Inventory information processing method and device, electronic equipment and computer readable medium

Abstract

Embodiments of the present disclosure disclose inventory information processing methods and devices, electronic equipment and computer readable media. A specific embodiment of the method includes: obtaining a stock period turnover quantity prediction table corresponding to a target item, wherein the stock period turnover quantity prediction table includes a stock period distribution probability of each stock period and a plurality of predicted turnover quantity sequences, the stock period distribution probability of each stock period corresponds to a predicted turnover quantity sequence in the plurality of predicted turnover quantity sequences, and the stock period turnover quantity prediction table represents the turnover quantity of the target item in different stock periods predicted by quantile; generating an inventory threshold and a target inventory quantity corresponding to the target item according to the stock period distribution probability and the predicted turnover quantity sequence included in the stock period turnover quantity prediction table, wherein the inventory threshold and the target inventory quantity are used for replenishment of the target item. The embodiment is related to intelligent supply chain, and reduces the loss of items and the waste of warehouse space resources.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of computer technology, and more specifically to inventory information processing methods, apparatus, electronic devices, and computer-readable media. Background Technology

[0002] In the inventory management field, it's generally desirable to prepare sufficient inventory in advance to meet sales demand over a future period and avoid stockouts. However, it's also important to avoid excessive inventory to prevent high working capital costs and warehousing expenses. Currently, the common method for replenishing inventory is to calculate the required inventory level based on a pre-set lead time (after an order is placed, the goods may not arrive immediately, but may take L days, known as the lead time), using average sales volume forecasts.

[0003] However, the above method usually has the following technical problems: the randomness of the lead time is not taken into account, the accuracy of the inventory quantity to be replenished is low, resulting in too much or too little inventory. When the inventory is too much, it is easy to cause damage to the goods; when the inventory is too little, it is easy to waste warehouse space resources.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion 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 this disclosure provide inventory information processing methods, apparatuses, electronic devices, and computer-readable media to address the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide an inventory information processing method, the method comprising: obtaining a stock preparation period turnover prediction table for a target item, wherein the stock preparation period turnover prediction table includes a stock preparation period distribution probability for each stock preparation period and a predicted turnover sequence for each stock preparation period, the stock preparation period distribution probability for each stock preparation period corresponds to a predicted turnover sequence in the predicted turnover sequence, and the stock preparation period turnover prediction table characterizes the turnover of the target item under different stock preparation periods predicted by quantiles; generating an inventory threshold and a target inventory quantity for the target item based on the stock preparation period distribution probability and the predicted turnover sequence included in the stock preparation period turnover prediction table, wherein the inventory threshold and the target inventory quantity are used to replenish the target item.

[0008] Optionally, generating the inventory threshold and target inventory quantity corresponding to the target item based on the inventory period turnover forecast table including the inventory period distribution probability and predicted turnover sequence includes: determining the maximum and minimum predicted turnover included in the inventory period turnover forecast table as the first predicted turnover and the second predicted turnover, respectively; performing golden section processing on the first predicted turnover and the second predicted turnover to generate the first alternative turnover and the second alternative turnover; generating the first turnover distribution probability corresponding to the first alternative turnover and the second turnover distribution probability corresponding to the second alternative turnover based on the inventory period turnover forecast table; and determining the inventory threshold based on the first turnover distribution probability and the second turnover distribution probability.

[0009] Optionally, determining the inventory threshold based on the first and second circulation volume distribution probabilities includes: in response to the first or second circulation volume distribution probability satisfying a first preset condition, determining the candidate circulation volume corresponding to the circulation volume distribution probability that satisfies the first preset condition in the first or second circulation volume distribution probability as the inventory threshold, wherein the first preset condition is: the first circulation volume distribution probability is equal to a preset circulation volume satisfaction rate, or the second circulation volume distribution probability is equal to the preset circulation volume satisfaction rate.

[0010] Optionally, determining the inventory threshold based on the first circulation volume distribution probability and the second circulation volume distribution probability includes: determining the first candidate circulation volume as the target circulation volume in response to the first circulation volume distribution probability satisfying a second preset condition, wherein the second preset condition is: the first circulation volume distribution probability is greater than a preset circulation volume satisfaction rate; and determining the target circulation volume as the inventory threshold in response to the difference between the target circulation volume and the second predicted circulation volume being less than or equal to a preset difference.

[0011] Optionally, determining the inventory threshold based on the first and second circulation volume distribution probabilities includes: in response to the first and second circulation volume distribution probabilities satisfying a third preset condition, determining the first candidate circulation volume as the first target circulation volume, and determining the second candidate circulation volume as the second target circulation volume, wherein the third preset condition is: the first circulation volume distribution probability is less than the second circulation volume distribution probability, and the first circulation volume distribution probability is less than a preset circulation volume satisfaction rate, and the second circulation volume distribution probability is greater than the preset circulation volume satisfaction rate; in response to the difference between the second target circulation volume and the first target circulation volume being less than or equal to a preset difference, determining the first target circulation volume as the inventory threshold.

[0012] Optionally, determining the inventory threshold based on the first and second circulation volume distribution probabilities includes: determining the second alternative circulation volume as the target circulation volume in response to the second circulation volume distribution probability satisfying a fourth preset condition, wherein the fourth preset condition is: the second circulation volume distribution probability is less than a preset circulation volume satisfaction rate; and determining the target circulation volume as the inventory threshold in response to the difference between the first predicted circulation volume and the target circulation volume being less than or equal to a preset difference.

[0013] Optionally, generating the corresponding inventory threshold and target inventory quantity for the target item based on the inventory period distribution probability and predicted inventory quantity sequence included in the inventory period turnover forecast table includes: in response to determining that the inventory threshold is greater than the current inventory quantity of the target item, determining the target inventory quantity of the target item based on the target period turnover forecast table, wherein the target period turnover forecast table includes the inventory period turnover forecast table.

[0014] Optionally, determining the target inventory level of the target item based on the target cycle turnover forecast table includes: determining the maximum and minimum predicted turnover levels included in the target cycle turnover forecast table as the first and second predicted turnover levels, respectively; performing a golden section operation on the first and second predicted turnover levels to generate a first segmented turnover level and a second segmented turnover level; generating a first predicted value distribution probability corresponding to the first segmented turnover level and a second predicted value distribution probability corresponding to the second segmented turnover level based on the target cycle turnover forecast table; and determining the target inventory level based on the first and second predicted value distribution probabilities.

