Buffer inventory information generation method, device, equipment and computer readable medium

Abstract

Embodiments of the present disclosure disclose a method, device and computer readable medium for generating buffer stock information. A specific embodiment of the method comprises: obtaining a set of historical daily turnover quantities of an item corresponding to item information and a set of historical daily estimated demand quantities of the item; generating a set of historical deviation information corresponding to a set of item information; generating a set of quantile fluctuation error groups corresponding to the set of item information based on the set of historical deviation information and a set of preset quantile points; determining historical quantile information corresponding to the set of item information; and generating buffer stock information corresponding to the set of item information based on the historical quantile information and the set of quantile fluctuation error groups. The embodiment improves the accuracy of the buffer stock information, reduces the overstock of items, and saves warehouse resources.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a method and apparatus for generating buffer inventory information, and a computer readable medium. BACKGROUND

[0002] With the development of online item information platforms, the flow of items through online item information platforms is increasing, and items in warehouses need to be replenished in advance. Currently, when determining the demand for an item, the commonly used methods are: determining the demand for the corresponding item according to the experience of humans; or generating the demand for the corresponding item by multiplying the standard deviation of the historical estimated demand by a certain coefficient.

[0003] However, the inventors have found that when the above methods are used to determine the demand for an item, the following technical problems often occur: when the demand for an item is determined by humans, the data samples are less, and there is a risk of missing items, resulting in low accuracy of the demand, and when the demand is too large, there is too much inventory, and when the demand is too small, there is a waste of warehouse resources; when the demand is generated by the historical estimated demand, the data of the historical estimated demand is assumed to be normally distributed, and the degree of conformity with the actual data is low, resulting in poor accuracy of the demand, and when the demand is too large, there is too much inventory, and when the demand is too small, there is a waste of warehouse resources.

[0004] The above information disclosed in this Background section is only for the purpose of enhancing the understanding of the background of the present inventive concepts, and therefore, it can include information that does not form the prior art known to those of ordinary skill in the art in the country. SUMMARY

[0005] The summary section of the present disclosure is intended to introduce the concepts in a simplified form, which will be described in detail in the specific embodiments section. The summary section of the present disclosure is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of the present disclosure propose a method and apparatus for generating buffer inventory information, and an electronic device and a computer readable medium, to solve one or more of the technical problems mentioned in the background section.

[0007] In a first aspect, some embodiments of the present disclosure provide a method for generating buffer stock information, the method comprising: for each preset historical day corresponding to each item information in an item information set, obtaining an item single-day historical turnover quantity set and an item historical single-day estimated demand quantity set corresponding to the item information, wherein the item single-day historical turnover quantity set comprises a same number of item single-day historical turnover quantities as the preset historical days, and the item historical single-day estimated demand quantity set comprises a same number of item historical single-day estimated demand quantities as the preset historical days; generating a historical deviation information set corresponding to the item information set according to each preset replenishment cycle day corresponding to each item information in the item information set, each obtained item single-day historical turnover quantity set, and each obtained item historical single-day estimated demand quantity set; generating a quantile fluctuation error group set corresponding to the item information set according to the historical deviation information set and a preset quantile set, wherein the historical deviation information set comprises a same number of historical deviation information as the preset quantile set; determining historical quantile information corresponding to the item information set according to the item information set, a preset maximum stockout rate corresponding to the item information set, and the preset historical days; and generating buffer stock information corresponding to the item information set according to the historical quantile information and the quantile fluctuation error group set.

[0008] In a second aspect, some embodiments of the present disclosure provide an apparatus for generating buffer stock information, the apparatus comprising: an obtaining unit configured to, for each preset historical day corresponding to each item information in an item information set, obtain an item single-day historical turnover quantity set and an item historical single-day estimated demand quantity set corresponding to the item information, wherein the item single-day historical turnover quantity set comprises a same number of item single-day historical turnover quantities as the preset historical days, and the item historical single-day estimated demand quantity set comprises a same number of item historical single-day estimated demand quantities as the preset historical days; a first generating unit configured to generate a historical deviation information set corresponding to the item information set according to each preset replenishment cycle day corresponding to each item information in the item information set, each obtained item single-day historical turnover quantity set, and each obtained item historical single-day estimated demand quantity set; a second generating unit configured to generate a quantile fluctuation error set corresponding to the item information set according to the historical deviation information set and a preset quantile set, wherein the historical deviation information set comprises a same number of historical deviation information as the preset quantile set; a determining unit configured to determine historical quantile information corresponding to the item information set according to the item information set, a preset maximum stockout rate corresponding to the item information set, and the preset historical days; and a third generating unit configured to generate buffer stock information corresponding to the item information set according to the historical quantile information and the quantile fluctuation error set.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having stored thereon one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation manner of the first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0011] The above various embodiments of the present disclosure have the following beneficial effects: through the buffer inventory information generation method of some embodiments of the present disclosure, the accuracy of the buffer inventory information is improved, the overstock of goods is reduced, and the warehouse resources are saved. Specifically, the reasons for causing overstock of goods and waste of warehouse resources are as follows: when the demand quantity of goods is determined artificially, the data sample is small, and there may be missing goods, resulting in low accuracy of the demand quantity. When the demand quantity is too large, it leads to too much overstock of goods, and when the demand quantity is too small, it leads to waste of warehouse resources; when the demand quantity is generated by historical estimated demand quantity, the data of the historical estimated demand quantity is assumed to be normally distributed, and the degree of conformity with the actual data is low, resulting in poor accuracy of the demand quantity. When the demand quantity is too large, it leads to too much overstock of goods, and when the demand quantity is too small, it leads to waste of warehouse resources. Based on this, the buffer inventory information generation method of some embodiments of the present disclosure first obtains a set of single-day historical turnover quantities of goods and a set of historical single-day estimated demand quantities of goods corresponding to a preset historical number of days for each item information in a set of item information. The set of single-day historical turnover quantities of goods includes a number of single-day historical turnover quantities of goods that is the same as the preset historical number of days, and the set of historical single-day estimated demand quantities of goods includes a number of historical single-day estimated demand quantities of goods that is the same as the preset historical number of days. In this way, a set of single-day historical turnover quantities of goods and a set of historical single-day estimated demand quantities of goods corresponding to the set of item information can be obtained. Then, according to each preset replenishment cycle day corresponding to each item information in the set of item information, the obtained set of single-day historical turnover quantities of goods and the set of historical single-day estimated demand quantities of goods, a set of historical deviation information corresponding to the set of item information is generated. In this way, each historical deviation information of each item information can be represented. Secondly, according to the set of historical deviation information and a set of preset quantile points, a set of quantile point fluctuation error groups corresponding to the set of item information is generated, wherein the number of historical deviation information included in the set of historical deviation information is the same as the number of preset quantile points included in the set of preset quantile points. In this way, the set of quantile point fluctuation error groups corresponding to the set of item information can be obtained. Next, according to the set of item information, the preset maximum stockout rate and the preset historical number of days corresponding to the set of item information, the historical quantile point information corresponding to the set of item information is determined. In this way, the historical quantile point information that meets the maximum stockout rate can be represented. Finally, according to the historical quantile point information and the set of quantile point fluctuation error information groups, the buffer inventory information corresponding to the set of item information is generated. In this way, the buffer inventory quantity of each item information in the set of item information corresponding to the set of item information can be represented.In addition, because the buffer stock information is generated based on the above-mentioned historical daily turnover volume set of the item and the above-mentioned historical daily estimated demand volume set of the item, the historical daily turnover volume and the historical daily estimated demand volume of the item within any preset time range can be obtained, the sample data can be obtained in a flexible manner, the sample data is actual historical data, and thus the accuracy of the generated buffer stock information can be improved, the accuracy of the demand volume can be improved, and the item backlog can be reduced, thereby saving warehouse resources. BRIEF DESCRIPTION OF DRAWINGS

