Method and apparatus for detecting inventory anomalies

Abstract

Embodiments of the present disclosure disclose a method and device for detecting inventory anomaly. The specific implementation of the method comprises: obtaining platform inventory management information and logistics inventory management information of a target commodity; determining overstock information of the target commodity according to the platform inventory management information and the logistics inventory management information; determining an inventory difference between the platform inventory and the logistics inventory according to the platform inventory management information, the logistics inventory management information and the overstock information; and outputting information related to the inventory difference of the target commodity if the inventory difference is greater than a predetermined threshold. This implementation can avoid over-selling in a mall, reduce complaints from merchants, and bring actual economic benefits. It greatly reduces the difficulty of investigating the reasons for the difference in commodities.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of logistics technology, and more specifically to methods and apparatus for detecting inventory anomalies. Background Technology

[0002] The inventory of goods from different merchants can be managed by different management systems. The warehouse manages the actual inventory of goods. Over time, due to various reasons for the interaction between the systems, the inventory between the management system and the warehouse may become inconsistent. This inventory difference will lead to overselling of goods, which often results in complaints from external merchants. There have been cases of contract termination with the company due to inventory differences, as well as the problem of having physical goods but no inventory to sell, resulting in lost sales opportunities and actual economic losses.

[0003] Existing technologies involve numerous business nodes, resulting in complex message types after combination. Inevitably, inconsistencies in inventory can arise due to system issues. For example, a warehouse might deduct inventory normally, but the batch information for the product might be missing after being sent back to the management system, causing the deduction to fail. Alternatively, a message might be missing due to system modifications or the addition of nodes, preventing one side from performing inventory modification operations. Furthermore, because inventory involves multiple systems, which generally process inventory asynchronously, the lack of corresponding information feedback mechanisms between some node systems can also lead to inventory inconsistencies. Over time, these discrepancies may accumulate, making it difficult to handle product discrepancies and investigate their causes. Summary of the Invention

[0004] Embodiments of this disclosure provide methods and apparatus for detecting inventory anomalies.

[0005] In a first aspect, embodiments of this disclosure provide a method for detecting inventory anomalies, comprising: acquiring platform inventory management information and logistics inventory management information of a target product; determining backlog information of the target product based on the platform inventory management information and logistics inventory management information; determining the inventory difference between platform inventory and logistics inventory based on the platform inventory management information, logistics inventory management information and the backlog information; and outputting information related to the inventory difference of the target product if the inventory difference is greater than a predetermined threshold.

[0006] In some embodiments, obtaining platform inventory management information and logistics inventory management information of the target product includes: adding the target product name to the push list; and synchronizing the platform inventory management information and logistics inventory management information of the target product in response to detecting changes in the inventory quantity in the platform inventory management information and / or the inventory quantity in the logistics inventory management information of the target product.

[0007] In some embodiments, determining the backlog information of the target product based on the platform inventory management information and logistics inventory management information includes: obtaining at least one of the following backlog information of the target product: received but not returned to the shelf; warehouse abnormality and not returned to the warehouse; partial outbound from the warehouse; change of ownership order; and calculating the backlog information of the target product based on the at least one backlog information.

[0008] In some embodiments, the method further includes: obtaining platform flow management information and logistics flow management information of the target product; calculating the flow difference in the platform flow management information and the logistics flow management information; and if the flow difference is greater than a predetermined threshold, outputting information related to the flow difference of the target product.

[0009] In some embodiments, the method further includes: in response to receiving a request to query an inventory discrepancy of a target product, displaying information related to the inventory difference of the target product on a query interface.

[0010] In some embodiments, the method further includes: in response to receiving a request to analyze inventory anomalies of a target product, displaying information related to the sales volume discrepancies of the target product on a query interface.

[0011] In some embodiments, the method further includes: determining the backlog information of the target product at regular intervals based on the platform inventory management information and the logistics inventory management information according to pre-configured task scheduling information, and determining the inventory difference between the platform inventory and the logistics inventory based on the platform inventory management information, the logistics inventory management information and the backlog information.