[0015] Optionally, the column fields of the aforementioned inventory turnover forecast table are the inventory turnover probability distribution for each inventory period. The aforementioned inventory turnover forecast table includes the inventory turnover probability distribution for each inventory period, sorted in ascending order by each inventory period. The aforementioned forecast turnover sequence includes the forecast turnover sequence, and the forecast turnover is sorted in ascending order by each forecast turnover. The generation of a first turnover probability distribution corresponding to the aforementioned first candidate turnover and a second turnover probability distribution corresponding to the aforementioned second candidate turnover based on the aforementioned inventory turnover forecast table includes: performing the following selection steps according to each inventory turnover probability distribution in the aforementioned inventory turnover probability distribution: selecting the forecast turnover sequence corresponding to the aforementioned inventory turnover probability distribution from the aforementioned inventory turnover forecast table as a candidate forecast turnover sequence; selecting the candidate forecast turnover corresponding to the aforementioned first candidate turnover from the candidate forecast turnover sequence as the first... The candidate predicted turnover volume is determined as follows: The first candidate predicted turnover volume is less than or equal to the first candidate turnover volume, and the first candidate predicted turnover volume is the largest candidate predicted turnover volume less than or equal to the first candidate turnover volume in the candidate predicted turnover volume sequence; A candidate predicted turnover volume corresponding to the second candidate turnover volume is selected from the candidate predicted turnover volume sequence as the second candidate predicted turnover volume, wherein the second candidate predicted turnover volume is less than or equal to the second candidate turnover volume, and the second candidate predicted turnover volume is the largest candidate predicted turnover volume less than or equal to the second candidate turnover volume in the candidate predicted turnover volume sequence; A first turnover volume distribution probability is generated based on the stock preparation period distribution probability of each stock preparation period and the selected first candidate predicted turnover volumes; A second turnover volume distribution probability is generated based on the stock preparation period distribution probability of each stock preparation period and the selected second candidate predicted turnover volumes.

[0016] Optionally, the above method further includes: replenishing the warehouse corresponding to the target item based on the target inventory level.

[0017] Secondly, some embodiments of this disclosure provide an inventory information processing apparatus, the apparatus comprising: an acquisition unit configured to acquire a stock preparation period turnover prediction table corresponding to a target item, wherein the stock preparation period turnover prediction table includes a stock preparation period distribution probability for each stock preparation period and a predicted turnover sequence for each predicted turnover sequence, the stock preparation period turnover prediction table representing the turnover of the target item under different stock preparation periods as predicted by quantiles; and a generation unit configured to generate an inventory threshold and a target inventory quantity corresponding to the target item based on the stock preparation period distribution probability and the predicted turnover sequence included in the stock preparation period turnover prediction table, wherein the inventory threshold and the target inventory quantity are used to replenish the target item.

[0018] Optionally, the generation unit is further configured to: determine the maximum and minimum predicted turnover included in the above-mentioned inventory turnover forecast table as the first predicted turnover and the second predicted turnover, respectively; perform golden section processing on the first predicted turnover and the second predicted turnover to generate the first alternative turnover and the second alternative turnover; generate a first turnover distribution probability corresponding to the first alternative turnover and a second turnover distribution probability corresponding to the second alternative turnover based on the above-mentioned inventory turnover forecast table; and determine the inventory threshold based on the first turnover distribution probability and the second turnover distribution probability.

[0019] Optionally, the generation unit is further configured to: in response to the first circulation volume distribution probability or the second circulation volume distribution probability satisfying a first preset condition, determine the candidate circulation volume corresponding to the circulation volume distribution probability that satisfies the first preset condition in the first circulation volume distribution probability or the second circulation volume distribution probability as an inventory threshold, wherein the first preset condition is: the first circulation volume distribution probability is equal to a preset circulation volume satisfaction rate, or the second circulation volume distribution probability is equal to the preset circulation volume satisfaction rate.

[0020] Optionally, the generation unit is further configured to: in response to the first circulation volume distribution probability satisfying a second preset condition, determine the first candidate circulation volume as the target circulation volume, wherein the second preset condition is: the first circulation volume distribution probability is greater than a preset circulation volume satisfaction rate; in response to the difference between the target circulation volume and the second predicted circulation volume being less than or equal to a preset difference, determine the target circulation volume as an inventory threshold.

[0021] Optionally, the generation unit is further configured to: in response to the first flow volume distribution probability and the second flow volume distribution probability satisfying a third preset condition, determine the first candidate flow volume as the first target flow volume, and determine the second candidate flow volume as the second target flow volume, wherein the third preset condition is: the first flow volume distribution probability is less than the second flow volume distribution probability, and the first flow volume distribution probability is less than a preset flow volume satisfaction rate, and the second flow volume distribution probability is greater than the preset flow volume satisfaction rate; in response to the difference between the second target flow volume and the first target flow volume being less than or equal to a preset difference, determine the first target flow volume as an inventory threshold.

[0022] Optionally, the generation unit is further configured to: in response to the second turnover distribution probability satisfying a fourth preset condition, determine the second candidate turnover as the target turnover, wherein the fourth preset condition is: the second turnover distribution probability is less than a preset turnover satisfaction rate; and in response to the difference between the first predicted turnover and the target turnover being less than or equal to a preset difference, determine the target turnover as an inventory threshold.

[0023] Optionally, the generation unit is further configured to: in response to determining that the above-mentioned inventory threshold is greater than the current inventory of the above-mentioned target item, determine the target inventory of the above-mentioned target item according to the target cycle turnover forecast table, wherein the above-mentioned target cycle turnover forecast table includes the above-mentioned stock preparation period turnover forecast table.

[0024] Optionally, the generation unit is further configured to: determine the maximum and minimum predicted turnover included in the target cycle turnover prediction table as the first turnover prediction and the second turnover prediction, respectively; perform golden section processing on the first turnover prediction and the second turnover prediction to generate the first segmented turnover and the second segmented turnover; generate a first predicted value distribution probability corresponding to the first segmented turnover and a second predicted value distribution probability corresponding to the second segmented turnover based on the target cycle turnover prediction table; and determine the target inventory based on the first predicted value distribution probability and the second predicted value distribution probability.

[0025] Optionally, the column fields of the above-mentioned inventory turnover forecast table are the inventory turnover probability distribution for each inventory turnover period. The above-mentioned inventory turnover forecast table includes the inventory turnover probability distribution for each inventory turnover period, sorted in ascending order for each inventory turnover period. The predicted turnover sequence in the above-mentioned predicted turnover sequence includes each predicted turnover, sorted in ascending order.

[0026] Optionally, the generation unit is further configured to: generate a first turnover distribution probability corresponding to the first candidate turnover quantity and a second turnover distribution probability corresponding to the second candidate turnover quantity according to the above-mentioned inventory preparation period turnover quantity prediction table, including: performing the following selection steps according to each inventory preparation period distribution probability in the above-mentioned inventory preparation period distribution probabilities: selecting a predicted turnover quantity sequence corresponding to the above-mentioned inventory preparation period distribution probability from the above-mentioned inventory preparation period turnover quantity prediction table as a candidate predicted turnover quantity sequence; selecting a candidate predicted turnover quantity corresponding to the above-mentioned first candidate turnover quantity from the above-mentioned candidate predicted turnover quantity sequence as a first candidate predicted turnover quantity, wherein the above-mentioned first candidate predicted turnover quantity is less than or equal to the above-mentioned first candidate turnover quantity, and the above-mentioned first candidate predicted turnover quantity... The largest candidate predicted turnover volume less than or equal to the first candidate turnover volume in the above candidate predicted turnover volume sequence is selected as the second candidate predicted turnover volume. The second candidate predicted turnover volume is selected from the above candidate predicted turnover volume sequence, corresponding to the second candidate turnover volume, wherein the second candidate predicted turnover volume is less than or equal to the first candidate turnover volume, and the second candidate predicted turnover volume is the largest candidate predicted turnover volume less than or equal to the second candidate turnover volume in the above candidate predicted turnover volume sequence. A first turnover volume distribution probability is generated based on the distribution probability of the stock preparation period for each stock preparation period and the selected first candidate predicted turnover volumes. A second turnover volume distribution probability is generated based on the distribution probability of the stock preparation period for each stock preparation period and the selected second candidate predicted turnover volumes.