[0012] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail some embodiments thereof with reference to the attached drawings. The same or similar components have the same or similar reference labels. It should be understood that the drawings are schematic and elements and features are not necessarily to scale.

[0013] Figure 1 is a flowchart of some embodiments of a buffer stock information generation method according to the present disclosure;

[0014] Figure 2 is a structural schematic diagram of some embodiments of a buffer stock information generation apparatus according to the present disclosure;

[0015] Figure 3 is a structural schematic diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in detail with reference to the drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present disclosure are merely for exemplary purposes, and are not intended to limit the scope of protection of the present disclosure.

[0017] In addition, it should be noted that only parts related to the invention are shown in the drawings for ease of description. The embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0018] It should be noted that the terms "first", "second", and the like mentioned in the present disclosure are merely used to distinguish different devices, modules, or units, and are not intended to limit the functions performed by these devices, modules, or units or the mutual dependency therebetween.

[0019] It should be noted that the adjectives "one", "multiple" mentioned in the present disclosure are illustrative rather than limiting, and those skilled in the art should understand that "one" or "multiple" should be understood as "one or more" unless otherwise explicitly stated in the context.

[0020] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0021] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Figure 1 A flow 100 of some embodiments of a buffer inventory information generation method according to the present disclosure is shown. The buffer inventory information generation method includes the following steps:

[0023] Step 101: For each item in the item information set, for the preset number of historical days, obtain the set of historical daily turnover volume and the set of historical daily estimated demand volume of the corresponding item information.

[0024] In some embodiments, the executing entity (e.g., a computing device) of the buffer inventory information generation method can retrieve, from a database, a set of daily historical turnover volumes and a set of daily estimated demand volumes for each item in the item information set, corresponding to a preset number of historical days. The daily historical turnover volume set includes the same number of items as the preset number of historical days. The daily estimated demand volume represents an estimate of daily demand within a preset number of days. For example, it can estimate the demand for each day within the next three days. The preset number of days can be three days. The item information set represents a collection of items of the same category. For example, apples and bananas can be items of the same category. The item information can include the name of the item. The daily estimated demand volume set includes the same number of items as the preset number of historical days. The preset number of historical days can be 100. The specific setting of the preset number of historical days is not limited. The aforementioned set of daily historical turnover volumes for each item can represent the daily turnover volume of each item. Therefore, we can obtain the set of daily historical turnover volumes and the set of estimated daily historical demand volumes for each item within the corresponding item information set.

[0025] Step 102: Based on the preset replenishment cycle days corresponding to each item information in the item information set, the obtained daily historical turnover set of each item, and the historical daily estimated demand set of each item, generate the historical deviation information set corresponding to the item information set.

[0026] In some embodiments, the execution subject can generate a historical deviation information set corresponding to the item information set according to each preset replenishment period corresponding to each item information in the item information set, each obtained single-day historical turnover quantity set of each item, and each obtained single-day historical estimated demand quantity set of each item. The preset replenishment period can represent the average replenishment interval of the item information. The correspondence between each item information and each preset replenishment period can be one-to-one. In practice, the execution subject can generate a historical deviation information set corresponding to the item information set according to each preset replenishment period corresponding to each item information in the item information set, each obtained single-day historical turnover quantity set of each item, and each obtained single-day historical estimated demand quantity set of each item in various ways.

[0027] In some optional implementations of some embodiments, the execution subject can generate a historical deviation information set corresponding to the item information set according to each preset replenishment period corresponding to each item information in the item information set, each obtained single-day historical turnover quantity set of each item, and each obtained single-day historical estimated demand quantity set of each item by the following steps:

[0028] First, determine each single-day historical deviation information of each item information in the item information set according to the preset replenishment period and the number sequence set corresponding to the preset historical days. Each single-day historical deviation information can represent the deviation information between the daily historical turnover quantity and the daily estimated demand quantity predicted in the future. The number sequence set can represent a sequence of sorting the preset historical days from low to high. For example, the preset replenishment period can be 5. Thus, each single-day historical deviation information corresponding to each item information can be obtained.

