Method, device and readable medium for processing commodity objects

Abstract

Embodiments of the present application provide a commodity object-based processing method, device and readable medium, the method comprising: obtaining commodity exception information, the commodity exception information comprising a stock keeping unit SKU of a commodity object and an exception type; matching the commodity exception information according to a matching rule to determine at least one exception type corresponding to a judgment sheet; and analyzing the at least one judgment sheet to determine responsibility attribution information of the at least one judgment sheet. The responsibility attribution information of the at least one judgment sheet can be automatically determined, without manual determination of responsibility, and the processing efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a processing method based on commodity objects, an electronic device, and a machine-readable medium. Background Technology

[0002] Community group buying is a shopping and consumption behavior of residents within a residential community. It is a regionalized, niche, and localized form of group buying that relies on a real community.

[0003] Community group buying is a sales model that mainly involves community shops offering group buying discounts to residents in the surrounding area (within the community). After users order goods, they will be delivered to the corresponding shops or other stations, and users will pick up their goods at the stations themselves.

[0004] However, since the goods purchased by multiple users are delivered to one station, various problems may occur during the delivery process, such as excess or shortage of goods, or damaged goods. These problems often require manual determination of responsibility, which is inefficient. Summary of the Invention

[0005] This application provides a processing method based on commodity objects to improve processing efficiency.

[0006] Accordingly, embodiments of this application also provide a processing device based on commodity objects, an electronic device, and a storage medium to ensure the implementation and application of the above system.

[0007] Accordingly, this application also provides a processing method based on commodity objects, the method comprising:

[0008] Obtain product anomaly information, which includes the product object's inventory unit (SKU) and anomaly type;

[0009] The abnormal information of the goods is matched according to the matching rules to determine the judgment order corresponding to at least one abnormal type;

[0010] Analyze the at least one liability judgment to determine the liability attribution information of the at least one liability judgment.

[0011] Optionally, obtaining product anomaly information includes:

[0012] Obtain at least one abnormal information reported by a grid warehouse, wherein the grid warehouse is a node in the transportation process of the commodity object, and the nodes in the transportation process also include the central warehouse;

[0013] Collect abnormal information from at least one grid warehouse to determine abnormal product information.

[0014] Optionally, the product anomaly information also includes the product quantity and the reason for the anomaly; the step of matching the product anomaly information according to matching rules to determine at least one judgment order corresponding to anomaly type includes:

[0015] Obtain the SKU of the product object from the product error information;

[0016] Match the anomaly type and cause of each record according to the SKU;

[0017] When two records meet the deduction rules, the abnormality type is determined and the corresponding judgment form is generated;

[0018] For SKUs that meet the deduction rules, deductions are applied based on the quantity of goods. The anomaly type of the remaining SKUs is determined, and at least one judgment slip is generated.

[0019] Optionally, the anomaly types include overstock and out-of-stock types; the matching of anomaly types and reasons for each record according to the SKU includes:

[0020] Obtain the exception type and reason for each record according to SKU;

[0021] The records of multiple goods types are matched with the records of out-of-stock types according to the reason for the anomaly to determine whether they meet the deduction rules.

[0022] Optionally, the step of matching records of multiple goods types with records of out-of-stock types according to the reason for the anomaly to determine whether they meet the deduction rules includes:

[0023] When the reason for the anomaly of the "multiple goods" type is a quality problem, and the reason for the anomaly of the "out of stock" type is an out of stock, it is determined that the two records meet the deduction rules.

[0024] When the reason for the anomaly of multiple goods is that the quality is intact, it is determined that the two records do not meet the deduction rules.

[0025] Optionally, when two records meet the deduction rules, determining the anomaly type and generating a corresponding judgment slip includes:

[0026] When two records meet the deduction rules, the anomaly type is determined to be out of stock, and a corresponding judgment order is generated. The anomaly type of the judgment order is out of stock, the anomaly reason is a quality problem, and the anomaly quantity is the deduction quantity.

[0027] Determining the anomaly type of the remaining SKUs and generating at least one accountability report includes at least one of the following steps:

[0028] The remaining SKUs are identified as out of stock, and a corresponding judgment order is generated. The judgment order specifies that the anomaly type is out of stock, the reason for the anomaly is out of stock, and the quantity of the anomaly is the quantity of the product.

[0029] The remaining SKUs are identified as having an anomaly type of "overstock" and a corresponding judgment slip is generated. The anomaly type of the judgment slip is "overstock", the reason for the anomaly is "quality intact", and the anomaly quantity is the quantity of goods.

[0030] The remaining SKUs are identified as having an "overstock" anomaly, and a corresponding judgment slip is generated. The anomaly type in the judgment slip is "overstock," the cause of the anomaly is a quality issue, and the anomaly quantity is the remaining quantity. The remaining quantity is determined based on the quantity of goods and the quantity deducted.

[0031] Optionally, the at least one liability assessment report is analyzed to determine the liability attribution information of the at least one liability assessment report, including:

[0032] At least one node is used to determine the attribution of responsibility based on the anomaly type and cause corresponding to the liability assessment form, and the liability attribution information of the liability assessment form is generated based on the at least one node.

