Abnormal performance information processing method and electronic equipment

Abstract

The embodiment of the present application discloses a method for processing abnormal fulfillment information and an electronic device, the method may include: receiving abnormal information reported by at least one first warehouse operation node within the current fulfillment cycle, wherein, within the current fulfillment cycle, the first warehouse operation node is used to receive goods from the second warehouse operation node upstream of the fulfillment link, and sort the received goods in units of user self-pickup sites; classifying the causes of abnormal information corresponding to the SKU according to the matching between the overstock quantity and the out-of-stock quantity corresponding to the same SKU; based on the category of the cause, generating a corresponding judgment sheet for the SKU for judgment processing. Through the embodiment of the present application, the online judgment data can be realized, so that various abnormal situations of overstock or out-of-stock can be checked and traced.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to a method and electronic device for processing contract performance exception information. Background Art

[0002] Community group buying is a shopping and consumption behavior of resident groups in real residential communities. It is a regionalized, niche, and localized form of group buying based on real communities. Through group buying discounts provided by community shops to surrounding residents (such as in the community), shops can promote precise marketing and consumption stimulation for core customers, achieve rapid improvement in regional visibility and reputation of shops, and have a significant effect on shop marketing. At present, fresh food categories, as high-frequency + rigid demand, are an important source of traffic for community group buying. The operating model is mainly online pre-sale one day in advance. Customers place orders in advance through instant messaging groups, APP (application presentation), mini-programs and other channels, and can go to the community group leader to pick up the goods the next day. This method mainly solves the commodity needs of household consumers to purchase fresh fruits.

[0003] In terms of warehousing and distribution logistics, the community group buying model mainly includes multiple operating nodes such as central warehouses, grid warehouses, and group points. Among them, there will be multiple grid warehouses under a central warehouse, but a grid warehouse will only belong to one central warehouse. All the goods purchased by consumers can be sent from the central warehouse and flow into the grid warehouse. In the grid warehouse, they will be sorted according to the group point and shipped according to the route dimension. The group point is a node that directly connects with consumers. When the goods arrive at the group leader, the group leader will notify the consumer to pick up the goods at the door.

[0004] In the whole process of shipping from the central warehouse to the grid warehouse, and then sorting from the grid warehouse to the group point, there are a lot of out-of-stock problems due to logistics reasons, which lead to refunds. Many of these out-of-stock problems cannot be traced back and cannot be determined. This phenomenon not only brings bad shopping experience to consumers, but also brings huge financial losses to the platform. Summary of the invention

[0005] The present application provides a method, device and electronic device for processing abnormal fulfillment information, which can realize the online judgment data, so that various abnormal situations of overstocking or out-of-stocking can be checked and traced.

[0006] This application provides the following solutions:

[0007] A method for processing abnormal performance information, comprising:

[0008] Receive abnormal information reported by at least one first warehouse operation node in the current fulfillment cycle, wherein in the current fulfillment cycle, the first warehouse operation node is used to receive goods from the second warehouse operation node upstream of the fulfillment link, and sort the received goods in units of user self-pickup sites for delivery to the user self-pickup sites; the abnormal information includes the minimum inventory unit SKU identifier, quantity and abnormal type of the goods, and the abnormal type includes overstock or out of stock;

[0009] Classify the causes of abnormal information corresponding to the SKU according to the matching between the excess quantity and the out-of-stock quantity corresponding to the same SKU;

[0010] Based on the category of the cause, a corresponding accountability sheet is generated for the SKU for accountability processing.

[0011] The receiving of abnormal information reported by at least one first warehouse operation node in the current fulfillment cycle includes:

[0012] Receive the out-of-stock exception information in the SKU dimension submitted by the first warehouse operation node during the process of sorting the received goods in units of user pick-up sites, and / or, after the first warehouse operation node completes the sorting, submit the excess stock exception information for the excess stock situation generated in the first warehouse operation node.

[0013] Among them, the multiple goods generated in the first warehouse operation node include one or more of the following: SKUs that are intercepted in the first warehouse operation node due to damage, loss of temperature or other quality problems when the first warehouse operation node receives the goods, or SKUs with good quality remaining after sorting in the first warehouse operation node, or SKUs included in containers received by the first warehouse operation node that do not belong to the first warehouse operation node.

[0014] Wherein, for the intercepted SKU, the abnormal information reported by the first warehouse operation node also includes: evidence information of the abnormal information;

[0015] The generating a corresponding judgment sheet for the SKU based on the category of the generating reason includes:

[0016] The evidence information is added to the generated accountability sheet so that the second warehouse operation node can take responsibility.

[0017] Wherein, for the container received by the first warehouse operation node that does not belong to the first warehouse operation node, the abnormal information reported by the first warehouse operation node also includes: a container identifier of the container.

[0018] The classifying of the causes of the abnormal information corresponding to the SKU includes:

[0019] According to the matching between the overstock quantity and the out-of-stock quantity corresponding to the same SKU, determining whether the cause of the abnormality of the SKU is the first category or the second category;

[0020] The first category of causes includes: anomalies caused by being intercepted in the first warehouse operation node due to damage, loss of temperature or other quality problems, or anomalies caused by misdelivery between different first warehouse operation nodes;

[0021] The causes of the second category include: anomalies caused by simply over-delivery or under-delivery of the second warehouse operation node, or anomalies caused by errors in the sorting process of the first warehouse operation node.

[0022] Wherein, determining whether the cause of the abnormality of the SKU is the first category or the second category includes:

[0023] Determine whether the same first warehouse operation node has reported out-of-stock abnormal information and over-stock abnormal information for the same SKU;

[0024] If yes, it is determined that the SKU has an abnormality caused by being intercepted in the first warehouse operation node, and the intercepted quantity of the SKU in the first warehouse operation node is determined according to the smaller of the out-of-stock quantity and the overstock quantity reported by the first warehouse operation node for the SKU;

[0025] According to the intercepted quantity of the SKU, the larger one between the out-of-stock quantity and the over-stock quantity is deducted to determine the pure out-of-stock or pure over-stock quantity of the SKU in the first warehouse operation node;

[0026] The quantity of simple out-of-stock or simple over-stock of the SKU in the first warehouse operation node is matched and judged with the quantity of simple out-of-stock or simple over-stock of the SKU in other first warehouse operation nodes, so as to identify the mis-delivery anomaly of the SKU between different first warehouse operation nodes;

[0027] If after deducting the number of intercepted items and the number of mis-delivered items in the first warehouse operation node, the SKU still has abnormal information, it is determined that the SKU has an abnormality corresponding to the cause of the second category.