[0029] Second, for each item information in the item information set and each number sequence in the number sequence set, perform the following steps:

[0030] A first sub-step is to determine each single-day historical turnover quantity of each item corresponding to the replenishment cycle days and each historical single-day estimated demand quantity according to the replenishment cycle days and the sequence of days. In practice, first, according to the xth day in the sequence of days and the replenishment cycle days, the single-day historical turnover quantity corresponding to the xth day of the item information and the historical single-day estimated demand quantity corresponding to the xth day are determined. Then, the single-day historical turnover quantity corresponding to the xth day of the item information in the set of single-day historical turnover quantities is determined. Next, the single-day historical turnover quantity corresponding to the xth day of the item information in the set of historical single-day estimated demand quantities is determined. Then, the sum of the single-day historical turnover quantity corresponding to the xth day of the item information and the single-day historical turnover quantity after y single-day historical turnover quantities of the xth day is determined as each single-day historical turnover quantity corresponding to the item information. The sum of the historical single-day estimated demand quantity corresponding to the xth day and the single-day historical turnover quantity after y single-day historical turnover quantities of the xth day is determined as each historical single-day estimated demand quantity. The replenishment cycle days can be y. The initial value of x is 1. Thus, each single-day historical turnover quantity and each historical single-day estimated demand quantity within the cycle can be obtained.

[0031] A second sub-step is to determine the sum of each single-day historical turnover quantity as a first value. Thus, each single-day historical turnover quantity corresponding to the sequence of days can be obtained.

[0032] A third sub-step is to determine the sum of each historical single-day estimated demand quantity as a second value. Thus, each historical single-day estimated demand quantity corresponding to the sequence of days can be obtained.

[0033] A fourth sub-step is to determine the difference between the first value and the second value as a third value. Thus, the historical deviation information within the cycle can be obtained.

[0034] A fifth sub-step is to determine the ratio of the third value to the preset replenishment cycle days as the historical deviation information corresponding to the sequence of days. Thus, the historical deviation information corresponding to the sequence of days can be obtained.

[0035] A third step is to determine each historical deviation information as a set of historical deviation information corresponding to the set of item information. Thus, the set of historical deviation information corresponding to the set of item information can be obtained.

[0036] Step 103 is to generate a set of quantile fluctuation error groups corresponding to the set of item information according to the set of historical deviation information and a set of preset quantiles.

[0037] In some embodiments, the execution subject can generate a set of quantile fluctuation error groups corresponding to the set of item information according to the set of historical deviation information and the set of preset quantile points. The set of historical deviation information includes the same number of historical deviation information as the set of preset quantile points. The set of preset quantile points can be [1%, 2%, 3%, …, 100%]. A set of quantile sequence numbers corresponding to the set of preset quantile points can be obtained. The set of quantile sequence numbers can be [0, 1, 2, …, 100]. The preset quantile points in the set of preset quantile points can correspond to the quantile sequence numbers in the set of quantile sequence numbers in a one-to-one manner. The quantile fluctuation error group in the set of quantile fluctuation error groups includes a quantile fluctuation error representing the difference between the zth quantile error information and the (z-1)th quantile error information of the item information. The initial value of z can be 0, and when z is 0, it can correspond to 1% in the set of preset quantile points. Z can represent the quantile sequence number in the set of quantile sequence numbers. In practice, the execution subject can generate a set of quantile fluctuation error groups corresponding to the set of item information according to the set of historical deviation information and the set of preset quantile points in various ways.

[0038] In some optional implementations of some embodiments, the execution subject can generate a set of quantile fluctuation error groups corresponding to the set of item information according to the set of historical deviation information and the set of preset quantile points by the following steps:

[0039] First, for each item information in the set of item information, the following steps are performed according to the historical deviation information corresponding to the item information in the set of historical deviation information:

[0040] First sub-step, determine the historical deviation information corresponding to the item information as a historical deviation information group. In this way, each historical deviation information group corresponding to the set of item information can be obtained.

[0041] Second sub-step, determine the quantile error information group corresponding to the item information according to the historical deviation information group and the set of preset quantile points. The quantile error information in the quantile error information group corresponds to the preset quantile point in the set of preset quantile points. The correspondence between the quantile error information in the quantile error information group and the preset quantile point in the set of preset quantile points can be one-to-one. In practice, first, for each preset quantile point in the set of preset quantile points, the execution subject can determine the historical deviation information corresponding to the preset quantile point in the historical deviation information group as the quantile error information. Then, each quantile error information obtained can be determined as the quantile error information group. In this way, each quantile error information group corresponding to each item information can be obtained.

[0042] A third sub-step, for each quantile error information in the quantile error information set, the following steps are performed:

[0043] A first sub-step, in response to determining that the quantile error information is greater than a first preset value, determining whether there is quantile error information in the quantile error information set that satisfies a first preset condition. Wherein the quantile sequence number corresponding to the quantile error information is z, the first preset condition can be the quantile error information corresponding to the “z-1” quantile. The first preset value can be 0. Thus, it can be determined whether there is quantile error information that satisfies the first preset condition.

[0044] A second sub-step, in response to there being quantile error information in the quantile error information set that satisfies the first preset condition, determining the quantile error information that satisfies the first preset condition as the target quantile error information. Thus, the target quantile error information corresponding to the first preset condition can be determined.

[0045] A third sub-step, determining the difference between the quantile error information and the target quantile error information as the quantile fluctuation error information. Thus, the quantile fluctuation error information corresponding to the quantile error information can be obtained.

[0046] A fourth sub-step, in response to determining that the quantile error information is less than or equal to the first preset value, determining a second preset value as the quantile fluctuation error information. Wherein the second preset value can be “-1”. The second preset value can represent that the single-day historical turnover of the item is less than the historical single-day estimated demand of the item, and the current is not out of stock. Thus, the quantile fluctuation error information corresponding to the quantile error information can be obtained.

[0047] A third step, determining each generated quantile fluctuation error information as a quantile fluctuation error information set corresponding to the item information. Thus, the quantile fluctuation error information set corresponding to each item information in the item information set can be obtained.