[0027] Optionally, the apparatus further includes a replenishment unit configured to replenish the warehouse corresponding to the target item based on the target inventory level.

[0028] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0029] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0030] The above-described embodiments of this disclosure have the following beneficial effects: the inventory information processing method of some embodiments of this disclosure improves the accuracy of the determined inventory quantity of goods and reduces the loss of goods and the waste of warehouse space resources. Specifically, the reason why goods are easily lost or warehouse space resources are wasted is that the randomness of the preparation period is not taken into account, the accuracy of the determined inventory quantity that needs to be replenished is low, resulting in an excessive or insufficient inventory quantity of goods. When the inventory quantity of goods is excessive, it is easy to cause the loss of goods; when the inventory quantity of goods is insufficient, it is easy to cause the waste of warehouse space resources. Based on this, the inventory information processing method of some embodiments of this disclosure first obtains a preparation period turnover prediction table for the target goods. The preparation period turnover prediction table includes the preparation period distribution probability of each preparation period and each predicted turnover sequence. The preparation period distribution probability of each preparation period corresponds to the predicted turnover sequence in each predicted turnover sequence. The preparation period turnover prediction table represents the turnover of the target goods under different preparation periods predicted by quantiles. Thus, the target inventory quantity of goods can be determined according to different preparation periods. This improves the accuracy of the calculated required inventory replenishment for each item. Then, based on the lead time distribution probability and predicted turnover sequence included in the lead time turnover forecast table, inventory thresholds and target inventory levels for the target items are generated. These thresholds and target inventory levels are used to replenish the target items. Thus, different lead time distribution probabilities can be used to determine the inventory thresholds and target inventory levels for target items. This improves the accuracy of the determined target inventory levels even when the lead time is uncertain. Consequently, it reduces item loss and waste of warehouse space resources. Attached Figure Description

[0031] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0032] Figure 1 This is a schematic diagram illustrating an application scenario of the inventory information processing method according to some embodiments of this disclosure;

[0033] Figure 2 This is a flowchart of some embodiments of the inventory information processing method according to this disclosure;

[0034] Figure 3 These are flowcharts of other embodiments of the inventory information processing method according to this disclosure;

[0035] Figure 4 These are schematic diagrams illustrating the structure of some embodiments of the inventory information processing apparatus according to this disclosure;

[0036] Figure 5 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0037] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0038] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0039] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0040] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0041] 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.

[0042] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0043] Figure 1 This is a schematic diagram of an application scenario of an inventory information processing method according to some embodiments of the present disclosure.

[0044] exist Figure 1In the application scenario, firstly, the computing device 101 can obtain a turnover prediction table 102 for the stock preparation period corresponding to the target item. This table 102 includes the stock preparation period distribution probability for each stock preparation period and each predicted turnover sequence. The stock preparation period distribution probability for each stock preparation period corresponds to the predicted turnover sequence in each predicted turnover sequence. The stock preparation period turnover prediction table 102 represents the turnover of the target item under different stock preparation periods, predicted by quantiles. Then, the computing device 101 can generate an inventory threshold 103 and a target inventory quantity 104 for the target item based on the stock preparation period distribution probability and predicted turnover sequence included in the stock preparation period turnover prediction table 102. The inventory threshold and the target inventory quantity are used to replenish the target item.

[0045] For example, Table 102, which forecasts the turnover during the stock preparation period, is shown below:

[0046] g(Li) 0 0.1 0.2 … L1 0.25 20 108 174 … L2 0.3 40 146 223 … L3 0.3 50 194 282 … … … … … … … .

[0047] Where g(Li) represents the probability distribution of the stock preparation period Li. Li represents the i-th stock preparation period. L1, L2, and L3 represent the 1st, 2nd, and 3rd stock preparation periods, respectively. The probability distribution of the stock preparation period corresponding to the 1st stock preparation period is 0.25. The probability distribution of the stock preparation period corresponding to the 2nd stock preparation period is 0.3. The probability distribution of the stock preparation period corresponding to the 3rd stock preparation period is 0.3. 0, 0.1, and 0.2 can represent different quantiles. 20, 108, and 174 can represent the predicted turnover volume of different quantiles under the stock preparation period L1. 40, 146, and 223 can represent the predicted turnover volume of different quantiles under the stock preparation period L2. 50, 194, and 282 can represent the predicted turnover volume of different quantiles under the stock preparation period L3.

[0048] It should be noted that the aforementioned computing device 101 can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed within the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0049] It should be understood that Figure 1 The number of computing devices shown is merely illustrative. Any number of computing devices can be used depending on implementation needs.

[0050] Continue to refer to Figure 2The diagram illustrates a flow 200 of some embodiments of an inventory information processing method according to the present disclosure. This inventory information processing method includes the following steps:

[0051] Step 201: Obtain the inventory turnover forecast table for the target item during the preparation period.

[0052] In some embodiments, the entity executing the inventory information processing method (e.g. Figure 1 The computing device 101 shown can obtain a forecast table of the turnover volume for the target item during the preparation period from a terminal device via a wired or wireless connection. This forecast table includes the probability distribution of the preparation period for each preparation period and a sequence of predicted turnover volumes. The probability distribution of the preparation period for each preparation period corresponds to a predicted turnover volume sequence within the predicted turnover volume sequence. The forecast table represents the turnover volume of the target item under different preparation periods, predicted using quantiles. Here, the target item can be an item in the warehouse awaiting replenishment. Each preparation period corresponds to a probability distribution of the preparation period. Each probability distribution of the preparation period corresponds to a sequence of predicted turnover volumes. The predicted turnover volume in each sequence of predicted turnover volumes corresponds to a quantile (decimal). Here, each sequence of predicted turnover volumes is predicted based on the historical turnover volumes (sales) of the target item, using quantile regression to predict the predicted turnover volume at different quantiles under a given preparation period. The probability distribution of lead time can be used to represent the probability distribution of a lead time across different lead times.

[0053] For example, a turnover forecast table during the stock preparation period could be:

[0054] g(Li) 0 0.1 0.2 … L1 0.25 20 108 174 … L2 0.3 40 146 223 … L3 0.3 50 194 282 … … … … … … … .