[0028] Wherein, determining whether the SKU has a first category of abnormality or a second category of abnormality includes:

[0029] Summarize and count the abnormal information reported by the at least one first warehouse operation node for the same SKU to determine the total overstock quantity and total out-of-stock quantity corresponding to the same SKU;

[0030] If the total overstock quantity and the total out-of-stock quantity of the same SKU are equal, or the difference between the two is less than the target threshold, it is determined that the SKU has the first category of anomalies;

[0031] If the difference between the total overstock quantity and the total out-of-stock quantity of the same SKU is greater than the target threshold, it is determined that the SKU has an anomaly of the second category.

[0032] Among them, it also includes:

[0033] The responsibility sheet is provided to the second warehouse operation node so that the second warehouse operation node can acknowledge responsibility or provide evidence according to the cause information corresponding to the responsibility sheet.

[0034] The step of providing the judgment sheet to the second warehouse operation node includes:

[0035] The multiple accountability sheets are summarized and provided to the second warehouse operation node, and an operation control for batch accountability of the multiple accountability sheets is provided.

[0036] Wherein, when providing the judgment sheet to the second warehouse operation node, it also includes:

[0037] An operation control for appealing the judgment order is provided so that the second warehouse operation node can initiate an appeal against the specified judgment order.

[0038] Among them, multiple video monitoring devices are deployed in the second warehouse operation node for recording the sorting operation process in the second warehouse operation node, so as to provide evidence information for the second warehouse operation node when the second warehouse operation node needs to file a complaint.

[0039] Wherein, during the sorting operation, the second warehouse operation node carries the goods corresponding to the SKU through a preset container, and saves the corresponding relationship between the container identifier and the location, as well as the time information of the sorting operation for each SKU;

[0040] The abnormal information reported by the first warehouse operation node also includes: container identification information of the SKU with excess or shortage of goods;

[0041] The method further comprises:

[0042] Determine the SKU ID associated with the judgment form and the target container ID corresponding to the SKU;

[0043] Determine the location information of the target container when the sorting operation is performed in the second warehouse operation node, and the time information of the second warehouse operation node picking the SKU;

[0044] According to the location information and the time information, a video clip corresponding to the time information is extracted from the video recorded by the video monitoring device corresponding to the location information, and provided to the second warehouse operation node so that the second warehouse operation node can file a complaint and provide evidence based on the video clip.

[0045] A device for processing contract performance exception information, comprising:

[0046] An exception information receiving unit is used to receive exception information reported by at least one first warehouse operation node in a current fulfillment cycle, wherein in the current fulfillment cycle, the first warehouse operation node is used to receive goods from a second warehouse operation node upstream of the fulfillment link, and sort the received goods in units of user self-pickup sites for delivery to the user self-pickup sites; the exception information includes the minimum inventory unit SKU identifier, quantity and exception type of the goods, and the exception type includes overstock or out of stock;

[0047] An abnormality reason classification unit is used to classify the causes of abnormal information corresponding to the SKU according to the matching between the overstock quantity and the out-of-stock quantity corresponding to the same SKU;

[0048] The accountability sheet generating unit is used to generate a corresponding accountability sheet for the SKU based on the category of the cause so as to carry out accountability processing.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the methods described above.

[0050] An electronic device, comprising:

[0051] one or more processors; and

[0052] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of any of the methods described above.

[0053] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0054] Through the embodiment of the present application, the specific first warehouse operation node (for example, grid warehouse) can report the abnormal situation generated in the current fulfillment cycle, wherein the abnormal report can be made in units of SKU, and the specific abnormal information reported can include SKU identification, quantity and abnormal type, and the abnormal type includes overstock or out of stock. Afterwards, the cause of the abnormal information corresponding to the SKU can be classified according to the matching between the overstock quantity and the out of stock quantity corresponding to the same SKU, and the corresponding judgment and responsibility sheet can be generated for the SKU based on the category of the cause. In this way, the online judgment and responsibility data can be realized, so that various overstock or out of stock abnormal situations generated in the first warehouse operation node can be checked and traced. On this basis, the mistakes made by the operators in the specific warehouse at work can be traced, and if it is determined that the operators have made mistakes, they can also be punished accordingly, and they are no longer in a state where they will not be held accountable for mistakes. Therefore, this method is also conducive to better standardizing the operations of the operators in the warehouse, prompting them to complete their work more carefully, reducing the probability of human errors, and thus reducing the probability of out of stock as a whole.

[0055] In addition, by classifying the causes of abnormal information into two categories, those that can be automatically judged and those that are difficult to judge, it is also possible to automatically judge the responsibility for some situations. For example, SKUs that are intercepted by the first warehouse operation node, or SKUs that are mis-sent between different first warehouse operation nodes, can be directly judged to the second warehouse operation node. In addition, for situations that are difficult to determine responsibility, such as simple overstocking or shortages caused by the first warehouse operation node, the video monitoring system in the warehouse can also provide reliable evidence for specific determination of responsibility, thereby greatly increasing the proportion of various abnormalities that can be determined, providing a more objective data basis for rewarding and punishing warehouses.

[0056] Of course, any product implementing the present application does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a schematic diagram of the system architecture provided by the embodiment of the present application;

[0059] Figure 2 is a flow chart of the method provided in the embodiment of the present application;

[0060] Figure 3 It is a schematic diagram of classification matching of abnormal causes provided in an embodiment of the present application;

[0061] Figure 4 is a schematic diagram of a device system provided in an embodiment of the present application;

[0062] Figure 5 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0064] In order to facilitate the understanding of the technical solution provided by the embodiments of the present application, the following first briefly introduces the warehouse and distribution logistics solution under the community group buying model. Under the community group buying model, consumers select a group point and select specific product SKUs (Stock Keeping Units), quantities, etc. before placing an order. Among them, the SKU here can specifically refer to a single product, which is used to distinguish the different attributes of the product: for a product, when its model, configuration, grade, color, packaging capacity and other attributes are different from other products, it can be called a single product. For example, if a product is multi-colored, including red, white, blue, etc., then the product can correspond to multiple SKUs, and the code of each SKU is also different. Consumers can select a specific SKU and place an order.