[0012] Secondly, embodiments of this disclosure provide an apparatus for detecting inventory anomalies, comprising: an acquisition unit configured to acquire platform inventory management information and logistics inventory management information of a target product; a determination unit configured to determine backlog information of the target product based on the platform inventory management information and logistics inventory management information; a calculation unit configured to determine the inventory difference between platform inventory and logistics inventory based on the platform inventory management information, logistics inventory management information, and the backlog information; and an output unit configured to output information related to the inventory difference of the target product if the inventory difference is greater than a predetermined threshold.

[0013] In some embodiments, the acquisition unit is further configured to: add the target product name to the push list; and synchronize the platform inventory management information and logistics inventory management information of the target product in response to detecting changes in the inventory quantity in the platform inventory management information and / or the inventory quantity in the logistics inventory management information of the target product.

[0014] In some embodiments, the determining unit is further configured to: obtain at least one of the following backlog information for the target product: received but not returned to the shelf; abnormal warehouse and not returned to the warehouse; partial warehouse outbound; change of ownership order; and to calculate the backlog information of the target product based on the at least one backlog information.

[0015] In some embodiments, the apparatus further includes an analysis unit configured to: acquire platform flow management information and logistics flow management information of the target product; calculate the flow difference in the platform flow management information and the logistics flow management information; and if the flow difference is greater than a predetermined threshold, output information related to the flow difference of the target product.

[0016] In some embodiments, the apparatus further includes a query unit configured to: in response to receiving a query request for an inventory discrepancy of a target product, display information related to the inventory difference of the target product on a query interface.

[0017] In some embodiments, the apparatus further includes a query unit configured to: in response to receiving a request to analyze inventory anomalies of a target product, display information related to the sales volume discrepancies of the target product on a query interface.

[0018] In some embodiments, the apparatus further includes a scheduling unit configured to: periodically determine the backlog information of the target product based on the platform inventory management information and the logistics inventory management information according to pre-configured task scheduling information, and determine the inventory difference between the platform inventory and the logistics inventory based on the platform inventory management information, the logistics inventory management information and the backlog information.

[0019] Thirdly, embodiments of this disclosure provide an electronic device for detecting inventory anomalies, comprising: one or more processors; and a storage device having one or more computer programs stored thereon, wherein when the one or more computer programs are executed by the one or more processors, the one or more processors perform the method as described in any one of the first aspects.

[0020] Fourthly, embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of the first aspects.

[0021] This application resolves inventory discrepancies between various systems, establishes an inventory discrepancy early warning system, ensures that actual salable goods are in stock, addresses overselling issues and merchant complaints, and brings tangible economic efficiency to the warehouse. After approximately one year of operation, the solution has resolved discrepancies of approximately 2,000 large-item domestic orders and approximately 1,400 large-item external orders, totaling approximately 3,400 items. At a cost of 2,000 yuan per item, this translates to approximately 7 million yuan in economic efficiency. Furthermore, the early warning mechanism allows for detailed viewing of discrepancy quantities and document information, significantly reducing the difficulty of inventory discrepancy investigation. Attached Figure Description

[0022] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0023] Figure 1 This is an exemplary system architecture diagram to which one embodiment of this disclosure can be applied;

[0024] Figure 2 This is a flowchart of one embodiment of the method for detecting inventory anomalies according to the present disclosure;

[0025] Figure 3 This is a flowchart of yet another embodiment of the method for detecting inventory anomalies according to the present disclosure;

[0026] Figures 4a-4c This is a schematic diagram illustrating an application scenario of the method for detecting inventory anomalies according to this disclosure;

[0027] Figure 5 This is a schematic diagram of one embodiment of the apparatus for detecting inventory anomalies according to the present disclosure;

[0028] Figure 6 This is a schematic diagram of the structure of a computer system suitable for implementing embodiments of the present disclosure. Detailed Implementation

[0029] The present disclosure will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0030] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] Figure 1An exemplary system architecture is shown for embodiments of the methods or apparatus for detecting inventory anomalies that can be applied according to this disclosure.