[0055] Where g(Li) represents the probability distribution of the stock preparation period Li. Li represents the i-th stock preparation period. L1, L2, and L3 represent the 1st, 2nd, and 3rd stock preparation periods, respectively. The probability distribution of the stock preparation period corresponding to the 1st stock preparation period is 0.25. The probability distribution of the stock preparation period corresponding to the 2nd stock preparation period is 0.3. The probability distribution of the stock preparation period corresponding to the 3rd stock preparation period is 0.3. 0, 0.1, and 0.2 can represent different quantiles. 20, 108, and 174 can represent the predicted turnover volume of different quantiles under the stock preparation period L1. 40, 146, and 223 can represent the predicted turnover volume of different quantiles under the stock preparation period L2. 50, 194, and 282 can represent the predicted turnover volume of different quantiles under the stock preparation period L3.

[0056] Step 202: Based on the stock preparation period distribution probability and predicted turnover sequence included in the above stock preparation period turnover forecast table, generate the corresponding inventory threshold and target inventory quantity for the above target items.

[0057] In some embodiments, the executing entity may generate an inventory threshold and a target inventory quantity corresponding to the target item based on the inventory period distribution probability and predicted inventory quantity sequence included in the inventory period turnover forecast table. The inventory threshold and the target inventory quantity are used to replenish the target item.

[0058] In practice, based on the stock preparation period distribution probability and predicted turnover sequence included in the aforementioned stock preparation period turnover forecast table, the executing entity can generate the corresponding inventory threshold and target inventory quantity for the aforementioned target items:

[0059] The first step is to determine the maximum and minimum predicted turnover included in the above-mentioned inventory turnover forecast table as the first predicted turnover and the second predicted turnover, respectively.

[0060] The second step involves segmenting the first and second predicted turnover volumes to generate a first segmented predicted turnover volume and a second segmented predicted turnover volume. First, the difference between the first predicted turnover volume and the two candidate turnover volumes is determined as the turnover volume difference. Next, the product of the turnover volume difference and the first segmented value is determined as the first product. Then, the sum of the first product and the second predicted turnover volume is determined as the first candidate turnover volume. Then, the product of the turnover volume difference and the second segmented value is determined as the second product. Finally, the sum of the second product and the second predicted turnover volume is determined as the second candidate turnover volume. Here, the first segmented value ranges from (0, 0.5). The second segmented value ranges from (0.5, 1). The sum of the first and second segmented values ​​is 1.

[0061] Third, for each of the predicted flow rate sequences mentioned above, select the predicted flow rate corresponding to the first candidate flow rate as the first candidate predicted flow rate. Wherein, the first candidate predicted flow rate is less than or equal to the first candidate flow rate, and the first candidate predicted flow rate is the largest predicted flow rate in the predicted flow rate sequence that is less than or equal to the first candidate flow rate.

[0062] Fourth step: For each of the predicted flow rate sequences mentioned above, select the predicted flow rate corresponding to the second candidate flow rate from the predicted flow rate sequence as the second candidate predicted flow rate. Wherein, the second candidate predicted flow rate is less than or equal to the second candidate flow rate, and the second candidate predicted flow rate is the largest predicted flow rate in the predicted flow rate sequence that is less than or equal to the second candidate flow rate.

[0063] Fifth step: For each selected first candidate predicted turnover, the product of the first candidate predicted turnover and the quantile corresponding to the first candidate predicted turnover is determined as the probability distribution of the first predicted turnover.

[0064] The sixth step is to determine the sum of the probabilities of each of the first predicted turnover volume distributions as the first turnover volume distribution probability.

[0065] Step 7: For each selected second alternative predicted turnover, the product of the second alternative predicted turnover and the quantile corresponding to the second alternative predicted turnover is determined as the probability distribution of the second predicted turnover.

[0066] The eighth step is to determine the sum of the probabilities of each of the determined second predicted turnover distributions as the second turnover distribution probability.

[0067] Step 9: Determine the maximum value of the first and second circulation volume distribution probabilities as the target circulation volume distribution probability.

[0068] Step 10: Determine the alternative turnover volume corresponding to the above target turnover volume distribution probability as the inventory threshold.

[0069] Step 11: In response to the aforementioned inventory threshold being greater than the current inventory level of the target item, the aforementioned inventory threshold and a pre-set replenishment value corresponding to the target item are determined as the target inventory level. Here, there are no restrictions on the setting of the replenishment value corresponding to the target item.

[0070] The above-described embodiments of this disclosure have the following beneficial effects: the inventory information processing method of some embodiments of this disclosure improves the accuracy of the determined inventory quantity of goods and reduces the loss of goods and the waste of warehouse space resources. Specifically, the reason why goods are easily lost or warehouse space resources are wasted is that the randomness of the preparation period is not taken into account, the accuracy of the determined inventory quantity that needs to be replenished is low, resulting in an excessive or insufficient inventory quantity of goods. When the inventory quantity of goods is excessive, it is easy to cause the loss of goods; when the inventory quantity of goods is insufficient, it is easy to cause the waste of warehouse space resources. Based on this, the inventory information processing method of some embodiments of this disclosure first obtains a preparation period turnover prediction table for the target goods. The preparation period turnover prediction table includes the preparation period distribution probability of each preparation period and each predicted turnover sequence. The preparation period distribution probability of each preparation period corresponds to the predicted turnover sequence in each predicted turnover sequence. The preparation period turnover prediction table represents the turnover of the target goods under different preparation periods predicted by quantiles. Thus, the target inventory quantity of goods can be determined according to different preparation periods. This improves the accuracy of the calculated required inventory replenishment for each item. Then, based on the lead time distribution probability and predicted turnover sequence included in the lead time turnover forecast table, inventory thresholds and target inventory levels for the target items are generated. These thresholds and target inventory levels are used to replenish the target items. Thus, different lead time distribution probabilities can be used to determine the inventory thresholds and target inventory levels for target items. This improves the accuracy of the determined target inventory levels even when the lead time is uncertain. Consequently, it reduces item loss and waste of warehouse space resources.

[0071] Further reference Figure 3 This illustration shows some other embodiments of the inventory information processing method according to the present disclosure. The inventory information processing method includes the following steps:

[0072] Step 301: Obtain the inventory turnover forecast table for the target item during the preparation period.

[0073] In some embodiments, the specific implementation of step 301 and its resulting technical effects can be found in [reference needed]. Figure 2 Step 201 in the corresponding embodiments will not be repeated here.

[0074] Step 302: Based on the stock preparation period distribution probability and predicted turnover sequence included in the above stock preparation period turnover prediction table, generate the corresponding inventory threshold and target inventory quantity for the above target items.

[0075] In some embodiments, the executing entity can generate an inventory threshold and a target inventory level for the target item based on the inventory period distribution probability and predicted inventory level sequence included in the inventory period turnover forecast table. The inventory threshold and target inventory level are used to replenish the target item. Here, the column fields of the inventory period turnover forecast table are the inventory period distribution probabilities for each inventory period. The inventory period turnover forecast table includes inventory period distribution probabilities sorted in ascending order for each inventory period. The predicted inventory levels in each predicted inventory level sequence are sorted in ascending order.

[0076] For example, a turnover forecast table during the stock preparation period could be:

[0077] g(Li) 0 0.1 0.2 0.3 … L1 0.25 20 108 174 194 … L2 0.3 40 146 223 265 … L3 0.3 50 194 282 313 … … … … … … … … .