[0033] Optionally, the step of determining the attribution of responsibility according to the anomaly type and cause corresponding to the liability assessment form includes at least one of the following steps:

[0034] According to the out-of-stock and overstock matching rules, the out-of-stock type and overstock type judgment orders reported by the same grid warehouse are matched. For the matching results, the node to which the responsibility of the judgment order is determined is the grid warehouse.

[0035] For liability assessment orders involving multiple cargo types, match them according to the multiple cargo liability assessment rules to determine at least one node where liability is assigned;

[0036] For out-of-stock type liability assessment orders, match them according to the out-of-stock liability assessment rules to determine at least one node to which liability belongs.

[0037] Optionally, it also includes: analyzing the abnormal results of goods based on the responsibility attribution information, and adjusting the transportation operation information according to the abnormal results of goods.

[0038] This application also discloses an electronic device, including: a processor; and a memory storing executable code thereon, which, when executed, causes the processor to perform the method described in this application.

[0039] This application also discloses one or more machine-readable media storing executable code thereon, which, when executed, causes a processor to perform the method described in this application.

[0040] Compared with the prior art, the embodiments of this application have the following advantages:

[0041] In this embodiment of the application, by matching the abnormal information of the goods according to the matching rules, at least one judgment order corresponding to the abnormality type can be automatically generated. By analyzing each judgment order, the responsibility attribution information of the at least one judgment order can be automatically determined without the need for manual determination of responsibility, which can improve processing efficiency. Attached Figure Description

[0042] Figure 1 This is a schematic diagram illustrating a node structure example for a group-buying scenario according to an embodiment of this application;

[0043] Figure 2 This is a flowchart illustrating the steps of an embodiment of a processing method based on commodity objects according to this application;

[0044] Figure 3 This is a schematic diagram illustrating an example of a judgment order matching in an embodiment of this application;

[0045] Figure 4 This is a flowchart illustrating the steps of another embodiment of the processing method based on commodity objects in this application;

[0046] Figure 5 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Detailed Implementation

[0047] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] This application's embodiments can be applied to group buying scenarios such as community group buying, where unified delivery is implemented. In this group buying scenario, goods purchased by multiple users are packaged together and transported to a pickup point. For example... Figure 1 This diagram illustrates an example architecture of the transportation process for goods in a group-buying scenario. The nodes include a central warehouse, grid warehouses, and stations. One central warehouse can correspond to multiple grid warehouses, and one grid warehouse can correspond to multiple stations. After a user purchases a goods item, the goods are sorted and dispatched from the central warehouse to the grid warehouse. The grid warehouse then sorts the goods for each station and delivers them to the respective station. At each station, the group leader sorts and identifies one or more goods purchased by each user, and then notifies the user to pick up the goods.

[0049] As can be seen from the above process, in group-buying scenarios, the transportation of goods involves multiple sorting processes, each targeting the next lower level. This can lead to various problems during transportation. For example, frozen goods may thaw, or damage may occur during transportation. Sorting errors may result in some stations having more goods than others. It is necessary to determine which stage the problem occurred in order to resolve it.

[0050] Reference Figure 2 The diagram illustrates a flowchart of an embodiment of a processing method based on commodity objects according to this application.

[0051] Step 202: Obtain product anomaly information, which includes the product object's inventory quantity unit (SKU) and anomaly type.

[0052] The step of obtaining abnormal product information includes: obtaining abnormal information reported by at least one grid warehouse, wherein the grid warehouse is a node in the transportation process of the product object, and the nodes in the transportation process also include a central warehouse; and statistically analyzing the abnormal information of the at least one grid warehouse to determine the abnormal product information.

[0053] It can retrieve abnormal information about product objects. After a product object is delivered to the station, the user picks it up at the station. The station can then report abnormal information about the product object to the grid warehouse, such as returns due to thawing, poor quality, or damage, shortages due to non-delivery, and excess items. When reporting, the station can report the product object's stock keeping unit (SKU), quantity, and reason for the abnormality. The SKU is the basic unit for measuring inventory inflow and outflow, and can be used as an identifier for a product object, thus uniquely representing it.

[0054] After compiling the anomaly information reported by each station, the grid warehouse can organize the anomaly information. This anomaly information can include: the SKU of the product object, the quantity of the product, and the reason for the anomaly. The anomaly type can also be determined based on the reason and added to the anomaly information. For example, the anomaly type can include "excess stock" and "out of stock." Since the grid warehouse receives returned products from stations, the anomaly type for returns can be set to "excess stock," and other situations such as excess stock or shortage stock can be set to "excess stock" or "out of stock" respectively. After compiling the anomaly information, the grid warehouse can report it to the central warehouse. The central warehouse can obtain the anomaly information reported by each grid warehouse, and after compiling it, obtain the product anomaly information. Each record in the product anomaly information can record the SKU of the product object, the quantity of the product, the anomaly type, and the reason for the anomaly. An example of product anomaly information is shown in Table 1.

[0055] Serial Number SKU Quantity of goods Exception types abnormal reason 1 0001 2 Multiple goods Damaged goods 2 0002 3 Multiple goods Commercial thawing 3 0003 3 Multiple goods In good condition 4 0001 1 out of stock out of stock 5 0002 3 out of stock out of stock … … … … …

[0056] Step 204: Match the abnormal information of the goods according to the matching rules to determine the judgment order corresponding to at least one abnormal type.