[0032] like Figure 1 As shown, the system architecture can include a production system, a big data platform, and application systems. These systems communicate with each other via a network. The network can include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0033] The production system provides data sources from three sources: Eclipse CLP, Warehouse Management System (WMS), and ERP. ERP is an internal order management system, including internal order inventory management. Eclipse CLP is an external order management system, including external order inventory management. WMS is a warehouse management system for the logistics industry, providing a range of essential functions such as goods inbound and outbound operations, order sales, and shipment. Internal order goods are products provided by the system itself, while external order goods are products provided by third-party sellers.

[0034] The big data platform is a high-performance, highly stable, and highly secure data storage, data governance, data analysis, and data mining platform based on Hadoop. It provides data access, data processing, and data distribution functions. Data can be pulled from data sources via Plumber tasks, ensuring data synchronization between the platform and data sources. The platform can also schedule data processing tasks to calculate inventory discrepancies, cash flow discrepancies, and in-transit quantities (i.e., backlog information) for different types of commodity management systems. The calculated data is then pushed to the target data source report in the application system via Plumber push tasks.

[0035] The application system provides an alarm and query platform to read inventory anomaly information from the target data source. It promptly outputs anomaly alarm information and also provides a query interface for users to query and locate inventory anomalies.

[0036] It's important to note that a big data platform can be either hardware or software. When it's hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When it's software, it can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services), or as a single software program or software module. No specific limitations are made here. A big data platform can also be a server for a distributed system, or a server integrated with blockchain technology. A big data platform can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology.

[0037] For example, in the existing interaction process of export orders, the ECLP (Export Order Product Management System, including Export Order Product Inventory Management) creates a sales order and sends it to the WMS (Warehouse Management System for the Logistics Industry, a system that provides a series of important functions such as product inbound and outbound, order sales, and shipment). The WMS receives the order, locates the warehouse, picks, dispatches, and then verifies and ships the goods. After shipment, the WMS deducts inventory and sends an inventory deduction instruction to the ECLP system. The ECLP will deduct the corresponding inventory based on information such as product grade, batch, and quantity shipped. Currently, the document dimensions involve rejection receipts, spare parts receipts, spare parts warehouse rejection receipts, purchase receipts, sales order shipments, internal distribution order shipments, return order shipments, and owner change orders. Each document involves product grade, batch, and quantity shipped. The systems use various combinations to organize different messages to perform corresponding inventory increase and modification operations.

[0038] It should be noted that the method for detecting inventory anomalies provided in the embodiments of this disclosure is generally executed by a big data platform, and correspondingly, the device for detecting inventory anomalies is generally set in the big data platform.

[0039] It should be understood that Figure 1 The number of production systems, big data platforms, and application systems shown is merely illustrative. Depending on implementation needs, any number of production systems, big data platforms, and application systems can be included.

[0040] Continue to refer to Figure 2 The diagram illustrates a flow 200 of an embodiment of a method for detecting inventory anomalies according to the present disclosure. The method for detecting inventory anomalies includes the following steps:

[0041] Step 201: Obtain the platform inventory management information and logistics inventory management information of the target product.

[0042] In this embodiment, the execution entity of the method for detecting inventory anomalies (e.g.) Figure 1 The big data platform shown can obtain platform inventory management information and logistics inventory management information from the production system via wired or wireless connections. Plumber tasks can be pre-configured to pull data from the production system, and the data retrieval operation can be executed whenever a change in inventory levels is detected in the production system. The retrieval process is as follows: Figure 4a As shown.

[0043] The business systems involved in this process include inventory, inventory flow, and document data tables. This data is used to build a big data comparison platform to compare basic inventory data with inventory and flow data. For example, the WMS system involves warehouse inventory, the ERP system involves online store inventory, and the ECLP system involves external order product inventory. The data tables are extracted into a data warehouse and eventually synchronized to a data mart. Scheduling tasks are created through the data mart, and task execution rules, time, cycle, retry strategies, etc. are set. Scheduling tasks can be used to calculate the inventory of each system separately using scripts such as Python.

[0044] Platform inventory management information refers to inventory management information within an ERP or ECLP system. Logistics inventory management information refers to inventory management information within a WMS (Windows Management System).