[0078] In practice, based on the stock preparation period distribution probability and predicted turnover sequence included in the aforementioned stock preparation period turnover forecast table, the executing entity can generate the corresponding inventory threshold and target inventory quantity for the aforementioned target items through the following steps:

[0079] The first step is to determine the maximum and minimum predicted turnover included in the above-mentioned inventory turnover forecast table as the first predicted turnover and the second predicted turnover, respectively.

[0080] The second step involves applying the golden ratio to the first and second predicted turnover volumes to generate a first and a second candidate turnover volume. First, the difference between the first and second predicted turnover volumes is determined as the turnover volume difference. Next, the product of this turnover volume difference and 0.382 is determined as the first product. Then, the sum of the first product and the second predicted turnover volume is determined as the first candidate turnover volume. Next, the product of the turnover volume difference and 0.618 is determined as the second product. Finally, the sum of the second product and the second predicted turnover volume is determined as the second candidate turnover volume.

[0081] The third step is to generate the first circulation volume distribution probability corresponding to the first candidate circulation volume and the second circulation volume distribution probability corresponding to the second candidate circulation volume based on the above-mentioned inventory turnover forecast table.

[0082] In practice, the third step above may include the following sub-steps:

[0083] The first sub-step involves performing the following selection steps based on the probability distribution of each of the above-mentioned lead times:

[0084] 1. Select the predicted turnover sequence corresponding to the above-mentioned distribution probability of the stock preparation period from the above-mentioned stock preparation period turnover prediction table as the candidate predicted turnover sequence.

[0085] 2. Select the candidate predicted flow volume corresponding to the first candidate flow volume from the above candidate predicted flow volume sequence as the first candidate predicted flow volume. Wherein, the first candidate predicted flow volume is less than or equal to the first candidate flow volume, and the first candidate predicted flow volume is the largest candidate predicted flow volume in the above candidate predicted flow volume sequence that is less than or equal to the first candidate flow volume.

[0086] 3. Select the candidate predicted flow volume corresponding to the second candidate flow volume from the above candidate predicted flow volume sequence as the second candidate predicted flow volume. Wherein, the second candidate predicted flow volume is less than or equal to the second candidate flow volume, and the second candidate predicted flow volume is the largest candidate predicted flow volume in the above candidate predicted flow volume sequence that is less than or equal to the second candidate flow volume.

[0087] The second sub-step generates the first turnover distribution probability based on the turnover distribution probability of each of the above-mentioned turnover periods and the selected first alternative predicted turnover volumes.

[0088] In practice, the second sub-step described above may include the following steps:

[0089] 1. For each of the aforementioned first candidate predicted turnover quantities, the average of the quantiles corresponding to the first candidate predicted turnover quantity and the first target quantile is determined as the first distribution probability corresponding to the first candidate predicted turnover quantity. Wherein, the first target quantile is: the quantile corresponding to the largest candidate predicted turnover quantity in the sequence of candidate predicted turnover quantities corresponding to the first candidate predicted turnover quantity that is greater than or equal to the first candidate predicted turnover quantity.

[0090] 2. The sum of the determined first distribution probabilities is determined as the first circulation volume distribution probability.

[0091] The third sub-step generates the second turnover distribution probability based on the turnover distribution probability of each of the above-mentioned turnover periods and the selected second alternative predicted turnover volumes.

[0092] In practice, the third sub-step described above may include the following steps:

[0093] 1. For each of the aforementioned second candidate predicted turnover quantities, the average of the quantiles corresponding to the second candidate predicted turnover quantity and the second target quantile is determined as the second distribution probability corresponding to the second candidate predicted turnover quantity. Wherein, the second target quantile is: the quantile corresponding to the largest candidate predicted turnover quantity in the candidate predicted turnover quantity sequence corresponding to the second candidate predicted turnover quantity that is greater than or equal to the second candidate predicted turnover quantity.

[0094] 2. The sum of the determined second distribution probabilities is taken as the second circulation volume distribution probability.

[0095] The fourth step is to determine the inventory threshold based on the first and second circulation volume distribution probabilities. In practice, in response to the first or second circulation volume distribution probability satisfying a first preset condition, the candidate circulation volume corresponding to the circulation volume distribution probability that satisfies the first preset condition from the first or second circulation volume distribution probabilities is determined as the inventory threshold. The first preset condition is: the first circulation volume distribution probability equals a preset circulation volume satisfaction rate, or the second circulation volume distribution probability equals the preset circulation volume satisfaction rate. Here, the setting of the preset circulation volume satisfaction rate is not limited. For example, the preset circulation volume satisfaction rate can be 0.95.

[0096] In some optional implementations of certain embodiments, the fourth step described above can be achieved through the following steps:

[0097] 1. In response to the first circulation volume distribution probability satisfying the second preset condition, the first candidate circulation volume is determined as the target circulation volume. The second preset condition is: the first circulation volume distribution probability is greater than the preset circulation volume satisfaction rate.

[0098] 2. In response to the fact that the difference between the target turnover and the second predicted turnover is less than or equal to a preset difference, the target turnover is determined as the inventory threshold. Here, there is no restriction on the setting of the preset difference. For example, the preset difference can be 1.

[0099] In some alternative implementations of certain embodiments, the fourth step described above can be achieved through the following steps:

[0100] 1. In response to the first flow volume distribution probability and the second flow volume distribution probability satisfying a third preset condition, the first candidate flow volume is determined as the first target flow volume, and the second candidate flow volume is determined as the second target flow volume. The third preset condition is: the first flow volume distribution probability is less than the second flow volume distribution probability, and the first flow volume distribution probability is less than a preset flow volume satisfaction rate, and the second flow volume distribution probability is greater than the preset flow volume satisfaction rate.

[0101] 2. In response to the fact that the difference between the second target turnover and the first target turnover is less than or equal to a preset difference, the first target turnover is determined as the inventory threshold.

[0102] In some alternative implementations of certain embodiments, the fourth step described above can be achieved through the following steps:

[0103] 1. In response to the second circulation volume distribution probability satisfying the fourth preset condition, the second candidate circulation volume is determined as the target circulation volume. The fourth preset condition is: the second circulation volume distribution probability is less than the preset circulation volume satisfaction rate.

[0104] 2. In response to the fact that the difference between the first predicted turnover and the target turnover is less than or equal to a preset difference, the target turnover is determined as the inventory threshold.

[0105] Step 5: In response to determining that the aforementioned inventory threshold is greater than the current inventory level of the aforementioned target item, the target inventory level of the aforementioned target item is determined according to the target cycle turnover forecast table. The target cycle turnover forecast table includes the aforementioned stock preparation period turnover forecast table. Here, the target cycle turnover forecast table can represent the turnover of the aforementioned target item under different target cycles predicted by quantiles. Here, the target cycle can refer to the sum of the stock preparation period and the corresponding procurement cycle of the aforementioned target item. The target cycle turnover forecast table includes the target cycle distribution probability for each target cycle and each turnover forecast sequence. The target cycle distribution probability for each target cycle corresponds to the turnover forecast sequence in each turnover forecast sequence. Each target cycle corresponds to a target cycle distribution probability. A target cycle distribution probability corresponds to a turnover forecast sequence. The turnover forecast in each turnover forecast sequence corresponds to a quantile (decimal). Here, each predicted turnover sequence is based on the historical turnover (historical sales) of the target item, predicted using quantile regression to represent the turnover at different quantiles within a specific target period. The target period probability distribution can represent the probability distribution of a target period across various target periods.