[0065] After the consumer completes the order, the central warehouse usually counts the orders by wave according to the specific delivery cycle and delivers them to the grid warehouse. For example, assuming that the goods ordered by the consumer on the same day are delivered the next day, the delivery cycle is one day. At this time, the central warehouse can pull a wave of orders at 22:00 every day (or other time points), including orders generated by multiple group points. Afterwards, unlike the traditional e-commerce method of picking, packing and shipping orders in units, in the community group buying model, the central warehouse ships on the SKU dimension, that is, after pulling multiple orders in a wave, it can count which SKUs are involved in these orders, the number of each SKU and other information, and then pick up goods in units of SKU and pack them according to the grid warehouse dimension. For example, assuming there are group points A, B, C, etc., each group point generates multiple orders in one day, and each order involves at least one SKU. After pulling the data of these orders, the central warehouse can aggregate the orders belonging to multiple group points in the same grid warehouse and determine the total number of SKUs involved and the number of each SKU. For example, the above-mentioned group points A, B, and C belong to the same grid warehouse, where group point A includes orders A1, A2, A3, etc., group point B includes orders B1, B2, B3, etc., and group point C includes orders C1, C2, C3, etc. Among them, order A1 includes 1 box of tomatoes of a certain brand and specification, and order B2 may include 2 boxes of tomatoes of the same brand and specification, and so on. When picking goods in the central warehouse, tomatoes of this brand and specification can be collected together for picking, and other SKUs are processed in the same way.

[0066] After the central warehouse completes the picking of goods for each grid warehouse according to the SKU dimension, the goods can be transported to the grid warehouse through trunk transportation. The grid warehouse can be packaged according to the route dimension. Among them, one route can usually involve multiple group points. In addition, the grid warehouse can know which orders each group point on each route corresponds to, as well as the specific SKU, quantity, etc., and can allocate a container for each group order, and divide the goods of each group order into different containers. The containers of a route are arranged together, and the driver delivers the goods to each group point according to the route.

[0067] In the above process, some orders often fail to be fulfilled normally. There may be many specific reasons. For example, during the transportation from the central warehouse to the grid warehouse, the goods may be damaged, or lose temperature, or have other quality problems, and cannot continue to flow downstream as normal products. Or, because the picking process of the central warehouse usually relies on manual operations, there may be a difference between the number of items to be sorted and the actual number of items to be sorted during the manual operation, including under-delivery, wrong delivery, and wrong delivery of the whole pallet. For example, under-delivery means that for a certain SKU, 20 items need to be picked from a certain grid warehouse, but in fact, only 19 items are picked due to counting operations, which will cause one item to be out of stock on the SKU when the grid warehouse receives the goods. Wrong delivery means that for a certain SKU, 20 items need to be picked from grid warehouses A and B respectively, but one of the items that should have been sent to grid warehouse A is placed in the container of grid warehouse B, so that grid warehouse A has one item out of stock on the SKU, and correspondingly, grid warehouse B has one item more on the SKU. In addition, when transporting goods from the central warehouse to the grid warehouse, it is possible that due to human errors and other reasons, an entire pallet of goods that should be sent to grid warehouse A is actually sent to grid warehouse B. When grid warehouse B receives the wrong pallet of goods, there will be an entire pallet of excess goods in grid warehouse B, and a whole pallet of shortages in grid warehouse A, and so on.

[0068] For the above situation, see Figure 1 , the embodiment of the present application can provide a contract performance abnormality judgment system, and can also provide corresponding operating specifications. Specifically, when the grid warehouse receives the goods from the central warehouse, it can judge whether the goods are damaged, hypothermic or have other quality problems. If these problems exist, the corresponding goods can be selected and intercepted in the grid warehouse, and no longer circulate downstream. At the same time, the goods with these problems can also be photographed, etc., for use as evidence. In the process of sorting for a specific group point by the grid warehouse, the abnormality judgment system can provide the grid warehouse with an operation entry for abnormal reporting. If the grid warehouse finds that a certain SKU is out of stock, it can report the out-of-stock abnormality for the SKU. At the same time, the container code of the specific out-of-stock SKU, as well as the out-of-stock quantity and other information can be reported. After the grid warehouse completes the sorting, the goods can be cleared. At this time, the goods that were previously selected due to damage, hypothermia or other quality problems, or the simply extra goods, are still in the grid warehouse, which belongs to the "multiple goods" situation. Therefore, the grid warehouse can also report the overstock situation through a specific abnormal reporting portal, which can also include SKU identification, quantity, etc. Each grid warehouse can report out-of-stock or overstock abnormalities in the above manner.

[0069] After that, the exceptions reported by each grid warehouse can be collected on the server side, and the matching of the overstock quantity and the out-of-stock quantity can be determined in the SKU dimension, so as to roughly determine the causes of some exceptions. For example, if a grid warehouse reports both out-of-stock and overstock exceptions for a certain SKU, it proves that the situation may be caused by the grid warehouse intercepting the SKU. That is, assuming that the central warehouse has shipped a total of 10 pieces of a certain SKU to a certain grid warehouse, but one of them is damaged and intercepted in the grid warehouse. After that, the intercepted SKU will be reported as an overstock exception; at the same time, since the grid warehouse has one less SKU that can be shipped, it is inevitable that one of the orders will be out of stock on the SKU. It can be seen that in the case of such goods being intercepted in the grid warehouse, the overstock and out-of-stock situations of the same SKU always appear in pairs. Therefore, according to the matching of the overstock and out-of-stock quantities of the same SKU, it can be determined whether there is an exception caused by being intercepted in the grid warehouse.

[0070] In addition, there may also be anomalies caused by wrong shipments from the central warehouse. At this time, the number of overstocks and out-of-stocks of the same SKU between different grid warehouses may match. For example, one overstock occurs on a certain SKU in grid warehouse A, and the SKU does not have any damage, loss of temperature or other quality problems. Another out-of-stock occurs on the SKU in another grid warehouse B, and there is no overstock anomaly corresponding to the SKU in grid warehouse B. Therefore, it can be roughly determined that the central warehouse sent the SKU that should have been sent to grid warehouse B to grid warehouse A.

[0071] It is usually easy to determine the responsibility for the above reasons. For example, in the case of interception, as long as the grid warehouse can provide evidence such as photos to prove that the goods were indeed damaged, lost temperature or had other quality problems when they were received, it can be determined that it is the responsibility of the central warehouse. Or, in the case of wrong shipments between different grid warehouses, it can also be determined that it is the responsibility of the central warehouse. Therefore, the above situations can be attributed to abnormalities caused by the same type of reasons.

[0072] In addition to the above situations, there may be another abnormality, that is, after deducting the situations of being intercepted due to other quality problems and being mis-delivered between different grid warehouses, there are still abnormalities of overstock or understock in a certain grid warehouse, or there are no other abnormalities matching the abnormality of a certain SKU. It is a simple situation of overstock or understock in the grid warehouse, which can be classified as an abnormality caused by the second type of reasons. The second type of reasons here may include over-delivery or under-delivery in the central warehouse, or operational errors in the grid warehouse during sorting, etc. However, it is impossible to identify whether it is caused by errors in the central warehouse or the grid warehouse from the abnormal information reported by the grid warehouse. In other words, for abnormalities caused by the second type of reasons, it is not as easy for the grid warehouse to collect evidence as in the case of damage, and it may be caused by errors in the central warehouse or the grid warehouse. Therefore, it is usually difficult to determine the responsibility.