[0045] The target products are those that the user pre-specifies as requiring anomaly detection. The user can also specify a whitelist of products that should not be subject to anomaly detection, thus preventing the retrieval of inventory data for products on the whitelist. If no whitelist is set, inventory data for all products will be retrieved. The whitelist is as follows: Figure 4b As shown. The whitelist can be set based on attributes such as name, price, and warehouse. For example, if products priced less than 1,000 yuan are included in the whitelist, the system will not perform inventory anomaly detection on small items, but only on large items.

[0046] Platform inventory management information and logistics inventory management information involve inventory tables, inventory transaction logs, inbound reservation tables, reservation order details tables, acceptance record tables, and shelving task tables. Outbound information involves collection order tables, collection order details tables, and order tables. Through big data push tasks, inventory data is written to the corresponding linked tables in the big data inventory application tables. These linked tables can include information such as: English name, Chinese name, data type, security level, and description. For example, Chinese names might include: Merchant ID, Business Unit ID, WMS Number, ECLP Number, ERP Number, etc.

[0047] Optionally, it can also sort out ERP and ECLP source data, domestic order inventory and domestic order transaction flow, and foreign order inventory and foreign order inventory transaction flow.

[0048] In some optional implementations of this embodiment, obtaining the platform inventory management information and logistics inventory management information of the target product includes: adding the target product name to the push list; and synchronizing the platform inventory management information and logistics inventory management information of the target product in response to detecting changes in the inventory quantity in the platform inventory management information and / or the inventory quantity in the logistics inventory management information. This allows for targeted monitoring of target product inventory anomalies. Target products can be selected using parameters such as product price (e.g., large items priced over 1,000), thereby effectively utilizing the resources of the big data platform, reducing computational load, and saving operational costs. Optionally, based on the warehouses where the detected target products are concentrated, it can be reasonably inferred that other products in those warehouses may also have anomalies. Then, all products in the warehouses with anomalies can be used as target products for detection. This allows for rapid problem localization and prevents omissions.

[0049] Step 202: Determine the backlog information of the target product based on the platform's inventory management information and logistics inventory management information.

[0050] In this embodiment, since some documents are in transit (i.e., backlogged), such data also needs to be calculated.

[0051] Backlog information includes received and shelved goods not reported back; abnormal warehouse operations not returned to the warehouse; partial outbound shipments from warehouses; and change of ownership orders. The specific logic is as follows:

[0052] a) Logic for receiving and putting away goods without feedback: The receiving and putting away status includes received but no task generated or not put away; partially put away; fully put away but not fully inspected; separately count purchase orders, internal distribution receipts, rejected receipts, and spare parts receipts; the statistical logic is based on the reservation order table and reservation order details table, receiving records, putting away tasks, etc., according to the corresponding logic; the counted quantity is written to the no_task field of the inventory summary wide table app_laga_store_eclp_inv_wid_orc; the wide table contains multi-day statistical data, such as 15 days.

[0053] b) Logic for abnormal warehouse return: Based on the aggregate order table and the detailed table, the warehouse shipment is counted. If the order is canceled, it is determined whether a return order is generated. If a return order exists, the return type of the reservation order is counted. The quantity of the return order is calculated and the data is written to the order_return field of the inventory summary wide table app_laga_store_eclp_inv_wid_orc.

[0054] c) Warehouse-specific outbound logic: Based on orders and order details, calculate the outbound volume of warehouse-specific orders and write the data to the sql_mut_section field of the inventory summary wide table app_laga_store_eclp_inv_wid_orc;

[0055] d) Ownership Change Order Logic: Based on the ownership order and ownership task, count the owner change orders in execution. Based on the ownership order status and task quantity, count the quantity in execution and write the data to the mv_right field of the inventory summary wide table app_laga_store_eclp_inv_wid_orc.

[0056] In some optional implementations of this embodiment, determining the backlog information of the target product based on the platform inventory management information and logistics inventory management information includes: obtaining at least one of the following backlog information for the target product: received but not returned after shelving; warehouse anomaly and not returned to the warehouse; partial outbound shipment from the warehouse; change of ownership document; and calculating the backlog information of the target product based on the at least one backlog information. By considering every possible backlog situation, the final total is calculated. This ensures that no backlog information is missed, improving the accuracy of inventory detection.

