result of the candidate disposal item; When the adopted candidate disposal item is a data supplementation candidate disposal item, the adopted candidate disposal item is converted into a data supplementation work order and pushed to the upstream business end for processing; Based on the grayscale verification result or the processing result of the data supplement work order, the signature of the corresponding non-closed loop link is updated with an anomaly identifier.

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