[0073] Therefore, in the embodiment of the present application, the specific causes of abnormality can be roughly classified according to the matching between the quantity of overstock and the quantity of out-of-stock of the same SKU. Based on the different causes, judgment orders can be generated respectively, and the judgment orders can be provided to the central warehouse later, and the central warehouse will judge the responsibility based on this judgment order. For example, for the judgment order corresponding to the first type of reason, it is usually easier to judge the responsibility, and the operation of the central warehouse will be relatively simple. After receiving the judgment order, the relevant evidence photos (provided by the grid warehouse) in the judgment order can be directly checked. If there is no problem, the responsibility can be directly recognized, and even automatic recognition can be achieved. For the judgment order corresponding to the second type of reason, that is, the simple overstock or out-of-stock situation in the grid warehouse (there is no interception by the grid warehouse, or the situation of misdelivery between different grid warehouses), the central warehouse can first determine whether there is an over-delivery or under-delivery situation. Specifically, in order to facilitate the judgment of the central warehouse, video monitoring equipment can also be deployed in the central warehouse. During the sorting operation of the central warehouse, the work process of the specific operator can be video collected and saved, and the collected video data can be used as the basis for specific determination of responsibility.

[0074] For example, if a judgment sheet shows that a certain SKU is simply out of stock in a grid warehouse, the quality control personnel of the central warehouse can retrieve the video data saved by the video monitoring equipment of the central warehouse to check whether the sorting personnel made mistakes in the sorting process, that is, whether there is a difference between the number of items to be sorted and the actual number of items to be sorted. If there is a difference, for example, there is indeed a shortage, the central warehouse can take responsibility for the judgment sheet. If it is found that there is no difference by viewing the video, the judgment sheet can be transferred to the grid warehouse, and the specific video data can be provided to the grid warehouse as evidence. Video monitoring equipment can also be deployed in the grid warehouse to collect and record the picking process of the grid warehouse. After receiving the judgment sheet, the quality control personnel of the grid warehouse can also check the video monitoring data related to the sorting situation in the grid warehouse to determine whether there is a sorting error in the sorting process of the grid warehouse, etc. If an error is found, the grid warehouse can take responsibility. Otherwise, arbitration can be initiated to the system, etc.

[0075] In short, after roughly classifying the causes of the abnormality, it is possible to generate a judgment sheet that is easier to determine responsibility, and a judgment sheet that is relatively difficult to determine responsibility. For the former, the central warehouse can use a simple procedure to handle it, and can even determine responsibility directly without manual processing. For the latter, the video monitoring system in the warehouse can be used to obtain relevant evidence to facilitate proof and ultimately determine responsibility.

[0076] Therefore, through the embodiments of the present application, it is possible to realize the online judgment of accountability data, and it is also possible to realize the automatic judgment of accountability for some situations, so that various overstocking or out-of-stock anomalies generated in the grid warehouse can be visually checked and traced. Even for situations where it is difficult to determine accountability, accountability can be determined through the video monitoring system in the warehouse. Therefore, it is ultimately possible to greatly increase the proportion of various anomalies that can be determined, providing a more objective data basis for rewarding and punishing warehouses. On this basis, the mistakes made by specific operators at work can be traced, and if mistakes occur, they can be punished accordingly, instead of being in a state where they will not be held accountable for mistakes. Therefore, this method can also be conducive to better standardizing the operations of operators in the warehouse, prompting them to complete their work more carefully, reducing the probability of human errors, and thus reducing the overall probability of out-of-stock situations.

[0077] The specific implementation scheme provided in the embodiments of the present application is described in detail below.

[0078] Specifically, the present application embodiment first provides a method for processing abnormal performance information, see Figure 2 , the method may specifically include:

[0079] S201: Receive exception information reported by at least one first warehouse operation node during the current fulfillment cycle, wherein, during the current fulfillment cycle, the first warehouse operation node is used to receive goods from the second warehouse operation node upstream of the fulfillment link, and sort the received goods in units of user self-pickup sites for delivery to the user self-pickup sites; the exception information includes the minimum inventory unit SKU identifier, quantity and exception type of the goods, and the exception type includes overstock or out of stock.

[0080] Among them, the first warehouse operation node may refer to the grid warehouse in the aforementioned example, and the second warehouse operation node may correspond to the central warehouse. Of course, in actual applications, there may be other specific naming methods.

[0081] The first warehouse operation node may report out-of-stock exception information in the SKU dimension during the process of sorting the received goods in units of user pick-up sites (for example, group points), and / or, after the first warehouse operation node completes the sorting, report excess stock exception information for the excess stock situation generated in the first warehouse operation node.

[0082] Among them, the multiple goods generated in the first warehouse operation node may specifically include one or more of the following: the SKUs intercepted in the first warehouse operation node due to damage, loss of temperature or other quality problems when the first warehouse operation node receives the goods, or the SKUs remaining after the sorting is completed in the first warehouse operation node, or the SKUs included in the container received by the first warehouse operation node that does not belong to the first warehouse operation node. For the intercepted SKU, since it is discovered by the first warehouse operation node, and this situation is more convenient to determine the cause of the abnormality and to retain evidence, the abnormal information reported by the first warehouse operation node may also include: the cause information and / or evidence information of the abnormal information, and this evidence information may include: photos taken of damage, loss of temperature, other quality problems, etc. This evidence information can be added to the judgment sheet later as a basis for determining responsibility. That is to say, for the situation where a certain SKU is intercepted in the first warehouse operation node due to damage, loss of temperature, other quality problems, etc., so that multiple goods are generated, the first warehouse operation node can determine the corresponding cause when reporting the abnormal information, and can provide relevant evidence.

[0083] In addition, the first warehouse operation node may also receive a whole pallet of goods that are sent by mistake. At this time, for the container that does not belong to the first warehouse operation node and is received by the first warehouse operation node, the abnormal information reported by the first warehouse operation node may also include: the container identification of the container. In other words, the first warehouse operation node can be supported to report the abnormality of the whole pallet. At this time, it is not necessary to report based on a specific SKU, but directly enter the container code of a specific pallet or other container. It should be noted here that when the second warehouse operation node ships to each first warehouse operation node, it will use a specific container to load the goods. Each container has its own container code and other identifications. The second warehouse operation node can provide which containers are used to load the goods corresponding to each first warehouse operation node through the logistics information system. In this way, when the first warehouse operation node receives specific goods, it can compare the container code of the container actually received with the container code recorded in the system to determine whether the container actually received belongs to the container of the first warehouse operation node. If not, it can usually be directly determined that the second warehouse operation node has a whole pallet mis-delivered. Therefore, the first warehouse operation node can enter the container code of such container. After that, a separate responsibility sheet can be generated for the container code, and the responsibility can be directly determined to be the second warehouse operation node.