[0057] After obtaining product anomaly information from the central warehouse, this information can be matched according to pre-defined rules. These rules categorize anomalies to facilitate determining responsibility for the anomalies. Matching rules can be based on anomaly type and cause. For example, some warehouses might be out of stock while others are overstocked due to products being delivered to the wrong warehouse. Alternatively, some out-of-stock situations might be due to product quality issues, such as damage or thawing, requiring re-delivery. Therefore, matching rules can be set based on various potential problems to automatically match and determine the cause of the out-of-stock situation, generating a corresponding liability assessment form. This form serves as the document for determining responsibility.

[0058] The step of matching the product anomaly information according to matching rules to determine at least one judgment order corresponding to anomaly type includes: obtaining the SKU of the product object from the product anomaly information; determining the anomaly type and anomaly reason of each record according to the SKU; when two records meet the deduction rules, determining the anomaly type and generating the corresponding judgment order; deducting the SKUs that meet the deduction rules according to the product quantity; determining the anomaly type of the remaining SKUs and generating at least one judgment order. The SKU of the product object can be obtained from the product anomaly information, such as obtaining each SKU in sequence, then determining the anomaly type and anomaly reason for several records of that SKU; matching two records with different anomaly types according to the anomaly reason to determine whether they meet the deduction rules; if they do not meet the rules, matching is performed on other records; if they meet the rules, the corresponding anomaly type is determined and the corresponding judgment order is generated.

[0059] The anomaly types include overstock and out-of-stock. Matching the anomaly type and cause of each record according to the SKU includes: obtaining the anomaly type and cause of each record by SKU; matching the overstock type records with the out-of-stock type records according to the anomaly cause to determine whether they meet the deduction rules. Matching the overstock type records with the out-of-stock type records according to the anomaly cause to determine whether they meet the deduction rules includes: when the anomaly cause for the overstock type is a quality problem and the anomaly cause for the out-of-stock type is an out-of-stock condition, determining that both records meet the deduction rules; when the anomaly cause for the overstock type is that the quality is intact, determining that both records do not meet the deduction rules. When two records meet the deduction rules, determining the anomaly type and generating a corresponding responsibility report includes: determining the anomaly type as out of stock and generating a corresponding responsibility report, wherein the anomaly type of the responsibility report is out of stock, the anomaly reason is quality issue, and the anomaly quantity is the deduction quantity; determining the anomaly type of the remaining SKU and generating at least one responsibility report includes at least the following steps: determining the anomaly type of the remaining SKU as out of stock and generating a corresponding responsibility report, wherein the anomaly type of the responsibility report is out of stock, the anomaly reason is out of stock, and the anomaly quantity is the product quantity; determining the anomaly type of the remaining SKU as excess stock and generating a corresponding responsibility report, wherein the anomaly type of the responsibility report is excess stock, the anomaly reason is good quality, and the anomaly quantity is the product quantity; determining the anomaly type of the remaining SKU as out of stock and generating a corresponding responsibility report, wherein the anomaly type of the responsibility report is excess stock, the anomaly reason is quality issue, and the anomaly quantity is the remaining quantity, wherein the remaining quantity is determined based on the product quantity and the deduction quantity.

[0060] The deduction rule refers to the matching rules for anomalies caused by quality issues with goods. Records of "excess stock" and "out of stock" types can be matched according to the cause of the anomaly. If the cause of the anomaly for the "excess stock" type is a quality issue, such as damaged goods, thawed goods, or poor quality, then it matches the common cause of the "out of stock" type, which is "out of stock," thus meeting the deduction rule and confirming that the out of stock is due to a quality issue. However, if the cause of the anomaly for the "excess stock" type is that the goods are of good quality, i.e., not a quality issue but simply an excess of goods, then it does not meet the deduction rule. For two records that meet the deduction rule, a deduction can be made based on the quantity of goods for which the out of stock is due to a quality issue. The anomaly type for the SKU of the deducted quantity is determined as "out of stock," and the anomaly cause is determined according to the corresponding quality issue. A corresponding judgment slip is generated, which includes the SKU, quantity, anomaly type, and anomaly cause. If, after deduction, there are still goods with quality issues remaining, the remaining SKUs are classified as "overstock," and a corresponding responsibility report is generated. The responsibility report's exception type is "out of stock," the reason is "quality issue," and the exception quantity is the remaining quantity, determined based on the quantity of goods and the deducted quantity. For overstock items with good quality, no deduction is applied, but a responsibility report is generated based on their corresponding exception type and reason. Any remaining goods after deduction are also subject to responsibility reports based on their respective exception types and reasons. For any remaining out-of-stock goods after matching, the remaining SKUs are classified as "out of stock," and a corresponding responsibility report is generated. The exception type on the responsibility report is "out of stock," the reason is "out of stock," and the exception quantity is the quantity of goods.