[0048] A fourth step, combining each determined quantile fluctuation error information set into a quantile fluctuation error information set collection. Thus, the quantile fluctuation error information set collection corresponding to the item information set can be obtained.

[0049] Step 104, determining the historical quantile information corresponding to the item information set according to the item information set, the preset maximum out-of-stock rate and the preset historical days corresponding to the item information set.

[0050] In some embodiments, the execution subject can determine historical quantile information of the corresponding item information set according to the item information set, a preset maximum stockout rate of the corresponding item information set, and a preset historical day number. The preset maximum stockout rate can represent a stockout rate when the buffer inventory of the item information set is the least. The historical quantile information can represent a quantity of each quantile corresponding to each item information in the item information set that meets the preset maximum stockout rate. The buffer inventory can represent the quantity of items that need to be stored when the item information meets the maximum stockout rate. In practice, the execution subject can determine the historical quantile information of the corresponding item information set according to the item information set, the preset maximum stockout rate of the corresponding item information set, and the preset historical day number in various ways.

[0051] In some optional implementations of some embodiments, the execution subject can determine the historical quantile information of the corresponding item information set according to the item information set, the preset maximum stockout rate of the corresponding item information set, and the preset historical day number by the following steps:

[0052] Firstly, the quantity of each item information included in the item information set is determined as quantity information. In this way, the quantity information can represent the quantity of item categories in the item information set.

[0053] Secondly, the product of the quantity information, the preset maximum stockout rate, and the preset historical day number is determined as the historical quantile information of the corresponding item information set. The historical quantile information includes a total quantity of historical quantiles. In this way, the quantity of quantiles required to meet the maximum stockout rate can be obtained.

[0054] Step 105, generating buffer inventory information of the corresponding item information set according to the historical quantile information and the quantile fluctuation error information group set.

[0055] In some embodiments, the execution subject can generate buffer inventory information of the corresponding item information set according to the historical quantile information and the quantile fluctuation error information group set. The buffer inventory information can represent the buffer inventory of each item information in the item information set. In practice, the execution subject can generate the buffer inventory information of the corresponding item information set according to the historical quantile information and the quantile fluctuation error information group set in various ways.

[0056] In some optional implementations of some embodiments, the execution subject can generate the buffer inventory information of the corresponding item information set according to the historical quantile information and the quantile fluctuation error information group set by the following steps:

[0057] In the first step, each quantile fluctuation error information corresponding to a target quantile and a preset number of quantiles is selected from each quantile fluctuation error information set to form a first quantile fluctuation error information set, thereby obtaining a first quantile fluctuation error information set collection. The target quantile can represent the first preset quantile in the preset quantile set. The preset number of quantiles can represent the number of quantiles to be selected from the target quantile. In practice, the subject can first select the quantile fluctuation error information corresponding to the target quantile from each quantile fluctuation error information set. Then, the quantiles corresponding to the preset number of quantiles can be determined. Finally, the quantile fluctuation error information corresponding to each quantile determined from each quantile fluctuation error information set is selected. For example, the preset number of quantiles can be 3. The quantiles corresponding to the preset number of quantiles can be the second quantile and the third quantile. The quantile fluctuation error information corresponding to the second quantile and the third quantile from each quantile fluctuation error information set is selected. Finally, the selected quantile fluctuation error information is determined as the first quantile fluctuation error information set. Thus, the first quantile fluctuation error information set collection corresponding to the item information set can be obtained.

[0058] In the second step, for each first quantile fluctuation error information set in the first quantile fluctuation error information set collection, an average quantile fluctuation error information set is generated based on the first quantile fluctuation error information set. In practice, for the i-th first quantile fluctuation error information in the first quantile fluctuation error information set, the average value of the first i first quantile fluctuation error information in the first quantile fluctuation error information set is determined as the average quantile fluctuation error information, thereby obtaining the average quantile fluctuation error information set. The initial value of i is 1. For example, when the target quantile represents the second preset quantile in the preset quantile set, the average quantile fluctuation error information is determined as the ratio of the first first quantile fluctuation error information to the second first quantile fluctuation error information and i. Thus, the average quantile fluctuation error information set corresponding to each item information can be obtained.

[0059] In the third step, each generated average quantile fluctuation error information set is determined as the average quantile fluctuation error information set collection. Thus, the average quantile fluctuation error information set collection corresponding to the item information set can be obtained.

[0060] In the fourth step, the average quantile fluctuation error information satisfying a preset condition is selected from the average quantile fluctuation error information set collection. The preset condition can be the largest average quantile fluctuation error information in the average quantile fluctuation error information set collection.

[0061] In the fifth step, the item information corresponding to the selected average quantile fluctuation error information in the item information set is determined as target item information. Thus, the target item information corresponding to the maximum average quantile fluctuation error information can be determined.

[0062] In the sixth step, the target quantile is updated according to the preset quantile number. In practice, the sum of the preset quantile number and 1 is determined as s according to the preset quantile number and the target quantile. The s can represent the quantile sequence number of the updated target quantile corresponding to the quantile sequence number set. The updated target quantile is the s-th preset quantile in the preset quantile set. For example, the target quantile before updating can represent the first preset quantile in the preset quantile set. The preset quantile number can be 3. Thus, the updated target quantile can represent the fourth preset quantile in the preset quantile set.

[0063] In the seventh step, according to the updated target quantile, the average quantile fluctuation error information group set, and the target item information, the following iterative steps are performed:

[0064] In the first sub-step, the quantile fluctuation error information group corresponding to the target item information in the quantile fluctuation error information group set is determined as the target quantile fluctuation error information group. Thus, the target quantile fluctuation error information group corresponding to the target item information can be determined.

[0065] In the second sub-step, each quantile fluctuation error information corresponding to the target quantile and the preset quantile number is selected from the target quantile fluctuation error information group as a second quantile fluctuation error information group. It should be noted that the selection of each quantile fluctuation error information corresponding to the target quantile and the preset quantile number according to the updated target quantile is consistent with the selection of each quantile fluctuation error information corresponding to the target quantile and the preset quantile number according to the target quantile before updating, which will not be described here. Thus, the updated second quantile fluctuation error information group can be obtained.