[0084] S202: Classify the causes of abnormal information corresponding to the SKU according to the matching between the excess quantity and the out-of-stock quantity corresponding to the same SKU.

[0085] After receiving multiple abnormal information reported by multiple first warehouse operation nodes, the quantity of overstock and out-of-stock can be matched on the SKU dimension to distinguish which abnormalities are easier to judge and which are more difficult to judge, and then generate judgment orders respectively. In this way, the number of judgment orders that are difficult to judge is reduced, and the second warehouse operation node only needs to provide evidence and other processing for these judgment orders that are more difficult to judge, which can reduce the workload of the second warehouse operation node.

[0086] That is to say, it is possible to determine whether the cause of the SKU abnormality is the first category or the second category based on the match between the excess quantity and the out-of-stock quantity corresponding to the same SKU; wherein, the causes of the first category include: abnormalities caused by being intercepted in the first warehouse operation node due to damage, loss of temperature or other quality problems, or abnormalities caused by misdelivery between different first warehouse operation nodes; the causes of the second category include: abnormalities caused by simply over-delivery or under-delivery at the second warehouse operation node, or abnormalities caused by mistakes made by the first warehouse operation node during the sorting process. The first category corresponds to situations that are easier to determine responsibility, and it is usually the responsibility of the second warehouse operation node, and the second warehouse operation node can directly take responsibility; the second category corresponds to situations that are more difficult to determine responsibility, and the second warehouse operation node may be required to provide evidence, and may even require the first warehouse operation node to provide further counter-evidence, and so on.

[0087] Specifically, when matching and judging the overstock quantity and out-of-stock quantity corresponding to the same SKU, two methods can be used: exact matching or approximate matching.

[0088] The so-called accurate matching means strictly following the quantity of multiple goods and the quantity of out-of-stock of the same SKU to determine various possible causes of abnormality. Specifically, first, it can be determined whether the same first warehouse operation node reports the out-of-stock abnormality information and the multiple goods abnormality information for the same SKU; if so, it can be determined that the SKU has an abnormality caused by being intercepted in the first warehouse operation node, and the number of intercepted SKUs in the first warehouse operation node can be determined according to the smaller of the number of multiple goods and the number of out-of-stock goods reported by the first warehouse operation node for the SKU. Afterwards, the larger of the number of multiple goods and the number of out-of-stock goods can be deducted according to the number of intercepted SKUs to determine the number of simple out-of-stock or simple multiple goods for the SKU in the first warehouse operation node. Then, the number of simple out-of-stock or simple multiple goods for the SKU in the first warehouse operation node can also be matched and judged with the number of simple out-of-stock or simple multiple goods for the SKU in other first warehouse operation nodes to identify the mis-shipping abnormality of the SKU between different first warehouse operation nodes. Afterwards, if the SKU still has abnormal information after deducting the number of intercepted items and the number of mis-delivered items in the first warehouse operation node, it is determined that the SKU has an abnormality corresponding to the cause of the second category.

[0089] For example, assuming that a first warehouse operation node A reported 3 extra items and 1 out of stock for a certain SKU, it can be proved that 1 item of the SKU was intercepted in the first warehouse operation node A. Accordingly, a judgment sheet can be generated for the SKU, and the abnormal reason is that it was intercepted by the first warehouse operation node A. Of course, more specific reasons can also be provided, including damage, or loss of temperature (such as thawing, etc.), or other quality problems. However, since the number of extra items reported by the first warehouse operation node A for a certain SKU is 3, but only 1 item of the SKU is out of stock, the other 2 extra items need to be further determined. In addition, suppose that another first warehouse operation node B reported 1 out of stock for the SKU, but did not report the situation of extra items, and no other first warehouse operation node reported other abnormalities for the SKU. At this point, it can be determined that among the other two multiple goods in the first warehouse operation node A, one may have been mistakenly sent between the first warehouse operation node A and the first warehouse operation node B, and the other is a simple multiple goods generated in the first warehouse operation node A, that is, it can be attributed to the second type of reason, which is a situation that is more difficult to determine responsibility.

[0090] For example, Figure 3 As shown in the figure, a first warehouse operation node reported 8 overstocks and 4 out-of-stocks for a certain SKU. Among the 8 overstocks, 2 were "damaged goods", 3 were "thawed goods", and 3 were "in good quality". In addition, 4 out-of-stocks were reported. After automatic matching, it can be determined that among the 4 out-of-stock items, 3 of them may be out-of-stock due to "thawed goods", which were intercepted in the first warehouse operation node and could not be transferred downstream; the other 1 may be out-of-stock due to "damaged goods", which was also intercepted in the first warehouse operation node and could not be transferred downstream. After completing the above matching, there is still 1 overstock of "damaged goods" and 3 overstock of "in good quality" in the first warehouse operation node. In the future, it can be further matched with the results of matching with other first warehouse operation nodes to determine whether there is a situation of wrong shipment between different first warehouse operation nodes, etc.

[0091] The so-called approximate match means that the abnormal information reported by multiple first warehouse operation nodes for the same SKU can be aggregated together to determine the total excess quantity and total out-of-stock quantity corresponding to the same SKU; then, if the total excess quantity and total out-of-stock quantity of the same SKU are equal, or the difference between the two is less than the target threshold, it can be determined that the SKU has the first category of abnormalities; only when the difference between the total excess quantity and the total out-of-stock quantity of the same SKU is greater than the target threshold, it is determined that the SKU may have the second category of abnormalities. That is to say, in the case of approximate matching, as long as the difference between the total excess quantity and the total out-of-stock quantity of the same SKU is not too large, for example, within 3 pieces, etc., it is not necessary to generate a judgment sheet corresponding to the second category. In this way, the number of judgment sheets that are difficult to judge can be further reduced, and the workload of providing evidence for the second warehouse operation node can be reduced.

[0092] S203: Based on the category of the cause, a corresponding accountability sheet is generated for the SKU for accountability processing.

[0093] After determining the category of the cause corresponding to the specific abnormal information, a corresponding judgment and accountability sheet can be generated for the specific SKU. Specifically, for the abnormal information corresponding to the first category of causes, since the out-of-stock abnormality and the over-stock abnormality of the same SKU can be matched, a pair of successfully matched abnormal information corresponds to a judgment and accountability sheet. For abnormal information that cannot be successfully matched, a separate judgment and accountability sheet can be generated.