[0057] Step 203: Determine the inventory difference between platform inventory and logistics inventory based on platform inventory management information, logistics inventory management information, and backlog information.

[0058] In this embodiment, the calculated actual internal order discrepancy is calculated as follows: ERP inventory - WMS inventory - in-transit inventory; external order discrepancy is calculated as: ECLP inventory - WMS inventory - in-transit inventory. In-transit inventory refers to backlog information. After calculating the inventory discrepancy, the inventory flow is analyzed through a scheduling task. By comparing and associating the flow document number, warehouse number, product, and product level, the discrepancy amount and the document associated with the discrepancy amount are finally generated.

[0059] Write a Python script for inventory comparison. Logically compare the WMS inventory table, ECLP inventory table, and ERP inventory table retrieved from big data. Group and statistically analyze the data by warehouse, product, product grade, and owner. Deduct data from the whitelist data table and the corresponding backlog information data to generate difference data and record it in the app_openstage_laga_stockdiff_sum_d table.

[0060] In some optional implementations of this embodiment, the method further includes: periodically determining the backlog information of the target product based on the platform inventory management information and logistics inventory management information according to pre-configured task scheduling information; and determining the inventory difference between the platform inventory and the logistics inventory based on the platform inventory management information, logistics inventory management information, and the backlog information. The dependency diagram of the task relationship for the foreign order product script difference is shown below. Figure 4cAs shown, the settings for calculating inventory discrepancies in overseas orders can include: basic task information (e.g., task type, task name, task level, etc.) and task execution rules (e.g., scheduling cycle, execution time, timeout duration, maximum number of concurrent instances, etc.). Inventory anomaly detection can be automatically triggered based on the scheduling task configuration information, effectively utilizing resources and improving work efficiency.

[0061] Step 204: If the inventory difference is greater than the predetermined threshold, output the information related to the inventory difference of the target product.

[0062] In this embodiment, a predetermined threshold (e.g., 0) can be set as an alarm threshold. When this value is exceeded, an alarm is triggered, outputting information related to the inventory discrepancy of the target product. This includes not only the inventory quantity difference but also information such as the warehouse name, storage location, and time, facilitating the identification of the cause of the inventory discrepancy. The output can be delivered via webpage display, SMS, email, or other methods. This allows for timely notification of any anomalies to management personnel. Furthermore, the inventory discrepancy information can be saved for future reference.

[0063] The method provided by the above embodiments of this disclosure can prevent overselling in online stores, reduce merchant complaints, and bring actual economic benefits. It also greatly reduces the difficulty of investigating the causes of discrepancies in products.

[0064] In some optional implementations of this embodiment, the method further includes: in response to receiving a request to query an inventory discrepancy of a target product, displaying information related to the inventory discrepancy of the target product on a query interface. The application system can provide an inventory discrepancy query services; users can enter the product name on the query interface, and the application system can find the information related to the inventory discrepancy of the target product from stored inventory discrepancy-related information based on the product name. Optionally, other query conditions can also be set to facilitate queries anytime, anywhere.

[0065] Further reference Figure 3 This illustrates a flow 300 of another embodiment of a method for detecting inventory anomalies. Flow 300 of this method for detecting inventory anomalies includes the following steps:

[0066] Step 301: Obtain the platform inventory management information and logistics inventory management information of the target product.

[0067] Step 302: Determine the backlog information of the target product based on the platform's inventory management information and logistics inventory management information.

[0068] Step 303: Determine the inventory difference between platform inventory and logistics inventory based on platform inventory management information, logistics inventory management information, and backlog information.

[0069] Step 304: If the inventory difference is greater than the predetermined threshold, output the information related to the inventory difference of the target product.

[0070] Steps 301-304 are basically the same as steps 201-204, so they will not be described again.

[0071] Step 305: Obtain the platform transaction management information and logistics transaction management information of the target product.

[0072] In this embodiment, the platform's transaction management information, such as ERP and ECLP source data, is sorted out to extract domestic order inventory and domestic order transaction flow, as well as foreign order inventory and foreign order inventory transaction flow.

[0073] Step 306: Calculate the difference in flow between the platform flow management information and the logistics flow management information.