[0066] In the third sub-step, a replacement average quantile fluctuation error information group is generated according to the second quantile fluctuation error information group. It should be noted that the generation of the replacement average quantile fluctuation error information group according to the second quantile fluctuation error information group is consistent with the generation of the average quantile fluctuation error information group according to the first quantile fluctuation error information group, which will not be described here. Thus, the updated average quantile fluctuation error information group can be obtained.

[0067] A fourth sub-step is to determine the sum of the quantile numbers corresponding to each average quantile fluctuation error information in the set of average quantile fluctuation error information groups as the total number of quantiles. In practice, first, the subject performing the method can determine the target quantile corresponding to each average quantile fluctuation error information group after updating. According to the updated target quantile and the preset number of quantiles, the number of quantiles corresponding to each average quantile fluctuation error information group is determined. For example, when the updated target quantile corresponds to the fourth preset quantile in the set of preset quantiles, the quantile number corresponding to the fourth preset quantile is determined from the set of quantile numbers. The preset number of quantiles can be 3. The number of quantiles corresponding to the average quantile fluctuation error information group is the sum of the determined quantile number and the preset number of quantiles. Then, the number of quantiles corresponding to each average quantile fluctuation error information group is determined. Finally, the sum of the number of quantiles corresponding to each average quantile fluctuation error information group is determined as the total number of quantiles.

[0068] A fifth sub-step is to determine whether the total number of quantiles is consistent with the total number of historical quantiles contained in the historical quantile information.

[0069] A sixth sub-step is to determine whether the average quantile fluctuation error information satisfying the preset condition is less than a first preset value. The first preset value can be 0. Thus, it can be determined whether the maximum average quantile fluctuation error information is less than the first preset value.

[0070] A seventh sub-step is to determine the buffer quantile corresponding to each average quantile fluctuation error information group in the set of average quantile fluctuation error information groups in response to the total number of quantiles being consistent with the total number of historical quantiles contained in the historical quantile information or the average quantile fluctuation error information satisfying the preset condition being less than the first preset value. The buffer quantile can represent the quantile corresponding to each average quantile fluctuation error information group. In practice, the subject performing the method can determine the quantile corresponding to the last average quantile fluctuation error information in the average quantile fluctuation error information group as the buffer quantile corresponding to the average quantile fluctuation error information group. For example, when the average quantile fluctuation error information group includes the average quantile fluctuation error information corresponding to the first quantile and the average quantile fluctuation error information corresponding to the second quantile, the buffer quantile corresponding to the average quantile fluctuation error information group is the quantile corresponding to the average quantile fluctuation error information corresponding to the second quantile. Thus, each buffer quantile corresponding to each average quantile fluctuation error information group can be obtained.

[0071] In the eighth sub-step, for each item information in the item information set, the buffer quantile error information corresponding to the buffer quantile of the item information is selected from the quantile error information set corresponding to the item information as the buffer quantile error information. The buffer quantile error information can represent the buffer inventory corresponding to the buffer quantile. Thus, the buffer quantile error information corresponding to each item information in the item information set can be obtained.

[0072] In the ninth sub-step, the selected buffer quantile error information is combined as the buffer inventory information corresponding to the item information set. Thus, the buffer inventory corresponding to each item information in the item information set can be obtained.

[0073] Optionally, in the iteration step, first, the execution subject can also update the average quantile fluctuation error information set according to the replacement average quantile fluctuation error information set, in response to that the total number of quantiles is inconsistent with the total number of historical quantiles contained in the historical quantile information and the average quantile fluctuation error information satisfying the preset condition is greater than or equal to the first preset value. In practice, the execution subject can replace the average quantile fluctuation error information set corresponding to the item information in the average quantile fluctuation error information set with the replacement average quantile fluctuation error information set. Thus, the average quantile fluctuation error information set can be updated by replacing the average quantile fluctuation error information set.

[0074] Then, the average quantile fluctuation error information satisfying the preset condition can be selected from the updated average quantile fluctuation error information set as the target average quantile fluctuation error information. Thus, the average quantile fluctuation error information with the largest value can be determined.

[0075] Next, the item information corresponding to the target average quantile fluctuation error information in the item information set can be determined as the target item information, so as to update the target item information. Thus, the item information with the largest average quantile fluctuation error information can be determined.

[0076] Subsequently, the target quantile can be updated according to the preset number of quantiles. It should be noted that the process of updating the target quantile according to the preset number of quantiles is consistent with the process of updating the target quantile in the sixth step.

[0077] Finally, the iteration step can be executed again according to the updated target quantile, the updated average quantile fluctuation error information set and the updated target item information. Thus, the iteration step can be continued to be executed under the condition that the iteration related conditions are met.

[0078] Optionally, after step 105, first, the execution subject can further execute the following steps for each item information in the item information set:

[0079] In a first sub-step, a target buffer bin error information is determined from buffer bin error information corresponding to the item information in each buffer bin error information included in the buffer inventory information. The buffer bin error information can represent the buffer inventory quantity corresponding to the buffer bin. Thus, each target buffer bin error information corresponding to the item information set can be obtained.

[0080] In a second sub-step, a remaining inventory quantity corresponding to the item information is obtained. The remaining inventory quantity can represent the number of the item remaining in the warehouse storing the item corresponding to the item information. Thus, the inventory quantity corresponding to the current buffer inventory information can be determined.

[0081] In a third sub-step, in response to determining that the target buffer bin error information is greater than the remaining inventory quantity, a difference between the target buffer bin error information and the remaining inventory quantity is determined as replenishment information of the item information. The replenishment information can represent the number of the item required to meet the remaining inventory quantity. Thus, each replenishment information corresponding to the item information set can be determined.

[0082] In a fourth sub-step, an item scheduling device associated with the item information is controlled to perform an item scheduling operation according to the replenishment information. The item scheduling device can be an unmanned transport vehicle. For example, when the number of the item corresponding to the replenishment information is 5, the execution subject can control the unmanned transport vehicle to obtain 5 items. Then, the unmanned transport vehicle can be controlled to transport the 5 items to the warehouse. Thus, the buffer inventory information corresponding to the item information set can be adjusted.