[0094] In specific implementation, after the judgment sheet is generated, the judgment sheet can also be provided to the second warehouse operation node so that the second warehouse operation node can acknowledge responsibility or provide evidence based on the cause information corresponding to the judgment sheet.

[0095] In order to facilitate the operation of the second warehouse operation node, multiple judgment and responsibility orders can be summarized and provided to the second warehouse operation node, and an operation control for batch recognition of the multiple judgment and responsibility orders can be provided. In particular, for the first category of judgment and responsibility orders, since the second warehouse operation node can usually directly recognize the responsibility, the batch operation option can improve the operation efficiency of the second warehouse operation node.

[0096] In addition, an operation control for appealing the judgment and responsibility form can also be provided for the second warehouse operation node, so that the second warehouse operation node can appeal against the specified judgment and responsibility form. For example, for the second category of judgment and responsibility forms, since the first warehouse operation node is simply overstocked or out of stock, there may be many reasons, which may be the responsibility of the second warehouse operation node or the responsibility of the first warehouse operation node. Therefore, after the quality control personnel of the second warehouse operation node receive such a judgment and responsibility form, if there is evidence to prove that it is not the responsibility of the second warehouse operation node, they can also appeal.

[0097] Among them, in order to provide more effective evidence, multiple video monitoring devices can be deployed in the second warehouse operation node to record the sorting operation process in the second warehouse operation node. In this way, the recorded video images can be used as evidence. Of course, since there may be multiple video monitoring devices, each used for video acquisition in different areas, and the video recorded by the same video monitoring device may last for a long time, or there may be multiple videos, etc. Therefore, if quality control personnel are required to view all video content and obtain relevant evidence from it, the efficiency will be very low.

[0098] However, since the specific judgment sheet corresponds to a single SKU, the area where a single SKU is located when picking in the warehouse is usually fixed, and the picking time and other information of each SKU can also be recorded in the system, so that the recorded video content can be automatically located according to the specific location information and time information, so that the quality control personnel can directly view the located video clips, thereby improving efficiency. Specifically, during the sorting operation, the second warehouse operation node can carry the goods corresponding to the specific SKU through a preset container, wherein a container may include one SKU or multiple different SKUs; the location where the specific container is placed in the second warehouse operation node can be fixed, or it can be recorded during the picking process. In short, the correspondence between the container identification and the location can be known. In addition, the time information of the sorting operation of each SKU can also be saved. The abnormal information reported by the first warehouse operation node can also include: the container identification information of the SKU with more or less goods, which is the container used by the second warehouse operation node to distribute the SKU to the first warehouse operation node. In this way, for the second category of accountability sheets, the SKU identifier associated with the specific accountability sheet and the target container identifier corresponding to the SKU can be determined; then, the location information of the target container when the sorting operation is performed in the second warehouse operation node and the time information when the second warehouse operation node picks the SKU can be determined; in this way, based on the location information and the time information, a video clip corresponding to the time information can be captured from the video recorded by the video monitoring device corresponding to the location information, and provided to the second warehouse operation node so that the second warehouse operation node can provide evidence based on the video clip.

[0099] In summary, through the embodiment of the present application, the specific first warehouse operation node (for example, grid warehouse) can report the abnormal situation generated in the current fulfillment cycle, wherein the abnormal report can be made in units of SKU, and the specific abnormal information reported can include SKU identification, quantity and abnormal type, and the abnormal type includes overstock or out of stock. Afterwards, the cause of the abnormal information corresponding to the SKU can be classified according to the matching between the overstock quantity and the out of stock quantity corresponding to the same SKU, and the corresponding judgment and responsibility sheet can be generated for the SKU based on the category of the cause. In this way, the online judgment and responsibility data can be realized, so that the various overstock or out of stock abnormalities generated in the first warehouse operation node can be visually checked and traceable. On this basis, the mistakes made by the operators in the specific warehouse at work can be traced back. If it is determined that the operators make mistakes, they can also be punished accordingly, and they are no longer in a state where they will not be held accountable for the mistakes. Therefore, this method is also conducive to better standardizing the operations of the operators in the warehouse, prompting them to complete their work more carefully, reducing the probability of human errors, and thus reducing the probability of out of stock as a whole.

[0100] In addition, by classifying the causes of abnormal information into two categories, those that can be automatically judged and those that are difficult to judge, it is also possible to automatically judge the responsibility for some situations. For example, SKUs that are intercepted by the first warehouse operation node, or SKUs that are mis-sent between different first warehouse operation nodes, can be directly judged to the second warehouse operation node. In addition, for situations that are difficult to determine responsibility, such as simple overstocking or shortages caused by the first warehouse operation node, the video monitoring system in the warehouse can also provide reliable evidence for specific determination of responsibility, thereby greatly increasing the proportion of various abnormalities that can be determined, providing a more objective data basis for rewarding and punishing warehouses.

[0101] It should be noted that the embodiments of the present application may involve the use of user data. In actual applications, user-specific personal data can be used in the scheme described herein within the scope permitted by applicable laws and regulations, subject to the requirements of applicable laws and regulations of the country where the user is located (for example, with the user's explicit consent, effective notification to the user, etc.).

[0102] Corresponding to the above method embodiment, the present application embodiment also provides a performance abnormality information processing device, see Figure 4 , the device may include:

[0103] The abnormal information receiving unit 401 is used to receive abnormal information reported by at least one first warehouse operation node in the current fulfillment cycle, wherein in the current fulfillment cycle, the first warehouse operation node is used to receive goods from the second warehouse operation node upstream of the fulfillment link, and sort the received goods in units of user self-pickup sites for delivery to the user self-pickup sites; the abnormal information includes the minimum inventory unit SKU identifier, quantity and abnormal type of the goods, and the abnormal type includes overstock or out of stock;

[0104] The abnormality reason classification unit 402 is used to classify the causes of abnormal information corresponding to the SKU according to the matching between the overstock quantity and the out-of-stock quantity corresponding to the same SKU;

[0105] The accountability sheet generating unit 403 is used to generate a corresponding accountability sheet for the SKU based on the category of the cause so as to perform accountability processing.

[0106] Specifically, the abnormal information receiving unit can be used to:

[0107] Receive the out-of-stock exception information in the SKU dimension submitted by the first warehouse operation node during the process of sorting the received goods in units of user pick-up sites, and / or, after the first warehouse operation node completes the sorting, submit the excess stock exception information for the excess stock situation generated in the first warehouse operation node.