[0061] As shown in the examples in Table 1 above, such as Figure 2 As shown, for the product object with SKU 0001, records 1 and 4 are matched according to the anomaly type and cause. They are determined to meet the deduction rules, and the deduction quantity is 1. Therefore, a judgment order is generated for SKU 0001, quantity 1, out of stock, damaged goods. After the deduction, the remaining quantity is 1, so a judgment order is generated for SKU 0001, quantity 1, excess stock, damaged goods. For the product object with SKU 0002, records 2 and 5 are matched according to the anomaly type and cause. They are determined to meet the deduction rules, and the deduction quantity is 3. Therefore, a judgment order is generated for SKU 0002, quantity 3, out of stock, thawed goods. For the product object with SKU 0003, its anomaly type is excess stock, and the anomaly cause is good quality. No deduction is performed, and a judgment order is generated for SKU 0001, quantity 3, excess stock, good quality. The above table generates 4 judgment orders.

[0062] In this embodiment, the aforementioned liability assessment forms can be further categorized to facilitate subsequent determination of liability. Specifically, if the anomaly type of the assessment form is "out of stock," the cause of the anomaly is "out of stock," and the abnormal quantity is the quantity of goods, this assessment form can be classified as a first-category assessment form; if the anomaly type of the assessment form is "excess stock," the cause of the anomaly is "good quality," and the abnormal quantity is the quantity of goods, this can be classified as a second-category assessment form; if the anomaly type of the assessment form is "excess stock," the cause of the anomaly is "quality problem," and the abnormal quantity is the remaining quantity, this can be classified as a third-category assessment form; and if the anomaly type of the assessment form is "out of stock," the cause of the anomaly is "quality problem," and the abnormal quantity is the deducted quantity, this can be classified as a fourth-category assessment form. The reasons for poor quality can be varied, such as frozen goods thawing, damaged packaging, or poor quality of the goods, such as rotten vegetables. Further categorization based on the detailed reasons for the quality problem is possible; for example, goods thawing or poor quality are considered fifth-category assessment forms due to inherent product quality issues, while damaged goods are considered sixth-category assessment forms due to non-inherent quality issues. Alternatively, each type of quality issue can be categorized into a separate accountability form; this application embodiment does not impose any limitations on this approach. In this application embodiment, to facilitate the determination of responsibility, node fields can also be set for the accountability form, including a central warehouse field and a grid field. The central warehouse field sets the corresponding central warehouse, and the grid warehouse field sets the reporting grid warehouse.

[0063] Step 206: Analyze the at least one liability judgment to determine the liability attribution information of the at least one liability judgment.

[0064] Once a liability assessment report is generated, it can be analyzed to determine the attribution of responsibility and which stage in the transportation process of the goods it pertains to. The matching rules are set with the impact of product quality taken into account, as quality is also related to the transportation process and thus influences the determination of responsibility.

[0065] The process of analyzing the at least one responsibility assessment report to determine its responsibility attribution information includes: determining the node for responsibility attribution based on the anomaly type and cause corresponding to the responsibility assessment report, and generating the responsibility attribution information for the responsibility assessment report. In this embodiment, the anomaly types of the product object include excess stock and shortage stock. Correspondingly, excess stock and shortage stock assessment rules can be set. The excess stock assessment rule is used to determine the responsibility attribution for excess stock type responsibility assessment reports, and the shortage stock assessment rule is used to determine the responsibility attribution for shortage stock type responsibility assessment reports. It also includes a shortage stock and excess stock matching rule, which matches shortage stock and excess stock responsibility assessment reports reported by the same grid warehouse to offset simple shortage stock and excess stock responsibility assessment reports, confirming the grid warehouse's responsibility for the problem occurring during sorting in the grid warehouse.

[0066] The step of determining at least one node of responsibility based on the anomaly type and cause corresponding to the responsibility assessment form includes at least the following steps: matching responsibility assessment forms of the out-of-stock type and the multiple-stock type reported by the same grid warehouse according to the out-of-stock and multiple-stock matching rules; for the matching results, determining the grid warehouse as the node of responsibility for the responsibility assessment form; for the multiple-stock type responsibility assessment form, matching according to the multiple-stock responsibility assessment rules to determine at least one node of responsibility; for the out-of-stock type responsibility assessment form, matching according to the out-of-stock responsibility assessment rules to determine at least one node of responsibility.

[0067] According to the out-of-stock and over-stock matching rules, if the same SKU is reported in the same grid warehouse, the out-of-stock and over-stock judgment orders are matched. If the over-stock judgment order indicates the product is in good condition, and the out-of-stock judgment order indicates the product is out of stock, then the same grid warehouse has reported both out-of-stock and over-stock situations for the same SKU. This indicates that the product is within the same grid warehouse, and the problem originated from an operational error in the grid warehouse, such as a sorting issue. Therefore, the responsibility is assigned to the grid warehouse. The remaining quantity can be deducted to generate a judgment order, which is then matched according to the over-stock or out-of-stock judgment rules. After offsetting out-of-stock and over-stock situations for the same SKU in the same grid warehouse, the remaining judgment orders can be matched using either the over-stock or out-of-stock judgment rules. For over-stock judgment orders, matching according to the over-stock judgment rules can assign responsibility to the central warehouse. That is, if there is still an over-stock situation after offsetting out-of-stock and over-stock situations, it is considered that the central warehouse has over-shipped the product, and therefore the responsibility is assigned to the central warehouse. After matching out-of-stock and overstock items, if there are still out-of-stock judgment orders, they cannot be simply assigned to a single node. This is because the reasons for these out-of-stock judgment orders could be due to product damage, quality issues, or even insufficient shipments from the central warehouse. Therefore, they can be matched according to the out-of-stock judgment rules. In this embodiment, the out-of-stock judgment rules are further divided according to the reasons for the anomalies, which can increase the automatic judgment rate from 30% to over 51%, improving the accuracy of automatic judgment.