[0074] In this embodiment, a Python script is written to compare transaction flows. The logic compares the inventory transaction flow of the WMS (fdm_wms3_inv_transaction_chain) table, the transaction flow record table of the internal order ERP, and the transaction flow record table of the ECLP. It compares and queries by associated order number, calculates the difference based on warehouse, product, grade, and consignor, and finally writes the data into the flow difference summary tables app_laswms_erp_flow_group_diff (internal orders) and app_openstage_laga_stockdiff_sum_d (external orders).

[0075] Step 307: If the flow difference is greater than a predetermined threshold, output information related to the flow difference of the target product.

[0076] In this embodiment, a predetermined threshold can be set according to the alarm level. For example, the predetermined threshold for a regular alarm is set to 0. A predetermined threshold, such as 10, can also be set for a critical alarm. Different alarm levels are output in different display formats; for example, a critical alarm is displayed in red font.

[0077] In some optional implementations of this embodiment, the method further includes: in response to receiving a request to analyze inventory anomalies of a target product, displaying information related to the cash flow discrepancies of the target product on a query interface. The application system can provide inventory anomaly analysis services. Users can enter the name of the product to be analyzed on the query interface, and the application system can find the cash flow discrepancy-related information of the target product from the stored cash flow discrepancy-related information based on the product name. Optionally, other query conditions can also be set to facilitate queries anytime, anywhere.

[0078] Further reference Figure 5 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a device for detecting inventory anomalies, which is similar to... Figure 2Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0079] like Figure 5 As shown, the device 500 for detecting inventory anomalies in this embodiment includes: an acquisition unit 501, a determination unit 502, a calculation unit 503, and an output unit 504. The acquisition unit 501 is configured to acquire platform inventory management information and logistics inventory management information of the target product; the determination unit 502 is configured to determine the backlog information of the target product based on the platform inventory management information and logistics inventory management information; the calculation unit 503 is configured to determine the inventory difference between the platform inventory and the logistics inventory based on the platform inventory management information, the logistics inventory management information, and the backlog information; and the output unit 504 is configured to output information related to the inventory difference of the target product if the inventory difference exceeds a predetermined threshold.

[0080] In this embodiment, the specific processing of the acquisition unit 501, determination unit 502, calculation unit 503, and output unit 504 of the device 500 for detecting inventory anomalies can be referred to... Figure 2 The corresponding steps are 201, 202, 203, and 204 in the embodiment.

[0081] In some optional implementations of this embodiment, the acquisition unit 501 is further configured to: add the target product name to the push list; and synchronize the platform inventory management information and logistics inventory management information of the target product in response to detecting changes in the inventory quantity in the platform inventory management information and / or the inventory quantity in the logistics inventory management information of the target product.

[0082] In some optional implementations of this embodiment, the determining unit 502 is further configured to: obtain at least one of the following backlog information of the target product: received but not returned to the shelf; abnormal warehouse and not returned to the warehouse; partial warehouse outbound; change of ownership order; and statistically analyze the backlog information of the target product based on the at least one backlog information.

[0083] In some optional implementations of this embodiment, the device 500 further includes an analysis unit (not shown in the figures), configured to: acquire platform flow management information and logistics flow management information of the target product; calculate the flow difference in the platform flow management information and the logistics flow management information; and if the flow difference is greater than a predetermined threshold, output information related to the flow difference of the target product.

[0084] In some optional implementations of this embodiment, the device 500 further includes a query unit (not shown in the figures), configured to: in response to receiving a request to query an inventory discrepancy of a target product, display information related to the inventory difference of the target product on the query interface.

[0085] In some optional implementations of this embodiment, the device 500 further includes a query unit (not shown in the figures), configured to: in response to receiving a request to analyze the inventory anomaly of the target product, display information related to the flow difference of the target product on the query interface.

[0086] In some optional implementations of this embodiment, the device 500 further includes a scheduling unit (not shown in the figures), configured to: determine the backlog information of the target product periodically based on the platform inventory management information and the logistics inventory management information according to pre-configured task scheduling information, and determine the inventory difference between the platform inventory and the logistics inventory based on the platform inventory management information, the logistics inventory management information and the backlog information.