[0083] The above various embodiments of the present disclosure have the following beneficial effects: through the buffer inventory information generation method of some embodiments of the present disclosure, the accuracy of the buffer inventory information is improved, the overstock of goods is reduced, and the warehouse resources are saved. Specifically, the reasons for causing overstock of goods and waste of warehouse resources are as follows: when the demand quantity of goods is determined artificially, the data sample is small, and there may be missing goods, resulting in low accuracy of the demand quantity. When the demand quantity is too large, it leads to too much overstock of goods, and when the demand quantity is too small, it leads to waste of warehouse resources; when the demand quantity is generated by historical estimated demand quantity, the data of the historical estimated demand quantity is assumed to be normally distributed, and the degree of conformity with the actual data is low, resulting in poor accuracy of the demand quantity. When the demand quantity is too large, it leads to too much overstock of goods, and when the demand quantity is too small, it leads to waste of warehouse resources. Based on this, the buffer inventory information generation method of some embodiments of the present disclosure first obtains a set of single-day historical turnover quantities of goods and a set of historical single-day estimated demand quantities of goods corresponding to a preset historical number of days for each item information in a set of item information. The set of single-day historical turnover quantities of goods includes a number of single-day historical turnover quantities of goods that is the same as the preset historical number of days, and the set of historical single-day estimated demand quantities of goods includes a number of historical single-day estimated demand quantities of goods that is the same as the preset historical number of days. In this way, a set of single-day historical turnover quantities of goods and a set of historical single-day estimated demand quantities of goods corresponding to the set of item information can be obtained. Then, according to each preset replenishment cycle day corresponding to each item information in the set of item information, the obtained set of single-day historical turnover quantities of goods and the set of historical single-day estimated demand quantities of goods, a set of historical deviation information corresponding to the set of item information is generated. In this way, each historical deviation information of each item information can be represented. Secondly, according to the set of historical deviation information and a set of preset quantile points, a set of quantile point fluctuation error groups corresponding to the set of item information is generated, wherein the number of historical deviation information included in the set of historical deviation information is the same as the number of preset quantile points included in the set of preset quantile points. In this way, the set of quantile point fluctuation error groups corresponding to the set of item information can be obtained. Next, according to the set of item information, the preset maximum stockout rate and the preset historical number of days corresponding to the set of item information, the historical quantile point information corresponding to the set of item information is determined. In this way, the historical quantile point information that meets the maximum stockout rate can be represented. Finally, according to the historical quantile point information and the set of quantile point fluctuation error information groups, the buffer inventory information corresponding to the set of item information is generated. In this way, the buffer inventory quantity of each item information in the set of item information corresponding to the set of item information can be represented.Because the buffer stock information is generated based on the above-mentioned set of single-day historical turnover quantities of the item and the above-mentioned set of single-day historical estimated demand quantities of the item, the single-day historical turnover quantities of the item and the single-day historical estimated demand quantities of the item in any preset time range can be obtained, the sample data can be obtained in a flexible manner, and the sample data is actual historical data, thereby improving the accuracy of the generated buffer stock information and the accuracy of the demand quantity, reducing the overstock of the item, and saving warehouse resources.

[0084] Further referring to Figure 2 , as an implementation of the method shown in the above-mentioned figures, the present disclosure provides some embodiments of a buffer stock information generation device, which corresponds to the method embodiments shown in Figure 2 , and the device can be applied in various electronic devices.

[0085] As shown in Figure 2 , the cache data sending device 200 of some embodiments includes an obtaining unit 201, a first generating unit 202, a second generating unit 203, a determining unit 204, and a third generating unit 205. The obtaining unit 201 is configured to, for each preset historical day corresponding to each item information in the item information set, obtain a set of single-day historical turnover quantities of the item corresponding to the item information and a set of single-day historical estimated demand quantities of the item, wherein the set of single-day historical turnover quantities of the item includes a same number of single-day historical turnover quantities of the item as the preset historical day, and the set of single-day historical estimated demand quantities of the item includes a same number of single-day historical estimated demand quantities of the item as the preset historical day; the first generating unit 202 is configured to generate a set of historical deviation information corresponding to the item information set according to each preset replenishment period corresponding to each item information in the item information set, each set of single-day historical turnover quantities of the item, and each set of single-day historical estimated demand quantities of the item; the second generating unit 203 is configured to generate a set of quantile fluctuation errors corresponding to the item information set according to the set of historical deviation information and a set of preset quantiles, wherein the set of historical deviation information includes a same number of historical deviation information as the set of preset quantiles includes a same number of preset quantiles; the determining unit 204 is configured to determine historical quantile information corresponding to the item information set according to the item information set, a preset maximum stockout rate corresponding to the item information set, and a preset historical day; and the third generating unit 205 is configured to generate buffer stock information corresponding to the item information set according to the historical quantile loss information and the set of quantile fluctuation errors.

[0086] It can be understood that the units recorded in the device 200 are described with reference to Figure 1The individual steps in the described methods correspond. Thus, the operations, features and advantages described above for the methods apply equally to the apparatus 200 and the units contained therein, which will not be described again here.

[0087] Reference is made below Figure 3 , which shows a structural diagram of an electronic device 300 (e.g., a computing device) suitable for use in implementing some embodiments of the present disclosure. The electronic device in some embodiments of the present disclosure can include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet PC), a PMP (Portable Multimedia Player), a car terminal (e.g., a car navigation terminal), and the like, as well as a stationary terminal such as a digital TV, a desktop computer, and the like. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functions and scope of use of embodiments of the present disclosure.

[0088] As shown in Figure 3 , the electronic device 300 can include a processing device 301 (e.g., a central processor, a graphics processor, etc.) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or loaded into a random access memory (RAM) 303 from a storage device 308. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0089] In general, the following devices can be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; a storage device 308 including, for example, a magnetic tape, a hard disk, and the like; and a communication device 309. The communication device 309 can allow the electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it is understood that all of the devices shown are not required to be implemented or present. More or fewer devices can alternatively be implemented or present. Figure 3 Each block shown in the middle can represent a device or, as desired, multiple devices.