[0068] Out-of-Stock Liability Judgment Rule 1: Out-of-stock cases due to product quality issues such as thawing or poor quality fall under Category 5, and liability can be assigned to the central warehouse. This rule is determined through historical big data analysis. The journey of goods from the central warehouse to the grid warehouse and then to the station generally occurs within a certain timeframe. Therefore, if product quality issues arise within this timeframe, it's likely that the problem occurred during sorting at the central warehouse, and the central warehouse is generally responsible. If the product quality issue arises after this timeframe, it's likely that a problem occurred during sorting at the grid warehouse, which is generally the grid warehouse's responsibility. Based on historical big data analysis, for Category 5 liability cases involving product quality issues such as thawing or poor quality, 98% of the responsibility is ultimately assigned to the central warehouse, with less than 2% assigned to the grid warehouse. Therefore, when generating this type of liability case, the responsibility can be directly assigned to the central warehouse. For disputed liability cases, the central warehouse can appeal.

[0069] Rule Two for Determining Liability for Out-of-Stock Situations: For out-of-stock situations caused by non-quality issues such as product damage, this falls under Category Six. In this case, liability can be assigned as "shared responsibility," meaning the grid warehouse and the central warehouse each bear 50% of the responsibility. Historical data analysis has revealed that for non-quality issues like product damage, it's difficult for the central warehouse and grid warehouse to pinpoint the actual point of failure; therefore, they are generally assigned 50% responsibility each.

[0070] In summary, by matching abnormal product information according to matching rules, at least one liability assessment form corresponding to an abnormality type can be automatically generated. By analyzing each liability assessment form, the responsibility attribution information of the at least one liability assessment form can be automatically determined without the need for manual determination of responsibility, which can improve processing efficiency.

[0071] Based on the above embodiments, this application also provides a processing method based on product objects, which can automatically determine the responsibility for problems such as excess or shortage of product objects in scenarios such as community group buying, thereby improving efficiency.

[0072] Reference Figure 4 The diagram illustrates a flowchart of another embodiment of the processing method based on commodity objects in this application.

[0073] Step 402: Obtain at least one abnormal information reported by a grid warehouse.

[0074] Step 404: Collect abnormal information from at least one grid warehouse and determine abnormal product information.

[0075] Step 406: Obtain the SKU of the product object from the product error information.

[0076] Step 408: Match the exception type and exception reason of each record according to the SKU.

[0077] The anomaly types include overstock and out-of-stock types; the step of matching the anomaly type and anomaly reason of each record according to the SKU includes: obtaining the anomaly type and anomaly reason of each record according to the SKU; matching the overstock type record with the out-of-stock type record according to the anomaly reason, and determining whether it meets the deduction rules.

[0078] The process of matching records of the "multiple goods" type with records of the "out of stock" type according to the cause of the anomaly and determining whether they meet the deduction rules includes: when the cause of the anomaly for the "multiple goods" type is a quality problem and the cause of the anomaly for the "out of stock" type is an out of stock, it is determined that the two records meet the deduction rules; when the cause of the anomaly for the "multiple goods" type is that the quality is intact, it is determined that the two records do not meet the deduction rules.

[0079] Step 410: When two records meet the deduction rules, determine the exception type and generate the corresponding judgment form.

[0080] When two records meet the deduction rules, the abnormality type is determined and a corresponding judgment order is generated, including: when two records meet the deduction rules, the abnormality type is determined to be out of stock, and a corresponding judgment order is generated. The abnormality type of the judgment order is out of stock, the abnormality reason is a quality problem, and the abnormal quantity is the deduction quantity.

[0081] Step 412: Deduct the SKUs that meet the deduction rules according to the quantity of goods, determine the abnormality type of the remaining SKUs, and generate at least one judgment form.

[0082] Determining the anomaly type of the remaining SKUs and generating at least one accountability report includes at least one of the following steps:

[0083] The remaining SKUs are identified as out of stock, and a corresponding judgment order is generated. The judgment order specifies that the anomaly type is out of stock, the reason for the anomaly is out of stock, and the quantity of the anomaly is the quantity of the product.

[0084] The remaining SKUs are identified as having an anomaly type of "overstock" and a corresponding judgment slip is generated. The anomaly type of the judgment slip is "overstock", the reason for the anomaly is "quality intact", and the anomaly quantity is the quantity of goods.

[0085] The remaining SKUs are identified as having an "overstock" anomaly, and a corresponding judgment slip is generated. The anomaly type in the judgment slip is "overstock," the cause of the anomaly is a quality issue, and the anomaly quantity is the remaining quantity. The remaining quantity is determined based on the quantity of goods and the quantity deducted.

[0086] Step 414: Determine at least one node for attribution of responsibility according to the abnormality type and cause corresponding to the liability assessment form, and generate the liability attribution information of the liability assessment form according to the at least one node.