[0106] For example, the target cycle turnover prediction table can be shown in the following table:

[0107] g(Mi) 0 0.1 0.2 … M1 0.25 20 108 174 … M2 0.3 40 146 223 … M3 0.3 50 194 282 … M4 0.1 60 201 315 … … … … … … … .

[0108] Where g(Mi) represents the target period distribution probability of target period Mi. Mi represents the i-th target period. M1, M2, M3, and M4 represent the 1st, 2nd, 3rd, and 4th target periods, respectively. The target period distribution probability corresponding to the 1st target period is 0.25. The target period distribution probability corresponding to the 2nd target period is 0.3. The target period distribution probability corresponding to the 3rd target period is 0.3. 0, 0.1, and 0.2 can represent different quantiles. 20, 108, and 174 can represent the flow prediction amount of different quantiles in the case of target period M1. 40, 146, and 223 can represent the flow prediction amount of different quantiles in the case of target period M2. 50, 194, and 282 can represent the flow prediction amount of different quantiles in the case of target period M3. 60, 201, and 315 can represent the flow prediction amount of different quantiles in the case of target period M4.

[0109] In practice, the fifth step above may include the following sub-steps:

[0110] The first sub-step involves determining the maximum and minimum predicted turnover included in the target cycle turnover prediction table as the first turnover prediction amount and the second turnover prediction amount, respectively.

[0111] The second sub-step involves performing a golden ratio operation on the first and second predicted turnover quantities to generate a first segmented turnover quantity and a second segmented turnover quantity. For the specific method of performing the golden ratio operation on the first and second predicted turnover quantities, please refer to the implementation method described above, which will not be repeated here.

[0112] The third sub-step involves generating, based on the aforementioned target cycle turnover prediction table, a first predicted value distribution probability corresponding to the first segment turnover volume and a second predicted value distribution probability corresponding to the second segment turnover volume. The specific method for generating these distribution probabilities can be found in the detailed implementation of generating the first turnover volume distribution probability corresponding to the first candidate turnover volume and the second turnover volume distribution probability corresponding to the second candidate turnover volume, as described above, and will not be repeated here.

[0113] The fourth sub-step involves determining the target inventory level based on the probability distributions of the first and second predicted values. For the specific implementation of determining the target inventory level, please refer to the implementation of determining the inventory threshold described above; it will not be repeated here.

[0114] Step 303: Based on the target inventory level, replenish the warehouse corresponding to the target item.

[0115] In some embodiments, the executing entity can replenish the warehouse corresponding to the target item based on the target inventory level. In practice, firstly, the difference between the target inventory level and the current inventory level of the target item can be determined as the target replenishment quantity. Then, the executing entity can dispatch associated transport vehicles to transport the target replenishment quantity of target items to the warehouse corresponding to the target item. This completes the replenishment operation for the target item.

[0116] from Figure 3 It can be seen that, with Figure 2 Compared to the description of some corresponding embodiments, Figure 3 In some corresponding embodiments, process 300 firstly determines the maximum and minimum predicted turnover volumes included in the aforementioned inventory replenishment period turnover volume forecast table as the first predicted turnover volume and the second predicted turnover volume, respectively. Secondly, the first and second predicted turnover volumes are subjected to golden section processing to generate a first alternative turnover volume and a second alternative turnover volume. Thus, the predicted turnover volume in the aforementioned inventory replenishment period turnover volume forecast table can be divided into three regions using the golden section algorithm, facilitating quick retrieval of the predicted turnover volume that meets replenishment requirements. Next, based on the aforementioned inventory replenishment period turnover volume forecast table, a first turnover volume distribution probability corresponding to the first alternative turnover volume and a second turnover volume distribution probability corresponding to the second alternative turnover volume are generated. Thus, the distribution probabilities of different predicted turnover volumes can be determined to identify the predicted turnover volume that meets the condition (inventory replenishment requirement) as the inventory threshold. Then, based on the first and second turnover volume distribution probabilities, the inventory threshold is determined. Therefore, inventory thresholds can be determined based on the probability distribution of turnover to avoid the impact of outliers on replenishment quantities. For example, when determining the inventory threshold using the average turnover, the influence of outliers (large or small turnover) cannot be eliminated, leading to inaccurate replenishment calculations. Finally, in response to the determination that the aforementioned inventory threshold is greater than the current inventory of the target item, the target inventory of the target item can be determined based on the target cycle turnover forecast table. This target cycle turnover forecast table includes the stock preparation period turnover forecast table. Thus, the inventory of items under different target cycles can be determined. This improves the accuracy of the determined target inventory when the target cycle is uncertain. Consequently, when replenishing target items, the required quantity can be accurately calculated, reducing item loss and waste of warehouse space resources.

[0117] Further reference Figure 4As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of an inventory information processing apparatus, which are similar to... Figure 2 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0118] like Figure 4 As shown, an inventory information processing apparatus 400 in some embodiments includes an acquisition unit 401 and a generation unit 402. The acquisition unit 401 is configured to acquire a stock preparation period turnover prediction table corresponding to a target item. The stock preparation period turnover prediction table includes a stock preparation period distribution probability for each stock preparation period and a predicted turnover sequence for each predicted turnover sequence. The stock preparation period turnover prediction table represents the turnover of the target item under different stock preparation periods, predicted by quantiles. The generation unit 402 is configured to generate an inventory threshold and a target inventory quantity corresponding to the target item based on the stock preparation period distribution probability and predicted turnover sequence included in the stock preparation period turnover prediction table. The inventory threshold and the target inventory quantity are used to replenish the target item.

[0119] Optionally, the generation unit 402 is further configured to: determine the maximum and minimum predicted turnover included in the above-mentioned inventory turnover forecast table as the first predicted turnover and the second predicted turnover, respectively; perform golden section processing on the first predicted turnover and the second predicted turnover to generate the first alternative turnover and the second alternative turnover; generate a first turnover distribution probability corresponding to the first alternative turnover and a second turnover distribution probability corresponding to the second alternative turnover according to the above-mentioned inventory turnover forecast table; and determine the inventory threshold according to the first turnover distribution probability and the second turnover distribution probability.

[0120] Optionally, the generation unit 402 is further configured to: in response to the first turnover distribution probability or the second turnover distribution probability satisfying a first preset condition, determine the candidate turnover volume corresponding to the turnover distribution probability that satisfies the first preset condition in the first turnover distribution probability or the second turnover distribution probability as an inventory threshold, wherein the first preset condition is: the first turnover distribution probability is equal to a preset turnover satisfaction rate, or the second turnover distribution probability is equal to the preset turnover satisfaction rate.