[0090] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to some embodiments of the present disclosure. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-described functions defined in the methods of some embodiments of the present disclosure are performed.

[0091] It should be noted that the computer readable medium recorded with the program code according to some embodiments of the present disclosure can be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. In some embodiments of the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take on many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport program for use by or in connection with an instruction execution system, apparatus or device. The program code contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to a wire, cable, optical fiber, RF (radio frequency), or any suitable combination of the above.

[0092] In some embodiments, the client, server, or other machines communicating using the system can communicate using any current or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., communication networks). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current or future developed network.

[0093] The computer readable medium described above can be included in the electronic device described above; or can exist separately from the electronic device and be not assembled into the electronic device. The computer readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, cause the electronic device to: for each preset historical day corresponding to each item information in the item information set, obtain an item single-day historical turnover quantity set and an item historical single-day estimated demand quantity set of the corresponding item information, wherein the number of each item single-day historical turnover quantity included in the item single-day historical turnover quantity set is the same as the preset historical day, and the number of each item historical single-day estimated demand quantity included in the item historical single-day estimated demand quantity set is the same as the preset historical day; generate a historical deviation information set of the corresponding item information set according to each preset replenishment cycle day corresponding to each item information in the item information set, each item single-day historical turnover quantity set and each item historical single-day estimated demand quantity set obtained; generate a quantile fluctuation error set of the corresponding item information set according to the historical deviation information set and a preset quantile point set, wherein the number of each historical deviation information included in the historical deviation information set is the same as the number of each preset quantile point included in the preset quantile point set; determine historical quantile point information of the corresponding item information set according to the item information set, a preset maximum stockout rate of the corresponding item information set and the preset historical day; and generate buffer inventory information of the corresponding item information set according to the historical quantile point loss information and the quantile fluctuation error set.

[0094] Computer program code for carrying out operations of some embodiments of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0095] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks depicted in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and / or flow diagrams and combinations of blocks in the block diagrams and / or flow diagrams can be implemented by a dedicated hardware-based system that carries out specified functions or operations or combinations of dedicated hardware and computer instructions.

[0096] The units described in some embodiments of the present disclosure can be implemented by software, or can be implemented by hardware. The described units can also be arranged in a processor, for example, can be described as: a processor includes an acquisition unit, a first generation unit, a second generation unit, a determination unit and a third generation unit. Among them, the name of these units does not constitute a limitation to the unit itself in some cases, for example, the third generation unit can also be described as: "a unit for generating buffer inventory information corresponding to the item information set according to the historical quantile loss information and the quantile fluctuation error set".

[0097] The functionality described herein above can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program- specific Integrated Circuits (ASICs), Program- specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0098] The above description is merely exemplary of the disclosure and the application made use of the principles of the technology. It is to be understood that the application scope of the embodiments of the disclosure is not limited to the specific combinations of technical features described above, and should also cover other technical solutions formed by any combination of the above technical features or equivalent features thereof without departing from the inventive concept. For example, the technical solutions formed by replacing the above features with technical features having similar functions disclosed in the embodiments of the disclosure (but not limited to) with each other.

Claims

1. A method for generating buffer stock information, comprising: for each preset historical day corresponding to each item information in an item information set, obtaining a set of item single-day historical turnover quantities and a set of item historical single-day estimated demand quantities corresponding to the item information, wherein the set of item single-day historical turnover quantities comprises a same number of item single-day historical turnover quantities as the preset historical days, and the set of item historical single-day estimated demand quantities comprises a same number of item historical single-day estimated demand quantities as the preset historical days; generating a set of historical deviation information corresponding to the item information set according to each preset replenishment cycle corresponding to each item information in the item information set, each set of item single-day historical turnover quantities, and each set of item historical single-day estimated demand quantities; generating a set of quantile fluctuation error information groups corresponding to the item information set according to the set of historical deviation information and a set of preset quantiles; determining historical quantile information corresponding to the item information set according to the item information set, a preset maximum stockout rate corresponding to the item information set, and the preset historical days; generating buffer stock information corresponding to the item information set according to the historical quantile information and the set of quantile fluctuation error information groups.

2. The method of claim 1, wherein, The generating of the set of historical deviation information corresponding to the item information set according to each preset replenishment cycle corresponding to each item information in the item information set, each set of item single-day historical turnover quantities, and each set of item historical single-day estimated demand quantities comprises: determining each single-day historical deviation information of each item information in the item information set according to the preset replenishment cycle and a set of day orders corresponding to the preset historical days; for each item information in the item information set and each day order in the set of day orders, performing the following steps: determining each item single-day historical turnover quantity and each historical single-day estimated demand quantity corresponding to the replenishment cycle according to the replenishment cycle and the day order; determining a first value as a sum of each item single-day historical turnover quantity; determining a second value as a sum of each historical single-day estimated demand quantity; determining a third value as a difference between the first value and the second value; determining a historical deviation information corresponding to the day order as a ratio of the third value to the preset replenishment cycle; determining each historical deviation information obtained as the set of historical deviation information corresponding to the item information set.

3. The method of claim 1, wherein, The generating of the set of quantile fluctuation error information groups corresponding to the item information set according to the set of historical deviation information and the set of preset quantiles comprises: for each item information in the item information set, performing the following steps according to each historical deviation information corresponding to the item information in the set of historical deviation information: determining each historical deviation information corresponding to the item information as a historical deviation information group; According to the historical deviation information set and a preset quantile point set, a quantile point error information set corresponding to the item information is determined, wherein a quantile point error information in the quantile point error information set corresponds to a preset quantile point in the preset quantile point set; For each quantile point error information in the quantile point error information set, the following steps are performed: In response to determining that the quantile point error information is greater than a first preset value, it is determined whether there is a quantile point error information in the quantile point error information set that satisfies a first preset condition; In response to there being a quantile point error information in the quantile point error information set that satisfies the first preset condition, the quantile point error information that satisfies the first preset condition is determined as a target quantile point error information; The difference between the quantile point error information and the target quantile point error information is determined as a quantile point fluctuation error information; In response to determining that the quantile point error information is less than or equal to the first preset value, a second preset value is determined as a quantile point fluctuation error information; Each generated quantile point fluctuation error information is determined as a quantile point fluctuation error information set corresponding to the item information; Each determined quantile point fluctuation error information set is combined into a quantile point fluctuation error information set collection.