[0108] Among them, the multiple goods generated in the first warehouse operation node include one or more of the following: SKUs that are intercepted in the first warehouse operation node due to damage, loss of temperature or other quality problems when the first warehouse operation node receives the goods, or SKUs with good quality remaining after sorting in the first warehouse operation node, or SKUs included in containers received by the first warehouse operation node that do not belong to the first warehouse operation node.

[0109] Wherein, for the intercepted SKU, the abnormal information reported by the first warehouse operation node also includes: evidence information of the abnormal information;

[0110] The abnormal cause classification unit can be specifically used for:

[0111] The evidence information is added to the generated accountability sheet so that the second warehouse operation node can take responsibility.

[0112] Specifically, for a container received by the first warehouse operation node that does not belong to the first warehouse operation node, the abnormal information reported by the first warehouse operation node also includes: a container identifier of the container.

[0113] The abnormal cause classification unit may be specifically used for:

[0114] According to the matching between the overstock quantity and the out-of-stock quantity corresponding to the same SKU, determining whether the cause of the abnormality of the SKU is the first category or the second category;

[0115] The first category of causes includes: anomalies caused by being intercepted in the first warehouse operation node due to damage, loss of temperature or other quality problems, or anomalies caused by misdelivery between different first warehouse operation nodes;

[0116] The causes of the second category include: anomalies caused by simply over-delivery or under-delivery of the second warehouse operation node, or anomalies caused by errors in the sorting process of the first warehouse operation node.

[0117] Specifically, the abnormal cause classification unit can be used to:

[0118] Determine whether the same first warehouse operation node has reported out-of-stock abnormal information and over-stock abnormal information for the same SKU;

[0119] If yes, it is determined that the SKU has an abnormality caused by being intercepted in the first warehouse operation node, and the intercepted quantity of the SKU in the first warehouse operation node is determined according to the smaller of the out-of-stock quantity and the overstock quantity reported by the first warehouse operation node for the SKU;

[0120] According to the intercepted quantity of the SKU, the larger one between the out-of-stock quantity and the over-stock quantity is deducted to determine the pure out-of-stock or pure over-stock quantity of the SKU in the first warehouse operation node;

[0121] The quantity of simple out-of-stock or simple over-stock of the SKU in the first warehouse operation node is matched and judged with the quantity of simple out-of-stock or simple over-stock of the SKU in other first warehouse operation nodes, so as to identify the mis-delivery anomaly of the SKU between different first warehouse operation nodes;

[0122] If after deducting the number of intercepted items and the number of mis-delivered items in the first warehouse operation node, the SKU still has abnormal information, it is determined that the SKU has an abnormality corresponding to the cause of the second category.

[0123] Alternatively, in another manner, the abnormal cause classification unit may be specifically used for:

[0124] Summarize and count the abnormal information reported by the at least one first warehouse operation node for the same SKU to determine the total overstock quantity and total out-of-stock quantity corresponding to the same SKU;

[0125] If the total overstock quantity and the total out-of-stock quantity of the same SKU are equal, or the difference between the two is less than the target threshold, it is determined that the SKU has the first category of anomalies;

[0126] If the difference between the total overstock quantity and the total out-of-stock quantity of the same SKU is greater than the target threshold, it is determined that the SKU has an anomaly of the second category.

[0127] In a specific implementation, the device may further include:

[0128] The responsibility form providing unit is used to provide the responsibility form to the second warehouse operation node so that the second warehouse operation node can acknowledge responsibility or provide evidence based on the cause information corresponding to the responsibility form.

[0129] Specifically, the judgment sheet providing unit can be used to:

[0130] The multiple accountability sheets are summarized and provided to the second warehouse operation node, and an operation control for batch accountability of the multiple accountability sheets is provided.

[0131] Alternatively, the judgment sheet providing unit may be specifically used for:

[0132] An operation control for appealing the judgment order is provided so that the second warehouse operation node can initiate an appeal against the specified judgment order.

[0133] Specifically, a plurality of video monitoring devices are deployed in the second warehouse operation node for recording the sorting operation process in the second warehouse operation node, so as to provide evidence information for the second warehouse operation node when the second warehouse operation node needs to file a complaint.

[0134] Wherein, during the sorting operation, the second warehouse operation node carries the goods corresponding to the SKU through a preset container, and saves the corresponding relationship between the container identifier and the location, as well as the time information of the sorting operation for each SKU;

[0135] The abnormal information reported by the first warehouse operation node also includes: container identification information of the SKU with excess or shortage of goods;

[0136] At this time, the device may further include:

[0137] A container identification determination unit, used to determine the SKU identification associated with the judgment and responsibility form, and the target container identification corresponding to the SKU;

[0138] A location and time information determination unit, used to determine the location information of the target container when the sorting operation is performed in the second warehouse operation node, and the time information of the second warehouse operation node picking the SKU;

[0139] The video clip capture unit is used to capture the video clip corresponding to the time information from the video recorded by the video monitoring device corresponding to the location information based on the location information and the time information, and provide it to the second warehouse operation node so that the second warehouse operation node can file a complaint and provide evidence based on the video clip.

[0140] In addition, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods in the aforementioned method embodiments are implemented.

[0141] And an electronic device, comprising:

[0142] one or more processors; and

[0143] A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of the method described in any one of the aforementioned method embodiments.

[0144] in, Figure 5 The architecture of the electronic device is shown as an example, which may include a processor 510, a video display adapter 511, a disk drive 512, an input / output interface 513, a network interface 514, and a memory 520. The processor 510, the video display adapter 511, the disk drive 512, the input / output interface 513, the network interface 514, and the memory 520 may be communicatively connected via a communication bus 530.

[0145] Among them, the processor 510 can be implemented by a general-purpose CPU (Central Processing Unit, processor), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., to execute relevant programs to implement the technical solution provided in this application.

[0146] The memory 520 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 520 can store an operating system 521 for controlling the operation of the electronic device 500, and a basic input and output system (BIOS) for controlling the low-level operation of the electronic device 500. In addition, a web browser 523, a data storage management system 524, and a contract performance exception information processing system 525, etc. can also be stored. The above-mentioned contract performance exception information processing system 525 can be an application program that specifically implements the operations of the aforementioned steps in the embodiment of the present application. In short, when the technical solution provided by the present application is implemented by software or firmware, the relevant program code is stored in the memory 520 and is called and executed by the processor 510.

[0147] The input / output interface 513 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0148] The network interface 514 is used to connect to a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0149] The bus 530 comprises a pathway for transmitting information between the various components of the device (eg, the processor 510, the video display adapter 511, the disk drive 512, the input / output interface 513, the network interface 514, and the memory 520).