[0087] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0088] An electronic device for detecting inventory anomalies includes: one or more processors; and a storage device having one or more computer programs stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform the method as described in process 200 or 300.

[0089] A computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method as described in process 200 or 300.

[0090] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] like Figure 6As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0092] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0093] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as methods for detecting inventory anomalies. For example, in some embodiments, the method for detecting inventory anomalies may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method for detecting inventory anomalies described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method for detecting inventory anomalies by any other suitable means (e.g., by means of firmware).

[0094] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0098] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0099] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be servers in distributed systems or servers incorporating blockchain technology. Servers can also be cloud servers, or intelligent cloud computing servers or intelligent cloud hosts with artificial intelligence technology.

[0100] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for detecting inventory anomalies, comprising: Obtain the platform inventory management information and logistics inventory management information of the target product. Among them, all products in the warehouses where anomalies occur are taken as target products. Write the inventory data involved in the platform inventory management information and logistics inventory management information into the corresponding zipper tables of the big data inventory application table. Each zipper table includes at least one of the following: English name, Chinese name, data type, security level, and description. Determining the backlog information of the target product based on the platform's inventory management information and logistics inventory management information includes: obtaining at least one of the following backlog information of the target product: received but not returned after being put on the shelf; warehouse abnormality and not returned to the warehouse; partial outbound from the warehouse; change of ownership order; and compiling the backlog information of the target product based on the at least one backlog information. The inventory difference between the platform inventory and the logistics inventory is determined based on the platform inventory management information, the logistics inventory management information, and the backlog information. If the inventory discrepancy exceeds a predetermined threshold, information related to the inventory discrepancy of the target product will be output.

2. The method according to claim 1, wherein, The acquisition of platform inventory management information and logistics inventory management information for the target product includes: Add the target product name to the push notification list; In response to detecting changes in the inventory quantity in the platform inventory management information and / or logistics inventory management information of the target product, the platform inventory management information and logistics inventory management information of the target product are synchronized.

3. The method according to claim 1, wherein, The method further includes: Obtain platform transaction flow management information and logistics transaction flow management information for the target product; Calculate the difference in flow between the platform flow management information and the logistics flow management information; If the difference in flow rate exceeds a predetermined threshold, information related to the difference in flow rate for the target product will be output.

4. The method according to claim 1, wherein, The method further includes: In response to receiving a request to query an inventory discrepancy for a target product, information related to the inventory difference of the target product is displayed on the query interface.

5. The method according to claim 3, wherein, The method further includes: In response to receiving a request to analyze inventory anomalies of the target product, information related to the transaction volume discrepancies of the target product is displayed on the query interface.

6. The method according to claim 1, wherein, The method further includes: Based on pre-configured task scheduling information, the backlog information of the target product is determined periodically according to the platform inventory management information and logistics inventory management information, and the inventory difference between the platform inventory and the logistics inventory is determined according to the platform inventory management information, logistics inventory management information and the backlog information.

7. An apparatus for detecting inventory anomalies, comprising: The acquisition unit is configured to acquire platform inventory management information and logistics inventory management information of the target product. Specifically, all products in the warehouse where the anomaly occurred are taken as the target product. The inventory data involved in the platform inventory management information and logistics inventory management information are written into the corresponding zipper tables of the big data inventory application table. Each zipper table includes at least one of the following: English name, Chinese name, data type, security level, and description. The determining unit is configured to determine the backlog information of the target product based on the platform inventory management information and logistics inventory management information, including: obtaining at least one of the following backlog information of the target product: received but not returned after being put on the shelf; warehouse abnormality and not returned to the warehouse; partial outbound from the warehouse; change of ownership order; and calculating the backlog information of the target product based on the at least one backlog information. The calculation unit is configured to determine the inventory difference between the platform inventory and the logistics inventory based on the platform inventory management information, the logistics inventory management information, and the backlog information. The output unit is configured to output information related to the inventory difference of the target product if the inventory difference exceeds a predetermined threshold.

8. An electronic device for detecting inventory anomalies, comprising: One or more processors; Storage device, on which one or more computer programs are stored, When the one or more computer programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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