[0087] The step of determining the attribution of responsibility according to the anomaly type and cause corresponding to the liability assessment form includes at least one of the following steps:

[0088] According to the out-of-stock and overstock matching rules, the out-of-stock type and overstock type judgment orders reported by the same grid warehouse are matched. For the matching results, the node to which the responsibility of the judgment order is determined is the grid warehouse.

[0089] For liability assessment orders involving multiple cargo types, match them according to the multiple cargo liability assessment rules to determine at least one node where liability is assigned;

[0090] For out-of-stock type liability assessment orders, match them according to the out-of-stock liability assessment rules to determine at least one node to which liability belongs.

[0091] Based on the above embodiments, this application embodiment can also analyze commodity anomaly results based on liability attribution information and adjust transportation operation information accordingly. For example, regarding liability attribution information caused by commodity quality problems, the commodity anomaly results leading to the anomaly can be analyzed, and then the transportation operation information can be adjusted. For example, in cases of commodity damage, where the responsibility is shared equally between the central warehouse and the grid warehouse, it can be analyzed which commodity items experience more frequent damage or other non-quality-related issues, and then the packaging scheme for those commodity items can be adjusted, such as adding protective packaging measures like foam packaging before transportation. Alternatively, these commodity items can be sorted and placed into individual packages during transportation. For commodity quality problems such as thawing or poor quality, the commodity anomaly results leading to the anomaly can be analyzed in conjunction with factors like climate, thereby adjusting the storage conditions, operating procedures, and / or packaging scheme for those commodity items. For example, in summer, frozen products are prone to thawing, ice packs can be added to the packaging; these ice packs can be added after sorting in the central warehouse and grid warehouse. Also, some frozen products thaw more easily than others, so ice packs are added to the more easily thawable frozen products. For example, for perishable goods such as leafy greens, warehouse storage conditions can be adjusted according to the season, and they can be placed in a later position for sorting to reduce transportation time and prevent quality problems.

[0092] In this embodiment of the application, by conducting big data analysis on out-of-stock judgment orders, multiple automatic judgment rules are summarized, which increases the automatic judgment rate from about 30% to about 51%, reducing the workload of manual judgment required in central warehouses and grid warehouses, thereby reducing human and material costs.

[0093] Furthermore, it can analyze the causes of anomalies and adjust the transportation process accordingly to reduce the occurrence of problems. It can also ensure the quality of goods and provide users with better service.

[0094] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0095] Based on the above embodiments, this embodiment also provides a processing device based on commodity objects, which is applied in an electronic device of a server (cluster).

[0096] The acquisition module is used to acquire product anomaly information, which includes the product object's inventory quantity unit (SKU) and anomaly type.

[0097] The responsibility assessment form generation module is used to match the abnormal information of the goods according to the matching rules and determine the responsibility assessment form corresponding to at least one abnormal type.

[0098] The responsibility attribution module is used to analyze the at least one liability judgment and determine the responsibility attribution information of the at least one liability judgment.

[0099] In summary, by matching abnormal product information according to matching rules, at least one liability assessment form corresponding to an abnormality type can be automatically generated. By analyzing each liability assessment form, the responsibility attribution information of the at least one liability assessment form can be automatically determined without the need for manual determination of responsibility, which can improve processing efficiency.

[0100] Optionally, the acquisition module is used to acquire abnormal information reported by at least one grid warehouse, wherein the grid warehouse is a node in the transportation process of the commodity object, and the nodes in the transportation process also include a central warehouse; and to count the abnormal information of the at least one grid warehouse to determine the commodity abnormal information.

[0101] Optionally, the judgment order generation module is used to obtain the SKU of the product object from the product anomaly information; match the anomaly type and anomaly reason of each record according to the SKU; when two records meet the deduction rules, determine the anomaly type and generate the corresponding judgment order; deduct the SKUs that meet the deduction rules according to the product quantity, determine the anomaly type of the remaining SKUs and generate at least one judgment order.

[0102] Optionally, the anomaly types include overstock and outstock types; the judgment sheet generation module is used to obtain the anomaly type and anomaly reason of each record according to SKU; and to match the records of overstock and outstock types according to the anomaly reason to determine whether they meet the deduction rules.

[0103] Optionally, the judgment sheet generation module is used to determine that two records meet the deduction rules when the abnormality reason for the multi-goods type is a quality problem and the abnormality reason for the out-of-stock type is an out-of-stock; and to determine that two records do not meet the deduction rules when the abnormality reason for the multi-goods type is that the quality is intact.

[0104] Optionally, the judgment order generation module is used to determine the anomaly type as "out of stock" and generate a corresponding judgment order when two records meet the deduction rules. The anomaly type of the judgment order is "out of stock," the anomaly reason is "quality problem," and the anomaly quantity is the deduction quantity. It also determines the anomaly type of the remaining SKU as "out of stock" and generates a corresponding judgment order. The anomaly type of the judgment order is "out of stock," the anomaly reason is "out of stock," and the anomaly quantity is the product quantity. Furthermore, it determines the anomaly type of the remaining SKU as "excess stock" and generates a corresponding judgment order. The anomaly type of the judgment order is "excess stock," the anomaly reason is "quality intact," and the anomaly quantity is the product quantity. Finally, it determines the anomaly type of the remaining SKU as "excess stock" and generates a corresponding judgment order. The anomaly type of the judgment order is "excess stock," the anomaly reason is "quality problem," and the anomaly quantity is the remaining quantity, which is determined based on the product quantity and the deduction quantity.