[0150] It should be noted that, although the above device only shows a processor 510, a video display adapter 511, a disk drive 512, an input / output interface 513, a network interface 514, a memory 520, a bus 530, etc., in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include components necessary for implementing the solution of the present application, and does not necessarily include all the components shown in the figure.

[0151] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0152] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0153] The above is a detailed introduction to the performance abnormality information processing method and electronic device provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and its core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present application.

Claims

1. A method for processing abnormal performance information, It is characterized in that include: Receive abnormal information reported by at least one first warehouse operation node within the current fulfillment cycle, wherein within the current fulfillment cycle, the first warehouse operation node is used to receive goods picked up according to the minimum inventory unit SKU dimension of the goods from the second warehouse operation node upstream of the fulfillment link and packaged and sent after being packaged in units of the first warehouse operation node, and sort the received goods in units of user self-pickup sites for delivery to the user self-pickup sites; the abnormal information includes SKU identification, quantity and abnormal type, and the abnormal type includes overstocking or out of stock; the second warehouse operation node picking according to the SKU dimension of the goods includes: after the second warehouse operation node obtains multiple orders generated by multiple user self-pickup points belonging to the same first warehouse operation node, determines the SKU identification involved in multiple goods included in the multiple orders and the SKU quantity corresponding to each SKU identification, and picks according to the SKU identification and the SKU quantity corresponding to each SKU identification; Classify the causes of abnormal information corresponding to the SKU according to the matching between the excess quantity and the out-of-stock quantity corresponding to the same SKU; Based on the category of the cause, a corresponding accountability sheet is generated for the SKU for accountability processing.

2. The method according to claim 1, It is characterized in that The receiving of abnormal information reported by at least one first warehouse operation node in the current fulfillment cycle includes: Receive the out-of-stock exception information in the SKU dimension submitted by the first warehouse operation node during the process of sorting the received goods in units of user pick-up sites, and / or, after the first warehouse operation node completes the sorting, submit the excess stock exception information for the excess stock situation generated in the first warehouse operation node.

3. The method according to claim 2, It is characterized in that The multiple goods generated in the first warehouse operation node include one or more of the following: SKUs that are intercepted in the first warehouse operation node due to damage, loss of temperature or other quality problems when the first warehouse operation node receives the goods, or SKUs with good quality remaining after sorting is completed in the first warehouse operation node, or SKUs included in containers received by the first warehouse operation node that do not belong to the first warehouse operation node.

4. The method according to claim 1, It is characterized in that The classifying the causes of the abnormal information corresponding to the SKU includes: According to the matching between the overstock quantity and the out-of-stock quantity corresponding to the same SKU, determining whether the cause of the abnormality of the SKU is the first category or the second category; The first category of causes includes: anomalies caused by being intercepted in the first warehouse operation node due to damage, loss of temperature or other quality problems, or anomalies caused by misdelivery between different first warehouse operation nodes; The causes of the second category include: anomalies caused by simply over-delivery or under-delivery of the second warehouse operation node, or anomalies caused by errors in the sorting process of the first warehouse operation node.

5. The method according to claim 4, It is characterized in that The determining whether the cause of the abnormality of the SKU is the first category or the second category includes: Determine whether the same first warehouse operation node has reported out-of-stock abnormal information and over-stock abnormal information for the same SKU; If yes, it is determined that the SKU has an abnormality caused by being intercepted in the first warehouse operation node, and the intercepted quantity of the SKU in the first warehouse operation node is determined according to the smaller of the out-of-stock quantity and the overstock quantity reported by the first warehouse operation node for the SKU; According to the intercepted quantity of the SKU, the larger one between the out-of-stock quantity and the over-stock quantity is deducted to determine the pure out-of-stock or pure over-stock quantity of the SKU in the first warehouse operation node; The quantity of simple out-of-stock or simple over-stock of the SKU in the first warehouse operation node is matched and judged with the quantity of simple out-of-stock or simple over-stock of the SKU in other first warehouse operation nodes, so as to identify the mis-delivery anomaly of the SKU between different first warehouse operation nodes; If after deducting the number of intercepted items and the number of mis-delivered items in the first warehouse operation node, the SKU still has abnormal information, it is determined that the SKU has an abnormality corresponding to the cause of the second category.

6. The method according to claim 4, It is characterized in that The determining whether the SKU has a first category of abnormality or a second category of abnormality includes: Summarize and count the abnormal information reported by the at least one first warehouse operation node for the same SKU to determine the total overstock quantity and total out-of-stock quantity corresponding to the same SKU; If the total overstock quantity and the total out-of-stock quantity of the same SKU are equal, or the difference between the two is less than the target threshold, it is determined that the SKU has the first category of anomalies; If the difference between the total overstock quantity and the total out-of-stock quantity of the same SKU is greater than the target threshold, it is determined that the SKU has an anomaly of the second category.

7. The method according to claim 1, It is characterized in that Also includes: The responsibility sheet is provided to the second warehouse operation node so that the second warehouse operation node can acknowledge responsibility or provide evidence according to the cause information corresponding to the responsibility sheet.

8. The method according to claim 7, It is characterized in that When providing the judgment sheet to the second warehouse operation node, it also includes: An operation control for appealing the judgment order is provided so that the second warehouse operation node can initiate an appeal against the specified judgment order.

9. The method according to claim 8, It is characterized in that A plurality of video monitoring devices are deployed in the second warehouse operation node for recording the sorting operation process in the second warehouse operation node, so as to provide evidence information for the second warehouse operation node when the second warehouse operation node needs to file a complaint.

10. The method according to claim 9, It is characterized in that During the sorting operation, the second warehouse operation node carries the goods corresponding to the SKU through a preset container, and saves the correspondence between the container identifier and the location, as well as the time information of the sorting operation for each SKU; The abnormal information reported by the first warehouse operation node also includes: container identification information of the SKU with excess or shortage of goods; The method further comprises: Determine the SKU ID associated with the judgment form and the target container ID corresponding to the SKU; Determine the location information of the target container when the sorting operation is performed in the second warehouse operation node, and the time information of the second warehouse operation node picking the SKU; According to the location information and the time information, a video clip corresponding to the time information is extracted from the video recorded by the video monitoring device corresponding to the location information, and provided to the second warehouse operation node so that the second warehouse operation node can file a complaint and provide evidence based on the video clip.

11. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the program is executed by a processor, the steps of the method described in any one of claims 1 to 10 are implemented.

12. An electronic device, It is characterized in that include: one or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions, wherein the program instructions, when read and executed by the one or more processors, execute the steps of the method described in any one of claims 1 to 10.

Images