[0121] Optionally, the generation unit 402 is further configured to: in response to the first circulation volume distribution probability satisfying a second preset condition, determine the first candidate circulation volume as the target circulation volume, wherein the second preset condition is: the first circulation volume distribution probability is greater than a preset circulation volume satisfaction rate; in response to the difference between the target circulation volume and the second predicted circulation volume being less than or equal to a preset difference, determine the target circulation volume as an inventory threshold.

[0122] Optionally, the generation unit 402 is further configured to: in response to the first flow volume distribution probability and the second flow volume distribution probability satisfying a third preset condition, determine the first candidate flow volume as the first target flow volume, and determine the second candidate flow volume as the second target flow volume, wherein the third preset condition is: the first flow volume distribution probability is less than the second flow volume distribution probability, and the first flow volume distribution probability is less than a preset flow volume satisfaction rate, and the second flow volume distribution probability is greater than the preset flow volume satisfaction rate; in response to the difference between the second target flow volume and the first target flow volume being less than or equal to a preset difference, determine the first target flow volume as an inventory threshold.

[0123] Optionally, the generation unit 402 is further configured to: in response to the second turnover distribution probability satisfying a fourth preset condition, determine the second candidate turnover as the target turnover, wherein the fourth preset condition is: the second turnover distribution probability is less than a preset turnover satisfaction rate; in response to the difference between the first predicted turnover and the target turnover being less than or equal to a preset difference, determine the target turnover as an inventory threshold.

[0124] Optionally, the generation unit 402 is further configured to: in response to determining that the above-mentioned inventory threshold is greater than the current inventory of the above-mentioned target item, determine the target inventory of the above-mentioned target item according to the target cycle turnover forecast table, wherein the above-mentioned target cycle turnover forecast table includes the above-mentioned stock preparation period turnover forecast table.

[0125] Optionally, the generation unit 402 is further configured to: determine the maximum predicted turnover and the minimum predicted turnover included in the target cycle turnover prediction table as the first turnover prediction and the second turnover prediction, respectively; perform golden section processing on the first turnover prediction and the second turnover prediction to generate the first segmented turnover and the second segmented turnover; generate a first predicted value distribution probability corresponding to the first segmented turnover and a second predicted value distribution probability corresponding to the second segmented turnover according to the target cycle turnover prediction table; and determine the target inventory quantity according to the first predicted value distribution probability and the second predicted value distribution probability.

[0126] Optionally, the column fields of the above-mentioned inventory turnover forecast table are the inventory turnover probability distribution for each inventory turnover period. The above-mentioned inventory turnover forecast table includes the inventory turnover probability distribution for each inventory turnover period, sorted in ascending order for each inventory turnover period. The predicted turnover sequence in the above-mentioned predicted turnover sequence includes each predicted turnover, sorted in ascending order.

[0127] Optionally, the generation unit 402 is further configured to: generate a first turnover distribution probability corresponding to the first alternative turnover quantity and a second turnover distribution probability corresponding to the second alternative turnover quantity according to the above-mentioned inventory preparation period turnover quantity prediction table, including: performing the following selection steps according to each inventory preparation period distribution probability in the above-mentioned inventory preparation period distribution probabilities: selecting a predicted turnover quantity sequence corresponding to the above-mentioned inventory preparation period distribution probability from the above-mentioned inventory preparation period turnover quantity prediction table as a candidate predicted turnover quantity sequence; selecting a candidate predicted turnover quantity corresponding to the above-mentioned first alternative turnover quantity from the above-mentioned candidate predicted turnover quantity sequence as a first candidate predicted turnover quantity, wherein the above-mentioned first candidate predicted turnover quantity is less than or equal to the above-mentioned first alternative turnover quantity, and the above-mentioned first candidate predicted turnover quantity... The turnover volume is the largest candidate predicted turnover volume less than or equal to the first candidate turnover volume in the above candidate predicted turnover volume sequence; the candidate predicted turnover volume corresponding to the second candidate turnover volume is selected from the above candidate predicted turnover volume sequence as the second candidate predicted turnover volume, wherein the second candidate predicted turnover volume is less than or equal to the first candidate turnover volume, and the second candidate predicted turnover volume is the largest candidate predicted turnover volume less than or equal to the second candidate turnover volume in the above candidate predicted turnover volume sequence; a first turnover volume distribution probability is generated based on the stock preparation period distribution probability of each stock preparation period and the selected first candidate predicted turnover volume; a second turnover volume distribution probability is generated based on the stock preparation period distribution probability of each stock preparation period and the selected second candidate predicted turnover volume.

[0128] Optionally, the device 400 further includes a replenishment unit configured to replenish the warehouse corresponding to the target item based on the target inventory level.

[0129] It is understandable that the units described in the device 400 are related to the reference. Figure 2 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to device 400 and the units contained therein, and will not be repeated here.

[0130] The following is for reference. Figure 5 It illustrates electronic devices suitable for implementing some embodiments of this disclosure (e.g., Figure 1The diagram shows the structure of the computing device 101)500. Electronic devices in some embodiments of this disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0131] like Figure 5 As shown, the electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0132] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 5 Each box shown can represent a device or multiple devices as needed.

[0133] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of some embodiments of this disclosure.

[0134] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a 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 using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0135] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0136] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: acquire a stock preparation period turnover forecast table corresponding to the target item, wherein the stock preparation period turnover forecast table includes a stock preparation period distribution probability for each stock preparation period and a predicted turnover sequence for each stock preparation period, the stock preparation period distribution probability for each stock preparation period corresponds to a predicted turnover sequence in the predicted turnover sequence, and the stock preparation period turnover forecast table characterizes the turnover of the target item under different stock preparation periods predicted by quantiles; and generate an inventory threshold and a target inventory quantity corresponding to the target item based on the stock preparation period distribution probability and predicted turnover sequence included in the stock preparation period turnover forecast table, wherein the inventory threshold and the target inventory quantity are used to replenish the target item.

[0137] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0139] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit and a generation unit. The names of these units do not necessarily limit the specific unit itself. For instance, the generation unit may be described as "a unit that generates an inventory threshold and a target inventory quantity corresponding to the target item based on the inventory period distribution probability and predicted inventory quantity sequence included in the aforementioned inventory period turnover prediction table, wherein the inventory threshold and the target inventory quantity are used to replenish the target item."