[0105] Optionally, the responsibility attribution module is used to determine at least one node for responsibility attribution according to the abnormality type and abnormality cause corresponding to the responsibility assessment form, and to generate the responsibility attribution information of the responsibility assessment form according to the at least one node.

[0106] Optionally, the responsibility attribution module is used to match the responsibility assignment orders for the out-of-stock type and the responsibility assignment orders for the multiple-stock type reported by the same grid warehouse according to the out-of-stock and multiple-stock matching rules. For the matching results, the grid warehouse is determined as the node to which the responsibility is assigned for the responsibility assignment order; for the responsibility assignment order for the multiple-stock type, it is matched according to the multiple-stock responsibility assignment rules to determine at least one node to which the responsibility is assigned; for the responsibility assignment order for the out-of-stock type, it is matched according to the out-of-stock responsibility assignment rules to determine at least one node to which the responsibility is assigned.

[0107] Optionally, it also includes: an anomaly adjustment module, used to analyze the anomaly results of goods based on the responsibility attribution information, and adjust the transportation operation information according to the anomaly results of goods.

[0108] In this embodiment of the application, by conducting big data analysis on out-of-stock judgment orders, multiple automatic judgment rules are summarized, which increases the automatic judgment rate from about 30% to about 51%, reducing the workload of manual judgment required in central warehouses and grid warehouses, thereby reducing human and material costs.

[0109] Furthermore, it can analyze the causes of anomalies and adjust the transportation process accordingly to reduce the occurrence of problems. It can also ensure the quality of goods and provide users with better service.

[0110] This application also provides a non-volatile readable storage medium storing one or more modules (programs). When these modules are applied to a device, they enable the device to execute the instructions for the method steps in this application.

[0111] This application provides one or more machine-readable media storing instructions that, when executed by one or more processors, cause an electronic device to perform one or more of the methods described in the above embodiments. In this application, the electronic device includes various types of devices such as terminal devices and servers (clusters).

[0112] The embodiments of this disclosure can be implemented as an apparatus configured to use any suitable hardware, firmware, software, or any combination thereof, including electronic devices such as terminal devices, servers (clusters), etc., within a data center. Figure 5 An exemplary apparatus 500 is schematically shown that can be used to implement the various embodiments described in this application.

[0113] In one embodiment, Figure 5 An exemplary device 500 is shown, which includes one or more processors 502, a control module (chipset) 504 coupled to at least one of the processors 502, a memory 506 coupled to the control module 504, a non-volatile memory (NVM) / storage device 508 coupled to the control module 504, one or more input / output devices 510 coupled to the control module 504, and a network interface 512 coupled to the control module 504.

[0114] Processor 502 may include one or more single-core or multi-core processors, and processor 502 may include any combination of general-purpose processors or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In some embodiments, device 500 can serve as a terminal device, server (cluster), or other device as described in the embodiments of this application.

[0115] In some embodiments, the apparatus 500 may include one or more computer-readable media (e.g., memory 506 or NVM / storage device 508) having instructions 514 and one or more processors 502 that are combined with the one or more computer-readable media and configured to execute the instructions 514 to implement the module and thus perform the actions described in this disclosure.

[0116] In one embodiment, the control module 504 may include any suitable interface controller to provide any suitable interface to at least one of the processors 502 and / or any suitable device or component communicating with the control module 504.

[0117] The control module 504 may include a memory controller module to provide an interface to the memory 506. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0118] Memory 506 may be used, for example, to load and store data and / or instructions 514 for device 500. In one embodiment, memory 506 may include any suitable volatile memory, such as suitable DRAM. In some embodiments, memory 506 may include double data rate type quad synchronous dynamic random access memory (DDR4 SDRAM).

[0119] In one embodiment, the control module 504 may include one or more input / output controllers to provide an interface to the NVM / storage device 508 and (one or more) input / output devices 510.

[0120] For example, NVM / storage device 508 may be used to store data and / or instructions 514. NVM / storage device 508 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (one or more) non-volatile storage devices (e.g., one or more hard disk drives (HDDs), one or more optical disc drives (CDs), and / or one or more digital universal optical disc (DVD) drives).

[0121] NVM / storage device 508 may include storage resources that are physically part of a device on which device 500 is mounted, or that can be accessed by the device without needing to be part of the device. For example, NVM / storage device 508 may be accessed via a network through one or more input / output devices 510.

[0122] One or more input / output devices 510 may provide an interface for device 500 to communicate with any other suitable device. Input / output devices 510 may include communication components, audio components, sensor components, etc. A network interface 512 may provide an interface for device 500 to communicate via one or more networks. Device 500 may wirelessly communicate with one or more components of a wireless network according to any of one or more wireless network standards and / or protocols, such as accessing wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G, 5G, etc., or combinations thereof.