4. The method of claim 3, wherein, The historical quantile point information corresponding to the item information set is determined according to the item information set, a preset maximum out-of-stock rate corresponding to the item information set, and the preset historical days, including: The number of each item information included in the item information set is determined as quantity information; The product of the quantity information, the preset maximum out-of-stock rate, and the preset historical days is determined as the historical quantile point information corresponding to the item information set, wherein the historical quantile point information includes a historical quantile point total amount.

5. The method of claim 4, wherein, The buffer inventory information corresponding to the item information set is generated according to the historical quantile point information and the quantile point fluctuation error information set collection, including: From each quantile point fluctuation error information set in the quantile point fluctuation error information set collection, each quantile point fluctuation error information corresponding to a target quantile point and a preset quantile point number is selected as a first quantile point fluctuation error information set to obtain a first quantile point fluctuation error information set collection; For each first quantile point fluctuation error information set in the first quantile point fluctuation error information set collection, an average quantile point fluctuation error information set is generated according to the first quantile point fluctuation error information set; Each generated average quantile point fluctuation error information set is determined as an average quantile point fluctuation error information set collection; From the average quantile point fluctuation error information set collection, an average quantile point fluctuation error information that satisfies a preset condition is selected; An item information in the item information set corresponding to the selected average quantile point fluctuation error information is determined as a target item information; The target quantile point is updated according to the preset quantile point number; According to the updated target quantile point, the average quantile point fluctuation error information set, and the target item information, the following iterative steps are performed: A quantile point fluctuation error information set corresponding to the target item information in the quantile point fluctuation error information set collection is determined as a target quantile point fluctuation error information set; selecting, from the target quantile fluctuation error information set, quantile fluctuation error information corresponding to a target quantile and a preset number of quantiles as a second quantile fluctuation error information set; generating a replacement average quantile fluctuation error information set according to the second quantile fluctuation error information set; determining a sum of the number of quantiles corresponding to each average quantile fluctuation error information in the average quantile fluctuation error information set as a total number of quantiles; determining whether the total number of quantiles is consistent with a historical total number of quantiles contained in the historical quantile information; determining whether the average quantile fluctuation error information satisfying the preset condition is less than a first preset value; in response to the total number of quantiles being consistent with the historical total number of quantiles contained in the historical quantile information or the average quantile fluctuation error information satisfying the preset condition being less than the first preset value, determining a buffer quantile corresponding to each average quantile fluctuation error information set in the average quantile fluctuation error information set; for each item information in the item information set, selecting, from the quantile error information set corresponding to the item information, quantile error information corresponding to the buffer quantile of the item information as buffer quantile error information; combining the selected buffer quantile error information as buffer inventory information corresponding to the item information set.

6. The method of claim 5, wherein, The iteration step further comprises: in response to the total number of quantiles being inconsistent with the historical total number of quantiles contained in the historical quantile information and the average quantile fluctuation error information satisfying the preset condition being greater than or equal to the first preset value, updating the average quantile fluctuation error information set according to the replacement average quantile fluctuation error information set; selecting, from the updated average quantile fluctuation error information set, average quantile fluctuation error information satisfying the preset condition as target average quantile fluctuation error information; determining item information corresponding to the target average quantile fluctuation error information in the item information set as target item information to update the target item information; updating the target quantile according to the preset number of quantiles; performing the iteration step again according to the updated target quantile, the updated average quantile fluctuation error information set, and the updated target item information.

7. The method of claim 6, wherein, After the buffer inventory information corresponding to the item information set is generated according to the historical quantile information and the quantile fluctuation error information set, the method further comprises: for each item information in the item information set, performing the following steps: determining buffer quantile error information corresponding to the item information in each buffer quantile error information included in the buffer inventory information as target buffer quantile error information; obtaining a remaining inventory quantity corresponding to the item information; in response to determining that the target buffer quantile error information is greater than the remaining inventory quantity, determining a difference between the target buffer quantile error information and the remaining inventory quantity as replenishment information of the item information; controlling an item scheduling device associated with the item information to perform an item scheduling operation according to the replenishment information.

8. A buffer stock information generation apparatus, comprising: an acquisition unit configured to, for each preset historical day corresponding to each item information in a set of item information, acquire a set of item single-day historical turnover quantities corresponding to the item information and a set of item historical single-day estimated demand quantities, wherein the set of item single-day historical turnover quantities comprises a same number of item single-day historical turnover quantities as the preset historical day, and the set of item historical single-day estimated demand quantities comprises a same number of item historical single-day estimated demand quantities as the preset historical day; a first generation unit configured to generate a set of historical deviation information corresponding to the set of item information according to each preset replenishment cycle day corresponding to each item information in the set of item information, each set of item single-day historical turnover quantities acquired, and each set of item historical single-day estimated demand quantities; a second generation unit configured to generate a set of sets of quantile fluctuation error information corresponding to the set of item information according to the set of historical deviation information and a set of preset quantiles, wherein the set of historical deviation information comprises a same number of historical deviation information as the set of preset quantiles comprises of preset quantiles; a determination unit configured to determine historical quantile information corresponding to the set of item information according to the set of item information, a preset maximum stockout rate corresponding to the set of item information, and the preset historical day; a third generation unit configured to generate buffer stock information corresponding to the set of item information according to the historical quantile information and the set of sets of quantile fluctuation error information.

9. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-7.

10. A computer readable medium having stored thereon a computer program, wherein, The computer program is executed by the processor to implement the method of any one of claims 1-7.

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