[0140] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0141] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. An inventory information processing method, comprising: Obtain a turnover prediction table for the stock preparation period corresponding to the target item. The turnover prediction table includes the stock preparation period distribution probability for each stock preparation period and each predicted turnover sequence. The stock preparation period distribution probability for each stock preparation period corresponds to the predicted turnover sequence in each predicted turnover sequence. The turnover prediction table represents the turnover of the target item under different stock preparation periods predicted by quantiles. Based on the inventory period distribution probability and predicted turnover sequence included in the inventory period turnover forecast table, an inventory threshold and target inventory quantity corresponding to the target item are generated, wherein the inventory threshold and the target inventory quantity are used to replenish the target item, including: The maximum and minimum predicted turnover included in the inventory turnover forecast table are determined as the first predicted turnover and the second predicted turnover, respectively, and then subjected to the golden section to generate the first alternative turnover and the second alternative turnover. Based on the inventory turnover forecast table, generate a first turnover distribution probability corresponding to the first candidate turnover volume and a second turnover distribution probability corresponding to the second candidate turnover volume; In response to the first circulation volume distribution probability or the second circulation volume distribution probability satisfying a first preset condition, the candidate circulation volume corresponding to the circulation volume distribution probability that satisfies the first preset condition in the first circulation volume distribution probability or the second circulation volume distribution probability is determined as the inventory threshold, wherein the first preset condition is: the first circulation volume distribution probability is equal to the preset circulation volume satisfaction rate, or the second circulation volume distribution probability is equal to the preset circulation volume satisfaction rate.

2. The method according to claim 1, wherein, The step of determining the inventory threshold based on the first turnover distribution probability and the second turnover distribution probability includes: In response to the first flow volume distribution probability satisfying the second preset condition, the first candidate flow volume is determined as the target flow volume, wherein the second preset condition is: the first flow volume distribution probability is greater than the preset flow volume satisfaction rate; In response to the difference between the target turnover and the second predicted turnover being less than or equal to a preset difference, the target turnover is determined as an inventory threshold.

3. The method according to claim 1, wherein, The step of determining the inventory threshold based on the first turnover distribution probability and the second turnover distribution probability includes: In response to the first flow volume distribution probability and the second flow volume distribution probability satisfying a third preset condition, the first candidate flow volume is determined as the first target flow volume, and the second candidate flow volume is determined as the second target flow volume, wherein the third preset condition is: the first flow volume distribution probability is less than the second flow volume distribution probability, and the first flow volume distribution probability is less than a preset flow volume satisfaction rate, and the second flow volume distribution probability is greater than the preset flow volume satisfaction rate; In response to the fact that the difference between the second target turnover and the first target turnover is less than or equal to a preset difference, the first target turnover is determined as the inventory threshold.

4. The method according to claim 1, wherein, The step of determining the inventory threshold based on the first turnover distribution probability and the second turnover distribution probability includes: In response to the second flow volume distribution probability satisfying the fourth preset condition, the second candidate flow volume is determined as the target flow volume, wherein the fourth preset condition is: the second flow volume distribution probability is less than the preset flow volume satisfaction rate; In response to the fact that the difference between the first predicted turnover and the target turnover is less than or equal to a preset difference, the target turnover is determined as the inventory threshold.

5. The method according to claim 1, wherein, The step of generating the corresponding inventory threshold and target inventory quantity for the target item based on the inventory period distribution probability and predicted inventory quantity sequence included in the inventory period turnover prediction table includes: In response to determining that the inventory threshold is greater than the current inventory of the target item, the target inventory of the target item is determined according to the target cycle turnover forecast table, wherein the target cycle turnover forecast table includes the stock preparation period turnover forecast table.

6. The method according to claim 5, wherein, The step of determining the target inventory level of the target item based on the target cycle turnover forecast table includes: The maximum predicted turnover and the minimum predicted turnover included in the target cycle turnover prediction table are respectively determined as the first turnover prediction and the second turnover prediction; The first and second flow prediction quantities are subjected to the golden section to generate the first and second segmented flow quantities. Based on the target cycle turnover prediction table, generate a first predicted value distribution probability corresponding to the first segment turnover and a second predicted value distribution probability corresponding to the second segment turnover; The target inventory level is determined based on the probability distribution of the first predicted value and the probability distribution of the second predicted value.

7. The method according to claim 1, wherein, The column fields of the inventory turnover forecast table are the inventory turnover probability distribution for each inventory period. The inventory turnover forecast table includes the inventory turnover probability distribution for each inventory period, sorted in ascending order. The predicted turnover sequence in each predicted turnover sequence includes the predicted turnover, and each predicted turnover is sorted in ascending order. The step of generating a first turnover distribution probability corresponding to the first candidate turnover quantity and a second turnover distribution probability corresponding to the second candidate turnover quantity based on the inventory turnover forecast table includes: Based on each of the probability distributions of the various lead times, perform the following selection steps: Select the predicted turnover sequence corresponding to the distribution probability of the stock preparation period from the stock preparation period turnover prediction table as the candidate predicted turnover sequence; Select the candidate predicted flow volume corresponding to the first candidate flow volume from the candidate predicted flow volume sequence as the first candidate predicted flow volume, wherein the first candidate predicted flow volume is less than or equal to the first candidate flow volume, and the first candidate predicted flow volume is the largest candidate predicted flow volume in the candidate predicted flow volume sequence that is less than or equal to the first candidate flow volume. Select the candidate predicted flow volume corresponding to the second candidate flow volume from the candidate predicted flow volume sequence as the second candidate predicted flow volume, wherein the first candidate predicted flow volume is less than or equal to the second candidate flow volume, and the second candidate predicted flow volume is the largest candidate predicted flow volume in the candidate predicted flow volume sequence that is less than or equal to the second candidate flow volume. Based on the probability distribution of the preparation period for each preparation period and the selected first alternative predicted turnover, a probability distribution of the first turnover is generated. Based on the probability distribution of the preparation period for each preparation period and the selected second alternative predicted turnover, a second turnover distribution probability is generated.

8. The method according to claim 1, wherein, The method further includes: Based on the target inventory level, replenish the warehouse corresponding to the target item.

9. An inventory information processing device, comprising: The acquisition unit is configured to acquire a stock preparation period turnover prediction table for the target item, wherein the stock preparation period turnover prediction table includes the stock preparation period distribution probability for each stock preparation period and each predicted turnover sequence, the stock preparation period distribution probability for each stock preparation period corresponds to the predicted turnover sequence in each predicted turnover sequence, and the stock preparation period turnover prediction table represents the turnover of the target item under different stock preparation periods predicted by quantiles; The generation unit is configured to generate an inventory threshold and a target inventory quantity corresponding to the target item based on the inventory period distribution probability and predicted inventory quantity sequence included in the inventory period turnover prediction table, wherein the inventory threshold and the target inventory quantity are used to replenish the target item, including: The maximum and minimum predicted turnover included in the inventory turnover forecast table are determined as the first predicted turnover and the second predicted turnover, respectively, and then subjected to the golden section to generate the first alternative turnover and the second alternative turnover. Based on the inventory turnover forecast table, generate a first turnover distribution probability corresponding to the first candidate turnover volume and a second turnover distribution probability corresponding to the second candidate turnover volume; In response to the first circulation volume distribution probability or the second circulation volume distribution probability satisfying a first preset condition, the candidate circulation volume corresponding to the circulation volume distribution probability that satisfies the first preset condition in the first circulation volume distribution probability or the second circulation volume distribution probability is determined as the inventory threshold, wherein the first preset condition is: the first circulation volume distribution probability is equal to the preset circulation volume satisfaction rate, or the second circulation volume distribution probability is equal to the preset circulation volume satisfaction rate.

10. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-8.

11. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.

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