[0123] In one embodiment, at least one of the processors 502 may be logically packaged with one or more controllers (e.g., memory controller modules) of the control module 504. In one embodiment, at least one of the processors 502 may be logically packaged with one or more controllers of the control module 504 to form a system-in-package (SiP). In one embodiment, at least one of the processors 502 may be integrated with the logic of one or more controllers of the control module 504 on the same die. In one embodiment, at least one of the processors 502 may be integrated with the logic of one or more controllers of the control module 504 on the same die to form a system-on-a-chip (SoC).

[0124] In various embodiments, device 500 may be, but is not limited to, a terminal device such as a server, desktop computing device, or mobile computing device (e.g., laptop computing device, handheld computing device, tablet computer, netbook, etc.). In various embodiments, device 500 may have more or fewer components and / or different architectures. For example, in some embodiments, device 500 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touchscreen display), a non-volatile memory port, multiple antennas, a graphics chip, an application-specific integrated circuit (ASIC), and a speaker.

[0125] The detection device can use a main control chip as a processor or control module, and sensor data, position information, etc. can be stored in a memory or NVM / storage device. The sensor group can be used as an input / output device, and the communication interface can include a network interface.

[0126] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0127] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0128] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0132] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0133] The foregoing has provided a detailed description of a product object-based processing method, a terminal device, and a machine-readable medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A processing method based on commodity objects, characterized in that, The method includes: Obtain product anomaly information, which includes the product object's inventory unit SKU, anomaly type, product quantity, and anomaly reason; Obtain the SKU of the product object from the product error information; According to the SKU, obtain the anomaly type and anomaly reason for each record. The anomaly type includes excess stock and out-of-stock type. The records of multiple goods type and the records of out-of-stock type are matched according to the reason for the abnormality to determine whether they meet the deduction rules. The deduction rules are the matching rules for abnormalities caused by the quality problems of the goods. When the reason for the anomaly of the "multiple goods" type is a quality problem, and the reason for the anomaly of the "out of stock" type is an out of stock, it is determined that the two records meet the deduction rules. When two records meet the deduction rules, the abnormality type is determined and the corresponding judgment form is generated; For SKUs that meet the deduction rules, deduct the amount based on the quantity of goods, determine the anomaly type of the remaining SKUs, and generate at least one judgment slip; Analyze the at least one liability judgment to determine the liability attribution information of the at least one liability judgment.

2. The method according to claim 1, characterized in that, The acquisition of abnormal product information includes: Obtain at least one abnormal information reported by a grid warehouse, wherein the grid warehouse is a node in the transportation process of the commodity object, and the nodes in the transportation process also include the central warehouse; Collect abnormal information from at least one grid warehouse to determine abnormal product information.

3. The method according to claim 1, characterized in that, Also includes: When the reason for the anomaly of multiple goods is that the quality is intact, it is determined that the two records do not meet the deduction rules.

4. The method according to claim 1, characterized in that, When two records meet the deduction rules, the abnormality type is determined and a corresponding judgment report is generated, including: When two records meet the deduction rules, the anomaly type is determined to be out of stock, and a corresponding judgment order is generated. The anomaly type of the judgment order is out of stock, the anomaly reason is a quality problem, and the anomaly quantity is the deduction quantity. Determining the anomaly type of the remaining SKUs and generating at least one accountability report includes at least one of the following steps: The remaining SKUs are identified as out of stock, and a corresponding judgment order is generated. The judgment order specifies that the anomaly type is out of stock, the reason for the anomaly is out of stock, and the quantity of the anomaly is the quantity of the product. The remaining SKUs are identified as having an anomaly type of "overstock" and a corresponding judgment slip is generated. The anomaly type of the judgment slip is "overstock", the reason for the anomaly is "quality intact", and the anomaly quantity is the quantity of goods. The remaining SKUs are identified as having an "overstock" anomaly, and a corresponding judgment slip is generated. The anomaly type in the judgment slip is "overstock," the cause of the anomaly is a quality issue, and the anomaly quantity is the remaining quantity. The remaining quantity is determined based on the quantity of goods and the quantity deducted.

5. The method according to claim 1, characterized in that, Analyze the at least one liability judgment to determine the liability attribution information of the at least one liability judgment, including: At least one node is used to determine the attribution of responsibility based on the anomaly type and cause corresponding to the liability assessment form, and the liability attribution information of the liability assessment form is generated based on the at least one node.

6. The method according to claim 5, characterized in that, The step of determining liability at least once based on the anomaly type and cause corresponding to the liability assessment form includes at least one of the following steps: According to the out-of-stock and overstock matching rules, the out-of-stock type and overstock type judgment orders reported by the same grid warehouse are matched. For the matching results, the node to which the responsibility of the judgment order is determined is the grid warehouse. For liability assessment orders involving multiple cargo types, match them according to the multiple cargo liability assessment rules to determine at least one node where liability is assigned; For out-of-stock type liability assessment orders, match them according to the out-of-stock liability assessment rules to determine at least one node to which liability belongs.

7. An electronic device, characterized in that, include: processor; and A memory having executable code stored thereon, which, when executed, causes the processor to perform the method as described in any one of claims 1-6.

8. One or more machine-readable media having executable code stored thereon, which, when executed, causes a processor to perform the method as described in any one of claims 1-6.

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